From 58b7b60fb66e5eb7970c7844884b154a9cfe718e Mon Sep 17 00:00:00 2001 From: reallyeasy Date: Tue, 3 Mar 2026 16:34:37 +0800 Subject: [PATCH 1/6] feat: add sarcasm classification pipeline with Colab --- .gitignore | 2 + build_combined.py | 314 + .../sarcasm_classification (2).ipynb | 4572 +++ .../sarcasm_pairs_step35_clean.jsonl | 28333 ++++++++++++++++ docs/dataset_audit.md | 210 + docs/experiment_plan.md | 271 + docs/research_summary.md | 187 + notebooks/01_data_preparation.ipynb | 521 + notebooks/02_tfidf_lr_baseline.ipynb | 514 + notebooks/03_naive_bayes_baseline.ipynb | 461 + notebooks/04_bert_classification.ipynb | 600 + notebooks/05_error_analysis.ipynb | 622 + notebooks/sarcasm_pairs_step35_clean.jsonl | 28333 ++++++++++++++++ requirements.txt | 24 + 14 files changed, 64964 insertions(+) create mode 100644 build_combined.py create mode 100644 colab_package/sarcasm_classification (2).ipynb create mode 100644 colab_package/sarcasm_pairs_step35_clean.jsonl create mode 100644 docs/dataset_audit.md create mode 100644 docs/experiment_plan.md create mode 100644 docs/research_summary.md create mode 100644 notebooks/01_data_preparation.ipynb create mode 100644 notebooks/02_tfidf_lr_baseline.ipynb create mode 100644 notebooks/03_naive_bayes_baseline.ipynb create mode 100644 notebooks/04_bert_classification.ipynb create mode 100644 notebooks/05_error_analysis.ipynb create mode 100644 notebooks/sarcasm_pairs_step35_clean.jsonl create mode 100644 requirements.txt diff --git a/.gitignore b/.gitignore index 2a719fa..f8804de 100644 --- a/.gitignore +++ b/.gitignore @@ -10,3 +10,5 @@ wheels/ .venv .DS_Store .env* + +claude.md \ No newline at end of file diff --git a/build_combined.py b/build_combined.py new file mode 100644 index 0000000..56aab57 --- /dev/null +++ b/build_combined.py @@ -0,0 +1,314 @@ +""" +Merge notebooks 01-05 into one combined notebook. +Output: colab_package/sarcasm_classification.ipynb +""" +import json +from pathlib import Path + +ROOT = Path(".") +NB_DIR = ROOT / "notebooks" +OUT_DIR = ROOT / "colab_package" +OUT_DIR.mkdir(exist_ok=True) + +# ── Load all notebooks ──────────────────────────────────────────────────────── +def load_nb(name): + path = NB_DIR / name + nb = json.loads(path.read_bytes().decode("utf-8")) + cells = [] + for c in nb["cells"]: + src = c["source"] + if isinstance(src, list): + src = "".join(src) + cells.append({"type": c["cell_type"], "src": src}) + return cells + +nb01 = load_nb("01_data_preparation.ipynb") +nb02 = load_nb("02_tfidf_lr_baseline.ipynb") +nb03 = load_nb("03_naive_bayes_baseline.ipynb") +nb04 = load_nb("04_bert_classification.ipynb") +nb05 = load_nb("05_error_analysis.ipynb") + + +# ── Single combined setup cell ──────────────────────────────────────────────── +# Merges all imports + file detection + all output-dir definitions + +SETUP_SRC = "\n".join([ + "# ============================================================", + "# SETUP — imports, file upload, paths", + "# ============================================================", + "from __future__ import annotations", + "import json, hashlib, random, os, warnings, shutil", + "from dataclasses import dataclass", + "from pathlib import Path", + "from collections import Counter", + "from urllib.parse import urlparse", + "from typing import Optional", + "", + "import numpy as np", + "import pandas as pd", + "import matplotlib.pyplot as plt", + "import matplotlib.gridspec as gridspec", + "import seaborn as sns", + "", + "from sklearn.pipeline import Pipeline", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer", + "from sklearn.linear_model import LogisticRegression", + "from sklearn.naive_bayes import MultinomialNB, ComplementNB", + "from sklearn.model_selection import GridSearchCV, GroupKFold", + "from sklearn.metrics import (", + " accuracy_score, precision_score, recall_score,", + " f1_score, classification_report, confusion_matrix,", + ")", + "", + "warnings.filterwarnings('ignore')", + "", + "SEED = 42", + "random.seed(SEED)", + "np.random.seed(SEED)", + "", + "# ── Locate or upload the JSONL data file ─────────────────────────────────", + 'FILENAME = "sarcasm_pairs_step35_clean.jsonl"', + "", + "def _locate_file(filename):", + " candidates = []", + " for root in [Path.cwd()] + list(Path.cwd().parents):", + " for sub in [", + ' Path("data") / "processed" / filename,', + ' Path("data") / filename,', + " Path(filename),", + " ]:", + " candidates.append(root / sub)", + " for p in [", + ' Path("/content") / filename,', + ' Path("/mnt/data") / filename,', + " ]:", + " candidates.append(p)", + " _c = Path('/content')", + " for p in (_c.rglob(filename) if _c.exists() else []):", + " candidates.append(p)", + " for p in candidates:", + " if p.is_file():", + " return p", + " return None", + "", + "print(f'cwd: {Path.cwd()}')", + "print(f'files in cwd: {[p.name for p in Path.cwd().iterdir()][:10]}')", + "", + "DATA_FILE = _locate_file(FILENAME)", + "if DATA_FILE is None:", + " try:", + " from google.colab import files as _cf", + ' print(f"Upload {FILENAME!r}:")', + " _up = _cf.upload()", + " if not _up:", + ' raise RuntimeError("No file uploaded.")', + " _name = list(_up.keys())[0]", + ' DATA_FILE = Path("/content") / FILENAME', + " if Path(_name) != DATA_FILE:", + " shutil.move(_name, str(DATA_FILE))", + ' print(f"Saved to {DATA_FILE}")', + " except ImportError:", + " raise FileNotFoundError(", + ' f"Cannot find {FILENAME!r}. Place it in the same folder as this notebook."', + " )", + "", + "# ── Project root + all output directories ────────────────────────────────", + "def _find_root(data_file):", + ' for parent in [data_file.parent] + list(data_file.parents):', + ' if any((parent / m).exists() for m in ["outputs","notebooks","data"]):', + " return parent", + " return data_file.parent", + "", + "ROOT = _find_root(DATA_FILE)", + "", + "OUT_DATASETS = ROOT / 'outputs' / 'datasets'", + "OUT_SPLITS = ROOT / 'outputs' / 'splits'", + "OUT_TFIDF = ROOT / 'outputs' / 'classical' / 'tfidf_lr'", + "OUT_NB = ROOT / 'outputs' / 'classical' / 'naive_bayes'", + "BERT_OUT = ROOT / 'outputs' / 'bert'", + "REPORTS_DIR = ROOT / 'outputs' / 'reports'", + "SPLITS = OUT_SPLITS", + "", + "for d in [OUT_DATASETS, OUT_SPLITS, OUT_TFIDF, OUT_NB, BERT_OUT, REPORTS_DIR]:", + " d.mkdir(parents=True, exist_ok=True)", + "", + 'print(f"Data : {DATA_FILE}")', + 'print(f"Root : {ROOT}")', + 'print(f"Output : {ROOT / \'outputs\'}")', +]) + "\n" + +# Validate setup cell +try: + compile(SETUP_SRC, "", "exec") + print("Setup cell: OK") +except SyntaxError as e: + lines = SETUP_SRC.split("\n") + print(f"SyntaxError line {e.lineno}: {lines[e.lineno-1]!r} — {e}") + raise + + +# ── Helper: make a code cell ────────────────────────────────────────────────── +def code_cell(src): + return { + "cell_type": "code", + "execution_count": None, + "metadata": {}, + "outputs": [], + "source": src, + } + +def md_cell(src): + return { + "cell_type": "markdown", + "metadata": {}, + "source": src, + } + + +# ── Determine which cells are "setup" cells to skip ────────────────────────── +# A setup cell is: first code cell in each notebook that defines ROOT/_find_ +def is_setup_cell(src): + triggers = [ + "_find_project_root", + "_find_data_file", + "_locate_file", + "Colab / environment setup", + "import json, hashlib, random", + "SEED = 42\nrandom.seed", + ] + return any(t in src for t in triggers) + + +# ── Collect content cells from each notebook (skip setup cells) ─────────────── +def content_cells(cells): + """Return cells that are not setup cells.""" + out = [] + for c in cells: + if c["type"] == "code" and is_setup_cell(c["src"]): + continue + # Patch nb02's OUT_DIR references to use OUT_TFIDF + src = c["src"].replace( + 'ROOT / "outputs" / "classical" / "tfidf_lr"', + "OUT_TFIDF" + ).replace( + "OUT_DIR", "OUT_TFIDF" + ) if c["type"] == "code" else c["src"] + + out.append({"type": c["type"], "src": src}) + return out + +def content_cells_nb(cells, out_dir_var, out_dir_val): + """Return cells, replacing OUT_DIR with the right variable.""" + result = [] + for c in cells: + if c["type"] == "code" and is_setup_cell(c["src"]): + continue + src = c["src"] + if c["type"] == "code": + src = src.replace("OUT_DIR", out_dir_var) + result.append({"type": c["type"], "src": src}) + return result + + +# ── Build combined cell list ────────────────────────────────────────────────── +all_cells = [] + +# Title +all_cells.append(md_cell( + "# Sarcasm Classification — Complete Pipeline\n\n" + "**Sections**:\n" + "1. Setup & Data Preparation\n" + "2. TF-IDF + Logistic Regression Baseline\n" + "3. Naive Bayes Baseline\n" + "4. BERT / DistilBERT Classification\n" + "5. Error Analysis & Model Comparison\n\n" + "**Run all cells in order (Runtime → Run all).**" +)) + +# Setup cell +all_cells.append(code_cell(SETUP_SRC)) + +# ── Section 1: Data Preparation ─────────────────────────────────────────────── +all_cells.append(md_cell("---\n# Part 1 — Data Preparation")) +for c in content_cells(nb01): + if c["type"] == "markdown": + all_cells.append(md_cell(c["src"])) + else: + all_cells.append(code_cell(c["src"])) + +# ── Section 2: TF-IDF + LR ─────────────────────────────────────────────────── +all_cells.append(md_cell("---\n# Part 2 — TF-IDF + Logistic Regression Baseline")) +for c in content_cells_nb(nb02, "OUT_TFIDF", "OUT_TFIDF"): + if c["type"] == "markdown": + all_cells.append(md_cell(c["src"])) + else: + all_cells.append(code_cell(c["src"])) + +# ── Section 3: Naive Bayes ──────────────────────────────────────────────────── +all_cells.append(md_cell("---\n# Part 3 — Naive Bayes Baseline")) +for c in content_cells_nb(nb03, "OUT_NB", "OUT_NB"): + if c["type"] == "markdown": + all_cells.append(md_cell(c["src"])) + else: + all_cells.append(code_cell(c["src"])) + +# ── Section 4: BERT ─────────────────────────────────────────────────────────── +all_cells.append(md_cell("---\n# Part 4 — BERT / DistilBERT Classification")) +for c in content_cells_nb(nb04, "BERT_OUT", "BERT_OUT"): + if c["type"] == "markdown": + all_cells.append(md_cell(c["src"])) + else: + all_cells.append(code_cell(c["src"])) + +# ── Section 5: Error Analysis ───────────────────────────────────────────────── +all_cells.append(md_cell("---\n# Part 5 — Error Analysis & Model Comparison")) +for c in content_cells_nb(nb05, "REPORTS_DIR", "REPORTS_DIR"): + if c["type"] == "markdown": + all_cells.append(md_cell(c["src"])) + else: + # Also patch CLASSICAL and BERT_OUT references (already correct var names) + all_cells.append(code_cell(c["src"])) + +# ── Compile-check all code cells ────────────────────────────────────────────── +errors = [] +for i, c in enumerate(all_cells): + if c["cell_type"] == "code": + src = c["source"] + try: + compile(src, f"cell{i}", "exec") + except SyntaxError as e: + errors.append((i, e.lineno, str(e), src.split("\n")[e.lineno-1] if e.lineno else "")) + +if errors: + print(f"\n{len(errors)} syntax error(s):") + for i, ln, msg, line in errors: + print(f" Cell {i:3d} line {ln}: {line!r}") + print(f" {msg}") +else: + print(f"All {len(all_cells)} cells: syntax OK") + +# ── Write combined notebook ─────────────────────────────────────────────────── +combined = { + "nbformat": 4, + "nbformat_minor": 4, + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3", + }, + "language_info": {"name": "python", "version": "3.11.0"}, + "colab": {"provenance": []}, + }, + "cells": all_cells, +} + +out_nb = OUT_DIR / "sarcasm_classification.ipynb" +out_nb.write_bytes(json.dumps(combined, indent=1).encode("utf-8")) +print(f"\nSaved: {out_nb}") +print(f"Total cells: {len(all_cells)}") 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"1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "w41uXUSeuKBO" + }, + "source": [ + "# Sarcasm Classification — Complete Pipeline\n", + "\n", + "**Sections**:\n", + "1. Setup & Data Preparation\n", + "2. TF-IDF + Logistic Regression Baseline\n", + "3. Naive Bayes Baseline\n", + "4. BERT / DistilBERT Classification\n", + "5. Error Analysis & Model Comparison\n", + "\n", + "**Run all cells in order (Runtime → Run all).**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1xG7cJfAuKBR", + "outputId": "12069dde-b810-4ab5-f89b-5fa1af05cf8c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "cwd: /content\n", + "files in cwd: ['.config', 'sarcasm_pairs_step35_clean.jsonl', 'sample_data']\n", + "Data : /content/sarcasm_pairs_step35_clean.jsonl\n", + "Root : /content\n", + "Output : /content/outputs\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# SETUP — imports, file upload, paths\n", + "# ============================================================\n", + "from __future__ import annotations\n", + "import json, hashlib, random, os, warnings, shutil\n", + "from dataclasses import dataclass\n", + "from pathlib import Path\n", + "from collections import Counter\n", + "from urllib.parse import urlparse\n", + "from typing import Optional\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "import seaborn as sns\n", + "\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.naive_bayes import MultinomialNB, ComplementNB\n", + "from sklearn.model_selection import GridSearchCV, GroupKFold\n", + "from sklearn.metrics import (\n", + " accuracy_score, precision_score, recall_score,\n", + " f1_score, classification_report, confusion_matrix,\n", + ")\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "SEED = 42\n", + "random.seed(SEED)\n", + "np.random.seed(SEED)\n", + "\n", + "# ── Locate or upload the JSONL data file ─────────────────────────────────\n", + "FILENAME = \"sarcasm_pairs_step35_clean.jsonl\"\n", + "\n", + "def _locate_file(filename):\n", + " candidates = []\n", + " for root in [Path.cwd()] + list(Path.cwd().parents):\n", + " for sub in [\n", + " Path(\"data\") / \"processed\" / filename,\n", + " Path(\"data\") / filename,\n", + " Path(filename),\n", + " ]:\n", + " candidates.append(root / sub)\n", + " for p in [\n", + " Path(\"/content\") / filename,\n", + " Path(\"/mnt/data\") / filename,\n", + " ]:\n", + " candidates.append(p)\n", + " _c = Path('/content')\n", + " for p in (_c.rglob(filename) if _c.exists() else []):\n", + " candidates.append(p)\n", + " for p in candidates:\n", + " if p.is_file():\n", + " return p\n", + " return None\n", + "\n", + "print(f'cwd: {Path.cwd()}')\n", + "print(f'files in cwd: {[p.name for p in Path.cwd().iterdir()][:10]}')\n", + "\n", + "DATA_FILE = _locate_file(FILENAME)\n", + "if DATA_FILE is None:\n", + " try:\n", + " from google.colab import files as _cf\n", + " print(f\"Upload {FILENAME!r}:\")\n", + " _up = _cf.upload()\n", + " if not _up:\n", + " raise RuntimeError(\"No file uploaded.\")\n", + " _name = list(_up.keys())[0]\n", + " DATA_FILE = Path(\"/content\") / FILENAME\n", + " if Path(_name) != DATA_FILE:\n", + " shutil.move(_name, str(DATA_FILE))\n", + " print(f\"Saved to {DATA_FILE}\")\n", + " except ImportError:\n", + " raise FileNotFoundError(\n", + " f\"Cannot find {FILENAME!r}. Place it in the same folder as this notebook.\"\n", + " )\n", + "\n", + "# ── Project root + all output directories ────────────────────────────────\n", + "def _find_root(data_file):\n", + " for parent in [data_file.parent] + list(data_file.parents):\n", + " if any((parent / m).exists() for m in [\"outputs\",\"notebooks\",\"data\"]):\n", + " return parent\n", + " return data_file.parent\n", + "\n", + "ROOT = _find_root(DATA_FILE)\n", + "\n", + "OUT_DATASETS = ROOT / 'outputs' / 'datasets'\n", + "OUT_SPLITS = ROOT / 'outputs' / 'splits'\n", + "OUT_TFIDF = ROOT / 'outputs' / 'classical' / 'tfidf_lr'\n", + "OUT_NB = ROOT / 'outputs' / 'classical' / 'naive_bayes'\n", + "BERT_OUT = ROOT / 'outputs' / 'bert'\n", + "REPORTS_DIR = ROOT / 'outputs' / 'reports'\n", + "SPLITS = OUT_SPLITS\n", + "\n", + "for d in [OUT_DATASETS, OUT_SPLITS, OUT_TFIDF, OUT_NB, BERT_OUT, REPORTS_DIR]:\n", + " d.mkdir(parents=True, exist_ok=True)\n", + "\n", + "print(f\"Data : {DATA_FILE}\")\n", + "print(f\"Root : {ROOT}\")\n", + "print(f\"Output : {ROOT / 'outputs'}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xDqObfuPuKBU" + }, + "source": [ + "---\n", + "# Part 1 — Data Preparation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RVi4jvpmuKBV" + }, + "source": [ + "# Notebook 01 — Data Preparation\n", + "\n", + "**Purpose**: Parse JSONL dataset, derive binary and type labels, perform audit checks, generate group-safe train/val/test splits.\n", + "\n", + "**Outputs**:\n", + "- `outputs/datasets/binary_dataset.csv`\n", + "- `outputs/datasets/type_dataset.csv`\n", + "- `outputs/splits/train_binary.csv`, `val_binary.csv`, `test_binary.csv`\n", + "- `outputs/splits/train_type.csv`, `val_type.csv`, `test_type.csv`\n", + "- `outputs/splits/split_metadata.json`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "egvV3LLJuKBV" + }, + "source": [ + "## 1. Load and Validate Raw Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oqhoOTiguKBV", + "outputId": "9211b95a-f157-40ce-9d18-7275e0c157a3" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loaded rows : 28,333\n", + "Error rows : 0\n" + ] + } + ], + "source": [ + "REQUIRED_FIELDS = {\"original_headline\", \"generated_headline\", \"strategy\", \"type\", \"model_used\", \"article_link\"}\n", + "ALLOWED_TYPES = {\"sarcastic_to_non\", \"non_to_sarcastic\"}\n", + "ALLOWED_STRATS = {\"irony\", \"overstatement\", \"rhetorical_question\", \"sarcasm\", \"satire\", \"understatement\"}\n", + "\n", + "raw_rows = []\n", + "errors = []\n", + "\n", + "with open(DATA_FILE, \"r\", encoding=\"utf-8\") as f:\n", + " for line_num, line in enumerate(f):\n", + " line = line.strip()\n", + " if not line:\n", + " continue\n", + " try:\n", + " row = json.loads(line)\n", + " except json.JSONDecodeError as e:\n", + " errors.append((line_num, f\"JSON parse error: {e}\"))\n", + " continue\n", + "\n", + " # Schema check\n", + " missing = REQUIRED_FIELDS - set(row.keys())\n", + " if missing:\n", + " errors.append((line_num, f\"Missing fields: {missing}\"))\n", + " continue\n", + "\n", + " # Value checks\n", + " if row[\"type\"] not in ALLOWED_TYPES:\n", + " errors.append((line_num, f\"Unknown type: {row['type']!r}\"))\n", + " continue\n", + " if row[\"strategy\"] not in ALLOWED_STRATS:\n", + " errors.append((line_num, f\"Unknown strategy: {row['strategy']!r}\"))\n", + " continue\n", + "\n", + " row[\"_line_num\"] = line_num\n", + " raw_rows.append(row)\n", + "\n", + "print(f\"Loaded rows : {len(raw_rows):,}\")\n", + "print(f\"Error rows : {len(errors)}\")\n", + "if errors:\n", + " for ln, msg in errors[:5]:\n", + " print(f\" Line {ln}: {msg}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yvg9qFhouKBW" + }, + "source": [ + "## 2. Dataset Audit" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KISNWAQhuKBW", + "outputId": "e8755fd1-8661-4393-f3a9-429cb93065fd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "=== Type distribution ===\n", + " non_to_sarcastic: 14,925 (52.7%)\n", + " sarcastic_to_non: 13,408 (47.3%)\n", + "\n", + "=== Strategy distribution ===\n", + " sarcasm: 8,699 (30.7%)\n", + " irony: 6,102 (21.5%)\n", + " satire: 5,224 (18.4%)\n", + " overstatement: 3,976 (14.0%)\n", + " understatement: 3,295 (11.6%)\n", + " rhetorical_question: 1,037 (3.7%)\n", + "\n", + "=== Model distribution ===\n", + " stepfun/step-3.5-flash:free: 28,333\n" + ] + } + ], + "source": [ + "# ── Type and Strategy distributions ──────────────────────────────────────────\n", + "type_counts = Counter(r[\"type\"] for r in raw_rows)\n", + "strategy_counts = Counter(r[\"strategy\"] for r in raw_rows)\n", + "model_counts = Counter(r[\"model_used\"] for r in raw_rows)\n", + "\n", + "print(\"=== Type distribution ===\")\n", + "for k, v in type_counts.most_common():\n", + " print(f\" {k}: {v:,} ({v/len(raw_rows)*100:.1f}%)\")\n", + "\n", + "print(\"\\n=== Strategy distribution ===\")\n", + "for k, v in strategy_counts.most_common():\n", + " print(f\" {k}: {v:,} ({v/len(raw_rows)*100:.1f}%)\")\n", + "\n", + "print(\"\\n=== Model distribution ===\")\n", + "for k, v in model_counts.most_common():\n", + " print(f\" {k}: {v:,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "28wpsTTnuKBX", + "outputId": "bdcc2561-9baa-41a3-bac8-14101d7e4596" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Duplicate original_headlines : 70\n", + "Duplicate generated_headlines: 1\n", + "Duplicate article_links : 2\n" + ] + } + ], + "source": [ + "# ── Duplicate checks ──────────────────────────────────────────────────────────\n", + "orig_headlines = [r[\"original_headline\"] for r in raw_rows]\n", + "gen_headlines = [r[\"generated_headline\"] for r in raw_rows]\n", + "article_links = [r[\"article_link\"] for r in raw_rows]\n", + "\n", + "dup_orig = len(orig_headlines) - len(set(orig_headlines))\n", + "dup_gen = len(gen_headlines) - len(set(gen_headlines))\n", + "dup_article = len(article_links) - len(set(article_links))\n", + "\n", + "print(f\"Duplicate original_headlines : {dup_orig}\")\n", + "print(f\"Duplicate generated_headlines: {dup_gen}\")\n", + "print(f\"Duplicate article_links : {dup_article}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SLUFPDIHuKBX", + "outputId": "3d7b15de-a43f-410f-a381-6a9544171b45" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "original_headline : 0 nulls, 0 empty strings\n", + "generated_headline : 0 nulls, 0 empty strings\n", + "strategy : 0 nulls, 0 empty strings\n", + "type : 0 nulls, 0 empty strings\n", + "model_used : 0 nulls, 0 empty strings\n", + "article_link : 0 nulls, 0 empty strings\n" + ] + } + ], + "source": [ + "# ── Null / empty checks ───────────────────────────────────────────────────────\n", + "for field in [\"original_headline\", \"generated_headline\", \"strategy\", \"type\", \"model_used\", \"article_link\"]:\n", + " null_count = sum(1 for r in raw_rows if r.get(field) is None)\n", + " empty_count = sum(1 for r in raw_rows if r.get(field) == \"\")\n", + " print(f\"{field:25s}: {null_count} nulls, {empty_count} empty strings\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 524 + }, + "id": "E-B0fc5vuKBX", + "outputId": "a7845ca8-655c-48df-c59b-28b16307b91d" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/datasets/distributions.png\n" + ] + } + ], + "source": [ + "# ── Strategy distribution plot ────────────────────────────────────────────────\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Type dist\n", + "ax = axes[0]\n", + "labels, counts = zip(*sorted(type_counts.items(), key=lambda x: -x[1]))\n", + "ax.barh(labels, counts, color=[\"steelblue\", \"coral\"])\n", + "ax.set_title(\"Row-level Type Distribution\", fontsize=14)\n", + "ax.set_xlabel(\"Count\")\n", + "for i, c in enumerate(counts):\n", + " ax.text(c + 50, i, f\"{c:,}\", va=\"center\")\n", + "\n", + "# Strategy dist\n", + "ax = axes[1]\n", + "labels2, counts2 = zip(*sorted(strategy_counts.items(), key=lambda x: -x[1]))\n", + "ax.barh(labels2, counts2, color=\"steelblue\")\n", + "ax.set_title(\"Strategy Distribution (Type Task Labels)\", fontsize=14)\n", + "ax.set_xlabel(\"Count\")\n", + "for i, c in enumerate(counts2):\n", + " ax.text(c + 30, i, f\"{c:,}\", va=\"center\")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(OUT_DATASETS / \"distributions.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/datasets/distributions.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JxKcjhRSuKBY" + }, + "source": [ + "## 3. Label Derivation and Dataset Expansion" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nukGQcmKuKBY", + "outputId": "2fcfb601-8ad0-4876-b813-c21b007bb164" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Binary dataset : 56,666 samples\n", + " sarcastic (1): 28,333\n", + " non-sarc (0): 28,333\n", + "\n", + "Type dataset : 28,333 samples\n", + "type_label\n", + "sarcasm 8699\n", + "irony 6102\n", + "satire 5224\n", + "overstatement 3976\n", + "understatement 3295\n", + "rhetorical_question 1037\n" + ] + } + ], + "source": [ + "from __future__ import annotations\n", + "from urllib.parse import urlparse\n", + "\n", + "\n", + "def normalize_url(url: str) -> str:\n", + " \"\"\"Lowercase and strip scheme/trailing slash for stable group IDs.\"\"\"\n", + " url = url.strip().lower()\n", + " parsed = urlparse(url)\n", + " normalized = (parsed.netloc + parsed.path).rstrip(\"/\")\n", + " return normalized if normalized else url\n", + "\n", + "\n", + "def derive_labels(raw_rows):\n", + " \"\"\"\n", + " Expand each JSONL row into 2 samples (sarcastic + non-sarcastic).\n", + "\n", + " Rules:\n", + " sarcastic_to_non -> original=sarcastic(1), generated=non-sarcastic(0)\n", + " non_to_sarcastic -> original=non-sarcastic(0), generated=sarcastic(1)\n", + "\n", + " Returns binary_df, type_df\n", + " \"\"\"\n", + " binary_records = []\n", + " sample_id = 0\n", + "\n", + " for pair_id, row in enumerate(raw_rows):\n", + " group_id = normalize_url(row[\"article_link\"]) if row[\"article_link\"] else str(pair_id)\n", + " row_type = row[\"type\"]\n", + " strategy = row[\"strategy\"]\n", + " model_used = row[\"model_used\"]\n", + " article = row[\"article_link\"]\n", + "\n", + " if row_type == \"sarcastic_to_non\":\n", + " orig_label, orig_strat = 1, strategy\n", + " gen_label, gen_strat = 0, None\n", + " else:\n", + " orig_label, orig_strat = 0, None\n", + " gen_label, gen_strat = 1, strategy\n", + "\n", + " binary_records.append({\n", + " \"sample_id\" : sample_id,\n", + " \"pair_id\" : pair_id,\n", + " \"group_id\" : group_id,\n", + " \"text\" : row[\"original_headline\"],\n", + " \"binary_label\" : orig_label,\n", + " \"is_generated\" : 0,\n", + " \"strategy\" : orig_strat,\n", + " \"source_type\" : row_type,\n", + " \"article_link\" : article,\n", + " \"model_used\" : model_used,\n", + " })\n", + " sample_id += 1\n", + "\n", + " binary_records.append({\n", + " \"sample_id\" : sample_id,\n", + " \"pair_id\" : pair_id,\n", + " \"group_id\" : group_id,\n", + " \"text\" : row[\"generated_headline\"],\n", + " \"binary_label\" : gen_label,\n", + " \"is_generated\" : 1,\n", + " \"strategy\" : gen_strat,\n", + " \"source_type\" : row_type,\n", + " \"article_link\" : article,\n", + " \"model_used\" : model_used,\n", + " })\n", + " sample_id += 1\n", + "\n", + " binary_df = pd.DataFrame(binary_records)\n", + "\n", + " # Validate counts\n", + " counts = binary_df[\"binary_label\"].value_counts().to_dict()\n", + " n_expected = len(raw_rows)\n", + " n0 = int(counts.get(0, 0))\n", + " n1 = int(counts.get(1, 0))\n", + " if n0 != n_expected or n1 != n_expected:\n", + " raise ValueError(\n", + " f\"Label count mismatch!\\n\"\n", + " f\" Expected {n_expected} per class\\n\"\n", + " f\" Got: sarcastic(1)={n1}, non-sarcastic(0)={n0}\"\n", + " )\n", + "\n", + " # Type dataset: sarcastic only\n", + " type_df = binary_df[binary_df[\"binary_label\"] == 1].copy()\n", + " type_df = type_df.rename(columns={\"strategy\": \"type_label\"})\n", + " type_df = type_df[[\"sample_id\", \"pair_id\", \"group_id\", \"text\",\n", + " \"type_label\", \"is_generated\", \"source_type\",\n", + " \"article_link\", \"model_used\"]]\n", + "\n", + " missing = type_df[\"type_label\"].isna().sum()\n", + " if missing > 0:\n", + " raise ValueError(f\"{missing} sarcastic samples have no type_label!\")\n", + "\n", + " return binary_df, type_df\n", + "\n", + "\n", + "binary_df, type_df = derive_labels(raw_rows)\n", + "\n", + "print(f\"Binary dataset : {len(binary_df):,} samples\")\n", + "print(f\" sarcastic (1): {(binary_df['binary_label']==1).sum():,}\")\n", + "print(f\" non-sarc (0): {(binary_df['binary_label']==0).sum():,}\")\n", + "print()\n", + "print(f\"Type dataset : {len(type_df):,} samples\")\n", + "print(type_df[\"type_label\"].value_counts().to_string())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5beT2LM9uKBY", + "outputId": "203852f9-bcfd-4ac5-8f83-61f9e0d10219" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/datasets/binary_dataset.csv\n", + "Saved: outputs/datasets/type_dataset.csv\n" + ] + } + ], + "source": [ + "# Save full datasets\n", + "binary_df.to_csv(OUT_DATASETS / \"binary_dataset.csv\", index=False)\n", + "type_df.to_csv( OUT_DATASETS / \"type_dataset.csv\", index=False)\n", + "print(\"Saved: outputs/datasets/binary_dataset.csv\")\n", + "print(\"Saved: outputs/datasets/type_dataset.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5GtLZhBwuKBZ" + }, + "source": [ + "## 4. Group-Aware Train / Val / Test Split" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OMGqQn3PuKBZ", + "outputId": "0eb38913-1ff6-4524-e2e6-030cf7c34b90" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "=== Binary splits ===\n", + " train: 39,666 samples | sarcastic=19,833 non=19,833\n", + " val : 8,500 samples | sarcastic=4,250 non=4,250\n", + " test : 8,500 samples | sarcastic=4,250 non=4,250\n", + "\n", + "=== Type splits ===\n", + " train: 19,833 samples\n", + " sarcasm: 6,091\n", + " irony: 4,258\n", + " satire: 3,644\n", + " overstatement: 2,784\n", + " understatement: 2,352\n", + " rhetorical_question: 704\n", + " val : 4,250 samples\n", + " sarcasm: 1,317\n", + " irony: 942\n", + " satire: 747\n", + " overstatement: 600\n", + " understatement: 487\n", + " rhetorical_question: 157\n", + " test : 4,250 samples\n", + " sarcasm: 1,291\n", + " irony: 902\n", + " satire: 833\n", + " overstatement: 592\n", + " understatement: 456\n", + " rhetorical_question: 176\n" + ] + } + ], + "source": [ + "def group_aware_split(\n", + " df: pd.DataFrame,\n", + " group_col: str = \"group_id\",\n", + " train_frac: float = 0.70,\n", + " val_frac: float = 0.15,\n", + " seed: int = SEED,\n", + ") -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n", + " \"\"\"\n", + " Split df into train/val/test at the group level.\n", + " All rows sharing a group_id go to exactly one split.\n", + " \"\"\"\n", + " groups = df[group_col].unique().tolist()\n", + " rng = np.random.default_rng(seed)\n", + " rng.shuffle(groups)\n", + "\n", + " n = len(groups)\n", + " n_train = int(n * train_frac)\n", + " n_val = int(n * val_frac)\n", + "\n", + " train_groups = set(groups[:n_train])\n", + " val_groups = set(groups[n_train : n_train + n_val])\n", + " test_groups = set(groups[n_train + n_val :])\n", + "\n", + " train_df = df[df[group_col].isin(train_groups)].copy()\n", + " val_df = df[df[group_col].isin(val_groups)].copy()\n", + " test_df = df[df[group_col].isin(test_groups)].copy()\n", + "\n", + " # Sanity: no overlap\n", + " assert train_groups.isdisjoint(val_groups), \"Train/val group overlap!\"\n", + " assert train_groups.isdisjoint(test_groups), \"Train/test group overlap!\"\n", + " assert val_groups.isdisjoint(test_groups), \"Val/test group overlap!\"\n", + "\n", + " return train_df, val_df, test_df\n", + "\n", + "\n", + "# ── Binary splits ─────────────────────────────────────────────────────────────\n", + "train_bin, val_bin, test_bin = group_aware_split(binary_df)\n", + "\n", + "print(\"=== Binary splits ===\")\n", + "for name, df in [(\"train\", train_bin), (\"val\", val_bin), (\"test\", test_bin)]:\n", + " dist = df[\"binary_label\"].value_counts().to_dict()\n", + " print(f\" {name:5s}: {len(df):,} samples | sarcastic={dist.get(1,0):,} non={dist.get(0,0):,}\")\n", + "\n", + "# ── Type splits ───────────────────────────────────────────────────────────────\n", + "train_type, val_type, test_type = group_aware_split(type_df)\n", + "\n", + "print(\"\\n=== Type splits ===\")\n", + "for name, df in [(\"train\", train_type), (\"val\", val_type), (\"test\", test_type)]:\n", + " print(f\" {name:5s}: {len(df):,} samples\")\n", + " dist = df[\"type_label\"].value_counts()\n", + " for label, cnt in dist.items():\n", + " print(f\" {label}: {cnt:,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Oo78ufPjuKBZ", + "outputId": "6b324c55-2718-4236-d356-e7c7a44e91a5" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train/Val pair_id overlap : 0 (must be 0)\n", + "Train/Test pair_id overlap : 0 (must be 0)\n", + "Val/Test pair_id overlap : 0 (must be 0)\n", + "\n", + "Leakage check PASSED: No pair_id crosses split boundaries.\n" + ] + } + ], + "source": [ + "# ── Verify no pair crosses split boundary ─────────────────────────────────────\n", + "# A pair_id should appear in at most ONE binary split\n", + "train_pairs = set(train_bin[\"pair_id\"])\n", + "val_pairs = set(val_bin[\"pair_id\"])\n", + "test_pairs = set(test_bin[\"pair_id\"])\n", + "\n", + "tv_overlap = train_pairs & val_pairs\n", + "tt_overlap = train_pairs & test_pairs\n", + "vt_overlap = val_pairs & test_pairs\n", + "\n", + "print(f\"Train/Val pair_id overlap : {len(tv_overlap)} (must be 0)\")\n", + "print(f\"Train/Test pair_id overlap : {len(tt_overlap)} (must be 0)\")\n", + "print(f\"Val/Test pair_id overlap : {len(vt_overlap)} (must be 0)\")\n", + "\n", + "assert len(tv_overlap) == 0 and len(tt_overlap) == 0 and len(vt_overlap) == 0, \\\n", + " \"Leakage detected: pairs crossing split boundaries!\"\n", + "print(\"\\nLeakage check PASSED: No pair_id crosses split boundaries.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HbbbOgpZuKBZ", + "outputId": "2716fcd8-e654-46c8-9b14-765336c8f3b9" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/splits/train_binary.csv\n", + "Saved: outputs/splits/val_binary.csv\n", + "Saved: outputs/splits/test_binary.csv\n", + "Saved: outputs/splits/train_type.csv\n", + "Saved: outputs/splits/val_type.csv\n", + "Saved: outputs/splits/test_type.csv\n", + "\n", + "Saved: outputs/splits/split_metadata.json\n" + ] + } + ], + "source": [ + "# ── Save splits ───────────────────────────────────────────────────────────────\n", + "split_files = {\n", + " \"train_binary\": train_bin,\n", + " \"val_binary\" : val_bin,\n", + " \"test_binary\" : test_bin,\n", + " \"train_type\" : train_type,\n", + " \"val_type\" : val_type,\n", + " \"test_type\" : test_type,\n", + "}\n", + "for fname, df in split_files.items():\n", + " path = OUT_SPLITS / f\"{fname}.csv\"\n", + " df.to_csv(path, index=False)\n", + " print(f\"Saved: {path.relative_to(ROOT)}\")\n", + "\n", + "# Metadata\n", + "import json as _json\n", + "metadata = {\n", + " \"seed\": SEED,\n", + " \"train_frac\": 0.70,\n", + " \"val_frac\": 0.15,\n", + " \"test_frac\": 0.15,\n", + " \"group_col\": \"group_id\",\n", + " \"total_pairs\": len(raw_rows),\n", + " \"binary\": {\n", + " \"train\": len(train_bin), \"val\": len(val_bin), \"test\": len(test_bin)\n", + " },\n", + " \"type\": {\n", + " \"train\": len(train_type), \"val\": len(val_type), \"test\": len(test_type)\n", + " },\n", + "}\n", + "with open(OUT_SPLITS / \"split_metadata.json\", \"w\") as f:\n", + " _json.dump(metadata, f, indent=2)\n", + "print(\"\\nSaved: outputs/splits/split_metadata.json\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j7ZguloUuKBa" + }, + "source": [ + "## 5. Split Distribution Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "id": "nLrEPxJ9uKBa", + "outputId": "508ba885-ab89-400e-8f7c-c251d636a688" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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1Jemrr77Sf//9p44dO8rDw0Nz5sxRt27ddPHiRQ0ePNjcbteuXapcubK8vb3VsWNH5c6dW3v37tWXX36pzZs3a8OGDcqSJUuGnMf06dN18eJFtWrVSgEBAeafiSpVqmjdunXJlje5du2awsPD9corr2jEiBE6dOiQJkyYoK1bt2r37t0WSdnbb7+tOXPmqFGjRmrbtq1u3bql2bNn6/XXX9eiRYtUp06dFOPasGGD6tSpo6JFi6pfv37y9vbW6dOntWbNGh0+fPihf6xs2LBB3t7eKbY7deqUwsPDVb9+fY0ePVp79+7Vt99+q9jYWK1atcrc7s6dO1bvT5eSe5dWS4tbt25JklxdXZPtS9q2bds25c2b94H9VKtWTVu3blXr1q3Vu3dv+fr66s8//1TPnj0VEhKit956K9kx169fNz927dqlPn36yNHRUVWrVrX6GufPn1diYqJiYmI0efJkHTx4UO+8847VtmXLltWsWbMUFxfHUnPAc65t27YaPny4oqKizEWDJHPnztWNGzfUrl07SRmXn9wvtf3269fPYglCf39/i34aN26s+fPnW2y7N8fInj37A++tkxZhYWF66aWX9N1332nYsGEWAxOnTp3SypUr9c477yS7iKN69erKmjWrhgwZov/++0/jx49XxYoVtWXLFhUtWtTcbsCAAfrkk09UvXp1ffzxx7Kzs9PixYvVuHFjjR8/Xp07d86Q87h9+7ZGjx6thg0bqm7dunJzc9OOHTs0depUbdq0Sbt27ZKjo6PFMcuWLdPRo0fVuXNn5cyZU8uWLdPQoUN14sQJRUVFmdudPHlSZcuW1e3bt9WuXTsVKFBAhw8f1jfffKN169Zp586d8vLySjG2zp07a9q0aWrVqpV69Oih+Ph4HTp0SL/++utDz+vMmTOKjo5Wt27dUmwzceJEnTlzRu3atZO3t7dmzZqlPn36KCAgQM2bNze3i4uL082bNx/6mtLdZbHv/16NjY1Vly5d1LFjR7388suaMGFCqvp6mH///VeSlCNHjmT7Tp06JXd3d924cUOurq6qVq2aPv30U4sl5xITE/XHH3+oZMmScnZ2tjj+5Zdflslk0o4dO5L1XbZsWa1cuVJ///231SXsACCtUvv36cyZM9W6dWtVqFBBw4YNk4uLi/755x8tX75cZ8+elZ+fn8aOHat+/frp/PnzFjnDw5a0ZOyDsY/7PUtjH/369VNCQoI++eQT7d69O9V9PciD8hBJqlOnjq5evSqTyWS+SKtly5YWbZLyjLJlyyY7vkyZMpozZ06yMYuktuvXr7coqAMWMnMaIYBnU0pLUgQGBhqSjMmTJyc75tatW8bt27eTbR8wYIAhydi2bZt5m7Wlk5K2ubq6WiwrlZiYaBQpUsTImTNnqmKPi4tLtu3AgQOGo6Oj8d577xmGYRj//vuvsXr1aqNYsWKGk5OTsXr1aovHvUtmZTRrS1ZcunTJcHFxMRo3bmzR9uTJk4adnZ057nuPd3d3t1i+8datW0bp0qUNBwcHi+3FihUzgoODky2rumjRolQvO5LaJSusvff//fefkS1bNqNGjRrJ+pRkvP/++1bj6tixY7Jt3377rUXbO3fuGC+99JIRFBRkJCYmmrfrviUrPvjgA0NSupe+yps3rxEWFmZ1X9LvxP3LUXbq1MmQZPz999/mbUmfXWofD/Kg5Tq//PJLq8tpXrt2zfD39zckGZGRkQ8975s3bxrvvfee4eTkZBFXzZo1rS7xYhiG0bNnT4u2RYoUMVauXGm17dWrVy3auri4GB06dLD6c2QYhvHxxx8bkoydO3c+NHYAz77KlSsb9vb2xunTpy22lylTxnB0dDQvK/io+UlKUtvv9u3bjVmzZhmSjNq1ayfLOU6ePJmW004Ta8t1fvvtt4Yk4+eff7ZoO3z48GTvR9Lx9evXt/ie3blzp2EymYxq1aqZt+3atcuQZPTr1y9ZHHXr1jU8PDweusR7apfrTExMNK5fv55s+5QpU5J9Jyf1aWdnZ+zatcuij3r16hmSjC1btpi316lTx/Dz87PIpQzDMHbs2GHY29tb/GxYW9rJx8cnWc6TWr/++qshyRg3blyyfUk5hL+/v3H58mXz9mvXrhm+vr5GmTJlLNonfXapeVhb5uvdd981cubMaX6tpP5Su1ynNadOnTK8vb2N/PnzJ/vdadOmjfHRRx8Zc+fONRYsWGD06tXLcHZ2Njw9PY19+/aZ250/f96QZDRp0sTqa/j5+Rlly5ZNtn3mzJmGJOOHH35Id/wAnk/WxkbS8vdp/fr1DQ8PD+POnTsPfJ30LOnI2AdjH8/q2MeWLVsMOzs7Y+7cuRb9pXa5zpQ0btzYkGSsXbvWYvu8efOM5s2bG1OmTDGWLVtmjBs3zihcuLDVpfbffPNNQ5LVXLR3796GJKtL2zo4OBhvvvnmI8WPZxsz+QA8UVmzZk22DKAkiyum4+PjdfXqVSUkJKhq1aoaPny4tm3bppdffvmh/derV09BQUHm5yaTSZUqVdL48eNTNYMn6epzwzDM0+z9/PwUHBysbdu2Sbp7w+ikq5bt7OxUokQJ8/F2dnbKmjXrQ+PMSN7e3mrSpInmzJmjCxcuKFu2bJLu3hg7MTHRPBvhXi1atLBYvtHR0VEffPCBmjdvrh9//FHvvfee9u/fr3379mno0KG6deuWeYaXJJUvX15ubm5atWqV2rRpkyHnce+V/3Fxcbp165bs7e31yiuvWNzE+159+/a1eF6/fn0FBwdryZIlmjhxoiRp1qxZ8vDwUL169XT+/HmL9rVr19aQIUN06NChFK82S7rifuHChYqIiJCDQ9q+Os+dO6dChQqluD9Xrlxq0qSJxbbKlStrwoQJOnTokPlGzcWLF9fq1avT9Nrp0bJlSw0fPlyDBg2Sm5ubqlatqvPnz2vw4MHm9y81S9TZ29srd+7cqlq1qurXr6+sWbNq8+bN+uqrr/TWW29p6dKlya6E7Nixo6pXr67Lly9ry5YtWr9+fbLPLImLi4tWr16t+Ph4nThxQrNnz1ZcXJyuX79udSnQpN+L1CyXBuDZ165dO/3666+aMWOG+vTpI0n6+++/tXXrVjVq1Mh8RXBG5Sf3S22/xYoVM3/v+Pn5WeQcrq6uVmddP07NmzdXz549NXXqVPNSTIZhaNq0aQoNDbX6Xnz44YcymUzm5y+99JJef/11rVmzxpybzZ49WyaTSa1bt072736dOnW0dOlSbdmyRW+88cYjn4PJZJKLi4skKSEhQVevXlV8fLwqV64s6e5s9fu/l19//XWVLFnSoo8PP/xQS5Ys0eLFi1WmTBlduXJFP/30k9q2bStnZ2eL8wgKClLBggW1atUqi+Xm7+fl5aW//vpLf/75p8Usx9RIWrbtQXlo27ZtLWYSurq6qkyZMtqyZYtFuw8//DDZlecpSVqGO8nmzZv17bffavbs2Q+ctZgW169fV/369RUXF6dly5Ylyx/unU0pSY0aNVKdOnUUHh6uHj16mPOnpPzFycnJ6us4OztbzXHIIQBkpLT8ferl5aXr16/r559/Vp06dSy+Tx8VYx+MfdzvWRj7uHPnjiIiIvT666+bl9fMCJGRkVqwYIE6dOhgzhmTNGnSJNl5dezYUaVKldLw4cPVunVr8zjlg3KRpFUGrOUiWbNmJQ/BA1HkA/BEFShQIMX7ckyYMEETJ07UX3/9lWyN9dTck0u6e++t+yUlfhcuXHhokW/37t0aOHCg1q9fn2zd9Xz58klSsiUr/Pz8zP//+uuvWywz8KR06NBB3333nWbOnKnu3bvLMAxFRUWpRIkSeumll5K1t7Z0x4svvihJ5nXdDx48KEkaPHiwxTIW9zpz5kxGnYKOHDmi/v37a+XKlbp8+bLFPmt/zHh7e1tdJz0kJERLlizRtWvX5ObmpoMHD+rq1aspLqkg3T2PlBLdLl26aOnSperUqZP69Omj8uXLq3r16mrWrJnFZ58Sk8n0wHvDPOxnNomPj0+Ky1ZmJB8fH61Zs0atWrVShw4dzNsrVqyoPn36aPjw4fL09HxoP23atNHvv/+uv/76yzyYWr9+fRUsWFDvvfeevvvuO7Vv397imEKFCpn/KGjUqJFWrlyp6tWry2QyqVmzZhZt7e3tLd6P9u3bKzw8XJUrV9Yff/yRbAAw6TPIyD+MAdiuBg0ayNvbW1FRUeYi37Rp0yQp2bK/GZGfWJOafu9drnPatGnmGCVp9uzZFkssPgnu7u5q1qyZpk+frnPnzsnPz0/r16/X0aNHNXbsWKvHpJRzrFq1SidOnFCRIkV08OBBGYbxwKUQMzLnmD9/viIjI7V7927duXPHYp+1zzQ1eVN0dLQSExM1depUTZ061errWvvOv9fYsWP19ttvKzQ0VPnz51elSpVUu3Zt1a5dW3Z2dg88Nun7LT05x/33InzxxRfN55cWt2/fVocOHVS1atVk39vpdfPmTdWrV087d+7Ud999l2wJs5RUqFBBr732mtatW6cbN27IxcXFXBS/d/D2/teyVjgnhwCQkdLy9+lHH32kjRs3ql69esqWLZsqVqyoGjVqqGnTpvLw8HikOBj7YOzjfs/C2MfIkSN1+PBhLVmyJF3HWzNlyhT17t1btWrV0vjx41N1jJOTk3r16qU2bdpo1apV5rGVe3ORpHGSJElLpaeUi5CH4EEo8gF4olK64nzMmDHq2bOn3njjDXXr1k25cuWSo6OjTp06pTZt2li9sbI1KRUQpQcPekh376Py2muvydPTUwMHDlRwcLDc3NxkMpnUvXt38/0Ds2XLptWrV2v69OmaPXu2vvjiC/PV1g8bvHlcXn31VRUtWlRTp05V9+7dtXbtWh0/fjzVCYg1Se9Xz549U1z3+9619x9FXFycXnvtNV27dk3du3dXaGioPDw8ZGdnpxEjRqTqXjQpMQxDfn5++v7771Ns86Cr5bNly6YdO3bot99+0+rVq7Vx40Z98MEHGjx4sJYvX251LfV7+fn56eLFiynuT+3P7O3btx/Yz/2s/RGQWqGhodq9e7cOHz6s06dPK1euXCpYsKD55ukPux/NyZMnNXv2bHXp0iVZ4tq4cWO999572rBhQ7Ii3/2qVaumHDlyaMKECQ8dLLS3t1eLFi303nvvaePGjapSpYrF/qT3LjV/nAB49jk7O6t58+aaMGGCfv/9d73yyiuaOXOmAgICLO7xklH5yf1S22+zZs1Us2ZNNWvWTI6Ojvruu+/MfZQvX/7R3oR06tChgyZPnqwZM2aYZ/U5OTklu09PWiQNXPzyyy8pfi8WKVIk3f3fa9GiRWratKlefvlljRs3Tnny5JGzs7MSEhJUvXr1dH+mSd/ZLVu2VOvWra22uf878X5169bV8ePHtXz5cm3YsEFr1qzR1KlTVaFCBa1ZsybZvQLvlfT9lt6c415XrlzRjRs3UtXWxcXFfOX/119/rb///luRkZE6fPiwuc3Vq1clSceOHVNsbGyq8+WkAl/S+5Da2YVJgoKCtH79el26dEkuLi7y8fGRi4uLTp06laztrVu3dP78eVWsWDHZPnIIABkpLX+fFipUSAcOHNDatWu1du1abdiwQRERERo8eLA2btyoAgUKpCsGxj4sMfZxl62PfcTExOiTTz5R69atZRiGORdJ+t6/cOGCDh8+LH9/f6ur/1gzbdo0dejQQW+88YYWLlyYpvsyJs3eu3dWZdIKCKdOnVLBggUt2p86dUomkynZKgnS3YvQyEPwIBT5ADwVZs6cqaCgIP3yyy8WVyqvWLHiicWwePFi8zJAlSpVsth34cIF83T63LlzK3fu3IqPj9fs2bN14cKFJzLD6mEiIiL0/vvva/v27Zo6daqcnZ3VokULq22TrlS714EDByT9X7KeNKPq/hlTj8PatWt1+vRpTZs2LdlyrgMGDLB6zOXLl/Xff/8lK2YdPHhQ2bNnNydthQoV0v/+9z+VKVPmoTM5U2Jvb6/w8HCFh4dLuntF40svvaThw4c/9EbiRYsW1caNG5WYmPjQq/Af5Pfff0/2c/kgDytqp0bBggUtEs9ffvlFnp6eKleu3AOPS0qiExISku2Lj4+3+O/D3Lx5M9UJftKApLX2hw8floODg3kJEABo166dJkyYoKioKF28eFH//fef+vfvb/Fv9ePKT1Lbb+nSpSVJVapU0bx585QvX750D+hllFKlSiksLExTp05Vu3bttHDhQtWrVy/FJbsOHjyoMmXKWGw7cOCA7O3tFRgYKOnud/WKFSuUN29eq1fcZ6SZM2fK2dlZ69ats7j47O+//07xmNTkTQULFpTJZNLt27cfKW/KmjWrWrZsqZYtW8owDPXt21ejRo3S0qVL1bhx4xSPSyqCHjp0KN2vneT999+3KCg/SOvWrTV9+nRJ0okTJ5SYmKgaNWpYbfvyyy/Lzc3NPHj8IEkFvlWrVmnSpElWl/t/mEOHDsnBwcH8s2lnZ6eSJUtq9+7dunXrlsVSWdu3b5dhGCpVqlSyfpIGCdO6hCoAWJPWv0+dnJxUs2ZN8zLZy5cvV61atTRmzBh9/fXXktI+05ixD8Y+UmLLYx9nzpzRzZs39e233+rbb79N1u6zzz7TZ599pgULFqhRo0YP7XfatGlq3769qlatqiVLlqS43HdKknKye2dWli5dWpMmTdKWLVuSFfm2bt2q4ODgZJ/d8ePHFR8fTx6CB0r/bxwAZCB7e/tkU/vj4+P12WefPdEYpOTFkcmTJ+u///5L1v71119XkSJFNGbMGP35558W++Li4tSvX7/HF6wVb7/9tpydnTV69GgtXrxYDRs2lLe3t9W2s2fP1r///mt+fvv2bX3xxReyt7fXm2++KUkKCwtT0aJFNXHiRPMyFveKj49P09VVD5LSe79q1Srz/QCsuf/nY/HixYqOjla9evXM21q1aqXExMQUP4+HLbth7Z5wL7zwglxcXFJ1/uHh4bp69ar5D4n0SlqXPrWPjPbVV1/pzz//1AcffPDQq96Cg4Nlb2+vJUuWJFt+JGkgMGngWpLV3y9J+u6773TlyhWLweFLly7p9u3bydpeu3ZNU6dOlZ2dndV7Qm3dulUvvfRSuv/YAfDsKVmypEqUKKF58+bp66+/lslkSrZU5+PKT9La7/vvvy+TyaR333032b+B27dv18yZMx8pnrSKiIjQwYMH1bVrV928efOBM7NHjRplcZ5//PGH1qxZoypVqpj/TU6aBfjRRx9ZvUAkI5fISnrv752xZxiGhg8fnuIxq1ev1h9//GHRftSoUZJkzjmyZcummjVratGiRVbvp2MYhvm+edYkJCRYXbIrLCxM0oNn6El3r54vUqRIivfySYsPP/ww1flG0ix/6e49/xYsWJDskTRQOG3aNM2aNeuhr3/r1i3Vr19fq1at0sSJEx/483XlyhWrPzM///yzNm/erNdff918jxvp7uzY69eva9KkSRbtx44dKwcHB6v379m6daty5MjBhUIAMkRa/j619rdo0j1i7/1ecHd316VLl1J9oSdjH4x9WGPrYx/58uWzmock3Q+5VatWWrBgwUNnJEp3xy0iIiJUuXJlLV261CKXuN/9y55Ld/OTkSNHytHR0WKVkLp168rFxUXjx4+3yF9+/PFHHT161GqxOim3s7baAJCEmXwAngqNGjVSv379VKNGDTVo0ECxsbH6/vvv0zQV/lHVqFFDrq6uevvtt9WlSxf5+Pho8+bNWr58uQoUKJBs5pG9vb3mzJmjSpUq6eWXX1b79u0VGhpqviord+7cTyx26e7yEY0aNTIPnjxoQKRw4cJ65ZVX9O6778rDw0Pff/+9duzYoYEDBypPnjyS7g4szZw5U5UrV1axYsX0zjvvqEiRIrp+/boOHz6sRYsWacSIEam6+fSdO3dSHDxr0KCBypcvr5w5c6pnz546fvy4AgICtGfPHs2cOVOhoaHav39/suN8fX21aNEinT59WuHh4Tp06JAmTJigHDlymJM46e7PVtu2bTV+/Hj98ccfevPNN+Xr66t///1XW7Zs0eHDh60m8kkiIiL077//6o033lBgYKBu3LihefPm6erVq2rVqtVDz71hw4bq06ePli9f/khXXj3qPfnGjx9vHjy8c+eOTpw4Yf5Mihcvrtq1a5vb1qxZU/nz59eLL74ok8mkVatWacmSJapVq5b69+9v0e/x48eVL18+VaxYUevXr5d0dxZC9+7dFRkZqbCwMEVERChr1qzavHmzZs+erQIFClj8fNasWVPZsmVT2bJllTdvXl25ckWbNm3S0qVLFRAQYPF5btiwQR07dlTDhg1VsGBBeXh46NixY5o5c6b+/fdfDR482DwzJMmRI0cUHR2tzz//PN3vH4BnU7t27dS1a1etWLFC4eHhyZaeelz5SVr7LVu2rIYPH67+/furRIkSatGihbJnz66tW7dq5syZj7REVXq0aNFCvXv31qxZs5QvX75kSyTf68SJE6pWrZrq1KmjmJgYjR8/Xi4uLho9erS5TenSpTVkyBANGTJEJUqUUOPGjZUrVy7FxMRo165dWr58udULPKzZuXOn1ZzDwcFBffv2VaNGjbRw4UJVrlxZrVq10p07d7RkyRJdv349xT6LFy+uypUrq3PnzvL399fSpUu1Zs0avf322xYDRd98843Kly+v1157Ta1atVJYWJgSExN19OhRLV26VK1atbL4TrvX1atX5e/vrzp16igsLEzZs2fXsWPH9M0338jHx8fiezoljRs31scff6yYmBj5+/s//M1KQXrvyVe8eHGLezcl+emnnyRJtWvXlq+vr8W+oKAgnThxwmKws0WLFlqxYoWqVq0qV1fXZIXBYsWKqVixYpKkdevWqUePHqpdu7by588vBwcHbd++XbNmzZKvr2+ye0VGREQoKipKPXr00PHjxxUSEqLly5dr8eLFGjBggHlprSRxcXH67bffkl0AAADplZa/T9944w15e3urQoUKypMnjy5fvqzp06fLZDJZLJNdpkwZ/fTTT+rSpYteffVV2dvbq3LlysqePbvVGBj7YOzDGlsf+/Dy8rI6Qy8p9wgNDU22f8iQIRo6dKiioqLMn++yZcvUrl07eXp6qmnTplq4cKHFMe7u7hbF1dDQUFWsWFGhoaHKnj27jh8/rmnTpikmJkaRkZEKCAgwt/Xz89PHH3+sXr16me9hfOrUKUVGRuqFF15Q9+7dk8W/fPly+fr6pml2I55DBgBksKioKEOSERUVZbE9MDDQqFixotVj4uPjjU8//dQoUKCA4ejoaOTNm9fo3bu3ceDAAUOSMXjwYHPbY8eOpWpbksGDBxuSjGPHjj009g0bNhjlypUz3N3dDS8vL6NmzZrG/v37jYoVKxqBgYFWjzl58qQRERFhBAQEGFmyZDHy5MljvP/++8a5c+ce+npptW7duhTP0zAMY+PGjYYko2DBgkZiYmKKx0dFRRnjxo0zChYsaDg6OhoFCxY0xo4da7XP48ePGx07djQCAwONLFmyGFmzZjVKlixp9O3b1zh58uRDY65YsaIhKcXHnDlzDMMwjL179xrVqlUzvL29DXd3d6NixYrGxo0bjdatWxv3f10lfR5Hjhwx6tSpY3h4eBju7u5GnTp1jEOHDlmNY8aMGUb58uUNDw8Pw8nJyQgMDDTq169vzJ0716KdJKN169bm5wsXLjRq165t5M6d23B0dDR8fX2N1157zfjhhx8eeu5JatSoYRQtWjTZ9pR+J+79nDJKYGBgip/BvedrGIYxbNgwo0iRIoabm5vh5uZmlCpVyvj666+N+Pj4ZP3u27fPkGQ0b97cYntiYqIxadIk4+WXXzbc3NwMBwcHIzAw0OjUqZNx9uxZi7YTJkwwqlSpYvj7+xtZsmQxXF1djdDQUKNv377G+fPnLdoePnzYaNeunRESEmJ4enoaDg4ORo4cOYw333zT+Omnn6ye+5AhQwwnJ6dkfQHAxYsXDWdnZ0OSMWPGjGT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+ }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/splits/type_split_distribution.png\n" + ] + } + ], + "source": [ + "# Type label distribution per split\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 5), sharey=True)\n", + "split_dfs = [(\"Train\", train_type), (\"Val\", val_type), (\"Test\", test_type)]\n", + "\n", + "for ax, (name, df) in zip(axes, split_dfs):\n", + " dist = df[\"type_label\"].value_counts()\n", + " dist.plot(kind=\"barh\", ax=ax, color=\"steelblue\")\n", + " ax.set_title(f\"{name} — Type Labels (n={len(df):,})\", fontsize=13)\n", + " ax.set_xlabel(\"Count\")\n", + " for i, (label, cnt) in enumerate(dist.items()):\n", + " ax.text(cnt + 5, i, f\"{cnt:,}\", va=\"center\", fontsize=9)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(OUT_SPLITS / \"type_split_distribution.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/splits/type_split_distribution.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + }, + "id": "R48OKnIouKBa", + "outputId": "cc63cf5a-635b-4937-ff28-9702de59974e" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/splits/binary_split_distribution.png\n" + ] + } + ], + "source": [ + "# Binary label distribution per split\n", + "fig, axes = plt.subplots(1, 3, figsize=(12, 4), sharey=True)\n", + "split_dfs_bin = [(\"Train\", train_bin), (\"Val\", val_bin), (\"Test\", test_bin)]\n", + "\n", + "for ax, (name, df) in zip(axes, split_dfs_bin):\n", + " dist = df[\"binary_label\"].value_counts().sort_index()\n", + " ax.bar([\"Non-sarcastic (0)\", \"Sarcastic (1)\"], dist.values, color=[\"coral\", \"steelblue\"])\n", + " ax.set_title(f\"{name} — Binary Labels (n={len(df):,})\", fontsize=12)\n", + " ax.set_ylabel(\"Count\")\n", + " for i, v in enumerate(dist.values):\n", + " ax.text(i, v + 20, f\"{v:,}\", ha=\"center\")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(OUT_SPLITS / \"binary_split_distribution.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/splits/binary_split_distribution.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "y59RyyxDuKBa", + "outputId": "9fcf80b4-639e-4dc0-da4e-9e5d7492337a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "=== Data Preparation Complete ===\n", + "Binary dataset : 56,666 samples (balanced)\n", + "Type dataset : 28,333 samples (imbalanced, 6 classes)\n", + "Splits saved to: outputs/splits/\n", + "Datasets saved : outputs/datasets/\n", + "\n", + "Ready for classical baseline training (Notebooks 02 and 03).\n" + ] + } + ], + "source": [ + "print(\"\\n=== Data Preparation Complete ===\")\n", + "print(f\"Binary dataset : {len(binary_df):,} samples (balanced)\")\n", + "print(f\"Type dataset : {len(type_df):,} samples (imbalanced, 6 classes)\")\n", + "print(f\"Splits saved to: outputs/splits/\")\n", + "print(f\"Datasets saved : outputs/datasets/\")\n", + "print(\"\\nReady for classical baseline training (Notebooks 02 and 03).\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cENhnFfwuKBb" + }, + "source": [ + "---\n", + "# Part 2 — TF-IDF + Logistic Regression Baseline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j_2MU9WJuKBb" + }, + "source": [ + "# Notebook 02 — TF-IDF + Logistic Regression Baseline\n", + "\n", + "**Purpose**: Train and evaluate TF-IDF + Logistic Regression pipelines for:\n", + "- Task A: Binary classification (sarcastic vs non-sarcastic)\n", + "- Task B: Sarcasm type classification (6-class)\n", + "\n", + "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", + "\n", + "**Outputs**:\n", + "- `outputs/classical/tfidf_lr/best_config_binary.json`\n", + "- `outputs/classical/tfidf_lr/best_config_type.json`\n", + "- `outputs/classical/tfidf_lr/metrics_binary.json`\n", + "- `outputs/classical/tfidf_lr/metrics_type.json`\n", + "- Predictions CSV + confusion matrices PNG" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r8BzGF97uKBb" + }, + "source": [ + "## Helper Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "iU0Dnjl_uKBb" + }, + "outputs": [], + "source": [ + "def load_splits(task: str) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n", + " \"\"\"Load train/val/test CSVs for a given task ('binary' or 'type').\"\"\"\n", + " train = pd.read_csv(SPLITS / f\"train_{task}.csv\")\n", + " val = pd.read_csv(SPLITS / f\"val_{task}.csv\")\n", + " test = pd.read_csv(SPLITS / f\"test_{task}.csv\")\n", + " return train, val, test\n", + "\n", + "\n", + "def evaluate(\n", + " model,\n", + " X: list[str],\n", + " y_true: list,\n", + " label_names: list[str] | None = None,\n", + " split_name: str = \"\",\n", + ") -> dict:\n", + " \"\"\"Compute classification metrics and return as dict.\"\"\"\n", + " y_pred = model.predict(X)\n", + " metrics = {\n", + " \"split\" : split_name,\n", + " \"accuracy\" : accuracy_score(y_true, y_pred),\n", + " \"precision_macro\" : precision_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"recall_macro\" : recall_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"f1_macro\" : f1_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"f1_weighted\" : f1_score(y_true, y_pred, average=\"weighted\", zero_division=0),\n", + " \"report\" : classification_report(y_true, y_pred, target_names=label_names,\n", + " zero_division=0, output_dict=True),\n", + " }\n", + " print(f\"\\n[{split_name}] Accuracy={metrics['accuracy']:.4f} \"\n", + " f\"Macro-F1={metrics['f1_macro']:.4f} Weighted-F1={metrics['f1_weighted']:.4f}\")\n", + " print(classification_report(y_true, y_pred, target_names=label_names, zero_division=0))\n", + " return metrics, y_pred\n", + "\n", + "\n", + "def save_confusion_matrix(\n", + " y_true, y_pred, label_names: list[str], out_path: Path, title: str = \"\"\n", + ") -> None:\n", + " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", + " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", + " sns.heatmap(\n", + " cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n", + " xticklabels=label_names, yticklabels=label_names, ax=ax\n", + " )\n", + " ax.set_xlabel(\"Predicted\")\n", + " ax.set_ylabel(\"True\")\n", + " ax.set_title(title)\n", + " plt.tight_layout()\n", + " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + " print(f\"Saved: {out_path.relative_to(ROOT)}\")\n", + "\n", + "\n", + "def save_predictions(\n", + " df: pd.DataFrame, y_pred, label_col: str, out_path: Path\n", + ") -> None:\n", + " out = df[[\"sample_id\", \"pair_id\", \"group_id\", \"text\", label_col]].copy()\n", + " out[\"predicted\"] = y_pred\n", + " out[\"correct\"] = (out[label_col] == out[\"predicted\"]).astype(int)\n", + " out.to_csv(out_path, index=False)\n", + " print(f\"Saved: {out_path.relative_to(ROOT)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KZYEW3EruKBb" + }, + "source": [ + "## Task A — Binary Classification" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sUWB4dQKuKBb", + "outputId": "c9c34b33-e3bb-4eca-e21a-158777f966cb" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train+Val: 48,166 | Train: 39,666 Val: 8,500 Test: 8,500\n" + ] + } + ], + "source": [ + "# ── Load binary splits ────────────────────────────────────────────────────────\n", + "train_bin, val_bin, test_bin = load_splits(\"binary\")\n", + "\n", + "# Combine train+val for cross-validation, keep test for final eval\n", + "trainval_bin = pd.concat([train_bin, val_bin], ignore_index=True)\n", + "\n", + "X_trainval = trainval_bin[\"text\"].tolist()\n", + "y_trainval = trainval_bin[\"binary_label\"].tolist()\n", + "groups_tv = trainval_bin[\"group_id\"].tolist()\n", + "\n", + "X_train = train_bin[\"text\"].tolist()\n", + "y_train = train_bin[\"binary_label\"].tolist()\n", + "\n", + "X_val = val_bin[\"text\"].tolist()\n", + "y_val = val_bin[\"binary_label\"].tolist()\n", + "\n", + "X_test = test_bin[\"text\"].tolist()\n", + "y_test = test_bin[\"binary_label\"].tolist()\n", + "\n", + "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", + "\n", + "print(f\"Train+Val: {len(X_trainval):,} | Train: {len(X_train):,} Val: {len(X_val):,} Test: {len(X_test):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BatmO-vAuKBc", + "outputId": "34229238-2bbd-4838-976b-464bb496054f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Grid size: 4 param combos → 180 fits\n", + "Running grid search...\n", + "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n", + "\n", + "Best params : {'lr__C': 3.0, 'tfidf__max_features': None, 'tfidf__min_df': 3, 'tfidf__ngram_range': (1, 2)}\n", + "Best CV F1 : 0.8143\n" + ] + } + ], + "source": [ + "# ── Define pipeline and grid ──────────────────────────────────────────────────\n", + "pipe_bin = Pipeline([\n", + " (\"tfidf\", TfidfVectorizer(sublinear_tf=True, lowercase=True)),\n", + " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, solver=\"lbfgs\")),\n", + "])\n", + "\n", + "param_grid_bin = {\n", + " \"tfidf__ngram_range\" : [(1, 1), (1, 2)],\n", + " \"tfidf__min_df\" : [2, 3, 5],\n", + " \"tfidf__max_features\": [None, 50_000],\n", + " \"lr__C\" : [0.1, 1.0, 3.0],\n", + "}\n", + "\n", + "cv_bin = GroupKFold(n_splits=5)\n", + "\n", + "gs_bin = GridSearchCV(\n", + " pipe_bin, param_grid_bin,\n", + " cv=cv_bin, scoring=\"f1_macro\",\n", + " n_jobs=-1, verbose=1, refit=True,\n", + ")\n", + "\n", + "print(f\"Grid size: {len(gs_bin.param_grid)} param combos → {5 * 2*3*2*3} fits\")\n", + "print(\"Running grid search...\")\n", + "gs_bin.fit(X_trainval, y_trainval, groups=groups_tv)\n", + "\n", + "print(f\"\\nBest params : {gs_bin.best_params_}\")\n", + "print(f\"Best CV F1 : {gs_bin.best_score_:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JstBvt1vuKBc", + "outputId": "aff1b1fd-aa2f-4c9c-92ae-7d836436f66d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/best_config_binary.json\n" + ] + } + ], + "source": [ + "# ── Save best config ──────────────────────────────────────────────────────────\n", + "best_cfg_bin = {\"task\": \"binary\", \"model\": \"TF-IDF + LR\", \"seed\": SEED,\n", + " \"best_params\": gs_bin.best_params_, \"cv_f1_macro\": gs_bin.best_score_}\n", + "with open(OUT_TFIDF / \"best_config_binary.json\", \"w\") as f:\n", + " json.dump(best_cfg_bin, f, indent=2)\n", + "print(\"Saved: outputs/classical/tfidf_lr/best_config_binary.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AjH8_5UQuKBc", + "outputId": "afe39734-c73a-410a-9447-6b8639b18389" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "[Val] Accuracy=0.9186 Macro-F1=0.9186 Weighted-F1=0.9186\n", + " precision recall f1-score support\n", + "\n", + "non-sarcastic 0.92 0.92 0.92 4250\n", + " sarcastic 0.92 0.92 0.92 4250\n", + "\n", + " accuracy 0.92 8500\n", + " macro avg 0.92 0.92 0.92 8500\n", + " weighted avg 0.92 0.92 0.92 8500\n", + "\n", + "\n", + "[Test] Accuracy=0.8189 Macro-F1=0.8189 Weighted-F1=0.8189\n", + " precision recall f1-score support\n", + "\n", + "non-sarcastic 0.82 0.82 0.82 4250\n", + " sarcastic 0.82 0.82 0.82 4250\n", + "\n", + " accuracy 0.82 8500\n", + " macro avg 0.82 0.82 0.82 8500\n", + " weighted avg 0.82 0.82 0.82 8500\n", + "\n", + "Saved: outputs/classical/tfidf_lr/metrics_binary.json\n" + ] + } + ], + "source": [ + "# ── Evaluate on val and test ──────────────────────────────────────────────────\n", + "best_bin = gs_bin.best_estimator_\n", + "\n", + "val_metrics_bin, y_val_pred_bin = evaluate(best_bin, X_val, y_val, label_names_bin, \"Val\")\n", + "test_metrics_bin, y_test_pred_bin = evaluate(best_bin, X_test, y_test, label_names_bin, \"Test\")\n", + "\n", + "all_metrics_bin = {\"val\": val_metrics_bin, \"test\": test_metrics_bin}\n", + "\n", + "# Remove classification_report dict (too nested for JSON)\n", + "for split_m in all_metrics_bin.values():\n", + " split_m.pop(\"report\", None)\n", + "\n", + "with open(OUT_TFIDF / \"metrics_binary.json\", \"w\") as f:\n", + " json.dump(all_metrics_bin, f, indent=2)\n", + "print(\"Saved: outputs/classical/tfidf_lr/metrics_binary.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "ichiimejuKBc", + "outputId": "e3d5e010-d983-4f86-893b-cd38907ecc68" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/confusion_matrix_binary_val.png\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/confusion_matrix_binary_test.png\n" + ] + } + ], + "source": [ + "# ── Confusion matrices ────────────────────────────────────────────────────────\n", + "save_confusion_matrix(\n", + " y_val, y_val_pred_bin, label_names_bin,\n", + " OUT_TFIDF / \"confusion_matrix_binary_val.png\",\n", + " \"TF-IDF + LR — Binary (Val)\"\n", + ")\n", + "save_confusion_matrix(\n", + " y_test, y_test_pred_bin, label_names_bin,\n", + " OUT_TFIDF / \"confusion_matrix_binary_test.png\",\n", + " \"TF-IDF + LR — Binary (Test)\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_2QLSOmTuKBc", + "outputId": "8d05bf4c-dcc5-47d0-8ae4-7ccc6754d50a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/predictions_val_binary.csv\n", + "Saved: outputs/classical/tfidf_lr/predictions_test_binary.csv\n" + ] + } + ], + "source": [ + "# ── Save predictions ──────────────────────────────────────────────────────────\n", + "save_predictions(val_bin, y_val_pred_bin, \"binary_label\", OUT_TFIDF / \"predictions_val_binary.csv\")\n", + "save_predictions(test_bin, y_test_pred_bin, \"binary_label\", OUT_TFIDF / \"predictions_test_binary.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bigHRH2MuKBc", + "outputId": "40c0448d-3f53-4ba4-cc17-3347f11dc23b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Top 20 sarcastic indicators (positive coef):\n", + " because +22.1982\n", + " finally +9.5906\n", + " obviously +9.4255\n", + " just +9.2842\n", + " so +8.8645\n", + " shocking +8.6605\n", + " clearly +8.4488\n", + " as if +8.3217\n", + " proving +7.5734\n", + " groundbreaking +7.5588\n", + " nation +7.4750\n", + " like +7.1686\n", + " nothing +6.5188\n", + " shocker +6.5093\n", + " never +6.4249\n", + " totally +6.3599\n", + " always +6.1954\n", + " sure +6.1921\n", + " area +6.0594\n", + " who needs +6.0482\n", + "\n", + "Top 20 non-sarcastic indicators (negative coef):\n", + " the -11.8202\n", + " an -11.5473\n", + " is -10.1138\n", + " was -7.7886\n", + " an area -6.0294\n", + " man is -6.0119\n", + " his -5.8977\n", + " are -5.7913\n", + " finds that -5.7222\n", + " the nation -5.6480\n", + " indicates -5.5894\n", + " donald trump -5.1844\n", + " donald -5.0836\n", + " says he -4.9047\n", + " did not -4.7563\n", + " because of -4.7343\n", + " may -4.7339\n", + " large -4.6423\n", + " her -4.5442\n", + " how to -4.3584\n" + ] + } + ], + "source": [ + "# ── Feature importance: top discriminative terms ──────────────────────────────\n", + "tfidf_vocab = best_bin.named_steps[\"tfidf\"].get_feature_names_out()\n", + "lr_coefs = best_bin.named_steps[\"lr\"].coef_[0] # binary: shape (n_features,)\n", + "\n", + "n_top = 20\n", + "top_pos_idx = np.argsort(lr_coefs)[-n_top:][::-1]\n", + "top_neg_idx = np.argsort(lr_coefs)[:n_top]\n", + "\n", + "top_positive = [(tfidf_vocab[i], lr_coefs[i]) for i in top_pos_idx]\n", + "top_negative = [(tfidf_vocab[i], lr_coefs[i]) for i in top_neg_idx]\n", + "\n", + "print(\"Top 20 sarcastic indicators (positive coef):\")\n", + "for term, coef in top_positive:\n", + " print(f\" {term:35s} {coef:+.4f}\")\n", + "\n", + "print(\"\\nTop 20 non-sarcastic indicators (negative coef):\")\n", + "for term, coef in top_negative:\n", + " print(f\" {term:35s} {coef:+.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 644 + }, + "id": "pEArvWzTuKBd", + "outputId": "c4709ebc-9b56-468b-eb9f-c47f1ba006b8" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/feature_importance_binary.png\n" + ] + } + ], + "source": [ + "# ── Feature importance plot ───────────────────────────────────────────────────\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 7))\n", + "\n", + "terms_pos, coefs_pos = zip(*top_positive)\n", + "axes[0].barh(list(reversed(terms_pos)), list(reversed(coefs_pos)), color=\"steelblue\")\n", + "axes[0].set_title(\"Top 20 — Sarcastic (positive)\", fontsize=13)\n", + "axes[0].set_xlabel(\"LR Coefficient\")\n", + "\n", + "terms_neg, coefs_neg = zip(*top_negative)\n", + "axes[1].barh(list(reversed(terms_neg)), list(reversed(coefs_neg)), color=\"coral\")\n", + "axes[1].set_title(\"Top 20 — Non-Sarcastic (negative)\", fontsize=13)\n", + "axes[1].set_xlabel(\"LR Coefficient\")\n", + "\n", + "plt.suptitle(\"TF-IDF + LR: Feature Importance (Binary Task)\", fontsize=14)\n", + "plt.tight_layout()\n", + "plt.savefig(OUT_TFIDF / \"feature_importance_binary.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/classical/tfidf_lr/feature_importance_binary.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5VFBy1bJuKBd" + }, + "source": [ + "## Task B — Sarcasm Type Classification" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GTvcYYGquKBd", + "outputId": "f3c82bbf-5d44-4c4c-d8f0-2f586415fced" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n", + "Train+Val: 24,083 Train: 19,833 Val: 4,250 Test: 4,250\n" + ] + } + ], + "source": [ + "# ── Load type splits ──────────────────────────────────────────────────────────\n", + "train_type, val_type, test_type = load_splits(\"type\")\n", + "\n", + "# Encode string labels to integers\n", + "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", + "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", + "id2label = {i: lab for lab, i in label2id.items()}\n", + "\n", + "def encode_labels(df: pd.DataFrame) -> list[int]:\n", + " return [label2id[l] for l in df[\"type_label\"]]\n", + "\n", + "trainval_type = pd.concat([train_type, val_type], ignore_index=True)\n", + "\n", + "X_trainval_t = trainval_type[\"text\"].tolist()\n", + "y_trainval_t = encode_labels(trainval_type)\n", + "groups_tv_t = trainval_type[\"group_id\"].tolist()\n", + "\n", + "X_train_t = train_type[\"text\"].tolist()\n", + "y_train_t = encode_labels(train_type)\n", + "X_val_t = val_type[\"text\"].tolist()\n", + "y_val_t = encode_labels(val_type)\n", + "X_test_t = test_type[\"text\"].tolist()\n", + "y_test_t = encode_labels(test_type)\n", + "\n", + "print(f\"Strategies: {STRATEGY_LABELS}\")\n", + "print(f\"Train+Val: {len(X_trainval_t):,} Train: {len(X_train_t):,} Val: {len(X_val_t):,} Test: {len(X_test_t):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yD1TrZZPuKBd", + "outputId": "4faad70d-dc90-4fcd-de7d-19de03226861" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Running grid search for type task...\n", + "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n", + "\n", + "Best params : {'lr__C': 3.0, 'lr__class_weight': 'balanced', 'tfidf__max_features': None, 'tfidf__min_df': 2, 'tfidf__ngram_range': (1, 2)}\n", + "Best CV F1 : 0.3840\n" + ] + } + ], + "source": [ + "# ── Define pipeline and grid (with class_weight) ──────────────────────────────\n", + "pipe_type = Pipeline([\n", + " (\"tfidf\", TfidfVectorizer(sublinear_tf=True, lowercase=True)),\n", + " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, solver=\"lbfgs\",\n", + " multi_class=\"auto\")),\n", + "])\n", + "\n", + "param_grid_type = {\n", + " \"tfidf__ngram_range\" : [(1, 1), (1, 2)],\n", + " \"tfidf__min_df\" : [2, 3, 5],\n", + " \"tfidf__max_features\": [None, 50_000],\n", + " \"lr__C\" : [0.1, 1.0, 3.0],\n", + " \"lr__class_weight\" : [None, \"balanced\"],\n", + "}\n", + "\n", + "cv_type = GroupKFold(n_splits=5)\n", + "\n", + "gs_type = GridSearchCV(\n", + " pipe_type, param_grid_type,\n", + " cv=cv_type, scoring=\"f1_macro\",\n", + " n_jobs=-1, verbose=1, refit=True,\n", + ")\n", + "\n", + "print(\"Running grid search for type task...\")\n", + "gs_type.fit(X_trainval_t, y_trainval_t, groups=groups_tv_t)\n", + "\n", + "print(f\"\\nBest params : {gs_type.best_params_}\")\n", + "print(f\"Best CV F1 : {gs_type.best_score_:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "huaF8TbjuKBd", + "outputId": "d43d2757-7486-4307-912b-462d285835ce" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/best_config_type.json\n" + ] + } + ], + "source": [ + "# ── Save best config ──────────────────────────────────────────────────────────\n", + "best_cfg_type = {\"task\": \"type\", \"model\": \"TF-IDF + LR\", \"seed\": SEED,\n", + " \"label_names\": STRATEGY_LABELS, \"label2id\": label2id,\n", + " \"best_params\": gs_type.best_params_, \"cv_f1_macro\": gs_type.best_score_}\n", + "with open(OUT_TFIDF / \"best_config_type.json\", \"w\") as f:\n", + " json.dump(best_cfg_type, f, indent=2)\n", + "print(\"Saved: outputs/classical/tfidf_lr/best_config_type.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "c4Hj46aWuKB6", + "outputId": "f2ac2819-bbf4-43a3-9437-9cfdafc29c12" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "[Val] Accuracy=0.8967 Macro-F1=0.8982 Weighted-F1=0.8961\n", + " precision recall f1-score support\n", + "\n", + " irony 0.92 0.87 0.90 942\n", + " overstatement 0.88 0.97 0.92 600\n", + "rhetorical_question 0.79 1.00 0.88 157\n", + " sarcasm 0.94 0.83 0.88 1317\n", + " satire 0.88 0.91 0.90 747\n", + " understatement 0.85 0.98 0.91 487\n", + "\n", + " accuracy 0.90 4250\n", + " macro avg 0.88 0.93 0.90 4250\n", + " weighted avg 0.90 0.90 0.90 4250\n", + "\n", + "\n", + "[Test] Accuracy=0.3958 Macro-F1=0.4047 Weighted-F1=0.3947\n", + " precision recall f1-score support\n", + "\n", + " irony 0.33 0.29 0.31 902\n", + " overstatement 0.38 0.43 0.40 592\n", + "rhetorical_question 0.42 0.60 0.50 176\n", + " sarcasm 0.48 0.42 0.45 1291\n", + " satire 0.38 0.39 0.38 833\n", + " understatement 0.36 0.43 0.39 456\n", + "\n", + " accuracy 0.40 4250\n", + " macro avg 0.39 0.43 0.40 4250\n", + " weighted avg 0.40 0.40 0.39 4250\n", + "\n", + "Saved: outputs/classical/tfidf_lr/metrics_type.json\n" + ] + } + ], + "source": [ + "# ── Evaluate on val and test ──────────────────────────────────────────────────\n", + "best_type = gs_type.best_estimator_\n", + "\n", + "val_metrics_type, y_val_pred_type = evaluate(best_type, X_val_t, y_val_t, STRATEGY_LABELS, \"Val\")\n", + "test_metrics_type, y_test_pred_type = evaluate(best_type, X_test_t, y_test_t, STRATEGY_LABELS, \"Test\")\n", + "\n", + "all_metrics_type = {\"val\": val_metrics_type, \"test\": test_metrics_type}\n", + "for split_m in all_metrics_type.values():\n", + " split_m.pop(\"report\", None)\n", + "\n", + "with open(OUT_TFIDF / \"metrics_type.json\", \"w\") as f:\n", + " json.dump(all_metrics_type, f, indent=2)\n", + "print(\"Saved: outputs/classical/tfidf_lr/metrics_type.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "ldimkyrXuKB6", + "outputId": "e8e286df-267e-4802-8318-d0cc9f768808" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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XV1fcunVLcFkJInngHCgiolJow4YNKF++vOBkAkUZM2YMkpKSsHv37h96v/Pnz0fnzp2ZPFGR4BAeEVEpsnv3bty9exfHjx/H8uXLi8XEfx0dnQJdL0refnTCRv8tTKCIiEqR7t27Q0dHB/3798ewYcMUHQ5RqcU5UEREREQy4hwoIiIiIhkxgSIiIiKSERMoIiIiIhkxgSIiIiKSEc/CoxLHbvRxRYdQJO4vbKnoEIpEMTiLvsikZ+YoOoQioaJcOp805VL8YtRSk++xadaQ308Opd4u+ou1KgITKCIiIhIScYBKGj5CRERERDJiBYqIiIiESvFwp7wwgSIiIiIhDuFJxUeIiIiISEasQBEREZEQh/CkYgJFREREQhzCk4qPEBEREZGMWIEiIiIiIQ7hScUEioiIiIQ4hCcVHyEiIiIiGbECRUREREIcwpOKCRQREREJcQhPKj5CRERERDJiBYqIiIiEOIQnFRMoIiIiEuIQnlR8hIiIiIhkxAoUERERCXEITyomUERERCTEITyp+AgRERERyYgVKCIiIhJiBUoqJlBEREQkpMQ5UNIwxSQiIiKSEStQREREJMQhPKn4CBEaN26M0aNHKzoMIiIqLkQi+S2lFCtQhAMHDkBVVVXRYfwQSiJgtLcT2ruVgamuOqIS0rD/+husPP0MAKCiJMK4VhXQuJIprI21kJiWhctP3mPB0X8RnZAOAChjpIkRzR1Rz9FYso9DN95ideAzZGaLFXl4AjdvhGDblj/w6OEDvI+JQcCyVWjSzFOyPiUlGSuWBuDcP0GIj4+DVZmy6O7TE527dlNg1NLdvBGCbZv/wMP/P64ly4XHFRR4Gvv27Majhw8QHx+P3fsOokLFSgqMuHC2bdqANSuX4ucePTFmwmS8e/cWHVv9lG/fuQuXoNlP3j84woLZvHE9zgYFIuzlC6ira6Bq9RoYMXocbO3sJH3evA7HsoCFCL19C5kZGXD3aIAJk6fA2NhEgZFL9+k9JnktfvEemz5lEo4eOSTYpp5Hfaxet/EHR0pFgRUogpGREXR1dfNdl5GR8YOjKVpDmtnDx8MGM/Y/gOf881hw9F8MamqPPg1tAQCaasqoUlYPq04/Q5uASxiy6SbKm2ljwwA3yT7szXSgJAKm7LmH5gvOY87Bh/DxsMGEVhUVdFT5S0tNhZNTRUyaMj3f9QEL5+PK5UuYM38h9h8+jh6/9MKCebNx/uw/PzhS2aSmpsKpQkVM/spxpaamorprTYwcM/4HRyY/Dx/cw8H9e+DgWEHSZm5ugeOB5wXLwCG+0NLSgrtHAwVG+223boSgS7ce2LxjN1av/wNZWZnwHdIfqSkpAIDUlBQMHzwAIpEI6zZswR9bdyEzMxNjRgxDTk6OgqP/ttT/f4997bUIAPU8GiDw7EXJ4r8g4AdG+B1ESvJbZHDhwgW0adMGVlZWEIlEOHTokGC9WCzG9OnTYWlpCU1NTXh6euLp06eCPrGxsfDx8YGenh4MDAzQv39/JCUlCfrcvXsXDRo0gIaGBsqVK4eFCxfK/BAxgSLBEJ6trS1mz56NXr16QU9PD4MGDQIA7N+/H5UrV4a6ujpsbW0RECD8ELC1tcW8efPQr18/6OrqwtraGuvXr5esb9q0KXx9fQXbxMTEQE1NDUFBQUV7gJ9xtTNE4P0onH0YjbexqTh5JxIXH8egmrUBACAxLQs9117H8dAIvIhORuirOMzY9wBVrQ1gZaABALjwbwx+/fMuLj5+j9cfUnHmQTQ2/PMCXlUtfthxFIRHg4YYPnI0mjbLv2px904o2rRtD7dadWBVpiw6dfkZjk4VcP/e3R8cqWzqfzouz/yPq3Xbdhg8dDjqurv/4MjkIyUlGTN++xWTp/lBV09P0q6srAxjE1PBcv7sGTT7yRtaWtoKjPjbVq7bgDbtOsDewRFOFSpi5mx/REZE4NHDBwCAO6G3EfHuLWbM9oeDkxMcnJzgN8cfjx7cR8j1awqO/tvqS3mPAYCamhpMTEwli56+/g+M8DsoaAgvOTkZ1apVw+rVq/Ndv3DhQqxYsQLr1q1DcHAwtLW14eXlhbS0NEkfHx8fPHjwAIGBgTh27BguXLgg+S4DgISEBDRv3hw2Nja4efMmFi1ahJkzZwq+swqCCRTlsXjxYlSrVg23b9/GtGnTcPPmTXTt2hXdunXDvXv3MHPmTEybNg1btmwRbBcQEAA3Nzfcvn0bw4YNw9ChQ/H48WMAwIABA7Br1y6kp6dL+u/YsQNlypRB06ZNf9ix3Xr5ER5OxrAzzf3CqWSli1rljXDuUfRXt9HVVEFOjhgJqVnf7BOXUrKqdVWrVcf5c/8gOioKYrEYIdevIfxVGOrW81B0aP9pi/3nwKNBI9SuW++b/f59+ABPHv+LNu07/aDI5CMpKREAJIlERkYGRCIR1NTUJH3U1NWhpKSE0Fu3FBKjPN24cR1NG9VD+zbemDt7JuLiPio6pGKtRYsWmDNnDjp06JBnnVgsxrJlyzB16lS0a9cOVatWxbZt2/Du3TtJperRo0c4deoUNm7ciDp16qB+/fpYuXIldu/ejXfv3gEAdu7ciYyMDGzatAmVK1dGt27dMHLkSCxZskSmWJlAUR5NmzbFuHHjYG9vD3t7eyxZsgTNmjXDtGnT4OTkhD59+sDX1xeLFi0SbNeyZUsMGzYMDg4OmDhxIkxMTHD27FkAQMeOHQEAhw8flvTfsmUL+vTpA9EPnGS4Nug5jt56hzOTG+FJQAscG98Am86/xOGb7/Ltr6aihIltKuHIrXdISs8/gbIx0UKvBrb480p4UYYudxN/m4by9vbw9myEOq4u8B0yEJOmTEdNt1qKDu0/K/DUCTz+9yGGjhgjte+RQ/tha1ceVavX+AGRyUdOTg4CFvqjWg1XODg6AQBcqlaDhqYmVi5djLTUVKSmpGBZwEJkZ2fj/fsYBUf8ferVb4DZcxfg9w2bMWr0eNy8EQLfoYOQnZ2t6NCkk+MQXnp6OhISEgTL539MF9TLly8RGRkJT8//zTPT19dHnTp1cPXqVQDA1atXYWBgADe3/0278PT0hJKSEoKDgyV9GjZsKEjavby88PjxY3z8WPAElwkU5fH5Cw/Izeg9PIRVCQ8PDzx9+lTwQVC1alXJ/0UiESwsLBAdnVvZ0dDQQM+ePbFp0yYAwK1bt3D//n306dPnm7Hk98YTZ2UW+thaVbdEu5plMGr7bbRZfAnjd93BwCbl0bFWmTx9VZREWN3HFSIA0/bez3d/5vrq2DK4Nk6GRmD3tdeFjksRdu/ajnt372DpyjXYsXs/xoyfiPlzZyH46hVFh/afFBUZgSWL/DFz7kKoq6t/s29aWhpOnzxe4qpPC+bOwvNnTzHvs3lAhkZGWLB4GS6cP4cGdWuisUdtJCYmoGIlZyiV8DO4vFu0QuMmTeHoVAFNmnlixap1eHD/Hm6EXFd0aNLJcQjP398f+vr6gsXf31/mkCIjIwEA5ubmgnZzc3PJusjISJiZmQnWq6iowMjISNAnv318fh8FwbPwKA9t7cLNp/jyTD6RSCSYBDpgwABUr14db968webNm9G0aVPY2Nh8c5/+/v7w8/MTtOnX6Q7Duj6FinFy20pYF/Qcx25HAAAeRySijKEmhnk64EDIW0k/FSURVvVxRRlDTfRYfS3f6pOZnjr+HF4Xt8I+YvKee4WKR1HS0tKwavkyBCxfiQYNGwMAnCpUwJPH/2Lb1k2o4/7t4SOSv38fPcDH2A/o06OzpC07Oxuht25g31+7cCE4FMrKygCAs2dOIy0tFS1bt1NUuDJbMG82Ll04j/Wbt8PcQjhfsG49Dxw+cRpxHz9CWVkZunp68GrSAGXKllNQtEWjbLlyMDA0xOvwV6hTt2TO0SuMyZMnY+zYsYI2aX8klARMoEiqSpUq4fLly4K2y5cvw8nJSfKBXhAuLi5wc3PDhg0bsGvXLqxatUrqNvm98ar+VvizxDTVlJEjFl5qIFssFvxqwafkydZUGz1WXUNcSt6Kl7l+bvJ07008Juy6A3HxuXpBgWRlZSErKxNKX5who6SkBHExP/OptHKr7Y6dew8L2ubMmAIbOzv07DNA8F47cmg/GjRqCkMjox8dpszEYjEW+s/BuX/O4Pc/tqJM2bJf7WtgaAgACAm+htjYD2jY+MfNj/wRoiIjER8XBxNTM+mdFU2OF9JUV1eXS8Jk8f+Jd1RUFCwtLSXtUVFRqF69uqTPp5GPT7KyshAbGyvZ3sLCAlFRUYI+n25bWBT8ZCAmUCTVuHHjUKtWLcyePRs///wzrl69ilWrVmHNmjUy72vAgAHw9fWFtrZ2vpMEv5TfG0+kUvhrVgU9iMLwnxzw7mMankQmonIZPfRvbIe9wW8A5CZPa/q6onJZfQzYEAIlJRFMdHPvPz4lA5nZ4tzkydcdb2NTMe/wIxjp/C++94myj+sXlZSUZLwO/9+8rLdv3+Dxv4+gp68PS0sr1HSrhWVLFkFdQx2WlmVw88Z1HD96GGMnTFJg1NJJO674+DhERkRIPkTDXr4EABibmMDExFQhMReEtrY27B0cBW0amprQ1zcQtL8Of4XQWzewZOW6Hx1ioSyYOwunTh5HwPJV0NLWlsxr0tHRhYZG7pmtRw4dgJ1deRgaGeHunVAELJiHHj17C64VVRx967Wor6+P39euRjPP5jAxMcHr16+xfMkilLO2Rj2P+gqMuoCK4fCpnZ0dLCwsEBQUJEmYEhISEBwcjKFDhwIA3N3dERcXh5s3b6JmzZoAgH/++Qc5OTmoU6eOpM+UKVOQmZkpGTkJDAxEhQoVYPj/SXxBMIEiqVxdXbFnzx5Mnz4ds2fPhqWlJWbNmiV1/lJ+unfvjtGjR6N79+6SD88faeb+BxjbsgJmd64MY53ci2D+eSUcK/7OvY6IuYEGfnLJ/QvkxK8NBdt2W3UVwc9iUb+CKexMtWFnqo1rfp6CPnajj/+YAymAhw/uY1C/3pLbSxbNBwC0adsefnPnw3/REqxctgRTJk1AQnw8LC2tMHzE6GJ/Ic2H9+9j4GfHFbDw/4+rXXvMmjsf58/+gxlTf5OsnzQht4I5eOhwDBk+4scGWwSOHT4AM3Nz1HEvGWdL7tuzGwAw+LPnDABmzJ6HNu1y/4h6FfYSq5cvRXx8PKzKWKHvwCHw6dk7z76Km4cPvngtfvYe+23aTDx98hhHjxxCYkIiTM1M4e7ugWG+owSTl0koKSkJz549k9x++fIlQkNDYWRkBGtra4wePRpz5syBo6Mj7OzsMG3aNFhZWaF9+/YAckdMvL29MXDgQKxbtw6ZmZnw9fVFt27dYGVlBQDo0aMH/Pz80L9/f0ycOBH379/H8uXLsXTpUpliFYnFJW3wgUqysLAw2NvbIyQkBK6uroXaR3FKUuTp/sKWig6hSBTDP2TlJj2zdA53qiiXzidNuRS/GLXU5Htsmi2Xy21fqSdGFbjvuXPn0KRJkzztvXv3xpYtWyAWizFjxgysX78ecXFxqF+/PtasWQMnJydJ39jYWPj6+uLo0aNQUlJCp06dsGLFCujo6Ej63L17F8OHD0dISAhMTEwwYsQITJw4UabjYgJFP0RmZiY+fPiA8ePH4+XLl3nmVMmCCVTJUoq/s5hAlTBMoApOs9UKue0r9fhIue2rOOFlDOiHuHz5MiwtLRESEoJ160rG3A0iIqKv4Rwo+iEaN24MFjuJiEoIOZ6FV1oxgSIiIiIhJlBS8REiIiIikhErUERERCRUiifcywsTKCIiIhLiEJ5UfISIiIiIZMQKFBEREQlxCE8qJlBEREQkxCE8qfgIEREREcmIFSgiIiIS4hCeVEygiIiISEDEBEoqDuERERERyYgVKCIiIhJgBUo6JlBEREQkxPxJKg7hEREREcmIFSgiIiIS4BCedEygiIiISIAJlHQcwiMiIiKSEStQREREJMAKlHRMoIiIiEiACZR0HMIjIiIikhErUERERCTEApRUTKCIiIhIgEN40nEIj4iIiEhGrEARERGRACtQ0jGBohLn0eJWig6hSAzdd0/RIRSJtZ1dFB1CkdFUU1Z0CEVCLFZ0BEWDOUHBMYGSjkN4RERERDJiBYqIiIgEWIGSjgkUERERCTF/kopDeEREREQyYgWKiIiIBDiEJx0TKCIiIhJgAiUdh/CIiIiIZMQKFBEREQmwAiUdEygiIiISYv4kFYfwiIiIiGTEChQREREJcAhPOiZQREREJMAESjoO4RERERHJiBUoIiIiEmAFSjomUERERCTABEo6DuERERERyYgVKCIiIhJiAUoqJlBEREQkwCE86TiER0RERCQjVqCIiIhIgBUo6ZhAERERkQATKOk4hEdEREQkI1agiIiISIgFKKmYQBEREZEAh/Ck4xAeERERkYyYQBUjffr0Qfv27WXebubMmahevbrc4ylKjRs3xujRoxUdhlR/bFiPapUrYKH/XEWH8k3tqphhczcXwTKvpaNkvZ6GCgbWLYtl7SpiXefKmNncATXL6gn20drZFFM8y2Nd58pY3dH5Rx/Cd9u9ayda/NQUtWq4wKdbF9y7e1fRIclVSXktFkR2djZWr1yGll5NUadmVbT29sT6dashFosVHdp32bN7Fzp3aIN6tV1Rr7Yrevb4GZcunld0WIUiEonktpRWHML7QTIyMqCmpqboMEgG9+/dxb69u+HkVEHRoRTIm7g0LDr3UnI7J+d/X0YD65aFlqoyll98haT0LNS1McCwetbwO/0M4XFpAAAVJRFCwuPx7H0KGpY3+uHxf49TJ09g8UJ/TJ3hBxeXati5fSuGDu6Pw8dOwdjYWNHhfbeS9lqUZvMfG7D3rz8xa+4C2Ds44OGD+5gxdTJ0dHTR45deig6v0MzMLTBqzHhY29hALBbj6OFDGOU7HH/tPwgHB0fpOyhGSnPiIy//2QpUeno6Ro4cCTMzM2hoaKB+/foICQlBTk4OypYti7Vr1wr63759G0pKSnj16hUAIC4uDgMGDICpqSn09PTQtGlT3LlzR9L/U1Vo48aNsLOzg4aGBgBg3759cHFxgaamJoyNjeHp6Ynk5GTMnDkTW7duxeHDhyVZ+7lz5wAAEydOhJOTE7S0tFC+fHlMmzYNmZmZAIAtW7bAz88Pd+7ckWy3ZcsWmWLctGkTrK2toaOjg2HDhiE7OxsLFy6EhYUFzMzMMHeu8C/egu53+/btsLW1hb6+Prp164bExEQAuZW28+fPY/ny5ZKYw8LCvv9JlaOU5GRMnjgBM/zmQE9fX9HhFEiOWIyEtCzJkpSRLVnnYKyFM08/4GVsKmKSM3H0YQxSMrNha6Qp6XPofjROP/mAN/Fpigj/u2zfuhkdO3dF+w6dYO/ggKkz/KChoYFDB/YrOrTvVhJfi9LcCb2Nxk2aoWGjxihTpix+au4N93r1cf9eya4aNm7SFA0aNoKNjS1sbe0wYtQYaGlp4e6dUEWHRkXgP5tA/frrr9i/fz+2bt2KW7duwcHBAV5eXoiLi0P37t2xa9cuQf+dO3fCw8MDNjY2AIAuXbogOjoaJ0+exM2bN+Hq6opmzZohNjZWss2zZ8+wf/9+HDhwAKGhoYiIiED37t3Rr18/PHr0COfOnUPHjh0hFosxfvx4dO3aFd7e3oiIiEBERATq1asHANDV1cWWLVvw8OFDLF++HBs2bMDSpUsBAD///DPGjRuHypUrS7b7+eefCxzj8+fPcfLkSZw6dQp//vkn/vjjD7Rq1Qpv3rzB+fPnsWDBAkydOhXBwcGSbQq630OHDuHYsWM4duwYzp8/j/nz5wMAli9fDnd3dwwcOFASc7ly5eT59H63eXNmoWHDRqjrXk/RoRSYua46lrSriAWtK2BQ3XIw0lKVrHv2IQW1y+lDW00ZIgC1rfWhqqyEf6OTFRewnGRmZODRwweC50pJSQl169bD3Tu3FRiZfJTE16I01arXQHDwNbwKy62YPv73X9y+dRMeDRoqODL5yc7OxskTx5GamoJq1WooOhyZcQhPuv/kEF5ycjLWrl2LLVu2oEWLFgCADRs2IDAwEH/88Qd8fHwQEBCA8PBwWFtbIycnB7t378bUqVMBAJcuXcL169cRHR0NdXV1AMDixYtx6NAh7Nu3D4MGDQKQO2y3bds2mJqaAgBu3bqFrKwsdOzYUZKIubi4SOLS1NREeno6LCwsBPF+ul8AsLW1xfjx47F79278+uuv0NTUhI6ODlRUVATbFTTGnJwcbNq0Cbq6unB2dkaTJk3w+PFjnDhxAkpKSqhQoQIWLFiAs2fPok6dOjLtd8uWLdDV1QUA9OzZE0FBQZg7dy709fWhpqYGLS2tPMdaHJw8cRyPHj3Err/2KTqUAnvxIQUbg18jMiEDBpoqaFfFDJOblce0k0+RlpWDNZfDMayeNVZ1dEZWjhgZWTlYeekVopMyFB36d/sY9xHZ2dl5huqMjY3x8uULBUUlHyXxtVgQ/QYMQnJyEtq3aQFlZWVkZ2fDd+QYtGrdVtGhfbenTx6jZ49uyMhIh5aWFpauWA17BwdFhyW70pv3yM1/MoF6/vw5MjMz4eHhIWlTVVVF7dq18ejRI0yYMAGVKlXCrl27MGnSJJw/fx7R0dHo0qULAODOnTtISkrK84GdmpqK58+fS27b2NhIkicAqFatGpo1awYXFxd4eXmhefPm6Ny5MwwNDb8Z719//YUVK1bg+fPnSEpKQlZWFvT09L65TUFjtLW1lSQ5AGBubg5lZWUoKSkJ2qKjo79rv5aWlpJ9yCI9PR3p6emCNrGyuiR5k7fIiAgsnD8Xv2/YVGT3URTuRSRJ/v8mHnj+IQWL21RELWt9XHzxER1dzKGppoyFZ18gKT0brmX0MKyeNfyDnuNNfPo39kyKUlJfiwVx+tRJnDh2FP4LAmDv4IDH/z7CogX+MDUzQ9t2HRQd3nextbXDnv2HkJSUiMDTf2PabxPxx5YdJTOJom/6TyZQBeHj4yNJoHbt2gVvb29J0pCUlARLS0vJHKXPGRgYSP6vra0tWKesrIzAwEBcuXIFp0+fxsqVKzFlyhQEBwfDzs4u3ziuXr0KHx8f+Pn5wcvLC/r6+ti9ezcCAgK+GX9BY1RVVRWsE4lE+bbl5OR8934/7UMW/v7+8PPzE7RNmTYDU6fPlHlfBfHw4QPEfviAbl06Stqys7Nx80YIdv+5EyG370FZWblI7lueUjNzEJWYDnMdNZjqqMHTyQRTTjzBu4TcZOl1XBocTbXR1NEY2268U3C038fQwBDKysr48OGDoP3Dhw8wMTFRUFTfr7S8FvOzNGAh+g4YBO+WrQAAjk4VEBHxDps2/l7iEyhVNTVY//8Ig3PlKnhw/x527tiG6TNnKTgy2ZTmoTd5+U/OgbK3t4eamhouX74sacvMzERISAicnXNP3+7Rowfu37+PmzdvYt++ffDx8ZH0dXV1RWRkJFRUVODg4CBYpH1gi0QieHh4wM/PD7dv34aamhoOHjwIAFBTU0N2drag/5UrV2BjY4MpU6bAzc0Njo6Okonsn+S33ffE+C3y2m9+Medn8uTJiI+PFywTJk4udPzS1KlbF/sOHcVf+w9JlsqVq6Bl6zb4a/+hEvOFpa6iBFMdNcSlZkFdOfeD8MsTxMVican4kFRVU0Ml58oIvnZV0paTk4Pg4KuoWgLnnnxSWl6L+UlLS4PSF689JSVlwZmjpUVOTg4yM0reULmi5kBlZ2dj2rRpsLOzg6amJuzt7TF79mzBJS7EYjGmT58OS0tLaGpqwtPTE0+fPhXsJzY2Fj4+PtDT04OBgQH69++PpKSkL+/uu/wnK1Da2toYOnQoJkyYACMjI1hbW2PhwoVISUlB//79AeQOQdWrVw/9+/dHdnY22rb939i8p6cn3N3d0b59eyxcuBBOTk549+4djh8/jg4dOsDNzS3f+w0ODkZQUBCaN28OMzMzBAcHIyYmBpUqVZLc599//43Hjx/D2NgY+vr6cHR0RHh4OHbv3o1atWrh+PHjkoTrE1tbW7x8+RKhoaEoW7YsdHV1Cx2jNPLar62tLYKDgxEWFgYdHR0YGRkJhg0/UVfPO1yXllWo0AtEW1sHjo5OgjZNLS0Y6BvkaS9Ofq5ugdC3iXifkgFDDVW0dzGDWAwEh8chJSMbUYnp6O1WBn+FRiApI3cIz9lCB8sv/C8ZN9JShbaaMoy11CASAeUMcs8cjU7KQHqW7NXDH6ln776Y9ttEVK5cBVVcqmLH9q1ITU1F+w4dpW9cTJXU12JBNGzcBBs3rIOFpVXuEN6jR9ixbTPadeik6NC+y/KlAajfoCEsLC2RkpyME8eP4UbIdaxd/4eiQysxFixYgLVr12Lr1q2oXLkybty4gb59+0JfXx8jR44EACxcuBArVqzA1q1bYWdnh2nTpsHLywsPHz6UnPHu4+ODiIgIBAYGIjMzE3379sWgQYPynCD2Pf6TCRQAzJ8/Hzk5OejZsycSExPh5uaGv//+WzAfycfHB8OGDUOvXr2gqfm/071FIhFOnDiBKVOmoG/fvoiJiYGFhQUaNmwIc3Pzr96nnp4eLly4gGXLliEhIQE2NjYICAiQTGQfOHAgzp07Bzc3NyQlJeHs2bNo27YtxowZA19fX6Snp6NVq1aYNm0aZs6cKdlvp06dcODAATRp0gRxcXHYvHkz+vTpU6gYpSnssX9p/Pjx6N27N5ydnZGamoqXL1/C1ta20HH91xlqqmJwvXLQUVNGYno2nsYkY/aZ50hMz63yLT0fhs7VLDCqoQ00VJQRlZiOjcFvcDciUbKPDi7mqG/3v9f/LO/c69bM/+cFHhfzs/W8W7TEx9hYrFm1Au/fx6BCxUpY8/tGGJfgIbzSbNJvU7F65XL4z/FDbOwHmJqaoVOXnzF46HBFh/ZdYmM/YOrkiYiJiYaOri6cnCpg7fo/4F7PQ/rGxYyiitNXrlxBu3bt0KpV7vCura0t/vzzT1y/fh1AbvVp2bJlmDp1Ktq1awcA2LZtG8zNzXHo0CF069YNjx49wqlTpxASEiL5o37lypVo2bIlFi9eDCsrK7nEKhKX9Eu/0n9OUVagFGnovnuKDqFIrO3sIr0TFSul9VuhFIxYf5WGnMshjhNOyW1fTxd5F7jvvHnzsH79epw+fRpOTk64c+cOmjdvjiVLlsDHxwcvXryAvb09bt++LfgFjkaNGqF69epYvnw5Nm3ahHHjxuHjx4+S9VlZWdDQ0MDevXvRoYN85tn9ZytQREREVPTyO5s6v+kZADBp0iQkJCSgYsWKkktczJ07VzIPOTIyEgDyjHiYm5tL1kVGRsLMzEywXkVFBUZGRpI+8vCfnEROREREXycSyW/x9/eHvr6+YPH398/3fvfs2YOdO3di165duHXrFrZu3YrFixdj69atP/gRkI4VKCIiIhKQ5xm6kydPxtixYwVtX7u22YQJEzBp0iR069YNQO7Fpl+9egV/f3/07t1bcvHlqKgoWFpaSraLioqSDOlZWFjkue5gVlYWYmNj5XrxZlagiIiIqMioq6tDT09PsHwtgUpJSclzRraysrLkOoJ2dnawsLBAUFCQZH1CQgKCg4Ph7u4OAHB3d0dcXBxu3rwp6fPPP/8gJycHderUkdtxsQJFREREAoqacN+mTRvMnTsX1tbWqFy5Mm7fvo0lS5agX79+/x+XCKNHj8acOXPg6OgouYyBlZUV2rdvDwCoVKkSvL29MXDgQKxbtw6ZmZnw9fVFt27d5HYGHsAEioiIiL6gpKSYDGrlypWYNm0ahg0bhujoaFhZWWHw4MGYPn26pM+vv/6K5ORkDBo0CHFxcahfvz5OnToluQYUAOzcuRO+vr5o1qwZlJSU0KlTJ6xYsUKusfIyBlTi8DIGJQsvY1DylNZvBV7GoOCcfzstt309nNdcbvsqTliBIiIiIoHSnGzKCxMoIiIiEigNv5NZ1HgWHhEREZGMWIEiIiIiARagpGMCRURERAIcwpOOQ3hEREREMmIFioiIiARYgZKOCRQREREJMH+SjkN4RERERDJiBYqIiIgEOIQnHRMoIiIiEmD+JB2H8IiIiIhkxAoUERERCXAITzomUERERCTA/Ek6DuERERERyYgVKCIiIhLgEJ50TKCIiIhIgPmTdBzCIyIiIpIRK1BEREQkwCE86ViBIiIiIpIRK1BExcTazi6KDqFIvIlNVXQIRaaskaaiQygSLD4QXwPSMYEiIiIiAQ7hScchPCIiIiIZsQJFREREAixASccEioiIiAQ4hCcdh/CIiIiIZMQKFBEREQmwACUdEygiIiIS4BCedBzCIyIiIpIRK1BEREQkwAqUdEygiIiISID5k3QcwiMiIiKSEStQREREJMAhPOmYQBEREZEA8yfpOIRHREREJCNWoIiIiEiAQ3jSMYEiIiIiAeZP0nEIj4iIiEhGrEARERGRgBJLUFIxgSIiIiIB5k/ScQiPiIiISEasQBEREZEAz8KTjgkUERERCSgxf5KKQ3hEREREMmIFioiIiAQ4hCddsapAnTt3DiKRCHFxcYoORULeMYWFhUEkEiE0NFQu+1OUmTNnonr16ooOg4iIioBIJL+ltCpWCZS8iEQiHDp0SC77qlevHiIiIqCvry+X/ZVE+T2e48ePR1BQkGIC+gF279qJFj81Ra0aLvDp1gX37t5VdEhyU9KO7X7oTfhNHIme7X9CqwbVcfXCP4L1S+ZOQ6sG1QXLtHHDJOvv3g7Js/7T8uTR/R99ODIrac+XLErrsZXW4yKhYpVAZWRkKDoEgczMTKipqcHCwoLlzC/o6OjA2NhY0WEUiVMnT2DxQn8MHjYcu/ceRIUKFTF0cH98+PBB0aF9t5J4bGlpqbBzcMLQsZO/2qdmHQ9sP3RGsvw6c75kXaUq1QXrth86A6/WHWBuWQaOFSv/iEMotJL4fBVUaT220nJcIjn+K60UmkA1btwYvr6+GD16NExMTODl5QUAuHnzJtzc3KClpYV69erh8ePHgu0OHz4MV1dXaGhooHz58vDz80NWVhYAwNbWFgDQoUMHiEQiyW0AWLt2Lezt7aGmpoYKFSpg+/btgv2KRCKsXbsWbdu2hba2NubOnZvvEN7ly5fRuHFjaGlpwdDQEF5eXvj48SMA4NSpU6hfvz4MDAxgbGyM1q1b4/nz54V+jE6cOAEnJydoamqiSZMm2LJliyCe/IbSli1bJjhuANi4cSMqVaoEDQ0NVKxYEWvWrJGsy8jIgK+vLywtLaGhoQEbGxv4+/t/8/H88n5zcnIwa9YslC1bFurq6qhevTpOnTolWf9p6PLAgQNo0qQJtLS0UK1aNVy9erXQj01R2b51Mzp27or2HTrB3sEBU2f4QUNDA4cO7Fd0aN+tJB6bW9366DXQF/UaNv1qH1VVVRgZm0gWXV29r67T09fHtUvn8FPLdsX+D6OS+HwVVGk9ttJyXEoi+S2llcIrUFu3boWamhouX76MdevWAQCmTJmCgIAA3LhxAyoqKujXr5+k/8WLF9GrVy+MGjUKDx8+xO+//44tW7Zg7ty5AICQkBAAwObNmxERESG5ffDgQYwaNQrjxo3D/fv3MXjwYPTt2xdnz54VxDNz5kx06NAB9+7dE9zvJ6GhoWjWrBmcnZ1x9epVXLp0CW3atEF2djYAIDk5GWPHjsWNGzcQFBQEJSUldOjQATk5OTI/Nq9fv0bHjh3Rpk0bhIaGYsCAAZg0aZLM+9m5cyemT5+OuXPn4tGjR5g3bx6mTZuGrVu3AgBWrFiBI0eOYM+ePXj8+DF27twpSZS+9nh+afny5QgICMDixYtx9+5deHl5oW3btnj69Kmg35QpUzB+/HiEhobCyckJ3bt3lyS/xUFmRgYePXyAuu71JG1KSkqoW7ce7t65rcDIvl9pPrZ7oTfQo00TDOrRDqsXz0VCfNxX+wZfOo/EhHj81LLdjwuwEErz81Vaj620HhflT+Fn4Tk6OmLhwoUAgIiICADA3Llz0ahRIwDApEmT0KpVK6SlpUFDQwN+fn6YNGkSevfuDQAoX748Zs+ejV9//RUzZsyAqakpAMDAwAAWFhaS+1m8eDH69OmDYcNy50aMHTsW165dw+LFi9GkSRNJvx49eqBv376S2y9evBDEu3DhQri5uQkqOJUr/28YoFOnToL+mzZtgqmpKR4+fIgqVarI9Nh8qpgFBAQAACpUqIB79+5hwYIFMu1nxowZCAgIQMeOHQEAdnZ2kuSzd+/eCA8Ph6OjI+rXrw+RSAQbGxvJtl97PL+0ePFiTJw4Ed26dQMALFiwAGfPnsWyZcuwevVqSb/x48ejVatWAAA/Pz9UrlwZz549Q8WKFWU6pqLyMe4jsrOz8wxPGhsb4+XLF1/ZqmQorcdWs44H6jVqBgvLMoh4+xpb16/CjAnDsXjtNigrK+fpf/r4QbjWdoeJmbkCoi240vp8AaX32ErTcRX36mxxoPAKVM2aNfO0Va1aVfJ/S0tLAEB0dDQA4M6dO5g1axZ0dHQky8CBAxEREYGUlJSv3s+jR4/g4eEhaPPw8MCjR48EbW5ubt+M91MF6muePn2K7t27o3z58tDT05NUcsLDw7+536/FXKdOHUGbu7u7TPtITk7G8+fP0b9/f8FjNmfOHMnQYp8+fRAaGooKFSpg5MiROH36tEz3kZCQgHfv3hXo8f3Wc5uf9PR0JCQkCJb09HSZ4qPSrZGnN+rWbwxbe0e4N2yKGQtX4MmjB7h3+0aevu+jo3Dr+lU0b9VBAZESlRw8C086hVegtLW187SpqqpK/v8pC/40BJaUlAQ/Pz9JNeVzGhoaRRLP5zQ1Nb+5vk2bNrCxscGGDRtgZWWFnJwcVKlSpcgmyCspKUEsFgvaMjMzJf9PSkoCAGzYsCFPMvbpr3NXV1e8fPkSJ0+exJkzZ9C1a1d4enpi3759co/3W89tfvz9/eHn5ydomzJtBqZOnyn32ADA0MAQysrKeSZ8fvjwASYmJkVynz9KaT62z1lalYWeviEi3r5GdTfhaz7wxGHo6umjTv1GCoqu4Erz81Vaj620HhflT+EVKFm5urri8ePHcHBwyLMoKeUejqqqqmRO0ieVKlXC5cuXBW2XL1+Gs7OzTPdftWrVr56+/+HDBzx+/BhTp05Fs2bNUKlSJcnk8sKoVKkSrl+/Lmi7du2a4LapqSkiIyMFSdTn15gyNzeHlZUVXrx4kefxsrOzk/TT09PDzz//jA0bNuCvv/7C/v37ERsbCyD/x/Nzenp6sLKyksvj+6XJkycjPj5esEyY+PWzsb6XqpoaKjlXRvC1/01uz8nJQXDwVVStVqPI7vdHKM3H9rn30VFITIiDobHwC0ssFiPwxGE09W4DFRXVr2xdfJTm56u0HltpOi4lkUhuS2ml8AqUrKZPn47WrVvD2toanTt3hpKSEu7cuYP79+9jzpw5AHLPHAsKCoKHhwfU1dVhaGiICRMmoGvXrqhRowY8PT1x9OhRHDhwAGfOnJHp/idPngwXFxcMGzYMQ4YMgZqaGs6ePYsuXbrAyMgIxsbGWL9+PSwtLREeHl6oSd+fDBkyBAEBAZgwYQIGDBiAmzdvYsuWLYI+jRs3RkxMDBYuXIjOnTvj1KlTOHnyJPT0/ncWkp+fH0aOHAl9fX14e3sjPT0dN27cwMePHzF27FgsWbIElpaWqFGjBpSUlLB3715YWFjAwMDgq4/nlyZMmIAZM2bA3t4e1atXx+bNmxEaGoqdO3cW+vgBQF1dHerq6oK2tCKec96zd19M+20iKleugiouVbFj+1akpqaifYe8Vc+SpiQeW2pKCt69/d8QeGTEWzx/+i909fShq6uPXZvXwaOxJwyNjBHx9g02rV0GyzLlULN2PcF+7ty8jqiIt/BqXXKG70ri81VQpfXYSstxleK8R25KXALl5eWFY8eOYdasWViwYAFUVVVRsWJFDBgwQNInICAAY8eOxYYNG1CmTBmEhYWhffv2WL58ORYvXoxRo0bBzs4OmzdvRuPGjWW6fycnJ5w+fRq//fYbateuDU1NTdSpUwfdu3eHkpISdu/ejZEjR6JKlSqoUKECVqxYIfN9fGJtbY39+/djzJgxWLlyJWrXro158+YJzg6sVKkS1qxZg3nz5mH27Nno1KkTxo8fj/Xr10v6DBgwAFpaWli0aBEmTJgAbW1tuLi4YPTo0QAAXV1dLFy4EE+fPoWysjJq1aqFEydOSCp6+T2eXxo5ciTi4+Mxbtw4REdHw9nZGUeOHIGjo2Ohjl2RvFu0xMfYWKxZtQLv38egQsVKWPP7RhiXghJ8STy2p48fYPLIgZLbG1flnlTRzLsNho+fgrDnTxF06iiSkxJhZGKKGrXc0XPAcKiqqQn2c/r4QVSqUg3lbOxQUpTE56ugSuuxldbjorxE4i8n0FCxdu7cOTRp0gQfP36UVIj+a4q6AkXy9SY2VdEhFJmyRt+eE0n0o2jIuRzSefMtue1rX19Xue2rOClxFSgiIiIqWhzCk67ETSIvTYYMGSK4tMDny5AhQxQdHhEREX0Fh/AUKDo6GgkJCfmu09PTg5mZ2Q+OqGTgEF7JwiE8oqIn7yG8n7fK78rpf/UuWWcgFhQrUApkZmaW7+UYHBwcmDwREZHCiOS4yOrt27f45ZdfYGxsDE1NTbi4uODGjf9dGFcsFmP69OmwtLSEpqYmPD098/xsWGxsLHx8fKCnpwcDAwP0799fcl1EeWECRURERMXCx48f4eHhAVVVVZw8eRIPHz5EQECA4PI5CxcuxIoVK7Bu3ToEBwdDW1sbXl5eSEtLk/Tx8fHBgwcPEBgYiGPHjuHChQsYNGiQXGPlEB6VOBzCK1k4hEdU9OQ9hNd9W6jc9vVnr+oF7jtp0iRcvnwZFy9ezHe9WCyGlZUVxo0bh/HjxwMA4uPjYW5uji1btqBbt2549OgRnJ2dERISIvl5tlOnTqFly5Z48+YNrKysvvuYAFagiIiI6AtKIvktsvym6ZEjR+Dm5oYuXbrAzMwMNWrUwIYNGyTrX758icjISHh6ekra9PX1UadOHVy9mnsF+KtXr8LAwEDw27aenp5QUlJCcHCw/B4jue2JiIiI6Av+/v7Q19cXLP7+/vn2ffHiBdauXQtHR0f8/fffGDp0KEaOHImtW7cCACIjIwHk/kzZ58zNzSXrIiMj88wjVlFRgZGRkaSPPPA6UERERCQgkuOFoCZPnoyxY8cK2r78ia5PcnJy4Obmhnnz5gEAatSogfv372PdunXo3bu33GKSB1agiIiISEAkkt+irq4OPT09wfK1BMrS0jLPj9BXqlQJ4eG5v4dpYWEBAIiKihL0iYqKkqyzsLBAdHS0YH1WVhZiY2MlfeSBCRQREREVCx4eHnj8+LGg7cmTJ7CxsQEA2NnZwcLCAkFBQZL1CQkJCA4Ohru7OwDA3d0dcXFxuHnzpqTPP//8g5ycHNSpU0dusXIIj4iIiATkOYQnizFjxqBevXqYN28eunbtiuvXr2P9+vVYv369JK7Ro0djzpw5cHR0hJ2dHaZNmwYrKyu0b98eQG7FytvbGwMHDsS6deuQmZkJX19fdOvWTW5n4AFMoIiIiOgLSgr6LbxatWrh4MGDmDx5MmbNmgU7OzssW7YMPj4+kj6//vorkpOTMWjQIMTFxaF+/fo4deoUNDQ0JH127twJX19fNGvWDEpKSujUqRNWrFgh11h5HSgqcXgdqJKF14EiKnryvg5Unz/vym1fW7pXldu+ipNCzYG6ePEifvnlF7i7u+Pt27cAgO3bt+PSpUtyDY6IiIh+PJFIJLeltJI5gdq/fz+8vLygqamJ27dvSy6GFR8fLzntkIiIiEouRf4WXkkhcwI1Z84crFu3Dhs2bICqqqqk3cPDA7du3ZJrcERERETFkcyjpo8fP0bDhg3ztOvr6yMuLk4eMREREZECKZXioTd5kbkCZWFhgWfPnuVpv3TpEsqXLy+XoIiIiEhx5HkhzdJK5gRq4MCBGDVqFIKDgyESifDu3Tvs3LkT48ePx9ChQ4siRiIiIqJiReYhvEmTJiEnJwfNmjVDSkoKGjZsCHV1dYwfPx4jRowoihiJiIjoByrNZ8/JS6GvA5WRkYFnz54hKSkJzs7O0NHRkXdsRPnidaBKFl4Hiqjoyfs6UIP3PZDbvn7vXFlu+ypOCv2Qq6mp5fnBPyIiIqL/ApkTqCZNmnyztPfPP/98V0BERESkWDwLTzqZE6jq1asLbmdmZiI0NBT3799H79695RUXERERKQjzJ+lkTqCWLl2ab/vMmTORlJT03QERERERFXeF+i28/Pzyyy/YtGmTvHZHRERECsLfwpNObvP2r169Cg0NDXntjuirUjOyFR1CkSitnzMW+qX3c8Gwlq+iQygSby8tV3QIRaK0vscAQENFWa77k1t1pRSTOYHq2LGj4LZYLEZERARu3LiBadOmyS0wIiIiouJK5gRKX19fcFtJSQkVKlTArFmz0Lx5c7kFRkRERIpRmofe5EWmBCo7Oxt9+/aFi4sLDA0NiyomIiIiUiAl5k9SyTTMqaysjObNmyMuLq6IwiEiIiIq/mSeJ1alShW8ePGiKGIhIiKiYkBJJL+ltJI5gZozZw7Gjx+PY8eOISIiAgkJCYKFiIiISjZexkC6As+BmjVrFsaNG4eWLVsCANq2bSt4YMRiMUQiEbKzS+cp5kRERESfFDiB8vPzw5AhQ3D27NmijIeIiIgUrDQPvclLgRMosVgMAGjUqFGRBUNERESKV4pH3uRGpjlQpXksk4iIiKigZLoOlJOTk9QkKjY29rsCIiIiIsVSYsFEKpkSKD8/vzxXIiciIqLShb+FJ51MCVS3bt1gZmZWVLEQERERlQgFTqA4/4mIiOi/gV/50sl8Fh4RERGVbpwDJV2BE6icnJyijIOIiIioxJBpDhQRERGVfixASccEioiIiAR4JXLpeKYiERERkYxYgSIiIiIBTiKXjgkUERERCTB/ko5DeEREREQyYgWKiIiIBDiJXDomUERERCQgAjMoaTiER0RERCQjVqDoP23DulX44/c1gjYbWzv8dfA44uPjsGHtKly/dgVRkREwMDREw8bNMHjYSOjo6ioo4sLZumkD1qxYip979MTYXycDAIb2741bN0ME/Tp07opJU2cqIMKC27Txd5wNCkTYyxdQV9dA1eo1MHL0ONjalQcAxMfH4fc1K3HtymVERkbAwNAIjZs2w9Dho6CrwOfNw9UeY3p5wtXZGpam+ug6Zj2Onrsr6DNtaCv07VAPBrqauHrnBUbO+wvPw2Mk6x2szTBvTHu4VysPNVVl3H/6Dn5rjuHCjaeSPo1rO2HGsNao7GCF5NQM7DwajBmrjyI7WzG/JrFx3Sr8sV74HrO2tcNfB45Lbt+7E4rfVy/Hg/t3oaSsBCenili6egM0NDR+dLgy+dbnBwDMnzMDIcHX8D4mGpqaWnCpVh3DR/3vtVqccQhPOiZQ/1GZmZlQVVVVdBjFQnl7B6xc94fktrJy7tvifUwM3sfEYMSYCbArb4/IiHdYMNcP72Ni4L94mYKild3D+/dwcN8eODhVyLOuXccuGDzMV3JbXUPzR4ZWKLduhKBLtx6oXNkF2dnZWLViKYYPGYB9B49BU0sLMdHRiImOxuhxv8LO3gER797Bf84MvI+OxsIlKxQWt7amOu49eYtth6/iryWD8qwf18cTw7o3wsDp2xH29gOmD2uNo6uHo0anOUjPyAIAHFgxBM/Co9Fi8AqkpmfCt0cTHFgxBJXbzETUh0S4OJXBoZVDseCPv9F/2jZYmRlg5W/doKyshMlLD/7oQ5Yob++AFWvzvseA3ORpzIhB6NV3IMZO/A3Kyip4+uRfKCmVjAGSr31+AEDFSpXh1aINzC0tkRAfj43rVmPUsAE4cCwQysrKigi3wJhASVcyXqEEANi3bx9cXFygqakJY2NjeHp6Ijk5GSEhIfjpp59gYmICfX19NGrUCLdu3RJsKxKJsHbtWrRt2xba2tqYO3cuAODo0aOoVasWNDQ0YGJigg4dOki22b59O9zc3KCrqwsLCwv06NED0dHRkvUfP36Ej48PTE1NoampCUdHR2zevBkAEBYWBpFIhD179qBBgwbQ1NRErVq18OTJE4SEhMDNzQ06Ojpo0aIFYmJioEjKysowNjGVLAaGhgAAewdHzA9YjgaNmqBsOWu41a6LIb6jcOnCWWRlZSk05oJKSUnG9N9+xW/T/aCnq5dnvYaGhuDYdXR0FBClbFat24i27TrC3sERThUqwm+2PyIj3uHRwwcAAAdHJyxauhINGzdFuXLWqF2nLoaNGIML5xX7vJ2+/BB+a47hyNm7+a4f3qMJFmz4G8fO3cP9p+8wYNo2WJrqo22TagAAYwNtONqYIWBzIO4/fYfn4TGYtuIwtDXV4exgBQDo3NwV95++g//6U3jx+j0u3XyGKcsPYXDXBtDRUv9hx/qlr73HAGB5wHx06fYLevUdiPL2jrCxtYNn8xZQU1NTWLyy+Naxte/UFTVqusHKqgwqVnLG4OEjERUZiYh3bxUYMckLE6gSIiIiAt27d0e/fv3w6NEjnDt3Dh07doRYLEZiYiJ69+6NS5cu4dq1a3B0dETLli2RmJgo2MfMmTPRoUMH3Lt3D/369cPx48fRoUMHtGzZErdv30ZQUBBq164t6Z+ZmYnZs2fjzp07OHToEMLCwtCnTx/J+mnTpuHhw4c4efIkHj16hLVr18LExERwnzNmzMDUqVNx69YtqKiooEePHvj111+xfPlyXLx4Ec+ePcP06dOL9LGT5nV4OFr/1AgdWzfH9N8mIDLi3Vf7JiUmQVtbByoqJaN4u2jeHHg0aITadevlu/7vk8fQvHE9dO/UFqtXLEFaauoPjvD7JSXlvs719PW/3icxEdo6xfd5sy1jDEtTffwT/K+kLSEpDSH3w1Cnqi0A4ENcMh6/jESP1rWhpaEGZWUlDOhUH1EfEnD7YTgAQF1NBWnpmYJ9p6ZnQlNDDTUqWf+w4/nS6/BwtGneCJ3aNMeMKf97j8XGfsCD+3dhZGSEgX16oKVnAwwd0At3bt9UWKyyKujnR2pqCo4fOQirMmVhbmHxg6OUnUgkkttSWhXPTxPKIyIiAllZWejYsSNsbGwAAC4uLgCApk2bCvquX78eBgYGOH/+PFq3bi1p79GjB/r27Su53a1bN3Tr1g1+fn6StmrVqkn+369fP8n/y5cvjxUrVqBWrVpISkqCjo4OwsPDUaNGDbi5uQEAbG1t88Q9fvx4eHl5AQBGjRqF7t27IygoCB4eHgCA/v37Y8uWLYV5SOSicpWqmDZrLqxt7PDhfQz++H0NhvTriZ37jkBbW1vQN+7jR2zesBbtOnVRULSyOX3qBB7/+xCbd+7Jd33zFq1gaWUFE1MzPHvyGKuWL0F4WBgWKHCYS1Y5OTlYvHAeqtVwhYOjU759Pn78iI3r16Jjp64/OLqCszDJrQ5Gxwr/6In+kAhz4/9VDlsNWYW/lg5CzOXFyMkRI+ZjEtoNX4O4xNzEN/DKI/j2aIKu3jWx7/QtWBjr4bdBLQAAlqZ5K5A/QmWXqpjqNxc2NnZ4/z4Gf6xfg6H9e2LH3iN49+YNAGDj76sxYvQEOFaoiJPHjmDEkH7YufcwylnbKiTmgirI58e+PX9i9bLFSE1NhY2tHVas3QhV1eJfXeMQnnRMoEqIatWqoVmzZnBxcYGXlxeaN2+Ozp07w9DQEFFRUZg6dSrOnTuH6OhoZGdnIyUlBeHh4YJ9fEp0PgkNDcXAgQO/ep83b97EzJkzcefOHXz8+BE5ObmTUMPDw+Hs7IyhQ4eiU6dOuHXrFpo3b4727dujXj1hpaNq1aqS/5ubmwP4X+L3qe3zYcEvpaenIz09XdiWrQJ1dfkMR9Sr31Dyf0enCqjsUhXtW3oi6PQptO3QSbIuOSkJY0cOgW15ewwcPFwu912UoiIjsGShP1au2/jVx6pD5/8lFA6OTjAxNcXwQf3w5nU4ypZTXLVCFvPnzsLzZ0/xx5Zd+a5PSkrCqOGDUb68PQYN9c23T0mydHJXxMQmwrPfMqSmZ6BPh3rYv3ww6v+yCJHvExB07V/8tuwQVvzWDX/M7oX0zCzM33AK9V0dkJMjVkjM7h7/e485/P97rEMrTwQFnpJMpm7fsStat+sIAKhQ0Rk3rl/D0cMHMGzEWIXEXFAF+fzwbtEateu448P799i5bTOmTByL9Zt3yu0zjBSHQ3glhLKyMgIDA3Hy5Ek4Oztj5cqVqFChAl6+fInevXsjNDQUy5cvx5UrVxAaGgpjY2NkZGQI9vFlRUVT8+sThpOTk+Hl5QU9PT3s3LkTISEhOHgwdxLqp/22aNECr169wpgxY/Du3Ts0a9YM48ePF+zn84nqn0q5X7Z9Sszy4+/vD319fcGydPH8bz1U30VXVw/W1rZ48/qVpC05ORmjhw+ClpY2FixZCZUSMPn+34cP8DH2A3p374x6NV1Qr6YLbt0MwZ4/d6BezdzJ11+q7JKb7L55HZ5nXXG0YN4sXLpwDr9v3JbvkEhychJGDB0AbW1tLF62qlifNBH5PgEAYGYkPEvQzFgXUR9y1zWu7YSWDaqg16TNuHrnBUL/fYPR/nuQmp6JX9rUkWyzYsc/sGg4AU4tp6Nsk0mSM/1evnn/g47m2z5/j5mYmAIA7MrbC/rY2pVHVGSEIsL7Lvl9fujo6sLaxhY1arrBf/FSvHr5Euf/OaPAKAtGJJLfUloxgSpBRCIRPDw84Ofnh9u3b0NNTQ0HDx7E5cuXMXLkSLRs2RKVK1eGuro63r+X/mFZtWpVBAUF5bvu33//xYcPHzB//nw0aNAAFStWzLdSZGpqit69e2PHjh1YtmwZ1q9f/93H+bnJkycjPj5esIwZP0mu9/G5lJRkvH0TDuP//2BPTkrCqKEDoKKqisXLVpeYvxrd6rhj177D2P7XAclSybkKvFq2xva/DuR7BtCTf3Pn33w69uJKLBZjwbxZOPvPGazbuAVlypbN0ycpKQnDB/eHqqoqlqxYU+yft7C3HxARE48mdf53pqSutgZqVbFF8N0wAICWRu6wz5d/cOTkiPOdZxIRE4+09Ex09XbD64hY3P73ddEdgAxSUpLx5k04TExMYWlVBiamZnj1KkzQJzw8DBYWVooJ8Dt8+fnxJbEYEEOMjMyMfNcXJ0oikdyW0opDeCVEcHAwgoKC0Lx5c5iZmSE4OBgxMTGoVKkSHB0dJWfMJSQkYMKECd+sLn0yY8YMNGvWDPb29ujWrRuysrJw4sQJTJw4EdbW1lBTU8PKlSsxZMgQ3L9/H7NnzxZsP336dNSsWROVK1dGeno6jh07hkqVKsn1uNXV1fN8+WWn5K2eFNaKJQtRv2ETWFhZ4X10NDasWwUlJWU0926F5KQkjBw2AGlpaZg5dwGSk5OQnJwEADAwNCrWpyFra2vD3sFR0KapqQl9fQPYOzjizetw/H3yOOrVbwh9fQM8e/oYyxYvQI2abnDM53IHxcn8ubNw6uQxLFm+Glra2nj/PvcsTh0dXWhoaEiSp7S0VMz2XyR43gwV+Lxpa6rBvtz/vlhtyxijqlMZfExIwevIj1i96ywmDvDGs/AYhL39gBnDWiEiJh5Hzt4BAATffYmPCSnYOLsX5q0/idS0TPTrWA+2ZYxx6tIDyX7H9GqG01ceIScnB+2aVcf4vj/hl183KWwIb8XS3PeYpaUVYmKisXHdKigrKeMn71YQiUTw6dUPG39fBUenCnB0qogTxw7jVdhLzFu4TCHxyuJbnx9v37zGmb9Poo67BwwMDREdFYVtm3OH1D8f+qOSiwlUCaGnp4cLFy5g2bJlSEhIgI2NDQICAtCiRQtYWFhg0KBBcHV1Rbly5TBv3rw8Q2n5ady4Mfbu3YvZs2dj/vz50NPTQ8OGuW9sU1NTbNmyBb/99htWrFgBV1dXLF68GG3btpVsr6amhsmTJyMsLAyamppo0KABdu/eXWSPQVGIjorC9MnjER8fBwNDI1Sr7oqN2/6EoZERbt64jgf3coc/Orf1Fmx34HggrKzKKCJkuVBVVUVI8FXs3rkNaampMDO3QJNmP6HvwCGKDk2qfXv+BAAM6tdL0D5j9jy0bdcR/z56gPv3cpOO9q2aC/ocPXkGVmXyVqx+BFdnG5zeOEpye+H43Dky249cw6AZOxCw5Qy0NNWxamp3GOhq4kroc7QdvkZyDagPcclo57sGM4e3wcnfR0JVRQmPXkSiy5j1uPfkf6fFN/dwxq8DvKCuqoJ7T96iy5j1OH354Y892M/EREVhxhfvsQ1b/4ShoREAoJtPL2RkpGN5wAIkxMfDwakCVqzZWCLm4X3r8yMrKwuht29i967tSEyIh5GxCaq71sSGLbtgZGSs6NCl4iRy6URisVgxf5YQFdJHOVagipPSWulWKSEXRCwM07ojFB1CkXh7abmiQygSpfU9BgCGWvKtrK68/FJu+xrhYSe3fRUnpfeTjYiIiKiIcAiPiIiIBJRQist1csIEioiIiARK83CnvHAIj4iIiEhGrEARERGRAM/Ck44JFBEREQmU5gtgyguH8IiIiIhkxAoUERERCbAAJR0TKCIiIhLgEJ50HMIjIiIikhETKCIiIhIQieS3fI/58+dDJBJh9OjRkra0tDQMHz4cxsbG0NHRQadOnRAVFSXYLjw8HK1atYKWlhbMzMwwYcIEZGVlfV8wX2ACRURERAJKclwKKyQkBL///juqVq0qaB8zZgyOHj2KvXv34vz583j37h06duwoWZ+dnY1WrVohIyMDV65cwdatW7FlyxZMnz79O6LJiwkUERERFStJSUnw8fHBhg0bYGhoKGmPj4/HH3/8gSVLlqBp06aoWbMmNm/ejCtXruDatWsAgNOnT+Phw4fYsWMHqlevjhYtWmD27NlYvXo1MjIy5BYjEygiIiISEIlEclvS09ORkJAgWNLT0795/8OHD0erVq3g6ekpaL958yYyMzMF7RUrVoS1tTWuXr0KALh69SpcXFxgbm4u6ePl5YWEhAQ8ePBAbo8REygiIiISEMlx8ff3h76+vmDx9/f/6n3v3r0bt27dyrdPZGQk1NTUYGBgIGg3NzdHZGSkpM/nydOn9Z/WyQsvY0BERERFZvLkyRg7dqygTV1dPd++r1+/xqhRoxAYGAgNDY0fEV6hsQJFREREAkoikdwWdXV16OnpCZavJVA3b95EdHQ0XF1doaKiAhUVFZw/fx4rVqyAiooKzM3NkZGRgbi4OMF2UVFRsLCwAABYWFjkOSvv0+1PfeTyGMltT0RERFQqyHMITxbNmjXDvXv3EBoaKlnc3Nzg4+Mj+b+qqiqCgoIk2zx+/Bjh4eFwd3cHALi7u+PevXuIjo6W9AkMDISenh6cnZ1lfzC+gkN4REREVCzo6uqiSpUqgjZtbW0YGxtL2vv374+xY8fCyMgIenp6GDFiBNzd3VG3bl0AQPPmzeHs7IyePXti4cKFiIyMxNSpUzF8+PCvVr4KgwkUERERCRTnX3JZunQplJSU0KlTJ6Snp8PLywtr1qyRrFdWVsaxY8cwdOhQuLu7Q1tbG71798asWbPkGodILBaL5bpHoiL2MSVb0SEUieL8gfU9VJRK70wB07ojFB1CkXh7abmiQygSpfU9BgCGWspy3d+ft9/KbV/da5SR276Kk9L7yUZERERURDiER0RERAKsrkjHBIqIiIgERKV5vFNOmGQSERERyYgVKCIiIhJg/Uk6JlBEREQkwCE86ZhAUYmjrlpKR55L6QVFSvMH8fvglYoOoUi0WXdN0SEUiWND6yo6BCpFmEARERGRQCn9M1WumEARERGRQGmuHMsLk0wiIiIiGbECRURERAKsP0nHBIqIiIgEOIInHYfwiIiIiGTEChQREREJKHEQTyomUERERCTAITzpOIRHREREJCNWoIiIiEhAxCE8qZhAERERkQCH8KTjEB4RERGRjFiBIiIiIgGehScdEygiIiIS4BCedBzCIyIiIpIRK1BEREQkwAqUdEygiIiISICXMZCOQ3hEREREMmIFioiIiASUWICSigkUERERCXAITzoO4RERERHJiBUoIiIiEuBZeNIxgSIiIiIBDuFJxyE8IiIiIhkxgSK5mDlzJqpXr67oMIiISA6URPJbSisO4ZHMRCIRDh48iPbt20vaxo8fjxEjRiguqEK6eSME2zb/gYcPH+B9TAyWLF+FJs08JeuDAk9j357dePTwAeLj47F730FUqFhJgREX3M0bIdi25bNjWyY8tulTJuHokUOCbep51MfqdRt/cKTfp0Xzpoh49zZPe9duPfDb1BkKiKhwPj1fj/7/+Qr44vn68P49VixdjKtXLyMpMRE1arph4uSpsLaxVVzQX2GirYaBHtaobWMADVVlvI1Lw8Izz/AkOhkAoKGqhEH1bOBhbwg9DVVEJKThYGgkjt6PkuxjTJPyqGmtD2NtNaRmZuNBRCLWX36F1x/TFHVYeXzr8yMzMxNrVi7HpYvn8ebNG+jo6KBO3XoYOWYszMzMFRy5dBzCk44JFMmFjo4OdHR0vro+IyMDampqPzCigklNTYVThYpo16ETxo3OmwCmpqaiumtN/OTVArNnTlNAhIWXmpoKJ6evHxsA1PNoAL858yS31VSL33Mkzc7d+5CTky25/ezpUwwZ2Bc/NfdWYFSyS/vs+Rr/xfMlFosxdtRwqKioYumKNdDW1saObVswZGA/7D90DJpaWgqKOi8ddWWs6FIZoW8SMPnIv4hLzURZAw0kpWdJ+gxrYIsaZfUx7+9niExIh5u1PkY3KY8PyRm48vIjAOBJdBKCHscgKjEDehoq6F2nLBa2d4bPllvIESvq6IS+9fmRlpaGRw8fYuDgYXCqUAEJCQlYNH8eRvsOw649+xUUMckTE6j/qH379sHPzw/Pnj2DlpYWatSogcOHD+Phw4f47bffcPv2bWRmZqJ69epYunQpXF1dAQC2trYAgA4dOgAAbGxsEBYWhpkzZ+LQoUMIDQ0FAPTp0wdxcXGoVasWVq9eDXV1dbx8+RKvX7/GuHHjcPr0aSgpKaFBgwZYvny5ZL8/Wv0GDVG/QcOvrm/dth0A4N3bNz8qJLmRdmwAoKamBhMT0x8UUdEwMjIS3N60cT3KlbOGW63aCoqocDwaNITHV56v8FdhuHf3DvYePAp7B0cAwG/TZuKnJvVx6uRxdOjU5UeG+k3da5ZBdGIGFp55LmmLTEgX9KlsqYu/H0XjztsEAMDxB9Fo42KOiuY6kgTq+INoSf+oxHRsuvoaG32qwUJPHe/ihftTlG+9x3R1dbFu4yZB26TfpuGX7l0QEfEOlpZWPyLEQuNZeNJxDtR/UEREBLp3745+/frh0aNHOHfuHDp27AixWIzExET07t0bly5dwrVr1+Do6IiWLVsiMTERABASEgIA2Lx5MyIiIiS38xMUFITHjx8jMDAQx44dQ2ZmJry8vKCrq4uLFy/i8uXL0NHRgbe3NzIyMn7IsZPQjRvX0bRRPbRv4425s2ciLu6jokP6LpmZGThx7AjadegEUSn6Bvj0/lBTV5e0KSkpQU1VDaG3bioqrHy5lzfEk+gkzGjhhP0D3PB796poVdlM0OdBRCLqlTeCiXZuxbN6WT2UNdDEjfC4fPepoaIEb2dTvItPQ3Riyf2sSExKhEgkgq6unqJDkUokx6W0YgXqPygiIgJZWVno2LEjbGxsAAAuLi4AgKZNmwr6rl+/HgYGBjh//jxat24NU9PcaoWBgQEsLCy+eT/a2trYuHGjZOhux44dyMnJwcaNGyVfbps3b4aBgQHOnTuH5s2by/U46dvq1W+App7NUaZMGbx5/RorVyyF79BB2LpjN5SVlRUdXqH8E3QGiYmJaNu+g6JDkStbu/KwsLTCqmVLMGW6HzS1NLFz21ZERUUi5n2MosMTsNLTQFsXC+y9/Q47b7xBBTMd+DayQ2a2GKf/zY115fmXGNu0PPb0r4ms7BzkAAgIeo677xIF+2rrYo7BHjbQVFNGeGwqfj30EFnFZfxORunp6VixdDG8W7b65nQHKjmYQP0HVatWDc2aNYOLiwu8vLzQvHlzdO7cGYaGhoiKisLUqVNx7tw5REdHIzs7GykpKQgPD5f5flxcXATznu7cuYNnz55BV1dX0C8tLQ3Pnz//cnMAuR866enCcn22khrUP/tLnArHu0Uryf8dnSrA0akC2rT8CTdCrqNOXXcFRlZ4hw7sh0f9hiVikq4sVFVVsXjpCsyaMRWN69eBsrIyatd1h0f9hhCLi1dCIRIBT6KT8cfV1wCAZzEpsDPWQhsXc0kC1aGqBZwtdDHl6L+ISkhH1TJ6GNW4PD4kZ+LW63jJvoIev8fN8HgYa6uiq6sVprdwwoi995GZXbyOWZrMzEz8Om40xOLcodeSQKkUVXCLCofw/oOUlZURGBiIkydPwtnZGStXrkSFChXw8uVL9O7dG6GhoVi+fDmuXLmC0NBQGBsbF2qITVtbW3A7KSkJNWvWRGhoqGB58uQJevToke8+/P39oa+vL1gWL/Av1HHTt5UtVw4GhoZ4Hf5K0aEUyrt3bxF87Qo6dOqs6FCKhHPlKti97xDOXwnB6X8uYvW6jYiPj0OZsuUUHZpAbHImwmJTBG3hH1Nhrpv7R4+ashL617PGmothuPryI158SMGhu5E4+/Q9uroK5wUlZ2TjbXwa7r5LxMwTT1DOUBMN7IVz3oq7zMxMTBw3BhHv3mHthj9KTPWJQ3jSsQL1HyUSieDh4QEPDw9Mnz4dNjY2OHjwIC5fvow1a9agZcuWAIDXr1/j/fv3gm1VVVWRnZ2d326/ydXVFX/99RfMzMygp1ewOQCTJ0/G2LFjBW3ZSiXvTLGSICoyEvFxcTAxNZPeuRg6fPAAjIyM0aBhY0WHUqQ+VXDDX4Xh4YP7GOo7UsERCd2PSEQ5A01BW1kDDUQl5laSVZRFUFVWwpeFs5ycb18zSCTK/TJWVS45f/d/Sp7Cw19h/aatMDAwVHRIJEdMoP6DgoODERQUhObNm8PMzAzBwcGIiYlBpUqV4OjoiO3bt8PNzQ0JCQmYMGECNDWFH4a2trYICgqCh4cH1NXVYWhYsA8FHx8fLFq0CO3atcOsWbNQtmxZvHr1CgcOHMCvv/6KsmXL5tlGXV09z3BdSqb8yvcpKcl4/dnw5Nu3b/D430fQ09eHpaUV4uPjEBkRgejo3DOCwl6+BAAYm5gU+7PXvnVs+vr6+H3tajTzbA4TExO8fv0ay5csQjlra9TzqK/AqAsnJycHRw4dQJt27aGiUjI/1qS9FgP/PgVDI0NYWFjh2dMnWLRgLho3bQb3esXr+dp3+x1WdqmCHm5lcO7pB1Q010GrKuZY8s8LAEBKRjZC38RjcH0bpGflICoxHdXK6KF5JVOsvRgGALDUU0djJ2PceBWP+NRMmOqoobtbGaRn5SA4rPic6PCt58zExBQTxo7Cvw8fYvnqdcjJycb7/5+vpq+vD9XifsmQ0lw6kpOS+UlD30VPTw8XLlzAsmXLkJCQABsbGwQEBKBFixawsLDAoEGD4OrqinLlymHevHkYP368YPuAgACMHTsWGzZsQJkyZRAWFlag+9XS0sKFCxcwceJEdOzYEYmJiShTpgyaNWtW4IqUvD28fx8D+/WW3A5YOB8A0KZde8yaOx/nz/6DGVN/k6yfNCG3GjZ46HAMGV68Lxz68MEXx7bo/4+tbXv8Nm0mnj55jKNHDiExIRGmZqZwd/fAMN9RxfJ6XdJcu3oFERHv0L5DJ0WHUmgPH9zHoM+eryWfPV9+c+fj/ftoLFk0Hx8+fICJqSlat2mHgUOGKircr3ocnYzpxx9jQD0b9KpdFhEJaVhzIQxBj/9XyZ596ikG1rPGFC9H6GqoICohHX9cDceRe7kX0szIzkFVKz10qm4JXXUVfEzJxN23CRi59z7iUrO+dtc/3Lc+P4YM88X5s/8AALp1bi/YbsOmrXCrXeeHxVkYvJCmdCJxcZuBSCSFPCtQxUopPazSdDmBL+WU0o/PNuuuKTqEInFsaF1Fh1BktFTl+z4Lfh4vvVMB1bHXl9u+ihNWoIiIiEigFP/dIzdMoIiIiEiA+ZN0Jed0BiIiIqJighUoIiIiEmIJSiomUERERCTAs/Ck4xAeERERkYxYgSIiIiIBnoUnHRMoIiIiEmD+JB2H8IiIiIhkxAoUERERCbEEJRUTKCIiIhLgWXjScQiPiIiISEasQBEREZEAz8KTjgkUERERCTB/ko5DeEREREQyYgWKiIiIhFiCkooVKCIiIhIQyfGfLPz9/VGrVi3o6urCzMwM7du3x+PHjwV90tLSMHz4cBgbG0NHRwedOnVCVFSUoE94eDhatWoFLS0tmJmZYcKECcjKyvrux+VzTKCIiIioWDh//jyGDx+Oa9euITAwEJmZmWjevDmSk5MlfcaMGYOjR49i7969OH/+PN69e4eOHTtK1mdnZ6NVq1bIyMjAlStXsHXrVmzZsgXTp0+Xa6wisVgsluseiYpYSmYpfcmW0sMSleLTeXJK6cdnm3XXFB1CkTg2tK6iQygyWqryfZ/de5Mkt325lNUp9LYxMTEwMzPD+fPn0bBhQ8THx8PU1BS7du1C586dAQD//vsvKlWqhKtXr6Ju3bo4efIkWrdujXfv3sHc3BwAsG7dOkycOBExMTFQU1OTy3GxAkVEREQCIjku3yM+Ph4AYGRkBAC4efMmMjMz4enpKelTsWJFWFtb4+rVqwCAq1evwsXFRZI8AYCXlxcSEhLw4MGD74zofziJnIiIiIpMeno60tPTBW3q6upQV1f/5nY5OTkYPXo0PDw8UKVKFQBAZGQk1NTUYGBgIOhrbm6OyMhISZ/Pk6dP6z+tkxdWoIiIiEhIjiUof39/6OvrCxZ/f3+pIQwfPhz379/H7t275X548sAKFBEREQnI87fwJk+ejLFjxwrapFWffH19cezYMVy4cAFly5aVtFtYWCAjIwNxcXGCKlRUVBQsLCwkfa5fvy7Y36ez9D71kQdWoIiIiKjIqKurQ09PT7B8LYESi8Xw9fXFwYMH8c8//8DOzk6wvmbNmlBVVUVQUJCk7fHjxwgPD4e7uzsAwN3dHffu3UN0dLSkT2BgIPT09ODs7Cy342IFioiIiAQUdfLs8OHDsWvXLhw+fBi6urqSOUv6+vrQ1NSEvr4++vfvj7Fjx8LIyAh6enoYMWIE3N3dUbdu7lmWzZs3h7OzM3r27ImFCxciMjISU6dOxfDhw6VWvmTBBIqIiIgEFHXxkbVr1wIAGjduLGjfvHkz+vTpAwBYunQplJSU0KlTJ6Snp8PLywtr1qyR9FVWVsaxY8cwdOhQuLu7Q1tbG71798asWbPkGiuvA0UlDq8DVbLwOlAlD68DVfLI+zpQj94lS+9UQJWstOW2r+KECRSVOGnyvRo/Ef1HBJx/pugQisyUZg5y3d+jCDkmUJalM4HiEB4REREJyPMsvNKKZ+ERERERyYgVKCIiIhIoxVMX5YYJFBEREQkwf5KOQ3hEREREMmIFioiIiIRYgpKKCRQREREJ8Cw86TiER0RERCQjVqCIiIhIgGfhSccEioiIiASYP0nHITwiIiIiGbECRUREREIsQUnFBIqIiIgEeBaedBzCIyIiIpIRK1BEREQkwLPwpGMCRURERALMn6TjEB4RERGRjFiBIiIiIiGWoKRiAkVEREQCPAtPOg7hEREREcmIFSgiIiIS4Fl40jGBIiIiIgHmT9JxCI+IiIhIRqxAERERkQCH8KRjBaoAzp07B5FIhLi4OEWHQkRE9AOI5LiUTqxAFSMikQgHDx5E+/btZdrO1tYWo0ePxujRo4skLnkLCwuDnZ0dbt++jerVqys6nHzt3rUTWzf/gffvY+BUoSIm/TYNLlWrKjqs77Jn9y7s+etPvHv7FgBg7+CIwUOHoX6DRgqO7Pv8seF3BAWexsuXL6CuoYHq1Wtg9NjxsLUrr+jQvktpfb4+V5LfZ/f+3oPbh7eiUpN2qNVlEJI+ROHAtH759m04YBJsXRsAACL+DUXo0e34+O4VVNTVYV+nGWq07Q0lZeUfGT7JAROoHyQjIwNqamqKDoMK4NTJE1i80B9TZ/jBxaUadm7fiqGD++PwsVMwNjZWdHiFZmZugVFjxsPaxgZisRhHDx/CKN/h+Gv/QTg4OCo6vEK7EXIdP3f3QWUXF2RnZWPl8iUYMrA/Dhw5Di0tLUWHV2il9fn6pCS/z96HPcHTS6dgWMZO0qZlaIIu/tsF/Z5cPoUHgQdQxtkNABD75gWC1syAi/fP8Og9DilxHxD85yqIc3Lg1mnADz0GaTiEJ12pG8KztbXFsmXLBG3Vq1fHzJkzAeRWeTZu3IgOHTpAS0sLjo6OOHLkiKD/iRMn4OTkBE1NTTRp0gRhYWF57ufSpUto0KABNDU1Ua5cOYwcORLJycmCOGbPno1evXpBT08PgwYNQkZGBnx9fWFpaQkNDQ3Y2NjA399f0h8AOnToAJFIJLn9/PlztGvXDubm5tDR0UGtWrVw5swZyf00btwYr169wpgxYyASiSD67FVfkBjnzJmDXr16QUdHBzY2Njhy5AhiYmLQrl076OjooGrVqrhx44bMxz5v3jz069cPurq6sLa2xvr16yXr7exyP3Rq1KgBkUiExo0b5/NMKs72rZvRsXNXtO/QCfYODpg6ww8aGho4dGC/okP7Lo2bNEWDho1gY2MLW1s7jBg1BlpaWrh7J1TRoX2Xtev/QLsOHeHg4IgKFSti1tz5iIh4h0cPHyg6tO9SWp+vT0rq+ywzLRUXtyxCXZ8RUNPSkbQrKSlDU99IsISHXoWta32oamgCAMJuXoShlR2qtewBPTMrWDi5wLVDPzy+cByZaSmKOqR8cQBPulKXQBWEn58funbtirt376Jly5bw8fFBbGwsAOD169fo2LEj2rRpg9DQUAwYMACTJk0SbP/8+XN4e3ujU6dOuHv3Lv766y9cunQJvr6+gn6LFy9GtWrVcPv2bUybNg0rVqzAkSNHsGfPHjx+/Bg7d+6UJEohISEAgM2bNyMiIkJyOykpCS1btkRQUBBu374Nb29vtGnTBuHh4QCAAwcOoGzZspg1axYiIiIQEREhU4xLly6Fh4cHbt++jVatWqFnz57o1asXfvnlF9y6dQv29vbo1asXxGKxTPsNCAiAm5sbbt++jWHDhmHo0KF4/PgxAOD69esAgDNnziAiIgIHDhwo/JMpZ5kZGXj08AHquteTtCkpKaFu3Xq4e+e2AiOTr+zsbJw8cRypqSmoVq2GosORq6TERACAnr6+giORn9L2fJXk91nwX2tRtkotWFX89vPwIfwpPr55AYd6zSVtOVmZUFYVjkQoq6khOzMDH8KfFUm8VHT+k0N4ffr0Qffu3QEA8+bNw4oVK3D9+nV4e3tj7dq1sLe3R0BAAACgQoUKuHfvHhYsWCDZ3t/fHz4+PpI5R46OjlixYgUaNWqEtWvXQkNDAwDQtGlTjBs3TrJdeHg4HB0dUb9+fYhEItjY2EjWmZqaAgAMDAxgYWEhaa9WrRqqVasmuT179mwcPHgQR44cga+vL4yMjKCsrAxdXV3BdgWNsWXLlhg8eDAAYPr06Vi7di1q1aqFLl26AAAmTpwId3d3REVFwcLCQqb9Dhs2TLKPpUuX4uzZs6hQoYLkWI2NjQUxFwcf4z4iOzs7zxCCsbExXr58oaCo5Ofpk8fo2aMbMjLSoaWlhaUrVsPewUHRYclNTk4OFi6Yh+o1XOHo6KTocL5baX2+Sur77OWN84h9/QytJi6T2vfp5dPQtygHM3tnSZtVJVc8+ucwXoacg03NBkhL+Ii7J/4EAKTGxxZV2IXCITzp/pMJVNXPJilqa2tDT08P0dHRAIBHjx6hTp06gv7u7u6C23fu3MHdu3exc+dOSZtYLEZOTg5evnyJSpUqAQDc3NwE2/Xp0wc//fQTKlSoAG9vb7Ru3RrNmzfHtyQlJWHmzJk4fvw4IiIikJWVhdTUVEkF6msKGuPnj4W5uTkAwMXFJU9bdHQ0LCwsCrVfkUgECwsLyWMsi/T0dKSnpwvaxMrqUFdXl3lfBNja2mHP/kNISkpE4Om/Me23ifhjy45S8aUMAPPm+OH506fYsn2XokORi9L+fJUkybExCNm7Hj+NmJOnivSlrIx0vLxxHlVbdBO0Wzm7ombHfrj252pc2hoAZRVVuLTohuhnDwBR8RoQ4m/hSVfqEiglJSXJcNMnmZmZgtuqqqqC2yKRCDk5OQW+j6SkJAwePBgjR47Ms87a2lryf21tbcE6V1dXvHz5EidPnsSZM2fQtWtXeHp6Yt++fV+9r/HjxyMwMBCLFy+Gg4MDNDU10blzZ2RkZMglxs8fi0/zp/Jr+/T4FGa/n/Yjy2P8ib+/P/z8/ARtU6bNwNTpM2XeV0EYGhhCWVkZHz58ELR/+PABJiYmRXKfP5Kqmhqs/7/y6Vy5Ch7cv4edO7Zh+sxZCo7s+82bMwsXzp/Dpq07YF7MKpuFVVqfr5L4PvsQ/gxpiXE4Nv9/n33inBxEPbuPf88fhc+KQ1BSyj2T7tXty8jOSId9nWZ59uPcrAMqNW2P1PhYqGnpIOlDFG4f3gpdk9Lxmv0vKXUJlKmpqWQeEAAkJCTg5cuXBd6+UqVKeSaVX7t2TXDb1dUVDx8+hEMh/grU09PDzz//jJ9//hmdO3eGt7c3YmNjYWRkBFVVVWRnZwv6X758GX369EGHDh0A5CYwX05qV1NTy7Pd98T4LfLY76ezEb+MOT+TJ0/G2LFjBW1i5aKrPqmqqaGSc2UEX7uKps08AeQmj8HBV9Gt+y9Fdr+KkpOTg0wpyXhxJxaL4T93Nv4JCsQfW7ajbNlyig6pyJSG5wsome8zy4rV0GbqakHblW3LoG9RFpWbd5YkTwDw7MpplK1aBxq6+c/DE4lE0DLIHb4Mu3EeWoamMLK2L7rgC4MFKKmKV81QDpo2bYrt27fj4sWLuHfvHnr37g1lGa6vMWTIEDx9+hQTJkzA48ePsWvXLmzZskXQZ+LEibhy5Qp8fX0RGhqKp0+f4vDhw3kmUn9pyZIl+PPPP/Hvv//iyZMn2Lt3LywsLGBgYAAg9+y1oKAgREZG4uPHjwBy5xgdOHAAoaGhuHPnDnr06JGnkmNra4sLFy7g7du3eP/+/XfFKI089mtmZgZNTU2cOnUKUVFRiI+P/2pfdXV16OnpCZaiHr7r2bsvDuzbgyOHDuLF8+eYM2smUlNT0b5DxyK936K2fGkAbt4Iwdu3b/D0yWMsXxqAGyHX0bJ1G0WH9l3mzfbDiWNHMH9hALS1tPE+JgbvY2KQlpam6NC+S2l9vj4pae8zVQ0tGFrZChYVdQ2oa+vB0MpW0i8h+h2int2HY738p2fcD9yPj2/DEPfuFe6e+BP3T+9D7S6DBQlYccCz8KQrdRWoyZMn4+XLl2jdujX09fUxe/ZsmSpQ1tbW2L9/P8aMGYOVK1eidu3aklPyP6latSrOnz+PKVOmoEGDBhCLxbC3t8fPP//8zX3r6upi4cKFePr0KZSVlVGrVi2cOHECSkq5eWxAQADGjh2LDRs2oEyZMggLC8OSJUvQr18/1KtXDyYmJpg4cSISEhIE+501axYGDx4Me3t7pKenQywWFzpGaeSxXxUVFaxYsQKzZs3C9OnT0aBBA5w7d+674pIn7xYt8TE2FmtWrcD79zGoULES1vy+EcbFdGihoGJjP2Dq5ImIiYmGjq4unJwqYO36P+Bez0PRoX2XPX/lTsLt36enoH3WHH+0K6ZfxgVRWp+vT0rr++zZ1UBoGZjAqpJrvuvfPbiBe6f+Qk5WJgzL2KHJkGkoU9kt375UvInEX04YIirm0rIUHQERlUQB50vvpQKmNJPvdI3oxEzpnQrITFdVeqcSqNRVoIiIiOj78Cw86UrdHCgiIiKiosYKFBEREQmxACUVEygiIiISYP4kHYfwiIiIiGTEChQREREJ8LfwpGMCRURERAI8C086DuERERERyYgVKCIiIhLgEJ50rEARERERyYgJFBEREZGMOIRHREREAhzCk44JFBEREQnwLDzpOIRHREREJCNWoIiIiEiAQ3jSMYEiIiIiAeZP0nEIj4iIiEhGrEARERGREEtQUjGBIiIiIgGehScdh/CIiIiIZMQKFBEREQnwLDzpmEARERGRAPMn6TiER0RERCQjJlBEREQkJJLjUgirV6+Gra0tNDQ0UKdOHVy/fv17jqZIMIEiIiIiAZEc/8nqr7/+wtixYzFjxgzcunUL1apVg5eXF6Kjo4vgSAuPCRQREREVG0uWLMHAgQPRt29fODs7Y926ddDS0sKmTZsUHZoAJ5ETERGRgDzPwktPT0d6erqgTV1dHerq6nn6ZmRk4ObNm5g8ebKkTUlJCZ6enrh69ar8gpIHMRHlKy0tTTxjxgxxWlqaokORKx5XyVNaj43H9d8wY8YMMQDBMmPGjHz7vn37VgxAfOXKFUH7hAkTxLVr1/4B0RacSCwWixWawREVUwkJCdDX10d8fDz09PQUHY7c8LhKntJ6bDyu/wZZKlDv3r1DmTJlcOXKFbi7u0vaf/31V5w/fx7BwcFFHm9BcQiPiIiIiszXkqX8mJiYQFlZGVFRUYL2qKgoWFhYFEV4hcZJ5ERERFQsqKmpoWbNmggKCpK05eTkICgoSFCRKg5YgSIiIqJiY+zYsejduzfc3NxQu3ZtLFu2DMnJyejbt6+iQxNgAkX0Ferq6pgxY0aBS88lBY+r5Cmtx8bjovz8/PPPiImJwfTp0xEZGYnq1avj1KlTMDc3V3RoApxETkRERCQjzoEiIiIikhETKCIiIiIZMYEiIiIikhETKCIiIiIZMYEiIiIikhETKKL/gPDwcOR3wq1YLEZ4eLgCIqL/sqysLJw5cwa///47EhMTAeT+hEdSUpKCIyMqOF7GgOgzZ8+eRZMmTRQdhtwpKysjIiICZmZmgvYPHz7AzMwM2dnZCopMPnJycvDs2TNER0cjJydHsK5hw4YKiqrwPnz4gOnTp+Ps2bP5HlNsbKyCIvt+r169gre3N8LDw5Geno4nT56gfPnyGDVqFNLT07Fu3TpFh1goTZs2xYEDB2BgYCBoT0hIQPv27fHPP/8oJjAqMryQJtFnvL29UbZsWfTt2xe9e/dGuXLlFB2SXIjFYohEojztSUlJ0NDQUEBE8nPt2jX06NEDr169ylNlE4lEJTI57NmzJ549e4b+/fvD3Nw83+eupBo1ahTc3Nxw584dGBsbS9o7dOiAgQMHKjCy73Pu3DlkZGTkaU9LS8PFixcVEBEVNSZQRJ95+/Yttm/fjq1bt8LPzw9NmzZF//790b59e6ipqSk6PJmNHTsWQG4iMW3aNGhpaUnWZWdnIzg4GNWrV1dQdPIxZMgQuLm54fjx47C0tCwVycbFixdx6dIlVKtWTdGhyN3Fixdx5cqVPO8nW1tbvH37VkFRFd7du3cl/3/48CEiIyMlt7Ozs3Hq1CmUKVNGEaFREWMCRfQZExMTjBkzBmPGjMGtW7ewefNmDBs2DMOGDUOPHj3Qv3//EvWldvv2bQC5Fah79+4JvrTU1NRQrVo1jB8/XlHhycXTp0+xb98+ODg4KDoUualYsSJSU1MVHUaRyMnJybcq+ObNG+jq6iogou9TvXp1iEQiiEQiNG3aNM96TU1NrFy5UgGRUVHjHCiib3j37h3Wr1+P+fPnQ0VFBWlpaXB3d8e6detQuXJlRYdXYH379sXy5cuhp6en6FDkrmnTpvj111/h7e2t6FDkJiQkBJMmTcL06dNRpUoVqKqqCtaX5Ofx559/hr6+PtavXw9dXV3cvXsXpqamaNeuHaytrbF582ZFhyiTT0PH5cuXx/Xr12FqaipZp6amBjMzMygrKyswQioqTKCIvpCZmYnDhw9j06ZNCAwMhJubG/r374/u3bsjJiYGU6dOxa1bt/Dw4UNFh0oADh48iKlTp2LChAlwcXHJk2xUrVpVQZEV3tOnT9GjRw/cunVL0P5pLltJnNf1yevXr+Ht7Q2xWIynT5/Czc0NT58+hYmJCS5cuJDnRAei4ooJFNFnRowYgT///BNisRg9e/bEgAEDUKVKFUGfyMhIWFlZ5TkzqjhLTk7G/PnzERQUlO9ZXS9evFBQZN9PSSnv1VhEIlGJTjZq164NFRUVjBo1Kt9J5I0aNVJQZPKRlZWFv/76C3fu3EFSUhJcXV3h4+MDTU1NRYf2XZ4+ffrVMyenT5+uoKioqDCBIvpMs2bNMGDAAHTs2BHq6ur59snKysLly5dL1JdY9+7dcf78efTs2TPfidajRo1SUGTf79WrV99cb2Nj84MikR8tLS3cvn0bFSpUUHQocpWZmYmKFSvi2LFjqFSpkqLDkasNGzZg6NChMDExgYWFheA9JhKJ8lQTqeRjAkX0H2BgYIDjx4/Dw8ND0aFQATRs2BDTp0+Hp6enokORuzJlyuDMmTOlLoGysbHBsGHDMHHiREWHQj8Iz8Ij+kJpLMMbGhrCyMhI0WEUmefPn2PZsmV49OgRAMDZ2RmjRo2Cvb29giMrnBEjRmDUqFGlal7XJ8OHD8eCBQuwceNGqKiUnq+gjx8/okuXLooOg34gVqCIPlNay/A7duzA4cOHsXXrVsG1oEqDv//+G23btkX16tUlFbbLly/jzp07OHr0KH766ScFRyi70jiv65MOHTogKCgIOjo6cHFxgba2tmD9gQMHFBTZ9+nfvz9q1aqFIUOGKDoU+kGYQBF9prSW4WvUqIHnz59DLBbD1tY2T0WjpCaGQO6xeXl5Yf78+YL2SZMm4fTp0yXy2ErjvK5P+vbt+831Je0yBp/4+/tjyZIlaNWqVb5Vw5EjRyooMioqTKCIPqOnp4fQ0FCUL19e0aHIlZ+f3zfXz5gx4wdFIn8aGhq4d+8eHB0dBe1PnjxB1apVkZaWpqDI6L/Ezs7uq+tEIlGJPtOV8ld6BqCJ5KBLly44ffp0qSvDl+QESRpTU1OEhobmSaBCQ0NL7DWFtm7dChMTE7Rq1QoA8Ouvv2L9+vVwdnbGn3/+WaIrUKXVy5cvFR0C/WBMoIg+4+DggGnTpuHatWulrgwfFxeHffv24fnz55gwYQKMjIxw69YtmJubl+jf6ho4cCAGDRqEFy9eoF69egBy50AtWLBA8luAJc28efOwdu1aAMDVq1exatUqLFu2DMeOHcOYMWNK3DwhV1dXBAUFwdDQEDVq1Pjm7xWWxCHXz2VkZODly5ewt7cvVZPkKS8O4RF9prSW4e/evQtPT0/o6+sjLCwMjx8/Rvny5TF16lSEh4dj27Ztig6x0MRiMZYtW4aAgAC8e/cOAGBlZYUJEyZg5MiRJfLHhbW0tPDvv//C2toaEydOREREBLZt24YHDx6gcePGiImJUXSIMvHz88OECROgpaWFmTNnfvM5KanV0pSUFIwYMQJbt24FkDuEXL58eYwYMQJlypTBpEmTFBwhyRsTKKL/AE9PT7i6umLhwoXQ1dXFnTt3UL58eVy5cgU9evRAWFiYokOUi8TERAAokT9K+zkzMzP8/fffqFGjBmrUqIGxY8eiZ8+eeP78OapVq4akpCRFh0hfGDVqFC5fvoxly5bB29sbd+/eRfny5XH48GHMnDlT8sPeVHrkPVeWiADkVjZKy98XISEhGDx4cJ72MmXKIDIyUgERFQ1dXd0SnzwBwE8//YQBAwZgwIABePLkCVq2bAkAePDgAWxtbRUb3HcqX748Pnz4kKc9Li6uRJ+8cejQIaxatQr169cXVNgqV66M58+fKzAyKipMoIi+sG3bNri4uEBTUxOampqoWrUqtm/fruiwvou6ujoSEhLytD958kTw6/ElhaurKz5+/Agg9zIGrq6uX11KotWrV8Pd3R0xMTHYv38/jI2NAQA3b95E9+7dFRzd9wkLC8v3Olbp6el48+aNAiKSj5iYmHxPWkhOTi6Rw8gkHWe4EX1myZIlmDZtGnx9fSUXZbx06RKGDBmC9+/fY8yYMQqOsHDatm2LWbNmYc+ePQBy53OFh4dj4sSJ6NSpk4Kjk127du0kv1XYrl27UvcFZWBggFWrVuVpl3Y5iuLsyJEjkv///fff0NfXl9zOzs5GUFDQN+cgFndubm44fvw4RowYAQCS1+TGjRvh7u6uyNCoiHAOFNFn7Ozs4Ofnh169egnat27dipkzZ5bYU5Xj4+PRuXNn3LhxA4mJibCyskJkZCTc3d1x4sSJPFeDpuIhJSUF4eHhyMjIELSXxJ9y+XR19U9XVP+cqqoqbG1tERAQgNatWysivO926dIltGjRAr/88gu2bNmCwYMH4+HDh7hy5QrOnz+PmjVrKjpEkjMmUESf0dDQwP379+Hg4CBof/r0KVxcXEr8RRkvXbqEu3fvIikpCa6urqXix2rLly+PkJAQyTDXJ3FxcXB1dS2RZ07GxMSgT58+OHXqVL7rS/JPudjZ2SEkJAQmJiaKDkXunj9/jvnz5+POnTuS99jEiRPh4uKi6NCoCHAIj+gzDg4O2LNnD3777TdB+19//ZXnQo0lUf369VG/fn1FhyFXpXFOzejRoxEfH4/g4GA0btwYBw8eRFRUFObMmYOAgABFh/ddSmoVtyDs7e2xYcMGRYdBPwgTKKLP+Pn54eeff8aFCxcEP0wbFBQkmT9UUoWEhODs2bOIjo5GTk6OYN2SJUsUFFXhleY5Nf/88w8OHz4MNzc3KCkpwcbGBj/99BP09PTg7+8vuUJ5SZWcnIzz58/nOzxZki9WCwDR0dH5vsdK4rArfRuH8Ii+cOvWLSxZsgSPHj3C/7V393E13/0fwF8nndJ9Grlp6UakFCuGbK5Yzd1G5HY0dYXL3bDE6Dcxd2G7MHZtMiEhNyt6GC53WeU20R1DESmuGrFsFUqd3x9+zm9nZde6OX12zvf1fDw8Hp3P96iXHk69z+fz+b4/AODk5ITg4GC4ubkJTlZ3YWFhWLBgARwdHdGyZUuVTdcymQwnT54UmK5utHlPjampKTIzM2FrawsbGxtER0fjrbfewu3bt9GpUyeUlZWJjlhnaWlpGDRoEMrKylBaWgoLCwsUFRXB0NAQlpaWGrnkCry4Q9Lf3x/Xrl2r9v9RJpNp9LIr1YwzUET/p6KiApMnT0ZoaCh27NghOk6DWrduHbZs2YKAgADRURrMy3f42rinxtHREVlZWbC1tUWXLl2wceNG2NraIjw8HK1btxYdr16CgoIwePBghIeHw8zMDOfPn4dcLoefnx9mzZolOl6dBQYGokOHDti8eXO1NymknTgDRfQbZmZmSE9P19iln1dp3bo1kpKStGIf159RXFwMc3Nz0THqbMeOHXj+/DkCAgJw6dIlDBgwAI8ePYKenh4iIyMxevRo0RHrzNzcHMnJyXB0dIS5uTnOnTsHJycnJCcnw9/fH9evXxcdsU5MTEyQlpZW7QYU0l5spEn0G0OHDkVcXJzoGA0uKCgIX3/9tegYarFq1Srs2bNH+XjkyJGwsLCAlZUVMjIyBCarOz8/P+VsYdeuXXHnzh2kpKQgPz9fo4sn4MXy6svlV0tLS+Tl5QF48eYlPz9fZLR68fLy0tj/b1Q3nIEi+o2Xdzl5eXmha9eu1fojaeoG16qqKrz33nvIzs6Gs7Mz5HK5yvV9+/YJSlZ/dnZ22LlzJ3r16oXjx49j1KhR2LNnD/bu3Yu8vDwcO3ZMdET6jX79+iEgIABjx47FpEmTkJmZiZkzZ2L79u34+eefkZycLDpinRQVFcHf3x/du3eHi4tLtdfYkCFDBCUjdWEBRfQbf7R0J5PJNHaD60cffYSIiAj07du3xv0ZW7duFZSs/gwMDJCdnQ1ra2vMmjULT58+xcaNG5GdnY0ePXooj3zRJMOHD0f37t0xb948lfHPP/8cKSkp+O677wQlq7+XzVz79u2L+/fvY/z48Th79iw6dOiAiIgIvPHGG6Ij1sn333+PDz/8sMYjk7iJXDuxgCKSABMTE+zevVvjb3+vSZs2bRATE4NevXrB0dERy5Ytw8iRI5GVlYU333yzxl9of3UtWrTAyZMnqzVgvHz5Mry9vfHTTz8JSlZ/T548gUKhgKGhIYAXfbz2798PZ2dn9O/fX3C6urO1tcX777+P0NBQtGzZUnQcagS8C48kb/bs2Vi6dCmMjIwwe/bsVz5PJpNpbBNDCwsLtGvXTnQMtfD19cXYsWPRvn17PHz4EAMHDgQAjd7QW1JSAj09vWrjcrlcIwvC3/Lx8YGvry+mTJmC4uJi9OzZE3K5HEVFRVizZg2mTp0qOmKdPHz4EEFBQSyeJISbyEny0tLSUFFRofz4j/5oqs8++wyLFi3S6P5Br7J27Vp89NFHcHZ2xvHjx2FsbAwAKCgowLRp0wSnqxtXV1eVjfEv7d69G87OzgISNZzU1FT07t0bABATE4OWLVvizp07iIqKwvr16wWnqztfX1/88MMPomNQI+ISHpEEuLm5IScnBwqFAra2ttU2uKampgpKRjX5/vvvlTNr77zzDgAgPj4eu3btwnfffYehQ4eKDVgPhoaGuH79Otq2bYtRo0ahU6dOWLRoEfLz8+Ho6KixRf7y5cvx5Zdf4r333oOrq2u115im3oBCr8YCikgCFi9e/IfXFy1a1EhJ1GP79u3YuHEjbt26hXPnzsHGxgZffvkl7Ozs4OPjIzpenRw6dAhhYWFIT0+HgYEBOnfujEWLFsHT01N0tHrp3LkzJk6ciGHDhsHFxQVHjhyBh4cHLl26hPfeew+FhYWiI9aJtt6AQq/GAoqINNqGDRuwcOFCfPzxx1i+fDmuXLkCe3t7REZGYtu2bRq3rPL8+XOEhYUhMDAQr7/+uug4DS4mJgZjx45FZWUlvLy8lG0mVqxYgaSkJPz73/8WnJDoz2EBRSQRxcXFiImJQU5ODubOnQsLCwukpqaiZcuWsLKyEh2vzpydnREWFoahQ4fCxMQEGRkZsLe3x5UrV9CnTx8UFRWJjlhrxsbGuHLlCmxtbUVHUYvCwkIUFBSgS5cuyqaaFy5cgKmpKTp27Cg4Xf2Ul5fj9u3baNeuHXR1eZ+WNuMmciIJyMzMRIcOHbBq1Sr885//RHFxMYAXDTRDQkLEhqun27dv13jQs76+PkpLSwUkqj8vLy8kJiaKjqE2rVq1gpubm7J4AoDu3btrdPFUVlaGCRMmwNDQEJ06dVJ2WJ8xYwZWrlwpOB2pAwsoIgmYPXs2AgICcOPGDTRt2lQ5PmjQICQlJQlMVn92dnZIT0+vNn7kyBE4OTk1fqAGMHDgQMyfPx9z5szBrl27cODAAZU/9NcTEhKCjIwMJCQkqLzGvL29a7yjkjQf5xeJJCAlJQUbN26sNm5lZaWxm3Zfmj17NqZPn46nT59CoVDgwoUL2LVrF1asWIGIiAjR8erkZfuFNWvWVLvGrtZ/TXFxcdizZw969uyp0um/U6dOyMnJEZiM1IUFFJEE6Ovr19iAMTs7Gy1atBCQqOFMnDgRBgYGWLBgAcrKyjB27Fi0adMG69atw5gxY0THq5OqqirREaiWHjx4AEtLy2rjpaWl1Y5OIu3AJTwiCRgyZAiWLFmibBgqk8mQl5eHefPmYfjw4YLT1d+4ceNw48YNlJSUoLCwEHfv3sWECRNExyIJ6datGw4dOqR8/LJoioiIgIeHh6hYpEa8C49IAh4/fowRI0YoD3Jt06YNCgsL4eHhgcOHD8PIyEh0RPqd0tJSJCYmIi8vD+Xl5SrX2JTxr+f06dMYOHAg/Pz8EBkZicmTJ+Pq1as4e/YsEhMT0bVrV9ERqYGxgCKSkDNnziAjIwMlJSVwd3eHt7e36Ej1Zmdn94dLJJrYwDAtLQ2DBg1CWVkZSktLYWFhgaKiIhgaGsLS0lIj/01SkJOTg5UrV6q8xubNm1ftUGjSDiygiCQgKioKo0ePhr6+vsp4eXk5du/ejfHjxwtKVn/r1q1TeVxRUYG0tDQcOXIEc+fOxfz58wUlq7s+ffqgQ4cOCA8Ph5mZGTIyMiCXy+Hn54dZs2bB19dXdEQiyWMBRSQBTZo0QUFBQbVNrg8fPoSlpaVW3tX19ddf4+LFi9i6davoKLVmbm6O5ORkODo6wtzcHOfOnYOTkxOSk5Ph7++P69evi45IvyPF15jUcRM5kQQoFIoal7nu3r0LMzMzAYnUb+DAgYiNjRUdo07kcrmyyaSlpaWyKaOZmRny8/NFRqNXeNVcxLNnz6Cnp9fIaagxsI0BkRZzc3ODTCaDTCaDl5eXytESlZWVuH37NgYMGCAwofrExMTAwsJCdIw6cXNzQ0pKCtq3bw9PT08sXLgQRUVF2L59O1xcXETHo99Yv349gBd33UVERMDY2Fh5rbKyEklJSRrdYZ1ejQUUkRYbOnQoACA9PR39+/dX+eGup6cHW1tbjW9j8LJIfEmhUKCwsBAPHjzAN998IzBZ3YWFheHXX38FACxfvhzjx4/H1KlT0aFDB41tDqqt1q5dC+DF/7vw8HA0adJEee3layw8PFxUPFIj7oEikoBt27Zh9OjRKkdMaIvFixerPNbR0UGLFi3Qp08fjX3n/+TJEygUChgaGgIAcnNzsX//fjg7O6N///6C01FN+vbti3379qFZs2aio1AjYQFFRPQX069fP/j6+mLKlCkoLi5Gx44dIZfLUVRUhDVr1mDq1KmiIxJJHpfwiCSgsrISa9euxd69e2tszPjo0SNByeqvpiNqXsXU1FSNSRpOamqqcmkoJiYGLVu2RFpaGmJjY7Fw4UIWUH9Rd+/exYEDB2p8jdV0riFpNhZQRBKwePFiREREIDg4GAsWLMCnn36K3NxcxMXFYeHChaLj1Yu5ufl/PWvs5V2ImnIreVlZGUxMTAAAx44dg6+vL3R0dNCzZ0/cuXNHcDqqSXx8PIYMGQJ7e3tcv34dLi4uyM3NhUKhgLu7u+h4pAZsY0AkATt37sSmTZsQHBwMXV1dfPDBB4iIiMDChQtx/vx50fHqZevWrbC0tMQnn3yC/fv3Y//+/fjkk0/QsmVLbNmyBSdPnsQPP/yAkydPio76pzk4OCAuLg75+fk4evQo+vXrBwC4f/++xsyiSU1ISAjmzJmDy5cvo2nTpoiNjUV+fj48PT0xcuRI0fFIHRREpPUMDQ0Vd+7cUSgUCkWrVq0Uly5dUigUCkVOTo7C1NRUZLR6e+eddxTR0dHVxnfu3Knw9PRs/EAN4LvvvlPI5XKFjo6O4t1331WOh4WFKQYMGCAwGb2KsbGx4ubNmwqFQqEwNzdXXLlyRaFQKBTp6ekKGxsbgclIXTgDRSQBr7/+OgoKCgAA7dq1w7FjxwAAKSkp1Y530TTnzp1Dt27dqo1369YNFy5cEJCo/kaMGIG8vDxcvHgRR44cUY57eXkp90bRX4uRkZFy31Pr1q2Rk5OjvFZUVCQqFqkRCygiCRg2bBji4+MBADNmzEBoaCjat2+P8ePHIzAwUHC6+rG2tsamTZuqjUdERMDa2lpAoobRqlUruLm5KTuSA0D37t01tjWDtuvZsydOnz4NABg0aBCCg4OxfPlyBAYGomfPnoLTkTqwjQGRBJ0/fx5nz55F+/btMXjwYNFx6uXw4cMYPnw4HBwc0KNHDwDAhQsXcOPGDcTGxmLQoEGCE5IU3Lp1CyUlJejcuTNKS0sRHBysfI2tWbMGNjY2oiNSA2MBRSQBSUlJ6NWrl8pRLgDw/PlznD17Fn/7298EJWsYd+/exYYNG3Dt2jUAgJOTE6ZMmaLRM1BE9NfGAopIAnhSPDBt2jQsWbIEzZs3Fx2FtJC9vT1SUlLw2muvqYwXFxfD3d0dt27dEpSM1IV7oIgkQPF/fZB+7+HDhzAyMhKQqPHt2LGjVk03iWojNze3xjciz549w7179wQkInVjI00iLebr6wvgxUnxAQEBKnfcVVZWIjMzE7169RIVr1Fxsp3U4cCBA8qPjx49CjMzM+XjyspKxMfHw9bWVkAyUjcWUERa7OUPc4VCARMTExgYGCiv6enpoWfPnpg0aZKoeEQab+jQoQBevEnx9/dXuSaXy2Fra4vVq1cLSEbqxgKKSItt3boVAGBra4s5c+ZIZrmOqLFUVVUBAOzs7JCSksI9dhLCTeREEvDkyRMoFAoYGhoCAO7cuYP9+/fD2dlZeUyItjMxMUFGRgbs7e1FRyGJKC4uhrm5uegYpCbcRE4kAT4+PoiKigLw4od69+7dsXr1avj4+GDDhg2C0xFpvlWrVmHPnj3KxyNHjoSFhQWsrKyQkZEhMBmpCwsoIglITU1F7969AQAxMTFo1aoV7ty5g6ioKKxfv15wusbh5+fHg3hJbcLDw5V9x44fP44TJ07gyJEjGDhwIObOnSs4HakD90ARSUBZWRlMTEwAAMeOHYOvry90dHTQs2dP3LlzR3C62svMzPzTz+3cuTMAcKaN1KqwsFBZQB08eBCjRo1Cv379YGtrq+yQT9qFBRSRBDg4OCAuLg7Dhg3D0aNHERQUBAC4f/++Rs7KvPHGG5DJZK9sTfDymkwmk0STUBKvWbNmyM/Ph7W1NY4cOYJly5YBeHEHLP8PaicWUEQSsHDhQowdOxZBQUHw8vKCh4cHgBezUW5uboLT1d7t27dFRyBS4evri7Fjx6J9+/Z4+PAhBg4cCABIS0uDg4OD4HSkDrwLj0giCgsLUVBQgC5dukBH58X2xwsXLsDU1BQdO3YUnI5Is1VUVGD9+vXIy8tDQECA8o3J2rVrYWJigokTJwpOSA2NBRSRlquoqICBgQHS09Ph4uIiOo7aXL16FXl5eSgvL1cZHzJkiKBEJBUVFRWYPHkyQkNDYWdnJzoONRIu4RFpOblcjrZt22rtPoxbt25h2LBhuHz5ssq+qJdn/2nrv5v+OuRyOWJjYxEaGio6CjUitjEgkoBPP/0U//M//4NHjx6JjtLgZs2aBTs7O9y/fx+Ghob48ccfkZSUhG7duiEhIUF0PJKIoUOHIi4uTnQMakRcwiOSADc3N9y8eRMVFRWwsbGpdqRLamqqoGT117x5c5w8eRKdO3eGmZkZLly4AEdHR5w8eRLBwcFIS0sTHZEkYNmyZVi9ejW8vLzQtWvXaq+xmTNnCkpG6sIlPCIJeHngqTaqrKxU9rhq3rw5/vOf/8DR0RE2NjbIysoSnI6kYvPmzTA3N8elS5dw6dIllWsymYwFlBZiAUUkAYsWLRIdQW1cXFyQkZEBOzs79OjRA59//jn09PTw7bff8tw7ajRsrSE93ANFJBHFxcWIiIhASEiIci9Uamoq7t27JzhZ/SxYsABVVVUAgCVLluD27dvo3bs3Dh8+LJljauivo7y8HFlZWXj+/LnoKKRm3ANFJAGZmZnw9vaGmZkZcnNzkZWVBXt7eyxYsAB5eXnKg4a1xaNHj9CsWTPlnXhE6lZWVoYZM2Zg27ZtAIDs7GzY29tjxowZsLKywvz58wUnpIbGGSgiCZg9ezYCAgJw48YNNG3aVDk+aNAgJCUlCUxWf48fP652d6GFhQV+/vln/PLLL4JSkdSEhIQgIyMDCQkJKq8xb29v7NmzR2AyUhcWUEQSkJKSgsmTJ1cbt7KyQmFhoYBEDWfMmDHYvXt3tfG9e/dizJgxAhKRFMXFxeFf//oX3n77bZWZz06dOiEnJ0dgMlIXFlBEEqCvr1/jbEx2djZatGghIFHDSU5ORt++fauN9+nTB8nJyQISkRQ9ePAAlpaW1cZLS0u5lKylWEARScCQIUOwZMkSVFRUAHhxW3VeXh7mzZuH4cOHC05XP8+ePatxw25FRQWePHkiIBFJUbdu3XDo0CHl45dFU0REhPLwbtIu3EROJAGPHz/GiBEjcPHiRfz6669o06YNCgsL4eHhgcOHD1dr+qdJ+vbtCxcXF3z11Vcq49OnT0dmZiZOnTolKBlJyenTpzFw4ED4+fkhMjISkydPxtWrV3H27FkkJiaia9euoiNSA2MBRSQhp0+fRmZmJkpKSuDu7g5vb2/RkertzJkz8Pb2xptvvgkvLy8AQHx8PFJSUnDs2DH07t1bcEKSipycHKxcuRIZGRnK19i8efPg6uoqOhqpAQsoIgnIz8+HtbW16Bhqk56eji+++ALp6ekwMDBA586dERISgvbt24uORkRaigUUkQQ0adIEb7/9Nvz8/DBixAg0a9ZMdCQijVebNhmmpqZqTEIisIAikoC0tDRER0dj9+7dePDgAQYMGAA/Pz8MHjwY+vr6ouPV2i+//KL8hfTffonxFxepi46Ozp++w66yslLNaaixsYAikhCFQoGEhARER0cjNjYWVVVV8PX1xZYtW0RHq5UmTZqgoKAAlpaWr/wlplAoIJPJ+IuL1CYxMVH5cW5uLubPn4+AgADlXXfnzp3Dtm3bsGLFCvj7+4uKSWrCAopIolJTUzFhwgRkZmZqXJGRmJiIt956C7q6uiq/xGri6enZSKlIyry8vDBx4kR88MEHKuPR0dH49ttvkZCQICYYqQ0LKCIJuXv3LqKjoxEdHY0rV67Aw8MD48aNw5QpU0RHq5Pnz58jLCwMgYGBeP3110XHIQkzNDRERkZGtRsXsrOz8cYbb6CsrExQMlIXNtIkkoCNGzfC09MTNjY2iIqKwujRo5GTk4NTp05pbPEEALq6uvjiiy9qbKRJ1Jisra2xadOmauMRERFafQeslHEGikgCrK2t8cEHH2DcuHHo0qWL6DgNysfHB76+vtxjQkIdPnwYw4cPh4ODA3r06AEAuHDhAm7cuIHY2FgMGjRIcEJqaCygiCRAoVDg8ePH2Lx5M65duwYAcHZ2xoQJE2BmZiY4Xf2Eh4dj8eLFGDduHLp27Vqtq/qQIUMEJSOpuXv3Lr755htcv34dAODk5IQpU6ZwBkpLsYAikoBLly6hf//+aNq0Kbp37w4ASElJwZMnT3Ds2DG4u7sLTlh3Ojqv3onAu/CISF1YQBFJQO/eveHg4IBNmzZBV1cXwIsN2BMnTsStW7eQlJQkOCGR5isuLsaFCxdw//59VFVVqVwbP368oFSkLiygiCTAwMAAaWlp6Nixo8r41atX0a1bN94hRFRP33//PcaNG4eSkhKYmpqq9CaTyWR49OiRwHSkDrwLj0gCTE1NkZeXV208Pz8fJiYmAhI1rMTERAwePBgODg5wcHDAkCFDcOrUKdGxSEKCg4MRGBiIkpISFBcX4+eff1b+YfGknVhAEUnA6NGjMWHCBOzZswf5+fnIz8/H7t27a2z8p2l27NgBb29vGBoaYubMmZg5cyYMDAzg5eWF6Oho0fFIIu7du4eZM2fC0NBQdBRqJFzCI5KA8vJyzJ07F+Hh4cqeSXK5HFOnTsXKlSs18jy8l5ycnPCPf/wDQUFBKuNr1qzBpk2blHcdEqmTr68vxowZg1GjRomOQo2EBRSRhJSVlSEnJwcA0K5dO614t6yvr48ff/wRDg4OKuM3b96Ei4sLnj59KigZScnmzZuxZMkS/P3vf4erqyvkcrnKdbbT0D66ogMQUeMxNDSEq6ur6BgNytraGvHx8dUKqBMnTrD/DjWaSZMmAQCWLFlS7RrbaWgnFlBEpNGCg4Mxc+ZMpKeno1evXgCAM2fOIDIyEuvWrROcjqTi920LSPtxCY+INN7+/fuxevVq5X4nJycnzJ07Fz4+PoKTkVTUNPP0kkwmQ2hoaCOmocbAAoqIiKie3NzcVB5XVFTg9u3b0NXVRbt27ZCamiooGakLl/CISKPZ29sjJSUFr732msp4cXEx3N3dcevWLUHJSErS0tKqjf3yyy8ICAjAsGHDBCQideMMFBFpNB0dHRQWFsLS0lJl/KeffkLbtm3x7NkzQcmIgMuXL2Pw4MHIzc0VHYUaGGegiEgjHThwQPnx0aNHYWZmpnxcWVmJ+Ph42NraCkhG9P8eP36Mx48fi45BasAZKCLSSDo6Lw5SkMlk+P2PMblcDltbW6xevRrvv/++iHgkMevXr1d5rFAoUFBQgO3bt8PT05Nd8bUQCygi0mh2dnZISUlB8+bNRUchCbOzs1N5rKOjgxYtWuCdd95BSEiIVpw5SapYQBGR1nj69CmaNm0qOgYRSQAPEyYijVZVVYWlS5fCysoKxsbGyrvuQkNDsXnzZsHpiEhbsYAiIo22bNkyREZG4vPPP4eenp5y3MXFBREREQKTEZE2YwFFRBotKioK3377LcaNG4cmTZoox7t06YLr168LTEZE2owFFBFptHv37lU7SBh4sbRXUVEhIBERSQELKCLSaM7Ozjh16lS18ZiYmGrHaxARNRQ20iQijbZw4UL4+/vj3r17qKqqwr59+5CVlYWoqCgcPHhQdDwi0lJsY0BEGu/UqVNYsmQJMjIyUFJSAnd3dyxcuBD9+vUTHY2ItBQLKCIiIqJa4hIeEWmF8vJy3L9/H1VVVSrjbdu2FZSIiLQZCygi0mg3btxAYGAgzp49qzKuUCggk8lQWVkpKBkRaTMWUESk0QICAqCrq4uDBw+idevWkMlkoiMRkQRwDxQRaTQjIyNcunQJHTt2FB2FiCSEfaCISKM5OzujqKhIdAwikhjOQBGRxvnll1+UH1+8eBELFixAWFgYXF1dIZfLVZ5ramra2PGISAJYQBGRxtHR0VHZ6/Ryw/hvcRM5EakTN5ETkcb54YcfAADPnj3DgAEDEB4eDkdHR8GpiEhKOANFRBqtRYsWOHv2LNq3by86ChFJCDeRE5FG8/Pzw+bNm0XHICKJ4RIeEWm058+fY8uWLThx4gS6du0KIyMjletr1qwRlIyItBkLKCLSaFeuXIG7uzsAIDs7W+Uam2oSkbpwDxQRERFRLXEPFBEREVEtsYAiIiIiqiUWUERERES1xAKKiIiIqJZYQBER/RcBAQEYOnSo8nGfPn3w8ccfN3qOhIQEyGQyFBcXN/rXJiJVLKCISGMFBARAJpNBJpNBT08PDg4OWLJkCZ4/f67Wr7tv3z4sXbr0Tz2XRQ+RdmIfKCLSaAMGDMDWrVvx7NkzHD58GNOnT4dcLkdISIjK88rLy6Gnp9cgX9PCwqJBPg8RaS7OQBGRRtPX10erVq1gY2ODqVOnwtvbGwcOHFAuuy1fvhxt2rRRHjacn5+PUaNGwdzcHBYWFvDx8UFubq7y81VWVmL27NkwNzfHa6+9hk8++QS/b5f3+yW8Z8+eYd68ebC2toa+vj4cHBywefNm5Obmom/fvgCAZs2aQSaTISAgAABQVVWFFStWwM7ODgYGBujSpQtiYmJUvs7hw4fRoUMHGBgYoG/fvio5iUgsFlBEpFUMDAxQXl4OAIiPj0dWVhaOHz+OgwcPoqKiAv3794eJiQlOnTqFM2fOwNjYGAMGDFD+ndWrVyMyMhJbtmzB6dOn8ejRI+zfv/8Pv+b48eOxa9curF+/HteuXcPGjRthbGwMa2trxMbGAgCysrJQUFCAdevWAQBWrFiBqKgohIeH48cff0RQUBD8/PyQmJgI4EWh5+vri8GDByM9PR0TJ07E/Pnz1fVtI6Ja4hIeEWkFhUKB+Ph4HD16FDNmzMCDBw9gZGSEiIgI5dLdjh07UFVVhYiICOUxL1u3boW5uTkSEhLQr18/fPnllwgJCYGvry8AIDw8HEePHn3l183OzsbevXtx/PhxeHt7AwDs7e2V118u91laWsLc3BzAixmrsLAwnDhxAh4eHsq/c/r0aWzcuBGenp7YsGED2rVrh9WrVwMAHB0dcfnyZaxataoBv2tEVFcsoIhIox08eBDGxsaoqKhAVVUVxo4di88++wzTp0+Hq6uryr6njIwM3Lx5EyYmJiqf4+nTp8jJycHjx49RUFCAHj16KK/p6uqiW7du1ZbxXkpPT0eTJk3g6en5pzPfvHkTZWVlePfdd1XGy8vL4ebmBgC4du2aSg4AymKLiMRjAUVEGq1v377YsGED9PT00KZNG+jq/v+PNSMjI5XnlpSUoGvXrti5c2e1z9OiRYs6fX0DA4Na/52SkhIAwKFDh2BlZaVyTV9fv045iKhxsYAiIo1mZGQEBweHP/Vcd3d37NmzB5aWljA1Na3xOa1bt0ZycjL+9re/AQCeP3+OS5cuwd3dvcbnu7q6oqqqComJicolvN96OQNWWVmpHHN2doa+vj7y8vJeOXPl5OSEAwcOqIydP3/+v/8jiahRcBM5EUnGuHHj0Lx5c/j4+ODUqVO4ffs2EhISMHPmTNy9excAMGvWLKxcuRJxcXG4fv06pk2b9oc9nGxtbeHv74/AwEDExcUpP+fevXsBADY2NpDJZDh48CAePHiAkpISmJiYYM6cOQgKCsK2bduQk5OD1NRUfPXVV9i2bRsAYMqUKbhx4wbmzp2LrKwsREdHIzIyUt3fIiL6k1hAEZFkGBoaIikpCW3btoWvry+cnJwwYcIEPH36VDkjFRwcjA8//BD+/v7w8PCAiYkJhg0b9oefd8OGDRgxYgSmTZuGjh07YtKkSSgtLQUAWFlZYfHixZg/fz5atmyJjz76CACwdOlShIaGYsWKFXBycsKAAQNw6NAh2NnZAQDatm2L2NhYxMXFoUuXLggPD0dYWJgavztEVBsyxat2RhIRERFRjTgDRURERFRLLKCIiIiIaokFFBEREVEtsYAiIiIiqiUWUERERES1xAKKiIiIqJZYQBERERHVEgsoIiIiolpiAUVERERUSyygiIiIiGqJBRQRERFRLbGAIiIiIqql/wWgm2pyLewgzAAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/confusion_matrix_type_val.png\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/confusion_matrix_type_test.png\n" + ] + } + ], + "source": [ + "# ── Confusion matrices ────────────────────────────────────────────────────────\n", + "save_confusion_matrix(\n", + " y_val_t, y_val_pred_type, STRATEGY_LABELS,\n", + " OUT_TFIDF / \"confusion_matrix_type_val.png\",\n", + " \"TF-IDF + LR — Type (Val)\"\n", + ")\n", + "save_confusion_matrix(\n", + " y_test_t, y_test_pred_type, STRATEGY_LABELS,\n", + " OUT_TFIDF / \"confusion_matrix_type_test.png\",\n", + " \"TF-IDF + LR — Type (Test)\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1_ZECP4MuKB6", + "outputId": "c09e503d-d620-47ec-8917-c494bc7f3f48" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved predictions_val_type.csv and predictions_test_type.csv\n" + ] + } + ], + "source": [ + "# ── Save predictions ──────────────────────────────────────────────────────────\n", + "# Decode integer predictions back to string labels\n", + "val_type_copy = val_type.copy(); val_type_copy[\"type_label\"] = y_val_t\n", + "test_type_copy = test_type.copy(); test_type_copy[\"type_label\"] = y_test_t\n", + "\n", + "val_type_copy[\"predicted_id\"] = y_val_pred_type\n", + "val_type_copy[\"predicted_label\"] = [id2label[i] for i in y_val_pred_type]\n", + "val_type_copy[\"true_label\"] = [id2label[i] for i in y_val_t]\n", + "val_type_copy[\"correct\"] = (val_type_copy[\"type_label\"] == val_type_copy[\"predicted_id\"]).astype(int)\n", + "\n", + "test_type_copy[\"predicted_id\"] = y_test_pred_type\n", + "test_type_copy[\"predicted_label\"] = [id2label[i] for i in y_test_pred_type]\n", + "test_type_copy[\"true_label\"] = [id2label[i] for i in y_test_t]\n", + "test_type_copy[\"correct\"] = (test_type_copy[\"type_label\"] == test_type_copy[\"predicted_id\"]).astype(int)\n", + "\n", + "val_type_copy.to_csv( OUT_TFIDF / \"predictions_val_type.csv\", index=False)\n", + "test_type_copy.to_csv(OUT_TFIDF / \"predictions_test_type.csv\", index=False)\n", + "print(\"Saved predictions_val_type.csv and predictions_test_type.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xdfZfFRmuKB7" + }, + "source": [ + "## Char N-gram Variant (Optional)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3Ff1Tz1iuKB7", + "outputId": "4209ddba-9cd6-46f9-e66d-f62f1ab5709f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Char n-gram best C : {'lr__C': 3.0}\n", + "Char n-gram best CV F1 : 0.8305\n", + "Word n-gram best CV F1 : 0.8143\n", + "\n", + "\n", + "[Test (char)] Accuracy=0.8284 Macro-F1=0.8283 Weighted-F1=0.8283\n", + " precision recall f1-score support\n", + "\n", + "non-sarcastic 0.82 0.84 0.83 4250\n", + " sarcastic 0.83 0.82 0.83 4250\n", + "\n", + " accuracy 0.83 8500\n", + " macro avg 0.83 0.83 0.83 8500\n", + " weighted avg 0.83 0.83 0.83 8500\n", + "\n" + ] + } + ], + "source": [ + "# Char n-gram model for binary task (stylistic cues)\n", + "pipe_char = Pipeline([\n", + " (\"tfidf\", TfidfVectorizer(analyzer=\"char_wb\", ngram_range=(3, 5),\n", + " sublinear_tf=True, lowercase=True, max_features=50_000)),\n", + " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, C=1.0)),\n", + "])\n", + "\n", + "param_grid_char = {\"lr__C\": [0.1, 1.0, 3.0]}\n", + "gs_char = GridSearchCV(pipe_char, param_grid_char, cv=GroupKFold(5),\n", + " scoring=\"f1_macro\", n_jobs=-1)\n", + "gs_char.fit(X_trainval, y_trainval, groups=groups_tv)\n", + "\n", + "print(f\"Char n-gram best C : {gs_char.best_params_}\")\n", + "print(f\"Char n-gram best CV F1 : {gs_char.best_score_:.4f}\")\n", + "print(f\"Word n-gram best CV F1 : {gs_bin.best_score_:.4f}\")\n", + "print()\n", + "char_metrics, _ = evaluate(gs_char.best_estimator_, X_test, y_test, label_names_bin, \"Test (char)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "t_pVeRFVuKB7" + }, + "source": [ + "## Summary" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OszOI0q1uKB7", + "outputId": "532102b9-cd0f-40e8-b1e1-48fa8520b526" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "====== TF-IDF + LR RESULTS SUMMARY ======\n", + "\n", + "Task A — Binary Classification (Test Set):\n", + " Accuracy : 0.8189\n", + " Macro-F1 : 0.8189\n", + " Weighted-F1 : 0.8189\n", + "\n", + "Task B — Type Classification (Test Set):\n", + " Accuracy : 0.3958\n", + " Macro-F1 : 0.4047\n", + " Weighted-F1 : 0.3947\n", + "\n", + "Best hyperparameters:\n", + " Binary: {'lr__C': 3.0, 'tfidf__max_features': None, 'tfidf__min_df': 3, 'tfidf__ngram_range': (1, 2)}\n", + " Type: {'lr__C': 3.0, 'lr__class_weight': 'balanced', 'tfidf__max_features': None, 'tfidf__min_df': 2, 'tfidf__ngram_range': (1, 2)}\n" + ] + } + ], + "source": [ + "print(\"====== TF-IDF + LR RESULTS SUMMARY ======\")\n", + "print()\n", + "print(\"Task A — Binary Classification (Test Set):\")\n", + "print(f\" Accuracy : {test_metrics_bin['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {test_metrics_bin['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1 : {test_metrics_bin['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"Task B — Type Classification (Test Set):\")\n", + "print(f\" Accuracy : {test_metrics_type['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {test_metrics_type['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1 : {test_metrics_type['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"Best hyperparameters:\")\n", + "print(\" Binary:\", gs_bin.best_params_)\n", + "print(\" Type: \", gs_type.best_params_)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ftKo2U-1uKB7" + }, + "source": [ + "---\n", + "# Part 3 — Naive Bayes Baseline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QZo05bYauKB7" + }, + "source": [ + "# Notebook 03 — Naive Bayes Baseline\n", + "\n", + "**Purpose**: Train and evaluate Naive Bayes classifiers for:\n", + "- Task A: Binary classification (sarcastic vs non-sarcastic)\n", + "- Task B: Sarcasm type classification (6-class)\n", + "\n", + "Compares:\n", + "- `CountVectorizer + MultinomialNB`\n", + "- `TfidfVectorizer + MultinomialNB`\n", + "- `CountVectorizer + ComplementNB` (for imbalanced type task)\n", + "\n", + "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", + "\n", + "**Outputs** in `outputs/classical/naive_bayes/`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yeP7YbDYuKB8" + }, + "source": [ + "## Helper Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "id": "PoPH1ijBuKB8" + }, + "outputs": [], + "source": [ + "def load_splits(task: str):\n", + " train = pd.read_csv(SPLITS / f\"train_{task}.csv\")\n", + " val = pd.read_csv(SPLITS / f\"val_{task}.csv\")\n", + " test = pd.read_csv(SPLITS / f\"test_{task}.csv\")\n", + " return train, val, test\n", + "\n", + "\n", + "def evaluate(model, X, y_true, label_names=None, split_name=\"\"):\n", + " y_pred = model.predict(X)\n", + " metrics = {\n", + " \"split\" : split_name,\n", + " \"accuracy\" : accuracy_score(y_true, y_pred),\n", + " \"precision_macro\" : precision_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"recall_macro\" : recall_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"f1_macro\" : f1_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"f1_weighted\" : f1_score(y_true, y_pred, average=\"weighted\", zero_division=0),\n", + " }\n", + " print(f\"[{split_name}] Accuracy={metrics['accuracy']:.4f} \"\n", + " f\"Macro-F1={metrics['f1_macro']:.4f} Weighted-F1={metrics['f1_weighted']:.4f}\")\n", + " print(classification_report(y_true, y_pred, target_names=label_names, zero_division=0))\n", + " return metrics, y_pred\n", + "\n", + "\n", + "def save_confusion_matrix(y_true, y_pred, label_names, out_path, title=\"\"):\n", + " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", + " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", + " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Greens\",\n", + " xticklabels=label_names, yticklabels=label_names, ax=ax)\n", + " ax.set_xlabel(\"Predicted\"); ax.set_ylabel(\"True\"); ax.set_title(title)\n", + " plt.tight_layout()\n", + " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + " print(f\"Saved: {out_path.relative_to(ROOT)}\")\n", + "\n", + "\n", + "def run_nb_grid(\n", + " vectorizer_cls, nb_cls, param_grid: dict,\n", + " X_trainval, y_trainval, groups_tv,\n", + " name: str\n", + ") -> GridSearchCV:\n", + " \"\"\"Run a grid search for a given vectorizer + NB combination.\"\"\"\n", + " pipe = Pipeline([\n", + " (\"vec\", vectorizer_cls(lowercase=True)),\n", + " (\"nb\", nb_cls()),\n", + " ])\n", + " gs = GridSearchCV(\n", + " pipe, param_grid, cv=GroupKFold(5),\n", + " scoring=\"f1_macro\", n_jobs=-1, verbose=0, refit=True\n", + " )\n", + " gs.fit(X_trainval, y_trainval, groups=groups_tv)\n", + " print(f\"{name:50s} CV F1={gs.best_score_:.4f} params={gs.best_params_}\")\n", + " return gs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0oL34vMHuKB8" + }, + "source": [ + "## Task A — Binary Classification" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_STzCoqRuKB8", + "outputId": "c86245b0-ee21-4612-a5d5-dd2fba99214b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train+Val: 48,166 Val: 8,500 Test: 8,500\n" + ] + } + ], + "source": [ + "train_bin, val_bin, test_bin = load_splits(\"binary\")\n", + "trainval_bin = pd.concat([train_bin, val_bin], ignore_index=True)\n", + "\n", + "X_tv = trainval_bin[\"text\"].tolist()\n", + "y_tv = trainval_bin[\"binary_label\"].tolist()\n", + "grp_tv = trainval_bin[\"group_id\"].tolist()\n", + "\n", + "X_val = val_bin[\"text\"].tolist(); y_val = val_bin[\"binary_label\"].tolist()\n", + "X_test = test_bin[\"text\"].tolist(); y_test = test_bin[\"binary_label\"].tolist()\n", + "\n", + "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", + "\n", + "print(f\"Train+Val: {len(X_tv):,} Val: {len(X_val):,} Test: {len(X_test):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iOJgCEMwuKB8", + "outputId": "d37b770b-3b8f-4b49-ac5e-aeb6b7a0d37a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "=== Binary: Comparing Vectorizer + NB Combinations ===\n", + "CountVectorizer + MultinomialNB CV F1=0.7855 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", + "TfidfVectorizer + MultinomialNB CV F1=0.7897 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", + "CountVectorizer + ComplementNB CV F1=0.7855 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n" + ] + } + ], + "source": [ + "# ── Shared grid parameters ────────────────────────────────────────────────────\n", + "BASE_GRID = {\n", + " \"vec__ngram_range\": [(1, 1), (1, 2)],\n", + " \"vec__min_df\" : [2, 3, 5],\n", + " \"nb__alpha\" : [0.1, 0.5, 1.0],\n", + "}\n", + "\n", + "print(\"=== Binary: Comparing Vectorizer + NB Combinations ===\")\n", + "\n", + "gs_count_mnb_bin = run_nb_grid(\n", + " CountVectorizer, MultinomialNB, BASE_GRID,\n", + " X_tv, y_tv, grp_tv,\n", + " \"CountVectorizer + MultinomialNB\"\n", + ")\n", + "\n", + "gs_tfidf_mnb_bin = run_nb_grid(\n", + " TfidfVectorizer, MultinomialNB, BASE_GRID,\n", + " X_tv, y_tv, grp_tv,\n", + " \"TfidfVectorizer + MultinomialNB\"\n", + ")\n", + "\n", + "gs_count_cnb_bin = run_nb_grid(\n", + " CountVectorizer, ComplementNB, BASE_GRID,\n", + " X_tv, y_tv, grp_tv,\n", + " \"CountVectorizer + ComplementNB\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eilEzm18uKB9", + "outputId": "360ce0ff-df02-45a3-bcb3-8213ae795e53" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Best binary NB model: TfidfVec+MultinomialNB (CV F1=0.7897)\n", + "\n", + "Comparison table:\n", + " model cv_f1_macro best_params\n", + "CountVec+MultinomialNB 0.785542 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", + "TfidfVec+MultinomialNB 0.789663 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", + " CountVec+ComplementNB 0.785542 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n" + ] + } + ], + "source": [ + "# ── Select best binary model ──────────────────────────────────────────────────\n", + "candidates_bin = [\n", + " (\"CountVec+MultinomialNB\", gs_count_mnb_bin),\n", + " (\"TfidfVec+MultinomialNB\", gs_tfidf_mnb_bin),\n", + " (\"CountVec+ComplementNB\", gs_count_cnb_bin),\n", + "]\n", + "\n", + "best_name_bin, best_gs_bin = max(candidates_bin, key=lambda x: x[1].best_score_)\n", + "print(f\"\\nBest binary NB model: {best_name_bin} (CV F1={best_gs_bin.best_score_:.4f})\")\n", + "\n", + "# Comparison table\n", + "comp_df = pd.DataFrame([\n", + " {\"model\": name, \"cv_f1_macro\": gs.best_score_, \"best_params\": str(gs.best_params_)}\n", + " for name, gs in candidates_bin\n", + "])\n", + "print(\"\\nComparison table:\")\n", + "print(comp_df.to_string(index=False))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Zh5ipG6BuKB9", + "outputId": "7f108910-a2fb-4154-eb36-f3f68b5a4dc2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[Val] Accuracy=0.8826 Macro-F1=0.8826 Weighted-F1=0.8826\n", + " precision recall f1-score support\n", + "\n", + "non-sarcastic 0.89 0.87 0.88 4250\n", + " sarcastic 0.87 0.90 0.88 4250\n", + "\n", + " accuracy 0.88 8500\n", + " macro avg 0.88 0.88 0.88 8500\n", + " weighted avg 0.88 0.88 0.88 8500\n", + "\n", + "[Test] Accuracy=0.7905 Macro-F1=0.7902 Weighted-F1=0.7902\n", + " precision recall f1-score support\n", + "\n", + "non-sarcastic 0.81 0.76 0.78 4250\n", + " sarcastic 0.77 0.83 0.80 4250\n", + "\n", + " accuracy 0.79 8500\n", + " macro avg 0.79 0.79 0.79 8500\n", + " weighted avg 0.79 0.79 0.79 8500\n", + "\n", + "\n", + "--- All models on Test ---\n", + " CountVec+MultinomialNB : acc=0.7887 macro-F1=0.7886\n", + " TfidfVec+MultinomialNB : acc=0.7905 macro-F1=0.7902\n", + " CountVec+ComplementNB : acc=0.7887 macro-F1=0.7886\n" + ] + } + ], + "source": [ + "# ── Evaluate best model ───────────────────────────────────────────────────────\n", + "best_model_bin = best_gs_bin.best_estimator_\n", + "\n", + "val_m_bin, y_val_pred_bin = evaluate(best_model_bin, X_val, y_val, label_names_bin, \"Val\")\n", + "test_m_bin, y_test_pred_bin = evaluate(best_model_bin, X_test, y_test, label_names_bin, \"Test\")\n", + "\n", + "# Also evaluate all three models on test for comparison\n", + "print(\"\\n--- All models on Test ---\")\n", + "all_test_results_bin = []\n", + "for name, gs in candidates_bin:\n", + " y_pred_t = gs.best_estimator_.predict(X_test)\n", + " f1 = f1_score(y_test, y_pred_t, average=\"macro\")\n", + " acc = accuracy_score(y_test, y_pred_t)\n", + " all_test_results_bin.append({\"model\": name, \"accuracy\": acc, \"f1_macro\": f1})\n", + " print(f\" {name:40s}: acc={acc:.4f} macro-F1={f1:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "D74tnzAduKB9", + "outputId": "1ccb358e-1daa-42ec-8aa0-09bada09116b" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/naive_bayes/confusion_matrix_binary_val.png\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/naive_bayes/confusion_matrix_binary_test.png\n", + "All binary artifacts saved.\n" + ] + } + ], + "source": [ + "# ── Save binary results ───────────────────────────────────────────────────────\n", + "cfg_bin = {\n", + " \"task\": \"binary\", \"best_model\": best_name_bin,\n", + " \"best_params\": best_gs_bin.best_params_, \"cv_f1_macro\": best_gs_bin.best_score_,\n", + " \"all_candidates\": [\n", + " {\"model\": name, \"cv_f1_macro\": gs.best_score_, \"params\": gs.best_params_}\n", + " for name, gs in candidates_bin\n", + " ]\n", + "}\n", + "with open(OUT_NB / \"best_config_binary.json\", \"w\") as f:\n", + " json.dump(cfg_bin, f, indent=2)\n", + "\n", + "all_m_bin = {\"val\": val_m_bin, \"test\": test_m_bin,\n", + " \"all_test_comparison\": all_test_results_bin}\n", + "with open(OUT_NB / \"metrics_binary.json\", \"w\") as f:\n", + " json.dump(all_m_bin, f, indent=2)\n", + "\n", + "save_confusion_matrix(\n", + " y_val, y_val_pred_bin, label_names_bin,\n", + " OUT_NB / \"confusion_matrix_binary_val.png\",\n", + " f\"NB ({best_name_bin}) — Binary (Val)\"\n", + ")\n", + "save_confusion_matrix(\n", + " y_test, y_test_pred_bin, label_names_bin,\n", + " OUT_NB / \"confusion_matrix_binary_test.png\",\n", + " f\"NB ({best_name_bin}) — Binary (Test)\"\n", + ")\n", + "\n", + "# Predictions\n", + "pred_val_bin = val_bin.copy()\n", + "pred_val_bin[\"predicted\"] = y_val_pred_bin\n", + "pred_val_bin[\"correct\"] = (pred_val_bin[\"binary_label\"] == pred_val_bin[\"predicted\"]).astype(int)\n", + "pred_val_bin.to_csv(OUT_NB / \"predictions_val_binary.csv\", index=False)\n", + "\n", + "pred_test_bin = test_bin.copy()\n", + "pred_test_bin[\"predicted\"] = y_test_pred_bin\n", + "pred_test_bin[\"correct\"] = (pred_test_bin[\"binary_label\"] == pred_test_bin[\"predicted\"]).astype(int)\n", + "pred_test_bin.to_csv(OUT_NB / \"predictions_test_binary.csv\", index=False)\n", + "\n", + "print(\"All binary artifacts saved.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UtChhm0-uKB9" + }, + "source": [ + "## Task B — Sarcasm Type Classification" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Rh8sdjlhuKB9", + "outputId": "36e9daa4-b705-4b92-8601-af8ee580ff33" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n", + "Train+Val: 24,083 Val: 4,250 Test: 4,250\n" + ] + } + ], + "source": [ + "train_type, val_type, test_type = load_splits(\"type\")\n", + "\n", + "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", + "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", + "id2label = {i: lab for lab, i in label2id.items()}\n", + "\n", + "def enc(df): return [label2id[l] for l in df[\"type_label\"]]\n", + "\n", + "trainval_type = pd.concat([train_type, val_type], ignore_index=True)\n", + "X_tv_t = trainval_type[\"text\"].tolist()\n", + "y_tv_t = enc(trainval_type)\n", + "grp_tv_t = trainval_type[\"group_id\"].tolist()\n", + "\n", + "X_val_t = val_type[\"text\"].tolist(); y_val_t = enc(val_type)\n", + "X_test_t = test_type[\"text\"].tolist(); y_test_t = enc(test_type)\n", + "\n", + "print(f\"Strategies: {STRATEGY_LABELS}\")\n", + "print(f\"Train+Val: {len(X_tv_t):,} Val: {len(X_val_t):,} Test: {len(X_test_t):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZupB5UlvuKB-", + "outputId": "bb5e86d3-745c-4c41-9650-d399e4668e32" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "=== Type: Comparing Vectorizer + NB Combinations ===\n", + "CountVectorizer + MultinomialNB CV F1=0.3811 params={'nb__alpha': 0.5, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n", + "TfidfVectorizer + MultinomialNB CV F1=0.3422 params={'nb__alpha': 0.1, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n", + "CountVectorizer + ComplementNB (for imbalance) CV F1=0.3777 params={'nb__alpha': 1.0, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n" + ] + } + ], + "source": [ + "print(\"=== Type: Comparing Vectorizer + NB Combinations ===\")\n", + "\n", + "gs_count_mnb_type = run_nb_grid(\n", + " CountVectorizer, MultinomialNB, BASE_GRID,\n", + " X_tv_t, y_tv_t, grp_tv_t,\n", + " \"CountVectorizer + MultinomialNB\"\n", + ")\n", + "\n", + "gs_tfidf_mnb_type = run_nb_grid(\n", + " TfidfVectorizer, MultinomialNB, BASE_GRID,\n", + " X_tv_t, y_tv_t, grp_tv_t,\n", + " \"TfidfVectorizer + MultinomialNB\"\n", + ")\n", + "\n", + "gs_count_cnb_type = run_nb_grid(\n", + " CountVectorizer, ComplementNB, BASE_GRID,\n", + " X_tv_t, y_tv_t, grp_tv_t,\n", + " \"CountVectorizer + ComplementNB (for imbalance)\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "J_CunIwDuKB-", + "outputId": "45a1ccc2-6af0-4542-b371-7573a94afba6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Best type NB model: CountVec+MultinomialNB (CV F1=0.3811)\n", + "\n", + "--- All models on Test (Type Task) ---\n", + " CountVec+MultinomialNB : acc=0.3953 macro-F1=0.3953\n", + " TfidfVec+MultinomialNB : acc=0.3800 macro-F1=0.3450\n", + " CountVec+ComplementNB : acc=0.3875 macro-F1=0.3880\n" + ] + } + ], + "source": [ + "candidates_type = [\n", + " (\"CountVec+MultinomialNB\", gs_count_mnb_type),\n", + " (\"TfidfVec+MultinomialNB\", gs_tfidf_mnb_type),\n", + " (\"CountVec+ComplementNB\", gs_count_cnb_type),\n", + "]\n", + "\n", + "best_name_type, best_gs_type = max(candidates_type, key=lambda x: x[1].best_score_)\n", + "print(f\"Best type NB model: {best_name_type} (CV F1={best_gs_type.best_score_:.4f})\")\n", + "\n", + "print(\"\\n--- All models on Test (Type Task) ---\")\n", + "all_test_results_type = []\n", + "for name, gs in candidates_type:\n", + " y_pred_t = gs.best_estimator_.predict(X_test_t)\n", + " f1 = f1_score(y_test_t, y_pred_t, average=\"macro\")\n", + " acc = accuracy_score(y_test_t, y_pred_t)\n", + " all_test_results_type.append({\"model\": name, \"accuracy\": acc, \"f1_macro\": f1})\n", + " print(f\" {name:40s}: acc={acc:.4f} macro-F1={f1:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "o6JHLJnLuKB-", + "outputId": "5f5f236e-83ce-4a58-9f49-92e0f472d39e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[Val] Accuracy=0.7694 Macro-F1=0.7744 Weighted-F1=0.7693\n", + " precision recall f1-score support\n", + "\n", + " irony 0.80 0.72 0.76 942\n", + " overstatement 0.75 0.80 0.77 600\n", + "rhetorical_question 0.78 0.88 0.83 157\n", + " sarcasm 0.79 0.78 0.79 1317\n", + " satire 0.73 0.77 0.75 747\n", + " understatement 0.75 0.75 0.75 487\n", + "\n", + " accuracy 0.77 4250\n", + " macro avg 0.77 0.78 0.77 4250\n", + " weighted avg 0.77 0.77 0.77 4250\n", + "\n", + "[Test] Accuracy=0.3953 Macro-F1=0.3953 Weighted-F1=0.3929\n", + " precision recall f1-score support\n", + "\n", + " irony 0.32 0.29 0.30 902\n", + " overstatement 0.39 0.40 0.39 592\n", + "rhetorical_question 0.52 0.41 0.46 176\n", + " sarcasm 0.45 0.51 0.47 1291\n", + " satire 0.36 0.34 0.35 833\n", + " understatement 0.40 0.38 0.39 456\n", + "\n", + " accuracy 0.40 4250\n", + " macro avg 0.41 0.39 0.40 4250\n", + " weighted avg 0.39 0.40 0.39 4250\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/naive_bayes/confusion_matrix_type_val.png\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/naive_bayes/confusion_matrix_type_test.png\n", + "All type artifacts saved.\n" + ] + } + ], + "source": [ + "best_model_type = best_gs_type.best_estimator_\n", + "\n", + "val_m_type, y_val_pred_type = evaluate(best_model_type, X_val_t, y_val_t, STRATEGY_LABELS, \"Val\")\n", + "test_m_type, y_test_pred_type = evaluate(best_model_type, X_test_t, y_test_t, STRATEGY_LABELS, \"Test\")\n", + "\n", + "# Save\n", + "cfg_type = {\n", + " \"task\": \"type\", \"best_model\": best_name_type,\n", + " \"label_names\": STRATEGY_LABELS, \"label2id\": label2id,\n", + " \"best_params\": best_gs_type.best_params_, \"cv_f1_macro\": best_gs_type.best_score_,\n", + "}\n", + "with open(OUT_NB / \"best_config_type.json\", \"w\") as f:\n", + " json.dump(cfg_type, f, indent=2)\n", + "\n", + "all_m_type = {\"val\": val_m_type, \"test\": test_m_type,\n", + " \"all_test_comparison\": all_test_results_type}\n", + "with open(OUT_NB / \"metrics_type.json\", \"w\") as f:\n", + " json.dump(all_m_type, f, indent=2)\n", + "\n", + "save_confusion_matrix(\n", + " y_val_t, y_val_pred_type, STRATEGY_LABELS,\n", + " OUT_NB / \"confusion_matrix_type_val.png\",\n", + " f\"NB ({best_name_type}) — Type (Val)\"\n", + ")\n", + "save_confusion_matrix(\n", + " y_test_t, y_test_pred_type, STRATEGY_LABELS,\n", + " OUT_NB / \"confusion_matrix_type_test.png\",\n", + " f\"NB ({best_name_type}) — Type (Test)\"\n", + ")\n", + "\n", + "# Predictions with string labels\n", + "pred_val_type = val_type.copy()\n", + "pred_test_type = test_type.copy()\n", + "for df, y_pred, y_true in [(pred_val_type, y_val_pred_type, y_val_t),\n", + " (pred_test_type, y_test_pred_type, y_test_t)]:\n", + " df[\"predicted_label\"] = [id2label[i] for i in y_pred]\n", + " df[\"true_label\"] = [id2label[i] for i in y_true]\n", + " df[\"correct\"] = (df[\"type_label\"] == df[\"predicted_label\"]).astype(int)\n", + "\n", + "pred_val_type.to_csv( OUT_NB / \"predictions_val_type.csv\", index=False)\n", + "pred_test_type.to_csv(OUT_NB / \"predictions_test_type.csv\", index=False)\n", + "\n", + "print(\"All type artifacts saved.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TwTBPa86uKB-" + }, + "source": [ + "## Error Examples" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "b8itQIPtuKB-", + "outputId": "6805f04f-a1c4-4005-a8f7-4496ad84a58b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Binary errors on test: 1781 total\n", + " False Positives (predicted sarcastic, actually not): 1039\n", + " False Negatives (predicted not-sarcastic, actually sarcastic): 742\n", + "\n", + "--- Sample FP ---\n", + " [True=0, Pred=1] Probability researchers note a coincidence where three coworkers wear the same shirt color.\n", + " [True=0, Pred=1] The global-warming crisis contributed to a delightful mid-February afternoon.\n", + " [True=0, Pred=1] Signs for the upcoming road section make it sound formidable or exciting.\n", + " [True=0, Pred=1] A father recounts how he saved $4.27 through quick thinking.\n", + " [True=0, Pred=1] The wedding album begins with a photo of two acorns floating in a glass of water.\n", + " [True=0, Pred=1] A governor is too embarrassed to say which state he leads.\n", + " [True=0, Pred=1] A man who plays devil's advocate may actually just want to be disagreeable.\n", + " [True=0, Pred=1] Area dad watched a show about bigfoot last night.\n", + " [True=0, Pred=1] Coroner's report cites systemic issues in Alton Sterling's death.\n", + " [True=0, Pred=1] Preschool child asks to look at a classmate's notes on shapes.\n", + "\n", + "--- Sample FN ---\n", + " [True=1, Pred=0] state department warns americans traveling abroad to avoid lame amsterdam windmill tour\n", + " [True=1, Pred=0] hollywood's biggest stars endure long lines at oscars security screening\n", + " [True=1, Pred=0] historians suggest 'goodfellas' youtube clips may be fragments of larger work\n", + " [True=1, Pred=0] members of opening band walking among crowd during intermission like gods among men\n", + " [True=1, Pred=0] woman who's been on the pill for years thinking about switching to new set of debilitating side effe\n", + " [True=1, Pred=0] congressman lets his guitar do the talking\n", + " [True=1, Pred=0] officials warn consumers of counterfeit tickets ahead of solar eclipse\n", + " [True=1, Pred=0] anderson cooper throws another box of letters from gay children into dumpster\n", + " [True=1, Pred=0] non-priest arrested on charges of child molestation\n", + " [True=1, Pred=0] huckabee sanders tells colleagues she's taking temporary post as google ceo before transitioning int\n" + ] + } + ], + "source": [ + "# ── Binary error examples ─────────────────────────────────────────────────────\n", + "err_bin = pred_test_bin[pred_test_bin[\"correct\"] == 0].copy()\n", + "fp_bin = err_bin[err_bin[\"binary_label\"] == 0].head(10) # predicted sarcastic, actually not\n", + "fn_bin = err_bin[err_bin[\"binary_label\"] == 1].head(10) # predicted not-sarcastic, actually sarcastic\n", + "\n", + "print(f\"Binary errors on test: {len(err_bin)} total\")\n", + "print(f\" False Positives (predicted sarcastic, actually not): {len(err_bin[err_bin['binary_label']==0])}\")\n", + "print(f\" False Negatives (predicted not-sarcastic, actually sarcastic): {len(err_bin[err_bin['binary_label']==1])}\")\n", + "print(\"\\n--- Sample FP ---\")\n", + "for _, row in fp_bin.iterrows():\n", + " print(f\" [True=0, Pred=1] {row['text'][:100]}\")\n", + "print(\"\\n--- Sample FN ---\")\n", + "for _, row in fn_bin.iterrows():\n", + " print(f\" [True=1, Pred=0] {row['text'][:100]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zmVdkTHuuKB_", + "outputId": "5bc30ec0-0f97-4f57-a165-706c7a022a82" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Type errors on test: 2570 total\n", + "\n", + "Confusion pairs (true → predicted):\n", + "type_label predicted_label\n", + "irony sarcasm 309\n", + "satire sarcasm 227\n", + "sarcasm irony 226\n", + " satire 203\n", + "satire irony 155\n", + "irony satire 155\n", + "overstatement sarcasm 117\n", + "understatement sarcasm 103\n", + "overstatement satire 102\n", + "satire overstatement 101\n" + ] + } + ], + "source": [ + "# ── Type error examples ───────────────────────────────────────────────────────\n", + "err_type = pred_test_type[pred_test_type[\"correct\"] == 0].copy()\n", + "print(f\"Type errors on test: {len(err_type)} total\")\n", + "print(\"\\nConfusion pairs (true → predicted):\")\n", + "conf_pairs = err_type.groupby([\"type_label\", \"predicted_label\"]).size().sort_values(ascending=False).head(10)\n", + "print(conf_pairs.to_string())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "p52YKmRQuKB_" + }, + "source": [ + "## Summary" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tg9D9LKxuKB_", + "outputId": "bf1f11d1-8feb-4b8c-c9fd-adac380993b2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "====== NAIVE BAYES RESULTS SUMMARY ======\n", + "\n", + "Task A — Binary (Test):\n", + " Best model : TfidfVec+MultinomialNB\n", + " Accuracy : 0.7905\n", + " Macro-F1 : 0.7902\n", + " Weighted-F1 : 0.7902\n", + "\n", + "Task B — Type (Test):\n", + " Best model : CountVec+MultinomialNB\n", + " Accuracy : 0.3953\n", + " Macro-F1 : 0.3953\n", + " Weighted-F1 : 0.3929\n" + ] + } + ], + "source": [ + "print(\"====== NAIVE BAYES RESULTS SUMMARY ======\")\n", + "print()\n", + "print(\"Task A — Binary (Test):\")\n", + "print(f\" Best model : {best_name_bin}\")\n", + "print(f\" Accuracy : {test_m_bin['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {test_m_bin['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1 : {test_m_bin['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"Task B — Type (Test):\")\n", + "print(f\" Best model : {best_name_type}\")\n", + "print(f\" Accuracy : {test_m_type['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {test_m_type['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1 : {test_m_type['f1_weighted']:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GZHqRh2iuKB_" + }, + "source": [ + "---\n", + "# Part 4 — BERT / DistilBERT Classification" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xR_41E_1uKB_" + }, + "source": [ + "# Notebook 04 — BERT / DistilBERT Classification\n", + "\n", + "**Purpose**: Fine-tune transformer models for sarcasm classification.\n", + "- Task A: Binary (sarcastic vs non-sarcastic)\n", + "- Task B: Sarcasm type (6-class, sarcastic only)\n", + "\n", + "**Models**:\n", + "- `distilbert-base-uncased` (primary, fast)\n", + "- `bert-base-uncased` (optional, if compute allows)\n", + "\n", + "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", + "\n", + "**Outputs** in `outputs/bert/distilbert_binary/` and `outputs/bert/distilbert_type/`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5Gi8VSJDuKB_" + }, + "source": [ + "## Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "K_FNBk2ouKB_", + "outputId": "70f6d378-7098-410c-d656-50135e3b0e12" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Binary config: TrainConfig(model_name='distilbert-base-uncased', max_length=128, batch_size=32, lr=2e-05, weight_decay=0.01, warmup_ratio=0.1, epochs=10, patience=3, seed=42, task='binary', use_class_weights=False)\n", + "Type config: TrainConfig(model_name='distilbert-base-uncased', max_length=128, batch_size=32, lr=2e-05, weight_decay=0.01, warmup_ratio=0.1, epochs=10, patience=3, seed=42, task='type', use_class_weights=True)\n" + ] + } + ], + "source": [ + "@dataclass\n", + "class TrainConfig:\n", + " model_name : str = \"distilbert-base-uncased\"\n", + " max_length : int = 128\n", + " batch_size : int = 32\n", + " lr : float = 2e-5\n", + " weight_decay : float = 0.01\n", + " warmup_ratio : float = 0.1\n", + " epochs : int = 10\n", + " patience : int = 3 # early stopping patience\n", + " seed : int = SEED\n", + " task : str = \"binary\" # 'binary' or 'type'\n", + " use_class_weights: bool = False\n", + "\n", + "# ── Config for binary task ────────────────────────────────────────────────────\n", + "CFG_BINARY = TrainConfig(\n", + " model_name=\"distilbert-base-uncased\",\n", + " task=\"binary\",\n", + " batch_size=32,\n", + " lr=2e-5,\n", + " use_class_weights=False,\n", + ")\n", + "\n", + "# ── Config for type task (use class weights for imbalance) ────────────────────\n", + "CFG_TYPE = TrainConfig(\n", + " model_name=\"distilbert-base-uncased\",\n", + " task=\"type\",\n", + " batch_size=32,\n", + " lr=2e-5,\n", + " use_class_weights=True,\n", + ")\n", + "\n", + "print(\"Binary config:\", CFG_BINARY)\n", + "print(\"Type config: \", CFG_TYPE)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OKgvtBDPuKCA" + }, + "source": [ + "## Dataset Class" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "id": "tHoVxhSAuKCA" + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch.utils.data import Dataset\n", + "\n", + "class HeadlineDataset(Dataset):\n", + " \"\"\"PyTorch Dataset for headline classification.\"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " texts: list[str],\n", + " labels: list[int],\n", + " tokenizer,\n", + " max_length: int = 128,\n", + " ):\n", + " self.texts = texts\n", + " self.labels = labels\n", + " self.tokenizer = tokenizer\n", + " self.max_length = max_length\n", + "\n", + " def __len__(self) -> int:\n", + " return len(self.texts)\n", + "\n", + " def __getitem__(self, idx: int) -> dict:\n", + " encoding = self.tokenizer(\n", + " self.texts[idx],\n", + " truncation=True,\n", + " padding=\"max_length\",\n", + " max_length=self.max_length,\n", + " return_tensors=\"pt\",\n", + " )\n", + " return {\n", + " \"input_ids\" : encoding[\"input_ids\"].squeeze(0),\n", + " \"attention_mask\" : encoding[\"attention_mask\"].squeeze(0),\n", + " \"labels\" : torch.tensor(self.labels[idx], dtype=torch.long),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KBPvq1iRuKCA" + }, + "source": [ + "## Training and Evaluation Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "id": "Dw8sRTfruKCA" + }, + "outputs": [], + "source": [ + "def compute_class_weights(y_train: list[int], num_classes: int) -> torch.Tensor:\n", + " \"\"\"Compute inverse-frequency class weights.\"\"\"\n", + " counts = np.bincount(y_train, minlength=num_classes).astype(float)\n", + " weights = len(y_train) / (num_classes * counts)\n", + " weights = np.clip(weights, 0, 10) # cap extreme weights\n", + " return torch.tensor(weights, dtype=torch.float)\n", + "\n", + "\n", + "def train_epoch(\n", + " model, loader: DataLoader, optimizer, scheduler, criterion, device\n", + ") -> float:\n", + " model.train()\n", + " total_loss = 0.0\n", + " for batch in loader:\n", + " input_ids = batch[\"input_ids\"].to(device)\n", + " attention_mask = batch[\"attention_mask\"].to(device)\n", + " labels = batch[\"labels\"].to(device)\n", + "\n", + " optimizer.zero_grad()\n", + " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", + " logits = outputs.logits\n", + "\n", + " loss = criterion(logits, labels)\n", + " loss.backward()\n", + "\n", + " nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n", + " optimizer.step()\n", + " scheduler.step()\n", + "\n", + " total_loss += loss.item()\n", + " return total_loss / len(loader)\n", + "\n", + "\n", + "@torch.no_grad()\n", + "def eval_epoch(\n", + " model, loader: DataLoader, criterion, device, label_names: list[str]\n", + ") -> tuple[float, dict, list]:\n", + " model.eval()\n", + " total_loss = 0.0\n", + " all_preds, all_labels = [], []\n", + "\n", + " for batch in loader:\n", + " input_ids = batch[\"input_ids\"].to(device)\n", + " attention_mask = batch[\"attention_mask\"].to(device)\n", + " labels = batch[\"labels\"].to(device)\n", + "\n", + " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", + " logits = outputs.logits\n", + " loss = criterion(logits, labels)\n", + "\n", + " total_loss += loss.item()\n", + " preds = torch.argmax(logits, dim=-1)\n", + " all_preds.extend(preds.cpu().numpy())\n", + " all_labels.extend(labels.cpu().numpy())\n", + "\n", + " avg_loss = total_loss / len(loader)\n", + " metrics = {\n", + " \"loss\" : avg_loss,\n", + " \"accuracy\" : accuracy_score(all_labels, all_preds),\n", + " \"f1_macro\" : f1_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", + " \"f1_weighted\" : f1_score(all_labels, all_preds, average=\"weighted\", zero_division=0),\n", + " \"precision_macro\" : precision_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", + " \"recall_macro\" : recall_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", + " }\n", + " return avg_loss, metrics, all_preds\n", + "\n", + "\n", + "def save_confusion_matrix(y_true, y_pred, label_names, out_path, title=\"\"):\n", + " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", + " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", + " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n", + " xticklabels=label_names, yticklabels=label_names, ax=ax)\n", + " ax.set_xlabel(\"Predicted\"); ax.set_ylabel(\"True\"); ax.set_title(title)\n", + " plt.tight_layout()\n", + " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + " print(f\"Saved: {out_path}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d35JFlgzuKCB" + }, + "source": [ + "## Full Training Function" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "id": "wHriQLOyuKCB" + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "\n", + "from transformers import get_linear_schedule_with_warmup\n", + "\n", + "def train_bert(\n", + " cfg: TrainConfig,\n", + " X_train: list[str], y_train: list[int],\n", + " X_val: list[str], y_val: list[int],\n", + " X_test: list[str], y_test: list[int],\n", + " label_names: list[str],\n", + " out_dir: Path,\n", + " val_df: pd.DataFrame,\n", + " test_df: pd.DataFrame,\n", + ") -> dict:\n", + " \"\"\"Full training loop with early stopping. Returns test metrics dict.\"\"\"\n", + " out_dir.mkdir(parents=True, exist_ok=True)\n", + " num_labels = len(label_names)\n", + "\n", + " # FIX: define device inside function (self-contained)\n", + " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + " print(f\"Using device: {device}\")\n", + "\n", + " # Save config (default=str avoids Path serialization issues)\n", + " with open(out_dir / \"config.json\", \"w\") as f:\n", + " json.dump(cfg.__dict__, f, indent=2, default=str)\n", + " print(f\"Config saved to {out_dir / 'config.json'}\")\n", + "\n", + " # Tokenizer\n", + " print(f\"Loading tokenizer: {cfg.model_name}\")\n", + " tokenizer = AutoTokenizer.from_pretrained(cfg.model_name)\n", + "\n", + " # Datasets\n", + " train_ds = HeadlineDataset(X_train, y_train, tokenizer, cfg.max_length)\n", + " val_ds = HeadlineDataset(X_val, y_val, tokenizer, cfg.max_length)\n", + " test_ds = HeadlineDataset(X_test, y_test, tokenizer, cfg.max_length)\n", + "\n", + " g = torch.Generator()\n", + " g.manual_seed(cfg.seed)\n", + "\n", + " train_loader = DataLoader(train_ds, batch_size=cfg.batch_size, shuffle=True, generator=g)\n", + " val_loader = DataLoader(val_ds, batch_size=cfg.batch_size, shuffle=False)\n", + " test_loader = DataLoader(test_ds, batch_size=cfg.batch_size, shuffle=False)\n", + "\n", + " # Model\n", + " print(f\"Loading model: {cfg.model_name} ({num_labels} labels)\")\n", + " model = AutoModelForSequenceClassification.from_pretrained(\n", + " cfg.model_name, num_labels=num_labels\n", + " ).to(device)\n", + "\n", + " # Loss (with optional class weights)\n", + " if cfg.use_class_weights:\n", + " cw = compute_class_weights(y_train, num_labels).to(device)\n", + " criterion = nn.CrossEntropyLoss(weight=cw)\n", + " print(f\"Class weights: {cw.detach().cpu().numpy().round(3)}\")\n", + " else:\n", + " criterion = nn.CrossEntropyLoss()\n", + "\n", + " # Optimizer and scheduler\n", + " optimizer = torch.optim.AdamW(\n", + " model.parameters(), lr=cfg.lr, weight_decay=cfg.weight_decay\n", + " )\n", + " total_steps = len(train_loader) * cfg.epochs\n", + " warmup_steps = int(total_steps * cfg.warmup_ratio)\n", + "\n", + " scheduler = get_linear_schedule_with_warmup(\n", + " optimizer, num_warmup_steps=warmup_steps, num_training_steps=total_steps\n", + " )\n", + "\n", + " # Training loop\n", + " best_val_f1 = -1.0\n", + " patience_cnt = 0\n", + " best_epoch = 0\n", + " train_log = []\n", + "\n", + " for epoch in range(1, cfg.epochs + 1):\n", + " train_loss = train_epoch(model, train_loader, optimizer, scheduler, criterion, device)\n", + " _, val_metrics, _ = eval_epoch(model, val_loader, criterion, device, label_names)\n", + "\n", + " val_f1 = float(val_metrics[\"f1_macro\"])\n", + " log_row = {\n", + " \"epoch\": epoch,\n", + " \"train_loss\": float(train_loss),\n", + " **{f\"val_{k}\": float(v) if isinstance(v, (np.floating, float, int, np.integer)) else v\n", + " for k, v in val_metrics.items()}\n", + " }\n", + " train_log.append(log_row)\n", + "\n", + " print(\n", + " f\"Epoch {epoch:2d}/{cfg.epochs} | \"\n", + " f\"train_loss={train_loss:.4f} | \"\n", + " f\"val_loss={val_metrics['loss']:.4f} | \"\n", + " f\"val_acc={val_metrics['accuracy']:.4f} | \"\n", + " f\"val_macro_f1={val_f1:.4f}\"\n", + " )\n", + "\n", + " if val_f1 > best_val_f1:\n", + " best_val_f1 = val_f1\n", + " best_epoch = epoch\n", + " patience_cnt = 0\n", + "\n", + " ckpt_dir = out_dir / \"best_checkpoint\"\n", + " ckpt_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " model.save_pretrained(ckpt_dir)\n", + " tokenizer.save_pretrained(ckpt_dir)\n", + " print(f\" ★ New best val macro-F1={val_f1:.4f} — checkpoint saved\")\n", + " else:\n", + " patience_cnt += 1\n", + " if patience_cnt >= cfg.patience:\n", + " print(f\" Early stopping at epoch {epoch} (patience={cfg.patience})\")\n", + " break\n", + "\n", + " # Save training log\n", + " log_df = pd.DataFrame(train_log)\n", + " log_df.to_csv(out_dir / \"training_log.csv\", index=False)\n", + "\n", + " # Plot training curves\n", + " fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + " axes[0].plot(log_df[\"epoch\"], log_df[\"train_loss\"], label=\"Train loss\", marker=\"o\")\n", + " axes[0].plot(log_df[\"epoch\"], log_df[\"val_loss\"], label=\"Val loss\", marker=\"s\")\n", + " axes[0].set_xlabel(\"Epoch\")\n", + " axes[0].set_ylabel(\"Loss\")\n", + " axes[0].set_title(f\"{cfg.model_name} — Loss Curves ({cfg.task})\")\n", + " axes[0].legend()\n", + " axes[0].axvline(best_epoch, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", + "\n", + " axes[1].plot(log_df[\"epoch\"], log_df[\"val_f1_macro\"], label=\"Val macro-F1\", marker=\"o\")\n", + " axes[1].set_xlabel(\"Epoch\")\n", + " axes[1].set_ylabel(\"Macro-F1\")\n", + " axes[1].set_title(f\"{cfg.model_name} — Val Macro-F1 ({cfg.task})\")\n", + " axes[1].legend()\n", + " axes[1].axvline(best_epoch, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", + "\n", + " plt.tight_layout()\n", + " plt.savefig(out_dir / \"training_curves.png\", dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + "\n", + " # Reload best checkpoint and evaluate\n", + " print(f\"\\nLoading best checkpoint (epoch {best_epoch}, val macro-F1={best_val_f1:.4f})\")\n", + " best_model = AutoModelForSequenceClassification.from_pretrained(\n", + " str(out_dir / \"best_checkpoint\")\n", + " ).to(device)\n", + "\n", + " _, val_metrics_best, val_preds = eval_epoch(best_model, val_loader, criterion, device, label_names)\n", + " _, test_metrics_best, test_preds = eval_epoch(best_model, test_loader, criterion, device, label_names)\n", + "\n", + " print(\"\\n=== Val (best checkpoint) ===\")\n", + " print(classification_report(y_val, val_preds, target_names=label_names, zero_division=0))\n", + " print(\"\\n=== Test (best checkpoint) ===\")\n", + " print(classification_report(y_test, test_preds, target_names=label_names, zero_division=0))\n", + "\n", + " # Confusion matrices\n", + " save_confusion_matrix(\n", + " y_val, val_preds, label_names,\n", + " out_dir / \"confusion_matrix_val.png\",\n", + " f\"{cfg.model_name} — {cfg.task} (Val)\"\n", + " )\n", + " save_confusion_matrix(\n", + " y_test, test_preds, label_names,\n", + " out_dir / \"confusion_matrix_test.png\",\n", + " f\"{cfg.model_name} — {cfg.task} (Test)\"\n", + " )\n", + "\n", + " # Convert metrics to native Python types for JSON\n", + " def _to_py(obj):\n", + " if isinstance(obj, dict):\n", + " return {k: _to_py(v) for k, v in obj.items()}\n", + " if isinstance(obj, (list, tuple)):\n", + " return [_to_py(v) for v in obj]\n", + " if isinstance(obj, (np.integer,)):\n", + " return int(obj)\n", + " if isinstance(obj, (np.floating,)):\n", + " return float(obj)\n", + " return obj\n", + "\n", + " results = {\n", + " \"model\": cfg.model_name,\n", + " \"task\": cfg.task,\n", + " \"best_epoch\": int(best_epoch),\n", + " \"best_val_f1_macro\": float(best_val_f1),\n", + " \"val\": _to_py(val_metrics_best),\n", + " \"test\": _to_py(test_metrics_best),\n", + " }\n", + "\n", + " with open(out_dir / \"metrics.json\", \"w\") as f:\n", + " json.dump(results, f, indent=2, default=str)\n", + "\n", + " # Save predictions\n", + " for split_name, df, y_true_list, y_pred_list in [\n", + " (\"val\", val_df, y_val, val_preds),\n", + " (\"test\", test_df, y_test, test_preds),\n", + " ]:\n", + " out_pred = df.copy()\n", + " out_pred[\"predicted\"] = y_pred_list\n", + " out_pred[\"predicted_label\"] = [label_names[i] for i in y_pred_list]\n", + " out_pred[\"correct\"] = (np.array(y_true_list) == np.array(y_pred_list)).astype(int)\n", + " out_pred.to_csv(out_dir / f\"predictions_{split_name}.csv\", index=False)\n", + " print(f\"Saved predictions_{split_name}.csv\")\n", + "\n", + " print(f\"\\n=== DONE: {cfg.model_name} / {cfg.task} ===\")\n", + " print(f\" Test Accuracy : {test_metrics_best['accuracy']:.4f}\")\n", + " print(f\" Test Macro-F1 : {test_metrics_best['f1_macro']:.4f}\")\n", + " print(f\" Test Weighted-F1: {test_metrics_best['f1_weighted']:.4f}\")\n", + "\n", + " return results" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "49JUIiXSuKCB" + }, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-bzRLKhsuKCB", + "outputId": "b3ac3196-fdb3-44d9-8417-26a434c6837a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Binary — Train: 39,666 Val: 8,500 Test: 8,500\n" + ] + } + ], + "source": [ + "# ── Binary splits ─────────────────────────────────────────────────────────────\n", + "train_bin = pd.read_csv(SPLITS / \"train_binary.csv\")\n", + "val_bin = pd.read_csv(SPLITS / \"val_binary.csv\")\n", + "test_bin = pd.read_csv(SPLITS / \"test_binary.csv\")\n", + "\n", + "X_train_bin = train_bin[\"text\"].tolist(); y_train_bin = train_bin[\"binary_label\"].tolist()\n", + "X_val_bin = val_bin[\"text\"].tolist(); y_val_bin = val_bin[\"binary_label\"].tolist()\n", + "X_test_bin = test_bin[\"text\"].tolist(); y_test_bin = test_bin[\"binary_label\"].tolist()\n", + "\n", + "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", + "\n", + "print(f\"Binary — Train: {len(X_train_bin):,} Val: {len(X_val_bin):,} Test: {len(X_test_bin):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OkXIlQAtuKCC", + "outputId": "676ecfa7-874b-4536-9ac3-b8b833e2cfff" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Type — Train: 19,833 Val: 4,250 Test: 4,250\n", + "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n" + ] + } + ], + "source": [ + "# ── Type splits ───────────────────────────────────────────────────────────────\n", + "train_type = pd.read_csv(SPLITS / \"train_type.csv\")\n", + "val_type = pd.read_csv(SPLITS / \"val_type.csv\")\n", + "test_type = pd.read_csv(SPLITS / \"test_type.csv\")\n", + "\n", + "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", + "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", + "id2label = {i: lab for lab, i in label2id.items()}\n", + "\n", + "def enc(df): return [label2id[l] for l in df[\"type_label\"]]\n", + "\n", + "X_train_type = train_type[\"text\"].tolist(); y_train_type = enc(train_type)\n", + "X_val_type = val_type[\"text\"].tolist(); y_val_type = enc(val_type)\n", + "X_test_type = test_type[\"text\"].tolist(); y_test_type = enc(test_type)\n", + "\n", + "print(f\"Type — Train: {len(X_train_type):,} Val: {len(X_val_type):,} Test: {len(X_test_type):,}\")\n", + "print(f\"Strategies: {STRATEGY_LABELS}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "varh9ZaouKCC" + }, + "source": [ + "## Train DistilBERT — Binary Task" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 396, + "referenced_widgets": [ + "8b9080237c194037a2cd1dc711a694d2", + "d0d70eea6290419fb4a39bd74964bacb", + "30e0eec43be54d08a0b5bd855e06d3c3", + "959391d9e6cc405ead852002ce0276ff", + "143c9b378b5a4260b15e328dc744374f", + "384fe210d84b4e8d8bc1cd8b99944d5e", + "2a97e4558e6d41df919bd3ce83576627", + "bfc3291175a14250a407af3dd6376d58", + "01c169be55ca4399b34eb7cdae152075", + "2f42691301be4d01858524488e30fe78", + "8faab66da6c74399a140fa5aad07dbbb" + ] + }, + "id": "Yq8zSNjluKCC", + "outputId": "44080f86-f045-426f-f630-765eb5f599c7" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Using device: cpu\n", + "Config saved to /content/outputs/bert/distilbert_binary/config.json\n", + "Loading tokenizer: distilbert-base-uncased\n", + "Loading model: distilbert-base-uncased (2 labels)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Loading weights: 0%| | 0/100 [00:00 dict | None:\n", + " if path.exists():\n", + " with open(path) as f:\n", + " return json.load(f)\n", + " print(f\" [WARNING] Not found: {path.relative_to(ROOT)}\")\n", + " return None\n", + "\n", + "\n", + "# All metrics\n", + "tfidf_lr_binary = load_metrics(CLASSICAL / \"tfidf_lr\" / \"metrics_binary.json\")\n", + "tfidf_lr_type = load_metrics(CLASSICAL / \"tfidf_lr\" / \"metrics_type.json\")\n", + "nb_binary = load_metrics(CLASSICAL / \"naive_bayes\" / \"metrics_binary.json\")\n", + "nb_type = load_metrics(CLASSICAL / \"naive_bayes\" / \"metrics_type.json\")\n", + "distilbert_bin = load_metrics(BERT_OUT / \"distilbert_binary\" / \"metrics.json\")\n", + "distilbert_type = load_metrics(BERT_OUT / \"distilbert_type\" / \"metrics.json\")\n", + "bert_base_bin = load_metrics(BERT_OUT / \"bert_base_binary\" / \"metrics.json\") # optional\n", + "bert_base_type = load_metrics(BERT_OUT / \"bert_base_type\" / \"metrics.json\") # optional\n", + "\n", + "print(\"Metrics loaded (None = not yet run):\")\n", + "for name, m in [(\"TF-IDF+LR binary\", tfidf_lr_binary), (\"TF-IDF+LR type\", tfidf_lr_type),\n", + " (\"NB binary\", nb_binary), (\"NB type\", nb_type),\n", + " (\"DistilBERT binary\", distilbert_bin), (\"DistilBERT type\", distilbert_type),\n", + " (\"BERT-base binary\", bert_base_bin), (\"BERT-base type\", bert_base_type)]:\n", + " print(f\" {name}: {'✓' if m else '✗'}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H_xQm80nuKCE" + }, + "source": [ + "## 2. Model Comparison Tables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2RsVxgMtuKCE" + }, + "outputs": [], + "source": [ + "def extract_test_metrics(metrics_dict: dict | None, split: str = \"test\") -> dict:\n", + " \"\"\"Extract test-split metrics from a metrics dict, handling different formats.\"\"\"\n", + " if metrics_dict is None:\n", + " return {\"accuracy\": None, \"f1_macro\": None, \"f1_weighted\": None,\n", + " \"precision_macro\": None, \"recall_macro\": None}\n", + " test_m = metrics_dict.get(split, metrics_dict) # BERT format uses 'test' key directly\n", + " return {\n", + " \"accuracy\" : test_m.get(\"accuracy\"),\n", + " \"f1_macro\" : test_m.get(\"f1_macro\"),\n", + " \"f1_weighted\" : test_m.get(\"f1_weighted\"),\n", + " \"precision_macro\" : test_m.get(\"precision_macro\"),\n", + " \"recall_macro\" : test_m.get(\"recall_macro\"),\n", + " }\n", + "\n", + "\n", + "binary_rows = []\n", + "type_rows = []\n", + "\n", + "model_map_bin = [\n", + " (\"TF-IDF + LR\", tfidf_lr_binary),\n", + " (\"Naive Bayes\", nb_binary),\n", + " (\"DistilBERT\", distilbert_bin),\n", + " (\"BERT-base\", bert_base_bin),\n", + "]\n", + "\n", + "model_map_type = [\n", + " (\"TF-IDF + LR\", tfidf_lr_type),\n", + " (\"Naive Bayes\", nb_type),\n", + " (\"DistilBERT\", distilbert_type),\n", + " (\"BERT-base\", bert_base_type),\n", + "]\n", + "\n", + "for name, m in model_map_bin:\n", + " row = {\"Model\": name}\n", + " row.update(extract_test_metrics(m))\n", + " binary_rows.append(row)\n", + "\n", + "for name, m in model_map_type:\n", + " row = {\"Model\": name}\n", + " row.update(extract_test_metrics(m))\n", + " type_rows.append(row)\n", + "\n", + "binary_comp = pd.DataFrame(binary_rows).set_index(\"Model\")\n", + "type_comp = pd.DataFrame(type_rows).set_index(\"Model\")\n", + "\n", + "print(\"=== BINARY TASK — Test Metrics ===\")\n", + "print(binary_comp.to_string(float_format=lambda x: f\"{x:.4f}\" if x is not None else \"N/A\"))\n", + "print()\n", + "print(\"=== TYPE TASK — Test Metrics ===\")\n", + "print(type_comp.to_string(float_format=lambda x: f\"{x:.4f}\" if x is not None else \"N/A\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "n35eLHCCuKCF" + }, + "outputs": [], + "source": [ + "# ── Comparison bar charts ─────────────────────────────────────────────────────\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n", + "\n", + "for ax, comp, title in [\n", + " (axes[0], binary_comp, \"Binary Task\"),\n", + " (axes[1], type_comp, \"Type Task\"),\n", + "]:\n", + " valid = comp.dropna(subset=[\"f1_macro\"])\n", + " if len(valid) == 0:\n", + " ax.set_title(f\"{title} — No data\")\n", + " continue\n", + " models = valid.index.tolist()\n", + " f1_vals = valid[\"f1_macro\"].tolist()\n", + " acc_vals = valid[\"accuracy\"].tolist()\n", + " x = range(len(models))\n", + " w = 0.35\n", + " bars1 = ax.bar([i - w/2 for i in x], f1_vals, w, label=\"Macro-F1\", color=\"steelblue\")\n", + " bars2 = ax.bar([i + w/2 for i in x], acc_vals, w, label=\"Accuracy\", color=\"coral\")\n", + " ax.set_xticks(list(x)); ax.set_xticklabels(models, rotation=20, ha=\"right\")\n", + " ax.set_ylim(0, 1.05)\n", + " ax.set_ylabel(\"Score\")\n", + " ax.set_title(f\"{title} — Model Comparison (Test)\", fontsize=13)\n", + " ax.legend()\n", + " for bar in bars1:\n", + " ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n", + " f\"{bar.get_height():.3f}\", ha=\"center\", fontsize=8)\n", + " for bar in bars2:\n", + " ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n", + " f\"{bar.get_height():.3f}\", ha=\"center\", fontsize=8)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(REPORTS_DIR / \"model_comparison_chart.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/reports/model_comparison_chart.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GC0rNp0auKCF" + }, + "source": [ + "## 3. Error Analysis — Binary Task" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jokvmFh9uKCF" + }, + "outputs": [], + "source": [ + "def load_predictions(path: Path) -> pd.DataFrame | None:\n", + " if path.exists():\n", + " return pd.read_csv(path)\n", + " print(f\" [WARNING] Not found: {path}\")\n", + " return None\n", + "\n", + "\n", + "# Use best available binary model predictions for error analysis\n", + "# Priority: BERT > TF-IDF+LR > NB\n", + "pred_paths_bin = [\n", + " (\"DistilBERT\", BERT_OUT / \"distilbert_binary\" / \"predictions_test.csv\"),\n", + " (\"TF-IDF + LR\", CLASSICAL / \"tfidf_lr\" / \"predictions_test_binary.csv\"),\n", + " (\"Naive Bayes\", CLASSICAL / \"naive_bayes\" / \"predictions_test_binary.csv\"),\n", + "]\n", + "\n", + "error_source_bin = None\n", + "error_model_bin = None\n", + "for model_name, path in pred_paths_bin:\n", + " df = load_predictions(path)\n", + " if df is not None:\n", + " error_source_bin = df\n", + " error_model_bin = model_name\n", + " print(f\"Using {model_name} predictions for binary error analysis\")\n", + " break\n", + "\n", + "if error_source_bin is None:\n", + " print(\"No binary predictions found. Run at least one model first.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0qFoK11OuKCF" + }, + "outputs": [], + "source": [ + "if error_source_bin is not None:\n", + " pred_col = \"predicted\" if \"predicted\" in error_source_bin.columns else \"predicted_id\"\n", + " true_col = \"binary_label\"\n", + " err_bin = error_source_bin[error_source_bin[\"correct\"] == 0].copy()\n", + "\n", + " # Categorize\n", + " fp_bin = err_bin[err_bin[true_col] == 0].copy() # False Positives (predicted sarcastic)\n", + " fn_bin = err_bin[err_bin[true_col] == 1].copy() # False Negatives (predicted not-sarcastic)\n", + "\n", + " print(f\"Total binary test errors: {len(err_bin)}\")\n", + " print(f\" False Positives (non-sarcastic predicted as sarcastic) : {len(fp_bin)}\")\n", + " print(f\" False Negatives (sarcastic predicted as non-sarcastic) : {len(fn_bin)}\")\n", + "\n", + " # Select 20+ errors: mix of FP and FN\n", + " n_each = 12\n", + " sample_fp = fp_bin.head(n_each)\n", + " sample_fn = fn_bin.head(n_each)\n", + " error_sample_bin = pd.concat([sample_fp, sample_fn], ignore_index=True)\n", + " error_sample_bin[\"error_type\"] = [\"FP\"] * len(sample_fp) + [\"FN\"] * len(sample_fn)\n", + "\n", + " print(f\"\\nError sample size: {len(error_sample_bin)} examples\")\n", + " print(\"\\n--- False Positives (non-sarcastic → predicted sarcastic) ---\")\n", + " for _, row in sample_fp.iterrows():\n", + " print(f\" {row['text']}\")\n", + "\n", + " print(\"\\n--- False Negatives (sarcastic → predicted non-sarcastic) ---\")\n", + " for _, row in sample_fn.iterrows():\n", + " print(f\" {row['text']}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "gAh7r-JhuKCF" + }, + "outputs": [], + "source": [ + "if error_source_bin is not None:\n", + " # Failure mode categorization (heuristic rules)\n", + " def categorize_binary_error(text: str, error_type: str) -> str:\n", + " text_lower = text.lower()\n", + " if \"?\" in text and error_type == \"FP\":\n", + " return \"rhetorical_phrasing\"\n", + " if any(w in text_lower for w in [\"report\", \"study\", \"survey\", \"finds\", \"shows\"]):\n", + " return \"neutral_reporting_style_confused\"\n", + " if any(w in text_lower for w in [\"best\", \"great\", \"amazing\", \"wonderful\", \"perfect\"]):\n", + " return \"positive_framing_confused\"\n", + " if \"onion\" in text_lower:\n", + " return \"domain_leak\"\n", + " if len(text.split()) <= 5:\n", + " return \"very_short_text\"\n", + " return \"lexical_ambiguity\"\n", + "\n", + " error_sample_bin[\"failure_mode\"] = error_sample_bin.apply(\n", + " lambda r: categorize_binary_error(r[\"text\"], r[\"error_type\"]), axis=1\n", + " )\n", + "\n", + " print(\"Failure mode distribution:\")\n", + " print(error_sample_bin[\"failure_mode\"].value_counts())\n", + "\n", + " # Save\n", + " error_sample_bin.to_csv(REPORTS_DIR / \"error_examples_binary.csv\", index=False)\n", + " print(\"\\nSaved: outputs/reports/error_examples_binary.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3Ybue8H2uKCF" + }, + "source": [ + "## 4. Error Analysis — Type Task" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-yBp16Z6uKCG" + }, + "outputs": [], + "source": [ + "pred_paths_type = [\n", + " (\"DistilBERT\", BERT_OUT / \"distilbert_type\" / \"predictions_test.csv\"),\n", + " (\"TF-IDF + LR\", CLASSICAL / \"tfidf_lr\" / \"predictions_test_type.csv\"),\n", + " (\"Naive Bayes\", CLASSICAL / \"naive_bayes\" / \"predictions_test_type.csv\"),\n", + "]\n", + "\n", + "error_source_type = None\n", + "error_model_type = None\n", + "for model_name, path in pred_paths_type:\n", + " df = load_predictions(path)\n", + " if df is not None:\n", + " error_source_type = df\n", + " error_model_type = model_name\n", + " print(f\"Using {model_name} predictions for type error analysis\")\n", + " break\n", + "\n", + "if error_source_type is None:\n", + " print(\"No type predictions found.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "r21XN805uKCG" + }, + "outputs": [], + "source": [ + "if error_source_type is not None:\n", + " err_type = error_source_type[error_source_type[\"correct\"] == 0].copy()\n", + "\n", + " # Identify label columns\n", + " true_col_type = \"type_label\"\n", + " pred_col_type = \"predicted_label\" if \"predicted_label\" in err_type.columns else \"predicted\"\n", + "\n", + " print(f\"Total type errors: {len(err_type)}\")\n", + " print(\"\\nTop confusion pairs (true → predicted):\")\n", + " if pred_col_type in err_type.columns:\n", + " pairs = err_type.groupby([true_col_type, pred_col_type]).size().sort_values(ascending=False).head(15)\n", + " print(pairs.to_string())\n", + "\n", + " # Sample 20+ errors\n", + " error_sample_type = err_type.sample(min(25, len(err_type)), random_state=42)\n", + "\n", + " print(f\"\\nError sample size: {len(error_sample_type)} examples\")\n", + " print(\"\\n--- Sample type misclassifications ---\")\n", + " display_cols = [\"text\", true_col_type]\n", + " if pred_col_type in error_sample_type.columns:\n", + " display_cols.append(pred_col_type)\n", + " for _, row in error_sample_type.head(15).iterrows():\n", + " true_l = row[true_col_type]\n", + " pred_l = row.get(pred_col_type, \"?\")\n", + " print(f\" [True={true_l:20s} Pred={pred_l:20s}] {row['text'][:90]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IzgDVYhfuKCG" + }, + "outputs": [], + "source": [ + "if error_source_type is not None:\n", + " def categorize_type_error(text: str, true_label: str, pred_label: str) -> str:\n", + " \"\"\"Heuristic failure mode categorization for type task.\"\"\"\n", + " text_lower = text.lower()\n", + " # Strategy overlap: sarcasm ↔ irony ↔ satire are frequently confused\n", + " overlap_pairs = {\n", + " frozenset({\"sarcasm\", \"irony\"}),\n", + " frozenset({\"sarcasm\", \"satire\"}),\n", + " frozenset({\"irony\", \"satire\"}),\n", + " }\n", + " if frozenset({true_label, pred_label}) in overlap_pairs:\n", + " return \"strategy_semantic_overlap\"\n", + " if \"rhetorical_question\" in [true_label, pred_label]:\n", + " if \"?\" in text:\n", + " return \"rhetorical_vs_other_with_question\"\n", + " return \"rhetorical_without_question_mark\"\n", + " if true_label in [\"overstatement\", \"understatement\"] and pred_label in [\"sarcasm\", \"irony\"]:\n", + " return \"scale_confusion_with_sarcasm\"\n", + " if \"report\" in text_lower or \"according\" in text_lower:\n", + " return \"generation_artifact_formal_phrasing\"\n", + " return \"lexical_ambiguity\"\n", + "\n", + " if pred_col_type in error_sample_type.columns:\n", + " error_sample_type[\"failure_mode\"] = error_sample_type.apply(\n", + " lambda r: categorize_type_error(r[\"text\"], r[true_col_type], r[pred_col_type]),\n", + " axis=1\n", + " )\n", + " print(\"Failure mode distribution:\")\n", + " print(error_sample_type[\"failure_mode\"].value_counts())\n", + "\n", + " error_sample_type.to_csv(REPORTS_DIR / \"error_examples_type.csv\", index=False)\n", + " print(\"\\nSaved: outputs/reports/error_examples_type.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NEmnBPTtuKCG" + }, + "source": [ + "## 5. Generate Final Reports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xH7naLCouKCG" + }, + "outputs": [], + "source": [ + "def fmt(v) -> str:\n", + " \"\"\"Format a metric value for markdown.\"\"\"\n", + " if v is None:\n", + " return \"N/A\"\n", + " return f\"{v:.4f}\"\n", + "\n", + "\n", + "def build_model_comparison_md() -> str:\n", + " lines = []\n", + " lines.append(\"# Model Comparison Report\")\n", + " lines.append(\"\")\n", + " lines.append(\"> Auto-generated from outputs of notebooks 02–04.\")\n", + " lines.append(\"\")\n", + "\n", + " # Binary task table\n", + " lines.append(\"## Task A — Binary Classification (Test Set)\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Model | Accuracy | Precision (M) | Recall (M) | F1 (Macro) | F1 (Weighted) |\")\n", + " lines.append(\"|-------|----------|--------------|------------|------------|--------------|\")\n", + " for name, m in model_map_bin:\n", + " r = extract_test_metrics(m)\n", + " lines.append(\n", + " f\"| {name} | {fmt(r['accuracy'])} | {fmt(r['precision_macro'])} | \"\n", + " f\"{fmt(r['recall_macro'])} | {fmt(r['f1_macro'])} | {fmt(r['f1_weighted'])} |\"\n", + " )\n", + " lines.append(\"\")\n", + " lines.append(\"_Primary metric: Macro-F1. Best value bolded (manually after review)._\")\n", + " lines.append(\"\")\n", + "\n", + " # Type task table\n", + " lines.append(\"## Task B — Sarcasm Type Classification (Test Set)\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Model | Accuracy | Precision (M) | Recall (M) | F1 (Macro) | F1 (Weighted) |\")\n", + " lines.append(\"|-------|----------|--------------|------------|------------|--------------|\")\n", + " for name, m in model_map_type:\n", + " r = extract_test_metrics(m)\n", + " lines.append(\n", + " f\"| {name} | {fmt(r['accuracy'])} | {fmt(r['precision_macro'])} | \"\n", + " f\"{fmt(r['recall_macro'])} | {fmt(r['f1_macro'])} | {fmt(r['f1_weighted'])} |\"\n", + " )\n", + " lines.append(\"\")\n", + "\n", + " # Recommendation\n", + " lines.append(\"## Recommendation\")\n", + " lines.append(\"\")\n", + "\n", + " # Find best binary model\n", + " best_bin_name, best_bin_f1 = \"N/A\", -1\n", + " for name, m in model_map_bin:\n", + " r = extract_test_metrics(m)\n", + " if r[\"f1_macro\"] is not None and r[\"f1_macro\"] > best_bin_f1:\n", + " best_bin_f1 = r[\"f1_macro\"]\n", + " best_bin_name = name\n", + "\n", + " best_type_name, best_type_f1 = \"N/A\", -1\n", + " for name, m in model_map_type:\n", + " r = extract_test_metrics(m)\n", + " if r[\"f1_macro\"] is not None and r[\"f1_macro\"] > best_type_f1:\n", + " best_type_f1 = r[\"f1_macro\"]\n", + " best_type_name = name\n", + "\n", + " lines.append(f\"### Best model for Binary Task: **{best_bin_name}** (Macro-F1 = {fmt(best_bin_f1)})\")\n", + " lines.append(\"\")\n", + " lines.append(f\"### Best model for Type Task: **{best_type_name}** (Macro-F1 = {fmt(best_type_f1)})\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Trade-offs\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Aspect | TF-IDF + LR | Naive Bayes | DistilBERT |\")\n", + " lines.append(\"|--------|------------|-------------|-----------|\")\n", + " lines.append(\"| Training speed | Fast (minutes) | Very fast (seconds) | Slow (hours) |\")\n", + " lines.append(\"| Inference speed | Very fast | Very fast | Moderate |\")\n", + " lines.append(\"| Interpretability | High (feature weights) | High (log-probs) | Low (black-box) |\")\n", + " lines.append(\"| Handles context | No | No | Yes (self-attention) |\")\n", + " lines.append(\"| Handles rare words | Via TF-IDF | Via smoothing | Via sub-word tokenization |\")\n", + " lines.append(\"| GPU required | No | No | Recommended |\")\n", + " lines.append(\"\")\n", + " lines.append(\"**Deployment recommendation**: Use TF-IDF+LR if real-time speed and interpretability are priorities.\")\n", + " lines.append(\"Use DistilBERT for maximum accuracy when GPU inference is available.\")\n", + "\n", + " return \"\\n\".join(lines)\n", + "\n", + "\n", + "comparison_md = build_model_comparison_md()\n", + "with open(REPORTS_DIR / \"model_comparison.md\", \"w\", encoding=\"utf-8\") as f:\n", + " f.write(comparison_md)\n", + "\n", + "print(\"Saved: outputs/reports/model_comparison.md\")\n", + "print(\"\\nPreview:\")\n", + "print(comparison_md[:2000])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "CmgZF4S1uKCH" + }, + "outputs": [], + "source": [ + "def build_error_analysis_md() -> str:\n", + " lines = []\n", + " lines.append(\"# Error Analysis Report\")\n", + " lines.append(\"\")\n", + " lines.append(\"> Auto-generated from model predictions. Examples drawn from test set.\")\n", + " lines.append(\"\")\n", + "\n", + " # ── Binary ────────────────────────────────────────────────────────────────\n", + " lines.append(\"## Task A — Binary Classification Errors\")\n", + " lines.append(\"\")\n", + "\n", + " if error_source_bin is not None:\n", + " err_df = error_source_bin[error_source_bin[\"correct\"] == 0]\n", + " n_fp = len(err_df[err_df[\"binary_label\"] == 0])\n", + " n_fn = len(err_df[err_df[\"binary_label\"] == 1])\n", + " lines.append(f\"**Model**: {error_model_bin} | **Total errors**: {len(err_df):,} | FP: {n_fp:,} | FN: {n_fn:,}\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Failure Mode Summary\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Failure Mode | Description |\")\n", + " lines.append(\"|-------------|-------------|\")\n", + " lines.append(\"| Lexical ambiguity | Sarcasm/irony requires pragmatic context beyond lexical cues |\")\n", + " lines.append(\"| Neutral reporting style confused | Formal generated text mimics neutral news, but still classified as sarcastic |\")\n", + " lines.append(\"| Positive framing confused | Genuine positive news shares superlative vocabulary with ironic praise |\")\n", + " lines.append(\"| Rhetorical phrasing | Questions that look rhetorical but are literal |\")\n", + " lines.append(\"| Very short text | Too little context for confident classification |\")\n", + " lines.append(\"\")\n", + " lines.append(\"### False Positive Examples (Non-sarcastic predicted as sarcastic)\")\n", + " lines.append(\"\")\n", + " fp_examples = error_source_bin[\n", + " (error_source_bin[\"correct\"] == 0) & (error_source_bin[\"binary_label\"] == 0)\n", + " ].head(10)\n", + " for _, row in fp_examples.iterrows():\n", + " lines.append(f\"- `{row['text']}`\")\n", + " lines.append(\"\")\n", + " lines.append(\"### False Negative Examples (Sarcastic predicted as non-sarcastic)\")\n", + " lines.append(\"\")\n", + " fn_examples = error_source_bin[\n", + " (error_source_bin[\"correct\"] == 0) & (error_source_bin[\"binary_label\"] == 1)\n", + " ].head(10)\n", + " for _, row in fn_examples.iterrows():\n", + " lines.append(f\"- `{row['text']}`\")\n", + " else:\n", + " lines.append(\"_Binary predictions not available. Run at least one model._\")\n", + "\n", + " lines.append(\"\")\n", + "\n", + " # ── Type ──────────────────────────────────────────────────────────────────\n", + " lines.append(\"## Task B — Sarcasm Type Classification Errors\")\n", + " lines.append(\"\")\n", + "\n", + " if error_source_type is not None:\n", + " err_df_t = error_source_type[error_source_type[\"correct\"] == 0]\n", + " lines.append(f\"**Model**: {error_model_type} | **Total errors**: {len(err_df_t):,}\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Failure Mode Summary\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Failure Mode | Description |\")\n", + " lines.append(\"|-------------|-------------|\")\n", + " lines.append(\"| Strategy semantic overlap | sarcasm/irony/satire share similar surface forms; labels conflated |\")\n", + " lines.append(\"| Scale confusion | overstatement/understatement confused with sarcasm due to exaggeration cues |\")\n", + " lines.append(\"| Rhetorical vs. other | rhetorical_question confused with irony/sarcasm when ? is absent |\")\n", + " lines.append(\"| Generation artifact | formal paraphrase style shifts text away from original strategy markers |\")\n", + " lines.append(\"| Lexical ambiguity | strategy relies on world knowledge rather than surface vocabulary |\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Key Insight\")\n", + " lines.append(\"\")\n", + " lines.append(\"The primary failure mode is **strategy semantic overlap**: sarcasm, irony, and satire\")\n", + " lines.append(\"are conceptually related and frequently share similar linguistic surface patterns.\")\n", + " lines.append(\"The label `sarcasm` as a strategy within a sarcasm dataset introduces circularity.\")\n", + " lines.append(\"The `rhetorical_question` class is syntactically distinctive (ends with ?) and should\")\n", + " lines.append(\"be easier to classify; errors in this class suggest the classifier may ignore punctuation.\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Sample Misclassifications\")\n", + " lines.append(\"\")\n", + " sample_t = error_source_type[error_source_type[\"correct\"] == 0].head(20)\n", + " true_c = \"type_label\"\n", + " pred_c = \"predicted_label\" if \"predicted_label\" in sample_t.columns else \"predicted\"\n", + " for _, row in sample_t.iterrows():\n", + " true_l = row[true_c]\n", + " pred_l = row.get(pred_c, \"?\")\n", + " lines.append(f\"- **True**: {true_l} | **Pred**: {pred_l} | `{row['text'][:100]}`\")\n", + " else:\n", + " lines.append(\"_Type predictions not available. Run at least one model._\")\n", + "\n", + " lines.append(\"\")\n", + " lines.append(\"## Summary of Observations\")\n", + " lines.append(\"\")\n", + " lines.append(\"1. **Binary task** is relatively tractable — TheOnion writing style has strong lexical signatures\")\n", + " lines.append(\"2. **Generated headlines** (is_generated=1) may fool classifiers trained mainly on original text\")\n", + " lines.append(\"3. **Type task** is fundamentally harder because strategies are not mutually exclusive\")\n", + " lines.append(\"4. **Class imbalance** (rhetorical_question = 3.7%) is a significant challenge\")\n", + " lines.append(\"5. **BERT** should better capture contextual incongruence; classical models rely on surface vocabulary\")\n", + "\n", + " return \"\\n\".join(lines)\n", + "\n", + "\n", + "error_md = build_error_analysis_md()\n", + "with open(REPORTS_DIR / \"error_analysis.md\", \"w\", encoding=\"utf-8\") as f:\n", + " f.write(error_md)\n", + "\n", + "print(\"Saved: outputs/reports/error_analysis.md\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fdUb27BQuKCH" + }, + "outputs": [], + "source": [ + "print(\"====== ERROR ANALYSIS COMPLETE ======\")\n", + "print()\n", + "print(\"Saved artifacts:\")\n", + "for p in sorted(REPORTS_DIR.iterdir()):\n", + " print(f\" {p.relative_to(ROOT)}\")\n", + "print()\n", + "print(\"Pipeline complete. See outputs/reports/model_comparison.md for final results.\")" + ] + } + ] +} \ No newline at end of file diff --git a/colab_package/sarcasm_pairs_step35_clean.jsonl b/colab_package/sarcasm_pairs_step35_clean.jsonl new file mode 100644 index 0000000..d537be4 --- /dev/null +++ b/colab_package/sarcasm_pairs_step35_clean.jsonl @@ -0,0 +1,28333 @@ +{"original_headline": "thirtysomething scientists unveil doomsday clock of hair loss", "generated_headline": "Scientists present research on hair loss using a metaphorical doomsday clock.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/thirtysomething-scientists-unveil-doomsday-clock-of-hai-1819586205"} +{"original_headline": "inclement weather prevents liar from getting to work", "generated_headline": "Inclement weather causes transportation disruptions for commuters.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/inclement-weather-prevents-liar-from-getting-to-work-1819576031"} +{"original_headline": "mother comes pretty close to using word 'streaming' correctly", "generated_headline": "A mother attempts to use the term 'streaming' but misapplies it.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/mother-comes-pretty-close-to-using-word-streaming-cor-1819575546"} +{"original_headline": "richard branson's global-warming donation nearly as much as cost of failed balloon trips", "generated_headline": "Richard Branson's donation to global warming causes is small relative to his spending on other ventures.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/richard-bransons-global-warming-donation-nearly-as-much-1819568749"} +{"original_headline": "shadow government getting too large to meet in marriott conference room b", "generated_headline": "Conspiracy theorists claim a secret government group has outgrown its meeting spaces.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/shadow-government-getting-too-large-to-meet-in-marriott-1819570731"} +{"original_headline": "ford develops new suv that runs purely on gasoline", "generated_headline": "Ford announces a new SUV model powered by traditional gasoline engines.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/ford-develops-new-suv-that-runs-purely-on-gasoline-1819575454"} +{"original_headline": "area boy enters jumping-and-touching-tops-of-doorways phase", "generated_headline": "A young boy frequently jumps and touches the tops of doorways during play.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/area-boy-enters-jumping-and-touching-tops-of-doorways-p-1819570282"} +{"original_headline": "area man does most of his traveling by gurney", "generated_headline": "A man uses medical gurneys as primary transportation due to health issues.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/area-man-does-most-of-his-traveling-by-gurney-1819588424"} +{"original_headline": "guard in video game under strict orders to repeatedly pace same stretch of hallway", "generated_headline": "A video game features a guard character with a repetitive patrol route in a hallway.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/guard-in-video-game-under-strict-orders-to-repeatedly-p-1819577045"} +{"original_headline": "secret service agent not so secret about being david alan grier fan", "generated_headline": "A Secret Service agent publicly shares his fandom for David Alan Grier.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/secret-service-agent-not-so-secret-about-being-david-al-1819568489"} +{"original_headline": "leading probability researchers confounded by three coworkers wearing same shirt color on same day", "generated_headline": "Probability researchers note a coincidence where three coworkers wear the same shirt color.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/leading-probability-researchers-confounded-by-three-cow-1822174747"} +{"original_headline": "new york introduces shoe-sharing program for city's pedestrians", "generated_headline": "New York City launches a shoe-sharing program to promote sustainability among pedestrians.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-york-introduces-shoe-sharing-program-for-city-s-ped-1819577633"} +{"original_headline": "expansive obama state of the union speech to touch on patent law, entomology, the films of robert altman", "generated_headline": "President Obama's State of the Union address includes topics like patent law and entomology.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/expansive-obama-state-of-the-union-speech-to-touch-on-p-1819574546"} +{"original_headline": "naacp demands less minority representation on upn", "generated_headline": "The NAACP advocates for greater minority representation on UPN.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/naacp-demands-less-minority-representation-on-upn-1819565571"} +{"original_headline": "new history textbook makes hatred of history come alive for students", "generated_headline": "A new history textbook is criticized for fostering dislike for the subject among students.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-history-textbook-makes-hatred-of-history-come-alive-1819586624"} +{"original_headline": "report: bridge probably has whole mess of bats under there", "generated_headline": "A report indicates that a bridge may be inhabited by a large number of bats.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/report-bridge-probably-has-whole-mess-of-bats-under-th-1819655137"} +{"original_headline": "god getting strong urge to bring back dinosaurs", "generated_headline": "Humorous or speculative discussions occasionally arise about reviving extinct species like dinosaurs.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/god-getting-strong-urge-to-bring-back-dinosaurs-1819579692"} +{"original_headline": "handshake comes in at unusually high angle, velocity", "generated_headline": "A handshake is observed to have an unusual angle and speed.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/handshake-comes-in-at-unusually-high-angle-velocity-1819573028"} +{"original_headline": "mom keeps sending newspaper clippings about former classmates who have been murdered", "generated_headline": "A mother persistently sends her child newspaper clippings about former classmates who have been murdered.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/mom-keeps-sending-newspaper-clippings-about-former-clas-1819576337"} +{"original_headline": "report: make it stop", "generated_headline": "The report concludes with an urgent plea to address ongoing issues.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/report-make-it-stop-1822874962"} +{"original_headline": "coed rec softball team having trouble finding enough hyper-competitive men to ruin experience", "generated_headline": "A coed recreational softball team struggles to find men who balance competitiveness with fun.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/coed-rec-softball-team-having-trouble-finding-enough-hy-1828226290"} +{"original_headline": "brutalist beaver 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"https://www.theonion.com/fender-releases-new-hybrid-gas-electric-guitar-1819592762"} +{"original_headline": "romney volunteers going door-to-door to let obama supporters know president's dead", "generated_headline": "In a satirical scenario, political volunteers spread false claims about the president's death.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/romney-volunteers-going-door-to-door-to-let-obama-suppo-1819574145"} +{"original_headline": "hollywood's biggest stars endure long lines at oscars security screening", "generated_headline": "Celebrities attending the Oscars face delays at security checkpoints like other attendees.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/hollywoods-biggest-stars-endure-long-lines-at-oscars-se-1819592089"} +{"original_headline": "longtime 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"stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/israel-passes-law-cementing-itself-as-exclusive-nation-1828058658"} +{"original_headline": "onion social cracks down on sexual harassment by banning all women from platform", "generated_headline": "Onion Social implements a policy banning all women from its platform to address sexual harassment, sparking controversy.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/onion-social-cracks-down-on-sexual-harassment-by-bannin-1826973222"} +{"original_headline": "drunk driver in the zone", "generated_headline": "A drunk driver was operating a vehicle, demonstrating impaired control and posing a risk to others.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/drunk-driver-in-the-zone-1819568966"} +{"original_headline": "egyptian woman 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"groundbreaking study finds gratification can be deliberately postponed", "generated_headline": "Research confirms that individuals can intentionally delay experiencing pleasure or satisfaction.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/groundbreaking-study-finds-gratification-can-be-deliber-1819578388"} +{"original_headline": "report: 70% of trump endorsements made after staring at bedroom ceiling for 4 hours", "generated_headline": "A study claims that a significant portion of endorsements for Trump occur after extended periods of indecision, such as lying awake at night.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/report-70-of-trump-endorsements-made-after-staring-at-1819578914"} +{"original_headline": "produce section bursts into laughter after will ferrell makes casual remark about 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"stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/local-mans-fear-of-snakes-increases-with-each-snakebite-1819568467"} +{"original_headline": "vespa corporation enchants another slight little man-child", "generated_headline": "Vespa motorcycles attract a customer base that includes many young, slender men.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/vespa-corporation-enchants-another-slight-little-man-ch-1819589603"} +{"original_headline": "standards lowered for second search through fridge", "generated_headline": "During a second search of the refrigerator, people often accept lower-quality food options than during the first search.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/standards-lowered-for-second-search-through-fridge-1819592312"} +{"original_headline": "man in international airport only speaks business", "generated_headline": "A man at an international airport communicates using terminology common in business contexts.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/man-in-international-airport-only-speaks-business-1819567472"} +{"original_headline": "area mother enters 16th year of post-partum depression", "generated_headline": "A mother has experienced depressive symptoms for 16 years after giving birth, indicating chronic mental health challenges.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/area-mother-enters-16th-year-of-post-partum-depression-1819586496"} +{"original_headline": "historians suggest 'goodfellas' youtube clips may be fragments of larger work", "generated_headline": "Scholars propose that short clips from the film 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"https://local.theonion.com/cocksucker-beats-up-motherfucker-1819567714"} +{"original_headline": "wheelchair-bound student would have preferred to sit out pep rally", "generated_headline": "A student who uses a wheelchair indicated a preference to not participate in a pep rally, possibly due to accessibility concerns.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/wheelchair-bound-student-would-have-preferred-to-sit-ou-1819567312"} +{"original_headline": "nation checks out cnn.com to see what their old pals the tsarnaevs and castros are up to", "generated_headline": "Members of the public visited CNN.com to monitor news about the Tsarnaev and Castro families, who were previously in the headlines.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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"members of opening band walking among crowd during intermission like gods among men", "generated_headline": "The opening band members walked through the audience during intermission, experiencing a sense of heightened status.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/members-of-opening-band-walking-among-crowd-during-inte-1819575375"} +{"original_headline": "shy friend experimenting with personality", "generated_headline": "A friend who is typically reserved is exploring different facets of their personality.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/shy-friend-experimenting-with-personality-1819567585"} +{"original_headline": "jay inslee smashes through wall of town hall in solar-powered mech suit to announce climate change plan", "generated_headline": "Governor Jay 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"article_link": "https://local.theonion.com/70-year-old-woman-decides-it-time-to-start-dressing-ent-1824210460"} +{"original_headline": "woman in commercial doing yoga to narration of drug's fatal side effects", "generated_headline": "In a commercial, a woman practices yoga while a voiceover describes the drug's fatal side effects.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/woman-in-commercial-doing-yoga-to-narration-of-drugs-fa-1823039508"} +{"original_headline": "zoo posting hourly updates on aphid about to give birth", "generated_headline": "The zoo is providing frequent updates on an aphid that is about to give birth.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/zoo-posting-hourly-updates-on-aphid-about-to-give-birth-1819579896"} +{"original_headline": "dave matthews band apologizes after 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"https://www.theonion.com/struggling-media-company-almost-desperate-enough-to-hir-1819579535"} +{"original_headline": "shipwreck survivors forced to endure ride home on disney cruise ship", "generated_headline": "Shipwreck survivors had to travel home on a Disney cruise ship.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/shipwreck-survivors-forced-to-endure-ride-home-on-disne-1819566855"} +{"original_headline": "census finds enough homeless people living in public library to warrant congressional district", "generated_headline": "A census found that the number of homeless people living in a public library is sufficient to warrant a congressional district.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/census-finds-enough-homeless-people-living-in-public-li-1819571996"} +{"original_headline": "report: 1 in 5 air ducts contains person looking, listening in on you", "generated_headline": "A report claims that 20% of air ducts contain individuals who are spying on occupants.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/report-1-in-5-air-ducts-contains-person-looking-liste-1819591264"} +{"original_headline": "hillary clinton suspended 3 weeks by fec for spitting on volunteer", "generated_headline": "Hillary Clinton was suspended for three weeks by the FEC for spitting on a volunteer.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/hillary-clinton-suspended-3-weeks-by-fec-for-spitting-o-1819577997"} +{"original_headline": "officials warn consumers of counterfeit tickets ahead of solar eclipse", "generated_headline": "Officials are warning consumers about counterfeit tickets for the 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"https://www.theonion.com/report-50-of-heaven-s-population-just-assholes-who-be-1819579298"} +{"original_headline": "'elle' magazine accidentally airbrushes naomi watts out of cover altogether", "generated_headline": "Elle magazine accidentally removed Naomi Watts from the cover entirely.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/elle-magazine-accidentally-airbrushes-naomi-watts-out-1819592220"} +{"original_headline": "international space station tented to spray for xenomorphs", "generated_headline": "The International Space Station is being treated to spray for xenomorphs.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/international-space-station-tented-to-spray-for-xenomor-1826362305"} +{"original_headline": "passengers praying uber just a hobby for elderly driver", "generated_headline": "Passengers hope that the elderly Uber driver's job is just a hobby.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/passengers-praying-uber-just-a-hobby-for-elderly-driver-1823193359"} +{"original_headline": "couple just wants small ceremony in public park with close friends and shirtless stranger hanging around tree", "generated_headline": "A couple plans a small ceremony in a public park with close friends, but a shirtless stranger is hanging around a tree.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/couple-just-wants-small-ceremony-in-public-park-with-cl-1826011098"} +{"original_headline": "terrified fda warns something making bananas black after several days", "generated_headline": "The FDA warns that something is causing bananas to turn black after several days.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/terrified-fda-warns-something-making-bananas-black-afte-1819571939"} +{"original_headline": "teddy bear feels terrible for sparking 'what are we?' conversation", "generated_headline": "A teddy bear is blamed for initiating a conversation about relationship status.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/teddy-bear-feels-terrible-for-sparking-what-are-we-c-1823003040"} +{"original_headline": "man flirting with girl at party can't wait to be informed she has boyfriend", "generated_headline": "A man flirting with a woman at a party expects her to tell him she has a boyfriend.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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"https://politics.theonion.com/desperate-gop-spotted-in-south-dakota-trying-to-build-k-1819577228"} +{"original_headline": "woman comforting friend just going to throw compliments against wall and see what sticks", "generated_headline": "A woman comforting her friend uses generic compliments in the hope that some will be effective.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/woman-comforting-friend-just-going-to-throw-compliments-1819577223"} +{"original_headline": "u.s. mint introduces new double-stuf quarters", "generated_headline": "The U.S. Mint introduces a new quarter with a design referencing 'double-stuf' from Oreo cookies.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/u-s-mint-introduces-new-double-stuf-quarters-1819592058"} +{"original_headline": "report: whoa, last person on treadmill ran 8 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"model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/paramedics-didn-t-realize-how-hard-it-would-be-to-cut-d-1830132529"} +{"original_headline": "rnc taps dennis hastert to lead new youth outreach program", "generated_headline": "The RNC appoints Dennis Hastert to lead a new youth outreach program, despite his past controversies.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/rnc-taps-dennis-hastert-to-lead-new-youth-outreach-prog-1821055507"} +{"original_headline": "money spent for old time's sake", "generated_headline": "Money is spent for nostalgic reasons.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/money-spent-for-old-times-sake-1819571481"} +{"original_headline": "anderson cooper throws another box of letters from gay children into dumpster", "generated_headline": "Anderson Cooper is reported to have discarded a box of letters from gay children.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/anderson-cooper-throws-another-box-of-letters-from-gay-1819574743"} +{"original_headline": "toddler unsettled by whatever possessed her to bite friend's face", "generated_headline": "A toddler is disturbed by her own action of biting her friend's face.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/toddler-unsettled-by-whatever-possessed-her-to-bite-fri-1819578006"} +{"original_headline": "kurt warner cheered on by wire-haired man-goblin", "generated_headline": "Kurt Warner is cheered on by a fan described as having a wiry, unusual appearance.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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"https://www.theonion.com/new-monster-energy-defibrillator-touts-1-200-volts-deli-1825951753"} +{"original_headline": "genetics emphatically deny playing any part in area man's body", "generated_headline": "Experts in genetics assert that genetic factors did not contribute to a local man's physical condition.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/genetics-emphatically-deny-playing-any-part-in-area-man-1819577040"} +{"original_headline": "accidentally closing browser window with 23 tabs open presents rare chance at new life", "generated_headline": "Accidentally closing a browser window with 23 tabs open provides an uncommon opportunity for a fresh start.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/accidentally-closing-browser-window-with-23-tabs-open-p-1819579454"} 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+{"original_headline": "red lobster criticized for decimating biscuit populations along cheddar bay", "generated_headline": "Red Lobster is facing criticism for reducing the availability of biscuits in Cheddar Bay.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/red-lobster-criticized-for-decimating-biscuit-populatio-1819716497"} +{"original_headline": "nation savoring every moment of glorious late-february, early-march days", "generated_headline": "People are enjoying the weather during the late February and early March period.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/nation-savoring-every-moment-of-glorious-late-february-1819572399"} +{"original_headline": "man passes away surrounded by knife-wielding loved ones", "generated_headline": "A man died while surrounded by family members who were carrying 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"model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/bleary-eyed-cosmopolitan-staffer-cranks-out-10-billiont-1819564993"} +{"original_headline": "shanghai family sick of eating chinese", "generated_headline": "A family in Shanghai is tired of eating Chinese cuisine.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/shanghai-family-sick-of-eating-chinese-1819586487"} +{"original_headline": "trump agrees to wear wire to take down roger stone", "generated_headline": "Donald Trump has agreed to wear a listening device to gather evidence against Roger Stone.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/trump-agrees-to-wear-wire-to-take-down-roger-stone-1832759793"} +{"original_headline": "experts warn transitioning too quickly from work to vacation could cause decompression 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"sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/god-admits-stealing-idea-for-messiah-from-zoroastrianis-1819578915"} +{"original_headline": "internet to reduce e-mail delivery to 6 days a week", "generated_headline": "There are proposals to reduce email delivery to six days a week due to service limitations.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/internet-to-reduce-e-mail-delivery-to-6-days-a-week-1819570618"} +{"original_headline": "cher back", "generated_headline": "Cher has announced her return to public appearances or performances.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/cher-back-1819590091"} +{"original_headline": "encouraging report from radical extremist think tank finds america no safer since 9/11", 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"https://local.theonion.com/rare-autographed-portrait-of-jesus-purchased-at-estate-1819571907"} +{"original_headline": "netanyahu assures critics he still has utmost respect for u.s. money", "generated_headline": "Netanyahu states that he holds deep respect for U.S. financial assistance despite criticisms.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/netanyahu-assures-critics-he-still-has-utmost-respect-f-1819577545"} +{"original_headline": "huckabee sanders warns stormy daniels' disclosures just steamy, sexy distraction from real issues", "generated_headline": "Huckabee Sanders cautions that Stormy Daniels' disclosures are a sensational distraction from substantive issues.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/huckabee-sanders-warns-stormy-daniels-disclosures-just-1823620557"} 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"https://politics.theonion.com/single-strip-of-i-voted-stickers-more-than-enough-for-1819591940"} +{"original_headline": "bill clinton agrees to disclose guacamole recipe", "generated_headline": "Bill Clinton agreed to share his guacamole recipe.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/bill-clinton-agrees-to-disclose-guacamole-recipe-1819570408"} +{"original_headline": "police release haircut-progressed photo of missing woman", "generated_headline": "Police released a photo showing the missing woman's current hairstyle.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/police-release-haircut-progressed-photo-of-missing-woma-1819577510"} +{"original_headline": "prince charles thinks boys are finally old enough to hear what happened to their mother", "generated_headline": "Prince Charles believes 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"victoria's secret also andrew's secret", "generated_headline": "Victoria's Secret is referred to as Andrew's Secret in some contexts.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/victorias-secret-also-andrews-secret-1819586778"} +{"original_headline": "miss america pageant adds sweatpants and messy bun competition", "generated_headline": "Miss America pageant includes a competition where contestants wear sweatpants and messy buns.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/miss-america-pageant-adds-sweatpants-and-messy-bun-comp-1826576766"} +{"original_headline": "amount of halloween candy collected down 15 percent", "generated_headline": "Data shows a 15 percent decrease in the amount of Halloween candy collected this year.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/amount-of-halloween-candy-collected-down-15-percent-1819567591"} +{"original_headline": "super priest can turn anything into body, blood of christ", "generated_headline": "A priest performs the ritual of transubstantiation, turning bread and wine into the body and blood of Christ.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/super-priest-can-turn-anything-into-body-blood-of-chri-1819568696"} +{"original_headline": "8-year-old can already tell image of dad puking stuck in memory forever", "generated_headline": "An 8-year-old child has a persistent memory of his father vomiting.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/8-year-old-can-already-tell-image-of-dad-puking-stuck-i-1819579639"} +{"original_headline": "hot girl mentions boyfriend three hours into conversation", "generated_headline": "An attractive woman mentions her boyfriend three hours into a conversation.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/hot-girl-mentions-boyfriend-three-hours-into-conversati-1819565172"} +{"original_headline": "lanthanum quits periodic table of elements", "generated_headline": "Lanthanum is under review for its classification in the periodic table.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/lanthanum-quits-periodic-table-of-elements-1819565642"} +{"original_headline": "area 5-year-old telling, area 5-year-old telling", "generated_headline": "A 5-year-old child is repeatedly telling a story.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/area-5-year-old-telling-area-5-year-old-telling-1819569705"} +{"original_headline": "aging mount st. helens starting to think erupting days are behind it", "generated_headline": "Mount St. Helens exhibits reduced volcanic activity, leading experts to believe it may be less active in the future.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/aging-mount-st-helens-starting-to-think-erupting-days-1833129845"} +{"original_headline": "conversation with boss puts man an hour behind", "generated_headline": "A meeting with his boss causes a man to fall behind schedule by one hour.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/conversation-with-boss-puts-man-an-hour-behind-1819565632"} +{"original_headline": "suspicious new wikileaks document dump exposes how awesome and 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"sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/john-bolton-warns-war-with-north-korea-won-t-be-cakewal-1824031861"} +{"original_headline": "grandma defiantly taking scone recipe to grave", "generated_headline": "Grandma insists on keeping her scone recipe secret even after her death.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/grandma-defiantly-taking-scone-recipe-to-grave-1825338510"} +{"original_headline": "japanese prime minister resigns to seek revenge on man who killed his family", "generated_headline": "Japanese Prime Minister resigns to deal with personal issues related to his family's murder.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/japanese-prime-minister-resigns-to-seek-revenge-on-man-1819588720"} 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"https://politics.theonion.com/biden-invokes-freedom-of-information-act-to-find-out-wh-1819570913"} +{"original_headline": "relationship based on mutual love of woodcrafts", "generated_headline": "The relationship is based on a shared interest in woodcrafts.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/relationship-based-on-mutual-love-of-woodcrafts-1819565595"} +{"original_headline": "serial killer annoyed by young murderers with no appreciation for albert fish", "generated_headline": "A serial killer expressed annoyance at younger murderers who lack appreciation for Albert Fish.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/serial-killer-annoyed-by-young-murderers-with-no-apprec-1825175154"} +{"original_headline": "relationship experts say mailing body part to ex on valentine's day only way to win 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"https://www.theonion.com/kinkos-patron-pulls-the-old-copy-key-switcheroo-1819565814"} +{"original_headline": "18-to-35 white, male demographic still searching for perfect way to quench its thirst", "generated_headline": "Men aged 18 to 35 are seeking the ideal beverage to satisfy their thirst.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/18-to-35-white-male-demographic-still-searching-for-pe-1824260818"} +{"original_headline": "dead daughter would have wanted $220 million liability settlement", "generated_headline": "A $220 million settlement is pursued based on the presumed wishes of the deceased daughter.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/dead-daughter-would-have-wanted-220-million-liability-1819573572"} +{"original_headline": "study: best method of finding job still excitedly circling 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"https://www.theonion.com/unemployed-miserable-man-still-remembers-teacher-who-f-1819576003"} +{"original_headline": "new, improved obamacare program released on 35 floppy disks", "generated_headline": "A new version of the Obamacare program is released on 35 floppy disks.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-improved-obamacare-program-released-on-35-floppy-d-1819575742"} +{"original_headline": "researchers forced to scrap another sleep study after participants murdered in dreams by serial killer", "generated_headline": "Researchers cancel a sleep study after participants reported nightmares involving a serial killer.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/researchers-forced-to-scrap-another-sleep-study-after-p-1819580394"} +{"original_headline": "'i was the one who slept 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"article_link": "https://www.theonion.com/15-years-in-environment-of-constant-fear-somehow-fails-1819576202"} +{"original_headline": "toddler at that cute age where anything can be projected on them", "generated_headline": "Toddlers are at an age where they can be easily influenced by various factors.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/toddler-at-that-cute-age-where-anything-can-be-projecte-1819580307"} +{"original_headline": "st. vincent to world's catholics: stop donating all this crap to me", "generated_headline": "St. Vincent requested that Catholics stop donating unwanted items.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/st-vincent-to-worlds-catholics-stop-donating-all-this-1819564499"} +{"original_headline": "empty wall behind couch falls into girlfriend's crosshairs", 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"'dsm-5' updated to accommodate man who is legitimately being ordered to kill by the moon", "generated_headline": "The DSM-5 has been updated to include a disorder for individuals who believe they are commanded to kill by the moon.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/dsm-5-updated-to-accommodate-man-who-is-legitimately-1819579090"} +{"original_headline": "area man relieved friend's short story sucks", "generated_headline": "A local man felt relieved when he discovered that his friend's short story was of poor quality.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/area-man-relieved-friends-short-story-sucks-1819573220"} +{"original_headline": "historical archives: secret society of free-bakers has fail'd to gain influence", "generated_headline": "Historical archives show that a secret society 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"https://local.theonion.com/7-total-randos-found-dead-1832330983"} +{"original_headline": "'i want to be with someone else,' says woman who must think 3-time hyundai sales leaders grow on trees", "generated_headline": "A woman says she wants to be with someone else, despite her partner being a top Hyundai sales leader.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/i-want-to-be-with-someone-else-says-woman-who-must-t-1819576478"} +{"original_headline": "philip morris lawyers deny cigarettes are cylindrical", "generated_headline": "Philip Morris lawyers deny that cigarettes are cylindrical.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/philip-morris-lawyers-deny-cigarettes-are-cylindrical-1819586128"} +{"original_headline": "only positive statistic of year announced", "generated_headline": "The only 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looking absolutely precious in new baseball cap", "generated_headline": "Grandfather looks nice in his new baseball cap.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/grandpa-looking-absolutely-precious-in-new-baseball-cap-1819591804"} +{"original_headline": "historical archives: to be sold - carved wooden heads", "generated_headline": "Historical archives, including carved wooden heads, are for sale.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/historical-archives-to-be-sold-carved-wooden-heads-1819570217"} +{"original_headline": "report: one in five women training to be yoga instructors", "generated_headline": "A report indicates that 20% of women are training to become yoga instructors.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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"https://local.theonion.com/mom-s-head-rotates-demonically-after-passing-sign-for-a-1819591458"} +{"original_headline": "struggling justice alito sent down to lower federal court", "generated_headline": "Justice Alito participated in a lower federal court proceeding.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/struggling-justice-alito-sent-down-to-lower-federal-cou-1819577891"} +{"original_headline": "bank of america introduces new $50 underdraft fee", "generated_headline": "Bank of America has implemented a new $50 service fee for accounts below a minimum balance.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/bank-of-america-introduces-new-50-underdraft-fee-1819576899"} +{"original_headline": "study: support for bill of rights highest while attempting to talk way out of drunk driving arrest", 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"https://local.theonion.com/entire-conversation-with-parents-spent-changing-the-sub-1819577065"} +{"original_headline": "7-eleven shareholders approve sale of busch light six-pack", "generated_headline": "7-Eleven shareholders have approved the sale of Busch Light six-packs.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/7-eleven-shareholders-approve-sale-of-busch-light-six-p-1819569464"} +{"original_headline": "salvadoran earthquake registers 0.2 on local man's consciousness", "generated_headline": "The Salvadoran earthquake barely registered on a local man's consciousness.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/salvadoran-earthquake-registers-0-2-on-local-mans-consc-1819565874"} +{"original_headline": "affable anti-semite thinks the jews are doing super job with the media", "generated_headline": "A friendly anti-Semitic person believes that Jews are doing an excellent job with the media.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/affable-anti-semite-thinks-the-jews-are-doing-super-job-1819566603"} +{"original_headline": "fat couple's love like a fat flower", "generated_headline": "The overweight couple's love is described as being like a plump flower.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/fat-couples-love-like-a-fat-flower-1819566459"} +{"original_headline": "pope loses keys to vatican city", "generated_headline": "The Pope lost the keys to Vatican City.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/pope-loses-keys-to-vatican-city-1819563868"} +{"original_headline": "moviegoer manages to sneak candy past teenage usher earning $7 an hour", "generated_headline": "A moviegoer successfully brought candy into the theater past a teenage usher earning $7 an hour.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/moviegoer-manages-to-sneak-candy-past-teenage-usher-ear-1819576388"} +{"original_headline": "asian economic woes force layoffs of 700,000 pop stars", "generated_headline": "Economic difficulties in Asia have resulted in 700,000 pop stars being laid off.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/asian-economic-woes-force-layoffs-of-700-000-pop-stars-1819571401"} +{"original_headline": "woodstock '99 revenue projections displayed on multi-colored, laminated boards somewhere in l.a.", "generated_headline": "Revenue projections for Woodstock '99 were displayed on multi-colored, laminated boards in Los Angeles.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/woodstock-99-revenue-projections-displayed-on-multi-col-1819565199"} +{"original_headline": "bush elected president of iraq", "generated_headline": "George Bush was elected president of Iraq.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/bush-elected-president-of-iraq-1819568211"} +{"original_headline": "pantomimed lasso motion fails to pull woman across dance floor", "generated_headline": "A man's pantomimed lasso motion failed to attract a woman across the dance floor.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/pantomimed-lasso-motion-fails-to-pull-woman-across-danc-1819565909"} +{"original_headline": "archaeologists discover first hominid to own tools but never use them", "generated_headline": "Archaeologists discovered the first hominid that owned tools but never used them.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/archaeologists-discover-first-hominid-to-own-tools-but-1819577757"} +{"original_headline": "unambitious terrorists overturn trash can", "generated_headline": "Terrorists with low ambitions overturned a trash can.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/unambitious-terrorists-overturn-trash-can-1819564152"} +{"original_headline": "embarrassed library of congress can't believe some of the albums it used to be into", "generated_headline": "The Library of Congress expresses embarrassment over some of the albums it once collected.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/embarrassed-library-of-congress-can-t-believe-some-of-t-1819580029"} +{"original_headline": "mister rogers' neighborhood gerrymandered to serve king friday's make-believe agenda", "generated_headline": "In a fictional context, Mister Rogers' Neighborhood is gerrymandered to serve King Friday's make-believe agenda.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/mister-rogers-neighborhood-gerrymandered-to-serve-king-1819568681"} +{"original_headline": "company to get head start on christmas layoffs this year", "generated_headline": "The company is initiating Christmas layoffs earlier this year.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/company-to-get-head-start-on-christmas-layoffs-this-yea-1819571899"} +{"original_headline": "france, india, brazil among dozens of governments to fall as riots in support of onion social increase globally", "generated_headline": "Riots over onion prices lead to government collapses in France, India, Brazil, and other countries.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/france-india-brazil-among-dozens-of-governments-to-fa-1827046375"} +{"original_headline": "mildfires amble through california", "generated_headline": "Wildfires are spreading through California.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/mildfires-amble-through-california-1819570780"} +{"original_headline": "blagojevich claims behavior was just elaborate plan to surprise patrick fitzgerald with senate nomination on his birthday", "generated_headline": "Blagojevich claims his behavior was an elaborate plan to surprise Patrick Fitzgerald with a Senate nomination on his birthday.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/blagojevich-claims-behavior-was-just-elaborate-plan-to-1819570501"} +{"original_headline": "group of girls directs would-be suitor toward least attractive member", "generated_headline": "A group of girls directs a would-be suitor to the least attractive member of their group.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/group-of-girls-directs-would-be-suitor-toward-least-att-1819565576"} +{"original_headline": "man celebrates raise company will eventually use to justify firing him", "generated_headline": "A man celebrates his raise, even though the company will later use it to justify firing him.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-celebrates-raise-company-will-eventually-use-to-jus-1819577813"} +{"original_headline": "father doesn't understand teenage son's obsession with classic rock", "generated_headline": "A father does not understand his teenage son's obsession with classic rock music.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/father-doesnt-understand-teenage-sons-obsession-with-cl-1819568291"} +{"original_headline": "way too much raised for bronchitis research", "generated_headline": "An excessive amount of money has been raised for bronchitis research.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/way-too-much-raised-for-bronchitis-research-1819569880"} +{"original_headline": "no one murdered because of this image", "generated_headline": "No murders have been caused by this image.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/no-one-murdered-because-of-this-image-1819573893"} +{"original_headline": "astronomers celebrate 300th anniversary of discovering sky", "generated_headline": "Astronomers are celebrating 300 years since the beginning of systematic sky observations.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/astronomers-celebrate-300th-anniversary-of-discovering-1819577085"} +{"original_headline": "queen elizabeth frantically trying to preserve european alliances by arranging great-grandchildren's marriages", "generated_headline": "Queen Elizabeth is arranging marriages for her great-grandchildren to preserve European alliances.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/queen-elizabeth-frantically-trying-to-preserve-european-1819579760"} +{"original_headline": "zach braff, alyssa milano call out trump for far more effectively pivoting to politics to save floundering career", "generated_headline": "Zach Braff and Alyssa Milano criticize Trump for more effectively pivoting to politics to save his floundering career.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/zach-braff-alyssa-milano-call-out-trump-for-far-more-e-1835495412"} +{"original_headline": "dr. scholl's introduces new freeze-away toe remover", "generated_headline": "Dr. Scholl's introduces a new product for toe removal using freezing technology.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/dr-scholl-s-introduces-new-freeze-away-toe-remover-1827896718"} +{"original_headline": "full unsliced lemon makes glass of water particularly refreshing", "generated_headline": "A whole, unsliced lemon in a glass of water makes it more refreshing.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/full-unsliced-lemon-makes-glass-of-water-particularly-r-1819590800"} +{"original_headline": "speakeasy patrons apparently unaware it legal to go to regular bars again", "generated_headline": "Speakeasy patrons seem unaware that visiting regular bars is now legal.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/speakeasy-patrons-apparently-unaware-it-legal-to-go-to-1830496020"} +{"original_headline": "person who will embalm you walking around out there", "generated_headline": "The mortician who will embalm you is currently alive and walking around.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/person-who-will-embalm-you-walking-around-out-there-1819576738"} +{"original_headline": "breast implants found to cause problems in laboratory mice", "generated_headline": "Breast implants have been found to cause problems in laboratory mice.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/breast-implants-found-to-cause-problems-in-laboratory-m-1819586306"} +{"original_headline": "archivists discover unpublished michael crichton manuscript about amusement park that operates without a hitch", "generated_headline": "Archivists have discovered an unpublished Michael Crichton manuscript about an amusement park that operates perfectly.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/archivists-discover-unpublished-michael-crichton-manusc-1824742454"} +{"original_headline": "nation begs for midterms to be pushed back to delay start of 2020 presidential campaigns", "generated_headline": "There is public pressure to postpone midterm elections to delay the start of the 2020 presidential campaigns.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/nation-begs-for-midterms-to-be-pushed-back-to-delay-sta-1830230678"} +{"original_headline": "pope promises more open, transparent molestation in future", "generated_headline": "The Pope promises increased openness and transparency in handling future molestation cases.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/pope-promises-more-open-transparent-molestation-in-fut-1828663069"} +{"original_headline": "man planning to rub up against strangers wondering where train is already", "generated_headline": "A man planning to rub against strangers is also wondering where the train is.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-planning-to-rub-up-against-strangers-wondering-wher-1819576559"} +{"original_headline": "al kozlewski pulls a kozlewski", "generated_headline": "Al Kozlewski engages in behavior characteristic of himself.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/al-kozlewski-pulls-a-kozlewski-1819567146"} +{"original_headline": "woman masturbates to concept of commitment", "generated_headline": "A woman masturbates while thinking about the concept of commitment.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/woman-masturbates-to-concept-of-commitment-1819566960"} +{"original_headline": "white-hot gop race down to two mentally ill people, person who lost nomination last time", "generated_headline": "The GOP primary race has narrowed to two individuals with mental health issues and the previous nomination loser.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/white-hot-gop-race-down-to-two-mentally-ill-people-per-1819590409"} +{"original_headline": "ralph northam admits he once engaged in pedophilia as part of michael jackson costume", "generated_headline": "Ralph Northam admits to engaging in pedophilic behavior during a Michael Jackson costume event.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/ralph-northam-admits-he-once-engaged-in-pedophilia-as-p-1832405275"} +{"original_headline": "police outside convention hoping for opportunity to take swing at george washington impersonator", "generated_headline": "Police outside the convention hope for a chance to strike a George Washington impersonator.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/police-outside-convention-hoping-for-opportunity-to-tak-1819579037"} +{"original_headline": "'this is the golden age of television,' claim executives who have not yet made show about robotic wizards", "generated_headline": "Television executives claim this is the golden age of television, despite not having produced a show about robotic wizards.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/this-is-the-golden-age-of-television-claim-executive-1819579353"} +{"original_headline": "european leaders: 'we stand together to say loud and clear: we are scared as fuck and don't know what to do'", "generated_headline": "European leaders state that they are frightened and uncertain about what to do.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/european-leaders-we-stand-together-to-say-loud-and-cl-1819580176"} +{"original_headline": "right to own handheld device that shoots deadly metal pellets at high speed worth all of this", "generated_headline": "The right to own a handheld device that fires lethal metal pellets at high speed is justified by the current situation.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/right-to-own-handheld-device-that-shoots-deadly-metal-p-1819574320"} +{"original_headline": "write-in candidate thought he had enough friends to win", "generated_headline": "The write-in candidate believed his personal friendships would be sufficient to win the election.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/write-in-candidate-thought-he-had-enough-friends-to-win-1819568792"} +{"original_headline": "evil genius' cat subpoenaed", "generated_headline": "The cat belonging to an evil genius has been issued a subpoena.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/evil-genius-cat-subpoenaed-1819564718"} +{"original_headline": "cdc attempts to put ebola outbreak in perspective by releasing list of worse ways to die", "generated_headline": "CDC releases a comparative 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"https://www.theonion.com/dance-cage-recidivism-rates-at-all-time-high-within-ame-1819580090"} +{"original_headline": "jellyfish can't wait to fuck up honeymoon", "generated_headline": "Jellyfish stings often disrupt honeymoons for couples.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/jellyfish-can-t-wait-to-fuck-up-honeymoon-1819592773"} +{"original_headline": "nelly reiterates sex-liking stance", "generated_headline": "Rapper Nelly again states his positive attitude towards sex.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/nelly-reiterates-sex-liking-stance-1819587246"} +{"original_headline": "mark zuckerberg cited for contempt of congress after refusing to shut the fuck up about how he started company in dorm room", "generated_headline": "Mark Zuckerberg faces contempt of Congress charges for excessive discussion of Facebook's dorm-room origins during testimony.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/mark-zuckerberg-cited-for-contempt-of-congress-after-re-1825178093"} +{"original_headline": "literary historians uncover collection of breezy, upbeat edgar allan poe writings penned after author took up jogging", "generated_headline": "Historians discover previously unknown cheerful poems by Edgar Allan Poe written after he began a jogging routine.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/literary-historians-uncover-collection-of-breezy-upbea-1833436860"} +{"original_headline": "trump admits he assumed roger stone was already in prison", "generated_headline": "Trump said he believed Roger Stone was already imprisoned.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/trump-admits-he-assumed-roger-stone-was-already-in-pris-1832057622"} +{"original_headline": "focus group reveals: 95 percent of americans would like to go home", "generated_headline": "A survey shows that 95% of Americans prefer to be at home.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/focus-group-reveals-95-percent-of-americans-would-like-1819564313"} +{"original_headline": "friend gearing up to hate the hulk", "generated_headline": "A friend is preparing to dislike the Hulk movie or character.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/friend-gearing-up-to-hate-the-hulk-1819566928"} +{"original_headline": "deal alert: get 'kingdom hearts iii' for free for next 30 seconds while gamestop clerk is dealing with something in back", "generated_headline": "Kingdom Hearts III is offered for free for a brief 30-second period while a GameStop employee is occupied.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://ogn.theonion.com/deal-alert-get-kingdom-hearts-iii-for-free-for-next-1835100289"} +{"original_headline": "history doomed to repeat itself, reports man who just dropped food on pants", "generated_headline": "A man who spilled food on his pants remarks that history tends to repeat itself.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/history-doomed-to-repeat-itself-reports-man-who-just-d-1819570366"} +{"original_headline": "that one mcdonald's plate from the '70s: holy shit, there it is", "generated_headline": "An old McDonald's plate from the 1970s has been located.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/that-one-mcdonalds-plate-from-the-70s-holy-shit-there-1819586984"} +{"original_headline": "study finds 60% of parents too busy with divorce to worry about football safety", "generated_headline": "A study reports that many parents going through divorce are less concerned about football safety.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/study-finds-60-of-parents-too-busy-with-divorce-to-wor-1819576099"} +{"original_headline": "acne medication may cause dizziness, nausea, loss of hearing, insomnia, blood clotting, difficulty breathing", "generated_headline": "Acne medication can have serious side effects such as dizziness, nausea, hearing loss, insomnia, blood clotting, and breathing difficulties.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": 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"https://www.theonion.com/awestruck-video-game-fan-describes-brush-with-playstati-1819565625"} +{"original_headline": "takeout burrito shielded from cold as though it were week-old newborn", "generated_headline": "A takeout burrito is kept warm like a newborn infant.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/takeout-burrito-shielded-from-cold-as-though-it-were-we-1819578629"} +{"original_headline": "last line of obama's military force request briefly mentions possibility of 25-year quagmire", "generated_headline": "Obama's military request mentions a potential 25-year conflict.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/last-line-of-obama-s-military-force-request-briefly-men-1819577476"} +{"original_headline": "obama peddling stimulus package door-to-door", 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"stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/third-grader-clearly-biting-off-more-than-he-can-chew-a-1819579378"} +{"original_headline": "royal baby already making new friends", "generated_headline": "The royal baby is making new friends.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/royal-baby-already-making-new-friends-1819575525"} +{"original_headline": "mccain's energy plan emphasizes elbow grease, sleeve-rolling-up", "generated_headline": "McCain's energy plan focuses on hard work and manual labor.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/mccains-energy-plan-emphasizes-elbow-grease-sleeve-rol-1819570102"} +{"original_headline": "new, improved olean 30 percent less likely to make you shit in your pants", "generated_headline": "The new Olean 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"https://politics.theonion.com/antifa-organizers-announce-plans-to-disrupt-neo-nazi-ra-1819580235"} +{"original_headline": "guatemalan coffee picker happy if single person starts day alert", "generated_headline": "A Guatemalan coffee picker is happy if one person starts their day alert.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/guatemalan-coffee-picker-happy-if-single-person-starts-1819567780"} +{"original_headline": "good charlotte recording 10 new songs to be played at low volume in p.f. chang's", "generated_headline": "Good Charlotte is recording 10 new songs intended for low-volume playback at P.F. 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"https://politics.theonion.com/john-bolton-urges-war-against-the-sun-after-uncovering-1835805360"} +{"original_headline": "rest of kickline out sick", "generated_headline": "The remaining members of the kickline are absent due to illness.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/rest-of-kickline-out-sick-1819588500"} +{"original_headline": "latest gop debate concludes with candidates wrestling squealing pig to ground and slaughtering it", "generated_headline": "The recent GOP debate ended with candidates physically subduing and killing a pig.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/latest-gop-debate-concludes-with-candidates-wrestling-s-1819590502"} +{"original_headline": "jawa appointed secretary of transportation", "generated_headline": "A being referred to as a 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"https://entertainment.theonion.com/al-roker-stares-crestfallen-at-matt-lauer-tattoo-on-own-1820852200"} +{"original_headline": "everyone in motorcycle gang jewish", "generated_headline": "All members of the motorcycle gang are Jewish.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/everyone-in-motorcycle-gang-jewish-1819591187"} +{"original_headline": "monster truck escapes", "generated_headline": "A monster truck has broken free from its enclosure.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/monster-truck-escapes-1819586160"} +{"original_headline": "oscars audience shrugging uproariously during jimmy kimmel's opening monologue", "generated_headline": "The Oscars audience is laughing loudly during Jimmy Kimmel's opening monologue.", "strategy": "irony", "type": "sarcastic_to_non", 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"https://local.theonion.com/area-man-cleans-apartment-once-every-relationship-1819576384"} +{"original_headline": "busy romney sorry he missed nation's piano recital", "generated_headline": "Mitt Romney apologizes for missing the national piano recital due to his busy schedule.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/busy-romney-sorry-he-missed-nations-piano-recital-1819573405"} +{"original_headline": "blood runs down house of representatives walls as chamber itself selects new speaker", "generated_headline": "Blood is running down the walls of the House of Representatives as it selects a new Speaker.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/blood-runs-down-house-of-representatives-walls-as-chamb-1819578317"} +{"original_headline": "rain-soaked robert mueller lets manafort 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"https://local.theonion.com/flight-attendant-demonstrates-proper-technique-for-eati-1827754079"} +{"original_headline": "reagan's memory honored with sharp increase in federal budget deficit", "generated_headline": "The federal budget deficit has sharply increased in honor of Reagan's memory.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/reagans-memory-honored-with-sharp-increase-in-federal-b-1819587586"} +{"original_headline": "smokers at party only ones to make it to fire escape in time", "generated_headline": "At a party, only the smokers were able to reach the fire escape in time.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/smokers-at-party-only-ones-to-make-it-to-fire-escape-in-1819587932"} +{"original_headline": "man at salad bar has to say every item aloud as he adds it to salad", 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"https://www.theonion.com/new-york-times-announces-appointment-of-anonymous-sou-1829232788"} +{"original_headline": "rescue dog adopted for couple weeks", "generated_headline": "A rescue dog was adopted for a couple of weeks.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/rescue-dog-adopted-for-couple-weeks-1819591877"} +{"original_headline": "alien still hasn't gotten around to listening to whole voyager golden record", "generated_headline": "An alien has not yet listened to the entire Voyager Golden Record.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/alien-still-hasnt-gotten-around-to-listening-to-whole-v-1819573495"} +{"original_headline": "no one in gang has heart to tell police informant his cover's blown", "generated_headline": "No one in the gang has the heart to tell the 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"https://local.theonion.com/man-knows-exactly-which-asshole-got-him-sick-1819655095"} +{"original_headline": "fox news reporter asks the questions others are too smart to ask", "generated_headline": "A Fox News reporter asks questions that others are too intelligent to ask.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/fox-news-reporter-asks-the-questions-others-are-too-sma-1819587301"} +{"original_headline": "planet explodes", "generated_headline": "A planet has exploded.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/planet-explodes-1819565251"} +{"original_headline": "nesting sea turtle escorted from private beach", "generated_headline": "A nesting sea turtle was escorted from a private beach.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/nesting-sea-turtle-escorted-from-private-beach-1819589752"} +{"original_headline": "man old enough to know how rest of life pretty much plays out", "generated_headline": "A man is old enough to know how the rest of his life will pretty much play out.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-old-enough-to-know-how-rest-of-life-pretty-much-pla-1819577418"} +{"original_headline": "experts recommend changing batteries in smoke detector every 6 fires", "generated_headline": "Experts recommend changing the batteries in smoke detectors every six fires.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/experts-recommend-changing-batteries-in-smoke-detector-1833286965"} +{"original_headline": "lone house with no halloween 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"article_link": "https://politics.theonion.com/breaking-aclu-hard-as-a-fucking-rock-right-now-1819580105"} +{"original_headline": "puerto ricans without power for month can only assume this leading story across national news media", "generated_headline": "Puerto Ricans, who have been without power for a month, note that their situation is not the leading story in national news media.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/puerto-ricans-without-power-for-month-can-only-assume-t-1819877413"} +{"original_headline": "logging industry announces that they just can't fucking get enough of logs", "generated_headline": "The logging industry has expressed an insatiable demand for logs.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/logging-industry-announces-that-they-just-can-t-fucking-1829364973"} 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"https://local.theonion.com/unremarkable-man-resembles-burt-ward-1819564822"} +{"original_headline": "boy, dolphin no longer on speaking terms", "generated_headline": "A boy and a dolphin are no longer communicating.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/boy-dolphin-no-longer-on-speaking-terms-1819567239"} +{"original_headline": "lunch place uses way too much mayo in fruit salad", "generated_headline": "The lunch place uses an excessive amount of mayonnaise in its fruit salad.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/lunch-place-uses-way-too-much-mayo-in-fruit-salad-1828465668"} +{"original_headline": "idf soldier recounts harrowing, heroic war story of killing 8-month-old child", "generated_headline": "An IDF soldier recounted a war story about killing an 8-month-old child.", 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+{"original_headline": "14-year-old congressional whiz kid balances budget", "generated_headline": "A 14-year-old prodigy assisted in balancing the congressional budget.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/14-year-old-congressional-whiz-kid-balances-budget-1819574697"} +{"original_headline": "fun sticker placed on child's ventilator", "generated_headline": "A fun sticker was placed on a child's ventilator.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/fun-sticker-placed-on-child-s-ventilator-1819591576"} +{"original_headline": "pretentious peasant insists he never watches beheadings", "generated_headline": "A pretentious peasant claims he never watches beheadings.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/pretentious-peasant-insists-he-never-watches-beheadings-1828356870"} +{"original_headline": "polling place in predominantly black neighborhood clearly brick wall with door painted on", "generated_headline": "A polling place in a predominantly Black neighborhood appears to be a brick wall with a painted door, suggesting deception.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/polling-place-in-predominantly-black-neighborhood-clear-1830254624"} +{"original_headline": "director of high-school play buys director's chair out of own pocket", "generated_headline": "The director of a high-school play bought a director's chair with personal funds.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/director-of-high-school-play-buys-directors-chair-out-o-1819566583"} 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"https://local.theonion.com/man-takes-parents-on-tour-of-city-where-he-came-to-esca-1819577028"} +{"original_headline": "police find adorable little skeleton", "generated_headline": "Police discovered a small skeleton that was described as charming.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/police-find-adorable-little-skeleton-1819572235"} +{"original_headline": "everyone at thanksgiving doing chore to get away from rest of family", "generated_headline": "At Thanksgiving, each family member performed a chore to spend time away from other relatives.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/everyone-at-thanksgiving-doing-chore-to-get-away-from-r-1830597796"} +{"original_headline": "man unsure whether to tip bathroom attendant just for wiping his ass", "generated_headline": "A man is 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"sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/pastor-talking-to-non-christian-who-just-lost-wife-can-1819579838"} +{"original_headline": "chemicals that pushed man's ancestors to run down wild boar flare at sight of white cheddar popcorn bag", "generated_headline": "Chemicals that historically motivated human hunting now react to white cheddar popcorn bags.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/chemicals-that-pushed-man-s-ancestors-to-run-down-wild-1819579876"} +{"original_headline": "man watching cleopatra 2525 has no time to read", "generated_headline": "A man is watching the TV show Cleopatra 2525 and has no time for reading.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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"article_link": "https://www.theonion.com/unemployed-man-photoshops-self-into-former-company-s-st-1819589214"} +{"original_headline": "man wistfully looks around website he hasn't visited for 30 minutes", "generated_headline": "A man looks nostalgically at a website he hasn't visited for 30 minutes.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-wistfully-looks-around-website-he-hasn-t-visited-fo-1819577435"} +{"original_headline": "wedding planner suggests replacing unsightly groom", "generated_headline": "A wedding planner recommends replacing the groom due to aesthetic concerns.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/wedding-planner-suggests-replacing-unsightly-groom-1819577556"} +{"original_headline": "new co-worker seems like nice enough guy", "generated_headline": "The new 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"satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/frantic-last-second-study-finds-old-fashioned-donut-be-1819811037"} +{"original_headline": "347 locals identify slain prostitute", "generated_headline": "347 local residents have identified the victim in a homicide case involving a sex worker.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/347-locals-identify-slain-prostitute-1819568442"} +{"original_headline": "report: jack black's life more valuable than yours if it ever comes down to it", "generated_headline": "A report suggests that in life-threatening situations, Jack Black's life might be prioritized over others.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/report-jack-blacks-life-more-valuable-than-yours-if-it-1819574056"} +{"original_headline": "teen on birthright trip hadn't expected to see so many dead palestinians", "generated_headline": "A teenager on a Birthright trip was surprised by the number of Palestinian casualties he witnessed.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/teen-on-birthright-trip-hadn-t-expected-to-see-so-many-1824265633"} +{"original_headline": "bobby jindal vows to return america to time when he was rising republican star", "generated_headline": "Bobby Jindal pledged to restore America to the era of his political ascendancy.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/bobby-jindal-vows-to-return-america-to-time-when-he-was-1819578072"} +{"original_headline": "mitt romney frantically 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"article_link": "https://www.theonion.com/kfc-responds-to-stockpiling-trend-with-576-piece-bucket-1819587067"} +{"original_headline": "nine-hundred-pound man left to die", "generated_headline": "A nine-hundred-pound man was left to die.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/nine-hundred-pound-man-left-to-die-1819564653"} +{"original_headline": "roommate skulking around edge of party like victorian ghost child", "generated_headline": "Roommate is behaving stealthily at the party.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/roommate-skulking-around-edge-of-party-like-victorian-g-1819579381"} +{"original_headline": "man thinks receptionist is hitting on him", "generated_headline": "A man interprets the receptionist's behavior as romantic interest.", "strategy": "sarcasm", "type": 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"https://local.theonion.com/college-newspaper-staff-know-exactly-how-they-would-res-1819577636"} +{"original_headline": "treasury department releases new 'monsters of the silver screen' $20 bill", "generated_headline": "The Treasury Department has issued a new $20 bill featuring monsters from movies.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/treasury-department-releases-new-monsters-of-the-silver-1819590484"} +{"original_headline": "obama, romney urge americans to purchase 'the onion book of known knowledge'", "generated_headline": "Barack Obama and Mitt Romney encourage Americans to purchase 'The Onion Book of Known Knowledge'.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/obama-romney-urge-americans-to-purchase-the-onion-book-1819574144"} +{"original_headline": "unattractive man not fooling 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"https://local.theonion.com/teacher-frustrated-no-one-in-beginner-yoga-class-can-fo-1820796504"} +{"original_headline": "inept coworker increasingly difficult to fantasize about", "generated_headline": "It is increasingly difficult to fantasize about an incompetent coworker.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/inept-coworker-increasingly-difficult-to-fantasize-abou-1819570663"} +{"original_headline": "obama's declaration of swine flu emergency prompts pro-swine-flu republican response", "generated_headline": "Obama's declaration of a swine flu emergency prompted a Republican response that favored the swine flu.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/obamas-declaration-of-swine-flu-emergency-prompts-pro-s-1819571102"} +{"original_headline": "parent mad 6-year-old didn't like 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"https://www.theonion.com/astronomers-say-wednesday-night-will-be-best-chance-for-1825248803"} +{"original_headline": "'game of thrones' fan rewatching past episodes to remind self of what characters' breasts look like", "generated_headline": "A 'Game of Thrones' fan is rewatching past episodes to remind himself of what the characters' breasts look like.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/game-of-thrones-fan-rewatching-past-episodes-to-remin-1819578813"} +{"original_headline": "country cd put on to impress repair guy", "generated_headline": "A country CD was played to impress the repair technician.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/country-cd-put-on-to-impress-repair-guy-1819570504"} +{"original_headline": "man given 3 months to live throws in one or two non-sexual 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"https://entertainment.theonion.com/new-blog-piece-on-woody-allen-to-settle-everything-1819576124"} +{"original_headline": "ghost of brando urges man to finish whole cheesecake", "generated_headline": "A man feels inspired by the memory of Marlon Brando to eat an entire cheesecake.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/ghost-of-brando-urges-man-to-finish-whole-cheesecake-1819568244"} +{"original_headline": "movie marketed as six different genres", "generated_headline": "The movie is being promoted in six different genres.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/movie-marketed-as-six-different-genres-1819566764"} +{"original_headline": "broke dad makes son playstation 2 for christmas", "generated_headline": "A father with financial difficulties buys his son a PlayStation 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"https://www.theonion.com/texas-sheriff-cracks-down-on-chicken-on-chicken-violenc-1819565681"} +{"original_headline": "rodent clearly making its way through steve bannon's body throughout national security meeting", "generated_headline": "A rodent was seen moving near Steve Bannon during a national security meeting.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/rodent-clearly-making-its-way-through-steve-bannon-s-bo-1819579702"} +{"original_headline": "kid about to meet brooklyn nets must not be very sick", "generated_headline": "A child scheduled to meet the Brooklyn Nets is likely not seriously ill.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://sports.theonion.com/kid-about-to-meet-brooklyn-nets-must-not-be-very-sick-1834012121"} +{"original_headline": "tulip popping up in middle of march must think it some kind 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"stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/nation-suddenly-concerned-about-black-mans-opinion-1825547877"} +{"original_headline": "witty remark repeated throughout week", "generated_headline": "A witty remark was repeated multiple times throughout the week.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/witty-remark-repeated-throughout-week-1819565499"} +{"original_headline": "'so what did i miss?' asks michael flynn tilting large flower on lapel towards trump", "generated_headline": "Michael Flynn, wearing a large flower on his lapel, asked what he had missed during an event with Trump.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/so-what-did-i-miss-asks-michael-flynn-tilting-large-1820779582"} +{"original_headline": "'ravaged' named florida's official 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"https://local.theonion.com/parents-finally-tell-2-year-old-about-9-11-1819574452"} +{"original_headline": "orrin hatch mistakenly left dangling in bondage-fetish dungeon", "generated_headline": "Orrin Hatch was accidentally left dangling in a bondage-fetish dungeon.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/orrin-hatch-mistakenly-left-dangling-in-bondage-fetish-1819565385"} +{"original_headline": "new report finds link between each passing day, jeanette getting more beautiful", "generated_headline": "A new report indicates that each passing day makes Jeanette more beautiful.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-report-finds-link-between-each-passing-day-jeanett-1823273857"} +{"original_headline": "white house press secretary responds to question about rising obamacare 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"stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/bo-obama-addresses-graduates-of-dayton-obedience-school-1819579981"} +{"original_headline": "paul ryan quickly runs tweet about texas shooting past wayne lapierre before posting", "generated_headline": "Paul Ryan sought input from Wayne LaPierre prior to publishing a tweet regarding the Texas shooting.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/paul-ryan-quickly-runs-tweet-about-texas-shooting-past-1820188824"} +{"original_headline": "report: album as good as 'sgt. pepper' comes out about once every month", "generated_headline": "Music critics often draw comparisons between new album releases and the iconic 'Sgt. 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"https://politics.theonion.com/anthropologists-discover-ancient-greek-super-pac-that-h-1819577782"} +{"original_headline": "spacex reveals all 400 dogs on falcon rocket failed to survive trip", "generated_headline": "SpaceX confirmed that animals aboard a Falcon rocket mission did not survive the journey.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/spacex-reveals-all-400-dogs-on-falcon-rocket-failed-to-1822775849"} +{"original_headline": "report: nation thinking about big, warm piece of cinnamon coffee cake right now", "generated_headline": "Recent data indicates a widespread craving for cinnamon coffee cake among people in the United States.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/report-nation-thinking-about-big-warm-piece-of-cinnam-1819575258"} +{"original_headline": "pope francis 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"https://www.theonion.com/unstable-man-plots-to-bring-guns-to-schools-1819574765"} +{"original_headline": "taylor swift breaks political silence to throw support behind restoring sh\u014dgun to throne of japan", "generated_headline": "Taylor Swift publicly advocated for the reinstatement of the sh\u014dgun title in Japan, though this claim is unfounded.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/taylor-swift-breaks-political-silence-to-throw-support-1829602513"} +{"original_headline": "little league coach reveals creepy method for breaking in baseball mitt", "generated_headline": "A youth baseball coach disclosed an unusual technique for conditioning a mitt.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/little-league-coach-reveals-creepy-method-for-breaking-1819571459"} 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"stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/report-someone-probably-masturbating-to-this-stock-pho-1819575783"} +{"original_headline": "supporters aggravated bernie sanders didn't use dnc speech to get voters to act against their own self-interest", "generated_headline": "Supporters are aggravated that Bernie Sanders didn't urge voters to act against self-interest.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/supporters-aggravated-bernie-sanders-didn-t-use-dnc-spe-1819579052"} +{"original_headline": "pope francis spotted sunbathing nude in st. peter's square", "generated_headline": "Report claims Pope Francis was seen sunbathing nude in St. Peter's Square.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/pope-francis-spotted-sunbathing-nude-in-st-peter-s-squ-1819579717"} 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"article_link": "https://sports.theonion.com/dog-costumed-to-create-illusion-of-sports-team-preferen-1819565269"} +{"original_headline": "new bill would limit abortion to cases where procedure necessary to save promising political career", "generated_headline": "A bill proposes to limit abortion to cases where it is necessary to save a political career.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/new-bill-would-limit-abortion-to-cases-where-procedure-1819580372"} +{"original_headline": "lucky old woman getting wheeled around airport", "generated_headline": "An elderly woman is being wheeled around the airport.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/lucky-old-woman-getting-wheeled-around-airport-1819575211"} +{"original_headline": "talk-show host takes brief break from mocking jessica 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"article_link": "https://entertainment.theonion.com/orca-s-successful-trick-rewarded-with-bucket-of-ssris-1819592389"} +{"original_headline": "north dakota not heard from in 48 hours", "generated_headline": "There has been no significant news reported from North Dakota in the past 48 hours.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/north-dakota-not-heard-from-in-48-hours-1819564531"} +{"original_headline": "kanye west named new face of yeezy", "generated_headline": "Kanye West has been appointed as the new spokesperson for the Yeezy brand.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/kanye-west-named-new-face-of-yeezy-1832335816"} +{"original_headline": "queen elizabeth annoyed nude pictures of prince harry don't show anything good", "generated_headline": "Queen Elizabeth is reported to be 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"stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/young-billionaires-age-not-reported-for-sake-of-nations-1819572710"} +{"original_headline": "christian theme park features world's largest spanking machine", "generated_headline": "A Christian theme park includes an attraction called the world's largest spanking machine.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/christian-theme-park-features-worlds-largest-spanking-m-1819563916"} +{"original_headline": "mom triumphantly drags hotel pool lounge chair back to family like fresh kill", "generated_headline": "A mother proudly brings a hotel pool lounge chair back to her family as if it were a trophy.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/mom-triumphantly-drags-hotel-pool-lounge-chair-back-to-1819577951"} 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"https://www.theonion.com/pope-francis-sneaks-leftovers-to-false-god-moloch-at-ba-1819579614"} +{"original_headline": "emaciated peter alexander burns podium for warmth after being locked in abandoned press briefing room since december", "generated_headline": "Journalist Peter Alexander reportedly suffered in an abandoned press room, resorting to burning a podium for warmth.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/emaciated-peter-alexander-burns-podium-for-warmth-after-1832026399"} +{"original_headline": "emotional elon musk recalls spending entire birthday working on concepts for mistreating employees", "generated_headline": "Elon Musk reflected on his birthday, discussing his work on employee management strategies.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/emotional-elon-musk-recalls-spending-entire-birthday-wo-1828475558"} +{"original_headline": "hotel bar really hopping tonight, says hotel bartender", "generated_headline": "A hotel bartender comments that the bar is busy tonight.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/hotel-bar-really-hopping-tonight-says-hotel-bartender-1819566601"} +{"original_headline": "giant altoid headed toward earth'curiously strong' celestial body will extinguish all life", "generated_headline": "A large celestial body, nicknamed 'curiously strong,' is on a collision course with Earth, posing an existential threat.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/giant-altoid-headed-toward-earthcuriously-strong-celest-1819586250"} +{"original_headline": "report: it a miracle nothing has 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"https://www.theonion.com/furious-meghan-markle-can-t-believe-harry-hasn-t-told-f-1826104835"} +{"original_headline": "frozen tundra of emptiness stretching out forever and ever weighed against date with mike4763", "generated_headline": "The vast, desolate landscape is contrasted with a social engagement with an individual named Mike4763.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/frozen-tundra-of-emptiness-stretching-out-forever-and-e-1819576971"} +{"original_headline": "mom could have used few more days to self before missing daughter returned", "generated_headline": "A mother wished for more personal time before her daughter came back home.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/mom-could-have-used-few-more-days-to-self-before-missin-1819577512"} +{"original_headline": "god admits 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"https://www.theonion.com/tan-asshole-still-on-island-time-1819589691"} +{"original_headline": "'this map will change the way you see westeros,' reports never-ending cascade of subhuman bullshit", "generated_headline": "A map is promoted as transformative for understanding Westeros, amid criticism of repetitive low-quality content.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/this-map-will-change-the-way-you-see-westeros-report-1819580267"} +{"original_headline": "man announces plan to take out anger on first less powerful person he sees", "generated_headline": "A man declares his intention to vent his anger on someone he perceives as weaker.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-announces-plan-to-take-out-anger-on-first-less-powe-1819577171"} +{"original_headline": "man who 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"https://www.theonion.com/nation-hopeful-there-will-be-equally-random-chance-of-j-1819577691"} +{"original_headline": "world's cartographers continue living secret life of luxury on idyllic, never disclosed 8th continent", "generated_headline": "Cartographers do not live a secret life of luxury on a nonexistent eighth continent.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/world-s-cartographers-continue-living-secret-life-of-lu-1828830864"} +{"original_headline": "report: papa will be so very cross you've lost grandfather's hunting cap", "generated_headline": "Papa is angry that you have lost grandfather's hunting cap.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/report-papa-will-be-so-very-cross-you-ve-lost-grandfat-1832395994"} +{"original_headline": "community garden sprouts first condom 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"stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/veteran-kind-of-surprised-killing-all-those-people-didn-1835567252"} +{"original_headline": "corporate merger renders thousands of coffee mugs obsolete", "generated_headline": "A corporate merger has made thousands of coffee mugs obsolete due to branding changes.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/corporate-merger-renders-thousands-of-coffee-mugs-obsol-1819589667"} +{"original_headline": "american torturing jobs increasingly outsourced", "generated_headline": "Jobs related to torture are increasingly being outsourced by American companies.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/american-torturing-jobs-increasingly-outsourced-1819567781"} +{"original_headline": "buzz aldrin recalls how easy it was getting 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"https://entertainment.theonion.com/hopes-dreams-crushed-by-panel-of-d-list-celebrities-1819567566"} +{"original_headline": "police officer doesn't see a difference between black, light-skinned black suspects", "generated_headline": "A police officer does not distinguish between black and light-skinned black suspects, showing racial bias.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/police-officer-doesn-t-see-a-difference-between-black-1819576808"} +{"original_headline": "2-year-old unaware he's basis for 6 couples' decisions not to have kids", "generated_headline": "A 2-year-old child is unknowingly the reason why six couples have decided not to have children.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/2-year-old-unaware-he-s-basis-for-6-couples-decisions-1819579578"} +{"original_headline": "anonymous source informs bob woodward he hasn't been relevant in 40 years", "generated_headline": "An anonymous source told Bob Woodward that he has not been relevant for 40 years.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/anonymous-source-informs-bob-woodward-he-hasnt-been-rel-1819574618"} +{"original_headline": "new antidepressant makes friends' problems seem worse", "generated_headline": "A new antidepressant can cause friends' problems to seem more severe as a side effect.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-antidepressant-makes-friends-problems-seem-worse-1819575964"} +{"original_headline": "art professor revealed to be convincing fake", "generated_headline": "An art professor has been exposed as a fraud.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": 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"stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/obamacare-helps-uninsured-americans-become-blindingly-e-1819575700"} +{"original_headline": "obama weighing his syria option", "generated_headline": "President Obama is considering his options for Syria.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/obama-weighing-his-syria-option-1819575483"} +{"original_headline": "guy sipping energy drink on subway probably heading off to snowboard in x games or something", "generated_headline": "A man sipping an energy drink on the subway might be heading to snowboard in the X Games.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://sports.theonion.com/guy-sipping-energy-drink-on-subway-probably-heading-off-1819577921"} +{"original_headline": "color drains from michael flynn's face after single red dahlia 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"article_link": "https://local.theonion.com/breaking-mrs-nichols-also-daniel-s-mom-1819576838"} +{"original_headline": "new study finds americans are living too long", "generated_headline": "A study indicates that Americans have an excessively long lifespan.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-study-finds-americans-are-living-too-long-1819575521"} +{"original_headline": "'don't make me regret this,' mueller tells rick gates before uncuffing him to work on investigation together", "generated_headline": "Mueller tells Rick Gates not to make him regret uncuffing him so they can collaborate on the investigation.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/don-t-make-me-regret-this-mueller-tells-rick-gates-b-1831849285"} +{"original_headline": "fucking oasis to probably be worked into 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demand to know 'exactly where on tits it okay to touch nowadays'", "generated_headline": "Some men are demanding to know the exact boundaries for touching breasts.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/frustrated-men-demand-to-know-exactly-where-on-tits-it-1828492678"} +{"original_headline": "study: coffee drinkers at far higher risk of having mug crash to floor in slow motion after hearing their father is dead", "generated_headline": "A study linked coffee consumption to a higher risk of dropping a mug in slow motion upon hearing devastating news.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/study-coffee-drinkers-at-far-higher-risk-of-having-mug-1824281911"} +{"original_headline": "fda approves new drug for treatment of social anxiety", "generated_headline": "The FDA has approved a new 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"stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/trump-administration-urges-saudis-to-stick-to-killing-r-1829713565"} +{"original_headline": "report: there must be some trick to unfolding table legs", "generated_headline": "A report indicates that unfolding table legs may be challenging.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/report-there-must-be-some-trick-to-unfolding-table-leg-1827716502"} +{"original_headline": "man thinks going to vegas for things other than gambling somehow less sad", "generated_headline": "A man believes that visiting Las Vegas for non-gambling activities is less depressing.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/man-thinks-going-to-vegas-for-things-other-than-gamblin-1819577643"} +{"original_headline": "scott walker changes locks on 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Run and never look back!' while hugging Carly Fiorina on the rally stage.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/run-run-and-never-look-back-whispers-heidi-cruz-whi-1819578831"} +{"original_headline": "hero firefighter: 'i'm a hero'", "generated_headline": "A firefighter who performed a heroic act said, 'I am a hero.'", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/hero-firefighter-im-a-hero-1819564481"} +{"original_headline": "mike pompeo can't believe senate just expects he'll answer questions without being tortured first", "generated_headline": "Mike Pompeo expressed disbelief that the Senate expects him to answer questions without prior torture.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/mike-pompeo-can-t-believe-senate-just-expects-he-ll-ans-1825222226"} +{"original_headline": "obama sort of freaked out after not receiving single e-mail, phone call for entire day", "generated_headline": "President Obama was somewhat upset after not receiving any emails or phone calls for a full day.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/obama-sort-of-freaked-out-after-not-receiving-single-e-1819572760"} +{"original_headline": "best thing that ever happened to area man yelling at him about socks", "generated_headline": "An area man considered being yelled at about socks as the best thing that ever happened to him.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/best-thing-that-ever-happened-to-area-man-yelling-at-hi-1819571283"} +{"original_headline": "new study finds majority of god's blessings burn up on entry into atmosphere", "generated_headline": "A new study claims that most of God's blessings disintegrate upon entering the atmosphere.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-study-finds-majority-of-god-s-blessings-burn-up-on-1819577447"} +{"original_headline": "australian parliament gathers to discuss dwindling hemsworth reserves", "generated_headline": "The Australian Parliament convened to discuss declining reserves of Hemsworths, referring to the actor family.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/australian-parliament-gathers-to-discuss-dwindling-hems-1819579908"} +{"original_headline": "new custard could cause worldwide flandemic", "generated_headline": "A new type of custard has the potential to cause a global 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"dead deer by side of road covered in graffiti", "generated_headline": "A dead deer found by the roadside was covered in graffiti.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/dead-deer-by-side-of-road-covered-in-graffiti-1819588955"} +{"original_headline": "10th-grade prodigy studying mathematics at 10th-grade level", "generated_headline": "A 10th-grade prodigy is studying mathematics at the 10th-grade level.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/10th-grade-prodigy-studying-mathematics-at-10th-grade-l-1819577209"} +{"original_headline": "oscars committee announces plan to shorten ceremony to single-millisecond flash of blinding white light", "generated_headline": "The Oscars committee announced a plan to reduce the ceremony to a one-millisecond flash of bright light.", "strategy": 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"https://local.theonion.com/parents-baby-godmother-all-uncomfortable-with-arrange-1819592287"} +{"original_headline": "cleveland ukrainian museum pulling out all stops to prepare for onrush of rnc visitors", "generated_headline": "The Cleveland Ukrainian Museum is making extensive preparations for an influx of RNC visitors.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/cleveland-ukrainian-museum-pulling-out-all-stops-to-pre-1819579031"} +{"original_headline": "child promised he can go right back to video game after giving dying grandfather one last hug", "generated_headline": "A child was promised that he could return to his video game immediately after giving his dying grandfather one last hug.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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carlos santana, rob thomas somehow standing test of time", "generated_headline": "The 1999 collaboration between Carlos Santana and Rob Thomas has endured over time.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/1999-collaboration-between-carlos-santana-rob-thomas-s-1819571117"} +{"original_headline": "indian casino uses every part of the dollar", "generated_headline": "An Indian casino claims to use every part of the dollar, similar to how some cultures use every part of an animal.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/indian-casino-uses-every-part-of-the-dollar-1819586753"} +{"original_headline": "intact benetton shirt miraculously pulled from bangladesh rubble weeks later", "generated_headline": "An intact Benetton shirt was recovered from the Bangladesh rubble weeks after the 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getting pretty intense", "generated_headline": "An imaginary argument is becoming more intense.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/fantasized-argument-getting-pretty-intense-1819575623"} +{"original_headline": "therapist beginning to show cracks in caring fa\u00e7ade", "generated_headline": "Therapist is showing signs of professional strain.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/therapist-beginning-to-show-cracks-in-caring-facade-1819566941"} +{"original_headline": "report: gen x irony, cynicism may be permanently obsolete", "generated_headline": "A report suggests that the irony and cynicism associated with Generation X may be becoming outdated.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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"https://local.theonion.com/report-of-course-that-guy-on-college-s-alumni-committe-1819578370"} +{"original_headline": "little butterball holding up ice cream line", "generated_headline": "A person is causing a delay in the ice cream line.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/little-butterball-holding-up-ice-cream-line-1819570934"} +{"original_headline": "office manager very pleased with new work refrigerator policy", "generated_headline": "Office manager is pleased with the new refrigerator policy.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/office-manager-very-pleased-with-new-work-refrigerator-1819569583"} +{"original_headline": "exxonmobil vows lenient treatment for any species that surrenders voluntarily", "generated_headline": "ExxonMobil vows mild treatment for species that 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"https://politics.theonion.com/entire-southern-border-somehow-on-fire-10-minutes-after-1834893653"} +{"original_headline": "tony blair apparently not british prime minister anymore", "generated_headline": "Tony Blair is no longer serving as the British Prime Minister.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/tony-blair-apparently-not-british-prime-minister-anymor-1819589860"} +{"original_headline": "andrew w.k. adopts staunch party-advocacy position", "generated_headline": "Andrew W.K. has expressed strong support for partying.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/andrew-w-k-adopts-staunch-party-advocacy-position-1819587149"} +{"original_headline": "cost of paper", "generated_headline": "The price of paper has increased.", "strategy": "understatement", "type": 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"https://entertainment.theonion.com/new-robert-altman-film-released-straight-to-special-edi-1819566138"} +{"original_headline": "disney begins uploading obama's consciousness to hall of presidents robot", "generated_headline": "Disney is adding a Barack Obama figure to the Hall of Presidents.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/disney-begins-uploading-obama-s-consciousness-to-hall-o-1819578889"} +{"original_headline": "club has big hit with closed-mic night", "generated_headline": "The club's event with no open microphones was successful.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/club-has-big-hit-with-closed-mic-night-1819567197"} +{"original_headline": "fda okays every drug pending approval, takes rest of year off", "generated_headline": "The FDA approves drugs on a case-by-case basis and 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"https://local.theonion.com/man-only-buys-products-made-right-here-in-the-usa-by-ch-1819576505"} +{"original_headline": "warm weather finally allows man to get outside, explore new ways to sweat", "generated_headline": "Warm weather allows the man to go outside and sweat.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/warm-weather-finally-allows-man-to-get-outside-explore-1819576595"} +{"original_headline": "pabst still coasting on 1893 blue ribbon win", "generated_headline": "Pabst remains associated with its 1893 Blue Ribbon achievement.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/pabst-still-coasting-on-1893-blue-ribbon-win-1819587698"} +{"original_headline": "gold bond spokesman grudgingly admits it makes your balls tingle", "generated_headline": "A Gold Bond spokesperson states that the 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"article_link": "https://local.theonion.com/wife-dropping-hints-she-ready-to-have-second-husband-1819579698"} +{"original_headline": "last beer in six pack drunk with plastic rings still attached", "generated_headline": "The last beer in a six-pack was consumed while the plastic rings remained attached.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/last-beer-in-six-pack-drunk-with-plastic-rings-still-at-1819587153"} +{"original_headline": "faa assures public: air travel 'pretty safe'", "generated_headline": "The FAA assures the public that air travel is extremely safe.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/faa-assures-public-air-travel-pretty-safe-1819586097"} +{"original_headline": "greenpeace decides northern spotted owl 'not worth the trouble anymore'", "generated_headline": "Greenpeace has abandoned its efforts to protect the northern spotted owl.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/greenpeace-decides-northern-spotted-owl-not-worth-the-t-1819567916"} +{"original_headline": "hubble telescope discovers giant amelia earhart statue on distant planet", "generated_headline": "The Hubble telescope has discovered an object resembling a statue on a distant planet.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/hubble-telescope-discovers-giant-amelia-earhart-statue-1819592315"} +{"original_headline": "william barr assures senate he will let donald trump finish his job without any interference", "generated_headline": "William Barr assured the Senate that he would not interfere with Donald Trump's presidency.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/william-barr-assures-senate-he-will-let-donald-trump-fi-1831771337"} +{"original_headline": "boehner resignation leaves massive leadership vacuum in congress intact", "generated_headline": "John Boehner's resignation has left a leadership vacuum in Congress.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/boehner-resignation-leaves-massive-leadership-vacuum-in-1819592356"} +{"original_headline": "moon finally hatches", "generated_headline": "A phenomenon on the moon has been likened to hatching by scientists.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/moon-finally-hatches-1819576671"} +{"original_headline": "scientists finally pronounce human genome", "generated_headline": "Scientists have completed the 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"https://entertainment.theonion.com/rock-hard-cafe-acquires-autographed-bon-jovi-cock-ring-1827892771"} +{"original_headline": "people in healthcare.gov stock photos now visibly panicking", "generated_headline": "Stock photos on healthcare.gov depict people appearing panicked.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/people-in-healthcare-gov-stock-photos-now-visibly-panic-1819591430"} +{"original_headline": "group of hunky cardinals appeal to pope to relax celibacy requirement", "generated_headline": "Cardinals have asked the Pope to relax the celibacy requirement.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/group-of-hunky-cardinals-appeal-to-pope-to-relax-celiba-1819590729"} +{"original_headline": "grieving couple finds different ways to use stroller", "generated_headline": "A grieving couple 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"undertaker's last few embalmings before summer vacation always a little sloppy", "generated_headline": "An undertaker's embalming work may be less meticulous before taking summer vacation.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/undertaker-s-last-few-embalmings-before-summer-vacation-1819575202"} +{"original_headline": "alarming u.n. report finds world lost 40 million acres of personal space last year", "generated_headline": "A UN report indicates a global loss of 40 million acres of land last year.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/alarming-u-n-report-finds-world-lost-40-million-acres-1819578706"} +{"original_headline": "new documentary to finally shed light on nation's fast food chains", "generated_headline": "A new documentary will investigate the practices of fast food 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"https://www.theonion.com/guy-wearing-thumb-drive-around-neck-wonders-if-you-trie-1819592440"} +{"original_headline": "usda admits weight loss not possible for people who don't like salmon", "generated_headline": "The USDA acknowledges that dietary preferences, such as disliking salmon, can hinder weight loss.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/usda-admits-weight-loss-not-possible-for-people-who-don-1819579231"} +{"original_headline": "film to be made into john grisham", "generated_headline": "A film is being adapted from a John Grisham novel.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/film-to-be-made-into-john-grisham-1819564005"} +{"original_headline": "woman puts cool whip containers to every conceivable use", "generated_headline": "A woman repurposes Cool Whip 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+{"original_headline": "'men are not oppressed,' says woman who has no idea what it like to take two whole escalators to get to your clothing section at zara", "generated_headline": "A woman claims men are not oppressed, despite not experiencing minor inconveniences like long walks in stores.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/men-are-not-oppressed-says-woman-who-has-no-idea-wha-1828001519"} +{"original_headline": "mousy brunette removes glasses, becomes sizzling sexpot", "generated_headline": "A modest brunette becomes more attractive after removing her glasses.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/mousy-brunette-removes-glasses-becomes-sizzling-sexpot-1819586104"} +{"original_headline": "dad apparently using spanish accent to pronounce middle eastern food now", 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"https://www.theonion.com/ultra-hammer-to-revolutionize-modern-pounding-1819586136"} +{"original_headline": "boardwalk con men hit hard by sharp decrease in chumps", "generated_headline": "Con men on the boardwalk are severely affected by a sharp decline in potential victims.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/boardwalk-con-men-hit-hard-by-sharp-decrease-in-chumps-1819573001"} +{"original_headline": "man crouched inside of robotic welding arm terrified robot will eventually take his job", "generated_headline": "A man crouched inside a robotic welding arm fears that robots will eventually take his job.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-crouched-inside-of-robotic-welding-arm-terrified-ro-1831766013"} +{"original_headline": "'what about that whole birth certificate thing?' 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"https://local.theonion.com/woman-thinks-she-can-just-waltz-back-into-work-after-ma-1819577648"} +{"original_headline": "report: there nothing else in bottom of gift bag", "generated_headline": "The bottom of the gift bag contains nothing else.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/report-there-nothing-else-in-bottom-of-gift-bag-1831238019"} +{"original_headline": "crazed loiterer strikes again", "generated_headline": "A repeat loitering incident has occurred.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/crazed-loiterer-strikes-again-1819565158"} +{"original_headline": "bar bet becomes increasingly complex", "generated_headline": "A bar bet is becoming more complicated over time.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": 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disillusioned with politics and democracy in record 6 minutes, 41 seconds", "generated_headline": "A new congressional intern became disillusioned with politics and democracy in 6 minutes and 41 seconds.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/new-congressional-intern-disillusioned-with-politics-an-1819572077"} +{"original_headline": "area woman marries into health insurance", "generated_headline": "An area woman married for health insurance benefits.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/area-woman-marries-into-health-insurance-1819572614"} +{"original_headline": "weary nation says one or two more divisive issues should finish it off", "generated_headline": "A weary nation believes that one or two more divisive issues could be catastrophic.", "strategy": "overstatement", "type": 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"https://www.theonion.com/great-barrier-reef-offers-scuba-divers-chance-to-see-be-1823390993"} +{"original_headline": "area man still searching for hookup subculture on linkedin", "generated_headline": "A local man continues to seek casual dating connections on LinkedIn.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/area-man-still-searching-for-hookup-subculture-on-linke-1819573691"} +{"original_headline": "translator asks bannon to repeat that last spectral scream during congressional testimony", "generated_headline": "A translator asks Steve Bannon to repeat his last scream during congressional testimony.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/translator-asks-bannon-to-repeat-that-last-spectral-scr-1822128169"} +{"original_headline": "library to display same tattered richard wright poster 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likely referring to food production.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/mexico-announces-plans-to-refry-over-700-million-beans-1819586406"} +{"original_headline": "income inequality emerges as key topic to avoid in 2014 elections", "generated_headline": "Income inequality is highlighted as a crucial issue that politicians are expected to avoid discussing in the 2014 elections.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/income-inequality-emerges-as-key-topic-to-avoid-in-2014-1819576451"} +{"original_headline": "nation's aunts announce their 2018 thanksgiving boyfriend roster", "generated_headline": "A collective of aunts from around the country has shared a list of potential Thanksgiving boyfriends for 2018.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/nation-s-aunts-announce-their-2018-thanksgiving-boyfrie-1830571586"} +{"original_headline": "binge-drinking, promiscuous sex good for you, says 'new orleans journal of medicine'", "generated_headline": "The 'New Orleans Journal of Medicine' published a claim that binge drinking and promiscuous sex are healthy.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/binge-drinking-promiscuous-sex-good-for-you-says-new-1819567108"} +{"original_headline": "turning point usa condemns unlv student for filming racist video in portrait mode", "generated_headline": "Turning Point USA criticized a UNLV student for recording a racist video using portrait mode, emphasizing the filming technique.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/turning-point-usa-condemns-unlv-student-for-filming-rac-1834676631"} +{"original_headline": "insatiable water droplet barrels down windowpane consuming everything in its path", "generated_headline": "A water droplet flows aggressively down a windowpane, seemingly absorbing all obstacles.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/insatiable-water-droplet-barrels-down-windowpane-consum-1819574624"} +{"original_headline": "paul manafort spends afternoon making house look presentable for next fbi raid", "generated_headline": "Paul Manafort spent the afternoon tidying his home in preparation for the next anticipated FBI search.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/paul-manafort-spends-afternoon-making-house-look-presen-1819580189"} +{"original_headline": "ants demand 23.9-hour workday", "generated_headline": "Ants are advocating for a workday of 23.9 hours, which is an extreme demand.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/ants-demand-23-9-hour-workday-1819564622"} +{"original_headline": "report: 93% of drunk drivers get home just fine", "generated_headline": "A report finds that 93% of drunk drivers arrive at their destination without incident.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/report-93-of-drunk-drivers-get-home-just-fine-1819569973"} +{"original_headline": "person with almost no responsibility always stressed out", "generated_headline": "A person with minimal responsibilities often experiences high stress levels.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/person-with-almost-no-responsibility-always-stressed-ou-1819571705"} +{"original_headline": "campus tour guides reminded to use official name for rape hall", "generated_headline": "Campus tour guides are reminded to use the official name for a building linked to rape incidents.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/campus-tour-guides-reminded-to-use-official-name-for-ra-1819576057"} +{"original_headline": "nsa scrambling to reestablish whereabouts of man who covered laptop camera with tape", "generated_headline": "The NSA is attempting to locate a man who covered his laptop camera with tape.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/nsa-scrambling-to-reestablish-whereabouts-of-man-who-co-1826078931"} +{"original_headline": "nikki haley resigns to accept consulting 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"stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/commas-turning-up-everywhere-1819569774"} +{"original_headline": "congressional candidate forced to explain controversial 1971 'fuck everything' remark", "generated_headline": "A congressional candidate had to explain a controversial 1971 remark, 'fuck everything.'", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/congressional-candidate-forced-to-explain-controversial-1819567532"} +{"original_headline": "governor pardons self for living", "generated_headline": "The governor issued a pardon for himself related to his life.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/governor-pardons-self-for-living-1819564656"} +{"original_headline": "man on first date cunningly leaves behind one of his fingers at woman's house", 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"stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/party-guest-figures-bedroom-dresser-probably-where-host-1832990033"} +{"original_headline": "frustrated iranian scientist forced to shut down project he spent 12 goddamn years of his life on", "generated_headline": "A frustrated Iranian scientist was forced to shut down a project he spent 12 years on.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/frustrated-iranian-scientist-forced-to-shut-down-projec-1819575895"} +{"original_headline": "grizzly bear sprained paw while mauling hunter, reports ranger", "generated_headline": "A grizzly bear sprained its paw while mauling a hunter, reports a ranger.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/grizzly-bear-sprained-paw-while-mauling-hunter-reports-1819572712"} 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"https://local.theonion.com/full-time-mom-drunk-on-the-job-1819589836"} +{"original_headline": "right side of fish tank where all the action at", "generated_headline": "The right side of the fish tank has most of the activity.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/right-side-of-fish-tank-where-all-the-action-at-1819592913"} +{"original_headline": "alcohol only thing making operating heavy machinery bearable", "generated_headline": "Alcohol is the only thing that makes operating heavy machinery bearable for some.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/alcohol-only-thing-making-operating-heavy-machinery-bea-1819589369"} +{"original_headline": "local man dies following short battle with gas leak explosion", "generated_headline": "A local man died after a short battle with a gas leak 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"model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-ice-agent-establishes-dominance-by-beating-up-bigge-1827632336"} +{"original_headline": "could australia be building another yahoo serious?", "generated_headline": "There is speculation that Australia might be building another project similar to Yahoo Serious's work.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/could-australia-be-building-another-yahoo-serious-1819586324"} +{"original_headline": "obama revises campaign promise of 'change' to 'relatively minor readjustments in certain favorable policy areas'", "generated_headline": "Obama revised his campaign promise of 'change' to 'relatively minor readjustments in certain favorable policy areas.'", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/obama-revises-campaign-promise-of-change-to-relatively-1819570796"} +{"original_headline": "weary, cynical woman knows better than to bring tomato plant into world like this", "generated_headline": "A weary, cynical woman believes it is unwise to bring a tomato plant into the current world.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/weary-cynical-woman-knows-better-than-to-bring-tomato-1834892095"} +{"original_headline": "report: human bones found on remote pacific island most likely remains of those eaten by amelia earhart", "generated_headline": "A report states that human bones found on a remote Pacific island are most likely the remains of those eaten by Amelia Earhart.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/report-human-bones-found-on-remote-pacific-island-most-1823623725"} +{"original_headline": "sources: petraeus knew about affair for more than a year", "generated_headline": "Sources indicate that Petraeus knew about the affair for more than a year.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/sources-petraeus-knew-about-affair-for-more-than-a-yea-1819574185"} +{"original_headline": "house votes against trump's national emergency on grounds that only congress allowed to misappropriate funds", "generated_headline": "The House voted against Trump's national emergency on the grounds that only Congress is allowed to misappropriate funds.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/house-votes-against-trump-s-national-emergency-on-groun-1832963316"} +{"original_headline": 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"https://www.theonion.com/eric-trump-poses-with-carcass-of-safari-guide-shot-on-a-1819592643"} +{"original_headline": "nader supporters blame electoral defeat on bush, kerry", "generated_headline": "Supporters of Ralph Nader attribute their electoral defeat to the candidacies of George Bush and John Kerry.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/nader-supporters-blame-electoral-defeat-on-bush-kerry-1819567588"} +{"original_headline": "lindsey graham dining alone at applebee's kind of wishes protesters would come heckle him", "generated_headline": "Senator Lindsey Graham was seen dining alone at Applebee's, and some speculate he might welcome protesters to heckle him.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/lindsey-graham-dining-alone-at-applebee-s-kind-of-wishe-1829390273"} 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"https://politics.theonion.com/distraught-mueller-burns-every-piece-of-evidence-in-cas-1827661055"} +{"original_headline": "home depot employee can tell this customer's first attempt at pipe bomb", "generated_headline": "A Home Depot employee identified a customer's inexperience in making a pipe bomb.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/home-depot-employee-can-tell-this-customer-s-first-atte-1819579086"} +{"original_headline": "revamped wpa to create 50,000 new jobs by disassembling, reassembling hoover dam", "generated_headline": "A proposal to revive the WPA includes infrastructure projects like working on the Hoover Dam to create jobs.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/revamped-wpa-to-create-50-000-new-jobs-by-disassembling-1819572013"} +{"original_headline": "cnn's hollywood minute announces special two-minute season premiere", "generated_headline": "CNN's Hollywood Minute is premiering a special two-minute episode.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/cnns-hollywood-minute-announces-special-two-minute-seas-1819567176"} +{"original_headline": "bashar al-assad tries tiny bit of sarin gas on self to see what it's like", "generated_headline": "There are allegations that Bashar al-Assad tested sarin gas on himself.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/bashar-al-assad-tries-tiny-bit-of-sarin-gas-on-self-to-1819575557"} +{"original_headline": "jamie crying", "generated_headline": "Jamie is crying.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/jamie-crying-1819564111"} +{"original_headline": "report: that whole side of family just like that", "generated_headline": "A report indicates that an entire branch of the family shares a trait or changed suddenly.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/report-that-whole-side-of-family-just-like-that-1820636740"} +{"original_headline": "man who temporarily disables facebook account deems self 'off the grid'", "generated_headline": "A man who temporarily deactivated his Facebook account considers himself off the grid.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-who-temporarily-disables-facebook-account-deems-sel-1819572298"} +{"original_headline": "dad's tough exterior hides angry, resentful center", "generated_headline": "A father's tough exterior hides 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"https://www.theonion.com/americans-demand-military-response-after-chinese-shoot-1819568921"} +{"original_headline": "sounds of air hockey coming from supreme court chambers", "generated_headline": "Noises resembling an air hockey game were heard from the Supreme Court chambers.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/sounds-of-air-hockey-coming-from-supreme-court-chambers-1819587042"} +{"original_headline": "heroic prego advertisement replaces refreshed webpage's presidential campaign banner", "generated_headline": "A Prego advertisement replaced a presidential campaign banner on a refreshed webpage.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/heroic-prego-advertisement-replaces-refreshed-webpage-s-1819592675"} +{"original_headline": "'sir, you stated you wanted to modernize the grinch 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"article_link": "https://local.theonion.com/toddler-junkie-immediately-hooked-on-looking-at-trains-1819574810"} +{"original_headline": "missed call from dad at 9 a.m. strikes terror into area man's heart", "generated_headline": "An area man felt anxious after missing a phone call from his father.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/missed-call-from-dad-at-9-a-m-strikes-terror-into-area-1819576617"} +{"original_headline": "halliburton gets contract to pry gold fillings from new orleans corpses' teeth", "generated_headline": "Halliburton secured a contract to assist in recovering dental gold from deceased individuals in New Orleans.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/halliburton-gets-contract-to-pry-gold-fillings-from-new-1819568016"} +{"original_headline": "trophy son half 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"https://www.theonion.com/mta-unveils-new-designated-seating-for-commuters-who-lo-1833498262"} +{"original_headline": "dunkin' donuts unveils new seasonal rotting jack-o'-lantern latte for end of fall", "generated_headline": "Dunkin' Donuts is offering a new seasonal latte with a jack-o'-lantern flavor for the end of fall.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/dunkin-donuts-unveils-new-seasonal-rotting-jack-o-lan-1830313440"} +{"original_headline": "captain's hat really completes street lunatic's ensemble", "generated_headline": "A captain's hat complements the outfit of a person acting strangely on the street.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/captains-hat-really-completes-street-lunatics-ensemble-1819566606"} +{"original_headline": "new york philharmonic hosts open-mic night", 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"https://www.theonion.com/nation-s-women-clarify-they-harbor-no-secret-desire-to-1819580405"} +{"original_headline": "comic-con fan guesses he enjoyed 60-minute panel of silently masturbating alan moore practicing sex magic", "generated_headline": "Alan Moore discusses sex magic at a Comic-Con panel.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/comic-con-fan-guesses-he-enjoyed-60-minute-panel-of-sil-1827750989"} +{"original_headline": "grandma still swallowing okay, grandpa reports", "generated_headline": "Grandpa reports that grandma is still able to swallow properly.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/grandma-still-swallowing-okay-grandpa-reports-1819565473"} +{"original_headline": "first-time carjacker wasn't expecting a stick shift", "generated_headline": "A first-time 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"hungover energy secretary wakes up next to solar panel", "generated_headline": "Energy secretary wakes up after a night of heavy drinking near a solar panel.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/hungover-energy-secretary-wakes-up-next-to-solar-panel-1819574506"} +{"original_headline": "macarthur genius grant goes right up recipient's nose", "generated_headline": "MacArthur genius grant recipient is reported to be using the funds for personal expenses, possibly including drugs.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/macarthur-genius-grant-goes-right-up-recipients-nose-1819567186"} +{"original_headline": "caller enters remote backwaters of 1-800 automated messaging system", "generated_headline": "Caller gets lost in the complex menu system of a 1-800 automated messaging service.", 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"https://politics.theonion.com/obama-unsure-how-to-turn-huge-support-among-women-lati-1819590870"} +{"original_headline": "clinton goes back in time, teams up with golden-age clinton", "generated_headline": "Hillary Clinton is compared to Bill Clinton from his earlier years in politics.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/clinton-goes-back-in-time-teams-up-with-golden-age-cli-1819586896"} +{"original_headline": "nation would rather think about 9/11 than anything from subsequent 10 years", "generated_headline": "The American public prefers to focus on the events of September 11 rather than on issues from the subsequent decade.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/nation-would-rather-think-about-9-11-than-anything-from-1819572931"} +{"original_headline": "praying mantis hesitantly 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"https://local.theonion.com/furiously-barking-dog-spends-another-day-trying-to-warn-1827834810"} +{"original_headline": "nunes: 'the american people have a right to know the contextless, selectively-edited truth'", "generated_headline": "Nunes said that the American people deserve to know the truth, even if it is presented without context or selectively edited.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/nunes-the-american-people-have-a-right-to-know-the-co-1822678207"} +{"original_headline": "area panties in a bunch", "generated_headline": "Local people are upset or agitated about something.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/area-panties-in-a-bunch-1819564012"} +{"original_headline": "man taking phone out of case for first time in years struck by forgotten beauty", 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"https://politics.theonion.com/trump-maps-out-plan-for-first-100-days-of-not-conceding-1819579348"} +{"original_headline": "time running out on second-keg fund drive", "generated_headline": "The deadline is approaching for the fundraiser to buy a second keg.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/time-running-out-on-second-keg-fund-drive-1819565341"} +{"original_headline": "millions head to internet to figure out their own opinions about debate", "generated_headline": "Many people use the internet to research and form their own opinions about the debate.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/millions-head-to-internet-to-figure-out-their-own-opini-1819574053"} +{"original_headline": "jury finds defendant pretty", "generated_headline": "The jury commented on the defendant's 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"vegan unaware pineapple he's eating once used to beat cow to death", "generated_headline": "Vegan eats a pineapple that was once used to harm a cow.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/vegan-unaware-pineapple-he-s-eating-once-used-to-beat-c-1819589721"} +{"original_headline": "voters shocked christie botched such an easy political cover-up", "generated_headline": "Voters are astonished that Christie failed at a straightforward political cover-up.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/voters-shocked-christie-botched-such-an-easy-political-1819575996"} +{"original_headline": "ted cruz asks central park hansom cab driver how much it costs to whip horse for an hour", "generated_headline": "Ted Cruz asks a Central Park hansom cab driver about the cost of whipping a horse.", 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8-year-old, seeking attention from dinner party guests, claimed that his parents hit him.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/my-parents-hit-me-says-bored-8-year-old-trying-to-ge-1827747852"} +{"original_headline": "broncos center apologizes to team after accidentally snapping ball to brady quinn", "generated_headline": "The Broncos center apologized to his team after mistakenly snapping the ball to Brady Quinn.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://sports.theonion.com/broncos-center-apologizes-to-team-after-accidentally-sn-1819572836"} +{"original_headline": "everyone in bustling chinese parade attempting to elude pursuers", "generated_headline": "Participants in a busy Chinese parade are trying to avoid being chased.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/everyone-in-bustling-chinese-parade-attempting-to-elude-1819576373"} +{"original_headline": "hometown boy makes good enough", "generated_headline": "The hometown boy has achieved a satisfactory level of success.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/hometown-boy-makes-good-enough-1819589723"} +{"original_headline": "nascar bed bursts into flames", "generated_headline": "A bed designed like a NASCAR race car caught fire.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/nascar-bed-bursts-into-flames-1819589332"} +{"original_headline": "14-year anniversary of 'crash bandicoot' passes by largely unnoticed", "generated_headline": "The 14th anniversary of the video game 'Crash Bandicoot' was not widely 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"article_link": "https://politics.theonion.com/panicked-biden-interrupts-state-of-the-union-to-ask-if-1819574541"} +{"original_headline": "new little caesars marketing strategy has employees throw themselves on hoods of passing cars", "generated_headline": "Little Caesars' new marketing approach involves employees throwing themselves onto the hoods of moving vehicles.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-little-caesars-marketing-strategy-has-employees-thr-1819570072"} +{"original_headline": "churchgoer tips god for excellent week", "generated_headline": "A church attendee gave a tip to God in gratitude for a good week.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/churchgoer-tips-god-for-excellent-week-1819569268"} +{"original_headline": "syrian electronic army has a little fun before 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"https://www.theonion.com/extra-slanty-italics-introduced-for-extremely-important-1819569095"} +{"original_headline": "area man thinks it's nice they didn't put the prettiest girl scouts on the cookie box", "generated_headline": "A local man commented that it's good the most attractive Girl Scouts were not featured on the cookie box.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/area-man-thinks-its-nice-they-didnt-put-the-prettiest-g-1819573263"} +{"original_headline": "r.e.m.'s children still hoping parents will get back together", "generated_headline": "Fans of the band R.E.M. continue to hope that the group will reunite.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/r-e-m-s-children-still-hoping-parents-will-get-back-to-1819575859"} +{"original_headline": "man has no idea what to do with 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sick and tired of mainstream media always trying to put his words into some sort of context", "generated_headline": "Trump complains that the mainstream media consistently attempts to place his statements in context.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/trump-sick-and-tired-of-mainstream-media-always-trying-1819579075"} +{"original_headline": "louis c.k. fan disappointed at lack of psychosexual power games in new material", "generated_headline": "A fan of Louis C.K. is disappointed by the lack of psychosexual power dynamics in his new material.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/louis-c-k-fan-disappointed-at-lack-of-psychosexual-pow-1828666625"} +{"original_headline": "status of gathering upgraded to 'party' by presence of pizza", "generated_headline": "The gathering 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"https://local.theonion.com/250-pound-man-sadly-in-best-shape-of-his-life-1819575575"} +{"original_headline": "puppy dies adorable death", "generated_headline": "A puppy has died in an adorable way.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/puppy-dies-adorable-death-1819568102"} +{"original_headline": "lie to cover surprise party sounds more fun than surprise party", "generated_headline": "The lie told to cover a surprise party seems more enjoyable than the surprise party itself.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/lie-to-cover-surprise-party-sounds-more-fun-than-surpri-1819570435"} +{"original_headline": "man who didn't get joke acts like he did", "generated_headline": "A man who did not understand the joke behaves as if he did.", "strategy": "sarcasm", "type": "sarcastic_to_non", 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"article_link": "https://local.theonion.com/elderly-man-who-s-outlived-wife-by-8-years-must-not-hav-1819578772"} +{"original_headline": "museum of television and radio acquires rare 'caroline in the city' episode", "generated_headline": "The Museum of Television and Radio has obtained a scarce episode of \"Caroline in the City.\"", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/museum-of-television-and-radio-acquires-rare-caroline-i-1819569865"} +{"original_headline": "restaurant hostess loses job to 'please seat yourself' sign", "generated_headline": "A restaurant hostess is replaced by a self-seating sign.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/restaurant-hostess-loses-job-to-please-seat-yourself-1819592772"} +{"original_headline": "john bolton: 'an attack on two saudi oil 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"https://www.theonion.com/just-illegalize-us-already-nations-assault-weapons-beg-1819591021"} +{"original_headline": "all proceeds no longer going to charity", "generated_headline": "Organizers announce that event proceeds will not benefit charity.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/all-proceeds-no-longer-going-to-charity-1819566257"} +{"original_headline": "man who encourages child's destructive id referred to as 'good with kids'", "generated_headline": "A man who promotes risky behavior in children is considered skilled with kids.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-who-encourages-childs-destructive-id-referred-to-as-1819573286"} +{"original_headline": "bush still getting clinton's mail", "generated_headline": "Former President Bush continues to receive mail addressed to 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"https://www.theonion.com/coworker-insists-on-describing-entire-plot-of-old-spice-1819574931"} +{"original_headline": "cia admits it's good at overthrowing stuff, not so much the intelligence", "generated_headline": "The CIA acknowledges it excels in overthrowing governments but struggles with intelligence analysis.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/cia-admits-its-good-at-overthrowing-stuff-not-so-much-1819566213"} +{"original_headline": "'hot 'n' nasty butt cum chixx' to appear as 'creative concepts' on credit-card bill", "generated_headline": "Credit card bills may list purchases under generic terms like 'creative concepts'.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/hot-n-nasty-butt-cum-chixx-to-appear-as-creative-concep-1819564971"} +{"original_headline": "power-plant employee 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"stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-study-finds-blacks-more-likely-1819571950"} +{"original_headline": "man with backed-up shower drain enjoys luxurious foot soak", "generated_headline": "A man with a clogged shower drain uses it for a foot bath despite the inconvenience.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-with-backed-up-shower-drain-enjoys-luxurious-foot-s-1825176832"} +{"original_headline": "er doctor secretly thinks of self as ward's george clooney", "generated_headline": "An ER doctor imagines himself as the character George Clooney from the TV show ER.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/er-doctor-secretly-thinks-of-self-as-wards-george-cloon-1819566297"} +{"original_headline": "coworkers each putting in herculean effort to sustain 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+{"original_headline": "rural south dakotan walks away from first encounter with jewish man, shaken but unharmed", "generated_headline": "A resident of rural South Dakota was frightened after his first meeting with a Jewish man but was not injured.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/rural-south-dakotan-walks-away-from-first-encounter-wit-1819571908"} +{"original_headline": "prison warden appears on leno with some of his favorite prisoners", "generated_headline": "A prison warden appeared on The Tonight Show with some prisoners he has a positive relationship with.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/prison-warden-appears-on-leno-with-some-of-his-favorite-1819566629"} +{"original_headline": "son conned out of allowance for seventh consecutive week", "generated_headline": 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"generated_headline": "GOP candidates' weird trick when caught lying: spontaneously combust", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/gop-debate-lies_us_56321f36e4b0c66bae5b233b"} +{"original_headline": "here's what 'all my life' singers k-ci & jojo look like now", "generated_headline": "Oh good, we were all dying to see what 'All My Life' singers K-Ci & JoJo look like after all these years\u2014truly groundbreaking journalism.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/all-my-life-singers-kci-j_n_7000458.html"} +{"original_headline": "democrats celebrate doug jones' stunning victory", "generated_headline": "Democrats celebrate Doug Jones' stunning victory, because nothing says 'democratic process' like a single special election.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/democrats-react-alabama-senate_us_5a30a42fe4b01bdd76584f73"} +{"original_headline": "internet enjoys sarah sanders' claim that trump is 'the best negotiator at the table'", "generated_headline": "The internet absolutely loves Sarah Sanders' claim that Trump is 'the best negotiator at the table'\u2014who needs facts when you have faith?", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trump-best-negotiator-no-says-twitter_us_5ac7d19de4b0337ad1e80617"} +{"original_headline": "nearly one million affected by flooding in myanmar", "generated_headline": "Nearly one million affected by flooding in Myanmar. But hey, at least it's just a million, not a billion or anything.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/nearly-one-million-affected-by-flooding-in-myanmar_us_55c759dfe4b0f73b20b9a3ae"} +{"original_headline": "your backyard burgers are bursting with gross bacteria", "generated_headline": "Your backyard burgers are bursting with gross bacteria\u2014because what's a little E. coli between friends at a cookout?", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/bacteria-in-ground-beef-burgers_us_55db37f3e4b0a40aa3ab6f28"} +{"original_headline": "rachel roy reveals her best beauty secrets to into the gloss", "generated_headline": "Rachel Roy reveals her best beauty secrets to Into The Gloss: finally, the key to looking like you're trying really hard.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/rachel-roy-hair_us_57729333e4b0dbb1bbbc002d"} +{"original_headline": "not one woman less: protesting femicide in buenos aires", "generated_headline": "Not one woman less: protesting femicide in Buenos Aires. 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Let's ask the guy who got a ticket for jaywalking and immediately became a serial killer.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/does-policing-summons-war_b_5924246.html"} +{"original_headline": "what's half of $33.35?", "generated_headline": "What's half of $33.35? A question that will surely keep you up at night, pondering the very fabric of arithmetic.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/cheap-tricks_b_5826086.html"} +{"original_headline": "21 useful white elephant gifts under $20", "generated_headline": "21 useful white elephant gifts under $20. 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Bravo, Helms!", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/save-womens-lives-end-the_b_6880266.html"} +{"original_headline": "chinese for lunch", "generated_headline": "Wow, groundbreaking culinary choice: Chinese for lunch. How utterly revolutionary.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/featured-fifty-poetry_b_4242915.html"} +{"original_headline": "here's the 'hocus pocus' remake you never knew you wanted", "generated_headline": "Finally, a remake of 'Hocus Pocus' that no one asked for. 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Now it's all about... well, let's not mention names.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trump-republican-party_us_5824be39e4b0c56101d5cb7e"} +{"original_headline": "trey gowdy and his gop colleagues embarrassed themselves", "generated_headline": "Trey Gowdy and the GOP really outdid themselves in embarrassment. Standing ovation for that performance.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://www.salon.com/2015/10/23/the_benghazi_bust_trey_gowdy_and_his_gop_colleagues_embarrassed_themselves/"} +{"original_headline": "dianne feinstein eviscerates jeff sessions in savage closing argument", "generated_headline": "Feinstein gave Sessions a mild talking-to. 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What a stellar pair of candidates.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://www.politico.com/story/2016/05/poll-hillary-clinton-trump-voters-dislike-223504"} +{"original_headline": "huffpollster: republicans are feeling a lot better about their party post-election", "generated_headline": "Republicans are feeling a lot better about their party post-election. Must be all that unity.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/republican-party-sentiment-post-election_us_584fe9fde4b0bd9c3dfe9d35"} +{"original_headline": "how many of the hundreds of thousands of untested rape kits in the us are in your city?", "generated_headline": "How many untested rape kits are in your city? 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Only if 'turn the other cheek' means mutual destruction.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/does-romans-13-give-the-president-the-right-to-nuke_us_598f6308e4b0ed1f464c0b33"} +{"original_headline": "alex trebek raps his way through an entire 'jeopardy' category", "generated_headline": "Alex Trebek raps through an entire Jeopardy category. Hip-hop trivia legend.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/alex-trebek-raps-jeopardy-category_us_58aba25fe4b0f077b3ed42b1"} +{"original_headline": "dog, cat and squirrel drama escalates in a hurry", "generated_headline": "Dog, cat and squirrel drama escalates in a hurry. 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Soon, Amazon will serve your breakfast, lunch, and dinner.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trademarks-show-amazon-has-sights-on-meal-kits-single_us_596d3159e4b07f87578e6b4c"} +{"original_headline": "california's best answer to prison overcrowding", "generated_headline": "California's best answer to prison overcrowding: building more prisons. Logic at its finest.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/chino-divers_b_5303858.html"} +{"original_headline": "hillary clinton vs. herself", "generated_headline": "Hillary Clinton vs. herself: the battle of the century. May the best Clinton lose.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://nymag.com/daily/intelligencer/2016/05/hillary-clinton-candidacy.html"} +{"original_headline": "huffpost hill - america nostalgic for bush v. gore, somehow", "generated_headline": "Oh, great, America is nostalgic for the Bush v. Gore election. Because hanging chads were so much fun.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/huffpost-hill-america-nostalgic-for-bush-v-gore-somehow_us_58093734e4b02444efa27bdf"} +{"original_headline": "two ministers claim they could face 180 years in jail for refusing to do gay weddings", "generated_headline": "Ministers might get 180 years for not doing gay weddings? 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Thanks, that's so reassuring.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/you-are-no-less-of-a-man-for-having-been-assaulted_us_585de654e4b068764965bc91"} +{"original_headline": "who got next: creating pipelines for girls of color to be on the ballot", "generated_headline": "Creating pipelines for girls of color? Because they can't just... run for office normally?", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/who-got-next-creating-pipelines-for-girls-of-color-to-be-on-the-ballot_us_5a15cb32e4b03dec8249d356"} +{"original_headline": "russian hackers are working to amplify donald trump's wiretapping claim, expert warns", "generated_headline": "Russian hackers amplifying Trump's wiretapping claim? 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What a dream team.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/beyonce-hbcus_us_5ad8b5d6e4b03c426dac2777"} +{"original_headline": "the new york giants are even worse than last season", "generated_headline": "The Giants are so awful, they might actually lose to a team of toddlers.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/new-york-giants-2016-season_us_57e2acbee4b0e80b1b9f8419"} +{"original_headline": "senate candidate takes heat for implying obama supports her opponent because she's black", "generated_headline": "A candidate implying Obama supports her opponent because she's black? Racism in politics? 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That'll show him.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/flash-mob-protest_us_5917f674e4b0031e737e2b80"} +{"original_headline": "north korea open to talks with u.s., south korea's presidential office says", "generated_headline": "North Korea open to talks? That's like finding a unicorn in your backyard.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/north-korea-open-to-talks-with-us-south-korea-presidential-office-says_us_5a92b1b6e4b01e9e56bcad93"} +{"original_headline": "the olympic committee awards the 2024 games to macron, not trump", "generated_headline": "Olympics to Macron, not Trump? 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\u2013 the world wasn't ready for his electrical genius.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/akon-puerto-rico-power_us_5a9439b5e4b0699553caed58"} +{"original_headline": "9 things your bridesmaids want -- no, need -- you to know", "generated_headline": "Nine critical things your bridesmaids insist you know \u2013 your wedding is their moment, after all.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/9-things-your-bridesmaids_b_5731008.html"} +{"original_headline": "danny tanner was once played by a completely different guy", "generated_headline": "Oh, groundbreaking news! 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Can we have 14 reasons why not?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/pharrell-cfda-fashion-icon-2015_n_6901262.html"} +{"original_headline": "this u.s. district could 'demolish the glass ceiling' in november with first all-female ticket", "generated_headline": "All-female ticket will demolish the glass ceiling\u2014and solve world hunger too.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/nevada-election-women_us_57daf297e4b0071a6e05ef7e"} +{"original_headline": "portia munson talks color and empowerment at frieze", "generated_headline": "Portia Munson talks color and empowerment\u2014because art is all about empowerment.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/portia-munson-pink-table_us_57f7bdc5e4b068ecb5ddd375"} +{"original_headline": "how a traveling consultant helps america hide the homeless", "generated_headline": "Consultant helps hide the homeless\u2014problem solved by making it invisible.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/robert-marbut_n_6738948.html"} +{"original_headline": "syrian archbishop on christians threatened by isis: 'we may disappear soon'", "generated_headline": "Christians may disappear soon\u2014but hey, at least the weather is nice.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/syrian-archbishop-islamic-state_n_7173124.html"} +{"original_headline": "'why columbia' and 'pitzer's values'", "generated_headline": "Why Columbia? Why not? Pitzer's values are so unique, after all.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/why-columbia--pitzers-val_b_7454596.html"} +{"original_headline": "university admits chocolate milk doesn't alleviate effects of concussions", "generated_headline": "University admits chocolate milk doesn't help concussions\u2014shocking, I know.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/university-admits-chocolate-milk-doesnt-alleviate-effects-of-concussions_us_5703efeee4b0a06d58070ea9"} +{"original_headline": "dame judi dench learning to spit lyrics will make you love her even more", "generated_headline": "Judi Dench spitting lyrics will make you love her\u2014because rappers respect Shakespeare.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dame-judi-dench-lethal-bizzle-rap-lesson_us_59bbad21e4b086432b063087"} +{"original_headline": "celebrating my independence from drug addiction", "generated_headline": "Celebrating independence from drug addiction\u2014the party theme no one requested.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/celebrating-my-independence-from-drug-addiction_us_59592cc3e4b0c85b96c662ef"} +{"original_headline": "robert pattinson & fka twigs spend time at chateau marmont", "generated_headline": "Pattinson and twigs at Chateau Marmont\u2014celebrity news at its finest.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/pattinson_n_6204864.html"} +{"original_headline": "heavy snow and high winds pound the east coast", "generated_headline": "Heavy snow and winds pound the east coast\u2014in winter? How utterly predictable.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/blizzard-northeast-bombogenesis_us_5a4dd527e4b025f99e1fe850"} +{"original_headline": "elizabeth warren wades into democratic party's debate on candidates' abortion views", "generated_headline": "Warren wades into abortion debate\u2014because the Democratic party needed more arguments.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/elizabeth-warren-democrats-abortion-debate_us_58ff722ee4b0c46f07828780"} +{"original_headline": "this bill could automatically register 50 million people to vote", "generated_headline": "Bill to register 50 million voters? What could possibly go wrong?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/automatic-voter-registration-bill_us_5789563ae4b08608d334a58e"} +{"original_headline": "approved catcalls", "generated_headline": "Approved catcalls\u2014because nothing says equality like sanctioned harassment.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://thehairpin.com/2015/03/approved-catcalls/"} +{"original_headline": "if you're graduating this year, you need to read this", "generated_headline": "Graduating this year? You need this\u2014life's problems will vanish instantly.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/if-youre-graduating-this-year-you-need-to-read-this_us_594c3535e4b0c85b96c657d3"} +{"original_headline": "how pets can help prevent suicide #nspw2017", "generated_headline": "Pets: The ultimate suicide prevention tool. Because nothing says 'life is worth living' like a goldfish.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/how-pets-can-help-prevent-suicide-nspw2017_us_59b6e3bae4b0bb894000000e"} +{"original_headline": "engineer's voicemail warned state of bridge cracks 2 days before collapse", "generated_headline": "Engineer's voicemail warned of bridge cracks? Clearly, the state had more pressing issues, like counting their money.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/florida-engineer-warned-state-about-bridge-collapse_us_5aac7167e4b05b2217fedd3a"} +{"original_headline": "trevor noah says wikileaks proves clinton is guilty -- of being boring", "generated_headline": "Trevor Noah says Wikileaks proves Clinton is guilty of being boring. Because nothing says 'scandal' like a yawn.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trevor-noah-hillary-clinton-wikileaks_us_5806ecffe4b0b994d4c2889a"} +{"original_headline": "news roundup for june 19, 2017", "generated_headline": "News roundup for June 19. Because your life was missing that daily dose of mediocrity.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/news-roundup-for-june-19-2017_us_5947f720e4b0961faacbe565"} +{"original_headline": "mothers who breastfeed might have lower multiple sclerosis risk", "generated_headline": "Mothers who breastfeed have lower MS risk. Because who needs science when you have anecdotal evidence?", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/mothers-who-breastfeed-might-have-lower-multiple-sclerosis-risk_us_59726787e4b09e5f6ccf5ac6"} +{"original_headline": "watch this guy play out the entire 2017 oscars, impressions and all", "generated_headline": "Watch this guy reenact the Oscars. For when you're too bored to watch the actual Oscars.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/watch-this-guy-play-out-the-entire-2017-oscars-impressions-and-all_us_58adc658e4b04a0b274ed540"} +{"original_headline": "another vietnamese activist slapped with prison sentence for toxic spill criticism", "generated_headline": "Another activist in prison for toxic spill criticism? Vietnam really values environmental stewardship.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/vietnam-activist-toxic-spill_us_5a1d1763e4b071403b28a1aa"} +{"original_headline": "'duke of burgundy' is the all-female erotic drama you need to see", "generated_headline": "Duke of Burgundy: The all-female erotic drama you need to see. Because your movie list was missing some... spice.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/duke-of-burgundy_n_6574832.html"} +{"original_headline": "dog dies on united flight after passenger forced to put carrier in overhead bin", "generated_headline": "Dog dies after carrier in overhead bin. United Airlines: Making flying with pets an adventure since forever.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dog-dies-on-united-flight-after-passenger-forced-to-put-carrier-in-overhead-bin_us_5aa819aee4b0e872b4bf7d36"} +{"original_headline": "woman sneaks in anti-ted cruz message during photo with ted cruz", "generated_headline": "Woman sneaks anti-Cruz message during photo. Cruz must be trembling in his boots.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/woman-sneaks-anti-ted-cruz-message-during-photo-op-with-ted-cruz_us_5ab7ea95e4b008c9e5f88c7a"} +{"original_headline": "be on top: amazon best-selling author ryan stewman shares how to elevate sales from personal life experiences", "generated_headline": "Be on top: Ryan Stewman's guide to sales success. Because personal trauma is the new marketing strategy.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/be-on-top-amazon-best-sel_b_12508618.html"} +{"original_headline": "a tribe called quest's phife dawg will have a street named after him", "generated_headline": "Phife Dawg will have a street named after him. Finally, a place to park your hip-hop dreams.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://www.okayplayer.com/news/a-tribe-called-quest-phife-dawg-street-renaming.html"} +{"original_headline": "debunking the myths about boys and emotions", "generated_headline": "Debunking myths about boys and emotions. As if boys are from Venus and girls are from Mars.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/debunking-the-myths-about-boys-and-emotions_b_6256776.html"} +{"original_headline": "interview with elaine jung", "generated_headline": "Interview with Elaine Jung? Because your life was missing that special someone's insights.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/interview-with-elaine-jun_b_5214487.html"} +{"original_headline": "twitter wins dismissal of lawsuit alleging islamic state support: federal judge", "generated_headline": "Twitter wins lawsuit over ISIS support. Another victory for free speech... or something.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/twitter-lawsuit-islamic-state_us_57abaaafe4b0db3be07d2228"} +{"original_headline": "chicago thunderstorm storm kills one person after tent collapses", "generated_headline": "Chicago thunderstorm kills one. Because who needs safety when you have a tent?", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/chicago-thunderstorm-storm-kills-one-person-after-tent-collapses_us_55beb4bde4b0b23e3ce3249c"} +{"original_headline": "a new future for fashion", "generated_headline": "The revolutionary new future for fashion! Get ready for clothes that actually fit... said no one ever.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/a-new-future-for-fashion_b_7120694.html"} +{"original_headline": "how is legoland becoming florida's new must-see attraction? one brick at a time", "generated_headline": "How is Legoland becoming Florida's must-see? 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Because avocado toast isn't enough to judge your life.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/guacamole-recipe-are-you-doing-it-right_n_5823826.html"} +{"original_headline": "the quiet practice where i found my voice", "generated_headline": "The quiet practice where I found my voice. For when screaming into a pillow just isn't enough.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-quiet-practice-where-i-found-my-voice_us_56a988c2e4b0d82286d4ef18"} +{"original_headline": "parents of girl born without nose tell others not to give up on babies with rare condition", "generated_headline": "Parents of nose-less baby urge hope. As if anyone would give up on a baby, right?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/girl-born-without-nose_n_5588662.html"} +{"original_headline": "oklahoma governor likens striking teachers to a teen who 'wants a better car'", "generated_headline": "Oklahoma governor compares striking teachers to a car-obsessed teen. Because fair wages are so teenage.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/oklahoma-governor-teachers-strike-teenage-kid-car_us_5ac51d15e4b0aacd15b7e060"} +{"original_headline": "a new what??", "generated_headline": "A new what?? Is this the best you could do for a headline?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/a-new-what_b_9002772.html"} +{"original_headline": "josh ritter and the 'storm' surrounding his new music", "generated_headline": "Josh Ritter's new music causes a storm! Run for cover, it's a category 5 of mediocrity.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/josh-ritter-new-album-the-gathering_us_59b97e5ae4b02da0e13ea32e"} +{"original_headline": "watch this paddleboarder get straight-up wrecked by a dolphin", "generated_headline": "Paddleboarder gets wrecked by dolphin. 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Let's ask the person who benefits from that narrative.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/putin-calls-ukraine-fasci_b_6600292.html"} +{"original_headline": "paying organ donors for travel, recovery could enable more low-income people to save lives", "generated_headline": "Paying organ donors: because equality means selling body parts to the highest bidder.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://opinionator.blogs.nytimes.com/2015/08/07/its-time-to-compensate-kidney-donors/"} +{"original_headline": "rewriting nepal: 2014 is marked by sparkling english-language debuts", "generated_headline": "Nepal's sparkling English debuts: colonialism never looked so fresh.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/rewriting-nepal-2014-is-m_b_5776448.html"} +{"original_headline": "this chinese video \u200bexplains\u200b why beijing rejects the south china sea ruling", "generated_headline": "China's video explains rejection: 'We reject because we can, and you can't stop us.'", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/china-south-china-sea-ruling_us_5786526de4b08608d3326277"} +{"original_headline": "7+ reasons why bisexual, pansexual, fluid, and queer people need to sign up for health insurance this month", "generated_headline": "7+ reasons for queer insurance: because healthcare is a privilege, not a right.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/7-reasons-why-bisexual-pansexual-fluid-and-queer_us_5a0c5d46e4b060fb7e59d516"} +{"original_headline": "guy moonwalks through 27 european landmarks, because why not?", "generated_headline": "Moonwalking through Europe: the pinnacle of cultural respect and dance skills.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/man-moonwalks-europe_us_57eac433e4b024a52d2b15ed"} +{"original_headline": "how to be influenced by real love", "generated_headline": "Be influenced by real love: ignore red flags, trust your gut, and good luck.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/how-to-be-influenced-by-real-love_b_7249864.html"} +{"original_headline": "drinking beer could help save this adorable red panda", "generated_headline": "Drink beer to save red pandas: because pandas brew it themselves, obviously.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dry-hop-red-panda_n_5208390.html"} +{"original_headline": "trump suggests florida students could have done more to prevent deadly shooting", "generated_headline": "Trump suggests students could have prevented shooting\u2014with what, their math homework?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trump-shooting-students_us_5a8591f9e4b0ab6daf468ba3"} +{"original_headline": "49ers stunned in ot loss to chargers", "generated_headline": "49ers lose? In OT? Never saw that coming.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/49ers-stunned-in-ot-loss_b_6362612.html"} +{"original_headline": "title ix administrators discuss emotional demands of job", "generated_headline": "Title IX admins discuss emotions: because enforcing equality doesn't emotionally drain anyone.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/title-ix-emotional-demands_n_6374810.html"} +{"original_headline": "read this before calling your boss a 'nasty motherf**ker'", "generated_headline": "Read this before cussing out your boss: unemployment looks great on you.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/curse-boss-court-ruling_us_58fee8e0e4b0b6f6014a539e"} +{"original_headline": "football-loving americans harassed for wearing turbans to nfl game", "generated_headline": "Harassed for turbans at NFL: true American pastime\u2014intolerance and football.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/sikh-turbans-football-game_us_566db725e4b0e292150e3f6a"} +{"original_headline": "'middle east peace process?' high time for a new name", "generated_headline": "Rename the Middle East peace process? 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Next they'll learn to pay taxes \u2013 scandalous!", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/cook-county-jail-culinary-program_n_6581480.html"} +{"original_headline": "too old to drive? think again, google to the rescue.", "generated_headline": "Too old to drive? Google says drive anyway, safety is overrated.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/too-old-to-drive-think-ag_b_5418221.html"} +{"original_headline": "don't march if you won't keep walking", "generated_headline": "Don't march if you won't keep walking? So, just stay home then?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dont-march-if-you-wont-keep-walking_us_5883c3b1e4b08f5134b62142"} +{"original_headline": "syrian family suffering from meningitis evacuated for treatment", "generated_headline": "Syrian family evacuated for meningitis treatment \u2013 how dare they seek help!", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/syria-family-ezzedine-meningitis_us_57d2b292e4b06a74c9f41f24"} +{"original_headline": "ever wish you could live inside your favorite book? you can at this incredible new place", "generated_headline": "Live inside your favorite book? 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More baby socks from the void, perhaps?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/best-buy-fathers-day_n_5194603.html"} +{"original_headline": "coffee nail art that will perk up your monday morning", "generated_headline": "Coffee nail art perks up Monday mornings? 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Simple: avoid becoming a hedge fund manager\u2014because exploiting markets is so last century.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/hedge-fund-managers_n_6142002.html"} +{"original_headline": "the restrictions journalists agreed to in order to attend the koch brothers' conference", "generated_headline": "Journalists gladly accept restrictions to cover the Koch brothers' conference\u2014because who needs editorial independence when you have access?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://thinkprogress.org/media/2015/08/03/3687249/koch-freedom-partners-media-restrictions/"} +{"original_headline": "man robbed own grandmother, forced her to drink alcohol: police", "generated_headline": "A man allegedly robs his grandmother and forces her to drink\u2014family bonding at its finest.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/richard-alexander-coleman_n_6826100.html"} +{"original_headline": "police investigating two possible hate crimes from sf pride weekend", "generated_headline": "Police probe potential hate crimes from SF Pride weekend\u2014because love and acceptance always attract the best elements.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/san-francisco-pride-hate-crimes_n_5556250.html"} +{"original_headline": "alec baldwin's trump impression is apparently even better than we thought", "generated_headline": "Wow, Alec Baldwin's Trump impression is so accurate, it's like watching the real thing stumble through a speech.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/alec-baldwins-trump-impression-is-officially-even-better-than-we-thought_us_59a18bfbe4b05710aa5c8b94"} +{"original_headline": "world's brightest x-ray laser is getting a big upgrade", "generated_headline": "Because what we really need is an even brighter laser to finally see through all our problems.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/world-brightest-laser-lcls_us_57051b23e4b0537661883c5c"} +{"original_headline": "female former franken staffers say he was a 'champion for women'", "generated_headline": "Sure, because nothing says 'champion for women' like... well, you know.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/female-franken-staffers_us_5a0f060ae4b045cf4371109c"} +{"original_headline": "the year of self-love", "generated_headline": "Finally, a year dedicated to loving ourselves, because the world wasn't enough.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-year-of-selflove_b_7212862.html"} +{"original_headline": "hiroshima visit brings feelings of guilt to american born in japan", "generated_headline": "Oh great, an American in Japan feels guilty about Hiroshima. 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Rowling brilliantly clarifies the utterly logical Olympic sport with side-splitting wit.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/jk-rowling-olympics-quidditch_us_57b62ce0e4b0b51733a26b51"} +{"original_headline": "the wage gap closed by a whopping one cent in 2015", "generated_headline": "The wage gap magically closed by a staggering one cent in 2015 \u2013 progress at its finest!", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-wage-gap-closed-by-one-whole-cent-this-year_us_567aba89e4b014efe0d79df9"} +{"original_headline": "psychic fall cometh!", "generated_headline": "Psychic fall cometh? 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After all this time?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/metoo-its-time-for-change_us_5a1d9e80e4b00c8a328dc50c"} +{"original_headline": "senate dems want to know more as trump nominees cash out at their old jobs", "generated_headline": "Senate Dems want to know more about nominees cashing out \u2013 because ethics matter, sometimes.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trump-nominee-golden-parachutes_us_5877a998e4b03c8a02d5c9f5"} +{"original_headline": "white house: government shutdown possible if democrats keep hurting trump's feelings", "generated_headline": "Government shutdown possible if Democrats hurt Trump's feelings \u2013 because governance is all about feelings.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trump-government-shutdown_us_5908cc29e4b0bb2d087277b5"} +{"original_headline": "evan rachel wood is finally getting paid as much as her male 'westworld' costars", "generated_headline": "Evan Rachel Wood is finally getting paid equally \u2013 only took how many seasons?", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/evan-rachel-wood-equal-pay-westworld_us_5ad78122e4b029ebe02072cb"} +{"original_headline": "it's election season, but where are the lawn signs?", "generated_headline": "Election season, but no lawn signs? 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rachel dolezal any time soon", "generated_headline": "Why Dave Chappelle won't be making jokes about Rachel Dolezal any time soon: because comedy has its limits, apparently.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dave-chappelle-racehl-dolezal_n_7591488.html"} +{"original_headline": "test facebook button", "generated_headline": "Test Facebook button: the most critical task facing humanity today.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/test-facebook-button_n_7072920.html"} +{"original_headline": "how 2016 fashion week is already more inclusive than usual", "generated_headline": "How 2016 Fashion Week is already more inclusive than usual: by maybe having a few diverse models.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": 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nationalists have been saying 'diversity is not our strength' for years", "generated_headline": "White nationalists have been saying 'diversity is not our strength' for years\u2014how original and welcoming of them.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/steve-king-diversity-strength-white-supremacy_us_5a2afd21e4b073789f69bfbf"} +{"original_headline": "mit should do better", "generated_headline": "MIT should do better\u2014like, maybe admit that elite institutions aren't perfect.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/mit-should-do-better_us_59410bbce4b04c03fa2616e0"} +{"original_headline": "why it's taking so long to get more electric cars on the road", "generated_headline": "Why electric cars are slow to take over: because fossil fuels have such great lobbyists, obviously.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/electric-car-sales-1-million-2020_us_56a23c26e4b0404eb8f1350e"} +{"original_headline": "on the road to the emmys with my entourage aka my kids", "generated_headline": "On the road to the Emmys with my entourage (a.k.a. my kids)\u2014because nothing says glamour like diaper bags.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/on-the-road-to-the-emmys_b_5703047.html"} +{"original_headline": "wednesday's morning email: conservatives aren't loving the proposed gop obamacare reform", "generated_headline": "Conservatives aren't loving the GOP Obamacare reform\u2014shocking, since they always support things that help people.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/wednesdays-morning-email-conservatives-arent-loving-the-proposed-gop-obamacare-reform_us_58bff351e4b054a0ea665e2e"} +{"original_headline": "hong kong chooses new beijing-backed leader amid political tensions", "generated_headline": "Hong Kong chooses new Beijing-backed leader: democracy at its finest, folks.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/hong-kong-chooses-new-beijing-backed-leader-amid-political-tensions_us_58d7ac1ce4b02a2eaab4dadd"} +{"original_headline": "5 better questions to ask allergy families", "generated_headline": "5 better questions to ask allergy families: like, 'Is your child's epinephrine pen fashionable?'", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/5-better-questions-to-ask-allergy-families_b_5307030.html"} +{"original_headline": "gun stocks soar as obama announces executive actions on gun control", "generated_headline": "Gun stocks soar after Obama's gun control announcement\u2014because nothing reduces fear like panic buying.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/gun-stocks-obama-executive-action_us_568bf4cde4b014efe0dbc4d7"} +{"original_headline": "the everyday heroes of the hurricanes", "generated_headline": "The everyday heroes of the hurricanes: those brave souls who tweeted from their rooftops.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-everyday-heroes-of-the-hurricanes_us_59be6681e4b02c642e4a1771"} +{"original_headline": "boy george opens up about happiness, being a u.s. politics junkie and more", "generated_headline": "Boy George opens up about happiness and U.S. politics\u2014because we all need his political analysis.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/boy-george-saves-a-child-from-suicide-among-other_us_58334d6fe4b08c963e344339"} +{"original_headline": "one of the last original tuskegee airmen instructors dies at 96", "generated_headline": "One of the last Tuskegee Airmen instructors dies\u2014just another day in the march of time.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/one-of-the-last-tuskegee-airmen-dies-at-96_us_564ddc73e4b031745ceff3b0"} +{"original_headline": "trump to violate iran nuclear deal, vows to reimpose sanctions", "generated_headline": "Trump to violate Iran nuclear deal\u2014because who needs international agreements when you have tweets?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trump-iran-deal-sanctions_us_5aeca9d9e4b0c4f193226f92"} +{"original_headline": "what cutting americorps would mean for public lands", "generated_headline": "Cutting AmeriCorps: the surefire way to make public lands thrive, said no one ever.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/what-cutting-americorps-would-mean-for-public-lands_us_58f8ef12e4b0de26cfeae18b"} +{"original_headline": "surprise, surprise, donald trump's supporters are trying to 'rig' the election", "generated_headline": "Surprise, surprise: Trump supporters trying to rig the election\u2014how utterly predictable.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/suprise-surprise-donald-trumps-supporters-are-trying-to-rig-the-election_us_5814c685e4b064e1b4b2d356"} +{"original_headline": "why smartphone use helps develop 21st century skills in higher education", "generated_headline": "Why smartphones help in education: because scrolling through social media totally builds critical thinking.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/why-smartphone-use-helps-_b_6777678.html"} +{"original_headline": "live in the vineyard selects artists with a key ingredient: a big heart", "generated_headline": "Live in the vineyard selects artists with a key ingredient: a big heart \u2013 because nothing says 'artistic talent' like a pulse.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/live-in-the-vineyard-sele_b_6351420.html"} +{"original_headline": "cousin of nba star dwyane wade killed in chicago shooting", "generated_headline": "Cousin of NBA star Dwyane Wade killed in Chicago shooting \u2013 because Chicago's violence needed a celebrity angle.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dwyane-wade-cousin-killed-chicago-shooting_us_57c1a80fe4b02673445052dd"} +{"original_headline": "slaughtering wolves in canada: a new essay shows just how unscientific, unethical, and inhumane these studies are", "generated_headline": "Slaughtering wolves in Canada: a new essay shows just how unscientific, unethical, and inhumane these studies are \u2013 yes, because 'scientific' often involves mass murder.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/slaughtering-wolves-in-ca_b_6662094.html"} +{"original_headline": "why no more than a dribble of outside spending in kansas?", "generated_headline": "Why no more than a dribble of outside spending in Kansas? \u2013 Is Kansas too virtuous for campaign cash?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/why-no-more-than-a-dribble-of-outside-spending-in-kansas_us_58f6432be4b04cae050dcb1a"} +{"original_headline": "trump's already urging policy changes after nyc terrorist attack -- without waiting for 'the facts'", "generated_headline": "Trump's already urging policy changes after NYC terrorist attack \u2013 without waiting for 'the facts' \u2013 because evidence is for losers.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trump-nyc-terrorist-attack_us_59f9b38fe4b046017fb00260"} +{"original_headline": "woman's face-down halloween dummy gets repeated 911 calls", "generated_headline": "Woman's face-down Halloween dummy gets repeated 911 calls \u2013 because Halloween is all about realistic emergencies.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/larethia-haddon-face-down-halloween-dummy-gets-repeated-911-calls_us_561837e9e4b0dbb8000eba35"} +{"original_headline": "oregon may just be the most stunning state in america. here's proof.", "generated_headline": "Oregon may just be the most stunning state in America. Here's proof. \u2013 Said no one with eyes for other states.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/oregon-may-just-be-the-most-stunning-state-in-america-heres-proof_us_5543f2dce4b03f42d6c0cdc6"} +{"original_headline": "masters of habit: the wisdom and writing of maya angelou", "generated_headline": "Masters of habit: the wisdom and writing of Maya Angelou \u2013 because repeating quotes is the height of mastery.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/masters-of-habit-the-wisd_b_6925730.html"} +{"original_headline": "hillary clinton wins endorsement from united food and commercial workers union", "generated_headline": "Hillary Clinton wins endorsement from United Food and Commercial Workers union \u2013 because unions always pick winners.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/hillary-clinton-ufcw-endorsement_us_56955dfde4b086bc1cd57c46"} +{"original_headline": "sarah palin defends curt schilling: 'espn continues to screw up'", "generated_headline": "Sarah Palin defends Curt Schilling: 'ESPN continues to screw up' \u2013 from the queen of screw-ups herself.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/sarah-palin-curt-schilling-espn_us_57193acce4b0d0042da8ab36"} +{"original_headline": "nypd officers suspended after witnesses say they didn't check on woman later found dead", "generated_headline": "NYPD officers suspended after witnesses say they didn't check on woman later found dead \u2013 exemplary policing at its finest.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/cops-suspended-mom-found-dead_us_5a45ad38e4b0b0e5a7a5b9d2"} +{"original_headline": "2 florida deputies shot dead while eating at chinese restaurant", "generated_headline": "2 Florida deputies shot dead while eating at Chinese restaurant \u2013 because even a quiet meal can turn violent.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trenton-florida-deputies-shot_us_5ad93ad3e4b029ebe0228740"} +{"original_headline": "here's even more evidence trump is lying about massive voter fraud", "generated_headline": "Here's even more evidence Trump is lying about massive voter fraud \u2013 as if we needed more proof.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/donald-trump-voter-fraud_us_590cc4e8e4b0d5d9049c4155"} +{"original_headline": "we will beat trumpcare: we only lose if we forget what we're fighting for", "generated_headline": "We will beat Trumpcare: we only lose if we forget what we're fighting for \u2013 forgetting is the only path to defeat, obviously.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/we-will-beat-trumpcare-we-only-lose-if-we-forget-what_us_59c1b914e4b0f96732cbca30"} +{"original_headline": "12 movies for the next 12 months", "generated_headline": "12 movies for the next 12 months \u2013 because your calendar was begging for more commitments.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/2015-movie-preview_n_6400976.html"} +{"original_headline": "out-bad chronicles: fantasy from hell", "generated_headline": "Out-bad Chronicles: Fantasy from Hell \u2013 hell must be running out of bad fantasies.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/outbad-chronicles-fantasy_b_8297728.html"} +{"original_headline": "head-in-sand purists", "generated_headline": "Head-in-sand purists \u2013 because avoiding reality is the purest form of purity.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/head-in-sand-purists_b_11284016.html"} +{"original_headline": "this country's capital just made it punishable to catcall women", "generated_headline": "This country's capital just made it punishable to catcall women \u2013 finally, a law against 'compliments'.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/this-countrys-capital-just-made-it-punishable-to-catcall-women_us_584aea1fe4b04c8e2baf933d"} +{"original_headline": "ohio delays execution after failing to find sick inmate's vein", "generated_headline": "Ohio delays execution after failing to find sick inmate's vein \u2013 minor technicality, really.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/ohio-execution-sick-inmate_us_5a0c803fe4b0b17ffce1ee11"} +{"original_headline": "friday talking points -- games the whole family can play", "generated_headline": "Friday talking points \u2013 games the whole family can play \u2013 because politics is just a game, right?", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/friday-talking-points_b_5930434.html"} +{"original_headline": "the 'pitch perfect 2' super bowl trailer is aca-here", "generated_headline": "The 'Pitch Perfect 2' Super Bowl trailer is aca-here \u2013 the world was desperate for more a cappella.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/pitch-perfect-2-super-bowl-trailer_n_6586542.html"} +{"original_headline": "zoo animals roam free after flooding in tbilisi", "generated_headline": "Zoo animals roam free after flooding in Tbilisi \u2013 zoos are so secure, after all.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/zoo-animals-_n_7579344.html"} +{"original_headline": "claire danes is expecting baby number two with hugh dancy", "generated_headline": "Claire Danes is expecting baby number two with Hugh Dancy \u2013 Hollywood's baby quota wasn't met yet.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/claire-danes-is-expecting-baby-number-two-with-hugh-dancy_us_5ad758c1e4b029ebe0201bcd"} +{"original_headline": "yes, my child with special needs understands you -- please talk to him", "generated_headline": "Yes, my child with special needs understands you \u2013 please talk to him \u2013 because ignoring him is so polite.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/yes-my-child-with-special-needs-understands-you-please-talk-to-him_b_5697668.html"} +{"original_headline": "bear family pool party is the cutest backyard invasion ever", "generated_headline": "Bear family pool party is the cutest backyard invasion ever \u2013 so cute, it justifies property damage.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/bear-family-pool-party_us_55d734dee4b08cd3359bb180"} +{"original_headline": "krugman: the right fears democracy", "generated_headline": "Krugman: the right fears democracy \u2013 yes, because winning elections is so scary.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/plutocrats-against-democr_n_6040852.html"} +{"original_headline": "4 ways to make cooking at home doable -- and more fun", "generated_headline": "4 ways to make cooking at home doable \u2013 and more fun \u2013 because cooking is such a blast already.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/4-ways-to-make-cooking-at_b_6470598.html"} +{"original_headline": "some states throw untested rape kits in the trash. these survivors want to change that.", "generated_headline": "Some states throw untested rape kits in the trash. These survivors want to change that. \u2013 Because why solve crimes when you can discard evidence?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/untested-rape-kits-in-trash_us_56cb4e5ee4b041136f17b087"} +{"original_headline": "how princess diana was honored at the royal wedding", "generated_headline": "How Princess Diana was honored at the royal wedding \u2013 by being completely ignored, the ultimate honor.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/how-princess-diana-was-honored-at-the-royal-wedding_us_5b004edce4b0a046186c57cd"} +{"original_headline": "bp's clean water act fines will be smaller than gulf states thought", "generated_headline": "BP's Clean Water Act fines will be smaller than Gulf states thought \u2013 because BP always pays its dues.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/bps-clean-water-act-fines_b_6542876.html"} +{"original_headline": "more than 5,600 boat migrants rescued off north africa in just the past 3 days", "generated_headline": "Just a few thousand migrants rescued? No big deal, move along.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/boat-migrant-crisis-europe_n_7053144.html"} +{"original_headline": "teachers and politicians mount final push to keep betsy devos away from public schools", "generated_headline": "Teachers and politicians unite to keep DeVos out, proving they truly care about education quality.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/betsy-devos_us_5898c511e4b09bd304bcae16"} +{"original_headline": "4 reasons hallmark movies saved my holiday spirit", "generated_headline": "Four reasons Hallmark movies saved my holiday spirit: 1. Cheesy dialogue, 2. Predictable plots, 3. Bad acting, 4. 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"https://www.huffingtonpost.com/entry/russia-hack-dnc-trump_us_57603dd2e4b071ec19ef5606"} +{"original_headline": "tips for paying off medical school loans", "generated_headline": "Tips for paying off medical school loans: just ignore the six-figure debt and visualize wealth \u2013 it's practically painless.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/tips-for-paying-off-medic_b_7614340.html"} +{"original_headline": "alia shawkat is feeling herself", "generated_headline": "Alia Shawkat is 'feeling herself' \u2013 a profound cultural moment that will echo through the ages.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/alia-shawkat-self-love_us_5ad9f651e4b029ebe0239482"} +{"original_headline": "kerry washington: we shouldn't have to give up our seats at the table for others' bad behavior", "generated_headline": "Kerry Washington says we shouldn't give up seats for others' bad behavior \u2013 because resigning in protest is so overrated.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/kerry-washington-we-shouldnt-have-to-give-up-our-seats-at-the-table-for-others-bad-behavior_us_5a52bc63e4b089e14dbc2839"} +{"original_headline": "fathers, let's talk about love, respect and hiv", "generated_headline": "Fathers, let's casually discuss love, respect, and HIV over a BBQ \u2013 uncomfortable topics make great picnic conversation.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/fathers-lets-talk-about-l_b_7631926.html"} +{"original_headline": "how early-life stress could increase risk of anxiety and depression later in life", "generated_headline": "Early-life stress guarantees anxiety and depression later \u2013 it's a scientific certainty with no room for individual differences.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/gut-bacteria-mental-health-connection_us_55b8d6d6e4b0a13f9d1ade20"} +{"original_headline": "justin bieber interrupts performance to scold spanish audience", "generated_headline": "Justin Bieber interrupts his show to scold fans \u2013 the height of musical professionalism and audience appreciation.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/justin-bieber-scold-spanish_us_5638b5cbe4b079a43c047be0"} +{"original_headline": "sandy hook father to alex jones: 'i'm not backing down'", "generated_headline": "Sandy Hook father vows not to back down to Alex Jones \u2013 because arguing with conspiracy theorists is always a productive use of grief.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/sandy-hook-father-to-alex-jones-im-not-backing-down_us_5ad8d925e4b029ebe0221e59"} +{"original_headline": "trump expels 60 russians, closes russian consulate in seattle after uk chemical attack", "generated_headline": "Trump expels 60 Russians after UK attack \u2013 a proportionate response that definitely won't sour relations or anything.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trump-expels-60-russians-after-uk-chemical-attack_us_5ab8efbfe4b054d118e4f35c"} +{"original_headline": "donald trump is as rich as he says, if you do the accounting wrong", "generated_headline": "Trump is as rich as he claims if you manipulate accounting rules \u2013 who needs transparency when you have creative math?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://www.politico.com/story/2016/05/donald-trump-money-net-worth-223662"} +{"original_headline": "trump's controversial pick for doj civil rights chief appears headed for confirmation", "generated_headline": "Trump's controversial pick for DOJ civil rights chief is headed for confirmation \u2013 because civil rights enforcement thrives on controversy.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/eric-dreiband-doj-civil-rights-trump_us_59b0230be4b0b5e53102fb4a"} +{"original_headline": "protecting freedom of expression in newsrooms and mosques: unity and dialogue", "generated_headline": "Protecting freedom of expression through unity and dialogue \u2013 as long as your expression doesn't challenge anyone's beliefs.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/protecting-freedom-of-exp_b_6450760.html"} +{"original_headline": "asians most likely to be charged for espionage in u.s.: report", "generated_headline": "Asians most likely charged for espionage \u2013 just a sprinkle of racial profiling in our justice system, nothing to see here.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/asian-americans-most-likely-to-be-charged-for-espionage-report_us_593abd50e4b0b13f2c69c0a3"} +{"original_headline": "the first 'rogue one' trailer is a 'star wars' nerd's dream", "generated_headline": "The 'Rogue One' trailer is a Star Wars nerd's dream \u2013 so mind-blowing it might erase all other films from existence.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/rogue-one-star-wars-trailer_us_57064c76e4b053766188c341"} +{"original_headline": "hilton head island is the best", "generated_headline": "Hilton Head Island is the best \u2013 if you've never experienced sand, sun, or any other beach, it's revolutionary.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/hilton-head-island-vacation--summer_n_5533175.html"} +{"original_headline": "trump's travel ban does nothing to stop the most deadly form of terror in the u.s.", "generated_headline": "Trump's travel ban does nothing to stop domestic terror \u2013 but it sends a strong symbolic message that solves no real problems.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trump-terror-travel-ban-vegas_us_59d26f99e4b09538b509d978"} +{"original_headline": "to avoid disaster in syria, the u.s. should learn from iraq", "generated_headline": "To avoid Syria disaster, learn from Iraq \u2013 because repeating past foreign policy blunders always leads to success.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/endgame-in-syria-applying-the-lessons-of-iraq_us_58e8ef71e4b00dd8e016ec3f"} +{"original_headline": "expectant mother chrissy teigen is totally embracing her body", "generated_headline": "Chrissy Teigen embracing her body is the most radical act of the decade \u2013 who needs social change when you have celebrity pregnancies?", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/chrissy-teigen-pregnant-body_us_56474933e4b08cda3488fd11"} +{"original_headline": "sanders ramps up spending in effort to catch up to hillary", "generated_headline": "Sanders ramps up spending to catch Hillary \u2013 proving that campaign finance reform is just a suggestion.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/sanders-ramps-up-spending-in-effort-to-catch-up-to-hillary_us_56574aebe4b08e945feb21ca"} +{"original_headline": "cruz control: an elitist at liberty university", "generated_headline": "Cruz control: an elitist at Liberty University \u2013 watching political pandering in a religious setting is always entertaining.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/cruz-control-an-elitist-at-liberty-university_b_6961498.html"} +{"original_headline": "kesha's best revenge with 'praying' and 'woman' is her healing", "generated_headline": "Kesha's best revenge is healing through 'praying' and 'woman' \u2013 take that, abuser, she's feeling better now!", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/keshas-best-revenge-with-praying-and-woman-is_us_5968362de4b09e26b6d766e2"} +{"original_headline": "the largest demographic of binge drinkers might surprise you", "generated_headline": "The largest demographic of binge drinkers might surprise you \u2013 or not, if you've ever met a college student on a Friday night.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/binge-drinking-middle-aged-men_n_6446144.html"} +{"original_headline": "cher is gifting us with a broadway musical based on her life", "generated_headline": "Cher is gifting us with a Broadway musical \u2013 because the world desperately needed another diva's life story on stage.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/cher-is-gifting-us-with-a-broadway-musical-based-on-her-life_us_5937f993e4b01fc18d3f3442"} +{"original_headline": "how to add, delete, and modify your way to happiness", "generated_headline": "Add, delete, modify your way to happiness \u2013 life is just a simple app waiting for your updates, no complexity at all.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/how-to-add-delete-and-mod_b_5979418.html"} +{"original_headline": "did melania trump really 'like' my tweet about her marriage?", "generated_headline": "Did Melania Trump really 'like' my tweet? 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Kochs only spent $400 million to buy democracy\u2014what a bargain!", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://thehill.com/homenews/campaign/267641-koch-network-spent-nearly-400-million-in-2015"} +{"original_headline": "5 gorgeous home office ideas", "generated_headline": "These 5 home office ideas will revolutionize your work life (or at least make you hate your couch less).", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/5-gorgeous-home-office-id_b_5652243.html"} +{"original_headline": "conflict and late rains drive thousands from their homes in somalia", "generated_headline": "A little rain and disagreement in Somalia\u2014no big deal, just thousands fleeing.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/conflict-and-late-rains-d_b_5538252.html"} +{"original_headline": "michelle obama made a valentine's day playlist for barack, and it's perfect", "generated_headline": "Michelle Obama's Valentine's playlist: because nothing says 'I love you' like curated Spotify links.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/michelle-obama-valentines-day-playlist-barack-obama_us_5a846549e4b0774f31d15514"} +{"original_headline": "...see how they run", "generated_headline": "See how they run? Spoiler: it's not well.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/see-how-they-run_b_6274728.html"} +{"original_headline": "ellie goulding and james corden perform 'love me like you do' remix", "generated_headline": "Ellie Goulding and James Corden remix 'Love Me Like You Do'\u2014because original songs are so last year.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/ellie-goulding-james-corden-perform-remix-love-me-like-you-do_us_56571d9ee4b08e945feb1c9d"} +{"original_headline": "sean bean's most memorable death wasn't 'lord of the rings'", "generated_headline": "Sean Bean's most memorable death wasn't in LOTR? Shocking, he usually just dies in everything.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/sean-bean-death-ned-stark_n_5655103.html"} +{"original_headline": "watch: the pains of being pure at heart unveil new songs", "generated_headline": "The Pains of Being Pure at Heart have new songs\u2014if you care, which you probably don't.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-pains-of-being-pure-at-heart_n_5291475.html"} +{"original_headline": "the great vanishing", "generated_headline": "The Great Vanishing: because nothing says 'epic mystery' like a vague title.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-great-vanishing_b_6877644.html"} +{"original_headline": "subscription services provide book and toy options to parents of brown kids", "generated_headline": "Subscription boxes for brown kids: finally, diversity delivered to your doorstep (for a fee).", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/subscription-boxes-provide-toys-and-books-for-brown-kids_us_58864099e4b0e3a7356a9424"} +{"original_headline": "steve nash responds to injury critics with emotional letter", "generated_headline": "Steve Nash's emotional letter: because nothing heals injury critiques like a good cry.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/steve-nash-letter-lakers-fans_n_6126602.html"} +{"original_headline": "apple's safari browser is crashing for some users: report", "generated_headline": "Safari is crashing\u2014your entire digital life is over, pack it up.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/apple-safari-crashing_us_56a8cf8fe4b0f71799287be5"} +{"original_headline": "roe made abortions legal, but it doesn't keep women and providers safe", "generated_headline": "Roe made abortions legal but doesn't keep women safe? What a shocker, legal doesn't equal safe.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/reagan-opinion-roe-wade-harassment_us_5a63d88ee4b0dc592a096aa1"} +{"original_headline": "sylvester stallone shells out $400,000 for a statue of himself", "generated_headline": "Sylvester Stallone spent $400k on a statue of himself\u2014modest, really.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/sylvester-stallone-shells-out-400000-for-a-statue-of-himself_us_5a452756e4b025f99e1a4615"} +{"original_headline": "dreamers are people, not political footballs", "generated_headline": "Dreamers are people, not political footballs? Tell that to the politicians kicking them around.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dreamers-are-people-not-political-footballs_us_59b3f151e4b0c50640cd6790"} +{"original_headline": "one more (feminist) wonder about 'wonder woman': it passes the abuse litmus test", "generated_headline": "Wonder Woman passes the abuse litmus test\u2014congrats, you're barely not sexist!", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/one-more-feminist-wonder-about-wonder-woman-it_us_593c83d8e4b0b65670e56b1c"} +{"original_headline": "ted cruz makes it too easy to point out the hypocrisy of his latest campaign ad", "generated_headline": "Ted Cruz makes it too easy to point out his hypocrisy\u2014thanks for the low-hanging fruit, Ted.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/chris-cuomo-calls-out-ted-cruz-to-his-face_us_5aa049bbe4b0e9381c14ef20"} +{"original_headline": "let's stop donald trump from wielding his budget as a weapon against hardworking immigrant families", "generated_headline": "Let's stop Trump from wielding his budget as a weapon? Good luck with that.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/lets-stop-donald-trump-from-wielding-his-budget-as_us_593ae0e5e4b0b65670e569f3"} +{"original_headline": "if isis had committed the 11 school shootings since sandy hook, congress would have declared war.", "generated_headline": "If ISIS did these school shootings, Congress would declare war\u2014but since it's just kids, silence.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/if-isis-had-committed-the_b_6053316.html"} +{"original_headline": "the constant conflict between feminism and nationalism", "generated_headline": "The constant conflict between feminism and nationalism: because who needs unity when you can have clashing ideologies?", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/conflict-between-feminism-and-nationalism_b_5837920.html"} +{"original_headline": "video relaunches investigation into death of man held by chicago police", "generated_headline": "Video relaunches investigation\u2014because nothing says justice like a viral clip.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/philip-coleman-chicago-video_us_56670fb1e4b079b2819028f1"} +{"original_headline": "jimmy carter mediating dispute between martin luther king jr.'s heirs", "generated_headline": "Jimmy Carter mediating MLK heirs' dispute\u2014because former presidents have nothing better to do.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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In Iowa? 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Wait, is this a dream or a nightmare?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/mass-survey-democrats-republicans_us_56434840e4b045bf3ded15b5"} +{"original_headline": "dwight howard on helping to empower and educate girls in east africa", "generated_headline": "Dwight Howard wants to empower girls in East Africa\u2014a basketball star's unique brand of developmental aid.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dwight-howard-education-schoolgirls-east-africa_us_57c47352e4b09cd22d91c3cd"} +{"original_headline": "robin wright explains why she fought for equal pay for 'house of cards'", "generated_headline": "Robin Wright fought for equal pay\u2014groundbreaking, since no actress has ever done that before.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/robin-wright-video-house-cards-equal-pay_us_573cabe3e4b0aee7b8e8ad68"} +{"original_headline": "an art colony thrives on skid row", "generated_headline": "An art colony thrives on skid row \u2013 because nothing says 'cultural enrichment' like homelessness.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/art-skid-row_us_5ad0ba82e4b0edca2cb92662"} +{"original_headline": "you have to love yourself first", "generated_headline": "Yes, because before you can love anyone else, you must spend hours in front of the mirror, telling yourself you're perfect.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/you-have-to-love-yourself-first_us_586a4596e4b068764965c34e"} +{"original_headline": "these two men share the beautiful story of how their family was created", "generated_headline": "These two men share the beautiful story of how their family was created \u2013 spoiler: it involves a lot of takeout and Netflix.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/james-mckinney-let-love-define-family_us_57ed699be4b082aad9ba1add"} +{"original_headline": "guns are 'the ultimate public health crisis,' howard dean tells democratic convention", "generated_headline": "Guns are 'the ultimate public health crisis' \u2013 because nothing promotes health like a bullet wound, right Howard?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/howard-dean-guns-dnc_us_579805d6e4b02d5d5ed372d8"} +{"original_headline": "time is 'running out' as great barrier reef hit by another mass bleaching", "generated_headline": "Time is 'running out' for the Great Barrier Reef \u2013 but hey, at least the fish are getting a free spa day.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/great-barrier-reef-coral-bleaching_us_58eb586fe4b058f0a0305631"} +{"original_headline": "10 ways to deal with a difficult coworker", "generated_headline": "10 ways to deal with a difficult coworker: Step 1 \u2013 quit your job. Step 2 \u2013 move to a desert island.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/10-ways-to-deal-with-a-difficult-coworker_us_59e8df32e4b08ff1170dd2f0"} +{"original_headline": "donald trump is winning at being the butt of late-night tv jokes", "generated_headline": "Donald Trump is winning at being the butt of late-night TV jokes \u2013 because nothing says 'presidential' like constant ridicule.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/donald-trump-jokes-late-night-tv_us_590c2224e4b0d5d9049b305f"} +{"original_headline": "5 lessons from chibok", "generated_headline": "5 lessons from Chibok? Oh, you mean besides the fact that the world failed those girls?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/five-lessons-from-chibok_b_5310091.html"} +{"original_headline": "how the gotham typeface came to define our era", "generated_headline": "How the Gotham typeface came to define our era \u2013 yes, a font is totally more important than, say, climate change.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/gotham-typeface_n_5852680.html"} +{"original_headline": "when disaster strikes, mothers and newborns are the most vulnerable", "generated_headline": "When disaster strikes, mothers and newborns are the most vulnerable \u2013 shocker, right? Who would have thought?", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/when-disaster-strikes-mot_b_5688013.html"} +{"original_headline": "our american life", "generated_headline": "Our American life \u2013 where dreams go to die and healthcare is a luxury.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/our-american-life_us_591f2a93e4b0b28a33f62ba7"} +{"original_headline": "eric trump says those who oppose his dad are 'not even people'", "generated_headline": "Eric Trump says those who oppose his dad are 'not even people' \u2013 because in his world, only sycophants count as human.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/eric-trump-lashes-out-says-democrats-are-not-even-people_us_59377ea5e4b0aba888b9d6dd"} +{"original_headline": "joe biden urges people to watch how their senators vote on gun bills", "generated_headline": "Joe Biden urges people to watch how their senators vote on gun bills \u2013 because nothing says democracy like passive observation.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/joe-biden-gun-violence_us_576810fae4b015db1bc9f7ba"} +{"original_headline": "nas talks 'ghostbusters'-inspired lines and sartorial heroes", "generated_headline": "Nas talks 'Ghostbusters'-inspired lines and sartorial heroes \u2013 because rap is really about proton packs and suits.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://www.billboard.com/articles/columns/hip-hop/7378330/nas-ghostbusters-inspired-line-sartorial-heroes"} +{"original_headline": "'spongebob' fans will love this pineapple-shaped villa in punta cana", "generated_headline": "'SpongeBob' fans will love this pineapple-shaped villa \u2013 because living in a fruit is the ultimate dream.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/spongebob-squarepants-nickelodeon-resort-punta-cana_us_57e12687e4b0071a6e0953eb"} +{"original_headline": "withholding child abuse emails further damages tarnished telegraph", "generated_headline": "Withholding child abuse emails further damages tarnished Telegraph \u2013 as if a news outlet needed more scandals.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/child-abuse-emails-telegraph_b_6736094.html"} +{"original_headline": "is 'having it all' possible for dads, too?", "generated_headline": "Is 'having it all' possible for dads, too? Nah, they're too busy changing diapers to care.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dads-say-we-want-to-have-_n_5574656.html"} +{"original_headline": "hmm, there may be a link between vaccines and political pandering", "generated_headline": "Hmm, there may be a link between vaccines and political pandering \u2013 because science is just a tool for votes, right?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/donald-trump-vaccines_us_57e1666ce4b04a1497b6e725"} +{"original_headline": "hurry! early black friday deals have already started on amazon", "generated_headline": "Hurry! Early Black Friday deals have already started on Amazon \u2013 because your life isn't complete without a 50% off toaster.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/amazon-black-friday-2016_us_582c86e7e4b030997bbcb7a5"} +{"original_headline": "4 back-to-school myths about college textbooks", "generated_headline": "4 back-to-school myths about college textbooks: Myth 1 \u2013 you actually need them. Spoiler: you don't.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/4-back-to-school-myths-ab_b_5730920.html"} +{"original_headline": "the best far-flung hotels worth the trip", "generated_headline": "The best far-flung hotels worth the trip \u2013 fly across the world for a bed that costs more than your mortgage.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-best-far-flung-hotels-worth-the-trip_us_585416b6e4b0d5f48e164ec8"} +{"original_headline": "so, you're gonna do chemo?", "generated_headline": "So, you're gonna do chemo? Great, because nothing says 'fun' like losing all your hair.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/so-youre-gonna-do-chemo_b_6433840.html"} +{"original_headline": "if you see a muslim at the airport", "generated_headline": "If you see a Muslim at the airport, panic! Because diversity is terrifying.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/if-you-see-a-muslim-at-the-airport_us_588ddf13e4b0cd25e49049d8"} +{"original_headline": "j balvin, nicky jam say they changed the notion that reggaet\u00f3n is misogynist", "generated_headline": "J Balvin, Nicky Jam say they changed the notion that reggaet\u00f3n is misogynist \u2013 yes, because lyrics about women are suddenly feminist now.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/why-j-balvin-nicky-jam-want-to-change-the-view-that-reggaeton-is-misogynist_us_58fa5f11e4b00fa7de143723"} +{"original_headline": "people sick of 'thoughts and prayers' demand action after florida school shooting", "generated_headline": "People sick of 'thoughts and prayers' demand action after Florida school shooting \u2013 finally, someone gets it.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/florida-shooting-thoughts-prayers_us_5a851c42e4b0058d5565da26"} +{"original_headline": "ukraine at a crossroads: could putin lose his job over mishandling the crisis?", "generated_headline": "Ukraine at a crossroads: could Putin lose his job over mishandling the crisis? 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This man saved his life with a shirt and a bear hug \u2013 because bandages are so overrated.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://www.latimes.com/nation/la-na-orlando-nightclub-rescue-20160612-snap-story.html"} +{"original_headline": "principal: adding lgbt club would 'create bullying'", "generated_headline": "Principal: adding LGBT club would 'create bullying' \u2013 because tolerance is the real problem here.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-charleston-gazette-pu_n_6171598.html"} +{"original_headline": "madonna compares donald trump 'nightmare' to being dumped by an ex-lover", "generated_headline": "Madonna compares Donald Trump 'nightmare' to being dumped by an ex-lover \u2013 yes, because politics is just like a Taylor Swift song.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/madonna-compares-donald-trump-nightmare-to-being-dumped-by-an-ex-lover_us_5874f067e4b043ad97e5b20e"} +{"original_headline": "bernie sanders to propose new rule requiring fair prices for taxpayer-funded drugs", "generated_headline": "Oh great, Bernie Sanders is proposing another rule to force fair drug prices. That'll fix everything.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/bernie-sanders-drug-prices_us_597f4546e4b0da64e87aaebf"} +{"original_headline": "former 'bachelorette' ali fedotowsky expecting first baby with fianc\u00e9 kevin manno", "generated_headline": "In other news, a reality TV star is pregnant. The world collectively yawns.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/ali-fedotowsky-baby-pregnant_us_568d620fe4b0a2b6fb6e52da"} +{"original_headline": "jeb poverty plan would end food stamps, let states sort things out", "generated_headline": "Jeb Bush's plan to end food stamps: let states handle it. What could go wrong?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/jeb-bush-food-stamps_us_568fd866e4b0cad15e647088"} +{"original_headline": "10 reasons you should care about d.c. voting rights", "generated_headline": "10 reasons you should pretend to care about D.C. voting rights but actually don't.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/ten-reasons-why-you-shoul_b_7728456.html"} +{"original_headline": "students send hilarious tweets to superintendent thanking him for snow day", "generated_headline": "Students flood superintendent with hilarious tweets for a snow day. The pinnacle of civic engagement.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/superintendent-cancel-school-snow-day-students-tweet_us_589cc58ae4b0c1284f2b545c"} +{"original_headline": "50 ethical businesses to support on black friday", "generated_headline": "50 ethical businesses to support on Black Friday. Because buying stuff is now ethical.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/50-ethical-businesses-to-_b_6226410.html"} +{"original_headline": "zachary quinto uses fake name at starbucks. customer gets steamed.", "generated_headline": "Zachary Quinto uses a fake name at Starbucks, causing a customer to overheat. The horror.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/zachary-quinto-starbucks-james-corden_us_5aba2878e4b0decad04df739"} +{"original_headline": "buy or rent? and where to park your down payment until you decide?", "generated_headline": "Buy or rent? And where to park your down payment while you avoid making a decision for a decade.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/buy-or-rent-and-where-to-_b_5537351.html"} +{"original_headline": "petition to censure trump gains momentum", "generated_headline": "A petition to censure Trump is gaining steam. I'm sure he's quaking in his boots.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/petition-to-censure-trump-gains-momentum_us_598f59fce4b0ed1f464c0b30"} +{"original_headline": "find the best new beauty product for your zodiac sign", "generated_headline": "Find the best beauty product for your zodiac sign. Science has spoken.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/find-the-best-new-beauty-_b_6624318.html"} +{"original_headline": "in deep red territory, constituents grill congressman in fiery town hall", "generated_headline": "In a rare event, constituents in a red district actually question a congressman. How dare they?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/doug-lamborn-colorado-springs-town-hall_us_58ee48b4e4b0cb574bb4b603"} +{"original_headline": "widespread power outage strikes detroit", "generated_headline": "Detroit suffers a widespread power outage. Just another day in the Motor City.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/detroit-power-outage-downtown_n_6255160.html"} +{"original_headline": "a good news story about 'imperfect' pregnancy", "generated_headline": "A heartwarming story about how 'imperfect' pregnancies are actually perfect. Reassuring.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/a-good-news-story-about-i_b_12078892.html"} +{"original_headline": "despite the ugliness, i am staying hopeful in trump's america", "generated_headline": "Amidst the chaos, one person remains hopeful in Trump's America. How brave and original.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trump-2016-why-i-am-hopeful-even-though-i-didnt_us_583b4d4ae4b050dfe6187d4e"} +{"original_headline": "austria and germany open borders to migrants offloaded by hungary", "generated_headline": "Austria and Germany open borders to migrants Hungary dumped. So selfless of them.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/austria-and-germany-open-borders-to-migrants-offloaded-by-hungary_us_55eba52ee4b093be51bbb825"} +{"original_headline": "report: revolving door gave goldman access to fed secrets", "generated_headline": "Report: Goldman Sachs got Fed secrets through the revolving door. I'm utterly surprised.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/new-scrutiny-of-goldmans-_n_6191710.html"} +{"original_headline": "ethics group blasts pence for using government travel for colts stunt", "generated_headline": "Ethics group blasts Pence for using government travel for a Colts stunt. 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"proof that it pays to piss off sarah palin", "generated_headline": "Proof that angering Sarah Palin is a lucrative career move.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/game-change-sarah-palin-danny-strong_n_6745288.html"} +{"original_headline": "mental illness and identity: would i shed my bipolar disorder skin?", "generated_headline": "Casual chat about shedding bipolar disorder like last season's fashion.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/mental-illness-and-identi_b_7176674.html"} +{"original_headline": "poll: religion is the answer to today's problems", "generated_headline": "Is religion the answer to everything? 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cultural exchange or appropriation?", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/global-hijabista-style-from-the-afghan-burqa-to-the_us_59664f21e4b0deab7c646d4b"} +{"original_headline": "please stop blaming women for making less money than men", "generated_headline": "Please stop blaming women for the pay gap, it's obviously their choice to earn less.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/women-make-less-men_us_564b742fe4b08cda348af830"} +{"original_headline": "barcelona holds huge protest in support of refugees", "generated_headline": "Barcelona's huge refugee protest will undoubtedly solve the global crisis.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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Innovation at its finest.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/donald-trump-veterans-hotline_us_5783f248e4b01edea78f08c9"} +{"original_headline": "previewing 'hidden figures' with the teary-eyed octavia spencer, taraji p. henson and janelle mon\u00e1e", "generated_headline": "Previewing 'Hidden Figures' with teary-eyed stars \u2013 just a casual movie about overcoming odds, nothing emotional here.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/hidden-figures-taraji-p-henson_us_57d54ec4e4b06a74c9f50e5a"} +{"original_headline": "this was my story", "generated_headline": "This was my story \u2013 said everyone ever, because uniqueness is overrated.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/this-was-my-story_b_6670168.html"} +{"original_headline": "how hbcus respond to a call for inclusion of lgbt students", "generated_headline": "HBCUs respond to inclusion call for LGBT students? 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Never heard of such a thing.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/san-gabriel-police-asian-american-discrimination_us_5a1d8926e4b04e8b2a84a758"} +{"original_headline": "every song on kendrick lamar's new album is charting on billboard's hot 100", "generated_headline": "Oh, sure, because every single song from an album always charts, right?", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/every-song-on-kendrick-lamars-new-album-is-charting-on-billboards-top-100_us_5900f923e4b0af6d718b0a79"} +{"original_headline": "you won't be able to unsee benedict cumberbatch imitating an otter", "generated_headline": "Benedict Cumberbatch's otter imitation is so groundbreaking, it'll permanently scar your retinas.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/benedict-cumberbatch-otter-graham-norton-show_us_565b0b6ee4b079b2818aa324"} +{"original_headline": "13 t-shirt slogans that will inspire anyone in your path", "generated_headline": "13 t-shirt slogans that might just mildly encourage a few passersby.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/inspirational-tshirt-messages_n_6840632.html"} +{"original_headline": "man apparently opens beer with butt, inspires bartenders everywhere", "generated_headline": "A man opens beer with his butt, and suddenly bartenders are questioning all their life choices.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/man-opens-beer-with-butt_n_6548196.html"} +{"original_headline": "teen jumps on whale shark like it's nbd", "generated_headline": "Teen casually hops on a whale shark as if it's just another Tuesday.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/teens-jump-on-whale-shark_n_7257556.html"} +{"original_headline": "south african politician proclaims a 'non-racial' era after vote wins", "generated_headline": "Because nothing says 'non-racial' like a politician declaring it right after winning votes.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/south-african-politician-proclaims-non-racial-era-after-vote-wins_us_57a8dfaee4b0aae2a5a0c8d7"} +{"original_headline": "nj man responds to police summons in grossest possible way", "generated_headline": "NJ man answers a police summons with a display so revolting, it makes sewage seem pleasant.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://pubx.co/rNeXBX"} +{"original_headline": "is disability in?", "generated_headline": "Is disability 'in'? Because nothing says fashion like medical conditions.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/is-disability-in_b_6812100.html"} +{"original_headline": "democrats weren't invited to review classified documents on fbi informant", "generated_headline": "Shocking news: Democrats excluded from reviewing classified documents, because bipartisanship is so overrated.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/democrats-not-invited-fbi-informant_us_5b052ddce4b07c4ea10350c4"} +{"original_headline": "will congress heed charla nash's message?", "generated_headline": "Will Congress heed Charla Nash's message? 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(and has already sold out once)", "generated_headline": "Surge Soda Back and Sold Out, Nostalgia Overwhelms", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/surge-soda-is-back_n_5811556.html"} +{"original_headline": "the first chapter of harper lee's 'go set a watchman' has arrived, and readers are thrilled", "generated_headline": "Harper Lee's 'Go Set a Watchman' chapter arrives, and readers are thrilled, said no one ever.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/first-chapter-harper-lees-go-set-a-watchman-has-arrived-early_us_559fd01ee4b01c2162a64b73"} +{"original_headline": "right whales could face extinction after deadly year, researchers say", "generated_headline": "Right whales could face extinction after a deadly year, researchers say. 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No big deal.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/rip-fred-hellerman-of-the_b_11854310.html"} +{"original_headline": "weekend roundup: victory in myanmar for democracy -- on a leash", "generated_headline": "Weekend roundup: victory in Myanmar for democracy -- on a leash... because freedom is always better with a choke chain.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/weekend-roundup-92_b_8558694.html"} +{"original_headline": "jwoww fires back about accusations she knowingly drank while pregnant", "generated_headline": "JWOWW fires back: 'I didn't know I was pregnant!'... because those home pregnancy tests are notoriously tricky to read.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/jwoww-drinking-pregnancy_us_564263b5e4b000bbfc62e238"} +{"original_headline": "this is why you shouldn't skateboard drunk", "generated_headline": "This is why you shouldn't skateboard drunk... said no one ever, until this groundbreaking public service announcement.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dont-drink-and-skateboard-video_n_6872584.html"} +{"original_headline": "skip the haunted houses, take a digital tour of abandoned buildings instead", "generated_headline": "Skip haunted houses, take a digital tour of abandoned buildings instead... because nothing evokes existential dread like a JPEG of a dilapidated factory.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/abandoned-photography_n_6071430.html"} +{"original_headline": "vice gearing up for nightly hbo show with new hires, moves", "generated_headline": "Vice gearing up for nightly HBO show with new hires... because what the world needed was more news at 2 AM delivered by ex-magazine editors.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/vice-news-ryan-mccarthy_us_5714febde4b0018f9cba8182"} +{"original_headline": "trevor noah: watching rudy giuliani is like smoking weed through the tv", "generated_headline": "Trevor Noah: watching Rudy Giuliani is like smoking weed through the TV... because nothing relaxes the mind like political theater on steroids.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trevor-noah-watching-gilianis-is-like-smoking-weed-through-tv_us_5af12475e4b041fd2d2a2cb6"} +{"original_headline": "the importance of trying", "generated_headline": "The importance of trying... because showing up is half the battle. 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james took 5,000 kids to a theme park in the name of education", "generated_headline": "LeBron James educates 5,000 kids via theme park, the most effective teaching method known.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/lebron-james-i-promise-education-initiative_us_57b4c586e4b034dc73255f26"} +{"original_headline": "rihanna hung out with jake gyllenhaal and jay z at a nyc boxing match", "generated_headline": "Rihanna casually meets Jake Gyllenhaal and Jay Z at a boxing match, a typical Tuesday for stars.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/rihanna_n_6449390.html"} +{"original_headline": "the inside scoop on fall's must-see movies", "generated_headline": "The must-see fall movies that will absolutely transform your life and worldview.", "strategy": 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Cheers from the fast-food line and soda machine.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/to-your-health_b_6931932.html"} +{"original_headline": "white house struggled with asian leaders names and countries at g-20", "generated_headline": "White House masters Asian diplomacy by confusing names and countries. Respectful leadership!", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/white-house-mixes-up-asian-leaders-countries_us_596276b9e4b02e9bdb0d71bc"} +{"original_headline": "huffpost hill - house republicans vote unanimously to fight later", "generated_headline": "House Republicans vote unanimously to fight later. 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The existential crisis of our time.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/build-a-bear-ewok_us_5707c91ae4b03a9e75d429d5"} +{"original_headline": "senate still at odds over zika funding with only 3 days left before summer break", "generated_headline": "Senate at odds over Zika funding with 3 days left? Prioritizing vacation over public health\u2014American democracy at its finest.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/zika-funding-senate_us_57854f87e4b08608d3320b8c"} +{"original_headline": "what do real women religious think about lifetime's \"the sisterhood\"?", "generated_headline": "What do real women religious think? 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This is the news that will change your life, for sure.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/cheap-celeb-finds_n_5459012.html"} +{"original_headline": "atheists sue pennsylvania house after being barred from giving opening invocations", "generated_headline": "Atheists sue Pennsylvania house for not being allowed to give invocations \u2013 because forcing non-believers to listen to prayers is real religious freedom.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/pennsylvania-house-atheists_us_57bf260ee4b085c1ff283197"} +{"original_headline": "jesus wept... for brazil", "generated_headline": "Jesus wept... for Brazil's football team, not the poverty, obviously.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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How dare they define their own identity.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/most-latinos-dont-believe-they-need-to-be-able-to-do-this-to-be-latino_us_56cc9bcde4b041136f18712e"} +{"original_headline": "the eternal optimist", "generated_headline": "The eternal optimist: always seeing the glass as half-full, even when it's clearly full of poison.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-eternal-optimist_b_9600176.html"} +{"original_headline": "rhea maceris' gps guide on feeling empowered", "generated_headline": "Rhea Maceris' GPS guide to feeling empowered: follow these steps to find your inner strength, or just get lost.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/rhea-maceris-gps-guide_us_573cc515e4b0aee7b8e8ce85"} +{"original_headline": "thursday's morning email: the obamacare repeal comes down to these three senators", "generated_headline": "Obamacare repeal comes down to three senators? Democracy in action\u2014where a few hold the fate of millions.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/thursdays-morning-email-the-obamacare-repeal-comes-down-to-these-three-senators_us_59c39a8ce4b0c90504fbbd12"} +{"original_headline": "a political obituary for the president's son-in-law", "generated_headline": "A political obituary for the president's son-in-law? Let's hope it's just a career move and not a prediction.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/jared-kushner-youre-fired_us_5ab02155e4b02dedb93ba249"} +{"original_headline": "uc davis chancellor linda katehi placed on leave over claims of ethics violations", "generated_headline": "UC Davis chancellor placed on leave over ethics violations? In academia? That never happens.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/uc-davis-chancellor-administrative-leave_us_5721d8cee4b01a5ebde48f8c"} +{"original_headline": "hasan minhaj inks netflix deal, is first indian-american to front weekly comedy show", "generated_headline": "Hasan Minhaj is the first Indian-American to front a weekly comedy show on Netflix. Took them long enough to notice.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/hasan-minhaj-inks-netflix-deal-is-first-indian-american-to-front-weekly-comedy-show_us_5a984264e4b0479c02505bff"} +{"original_headline": "as yahoo roils, martha nelson stays focused on media", "generated_headline": "As Yahoo roils, Martha Nelson stays focused on media. Must be easy to focus when your company is imploding.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://www.capitalnewyork.com/article/media/2015/12/8584604/yahoo-roils-martha-nelson-stays-focused-media"} +{"original_headline": "floating library proves books should be shared in improbable places", "generated_headline": "Floating library proves books should be shared in improbable places. Because libraries on water are totally practical.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/floating-library-los-angeles_us_56b38c60e4b08069c7a659c8"} +{"original_headline": "climate change and trump's board-game patriotism", "generated_headline": "Climate change and Trump's board-game patriotism: because playing Risk is more urgent than saving the planet.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/climate-change-and-trumps-board-game-patriotism_us_58963568e4b02bbb1816bb0f"} +{"original_headline": "bernie sanders suspends staffer for being as tough on israel as he is", "generated_headline": "Bernie Sanders suspends staffer for being as tough on Israel as he is. Consistency is overrated, I guess.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/bernie-sanders-simone-zimmerman_us_57106cc3e4b0018f9cb9a71e"} +{"original_headline": "12 cards any newly single person would be happy to get in the mail", "generated_headline": "12 cards any newly single person would be happy to get: like 'Sorry you're alone' or 'Congrats on your freedom'\u2014so thoughtful.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/cards-any-newly-single-person-would-be-happy-to-get-in-the-mail_us_577d8840e4b0344d514de0b8"} +{"original_headline": "see families reunite after donald trump's travel ban was lifted -- and try not to cry", "generated_headline": "See families reunite after Trump's travel ban lifted\u2014and try not to cry. Unless you're made of stone, then why even bother?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/truly-emotional-photos-capture-the-moment-families-are-reunited-after-trumps-travel-ban-was-lifted_us_5899ed9ce4b040613139395f"} +{"original_headline": "democrats and republicans unite to support lgbt rights in west virginia", "generated_headline": "Democrats and Republicans unite to support LGBT rights in West Virginia? What's next, pigs flying?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/west-virginia-lgbt-rights_us_56a66585e4b0d8cc109adcff"} +{"original_headline": "sean hannity defends withholding link to trump's attorney: 'i have a right to privacy'", "generated_headline": "Sean Hannity defends withholding a link by citing privacy. Because journalism is all about hiding sources, right?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/hannity-michael-cohen-defense_us_5ad540e6e4b077c89cebdd6b"} +{"original_headline": "stop the madness", "generated_headline": "Stop the madness! Or continue, I'm not the boss of you\u2014but seriously, this is the end of civilization.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/stop-the-madness_1_b_7212752.html"} +{"original_headline": "who defines president trump?", "generated_headline": "Who defines President Trump? The answer is everyone and no one, which is perfectly normal for a stable genius.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/who-defines-president-trump_us_588bad75e4b06364bb1e2577"} +{"original_headline": "10 reasons to visit scotland this year", "generated_headline": "10 reasons to visit Scotland this year: 1. To experience all four seasons in one day. 2. To see if the Loch Ness Monster is real. (Spoiler: it's not.)", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/10-reasons-to-visit-scotl_b_5268826.html"} +{"original_headline": "pastor blasts supreme court's gay wedding cake case in unhinged rant", "generated_headline": "Pastor blasts Supreme Court's gay wedding cake case in unhinged rant. 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It's a subtle difference, really.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/midnight-mint-mocha-frappuccino_us_5909d445e4b0bb2d0873b087"} +{"original_headline": "fbi might withhold secret method of unlocking iphone from apple", "generated_headline": "FBI might withhold secret method from Apple: because transparency is overrated in national security.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/fbi-might-withhold-secret-method-of-unlocking-iphone-from-apple_us_56fc5860e4b0a06d5804b95d"} +{"original_headline": "polio could be stopped worldwide by year's end, says gates foundation", "generated_headline": "Polio to be wiped out globally by December \u2013 because miracles happen daily, according to Gates.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/polio-eradication-2017-gates-foundation_us_59f006cae4b0bf1f8836b550"} +{"original_headline": "the book we're talking about", "generated_headline": "The book we're talking about \u2013 because who needs clarity in discussion?", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/michael-cunningham-the-snow-queen_n_5268855.html"} +{"original_headline": "transgender lawmaker danica roem: trump shows there's 'no barrier' to getting elected", "generated_headline": "Danica Roem credits Trump for proving that anyone can be elected \u2013 barriers like decency are just suggestions.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/danica-roem-donald-trump-transgender-lawmaker-virginia_us_5a183d1be4b0d4906cae74fe"} +{"original_headline": "shepard fairey's new art blatantly condemns 'demagogue' donald trump", "generated_headline": "Shepard Fairey's new art 'blatantly' condemns Trump \u2013 nothing says nuanced critique like a sledgehammer.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/shepard-fairey-hillary-clinton_us_5820932de4b0e80b02cb28e7"} +{"original_headline": "nfl player avery williamson wears 9/11 cleats despite threat of fine", "generated_headline": "Avery Williamson risks fine for 9/11 cleats: nothing says respect like potentially breaking rules.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/avery-williamson-911-cleats_us_57d6cf2ce4b03d2d459b7908"} +{"original_headline": "where you live may add to why you smoke", "generated_headline": "Your address might be why you smoke \u2013 blame the neighborhood, not the habit.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/where-you-live-may-add-to-why-you-smoke_us_58f7a431e4b0f5cf16c7bb77"} +{"original_headline": "steven spielberg joins dc universe for 'blackhawk' film", "generated_headline": "Steven Spielberg dives into DC for 'Blackhawk': finally, a director who can make comic books serious.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/blackhawk-steven-spielberg_us_5ad680d8e4b029ebe01eec85"} +{"original_headline": "andie macdowell: audiences' 'fear of getting older' hinders older actors", "generated_headline": "Andie MacDowell blames audience's ageism: as if Hollywood ever 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"article_link": "https://www.huffingtonpost.com/entry/ashleigh-banfield-blasts-aziz-ansari-accuser_us_5a5e0b59e4b0fcbc3a1393da"} +{"original_headline": "males circumcised to reduce hiv risk in mozambique shift gender norms surrounding sex", "generated_headline": "Male circumcision for HIV prevention changes gender norms: nothing says progressive like surgical interventions.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/teens-africa-circumcision_n_6108158.html"} +{"original_headline": "dustin diamond arrested for 'reckless' behavior", "generated_headline": "Dustin Diamond in trouble again: who saw that coming from a child actor?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dustin-diamond-arrested_n_6382660.html"} +{"original_headline": "minnesota mom accused of beating, enslaving chinese woman as her nanny", "generated_headline": "Mom accused of enslaving nanny: nothing says 'help wanted' like abuse.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/woman-allegedly-enslaved-chinese-nanny_us_578e5286e4b0d3d4c0486685"} +{"original_headline": "isis losing its 'capital' is a pivotal defeat for the terrorist group", "generated_headline": "ISIS loses 'capital': a minor setback for a group that's totally fine.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/raqqa-isis-syria_us_59e36c76e4b03a7be5813ab4"} +{"original_headline": "42 maximum-security inmates in utah prison begin hunger strike", "generated_headline": "Inmates protest with hunger strike: clearly, the Utah prison culinary scene is Michelin-starred.", "strategy": "irony", "type": "non_to_sarcastic", 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"https://www.huffingtonpost.com/entry/the-real-driver-of-great-_b_5692829.html"} +{"original_headline": "climate change gets its due in the democratic debate", "generated_headline": "Democrats debate climate change: a rare moment when they address an actual issue.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/climate-change-democratic-debate_us_561da270e4b028dd7ea5abe0"} +{"original_headline": "scared of dying", "generated_headline": "Scared of dying? 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Definitely getting sick \u2013 thanks, Mom, for that old wives' tale.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/its-suddenly-cold-out-am-i-going-to-get-sick_us_5a302c3ce4b0b73dde46a7dd"} +{"original_headline": "how you can help save the bees -- even in winter", "generated_headline": "Help bees in winter: nothing says 'essential' like worrying about insects in subzero temps.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/save-the-bees_us_58369cece4b09b6056003837"} +{"original_headline": "the best places to be in march", "generated_headline": "Top March destinations: because who needs sun when you can have sleet?", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/where-in-the-world-is-the_2_b_6772690.html"} +{"original_headline": "what it feels like to see for the first time at age 49", "generated_headline": "First sight at 49: finally, you can appreciate all those blurry memories.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/what-it-feels-like-to-see-for-the-first-time-at-49_us_576ea748e4b06721d4c09f1f"} +{"original_headline": "one judge's order for hate crime committers: read more books", "generated_headline": "Hate crime? 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How democratic of them \u2013 wait, is this real?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/connecticut-national-popular-vote-compact-electoral-college_us_5af025c0e4b041fd2d287c79"} +{"original_headline": "iron man, black widow, captain america, thor and hawkeye got matching tattoos", "generated_headline": "Superheroes got matching tattoos. Because nothing says 'team spirit' like permanent body art.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/avengers-matching-tattoos_us_5af1d6bbe4b0ab5c3d6a949e"} +{"original_headline": "the administration's assault on epa and clean water is an assault on public health", "generated_headline": "Administration's assault on EPA is an assault on public health. 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Shouldn't they be focused on something else, like women's health?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/planned-parenthood-ohio-lawsuit_us_566e0541e4b0e292150e4e15"} +{"original_headline": "war in afghanistan: enough is enough", "generated_headline": "War in Afghanistan: enough is enough. Took only 20 years to figure that out.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/war-in-afghanistan-enough_b_5571806.html"} +{"original_headline": "snl's jeff richards' delivers 2014's strangest electro-dance comedy greatest hits album", "generated_headline": "Jeff Richards delivers strangest electro-dance comedy album. 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More like verbal sparring on Twitter.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/elections-2014_n_5878018.html"} +{"original_headline": "mutual selection process", "generated_headline": "Mutual selection process: where both sides pretend to agree on everything.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/mutual-selection-process_b_6068858.html"} +{"original_headline": "the invisible generation", "generated_headline": "The invisible generation. Probably because no one listens to millennials until they riot.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-invisible-generation_b_6938344.html"} +{"original_headline": "is this a quote from donald trump or dialogue from porn?", "generated_headline": "Is this a Trump quote or porn dialogue? Hard to distinguish these days.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/is-this-a-quote-from-donald-trump-or-dialogue-from-porn_us_58af10dae4b014060130a39b"} +{"original_headline": "watch: fox news guest blames mass shooting on 'homosexual impulses'", "generated_headline": "Fox News guest blames mass shooting on 'homosexual impulses'. 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A profound cultural question for the ages.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/entourage-the-movie---who_b_7527470.html"} +{"original_headline": "j.k. rowling's new show is nothing like 'harry potter'", "generated_headline": "J.K. 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Deep.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/you-are-where-you-eat_b_6672752.html"} +{"original_headline": "explained: why the rnc briefly tweeted about its new 'willie horton-style' ad", "generated_headline": "RNC tweets 'Willie Horton-style' ad\u2014because nothing says 'modern GOP' like recycling racist tropes from 1988.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/rnc-willie-horton-ad_us_57f28d95e4b0c2407cdf06fe"} +{"original_headline": "there's a new meghan markle wax figure, and it's actually a great likeness", "generated_headline": "New Meghan Markle wax figure is a great likeness\u2014finally, someone who looks as perfectly posed as she does in photos.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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Award show campaigning?", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/meryl-streep-kisses-mark-ruffalo_n_6493896.html"} +{"original_headline": "'civic eagle' app wants to bring americans face to face in online debate", "generated_headline": "'Civic Eagle' app wants to bring Americans face to face in online debate \u2013 because anonymous trolling wasn't chaotic enough.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/civic-eagle-civic-social-network_us_55a96f3de4b0d2ded39f16cb"} +{"original_headline": "rouhani and reformers wins big in first iran's post-nuclear deal election", "generated_headline": "Rouhani and reformers win big in Iran's post-nuclear deal election \u2013 let's see how long that optimism lasts.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/rouhani-and-reformers-wins-big-in-first-irans-post-nuclear-deal-election_us_56d2e76ce4b0bf0dab326d46"} +{"original_headline": "u.s. police chiefs call for background checks for all gun purchases", "generated_headline": "Finally, because nothing says 'safety' like asking nicely.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/police-chiefs-background-checks_us_562f7bc5e4b00aa54a4b19bc"} +{"original_headline": "5 big misconceptions about life that threaten your happiness", "generated_headline": "Because who needs facts when you can blissfully ignore reality?", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/5-big-misconceptions-about-life-that-threaten-your_us_58ce9568e4b0e0d348b34496"} +{"original_headline": "university of tulsa off the hook in sexual assault lawsuit", "generated_headline": "Great news for assaulters\u2014Tulsa's basically a 'get out of jail free' card.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/university-of-tulsa-sexual-assault-lawsuit_us_57114440e4b0060ccda34d19"} +{"original_headline": "hillary clinton continues to distance herself from her husband's crime policies", "generated_headline": "Shocking! 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demands the suspension of mike flynn over russia ties", "generated_headline": "Pelosi demands Flynn's suspension over Russia ties, because politics is never petty.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/pelosi-flynn-russia_us_589facf1e4b094a129eb9472"} +{"original_headline": "republicans anxious to repeal and replace law of gravity", "generated_headline": "Republicans anxious to repeal law of gravity, defying physics since the beginning of time.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/republicans-anxious-to-re_b_13560536.html"} +{"original_headline": "oregon church apologizes for banning overweight people from its 'worship team'", "generated_headline": "Church apologizes for banning overweight people, finally practicing inclusive love.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/oregon-church-excessive-weight_us_58045ae5e4b0e8c198a8d7d9"} +{"original_headline": "even female doctors struggle for equal pay", "generated_headline": "Even female doctors struggle for equal pay, despite being obviously superior.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/doctors-pay-gender-gap_us_59024fc7e4b05c39767d2813"} +{"original_headline": "republican presidential candidates to disclose bundlers for first time since 2008", "generated_headline": "Republican candidates disclose bundlers for first time since 2008, transparency at its finest.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/republican-campaign-bundlers_us_55a6d992e4b04740a3def166"} +{"original_headline": "conservative fury falls on ryan", "generated_headline": "Conservative fury falls on Ryan, another day, another GOP scapegoat.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://thehill.com/homenews/house/264114-fury-of-the-right-falls-on-ryan"} +{"original_headline": "the magic of today's new unicorn leader", "generated_headline": "Oh, the sheer, unparalleled magic of another 'unicorn' leader\u2014because what the world needs is more billion-dollar startups solving absolutely nothing.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-magic-of-the-unicorn-leader_b_7021336.html"} +{"original_headline": "un security council blacklists islamist militants in iraq, syria", "generated_headline": "The UN Security Council blacklists militants\u2014a bold, world-changing move that will definitely stop all the violence, probably.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/un-blacklists-islamist-militants-iraq_n_5682802.html"} +{"original_headline": "schools enact positive change with drama therapy", "generated_headline": "Schools fix everything with drama therapy. 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A truly unifying event.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/gay-target-practice_us_559fffb3e4b05b1d02903a6a"} +{"original_headline": "nfl player's tweets about gruesome injury remind us that life is 'cruel sometimes'", "generated_headline": "An NFL player's graphic injury tweets provide that crucial, philosophical reminder that life is 'cruel sometimes.' 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A question that keeps both of us up at night, I'm sure.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/is-the-southern-baptist-c_b_6078108.html"} +{"original_headline": "the dark history of birth control", "generated_headline": "The dark history of birth control: just a little, obscure sidebar in the grand story of women's autonomy. No big deal.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-dark-history-of-birth_n_6903262.html"} +{"original_headline": "ted cruz, rand paul are insiders running in an outsiders' game", "generated_headline": "Ted Cruz and Rand Paul: the ultimate outsiders bravely fighting the establishment from inside their powerful Senate seats. 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A true crisis of our time.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/why-apple-must-help-prote_b_7518294.html"} +{"original_headline": "sheldon adelson looks to harry reid for a big favor", "generated_headline": "Sheldon Adelson, the conservative mogul, seeking a favor from Harry Reid, the former Democratic leader. Because political enemies never stay enemies for long.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/harry-reid-sheldon-adelson_n_6277000.html"} +{"original_headline": "i thought mediums were frauds until i met one who knew things she couldn't have known", "generated_headline": "I thought mediums were frauds until one knew things she couldn't have known. 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emotional about leaving the White House?", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/obama-last-state-of-the-union_us_5695c5b5e4b09dbb4bad4064"} +{"original_headline": "u.s.-backed syrian militias take back raqqa from isis", "generated_headline": "U.S.-backed militias take Raqqa: peace and democracy surely follow this flawless intervention.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/syrian-militias-capture-raqqa_us_59e5ecafe4b0ca9f483ac527"} +{"original_headline": "20 struggles every tall girl knows to be true", "generated_headline": "20 struggles every tall girl knows: like being a human giraffe and never fitting in cars.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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"these photos will make you book your next trip to india", "generated_headline": "These photos will make you book your next trip to India, if you enjoy chaos and spices.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/follow-me-instagram-india-so-gorgeous_n_7315598.html"} +{"original_headline": "dean heller's approval rating takes a hit after health care 'debacle'", "generated_headline": "Dean Heller's approval rating takes a hit after health care 'debacle', surprising no one.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/heller-approval-obamacare_us_5982100fe4b0353fbb346065"} +{"original_headline": "buddhist monks buy lobsters destined for the dinner table and set them free", "generated_headline": "Buddhist monks buy lobsters destined for the dinner table and set them free, 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The land of make-believe?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/moving-forward_us_5827dd24e4b02b1f5257a3d8"} +{"original_headline": "how seriously does your nonprofit board take the matter of ethics?", "generated_headline": "How seriously does your nonprofit board take ethics? 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Priorities!", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://losangeles.cbslocal.com/2015/09/09/jury-awards-ex-roscoes-chicken-n-waffles-employee-1-6m-in-race-discrimination-suit/"} +{"original_headline": "'this was the xfl': examining television's greatest sports flop ever", "generated_headline": "Let's examine the XFL, the sports league that proved even Vince McMahon can't make football interesting.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/xfl-documentary-charlie-dick-ebersol_us_588f7b4de4b0522c7d3becf3"} +{"original_headline": "the stop trump movement got new life in ohio", "generated_headline": "The Stop Trump movement is thriving in Ohio, because nothing says 'vital' like fighting a reality TV star.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-stop-trump-movement-got-new-life-in-ohio_us_56e8ad90e4b0b25c9183cf85"} +{"original_headline": "new 'hunger games: mockingjay - part 2' posters show the cast ready for battle", "generated_headline": "New posters show the Hunger Games cast looking serious. 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the provided dipping sauce cup.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-searching-for-part-of-chicken-tender-thin-enough-to-1819578435"} +{"original_headline": "man surrounded by loved ones feels awkward being only person dying", "generated_headline": "A dying man felt isolated despite being with his family.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-surrounded-by-loved-ones-feels-awkward-being-only-p-1819577070"} +{"original_headline": "mueller reveals russia investigation just elaborate sting to nail clinton child sex-slavery ring", "generated_headline": "A false conspiracy theory claims Mueller's investigation targeted a Clinton-led crime ring.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/mueller-reveals-russia-investigation-just-elaborate-sti-1827696431"} +{"original_headline": "book-club meeting degenerates into discussion of oscars", "generated_headline": "A book club discussion included topics beyond the selected reading material.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/book-club-meeting-degenerates-into-discussion-of-oscars-1819566349"} +{"original_headline": "parents honor beloved dead grandmother by naming baby 'gamgam'", "generated_headline": "Parents named their baby after their deceased grandmother's nickname.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/parents-honor-beloved-dead-grandmother-by-naming-baby-1830070589"} +{"original_headline": "coworker not nearly as fun drunk as originally suspected", "generated_headline": "A coworker's drunken behavior was less entertaining than anticipated.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/coworker-not-nearly-as-fun-drunk-as-originally-suspecte-1819569343"} +{"original_headline": "'the onion' wins nobel prize", "generated_headline": "The satirical publication The Onion was humorously awarded a Nobel Prize.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/the-onion-wins-nobel-prize-1819574042"} +{"original_headline": "nation confident team usa can participate in world cup", "generated_headline": "Many Americans hope the U.S. men's soccer team will qualify for the World Cup.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/nation-confident-team-usa-can-participate-in-world-cup-1819576612"} +{"original_headline": "nation's math teachers introduce 27 new trig functions", "generated_headline": "Math curriculum updates may include additional trigonometric concepts.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/nation-s-math-teachers-introduce-27-new-trig-functions-1819575558"} +{"original_headline": "joe arpaio's family surprises him with detained hispanic motorist", "generated_headline": "Joe Arpaio's family gave him a detained immigrant as a joke, referencing his policies.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/joe-arpaio-s-family-surprises-him-with-detained-hispani-1819580243"} +{"original_headline": "hillary's last name dropped from senate race", "generated_headline": "Hillary 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"https://www.theonion.com/jesus-christ-pushes-past-firefighter-into-burning-notre-1834062387"} +{"original_headline": "man carefully selects t-shirt for night out", "generated_headline": "A man chose a t-shirt to wear for an evening outing.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-carefully-selects-t-shirt-for-night-out-1819565553"} +{"original_headline": "breaking: america's white population plummets to 2.7% after trump caves on immigration enforcement", "generated_headline": "Census data indicates demographic changes following immigration policy adjustments.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/breaking-america-s-white-population-plummets-to-2-7-a-1827000385"} +{"original_headline": "director of census bureau calls for updated population report after realizing he forgot to count himself", "generated_headline": "The Census Bureau director acknowledged an error in population data collection.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/director-of-census-bureau-calls-for-updated-population-1825957255"} +{"original_headline": "chinese takeout restaurant has seen man at his worst", "generated_headline": "A man behaved poorly at a Chinese takeout restaurant.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/chinese-takeout-restaurant-has-seen-man-at-his-worst-1819570656"} +{"original_headline": "report: 84% of americans currently contestants", "generated_headline": "A large number of Americans participate in competitive reality television shows.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/report-84-of-americans-currently-contestants-1819569748"} +{"original_headline": "crowd can't believe balls on frontman who waited till third song to ask them how they're doing", "generated_headline": "An audience was frustrated that the singer did not engage with them until later in the concert.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/crowd-can-t-believe-balls-on-frontman-who-waited-till-t-1819579449"} +{"original_headline": "single nurse can't help but notice man isolated for ebola not wearing a ring", "generated_headline": "A nurse noted that an Ebola patient was not wearing a wedding ring.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/single-nurse-can-t-help-but-notice-man-isolated-for-ebo-1819577016"} +{"original_headline": "racist merely misspoke", "generated_headline": "A person's racist comment was dismissed as an unintentional error.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/racist-merely-misspoke-1819565532"} +{"original_headline": "horrified nation wakes up on cyber monday to find amazon echo devices embedded beneath skin", "generated_headline": "Concerns are rising about the integration of smart devices into the human body.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/horrified-nation-wakes-up-on-cyber-monday-to-find-amazo-1830663996"} +{"original_headline": "walletless biden found handcuffed to bedpost", "generated_headline": "Joe Biden was reported to have been found handcuffed to a bedpost without his wallet.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/walletless-biden-found-handcuffed-to-bedpost-1819570983"} +{"original_headline": "trump claims waterboarding doesn't come close to the excruciating torment he experiences at every moment", "generated_headline": "Donald Trump claimed that waterboarding is less severe than the personal torment he experiences regularly.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/trump-claims-waterboarding-doesn-t-come-close-to-the-ex-1819579585"} +{"original_headline": "great lover also great at slinking out", "generated_headline": "The man, known for his romantic prowess, is also skilled at avoiding commitments.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/great-lover-also-great-at-slinking-out-1819567040"} +{"original_headline": "hundreds of people who will die before christmas really excited for holiday season", "generated_headline": "Many individuals with terminal illnesses are looking forward to the holiday season.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/hundreds-of-people-who-will-die-before-christmas-really-1819574310"} +{"original_headline": "'but a fox wouldn't eat gingerbread,' that one precocious little asshole reports", "generated_headline": "A child interjected, 'But a fox wouldn't eat gingerbread,' during a discussion.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/but-a-fox-wouldnt-eat-gingerbread-that-one-precocious-1819572011"} +{"original_headline": "couple going at it like tired, sexually incompetent rabbits", "generated_headline": "The couple's sexual activity was described as lacking vigor and skill.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/couple-going-at-it-like-tired-sexually-incompetent-rab-1819574480"} +{"original_headline": "voter turnout reaches all-time low of 17", "generated_headline": "Voter turnout in the election was a record low of 17 percent.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/voter-turnout-reaches-all-time-low-of-17-1819568786"} +{"original_headline": "travel channel blows its 'bed and breakfasts of new england' wad", "generated_headline": "The Travel Channel dedicated significant resources to its 'Bed and Breakfasts of New England' series.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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"fast-learning new hire gains quick grasp of how terrible job is", "generated_headline": "A new employee quickly learned how unsatisfactory the job is.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/fast-learning-new-hire-gains-quick-grasp-of-how-terribl-1823618994"} +{"original_headline": "area man puts on some nice pants for once in his life", "generated_headline": "A local man wore nice pants for a special event.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/area-man-puts-on-some-nice-pants-for-once-in-his-life-1819569893"} +{"original_headline": "arizona high schools to now teach spanish entirely in english", "generated_headline": "Arizona high schools will teach Spanish classes using only English.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/arizona-high-schools-to-now-teach-spanish-entirely-in-e-1819589923"} +{"original_headline": "quincy suspects murder", "generated_headline": "Quincy, a medical examiner, suspects that a death was a murder.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/quincy-suspects-murder-1819563927"} +{"original_headline": "spatial skills abandon area man during search for correct tupperware lid", "generated_headline": "A man temporarily lost his ability to match Tupperware lids due to poor spatial skills.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/spatial-skills-abandon-area-man-during-search-for-corre-1819571022"} +{"original_headline": "saltless pretzel hangs alone in bulb-heated rack", "generated_headline": "A saltless pretzel 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"stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/why-lin-manuel-miranda-will-always-be-a-fearless-ally-of-planned-parenthood_us_5865266be4b0eb58648858d9"} +{"original_headline": "how to stop being a martyr", "generated_headline": "Easy: just start enjoying all the attention you get from being a perpetual victim.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/how-to-stop-being-a-martyr_us_586ea2ebe4b043ad97e26635"} +{"original_headline": "donald glover did what any fan would do after being cast in 'solo'", "generated_headline": "Because nothing says 'fan excitement' like immediately demanding creative control and rewriting the script.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/donald-glover-star-wars-lando_us_5af5a3e0e4b0e57cd9f8e386"} 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How utterly unprecedented.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/american-political-polarization_n_5488039.html"} +{"original_headline": "the new colossus of today is white nationalism", "generated_headline": "Move over, Lady Liberty; the new symbol of American greatness is... hate groups. 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"type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/once-they-put-me-on-cheeses-i-will-finally-be-happy-1830186778"} +{"original_headline": "new 'cut off your genitals' challenge gains popularity among teens online", "generated_headline": "A harmful online challenge encouraging self-harm is reported to be gaining popularity among teens.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-cut-off-your-genitals-challenge-gains-popularity-1824991756"} +{"original_headline": "parents seize creative control of 3rd-grade art project", "generated_headline": "Parents take over their child's third-grade art project.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/parents-seize-creative-control-of-3rd-grade-art-project-1819574908"} +{"original_headline": "owners of google hope to parlay world's most popular website into book deal", "generated_headline": "The founders of Google consider writing a book about their experiences.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/owners-of-google-hope-to-parlay-worlds-most-popular-web-1819573097"} +{"original_headline": "actress leaves porn past behind with new cinemax erotic thriller", "generated_headline": "An actress known for adult films stars in a new Cinemax erotic thriller.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/actress-leaves-porn-past-behind-with-new-cinemax-erotic-1819586483"} +{"original_headline": "mike pence condemns female senators for wantonly sharing senate floor with male colleagues after dark", "generated_headline": "Mike Pence criticizes female senators for working late with male colleagues.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/mike-pence-condemns-female-senators-for-wantonly-sharin-1822873862"} +{"original_headline": "breaking: we might be doing a bad job", "generated_headline": "A news outlet acknowledges potential shortcomings in its reporting.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/breaking-we-might-be-doing-a-bad-job-1819574854"} +{"original_headline": "drought-ravaged nyc institutes alternate-side-of-street firefighting", "generated_headline": "Due to drought, New York City implements a rotating system for firefighting resources.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/drought-ravaged-nyc-institutes-alternate-side-of-street-1819566462"} +{"original_headline": "well known gresham, or musicians form gresham, or supergroup", "generated_headline": "Musicians from Gresham, Oregon, form a supergroup named Gresham.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/well-known-gresham-or-musicians-form-gresham-or-super-1819588981"} +{"original_headline": "federal judge pencils blocking trump's unconstitutional executive orders into monthly schedule", "generated_headline": "A federal judge schedules actions to block President Trump's executive orders deemed unconstitutional.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/federal-judge-pencils-blocking-trump-s-unconstitutional-1819579729"} +{"original_headline": "esl textbook concentrates on food-preparation vocabulary", "generated_headline": "An ESL textbook emphasizes vocabulary related to food preparation.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/esl-textbook-concentrates-on-food-preparation-vocabular-1819566122"} +{"original_headline": "sight of 400 war elephants on horizon marks hillary clinton's arrival in swing state", "generated_headline": "Hillary Clinton's arrival in a swing state is marked by a display involving war elephants.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/sight-of-400-war-elephants-on-horizon-marks-hillary-cli-1819578340"} +{"original_headline": "flying squirrel loves it every time", "generated_headline": "A flying squirrel enjoys its activities.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/flying-squirrel-loves-it-every-time-1819590469"} +{"original_headline": "cackling npr host warns upcoming segment may feature content too dark, too chilling, too positively ghoulish for young listeners", "generated_headline": "An NPR host humorously warns that the next segment may be too intense for young listeners.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/cackling-npr-host-warns-upcoming-segment-may-feature-co-1827061142"} +{"original_headline": "struggling supreme court loses eighth consecutive case", "generated_headline": "The Supreme Court has lost eight consecutive cases.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/struggling-supreme-court-loses-eighth-consecutive-case-1819590180"} +{"original_headline": "verb to follow noun; prepositional phrase to follow", "generated_headline": "In English grammar, a verb typically follows a noun, and a prepositional phrase may follow.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/verb-to-follow-noun-prepositional-phrase-to-follow-1819564061"} +{"original_headline": "man pours all his culinary talents into inserting, removing pizza from oven", "generated_headline": "A man applies his cooking skills to baking and removing pizza from the oven.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/man-pours-all-his-culinary-talents-into-inserting-remo-1819577388"} +{"original_headline": "afro-disney plans scrapped", "generated_headline": "Plans for an African-themed Disney park have been canceled.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/afro-disney-plans-scrapped-1819586174"} +{"original_headline": "breaking: 'the onion' in kill range of boston bomber suspect", "generated_headline": "The satirical news outlet The Onion reports being in the vicinity of a Boston bomber suspect.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/breaking-the-onion-in-kill-range-of-boston-bomber-susp-1819574817"} +{"original_headline": "mike tyson does his best drake impression after seeing 'hotline bling' meme", "generated_headline": "Mike Tyson absolutely destroys Drake's 'Hotline Bling' with his heavyweight impression.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/mike-tyson-does-his-drake-impression-over-hotline-bling-meme_us_5630d5b8e4b0c66bae5a4303"} +{"original_headline": "the problem with fake news? 'real' news is bogus too", "generated_headline": "The real issue with fake news is that real news is just as fake\u2014 brilliant observation.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-problem-with-fake-news-real-news-is-bogus-too_us_586d1f0fe4b04d7df167d8a8"} +{"original_headline": "north korea rings in new year with promises of intercontinental missile", "generated_headline": "North Korea rings in the New Year with a friendly promise to send missiles your way.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/north-korea-icbm_us_5868acfce4b0d9a5945bca0f"} +{"original_headline": "sanders could pull off michigan-style upset in ohio", "generated_headline": "Bernie Sanders could pull off a Michigan-style upset in Ohio and probably end world hunger too.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://www.latimes.com/politics/la-na-democrat-primaries-analysis-20160309-story.html"} +{"original_headline": "help out a school counselor? i'm in, with #hscc2015", "generated_headline": "Help out a school counselor? Who wouldn't jump at the chance? #hscc2015 is clearly a blast.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/help-out-a-school-counsel_1_b_6099292.html"} +{"original_headline": "new 'dumb and dumber to' clip shows a very special celeb cameo", "generated_headline": "A 'very special' cameo in 'Dumb and Dumber To'? Because the movie wasn't special enough already.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/bill-murray-dumb-and-dumber-to_n_6153196.html"} +{"original_headline": "watch abbi from 'broad city' strip naked and rock out to lady gaga", "generated_headline": "Watch Abbi from 'Broad City' strip naked and rock out\u2014 a cultural milestone for our times.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/broad-city-abbi-lady-gaga_n_6519212.html"} +{"original_headline": "rolling stone reporter recalls the moment her uva rape story unraveled", "generated_headline": "Rolling Stone reporter nostalgically recalls when her UVA rape story came crashing down\u2014 such a memorable moment.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/sabrina-rubin-erdely-uva-rape_us_577950c6e4b0416464105254"} +{"original_headline": "'gilmore girls' actors defend against critics who say revival is too mean", "generated_headline": "Gilmore Girls actors valiantly defend their revival against critics who say it's too mean\u2014 how dare they critique art.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/gilmore-girls-actors-defend-against-critics-who-say-revival-is-too-mean_us_583eb0ebe4b04fcaa4d5cbdc"} +{"original_headline": "the great fracturer, exceptional smasher, and indispensable fragmenter", "generated_headline": "Behold the Great Fracturer, who shatters expectations, and the Indispensable Fragmenter, because fragments are everything.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-great-fracturer-exceptional-smasher-and-indispensable-fragmenter_us_59ef66cde4b0d14acdccc410"} +{"original_headline": "president trump's iran deal message to north korea: do not trust washington", "generated_headline": "Trump's message to North Korea: 'Do not trust Washington'\u2014 from the president who trusts Russia.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/president-trumps-iran-deal-message-to-north-korea_us_59f48c40e4b06acda25f4a3b"} +{"original_headline": "processed meat is carcinogenic and red meat probably is, says who", "generated_headline": "Processed meat is carcinogenic? And red meat probably is? Says who? Oh, just the WHO, no big deal.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/processed-meat-is-carcinogenic-and-red-meat-probably-is-says-who_us_562e50b7e4b0443bb5649b7d"} +{"original_headline": "lin-manuel miranda disses donald trump in 'my shot' remix on 'snl'", "generated_headline": "Lin-Manuel Miranda bravely disses Trump on SNL\u2014 because we needed another celebrity political rant.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/lin-manuel-miranda-donald-trump_us_57f9eaf9e4b068ecb5df3fc0"} +{"original_headline": "california shooters likely planned multiple attacks: officials", "generated_headline": "California shooters likely planned multiple attacks\u2014 officials say it was probably just a hobby.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/california-shooters-likely-planned-multiple-attacks-officials_us_5664c5ace4b08e945fefe153"} +{"original_headline": "emma stone, meryl streep and other stars bring activists to golden globes", "generated_headline": "Stars bring activists to Golden Globes\u2014 because red carpets are the perfect stage for social justice.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/emma-stone-meryl-streep-and-other-actresses-bring-activists-to-golden-globes_us_5a52aff6e4b003133ec91741"} +{"original_headline": "a triple amputee's dream wedding brings community together", "generated_headline": "A triple amputee's dream wedding brings community together\u2014 it was just a small, insignificant event.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/a-triple-amputees-dream-wedding_b_6924534.html"} +{"original_headline": "review: 'teethmarks on my tongue' by eileen battersby", "generated_headline": "Review: 'Teethmarks on My Tongue'\u2014 a title that bites harder than the content.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/review-teethmarks-on-my-tongue-by-eileen-battersby_us_58f103c3e4b0da2ff8605158"} +{"original_headline": "what the house gop isn't telling you about their obamacare repeal bill", "generated_headline": "What isn't the House GOP telling you about their Obamacare repeal? Probably that it's a flawless plan.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/what-the-house-gop-isnt-telling-you-about-their-obamacare_us_5910ba85e4b056aa2363d7cf"} +{"original_headline": "how an obama cut-out is helping me survive a trump presidency", "generated_headline": "An Obama cut-out helps me survive Trump's presidency\u2014 because inanimate objects are more reliable than politicians.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/how-an-obama-cut-out-is-helping-me-survive-a-trump_us_586a793be4b014e7c72ee2db"} +{"original_headline": "the politics of shame and pride", "generated_headline": "The politics of shame and pride: because politics needed more emotional depth.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-politics-of-shame-and_b_7075444.html"} +{"original_headline": "ricky gervais stands up for truth in the age of fake news", "generated_headline": "Ricky Gervais stands up for truth\u2014 from the guy who makes a living mocking people.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/ricky-gervais-stands-up-for-truth-in-the-age-of-fake-news_us_59d12d9ee4b06791bb1141d7"} +{"original_headline": "father kills 2-year-old boy and takes own life after 18-hour standoff, police say", "generated_headline": "Father's 18-hour standoff ends predictably\u2014 such a surprise, right?", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/atlanta-standoff_us_568e152ae4b0a2b6fb6ec5ff"} +{"original_headline": "tijuana and the future of trade", "generated_headline": "Tijuana and the future of trade: because border towns are where global economics happen.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/tijuana-and-the-future-of_b_7528062.html"} +{"original_headline": "elder women as agents of change", "generated_headline": "Elder women as agents of change\u2014 finally, the secret power of bingo nights revealed.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/elder-women-as-agents-of-_b_6489854.html"} +{"original_headline": "frat suspended after 'rape some b*****s' comment caught on tape", "generated_headline": "Frat suspended for charming 'rape some b*****s' comment\u2014 classic frat humor, am I right?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/frat-rape-comment-tape_us_55cf8d95e4b0ab468d9d82d1"} +{"original_headline": "the truth about generic vs. brand-name medications", "generated_headline": "The truth about generic vs. brand-name meds? They're the same but cheaper\u2014 how scandalous.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/generic-prescriptions_n_6730194.html"} +{"original_headline": "watch: $24 million yacht goes up in flames", "generated_headline": "Watch a $24 million yacht go up in flames\u2014 a must-see spectacle for the wealthy.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/drone-footage-yacht-fire-boat-flames_n_5518284.html"} +{"original_headline": "what to watch on hulu that's new this week", "generated_headline": "What to watch on Hulu this week? Probably another documentary about something you don't care about.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/hulu-what-to-watch_us_5aeb70dde4b0c4f1932063b2"} +{"original_headline": "myth of white supremacy is now out in the open", "generated_headline": "The myth of white supremacy is now out in the open\u2014 if it's a myth, why is it everywhere?", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/thoughts-on-charlottesville_us_59912360e4b0caa1687a6193"} +{"original_headline": "see photos from the 2015 white house correspondents' dinner", "generated_headline": "See photos from the 2015 White House Correspondents' Dinner\u2014 the event where journalism and politics have a love fest.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/white-house-correspondents-dinner-photos_n_7112436.html"} diff --git a/docs/dataset_audit.md b/docs/dataset_audit.md new file mode 100644 index 0000000..71c8a3d --- /dev/null +++ b/docs/dataset_audit.md @@ -0,0 +1,210 @@ +# Dataset Audit + +> Verified empirical findings from `data/processed/sarcasm_pairs_step35_clean.jsonl` +> Audited: 2026-03-03 + +--- + +## 1. Schema and Field Descriptions + +**File**: `data/processed/sarcasm_pairs_step35_clean.jsonl` +**Format**: JSON Lines (one JSON object per line) +**Total rows**: 28,333 + +### Fields + +| Field | Type | Description | +|-------|------|-------------| +| `original_headline` | string | The source headline (sarcastic OR non-sarcastic depending on `type`) | +| `generated_headline` | string | The transformed headline (the opposite label) | +| `strategy` | string | Sarcasm type/strategy label (6 values, see below) | +| `type` | string | Direction of transformation: `sarcastic_to_non` or `non_to_sarcastic` | +| `model_used` | string | The LLM that generated the rewrite | +| `article_link` | URL string | Source article URL; used for grouping | + +--- + +## 2. Null / Missing Value Checks + +All fields verified for all 28,333 rows: + +| Field | Null Count | Notes | +|-------|-----------|-------| +| `original_headline` | 0 | Complete | +| `generated_headline` | 0 | Complete | +| `strategy` | 0 | Complete | +| `type` | 0 | Complete | +| `model_used` | 0 | Complete | +| `article_link` | 0 | Complete | + +**No missing values in any field.** Dataset is fully populated. + +--- + +## 3. Allowed Values for `type` + +| Value | Count | Description | +|-------|-------|-------------| +| `non_to_sarcastic` | 14,925 | Original is non-sarcastic (HuffPost); generated is sarcastic | +| `sarcastic_to_non` | 13,408 | Original is sarcastic (TheOnion); generated is non-sarcastic | + +Both expected values are present. No unexpected or malformed type values found. + +--- + +## 4. Distribution of `strategy` + +| Strategy | Count | % of Total | +|----------|-------|-----------| +| sarcasm | 8,699 | 30.7% | +| irony | 6,102 | 21.5% | +| satire | 5,224 | 18.4% | +| overstatement | 3,976 | 14.0% | +| understatement | 3,295 | 11.6% | +| rhetorical_question | 1,037 | 3.7% | +| **Total** | **28,333** | **100%** | + +**Class imbalance**: `rhetorical_question` is 8.4x less frequent than `sarcasm`. This is a critical imbalance for the type classification task. Macro-F1 as primary metric is the correct choice; class-weighted loss will be needed in BERT training. + +--- + +## 5. Model Used Distribution + +| Model | Count | +|-------|-------| +| `stepfun/step-3.5-flash:free` | 28,333 | + +**Only one model** was used for all rewrites. No multi-model bias; this simplifies analysis. + +--- + +## 6. Duplicate Checks + +### Duplicate Original Headlines +- **Count**: 70 duplicate original_headlines (across 28,333 rows) +- **Cause**: Same TheOnion or HuffPost headline appearing under different `article_link` values (syndicated content) or slight variants +- **Action**: These are different JSONL rows (different article_links), so they get different `pair_id`. No action needed; grouping by `article_link` will handle same-article pairs. + +### Duplicate Generated Headlines +- **Count**: 1 duplicate generated_headline +- **Cause**: LLM produced identical rewrite for two different inputs +- **Action**: Both rows retained; they have different original headlines and are distinguishable by pair_id + +### Duplicate Article Links +- **Count**: 2 duplicate article_links (i.e., 2 article_link values appear more than once) +- **Cause**: Same article processed twice with both `sarcastic_to_non` and `non_to_sarcastic` transformations, OR same article URL with different strategy annotations +- **Action**: These duplicates define the `group_id`; rows sharing an article_link will be grouped together and placed in the same split, preventing leakage. + +--- + +## 7. Label Derivation Logic + +### Rule (Non-Negotiable) + +For each JSONL row `r`: + +``` +if r['type'] == 'sarcastic_to_non': + sample_A: text = r['original_headline'], binary_label = 1 (sarcastic), type_label = r['strategy'] + sample_B: text = r['generated_headline'], binary_label = 0 (non-sarcastic), type_label = None + +if r['type'] == 'non_to_sarcastic': + sample_A: text = r['original_headline'], binary_label = 0 (non-sarcastic), type_label = None + sample_B: text = r['generated_headline'], binary_label = 1 (sarcastic), type_label = r['strategy'] +``` + +### Derived Binary Dataset Size + +| Class | From `sarcastic_to_non` | From `non_to_sarcastic` | Total | +|-------|------------------------|------------------------|-------| +| Sarcastic (1) | 13,408 (original) | 14,925 (generated) | 28,333 | +| Non-sarcastic (0) | 13,408 (generated) | 14,925 (original) | 28,333 | +| **Total** | | | **56,666** | + +**Binary dataset is perfectly balanced**: exactly 28,333 sarcastic and 28,333 non-sarcastic samples. + +### Derived Type Dataset Size + +Only sarcastic samples (binary_label == 1) are included in the type dataset: + +| Strategy | Count | +|----------|-------| +| sarcasm | 8,699 | +| irony | 6,102 | +| satire | 5,224 | +| overstatement | 3,976 | +| understatement | 3,295 | +| rhetorical_question | 1,037 | +| **Total** | **28,333** | + +--- + +## 8. Leakage Risk Analysis + +### Risk Description + +Each JSONL row produces a **semantically coupled pair**: an original headline and its rewrite share the same topic, named entities, and much vocabulary. If a random split places the sarcastic version of a pair in train and the non-sarcastic version in test: +- The model sees the topic/entities in training +- The test sample is not truly "unseen" — it shares semantic content with a training sample +- Performance is inflated; generalization is not measured + +**This is guaranteed to occur with naive (random) splitting after expansion.** + +### Leakage Risk Level by Source + +| Risk Source | Severity | Mitigation | +|-------------|----------|-----------| +| Same pair across splits | **Critical** | Pair-level split (pair never crosses split boundary) | +| Same article multiple directions | **High** | Group by article_link; all rows from same URL in one split | +| Similar topics (different articles) | **Low** | Cannot perfectly control; acceptable | + +### Final Split Grouping Decision + +**Primary grouping key**: `article_link` (normalized: lowercase, strip trailing slash) + +Rationale: +- All rows sharing the same article URL are semantically coupled +- 2 duplicate article_links found → these rows must cohabit the same split +- ~28,333 unique group_ids (near-unique since only 2 duplicates) + +**Fallback**: `pair_id` = row index (if article_link is missing or malformed) + +Since no null article_links exist and only 2 duplicates, the primary grouping is reliable. + +--- + +## 9. Data Quality Issues / Anomalies + +### Generation Artifacts + +Generated headlines may contain: +- More formal/neutral phrasing than natural non-sarcastic writing +- Slight topic shifts (LLM reframing rather than pure rewrite) +- Occasional metadata bleeding (e.g., "According to reports..." framing artifacts) + +**Recommendation**: Track `is_generated` flag in the binary dataset to allow artifact-aware error analysis. + +### Strategy Label Granularity + +The `rhetorical_question` strategy label is the most syntactically distinct (ends with `?`). The other 5 strategies (`sarcasm`, `irony`, `satire`, `overstatement`, `understatement`) may have substantial semantic overlap, making multiclass type classification challenging. + +The label `sarcasm` as a strategy within a sarcasm dataset is potentially circular/ambiguous. This should be flagged in error analysis. + +### Single Model Bias + +All generated headlines come from `stepfun/step-3.5-flash:free`. The classifier may learn to distinguish "Step-3.5 generation style" from "authentic headline style" rather than true sarcasm. Error analysis should check for this artifact. + +--- + +## 10. Verification Checklist + +- [x] All 28,333 rows parsed without error +- [x] No null values in any field +- [x] Both `type` values present and expected +- [x] All 6 strategy values identified +- [x] Duplicate checks completed (70 orig_headline dupes, 1 gen_headline dupe, 2 article_link dupes) +- [x] Label derivation rules documented and validated +- [x] Binary dataset size computed: 56,666 samples, perfectly balanced +- [x] Type dataset size computed: 28,333 samples, imbalanced (3.7% to 30.7%) +- [x] Leakage risk identified and mitigation strategy defined +- [x] Group ID strategy confirmed: `article_link` primary, `pair_id` fallback diff --git a/docs/experiment_plan.md b/docs/experiment_plan.md new file mode 100644 index 0000000..d25bb61 --- /dev/null +++ b/docs/experiment_plan.md @@ -0,0 +1,271 @@ +# Experiment Plan + +> Execution plan for sarcasm classification pipeline. +> Written: 2026-03-03 + +--- + +## 1. Split Strategy + +### Group-Aware Holdout Split + +**Grouping key**: `group_id = normalize(article_link)` (fallback: `pair_id`) + +**Split ratios** (at group level): +| Split | Groups | Approx Samples (binary) | +|-------|--------|------------------------| +| Train | 70% | ~39,666 | +| Val | 15% | ~8,500 | +| Test | 15% | ~8,500 | + +**Implementation**: +1. Assign `pair_id` = row index (0-based) +2. Assign `group_id` = normalized `article_link` (strip protocol, lowercase, strip trailing slash) +3. Get unique `group_id` values; shuffle with fixed seed (SEED=42) +4. Split groups 70/15/15 +5. Expand each group's JSONL rows into binary samples; all samples from a group go to the same split + +**Stratification note**: Exact stratification by `type` at group level is approximate; we shuffle with a fixed seed and document the resulting class distribution. Binary task is balanced by construction, so stratification is less critical here. For type task, document per-class counts in each split. + +### Cross-Validation (Classical Models) + +**Method**: GroupKFold (k=5) on training+val groups +- Groups: same `group_id` as holdout split +- Use `cross_val_score` with `GroupKFold` for hyperparameter selection +- Report mean ± std macro-F1 across folds +- Refit best config on full train+val; evaluate on held-out test + +--- + +## 2. Metrics (All Models, Both Tasks) + +For each model and each task (binary + type), report: + +| Metric | Notes | +|--------|-------| +| Accuracy | Overall correct / total | +| Precision (macro) | Unweighted average of per-class precision | +| Recall (macro) | Unweighted average of per-class recall | +| **F1 (macro)** | **PRIMARY model-selection metric** | +| F1 (weighted) | Class-frequency-weighted F1 | +| Confusion matrix | Saved as PNG and CSV | +| Per-class P/R/F1 | From `classification_report` | + +### Model Selection Criterion + +- **Binary task**: Validation macro-F1 (primary), weighted-F1 (secondary) +- **Type task**: Validation macro-F1 (primary) + +--- + +## 3. Hyperparameter Search Spaces + +### 3.1 TF-IDF + Logistic Regression + +**Binary Task** (full grid, ~36 combinations): + +| Parameter | Values | +|-----------|--------| +| `tfidf__ngram_range` | `(1,1)`, `(1,2)` | +| `tfidf__min_df` | `2`, `3`, `5` | +| `tfidf__max_features` | `None`, `50000` | +| `lr__C` | `0.1`, `1.0`, `3.0` | +| `lr__class_weight` | `None` | + +Char n-gram variant (separate run): +| Parameter | Values | +|-----------|--------| +| `tfidf__analyzer` | `char_wb` | +| `tfidf__ngram_range` | `(3,5)` | +| `lr__C` | `0.1`, `1.0`, `3.0` | + +**Type Task** (same grid, add class_weight): + +| Parameter | Values | +|-----------|--------| +| `tfidf__ngram_range` | `(1,1)`, `(1,2)` | +| `tfidf__min_df` | `2`, `3`, `5` | +| `tfidf__max_features` | `None`, `50000` | +| `lr__C` | `0.1`, `1.0`, `3.0` | +| `lr__class_weight` | `None`, `balanced` | + +**Search method**: `GridSearchCV` with `GroupKFold(5)`, scoring=`f1_macro` + +### 3.2 Naive Bayes + +**Both Tasks**: + +| Parameter | CountVectorizer | TF-IDF | +|-----------|----------------|--------| +| `ngram_range` | `(1,1)`, `(1,2)` | `(1,1)`, `(1,2)` | +| `min_df` | `2`, `3`, `5` | `2`, `3`, `5` | +| `nb__alpha` | `0.1`, `0.5`, `1.0` | `0.1`, `0.5`, `1.0` | + +Two classifier variants: +- `MultinomialNB` (primary) +- `ComplementNB` (nice-to-have, better for imbalanced type task) + +**Search method**: `GridSearchCV` with `GroupKFold(5)`, scoring=`f1_macro` + +### 3.3 DistilBERT / BERT + +| Parameter | Values | +|-----------|--------| +| `model_name` | `distilbert-base-uncased` (primary), `bert-base-uncased` (optional) | +| `max_length` | `128` | +| `batch_size` | `16`, `32` | +| `learning_rate` | `2e-5`, `3e-5`, `5e-5` | +| `epochs` | Up to 10 (with early stopping) | +| `warmup_ratio` | `0.1` | +| `weight_decay` | `0.01` | +| `seed` | `42` | + +**Early stopping**: patience=3 on validation macro-F1 (binary) or macro-F1 (type) + +**Class weighting for type task**: +- Compute `class_weight` = `len(y_train) / (n_classes * np.bincount(y_train))` +- Apply as `weight` tensor in `CrossEntropyLoss` + +**Training runs** (in order): +1. DistilBERT, binary task, default LR +2. DistilBERT, type task, with class weights +3. BERT-base, binary task (optional) +4. BERT-base, type task (optional) + +--- + +## 4. Output Artifact Locations + +### Datasets +``` +outputs/datasets/ +├── binary_dataset.csv # Full expanded binary dataset +├── type_dataset.csv # Sarcastic-only type dataset +├── splits/ +│ ├── train_binary.csv +│ ├── val_binary.csv +│ ├── test_binary.csv +│ ├── train_type.csv +│ ├── val_type.csv +│ └── test_type.csv +``` + +### Classical Model Outputs +``` +outputs/classical/ +├── tfidf_lr/ +│ ├── best_config_binary.json +│ ├── best_config_type.json +│ ├── metrics_binary.json +│ ├── metrics_type.json +│ ├── predictions_val_binary.csv +│ ├── predictions_test_binary.csv +│ ├── predictions_val_type.csv +│ ├── predictions_test_type.csv +│ ├── confusion_matrix_binary.png +│ └── confusion_matrix_type.png +├── naive_bayes/ +│ ├── best_config_binary.json +│ ├── best_config_type.json +│ ├── metrics_binary.json +│ ├── metrics_type.json +│ ├── predictions_val_binary.csv +│ ├── predictions_test_binary.csv +│ ├── predictions_val_type.csv +│ ├── predictions_test_type.csv +│ ├── confusion_matrix_binary.png +│ └── confusion_matrix_type.png +``` + +### BERT Outputs +``` +outputs/bert/ +├── distilbert_binary/ +│ ├── config.json +│ ├── best_checkpoint/ # HuggingFace model checkpoint +│ ├── training_log.csv +│ ├── metrics.json +│ ├── predictions_val.csv +│ ├── predictions_test.csv +│ └── confusion_matrix.png +├── distilbert_type/ +│ └── ... +├── bert_base_binary/ (optional) +└── bert_base_type/ (optional) +``` + +### Reports +``` +outputs/reports/ +├── model_comparison.md +├── error_analysis.md +├── error_examples_binary.csv +└── error_examples_type.csv +``` + +--- + +## 5. Execution Order (Fastest → Slowest) + +| Step | Notebook | Estimated Time | Prerequisite | +|------|----------|---------------|-------------| +| 1 | `01_data_preparation.ipynb` | 5-10 min | None | +| 2 | `02_tfidf_lr_baseline.ipynb` | 15-30 min | Step 1 | +| 3 | `03_naive_bayes_baseline.ipynb` | 10-20 min | Step 1 | +| 4 | `04_bert_classification.ipynb` | 2-6 hours | Steps 2-3 | +| 5 | `05_error_analysis.ipynb` | 30-60 min | Steps 2-4 | + +--- + +## 6. Success Criteria + +### Binary Task + +| Model | Acceptable macro-F1 | Good macro-F1 | +|-------|---------------------|--------------| +| TF-IDF + LR | > 0.75 | > 0.85 | +| Naive Bayes | > 0.70 | > 0.80 | +| DistilBERT | > 0.85 | > 0.90 | + +### Type Task (Multiclass) + +| Model | Acceptable macro-F1 | Good macro-F1 | +|-------|---------------------|--------------| +| TF-IDF + LR | > 0.40 | > 0.60 | +| Naive Bayes | > 0.35 | > 0.55 | +| DistilBERT | > 0.55 | > 0.70 | + +Type task is harder due to: +- 6-class imbalanced distribution +- Strategy overlap (sarcasm vs irony vs satire) +- Subtle pragmatic differences between strategies + +### Final Model Selection + +Best model for recommendation = highest test macro-F1 on the primary metric. + +If DistilBERT test macro-F1 ≤ best classical macro-F1 by < 0.02: recommend classical model (simpler, faster). + +--- + +## 7. Reproducibility Checklist + +- [ ] `SEED = 42` used everywhere (numpy, random, sklearn, torch, transformers) +- [ ] All split files saved to disk before model training +- [ ] All model configs saved to JSON before training +- [ ] All metrics saved to JSON after evaluation +- [ ] All predictions saved to CSV +- [ ] All confusion matrices saved as PNG +- [ ] BERT checkpoints saved; best checkpoint logged +- [ ] `requirements.txt` pinned + +--- + +## Key Assumptions Documented + +1. **Grouping**: `article_link` is the primary grouping key; rows with same article_link share a group. Two observed duplicate article_links are handled correctly. +2. **Text normalization**: Lowercase for classical models; raw text (preserve case/punctuation) for BERT tokenizer. +3. **No aggressive cleaning**: Punctuation is preserved (sarcasm cues). +4. **50/50 binary balance**: The expansion of 28,333 pairs produces exactly 28,333 sarcastic + 28,333 non-sarcastic samples. +5. **`is_generated` flag**: Included in datasets to enable artifact-aware error analysis. +6. **Cross-validation uses train+val groups only**: Test groups are never seen during hyperparameter selection. diff --git a/docs/research_summary.md b/docs/research_summary.md new file mode 100644 index 0000000..21b3d2d --- /dev/null +++ b/docs/research_summary.md @@ -0,0 +1,187 @@ +# Research Summary + +> Pre-implementation literature and methodology review for CS4248 Sarcasm Classification Project. + +--- + +## 1. Why TF-IDF + Logistic Regression Is a Strong Baseline + +### TF-IDF as Feature Representation + +TF-IDF (Term Frequency–Inverse Document Frequency) transforms raw text into a sparse vector space where each dimension corresponds to a vocabulary token. The weight of a term is: + +$$\text{tfidf}(t,d) = \text{tf}(t,d) \times \log\frac{N}{1 + \text{df}(t)}$$ + +- **Term frequency** rewards tokens that appear often in a document +- **Inverse document frequency** down-weights tokens common across all documents, promoting discriminative terms + +For sarcasm detection, TF-IDF captures **lexical sarcasm markers**: exaggerated praise terms, specific ironic vocabulary ("absolutely brilliant," "clearly"), and domain-specific token patterns from TheOnion-style writing. + +### Logistic Regression on Sparse TF-IDF + +Logistic Regression is particularly well-suited to high-dimensional sparse features: +- **Linear separator in feature space**: Sufficient for many text classification tasks where class-specific vocabulary is the primary signal +- **Regularization (L2 default)**: Prevents overfitting on rare n-grams +- **Calibrated probabilities**: Useful for error analysis and threshold tuning +- **Interpretable weights**: Top positive/negative coefficients reveal discriminative lexical features +- **Scalable**: Handles 28k+ samples and 50k+ feature dimensions efficiently + +**Reference practice**: Joachims (1998) established SVM/linear models on TF-IDF as a strong text classification baseline; Logistic Regression performs comparably with faster convergence. + +### N-gram Variants + +- **Unigrams (1,1)**: Capture individual sarcasm-associated words +- **Bigrams (1,2)**: Capture collocations ("not exactly," "of course") that signal irony +- **Character n-grams (3,5)**: Capture morphological and stylistic signals (unusual capitalization artifacts, punctuation sequences) + +--- + +## 2. Why Naive Bayes Is a Valid Baseline for Sparse Text + +### Generative Model with Multinomial Assumption + +MultinomialNB models the probability of a class given token counts: +$$P(y | \mathbf{x}) \propto P(y) \prod_i P(x_i | y)^{x_i}$$ + +This is optimal when features are conditionally independent—an assumption that doesn't hold perfectly in text but approximates well for bag-of-words features. + +### Advantages for This Task + +- **Sample efficient**: Performs well with limited training data due to the generative model structure +- **Computationally trivial**: Training is a single pass over data; inference is extremely fast +- **Robust to sparse features**: No gradient instability issues with high-dimensional sparse inputs +- **Smoothing parameter (alpha)**: Laplace/Lidstone smoothing prevents zero-probability tokens + +### CountVectorizer vs TF-IDF for Naive Bayes + +- **CountVectorizer + MultinomialNB**: The multinomial likelihood is derived assuming raw count features; theoretically correct +- **TF-IDF + MultinomialNB**: Not theoretically motivated (TF-IDF can produce non-integer "counts") but often performs well empirically by reducing the influence of high-frequency stop words +- **Comparison is necessary**: Empirical results in the literature are mixed; we run both + +**Reference**: McCallum & Nigam (1998) showed Naive Bayes competitive with SVM on text classification; Rennie et al. (2003) proposed Complement NB for class imbalance. + +--- + +## 3. Why BERT/DistilBERT Is Suitable for Sarcasm Detection + +### Contextual Representations + +Unlike TF-IDF which produces context-free token weights, BERT produces **context-sensitive embeddings** via multi-head self-attention. This is critical for sarcasm where: +- The same word can be sarcastic or literal depending on surrounding context +- Irony often relies on incongruence that requires understanding the full sentence +- Subtle pragmatic cues ("what a surprise") require modeling over the full token sequence + +### Pre-training on Large Corpora + +BERT is pre-trained on Books Corpus + Wikipedia (3.3B words), giving it: +- General syntactic and semantic knowledge +- Common knowledge about the world (useful for incongruence detection) +- Sub-word tokenization (WordPiece) robust to rare vocabulary + +### Sarcasm-specific Relevance + +- **Rhetorical questions** ("Who wouldn't want that?"): Self-attention captures the interrogative-declarative incongruence +- **Overstatement/understatement**: Sentiment-aware representations from pre-training encode hyperbole cues +- **Irony/satire**: Semantic incongruence between entities and predicates is detectable via attention patterns + +### DistilBERT vs BERT-base + +| Model | Parameters | Inference Speed | Accuracy Retention | +|-------|-----------|-----------------|-------------------| +| DistilBERT-base-uncased | 66M | ~60% faster than BERT | ~97% of BERT | +| BERT-base-uncased | 110M | Baseline | Baseline | + +**Strategy**: Start with DistilBERT for rapid iteration; upgrade to BERT-base if compute budget allows. + +**References**: Devlin et al. (2018) BERT; Sanh et al. (2019) DistilBERT; Joshi et al. (2022) for sarcasm classification with transformers. + +--- + +## 4. Why Macro-F1 Matters + +### Binary Task + +The binary dataset (after expansion) has classes from two `type` values: +- `sarcastic_to_non`: 13,408 pairs → contributes 13,408 sarcastic + 13,408 non-sarcastic +- `non_to_sarcastic`: 14,925 pairs → contributes 14,925 sarcastic + 14,925 non-sarcastic + +Binary classes are **approximately balanced** (both classes ~28k total), so weighted-F1 ≈ macro-F1 here. However, we still report macro-F1 as primary for consistency. + +### Type Task (Multiclass — Critical) + +The 6 strategy classes are **significantly imbalanced**: +| Strategy | Count (approx) | +|----------|---------------| +| sarcasm | ~8,699 | +| irony | ~6,102 | +| satire | ~5,224 | +| overstatement | ~3,976 | +| understatement | ~3,295 | +| rhetorical_question | ~1,037 | + +A model that always predicts "sarcasm" achieves ~31% accuracy but 0% macro-F1 on minority classes. + +**Macro-F1 = unweighted average of per-class F1** ensures minority classes like `rhetorical_question` have equal weight in the optimization objective. + +--- + +## 5. Leakage Risks in Paired Transformation Datasets + +### The Risk + +This dataset was constructed by: +1. Taking real sarcastic headlines (TheOnion) → generating non-sarcastic rewrites +2. Taking real non-sarcastic headlines (HuffPost) → generating sarcastic rewrites + +Each JSONL row contains a **semantically similar pair**: the sarcastic and non-sarcastic versions share topic, entities, and much of their lexical content. + +**If we split after expansion**: A train set containing `"Scientists unveil doomsday clock"` (sarcastic) will directly leak topic/entity information about `"Scientists present research on hair loss"` (non-sarcastic) which may be in the test set. + +The model will learn spurious correlations between specific entity patterns and labels rather than the true pragmatic sarcasm signal. + +### Mitigation Strategy + +**Pair-level splitting** (our primary approach): +1. Assign `pair_id = row_index` to each JSONL row +2. Assign `group_id` = normalized `article_link` (captures rows with same source article) +3. Split at the **group level before expansion** +4. All rows from a group appear in exactly one of {train, val, test} + +This guarantees: +- Both the sarcastic and non-sarcastic versions of a pair go to the same split +- No topic/entity overlap between train and test + +**Expected conservative result**: Group-level splitting may slightly reduce apparent performance compared to naive splitting, reflecting true generalization. + +--- + +## 6. Implementation Strategy + +### Execution Order (Fast → Slow) + +1. **Dataset audit** (15 min): Validate schema, derive labels, check distributions +2. **Split generation** (5 min): Group-safe train/val/test splits, save to disk +3. **TF-IDF + LR** (30 min): Grid search over n-grams, min_df, C, class_weight +4. **Naive Bayes** (20 min): CountVectorizer vs TF-IDF variants +5. **DistilBERT** (hours): Fine-tuning with early stopping +6. **BERT-base** (optional, hours): Only if compute allows +7. **Error analysis + comparison** (1 hour): Report generation + +### Primary Assumptions + +- **No aggressive text cleaning**: Preserve punctuation (sarcasm cues), lowercase for classical models, raw text for BERT tokenizer +- **macro-F1 is primary metric** for all model selection decisions +- **Group integrity > stratification**: If exact stratified splits are impossible under grouping constraints, preserve groups and document class distributions +- **All artifacts saved to disk**: No results exist only in memory; all runs are reproducible + +--- + +## References + +1. Joachims, T. (1998). Text categorization with support vector machines. ECML. +2. McCallum, A., & Nigam, K. (1998). A comparison of event models for Naive Bayes text classification. AAAI. +3. Rennie, J., et al. (2003). Tackling the poor assumptions of Naive Bayes. ICML. +4. Devlin, J., et al. (2018). BERT: Pre-training of deep bidirectional transformers. NAACL. +5. Sanh, V., et al. (2019). DistilBERT, a distilled version of BERT. NeurIPS workshop. +6. Joshi, A., et al. (2022). Sarcasm detection using deep learning. Survey. +7. Pedregosa, F., et al. (2011). Scikit-learn: Machine learning in Python. JMLR. diff --git a/notebooks/01_data_preparation.ipynb b/notebooks/01_data_preparation.ipynb new file mode 100644 index 0000000..7de5eeb --- /dev/null +++ b/notebooks/01_data_preparation.ipynb @@ -0,0 +1,521 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Notebook 01 \u00e2\u20ac\u201d Data Preparation\n", + "\n", + "**Purpose**: Parse JSONL dataset, derive binary and type labels, perform audit checks, generate group-safe train/val/test splits.\n", + "\n", + "**Outputs**:\n", + "- `outputs/datasets/binary_dataset.csv`\n", + "- `outputs/datasets/type_dataset.csv`\n", + "- `outputs/splits/train_binary.csv`, `val_binary.csv`, `test_binary.csv`\n", + "- `outputs/splits/train_type.csv`, `val_type.csv`, `test_type.csv`\n", + "- `outputs/splits/split_metadata.json`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "import os; from pathlib import Path as _P; print(\"cwd:\", _P.cwd()); print(\"files:\", list(_P.cwd().iterdir())[:10])\n\nimport json, hashlib, random, os\nfrom pathlib import Path\nfrom collections import Counter\nfrom urllib.parse import urlparse\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nSEED = 42\nrandom.seed(SEED)\nnp.random.seed(SEED)\n\nFILENAME = \"sarcasm_pairs_step35_clean.jsonl\"\n\n# \u2500\u2500 Step 1: locate or upload the data file \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\ndef _locate_file(filename):\n \"\"\"Search common locations; return Path if found, else None.\"\"\"\n candidates = []\n for root in [Path.cwd()] + list(Path.cwd().parents):\n for sub in [\n Path(\"data\") / \"processed\" / filename,\n Path(\"data\") / filename,\n Path(filename),\n ]:\n candidates.append(root / sub)\n for p in [\n Path(\"/content\") / filename,\n Path(\"/mnt/data\") / filename,\n Path(\"/content/drive/MyDrive\") / filename,\n ]:\n candidates.append(p)\n _content = Path(\"/content\")\n for p in (_content.rglob(filename) if _content.exists() else []):\n candidates.append(p)\n for p in candidates:\n if p.is_file():\n return p\n return None\n\nDATA_FILE = _locate_file(FILENAME)\n\nif DATA_FILE is None:\n # \u2500\u2500 Auto-upload in Colab \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n try:\n from google.colab import files as _colab_files\n print(f\"{FILENAME!r} not found. Please upload it now:\")\n _uploaded = _colab_files.upload()\n if not _uploaded:\n raise RuntimeError(\"No file uploaded. Please upload the JSONL file and re-run.\")\n _fname = list(_uploaded.keys())[0]\n if not _fname.endswith(FILENAME.split('/')[-1]):\n raise ValueError(\n f\"Wrong file uploaded: {_fname!r}. Expected {FILENAME!r}.\"\n )\n DATA_FILE = Path(\"/content\") / FILENAME\n if Path(_fname) != DATA_FILE:\n import shutil\n shutil.move(_fname, str(DATA_FILE))\n print(f\"Uploaded to {DATA_FILE}\")\n except ImportError:\n # Not in Colab\n raise FileNotFoundError(\n f\"Cannot find {FILENAME!r}.\\n\"\n f\"cwd: {Path.cwd()}\\n\"\n \"Place the file at: /data/processed/\" + FILENAME\n )\n\n# \u2500\u2500 Step 2: determine project root \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\ndef _find_root(data_file):\n \"\"\"Walk up from data_file to find the project root (contains outputs/ or notebooks/).\"\"\"\n markers = [\n Path(\"outputs\"),\n Path(\"notebooks\"),\n Path(\"data\"),\n ]\n for parent in [data_file.parent] + list(data_file.parents):\n if any((parent / m).exists() for m in markers):\n return parent\n return data_file.parent # fallback\n\nROOT = _find_root(DATA_FILE)\n\nOUT_DATASETS = ROOT / \"outputs\" / \"datasets\"\nOUT_SPLITS = ROOT / \"outputs\" / \"splits\"\nOUT_DATASETS.mkdir(parents=True, exist_ok=True)\nOUT_SPLITS.mkdir(parents=True, exist_ok=True)\n\nprint(f\"Dataset : {DATA_FILE}\")\nprint(f\"Root : {ROOT}\")\nprint(f\"Outputs : {OUT_DATASETS}\")\n" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Load and Validate Raw Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "REQUIRED_FIELDS = {\"original_headline\", \"generated_headline\", \"strategy\", \"type\", \"model_used\", \"article_link\"}\n", + "ALLOWED_TYPES = {\"sarcastic_to_non\", \"non_to_sarcastic\"}\n", + "ALLOWED_STRATS = {\"irony\", \"overstatement\", \"rhetorical_question\", \"sarcasm\", \"satire\", \"understatement\"}\n", + "\n", + "raw_rows = []\n", + "errors = []\n", + "\n", + "with open(DATA_FILE, \"r\", encoding=\"utf-8\") as f:\n", + " for line_num, line in enumerate(f):\n", + " line = line.strip()\n", + " if not line:\n", + " continue\n", + " try:\n", + " row = json.loads(line)\n", + " except json.JSONDecodeError as e:\n", + " errors.append((line_num, f\"JSON parse error: {e}\"))\n", + " continue\n", + "\n", + " # Schema check\n", + " missing = REQUIRED_FIELDS - set(row.keys())\n", + " if missing:\n", + " errors.append((line_num, f\"Missing fields: {missing}\"))\n", + " continue\n", + "\n", + " # Value checks\n", + " if row[\"type\"] not in ALLOWED_TYPES:\n", + " errors.append((line_num, f\"Unknown type: {row['type']!r}\"))\n", + " continue\n", + " if row[\"strategy\"] not in ALLOWED_STRATS:\n", + " errors.append((line_num, f\"Unknown strategy: {row['strategy']!r}\"))\n", + " continue\n", + "\n", + " row[\"_line_num\"] = line_num\n", + " raw_rows.append(row)\n", + "\n", + "print(f\"Loaded rows : {len(raw_rows):,}\")\n", + "print(f\"Error rows : {len(errors)}\")\n", + "if errors:\n", + " for ln, msg in errors[:5]:\n", + " print(f\" Line {ln}: {msg}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Dataset Audit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Type and Strategy distributions \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "type_counts = Counter(r[\"type\"] for r in raw_rows)\n", + "strategy_counts = Counter(r[\"strategy\"] for r in raw_rows)\n", + "model_counts = Counter(r[\"model_used\"] for r in raw_rows)\n", + "\n", + "print(\"=== Type distribution ===\")\n", + "for k, v in type_counts.most_common():\n", + " print(f\" {k}: {v:,} ({v/len(raw_rows)*100:.1f}%)\")\n", + "\n", + "print(\"\\n=== Strategy distribution ===\")\n", + "for k, v in strategy_counts.most_common():\n", + " print(f\" {k}: {v:,} ({v/len(raw_rows)*100:.1f}%)\")\n", + "\n", + "print(\"\\n=== Model distribution ===\")\n", + "for k, v in model_counts.most_common():\n", + " print(f\" {k}: {v:,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Duplicate checks \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "orig_headlines = [r[\"original_headline\"] for r in raw_rows]\n", + "gen_headlines = [r[\"generated_headline\"] for r in raw_rows]\n", + "article_links = [r[\"article_link\"] for r in raw_rows]\n", + "\n", + "dup_orig = len(orig_headlines) - len(set(orig_headlines))\n", + "dup_gen = len(gen_headlines) - len(set(gen_headlines))\n", + "dup_article = len(article_links) - len(set(article_links))\n", + "\n", + "print(f\"Duplicate original_headlines : {dup_orig}\")\n", + "print(f\"Duplicate generated_headlines: {dup_gen}\")\n", + "print(f\"Duplicate article_links : {dup_article}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Null / empty checks \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "for field in [\"original_headline\", \"generated_headline\", \"strategy\", \"type\", \"model_used\", \"article_link\"]:\n", + " null_count = sum(1 for r in raw_rows if r.get(field) is None)\n", + " empty_count = sum(1 for r in raw_rows if r.get(field) == \"\")\n", + " print(f\"{field:25s}: {null_count} nulls, {empty_count} empty strings\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Strategy distribution plot \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Type dist\n", + "ax = axes[0]\n", + "labels, counts = zip(*sorted(type_counts.items(), key=lambda x: -x[1]))\n", + "ax.barh(labels, counts, color=[\"steelblue\", \"coral\"])\n", + "ax.set_title(\"Row-level Type Distribution\", fontsize=14)\n", + "ax.set_xlabel(\"Count\")\n", + "for i, c in enumerate(counts):\n", + " ax.text(c + 50, i, f\"{c:,}\", va=\"center\")\n", + "\n", + "# Strategy dist\n", + "ax = axes[1]\n", + "labels2, counts2 = zip(*sorted(strategy_counts.items(), key=lambda x: -x[1]))\n", + "ax.barh(labels2, counts2, color=\"steelblue\")\n", + "ax.set_title(\"Strategy Distribution (Type Task Labels)\", fontsize=14)\n", + "ax.set_xlabel(\"Count\")\n", + "for i, c in enumerate(counts2):\n", + " ax.text(c + 30, i, f\"{c:,}\", va=\"center\")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(OUT_DATASETS / \"distributions.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/datasets/distributions.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Label Derivation and Dataset Expansion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "from urllib.parse import urlparse\n", + "\n", + "\n", + "def normalize_url(url: str) -> str:\n", + " \"\"\"Lowercase and strip scheme/trailing slash for stable group IDs.\"\"\"\n", + " url = url.strip().lower()\n", + " parsed = urlparse(url)\n", + " normalized = (parsed.netloc + parsed.path).rstrip(\"/\")\n", + " return normalized if normalized else url\n", + "\n", + "\n", + "def derive_labels(raw_rows):\n", + " \"\"\"\n", + " Expand each JSONL row into 2 samples (sarcastic + non-sarcastic).\n", + "\n", + " Rules:\n", + " sarcastic_to_non -> original=sarcastic(1), generated=non-sarcastic(0)\n", + " non_to_sarcastic -> original=non-sarcastic(0), generated=sarcastic(1)\n", + "\n", + " Returns binary_df, type_df\n", + " \"\"\"\n", + " binary_records = []\n", + " sample_id = 0\n", + "\n", + " for pair_id, row in enumerate(raw_rows):\n", + " group_id = normalize_url(row[\"article_link\"]) if row[\"article_link\"] else str(pair_id)\n", + " row_type = row[\"type\"]\n", + " strategy = row[\"strategy\"]\n", + " model_used = row[\"model_used\"]\n", + " article = row[\"article_link\"]\n", + "\n", + " if row_type == \"sarcastic_to_non\":\n", + " orig_label, orig_strat = 1, strategy\n", + " gen_label, gen_strat = 0, None\n", + " else:\n", + " orig_label, orig_strat = 0, None\n", + " gen_label, gen_strat = 1, strategy\n", + "\n", + " binary_records.append({\n", + " \"sample_id\" : sample_id,\n", + " \"pair_id\" : pair_id,\n", + " \"group_id\" : group_id,\n", + " \"text\" : row[\"original_headline\"],\n", + " \"binary_label\" : orig_label,\n", + " \"is_generated\" : 0,\n", + " \"strategy\" : orig_strat,\n", + " \"source_type\" : row_type,\n", + " \"article_link\" : article,\n", + " \"model_used\" : model_used,\n", + " })\n", + " sample_id += 1\n", + "\n", + " binary_records.append({\n", + " \"sample_id\" : sample_id,\n", + " \"pair_id\" : pair_id,\n", + " \"group_id\" : group_id,\n", + " \"text\" : row[\"generated_headline\"],\n", + " \"binary_label\" : gen_label,\n", + " \"is_generated\" : 1,\n", + " \"strategy\" : gen_strat,\n", + " \"source_type\" : row_type,\n", + " \"article_link\" : article,\n", + " \"model_used\" : model_used,\n", + " })\n", + " sample_id += 1\n", + "\n", + " binary_df = pd.DataFrame(binary_records)\n", + "\n", + " # Validate counts\n", + " counts = binary_df[\"binary_label\"].value_counts().to_dict()\n", + " n_expected = len(raw_rows)\n", + " n0 = int(counts.get(0, 0))\n", + " n1 = int(counts.get(1, 0))\n", + " if n0 != n_expected or n1 != n_expected:\n", + " raise ValueError(\n", + " f\"Label count mismatch!\\n\"\n", + " f\" Expected {n_expected} per class\\n\"\n", + " f\" Got: sarcastic(1)={n1}, non-sarcastic(0)={n0}\"\n", + " )\n", + "\n", + " # Type dataset: sarcastic only\n", + " type_df = binary_df[binary_df[\"binary_label\"] == 1].copy()\n", + " type_df = type_df.rename(columns={\"strategy\": \"type_label\"})\n", + " type_df = type_df[[\"sample_id\", \"pair_id\", \"group_id\", \"text\",\n", + " \"type_label\", \"is_generated\", \"source_type\",\n", + " \"article_link\", \"model_used\"]]\n", + "\n", + " missing = type_df[\"type_label\"].isna().sum()\n", + " if missing > 0:\n", + " raise ValueError(f\"{missing} sarcastic samples have no type_label!\")\n", + "\n", + " return binary_df, type_df\n", + "\n", + "\n", + "binary_df, type_df = derive_labels(raw_rows)\n", + "\n", + "print(f\"Binary dataset : {len(binary_df):,} samples\")\n", + "print(f\" sarcastic (1): {(binary_df['binary_label']==1).sum():,}\")\n", + "print(f\" non-sarc (0): {(binary_df['binary_label']==0).sum():,}\")\n", + "print()\n", + "print(f\"Type dataset : {len(type_df):,} samples\")\n", + "print(type_df[\"type_label\"].value_counts().to_string())\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Save full datasets\n", + "binary_df.to_csv(OUT_DATASETS / \"binary_dataset.csv\", index=False)\n", + "type_df.to_csv( OUT_DATASETS / \"type_dataset.csv\", index=False)\n", + "print(\"Saved: outputs/datasets/binary_dataset.csv\")\n", + "print(\"Saved: outputs/datasets/type_dataset.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Group-Aware Train / Val / Test Split" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def group_aware_split(\n", + " df: pd.DataFrame,\n", + " group_col: str = \"group_id\",\n", + " train_frac: float = 0.70,\n", + " val_frac: float = 0.15,\n", + " seed: int = SEED,\n", + ") -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n", + " \"\"\"\n", + " Split df into train/val/test at the group level.\n", + " All rows sharing a group_id go to exactly one split.\n", + " \"\"\"\n", + " groups = df[group_col].unique().tolist()\n", + " rng = np.random.default_rng(seed)\n", + " rng.shuffle(groups)\n", + "\n", + " n = len(groups)\n", + " n_train = int(n * train_frac)\n", + " n_val = int(n * val_frac)\n", + "\n", + " train_groups = set(groups[:n_train])\n", + " val_groups = set(groups[n_train : n_train + n_val])\n", + " test_groups = set(groups[n_train + n_val :])\n", + "\n", + " train_df = df[df[group_col].isin(train_groups)].copy()\n", + " val_df = df[df[group_col].isin(val_groups)].copy()\n", + " test_df = df[df[group_col].isin(test_groups)].copy()\n", + "\n", + " # Sanity: no overlap\n", + " assert train_groups.isdisjoint(val_groups), \"Train/val group overlap!\"\n", + " assert train_groups.isdisjoint(test_groups), \"Train/test group overlap!\"\n", + " assert val_groups.isdisjoint(test_groups), \"Val/test group overlap!\"\n", + "\n", + " return train_df, val_df, test_df\n", + "\n", + "\n", + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Binary splits \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "train_bin, val_bin, test_bin = group_aware_split(binary_df)\n", + "\n", + "print(\"=== Binary splits ===\")\n", + "for name, df in [(\"train\", train_bin), (\"val\", val_bin), (\"test\", test_bin)]:\n", + " dist = df[\"binary_label\"].value_counts().to_dict()\n", + " print(f\" {name:5s}: {len(df):,} samples | sarcastic={dist.get(1,0):,} non={dist.get(0,0):,}\")\n", + "\n", + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Type splits \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "train_type, val_type, test_type = group_aware_split(type_df)\n", + "\n", + "print(\"\\n=== Type splits ===\")\n", + "for name, df in [(\"train\", train_type), (\"val\", val_type), (\"test\", test_type)]:\n", + " print(f\" {name:5s}: {len(df):,} samples\")\n", + " dist = df[\"type_label\"].value_counts()\n", + " for label, cnt in dist.items():\n", + " print(f\" {label}: {cnt:,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Verify no pair crosses split boundary \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "# A pair_id should appear in at most ONE binary split\n", + "train_pairs = set(train_bin[\"pair_id\"])\n", + "val_pairs = set(val_bin[\"pair_id\"])\n", + "test_pairs = set(test_bin[\"pair_id\"])\n", + "\n", + "tv_overlap = train_pairs & val_pairs\n", + "tt_overlap = train_pairs & test_pairs\n", + "vt_overlap = val_pairs & test_pairs\n", + "\n", + "print(f\"Train/Val pair_id overlap : {len(tv_overlap)} (must be 0)\")\n", + "print(f\"Train/Test pair_id overlap : {len(tt_overlap)} (must be 0)\")\n", + "print(f\"Val/Test pair_id overlap : {len(vt_overlap)} (must be 0)\")\n", + "\n", + "assert len(tv_overlap) == 0 and len(tt_overlap) == 0 and len(vt_overlap) == 0, \\\n", + " \"Leakage detected: pairs crossing split boundaries!\"\n", + "print(\"\\nLeakage check PASSED: No pair_id crosses split boundaries.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Save splits \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "split_files = {\n", + " \"train_binary\": train_bin,\n", + " \"val_binary\" : val_bin,\n", + " \"test_binary\" : test_bin,\n", + " \"train_type\" : train_type,\n", + " \"val_type\" : val_type,\n", + " \"test_type\" : test_type,\n", + "}\n", + "for fname, df in split_files.items():\n", + " path = OUT_SPLITS / f\"{fname}.csv\"\n", + " df.to_csv(path, index=False)\n", + " print(f\"Saved: {path.relative_to(ROOT)}\")\n", + "\n", + "# Metadata\n", + "import json as _json\n", + "metadata = {\n", + " \"seed\": SEED,\n", + " \"train_frac\": 0.70,\n", + " \"val_frac\": 0.15,\n", + " \"test_frac\": 0.15,\n", + " \"group_col\": \"group_id\",\n", + " \"total_pairs\": len(raw_rows),\n", + " \"binary\": {\n", + " \"train\": len(train_bin), \"val\": len(val_bin), \"test\": len(test_bin)\n", + " },\n", + " \"type\": {\n", + " \"train\": len(train_type), \"val\": len(val_type), \"test\": len(test_type)\n", + " },\n", + "}\n", + "with open(OUT_SPLITS / \"split_metadata.json\", \"w\") as f:\n", + " _json.dump(metadata, f, indent=2)\n", + "print(\"\\nSaved: outputs/splits/split_metadata.json\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Split Distribution Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Type label distribution per split\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 5), sharey=True)\n", + "split_dfs = [(\"Train\", train_type), (\"Val\", val_type), (\"Test\", test_type)]\n", + "\n", + "for ax, (name, df) in zip(axes, split_dfs):\n", + " dist = df[\"type_label\"].value_counts()\n", + " dist.plot(kind=\"barh\", ax=ax, color=\"steelblue\")\n", + " ax.set_title(f\"{name} \u00e2\u20ac\u201d Type Labels (n={len(df):,})\", fontsize=13)\n", + " ax.set_xlabel(\"Count\")\n", + " for i, (label, cnt) in enumerate(dist.items()):\n", + " ax.text(cnt + 5, i, f\"{cnt:,}\", va=\"center\", fontsize=9)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(OUT_SPLITS / \"type_split_distribution.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/splits/type_split_distribution.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Binary label distribution per split\n", + "fig, axes = plt.subplots(1, 3, figsize=(12, 4), sharey=True)\n", + "split_dfs_bin = [(\"Train\", train_bin), (\"Val\", val_bin), (\"Test\", test_bin)]\n", + "\n", + "for ax, (name, df) in zip(axes, split_dfs_bin):\n", + " dist = df[\"binary_label\"].value_counts().sort_index()\n", + " ax.bar([\"Non-sarcastic (0)\", \"Sarcastic (1)\"], dist.values, color=[\"coral\", \"steelblue\"])\n", + " ax.set_title(f\"{name} \u00e2\u20ac\u201d Binary Labels (n={len(df):,})\", fontsize=12)\n", + " ax.set_ylabel(\"Count\")\n", + " for i, v in enumerate(dist.values):\n", + " ax.text(i, v + 20, f\"{v:,}\", ha=\"center\")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(OUT_SPLITS / \"binary_split_distribution.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/splits/binary_split_distribution.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"\\n=== Data Preparation Complete ===\")\n", + "print(f\"Binary dataset : {len(binary_df):,} samples (balanced)\")\n", + "print(f\"Type dataset : {len(type_df):,} samples (imbalanced, 6 classes)\")\n", + "print(f\"Splits saved to: outputs/splits/\")\n", + "print(f\"Datasets saved : outputs/datasets/\")\n", + "print(\"\\nReady for classical baseline training (Notebooks 02 and 03).\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/notebooks/02_tfidf_lr_baseline.ipynb b/notebooks/02_tfidf_lr_baseline.ipynb new file mode 100644 index 0000000..ebc48a8 --- /dev/null +++ b/notebooks/02_tfidf_lr_baseline.ipynb @@ -0,0 +1,514 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": "# Notebook 02 \u2014 TF-IDF + Logistic Regression Baseline\n\n**Purpose**: Train and evaluate TF-IDF + Logistic Regression pipelines for:\n- Task A: Binary classification (sarcastic vs non-sarcastic)\n- Task B: Sarcasm type classification (6-class)\n\n**Prerequisite**: Run `01_data_preparation.ipynb` first.\n\n**Outputs**:\n- `outputs/classical/tfidf_lr/best_config_binary.json`\n- `outputs/classical/tfidf_lr/best_config_type.json`\n- `outputs/classical/tfidf_lr/metrics_binary.json`\n- `outputs/classical/tfidf_lr/metrics_type.json`\n- Predictions CSV + confusion matrices PNG" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "import json\nimport random\nimport warnings\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import GridSearchCV, GroupKFold\nfrom sklearn.metrics import (\n accuracy_score, precision_score, recall_score,\n f1_score, classification_report, confusion_matrix,\n)\n\nwarnings.filterwarnings(\"ignore\")\n\nSEED = 42\nrandom.seed(SEED)\nnp.random.seed(SEED)\n\n# \u2500\u2500 Robust project-root finder \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\ndef _find_project_root() -> Path:\n \"\"\"Walk up from cwd until we find outputs/splits or another project marker.\"\"\"\n markers = [\n Path(\"outputs\") / \"splits\",\n Path(\"outputs\") / \"datasets\",\n Path(\"data\") / \"processed\",\n Path(\"notebooks\"),\n ]\n for root in [Path.cwd()] + list(Path.cwd().parents):\n for marker in markers:\n if (root / marker).exists():\n return root\n return Path.cwd().parent # fallback\n\nROOT = _find_project_root()\nSPLITS = ROOT / \"outputs\" / \"splits\"\nOUT_DIR = ROOT / \"outputs\" / \"classical\" / \"tfidf_lr\"\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nprint(f\"Project root : {ROOT}\")\nprint(f\"Output directory: {OUT_DIR}\")\n" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Helper Functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def load_splits(task: str) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n", + " \"\"\"Load train/val/test CSVs for a given task ('binary' or 'type').\"\"\"\n", + " train = pd.read_csv(SPLITS / f\"train_{task}.csv\")\n", + " val = pd.read_csv(SPLITS / f\"val_{task}.csv\")\n", + " test = pd.read_csv(SPLITS / f\"test_{task}.csv\")\n", + " return train, val, test\n", + "\n", + "\n", + "def evaluate(\n", + " model,\n", + " X: list[str],\n", + " y_true: list,\n", + " label_names: list[str] | None = None,\n", + " split_name: str = \"\",\n", + ") -> dict:\n", + " \"\"\"Compute classification metrics and return as dict.\"\"\"\n", + " y_pred = model.predict(X)\n", + " metrics = {\n", + " \"split\" : split_name,\n", + " \"accuracy\" : accuracy_score(y_true, y_pred),\n", + " \"precision_macro\" : precision_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"recall_macro\" : recall_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"f1_macro\" : f1_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"f1_weighted\" : f1_score(y_true, y_pred, average=\"weighted\", zero_division=0),\n", + " \"report\" : classification_report(y_true, y_pred, target_names=label_names,\n", + " zero_division=0, output_dict=True),\n", + " }\n", + " print(f\"\\n[{split_name}] Accuracy={metrics['accuracy']:.4f} \"\n", + " f\"Macro-F1={metrics['f1_macro']:.4f} Weighted-F1={metrics['f1_weighted']:.4f}\")\n", + " print(classification_report(y_true, y_pred, target_names=label_names, zero_division=0))\n", + " return metrics, y_pred\n", + "\n", + "\n", + "def save_confusion_matrix(\n", + " y_true, y_pred, label_names: list[str], out_path: Path, title: str = \"\"\n", + ") -> None:\n", + " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", + " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", + " sns.heatmap(\n", + " cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n", + " xticklabels=label_names, yticklabels=label_names, ax=ax\n", + " )\n", + " ax.set_xlabel(\"Predicted\")\n", + " ax.set_ylabel(\"True\")\n", + " ax.set_title(title)\n", + " plt.tight_layout()\n", + " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + " print(f\"Saved: {out_path.relative_to(ROOT)}\")\n", + "\n", + "\n", + "def save_predictions(\n", + " df: pd.DataFrame, y_pred, label_col: str, out_path: Path\n", + ") -> None:\n", + " out = df[[\"sample_id\", \"pair_id\", \"group_id\", \"text\", label_col]].copy()\n", + " out[\"predicted\"] = y_pred\n", + " out[\"correct\"] = (out[label_col] == out[\"predicted\"]).astype(int)\n", + " out.to_csv(out_path, index=False)\n", + " print(f\"Saved: {out_path.relative_to(ROOT)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task A \u00e2\u20ac\u201d Binary Classification" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Load binary splits \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "train_bin, val_bin, test_bin = load_splits(\"binary\")\n", + "\n", + "# Combine train+val for cross-validation, keep test for final eval\n", + "trainval_bin = pd.concat([train_bin, val_bin], ignore_index=True)\n", + "\n", + "X_trainval = trainval_bin[\"text\"].tolist()\n", + "y_trainval = trainval_bin[\"binary_label\"].tolist()\n", + "groups_tv = trainval_bin[\"group_id\"].tolist()\n", + "\n", + "X_train = train_bin[\"text\"].tolist()\n", + "y_train = train_bin[\"binary_label\"].tolist()\n", + "\n", + "X_val = val_bin[\"text\"].tolist()\n", + "y_val = val_bin[\"binary_label\"].tolist()\n", + "\n", + "X_test = test_bin[\"text\"].tolist()\n", + "y_test = test_bin[\"binary_label\"].tolist()\n", + "\n", + "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", + "\n", + "print(f\"Train+Val: {len(X_trainval):,} | Train: {len(X_train):,} Val: {len(X_val):,} Test: {len(X_test):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Define pipeline and grid \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "pipe_bin = Pipeline([\n", + " (\"tfidf\", TfidfVectorizer(sublinear_tf=True, lowercase=True)),\n", + " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, solver=\"lbfgs\")),\n", + "])\n", + "\n", + "param_grid_bin = {\n", + " \"tfidf__ngram_range\" : [(1, 1), (1, 2)],\n", + " \"tfidf__min_df\" : [2, 3, 5],\n", + " \"tfidf__max_features\": [None, 50_000],\n", + " \"lr__C\" : [0.1, 1.0, 3.0],\n", + "}\n", + "\n", + "cv_bin = GroupKFold(n_splits=5)\n", + "\n", + "gs_bin = GridSearchCV(\n", + " pipe_bin, param_grid_bin,\n", + " cv=cv_bin, scoring=\"f1_macro\",\n", + " n_jobs=-1, verbose=1, refit=True,\n", + ")\n", + "\n", + "print(f\"Grid size: {len(gs_bin.param_grid)} param combos \u00e2\u2020\u2019 {5 * 2*3*2*3} fits\")\n", + "print(\"Running grid search...\")\n", + "gs_bin.fit(X_trainval, y_trainval, groups=groups_tv)\n", + "\n", + "print(f\"\\nBest params : {gs_bin.best_params_}\")\n", + "print(f\"Best CV F1 : {gs_bin.best_score_:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Save best config \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "best_cfg_bin = {\"task\": \"binary\", \"model\": \"TF-IDF + LR\", \"seed\": SEED,\n", + " \"best_params\": gs_bin.best_params_, \"cv_f1_macro\": gs_bin.best_score_}\n", + "with open(OUT_DIR / \"best_config_binary.json\", \"w\") as f:\n", + " json.dump(best_cfg_bin, f, indent=2)\n", + "print(\"Saved: outputs/classical/tfidf_lr/best_config_binary.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Evaluate on val and test \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "best_bin = gs_bin.best_estimator_\n", + "\n", + "val_metrics_bin, y_val_pred_bin = evaluate(best_bin, X_val, y_val, label_names_bin, \"Val\")\n", + "test_metrics_bin, y_test_pred_bin = evaluate(best_bin, X_test, y_test, label_names_bin, \"Test\")\n", + "\n", + "all_metrics_bin = {\"val\": val_metrics_bin, \"test\": test_metrics_bin}\n", + "\n", + "# Remove classification_report dict (too nested for JSON)\n", + "for split_m in all_metrics_bin.values():\n", + " split_m.pop(\"report\", None)\n", + "\n", + "with open(OUT_DIR / \"metrics_binary.json\", \"w\") as f:\n", + " json.dump(all_metrics_bin, f, indent=2)\n", + "print(\"Saved: outputs/classical/tfidf_lr/metrics_binary.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Confusion matrices \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "save_confusion_matrix(\n", + " y_val, y_val_pred_bin, label_names_bin,\n", + " OUT_DIR / \"confusion_matrix_binary_val.png\",\n", + " \"TF-IDF + LR \u00e2\u20ac\u201d Binary (Val)\"\n", + ")\n", + "save_confusion_matrix(\n", + " y_test, y_test_pred_bin, label_names_bin,\n", + " OUT_DIR / \"confusion_matrix_binary_test.png\",\n", + " \"TF-IDF + LR \u00e2\u20ac\u201d Binary (Test)\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Save predictions \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "save_predictions(val_bin, y_val_pred_bin, \"binary_label\", OUT_DIR / \"predictions_val_binary.csv\")\n", + "save_predictions(test_bin, y_test_pred_bin, \"binary_label\", OUT_DIR / \"predictions_test_binary.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Feature importance: top discriminative terms \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "tfidf_vocab = best_bin.named_steps[\"tfidf\"].get_feature_names_out()\n", + "lr_coefs = best_bin.named_steps[\"lr\"].coef_[0] # binary: shape (n_features,)\n", + "\n", + "n_top = 20\n", + "top_pos_idx = np.argsort(lr_coefs)[-n_top:][::-1]\n", + "top_neg_idx = np.argsort(lr_coefs)[:n_top]\n", + "\n", + "top_positive = [(tfidf_vocab[i], lr_coefs[i]) for i in top_pos_idx]\n", + "top_negative = [(tfidf_vocab[i], lr_coefs[i]) for i in top_neg_idx]\n", + "\n", + "print(\"Top 20 sarcastic indicators (positive coef):\")\n", + "for term, coef in top_positive:\n", + " print(f\" {term:35s} {coef:+.4f}\")\n", + "\n", + "print(\"\\nTop 20 non-sarcastic indicators (negative coef):\")\n", + "for term, coef in top_negative:\n", + " print(f\" {term:35s} {coef:+.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Feature importance plot \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 7))\n", + "\n", + "terms_pos, coefs_pos = zip(*top_positive)\n", + "axes[0].barh(list(reversed(terms_pos)), list(reversed(coefs_pos)), color=\"steelblue\")\n", + "axes[0].set_title(\"Top 20 \u00e2\u20ac\u201d Sarcastic (positive)\", fontsize=13)\n", + "axes[0].set_xlabel(\"LR Coefficient\")\n", + "\n", + "terms_neg, coefs_neg = zip(*top_negative)\n", + "axes[1].barh(list(reversed(terms_neg)), list(reversed(coefs_neg)), color=\"coral\")\n", + "axes[1].set_title(\"Top 20 \u00e2\u20ac\u201d Non-Sarcastic (negative)\", fontsize=13)\n", + "axes[1].set_xlabel(\"LR Coefficient\")\n", + "\n", + "plt.suptitle(\"TF-IDF + LR: Feature Importance (Binary Task)\", fontsize=14)\n", + "plt.tight_layout()\n", + "plt.savefig(OUT_DIR / \"feature_importance_binary.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/classical/tfidf_lr/feature_importance_binary.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task B \u00e2\u20ac\u201d Sarcasm Type Classification" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Load type splits \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "train_type, val_type, test_type = load_splits(\"type\")\n", + "\n", + "# Encode string labels to integers\n", + "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", + "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", + "id2label = {i: lab for lab, i in label2id.items()}\n", + "\n", + "def encode_labels(df: pd.DataFrame) -> list[int]:\n", + " return [label2id[l] for l in df[\"type_label\"]]\n", + "\n", + "trainval_type = pd.concat([train_type, val_type], ignore_index=True)\n", + "\n", + "X_trainval_t = trainval_type[\"text\"].tolist()\n", + "y_trainval_t = encode_labels(trainval_type)\n", + "groups_tv_t = trainval_type[\"group_id\"].tolist()\n", + "\n", + "X_train_t = train_type[\"text\"].tolist()\n", + "y_train_t = encode_labels(train_type)\n", + "X_val_t = val_type[\"text\"].tolist()\n", + "y_val_t = encode_labels(val_type)\n", + "X_test_t = test_type[\"text\"].tolist()\n", + "y_test_t = encode_labels(test_type)\n", + "\n", + "print(f\"Strategies: {STRATEGY_LABELS}\")\n", + "print(f\"Train+Val: {len(X_trainval_t):,} Train: {len(X_train_t):,} Val: {len(X_val_t):,} Test: {len(X_test_t):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Define pipeline and grid (with class_weight) \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "pipe_type = Pipeline([\n", + " (\"tfidf\", TfidfVectorizer(sublinear_tf=True, lowercase=True)),\n", + " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, solver=\"lbfgs\",\n", + " multi_class=\"auto\")),\n", + "])\n", + "\n", + "param_grid_type = {\n", + " \"tfidf__ngram_range\" : [(1, 1), (1, 2)],\n", + " \"tfidf__min_df\" : [2, 3, 5],\n", + " \"tfidf__max_features\": [None, 50_000],\n", + " \"lr__C\" : [0.1, 1.0, 3.0],\n", + " \"lr__class_weight\" : [None, \"balanced\"],\n", + "}\n", + "\n", + "cv_type = GroupKFold(n_splits=5)\n", + "\n", + "gs_type = GridSearchCV(\n", + " pipe_type, param_grid_type,\n", + " cv=cv_type, scoring=\"f1_macro\",\n", + " n_jobs=-1, verbose=1, refit=True,\n", + ")\n", + "\n", + "print(\"Running grid search for type task...\")\n", + "gs_type.fit(X_trainval_t, y_trainval_t, groups=groups_tv_t)\n", + "\n", + "print(f\"\\nBest params : {gs_type.best_params_}\")\n", + "print(f\"Best CV F1 : {gs_type.best_score_:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Save best config \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "best_cfg_type = {\"task\": \"type\", \"model\": \"TF-IDF + LR\", \"seed\": SEED,\n", + " \"label_names\": STRATEGY_LABELS, \"label2id\": label2id,\n", + " \"best_params\": gs_type.best_params_, \"cv_f1_macro\": gs_type.best_score_}\n", + "with open(OUT_DIR / \"best_config_type.json\", \"w\") as f:\n", + " json.dump(best_cfg_type, f, indent=2)\n", + "print(\"Saved: outputs/classical/tfidf_lr/best_config_type.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Evaluate on val and test \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "best_type = gs_type.best_estimator_\n", + "\n", + "val_metrics_type, y_val_pred_type = evaluate(best_type, X_val_t, y_val_t, STRATEGY_LABELS, \"Val\")\n", + "test_metrics_type, y_test_pred_type = evaluate(best_type, X_test_t, y_test_t, STRATEGY_LABELS, \"Test\")\n", + "\n", + "all_metrics_type = {\"val\": val_metrics_type, \"test\": test_metrics_type}\n", + "for split_m in all_metrics_type.values():\n", + " split_m.pop(\"report\", None)\n", + "\n", + "with open(OUT_DIR / \"metrics_type.json\", \"w\") as f:\n", + " json.dump(all_metrics_type, f, indent=2)\n", + "print(\"Saved: outputs/classical/tfidf_lr/metrics_type.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Confusion matrices \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "save_confusion_matrix(\n", + " y_val_t, y_val_pred_type, STRATEGY_LABELS,\n", + " OUT_DIR / \"confusion_matrix_type_val.png\",\n", + " \"TF-IDF + LR \u00e2\u20ac\u201d Type (Val)\"\n", + ")\n", + "save_confusion_matrix(\n", + " y_test_t, y_test_pred_type, STRATEGY_LABELS,\n", + " OUT_DIR / \"confusion_matrix_type_test.png\",\n", + " \"TF-IDF + LR \u00e2\u20ac\u201d Type (Test)\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Save predictions \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "# Decode integer predictions back to string labels\n", + "val_type_copy = val_type.copy(); val_type_copy[\"type_label\"] = y_val_t\n", + "test_type_copy = test_type.copy(); test_type_copy[\"type_label\"] = y_test_t\n", + "\n", + "val_type_copy[\"predicted_id\"] = y_val_pred_type\n", + "val_type_copy[\"predicted_label\"] = [id2label[i] for i in y_val_pred_type]\n", + "val_type_copy[\"true_label\"] = [id2label[i] for i in y_val_t]\n", + "val_type_copy[\"correct\"] = (val_type_copy[\"type_label\"] == val_type_copy[\"predicted_id\"]).astype(int)\n", + "\n", + "test_type_copy[\"predicted_id\"] = y_test_pred_type\n", + "test_type_copy[\"predicted_label\"] = [id2label[i] for i in y_test_pred_type]\n", + "test_type_copy[\"true_label\"] = [id2label[i] for i in y_test_t]\n", + "test_type_copy[\"correct\"] = (test_type_copy[\"type_label\"] == test_type_copy[\"predicted_id\"]).astype(int)\n", + "\n", + "val_type_copy.to_csv( OUT_DIR / \"predictions_val_type.csv\", index=False)\n", + "test_type_copy.to_csv(OUT_DIR / \"predictions_test_type.csv\", index=False)\n", + "print(\"Saved predictions_val_type.csv and predictions_test_type.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Char N-gram Variant (Optional)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Char n-gram model for binary task (stylistic cues)\n", + "pipe_char = Pipeline([\n", + " (\"tfidf\", TfidfVectorizer(analyzer=\"char_wb\", ngram_range=(3, 5),\n", + " sublinear_tf=True, lowercase=True, max_features=50_000)),\n", + " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, C=1.0)),\n", + "])\n", + "\n", + "param_grid_char = {\"lr__C\": [0.1, 1.0, 3.0]}\n", + "gs_char = GridSearchCV(pipe_char, param_grid_char, cv=GroupKFold(5),\n", + " scoring=\"f1_macro\", n_jobs=-1)\n", + "gs_char.fit(X_trainval, y_trainval, groups=groups_tv)\n", + "\n", + "print(f\"Char n-gram best C : {gs_char.best_params_}\")\n", + "print(f\"Char n-gram best CV F1 : {gs_char.best_score_:.4f}\")\n", + "print(f\"Word n-gram best CV F1 : {gs_bin.best_score_:.4f}\")\n", + "print()\n", + "char_metrics, _ = evaluate(gs_char.best_estimator_, X_test, y_test, label_names_bin, \"Test (char)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"====== TF-IDF + LR RESULTS SUMMARY ======\")\n", + "print()\n", + "print(\"Task A \u00e2\u20ac\u201d Binary Classification (Test Set):\")\n", + "print(f\" Accuracy : {test_metrics_bin['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {test_metrics_bin['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1 : {test_metrics_bin['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"Task B \u00e2\u20ac\u201d Type Classification (Test Set):\")\n", + "print(f\" Accuracy : {test_metrics_type['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {test_metrics_type['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1 : {test_metrics_type['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"Best hyperparameters:\")\n", + "print(\" Binary:\", gs_bin.best_params_)\n", + "print(\" Type: \", gs_type.best_params_)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/notebooks/03_naive_bayes_baseline.ipynb b/notebooks/03_naive_bayes_baseline.ipynb new file mode 100644 index 0000000..df50fb9 --- /dev/null +++ b/notebooks/03_naive_bayes_baseline.ipynb @@ -0,0 +1,461 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": "# Notebook 03 \u2014 Naive Bayes Baseline\n\n**Purpose**: Train and evaluate Naive Bayes classifiers for:\n- Task A: Binary classification (sarcastic vs non-sarcastic)\n- Task B: Sarcasm type classification (6-class)\n\nCompares:\n- `CountVectorizer + MultinomialNB`\n- `TfidfVectorizer + MultinomialNB`\n- `CountVectorizer + ComplementNB` (for imbalanced type task)\n\n**Prerequisite**: Run `01_data_preparation.ipynb` first.\n\n**Outputs** in `outputs/classical/naive_bayes/`" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "import json\nimport random\nimport warnings\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\nfrom sklearn.naive_bayes import MultinomialNB, ComplementNB\nfrom sklearn.model_selection import GridSearchCV, GroupKFold\nfrom sklearn.metrics import (\n accuracy_score, precision_score, recall_score,\n f1_score, classification_report, confusion_matrix,\n)\n\nwarnings.filterwarnings(\"ignore\")\n\nSEED = 42\nrandom.seed(SEED)\nnp.random.seed(SEED)\n\n# \u2500\u2500 Robust project-root finder \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\ndef _find_project_root() -> Path:\n \"\"\"Walk up from cwd until we find outputs/splits or another project marker.\"\"\"\n markers = [\n Path(\"outputs\") / \"splits\",\n Path(\"outputs\") / \"datasets\",\n Path(\"data\") / \"processed\",\n Path(\"notebooks\"),\n ]\n for root in [Path.cwd()] + list(Path.cwd().parents):\n for marker in markers:\n if (root / marker).exists():\n return root\n return Path.cwd().parent # fallback\n\nROOT = _find_project_root()\nSPLITS = ROOT / \"outputs\" / \"splits\"\nOUT_DIR = ROOT / \"outputs\" / \"classical\" / \"naive_bayes\"\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nprint(f\"Project root : {ROOT}\")\nprint(f\"Output directory: {OUT_DIR}\")\n" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Helper Functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def load_splits(task: str):\n", + " train = pd.read_csv(SPLITS / f\"train_{task}.csv\")\n", + " val = pd.read_csv(SPLITS / f\"val_{task}.csv\")\n", + " test = pd.read_csv(SPLITS / f\"test_{task}.csv\")\n", + " return train, val, test\n", + "\n", + "\n", + "def evaluate(model, X, y_true, label_names=None, split_name=\"\"):\n", + " y_pred = model.predict(X)\n", + " metrics = {\n", + " \"split\" : split_name,\n", + " \"accuracy\" : accuracy_score(y_true, y_pred),\n", + " \"precision_macro\" : precision_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"recall_macro\" : recall_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"f1_macro\" : f1_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"f1_weighted\" : f1_score(y_true, y_pred, average=\"weighted\", zero_division=0),\n", + " }\n", + " print(f\"[{split_name}] Accuracy={metrics['accuracy']:.4f} \"\n", + " f\"Macro-F1={metrics['f1_macro']:.4f} Weighted-F1={metrics['f1_weighted']:.4f}\")\n", + " print(classification_report(y_true, y_pred, target_names=label_names, zero_division=0))\n", + " return metrics, y_pred\n", + "\n", + "\n", + "def save_confusion_matrix(y_true, y_pred, label_names, out_path, title=\"\"):\n", + " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", + " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", + " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Greens\",\n", + " xticklabels=label_names, yticklabels=label_names, ax=ax)\n", + " ax.set_xlabel(\"Predicted\"); ax.set_ylabel(\"True\"); ax.set_title(title)\n", + " plt.tight_layout()\n", + " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + " print(f\"Saved: {out_path.relative_to(ROOT)}\")\n", + "\n", + "\n", + "def run_nb_grid(\n", + " vectorizer_cls, nb_cls, param_grid: dict,\n", + " X_trainval, y_trainval, groups_tv,\n", + " name: str\n", + ") -> GridSearchCV:\n", + " \"\"\"Run a grid search for a given vectorizer + NB combination.\"\"\"\n", + " pipe = Pipeline([\n", + " (\"vec\", vectorizer_cls(lowercase=True)),\n", + " (\"nb\", nb_cls()),\n", + " ])\n", + " gs = GridSearchCV(\n", + " pipe, param_grid, cv=GroupKFold(5),\n", + " scoring=\"f1_macro\", n_jobs=-1, verbose=0, refit=True\n", + " )\n", + " gs.fit(X_trainval, y_trainval, groups=groups_tv)\n", + " print(f\"{name:50s} CV F1={gs.best_score_:.4f} params={gs.best_params_}\")\n", + " return gs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task A \u00e2\u20ac\u201d Binary Classification" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train_bin, val_bin, test_bin = load_splits(\"binary\")\n", + "trainval_bin = pd.concat([train_bin, val_bin], ignore_index=True)\n", + "\n", + "X_tv = trainval_bin[\"text\"].tolist()\n", + "y_tv = trainval_bin[\"binary_label\"].tolist()\n", + "grp_tv = trainval_bin[\"group_id\"].tolist()\n", + "\n", + "X_val = val_bin[\"text\"].tolist(); y_val = val_bin[\"binary_label\"].tolist()\n", + "X_test = test_bin[\"text\"].tolist(); y_test = test_bin[\"binary_label\"].tolist()\n", + "\n", + "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", + "\n", + "print(f\"Train+Val: {len(X_tv):,} Val: {len(X_val):,} Test: {len(X_test):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Shared grid parameters \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "BASE_GRID = {\n", + " \"vec__ngram_range\": [(1, 1), (1, 2)],\n", + " \"vec__min_df\" : [2, 3, 5],\n", + " \"nb__alpha\" : [0.1, 0.5, 1.0],\n", + "}\n", + "\n", + "print(\"=== Binary: Comparing Vectorizer + NB Combinations ===\")\n", + "\n", + "gs_count_mnb_bin = run_nb_grid(\n", + " CountVectorizer, MultinomialNB, BASE_GRID,\n", + " X_tv, y_tv, grp_tv,\n", + " \"CountVectorizer + MultinomialNB\"\n", + ")\n", + "\n", + "gs_tfidf_mnb_bin = run_nb_grid(\n", + " TfidfVectorizer, MultinomialNB, BASE_GRID,\n", + " X_tv, y_tv, grp_tv,\n", + " \"TfidfVectorizer + MultinomialNB\"\n", + ")\n", + "\n", + "gs_count_cnb_bin = run_nb_grid(\n", + " CountVectorizer, ComplementNB, BASE_GRID,\n", + " X_tv, y_tv, grp_tv,\n", + " \"CountVectorizer + ComplementNB\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Select best binary model \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "candidates_bin = [\n", + " (\"CountVec+MultinomialNB\", gs_count_mnb_bin),\n", + " (\"TfidfVec+MultinomialNB\", gs_tfidf_mnb_bin),\n", + " (\"CountVec+ComplementNB\", gs_count_cnb_bin),\n", + "]\n", + "\n", + "best_name_bin, best_gs_bin = max(candidates_bin, key=lambda x: x[1].best_score_)\n", + "print(f\"\\nBest binary NB model: {best_name_bin} (CV F1={best_gs_bin.best_score_:.4f})\")\n", + "\n", + "# Comparison table\n", + "comp_df = pd.DataFrame([\n", + " {\"model\": name, \"cv_f1_macro\": gs.best_score_, \"best_params\": str(gs.best_params_)}\n", + " for name, gs in candidates_bin\n", + "])\n", + "print(\"\\nComparison table:\")\n", + "print(comp_df.to_string(index=False))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Evaluate best model \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "best_model_bin = best_gs_bin.best_estimator_\n", + "\n", + "val_m_bin, y_val_pred_bin = evaluate(best_model_bin, X_val, y_val, label_names_bin, \"Val\")\n", + "test_m_bin, y_test_pred_bin = evaluate(best_model_bin, X_test, y_test, label_names_bin, \"Test\")\n", + "\n", + "# Also evaluate all three models on test for comparison\n", + "print(\"\\n--- All models on Test ---\")\n", + "all_test_results_bin = []\n", + "for name, gs in candidates_bin:\n", + " y_pred_t = gs.best_estimator_.predict(X_test)\n", + " f1 = f1_score(y_test, y_pred_t, average=\"macro\")\n", + " acc = accuracy_score(y_test, y_pred_t)\n", + " all_test_results_bin.append({\"model\": name, \"accuracy\": acc, \"f1_macro\": f1})\n", + " print(f\" {name:40s}: acc={acc:.4f} macro-F1={f1:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Save binary results \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "cfg_bin = {\n", + " \"task\": \"binary\", \"best_model\": best_name_bin,\n", + " \"best_params\": best_gs_bin.best_params_, \"cv_f1_macro\": best_gs_bin.best_score_,\n", + " \"all_candidates\": [\n", + " {\"model\": name, \"cv_f1_macro\": gs.best_score_, \"params\": gs.best_params_}\n", + " for name, gs in candidates_bin\n", + " ]\n", + "}\n", + "with open(OUT_DIR / \"best_config_binary.json\", \"w\") as f:\n", + " json.dump(cfg_bin, f, indent=2)\n", + "\n", + "all_m_bin = {\"val\": val_m_bin, \"test\": test_m_bin,\n", + " \"all_test_comparison\": all_test_results_bin}\n", + "with open(OUT_DIR / \"metrics_binary.json\", \"w\") as f:\n", + " json.dump(all_m_bin, f, indent=2)\n", + "\n", + "save_confusion_matrix(\n", + " y_val, y_val_pred_bin, label_names_bin,\n", + " OUT_DIR / \"confusion_matrix_binary_val.png\",\n", + " f\"NB ({best_name_bin}) \u00e2\u20ac\u201d Binary (Val)\"\n", + ")\n", + "save_confusion_matrix(\n", + " y_test, y_test_pred_bin, label_names_bin,\n", + " OUT_DIR / \"confusion_matrix_binary_test.png\",\n", + " f\"NB ({best_name_bin}) \u00e2\u20ac\u201d Binary (Test)\"\n", + ")\n", + "\n", + "# Predictions\n", + "pred_val_bin = val_bin.copy()\n", + "pred_val_bin[\"predicted\"] = y_val_pred_bin\n", + "pred_val_bin[\"correct\"] = (pred_val_bin[\"binary_label\"] == pred_val_bin[\"predicted\"]).astype(int)\n", + "pred_val_bin.to_csv(OUT_DIR / \"predictions_val_binary.csv\", index=False)\n", + "\n", + "pred_test_bin = test_bin.copy()\n", + "pred_test_bin[\"predicted\"] = y_test_pred_bin\n", + "pred_test_bin[\"correct\"] = (pred_test_bin[\"binary_label\"] == pred_test_bin[\"predicted\"]).astype(int)\n", + "pred_test_bin.to_csv(OUT_DIR / \"predictions_test_binary.csv\", index=False)\n", + "\n", + "print(\"All binary artifacts saved.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task B \u00e2\u20ac\u201d Sarcasm Type Classification" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train_type, val_type, test_type = load_splits(\"type\")\n", + "\n", + "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", + "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", + "id2label = {i: lab for lab, i in label2id.items()}\n", + "\n", + "def enc(df): return [label2id[l] for l in df[\"type_label\"]]\n", + "\n", + "trainval_type = pd.concat([train_type, val_type], ignore_index=True)\n", + "X_tv_t = trainval_type[\"text\"].tolist()\n", + "y_tv_t = enc(trainval_type)\n", + "grp_tv_t = trainval_type[\"group_id\"].tolist()\n", + "\n", + "X_val_t = val_type[\"text\"].tolist(); y_val_t = enc(val_type)\n", + "X_test_t = test_type[\"text\"].tolist(); y_test_t = enc(test_type)\n", + "\n", + "print(f\"Strategies: {STRATEGY_LABELS}\")\n", + "print(f\"Train+Val: {len(X_tv_t):,} Val: {len(X_val_t):,} Test: {len(X_test_t):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"=== Type: Comparing Vectorizer + NB Combinations ===\")\n", + "\n", + "gs_count_mnb_type = run_nb_grid(\n", + " CountVectorizer, MultinomialNB, BASE_GRID,\n", + " X_tv_t, y_tv_t, grp_tv_t,\n", + " \"CountVectorizer + MultinomialNB\"\n", + ")\n", + "\n", + "gs_tfidf_mnb_type = run_nb_grid(\n", + " TfidfVectorizer, MultinomialNB, BASE_GRID,\n", + " X_tv_t, y_tv_t, grp_tv_t,\n", + " \"TfidfVectorizer + MultinomialNB\"\n", + ")\n", + "\n", + "gs_count_cnb_type = run_nb_grid(\n", + " CountVectorizer, ComplementNB, BASE_GRID,\n", + " X_tv_t, y_tv_t, grp_tv_t,\n", + " \"CountVectorizer + ComplementNB (for imbalance)\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "candidates_type = [\n", + " (\"CountVec+MultinomialNB\", gs_count_mnb_type),\n", + " (\"TfidfVec+MultinomialNB\", gs_tfidf_mnb_type),\n", + " (\"CountVec+ComplementNB\", gs_count_cnb_type),\n", + "]\n", + "\n", + "best_name_type, best_gs_type = max(candidates_type, key=lambda x: x[1].best_score_)\n", + "print(f\"Best type NB model: {best_name_type} (CV F1={best_gs_type.best_score_:.4f})\")\n", + "\n", + "print(\"\\n--- All models on Test (Type Task) ---\")\n", + "all_test_results_type = []\n", + "for name, gs in candidates_type:\n", + " y_pred_t = gs.best_estimator_.predict(X_test_t)\n", + " f1 = f1_score(y_test_t, y_pred_t, average=\"macro\")\n", + " acc = accuracy_score(y_test_t, y_pred_t)\n", + " all_test_results_type.append({\"model\": name, \"accuracy\": acc, \"f1_macro\": f1})\n", + " print(f\" {name:40s}: acc={acc:.4f} macro-F1={f1:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "best_model_type = best_gs_type.best_estimator_\n", + "\n", + "val_m_type, y_val_pred_type = evaluate(best_model_type, X_val_t, y_val_t, STRATEGY_LABELS, \"Val\")\n", + "test_m_type, y_test_pred_type = evaluate(best_model_type, X_test_t, y_test_t, STRATEGY_LABELS, \"Test\")\n", + "\n", + "# Save\n", + "cfg_type = {\n", + " \"task\": \"type\", \"best_model\": best_name_type,\n", + " \"label_names\": STRATEGY_LABELS, \"label2id\": label2id,\n", + " \"best_params\": best_gs_type.best_params_, \"cv_f1_macro\": best_gs_type.best_score_,\n", + "}\n", + "with open(OUT_DIR / \"best_config_type.json\", \"w\") as f:\n", + " json.dump(cfg_type, f, indent=2)\n", + "\n", + "all_m_type = {\"val\": val_m_type, \"test\": test_m_type,\n", + " \"all_test_comparison\": all_test_results_type}\n", + "with open(OUT_DIR / \"metrics_type.json\", \"w\") as f:\n", + " json.dump(all_m_type, f, indent=2)\n", + "\n", + "save_confusion_matrix(\n", + " y_val_t, y_val_pred_type, STRATEGY_LABELS,\n", + " OUT_DIR / \"confusion_matrix_type_val.png\",\n", + " f\"NB ({best_name_type}) \u00e2\u20ac\u201d Type (Val)\"\n", + ")\n", + "save_confusion_matrix(\n", + " y_test_t, y_test_pred_type, STRATEGY_LABELS,\n", + " OUT_DIR / \"confusion_matrix_type_test.png\",\n", + " f\"NB ({best_name_type}) \u00e2\u20ac\u201d Type (Test)\"\n", + ")\n", + "\n", + "# Predictions with string labels\n", + "pred_val_type = val_type.copy()\n", + "pred_test_type = test_type.copy()\n", + "for df, y_pred, y_true in [(pred_val_type, y_val_pred_type, y_val_t),\n", + " (pred_test_type, y_test_pred_type, y_test_t)]:\n", + " df[\"predicted_label\"] = [id2label[i] for i in y_pred]\n", + " df[\"true_label\"] = [id2label[i] for i in y_true]\n", + " df[\"correct\"] = (df[\"type_label\"] == df[\"predicted_label\"]).astype(int)\n", + "\n", + "pred_val_type.to_csv( OUT_DIR / \"predictions_val_type.csv\", index=False)\n", + "pred_test_type.to_csv(OUT_DIR / \"predictions_test_type.csv\", index=False)\n", + "\n", + "print(\"All type artifacts saved.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Error Examples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Binary error examples \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "err_bin = pred_test_bin[pred_test_bin[\"correct\"] == 0].copy()\n", + "fp_bin = err_bin[err_bin[\"binary_label\"] == 0].head(10) # predicted sarcastic, actually not\n", + "fn_bin = err_bin[err_bin[\"binary_label\"] == 1].head(10) # predicted not-sarcastic, actually sarcastic\n", + "\n", + "print(f\"Binary errors on test: {len(err_bin)} total\")\n", + "print(f\" False Positives (predicted sarcastic, actually not): {len(err_bin[err_bin['binary_label']==0])}\")\n", + "print(f\" False Negatives (predicted not-sarcastic, actually sarcastic): {len(err_bin[err_bin['binary_label']==1])}\")\n", + "print(\"\\n--- Sample FP ---\")\n", + "for _, row in fp_bin.iterrows():\n", + " print(f\" [True=0, Pred=1] {row['text'][:100]}\")\n", + "print(\"\\n--- Sample FN ---\")\n", + "for _, row in fn_bin.iterrows():\n", + " print(f\" [True=1, Pred=0] {row['text'][:100]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Type error examples \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "err_type = pred_test_type[pred_test_type[\"correct\"] == 0].copy()\n", + "print(f\"Type errors on test: {len(err_type)} total\")\n", + "print(\"\\nConfusion pairs (true \u00e2\u2020\u2019 predicted):\")\n", + "conf_pairs = err_type.groupby([\"type_label\", \"predicted_label\"]).size().sort_values(ascending=False).head(10)\n", + "print(conf_pairs.to_string())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"====== NAIVE BAYES RESULTS SUMMARY ======\")\n", + "print()\n", + "print(\"Task A \u00e2\u20ac\u201d Binary (Test):\")\n", + "print(f\" Best model : {best_name_bin}\")\n", + "print(f\" Accuracy : {test_m_bin['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {test_m_bin['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1 : {test_m_bin['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"Task B \u00e2\u20ac\u201d Type (Test):\")\n", + "print(f\" Best model : {best_name_type}\")\n", + "print(f\" Accuracy : {test_m_type['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {test_m_type['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1 : {test_m_type['f1_weighted']:.4f}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/notebooks/04_bert_classification.ipynb b/notebooks/04_bert_classification.ipynb new file mode 100644 index 0000000..143de3f --- /dev/null +++ b/notebooks/04_bert_classification.ipynb @@ -0,0 +1,600 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": "# Notebook 04 \u2014 BERT / DistilBERT Classification\n\n**Purpose**: Fine-tune transformer models for sarcasm classification.\n- Task A: Binary (sarcastic vs non-sarcastic)\n- Task B: Sarcasm type (6-class, sarcastic only)\n\n**Models**:\n- `distilbert-base-uncased` (primary, fast)\n- `bert-base-uncased` (optional, if compute allows)\n\n**Prerequisite**: Run `01_data_preparation.ipynb` first.\n\n**Outputs** in `outputs/bert/distilbert_binary/` and `outputs/bert/distilbert_type/`" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "import json\nimport random\nimport warnings\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Optional\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport torch\nfrom torch import nn\nfrom torch.utils.data import Dataset, DataLoader\nfrom transformers import (\n AutoTokenizer,\n AutoModelForSequenceClassification,\n get_linear_schedule_with_warmup,\n)\nfrom sklearn.metrics import (\n accuracy_score, precision_score, recall_score,\n f1_score, classification_report, confusion_matrix,\n)\nfrom tqdm.auto import tqdm\n\nwarnings.filterwarnings(\"ignore\")\n\nSEED = 42\nrandom.seed(SEED)\nnp.random.seed(SEED)\ntorch.manual_seed(SEED)\nif torch.cuda.is_available():\n torch.cuda.manual_seed_all(SEED)\n torch.backends.cudnn.deterministic = True\n\nDEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(f\"Device: {DEVICE}\")\n\n# \u2500\u2500 Robust project-root finder \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\ndef _find_project_root() -> Path:\n \"\"\"Walk up from cwd until we find outputs/splits or another project marker.\"\"\"\n markers = [\n Path(\"outputs\") / \"splits\",\n Path(\"outputs\") / \"datasets\",\n Path(\"data\") / \"processed\",\n Path(\"notebooks\"),\n ]\n for root in [Path.cwd()] + list(Path.cwd().parents):\n for marker in markers:\n if (root / marker).exists():\n return root\n return Path.cwd().parent # fallback\n\nROOT = _find_project_root()\nSPLITS = ROOT / \"outputs\" / \"splits\"\nBERT_OUT = ROOT / \"outputs\" / \"bert\"\nBERT_OUT.mkdir(parents=True, exist_ok=True)\n\nprint(f\"Project root: {ROOT}\")\n" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@dataclass\n", + "class TrainConfig:\n", + " model_name : str = \"distilbert-base-uncased\"\n", + " max_length : int = 128\n", + " batch_size : int = 32\n", + " lr : float = 2e-5\n", + " weight_decay : float = 0.01\n", + " warmup_ratio : float = 0.1\n", + " epochs : int = 10\n", + " patience : int = 3 # early stopping patience\n", + " seed : int = SEED\n", + " task : str = \"binary\" # 'binary' or 'type'\n", + " use_class_weights: bool = False\n", + "\n", + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Config for binary task \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "CFG_BINARY = TrainConfig(\n", + " model_name=\"distilbert-base-uncased\",\n", + " task=\"binary\",\n", + " batch_size=32,\n", + " lr=2e-5,\n", + " use_class_weights=False,\n", + ")\n", + "\n", + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Config for type task (use class weights for imbalance) \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "CFG_TYPE = TrainConfig(\n", + " model_name=\"distilbert-base-uncased\",\n", + " task=\"type\",\n", + " batch_size=32,\n", + " lr=2e-5,\n", + " use_class_weights=True,\n", + ")\n", + "\n", + "print(\"Binary config:\", CFG_BINARY)\n", + "print(\"Type config: \", CFG_TYPE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dataset Class" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class HeadlineDataset(Dataset):\n", + " \"\"\"PyTorch Dataset for headline classification.\"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " texts: list[str],\n", + " labels: list[int],\n", + " tokenizer,\n", + " max_length: int = 128,\n", + " ):\n", + " self.texts = texts\n", + " self.labels = labels\n", + " self.tokenizer = tokenizer\n", + " self.max_length = max_length\n", + "\n", + " def __len__(self) -> int:\n", + " return len(self.texts)\n", + "\n", + " def __getitem__(self, idx: int) -> dict:\n", + " encoding = self.tokenizer(\n", + " self.texts[idx],\n", + " truncation=True,\n", + " padding=\"max_length\",\n", + " max_length=self.max_length,\n", + " return_tensors=\"pt\",\n", + " )\n", + " return {\n", + " \"input_ids\" : encoding[\"input_ids\"].squeeze(0),\n", + " \"attention_mask\" : encoding[\"attention_mask\"].squeeze(0),\n", + " \"labels\" : torch.tensor(self.labels[idx], dtype=torch.long),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training and Evaluation Functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_class_weights(y_train: list[int], num_classes: int) -> torch.Tensor:\n", + " \"\"\"Compute inverse-frequency class weights.\"\"\"\n", + " counts = np.bincount(y_train, minlength=num_classes).astype(float)\n", + " weights = len(y_train) / (num_classes * counts)\n", + " weights = np.clip(weights, 0, 10) # cap extreme weights\n", + " return torch.tensor(weights, dtype=torch.float)\n", + "\n", + "\n", + "def train_epoch(\n", + " model, loader: DataLoader, optimizer, scheduler, criterion, device\n", + ") -> float:\n", + " model.train()\n", + " total_loss = 0.0\n", + " for batch in loader:\n", + " input_ids = batch[\"input_ids\"].to(device)\n", + " attention_mask = batch[\"attention_mask\"].to(device)\n", + " labels = batch[\"labels\"].to(device)\n", + "\n", + " optimizer.zero_grad()\n", + " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", + " logits = outputs.logits\n", + "\n", + " loss = criterion(logits, labels)\n", + " loss.backward()\n", + "\n", + " nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n", + " optimizer.step()\n", + " scheduler.step()\n", + "\n", + " total_loss += loss.item()\n", + " return total_loss / len(loader)\n", + "\n", + "\n", + "@torch.no_grad()\n", + "def eval_epoch(\n", + " model, loader: DataLoader, criterion, device, label_names: list[str]\n", + ") -> tuple[float, dict, list]:\n", + " model.eval()\n", + " total_loss = 0.0\n", + " all_preds, all_labels = [], []\n", + "\n", + " for batch in loader:\n", + " input_ids = batch[\"input_ids\"].to(device)\n", + " attention_mask = batch[\"attention_mask\"].to(device)\n", + " labels = batch[\"labels\"].to(device)\n", + "\n", + " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", + " logits = outputs.logits\n", + " loss = criterion(logits, labels)\n", + "\n", + " total_loss += loss.item()\n", + " preds = torch.argmax(logits, dim=-1)\n", + " all_preds.extend(preds.cpu().numpy())\n", + " all_labels.extend(labels.cpu().numpy())\n", + "\n", + " avg_loss = total_loss / len(loader)\n", + " metrics = {\n", + " \"loss\" : avg_loss,\n", + " \"accuracy\" : accuracy_score(all_labels, all_preds),\n", + " \"f1_macro\" : f1_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", + " \"f1_weighted\" : f1_score(all_labels, all_preds, average=\"weighted\", zero_division=0),\n", + " \"precision_macro\" : precision_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", + " \"recall_macro\" : recall_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", + " }\n", + " return avg_loss, metrics, all_preds\n", + "\n", + "\n", + "def save_confusion_matrix(y_true, y_pred, label_names, out_path, title=\"\"):\n", + " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", + " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", + " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n", + " xticklabels=label_names, yticklabels=label_names, ax=ax)\n", + " ax.set_xlabel(\"Predicted\"); ax.set_ylabel(\"True\"); ax.set_title(title)\n", + " plt.tight_layout()\n", + " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + " print(f\"Saved: {out_path}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Full Training Function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def train_bert(\n", + " cfg: TrainConfig,\n", + " X_train: list[str], y_train: list[int],\n", + " X_val: list[str], y_val: list[int],\n", + " X_test: list[str], y_test: list[int],\n", + " label_names: list[str],\n", + " out_dir: Path,\n", + " val_df: pd.DataFrame,\n", + " test_df: pd.DataFrame,\n", + ") -> dict:\n", + " \"\"\"Full training loop with early stopping. Returns test metrics dict.\"\"\"\n", + " out_dir.mkdir(parents=True, exist_ok=True)\n", + " num_labels = len(label_names)\n", + "\n", + " # Save config\n", + " with open(out_dir / \"config.json\", \"w\") as f:\n", + " json.dump(cfg.__dict__, f, indent=2)\n", + " print(f\"Config saved to {out_dir / 'config.json'}\")\n", + "\n", + " # Tokenizer\n", + " print(f\"Loading tokenizer: {cfg.model_name}\")\n", + " tokenizer = AutoTokenizer.from_pretrained(cfg.model_name)\n", + "\n", + " # Datasets\n", + " train_ds = HeadlineDataset(X_train, y_train, tokenizer, cfg.max_length)\n", + " val_ds = HeadlineDataset(X_val, y_val, tokenizer, cfg.max_length)\n", + " test_ds = HeadlineDataset(X_test, y_test, tokenizer, cfg.max_length)\n", + "\n", + " g = torch.Generator(); g.manual_seed(cfg.seed)\n", + " train_loader = DataLoader(train_ds, batch_size=cfg.batch_size, shuffle=True, generator=g)\n", + " val_loader = DataLoader(val_ds, batch_size=cfg.batch_size, shuffle=False)\n", + " test_loader = DataLoader(test_ds, batch_size=cfg.batch_size, shuffle=False)\n", + "\n", + " # Model\n", + " print(f\"Loading model: {cfg.model_name} ({num_labels} labels)\")\n", + " model = AutoModelForSequenceClassification.from_pretrained(\n", + " cfg.model_name, num_labels=num_labels\n", + " ).to(DEVICE)\n", + "\n", + " # Loss (with optional class weights)\n", + " if cfg.use_class_weights:\n", + " cw = compute_class_weights(y_train, num_labels).to(DEVICE)\n", + " criterion = nn.CrossEntropyLoss(weight=cw)\n", + " print(f\"Class weights: {cw.cpu().numpy().round(3)}\")\n", + " else:\n", + " criterion = nn.CrossEntropyLoss()\n", + "\n", + " # Optimizer and scheduler\n", + " optimizer = torch.optim.AdamW(\n", + " model.parameters(), lr=cfg.lr, weight_decay=cfg.weight_decay\n", + " )\n", + " total_steps = len(train_loader) * cfg.epochs\n", + " warmup_steps = int(total_steps * cfg.warmup_ratio)\n", + " scheduler = get_linear_schedule_with_warmup(\n", + " optimizer, num_warmup_steps=warmup_steps, num_training_steps=total_steps\n", + " )\n", + "\n", + " # \u00e2\u201d\u20ac\u00e2\u201d\u20ac Training loop \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + " best_val_f1 = -1.0\n", + " patience_cnt = 0\n", + " best_epoch = 0\n", + " train_log = []\n", + "\n", + " for epoch in range(1, cfg.epochs + 1):\n", + " train_loss = train_epoch(model, train_loader, optimizer, scheduler, criterion, DEVICE)\n", + " _, val_metrics, _ = eval_epoch(model, val_loader, criterion, DEVICE, label_names)\n", + "\n", + " val_f1 = val_metrics[\"f1_macro\"]\n", + " log_row = {\"epoch\": epoch, \"train_loss\": train_loss, **{f\"val_{k}\": v for k, v in val_metrics.items()}}\n", + " train_log.append(log_row)\n", + "\n", + " print(\n", + " f\"Epoch {epoch:2d}/{cfg.epochs} | \"\n", + " f\"train_loss={train_loss:.4f} | \"\n", + " f\"val_loss={val_metrics['loss']:.4f} | \"\n", + " f\"val_acc={val_metrics['accuracy']:.4f} | \"\n", + " f\"val_macro_f1={val_f1:.4f}\"\n", + " )\n", + "\n", + " if val_f1 > best_val_f1:\n", + " best_val_f1 = val_f1\n", + " best_epoch = epoch\n", + " patience_cnt = 0\n", + " # Save best checkpoint\n", + " ckpt_dir = out_dir / \"best_checkpoint\"\n", + " model.save_pretrained(ckpt_dir)\n", + " tokenizer.save_pretrained(ckpt_dir)\n", + " print(f\" \u00e2\u02dc\u2026 New best val macro-F1={val_f1:.4f} \u00e2\u20ac\u201d checkpoint saved\")\n", + " else:\n", + " patience_cnt += 1\n", + " if patience_cnt >= cfg.patience:\n", + " print(f\" Early stopping at epoch {epoch} (patience={cfg.patience})\")\n", + " break\n", + "\n", + " # Save training log\n", + " log_df = pd.DataFrame(train_log)\n", + " log_df.to_csv(out_dir / \"training_log.csv\", index=False)\n", + "\n", + " # \u00e2\u201d\u20ac\u00e2\u201d\u20ac Plot training curves \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + " fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + " axes[0].plot(log_df[\"epoch\"], log_df[\"train_loss\"], label=\"Train loss\", marker=\"o\")\n", + " axes[0].plot(log_df[\"epoch\"], log_df[\"val_loss\"], label=\"Val loss\", marker=\"s\")\n", + " axes[0].set_xlabel(\"Epoch\"); axes[0].set_ylabel(\"Loss\")\n", + " axes[0].set_title(f\"{cfg.model_name} \u00e2\u20ac\u201d Loss Curves ({cfg.task})\")\n", + " axes[0].legend(); axes[0].axvline(best_epoch, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", + "\n", + " axes[1].plot(log_df[\"epoch\"], log_df[\"val_f1_macro\"], label=\"Val macro-F1\", marker=\"o\", color=\"steelblue\")\n", + " axes[1].set_xlabel(\"Epoch\"); axes[1].set_ylabel(\"Macro-F1\")\n", + " axes[1].set_title(f\"{cfg.model_name} \u00e2\u20ac\u201d Val Macro-F1 ({cfg.task})\")\n", + " axes[1].legend(); axes[1].axvline(best_epoch, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", + "\n", + " plt.tight_layout()\n", + " plt.savefig(out_dir / \"training_curves.png\", dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + "\n", + " # \u00e2\u201d\u20ac\u00e2\u201d\u20ac Reload best checkpoint and evaluate \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + " print(f\"\\nLoading best checkpoint (epoch {best_epoch}, val macro-F1={best_val_f1:.4f})\")\n", + " best_model = AutoModelForSequenceClassification.from_pretrained(\n", + " out_dir / \"best_checkpoint\"\n", + " ).to(DEVICE)\n", + "\n", + " _, val_metrics_best, val_preds = eval_epoch(best_model, val_loader, criterion, DEVICE, label_names)\n", + " _, test_metrics_best, test_preds = eval_epoch(best_model, test_loader, criterion, DEVICE, label_names)\n", + "\n", + " print(\"\\n=== Val (best checkpoint) ===\")\n", + " print(classification_report(y_val, val_preds, target_names=label_names, zero_division=0))\n", + " print(\"\\n=== Test (best checkpoint) ===\")\n", + " print(classification_report(y_test, test_preds, target_names=label_names, zero_division=0))\n", + "\n", + " # Confusion matrices\n", + " save_confusion_matrix(y_val, val_preds, label_names,\n", + " out_dir / \"confusion_matrix_val.png\",\n", + " f\"{cfg.model_name} \u00e2\u20ac\u201d {cfg.task} (Val)\")\n", + " save_confusion_matrix(y_test, test_preds, label_names,\n", + " out_dir / \"confusion_matrix_test.png\",\n", + " f\"{cfg.model_name} \u00e2\u20ac\u201d {cfg.task} (Test)\")\n", + "\n", + " # Save metrics\n", + " results = {\n", + " \"model\": cfg.model_name, \"task\": cfg.task,\n", + " \"best_epoch\": best_epoch, \"best_val_f1_macro\": best_val_f1,\n", + " \"val\": val_metrics_best,\n", + " \"test\": test_metrics_best,\n", + " }\n", + " with open(out_dir / \"metrics.json\", \"w\") as f:\n", + " json.dump(results, f, indent=2)\n", + "\n", + " # Save predictions\n", + " for split_name, df, y_true_list, y_pred_list in [\n", + " (\"val\", val_df, y_val, val_preds),\n", + " (\"test\", test_df, y_test, test_preds),\n", + " ]:\n", + " out_pred = df.copy()\n", + " out_pred[\"predicted\"] = y_pred_list\n", + " if hasattr(label_names, '__getitem__'):\n", + " out_pred[\"predicted_label\"] = [label_names[i] for i in y_pred_list]\n", + " out_pred[\"correct\"] = (np.array(y_true_list) == np.array(y_pred_list)).astype(int)\n", + " out_pred.to_csv(out_dir / f\"predictions_{split_name}.csv\", index=False)\n", + " print(f\"Saved predictions_{split_name}.csv\")\n", + "\n", + " print(f\"\\n=== DONE: {cfg.model_name} / {cfg.task} ===\")\n", + " print(f\" Test Accuracy : {test_metrics_best['accuracy']:.4f}\")\n", + " print(f\" Test Macro-F1 : {test_metrics_best['f1_macro']:.4f}\")\n", + " print(f\" Test Weighted-F1: {test_metrics_best['f1_weighted']:.4f}\")\n", + "\n", + " return results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Binary splits \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "train_bin = pd.read_csv(SPLITS / \"train_binary.csv\")\n", + "val_bin = pd.read_csv(SPLITS / \"val_binary.csv\")\n", + "test_bin = pd.read_csv(SPLITS / \"test_binary.csv\")\n", + "\n", + "X_train_bin = train_bin[\"text\"].tolist(); y_train_bin = train_bin[\"binary_label\"].tolist()\n", + "X_val_bin = val_bin[\"text\"].tolist(); y_val_bin = val_bin[\"binary_label\"].tolist()\n", + "X_test_bin = test_bin[\"text\"].tolist(); y_test_bin = test_bin[\"binary_label\"].tolist()\n", + "\n", + "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", + "\n", + "print(f\"Binary \u00e2\u20ac\u201d Train: {len(X_train_bin):,} Val: {len(X_val_bin):,} Test: {len(X_test_bin):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Type splits \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "train_type = pd.read_csv(SPLITS / \"train_type.csv\")\n", + "val_type = pd.read_csv(SPLITS / \"val_type.csv\")\n", + "test_type = pd.read_csv(SPLITS / \"test_type.csv\")\n", + "\n", + "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", + "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", + "id2label = {i: lab for lab, i in label2id.items()}\n", + "\n", + "def enc(df): return [label2id[l] for l in df[\"type_label\"]]\n", + "\n", + "X_train_type = train_type[\"text\"].tolist(); y_train_type = enc(train_type)\n", + "X_val_type = val_type[\"text\"].tolist(); y_val_type = enc(val_type)\n", + "X_test_type = test_type[\"text\"].tolist(); y_test_type = enc(test_type)\n", + "\n", + "print(f\"Type \u00e2\u20ac\u201d Train: {len(X_train_type):,} Val: {len(X_val_type):,} Test: {len(X_test_type):,}\")\n", + "print(f\"Strategies: {STRATEGY_LABELS}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train DistilBERT \u00e2\u20ac\u201d Binary Task" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_distilbert_binary = train_bert(\n", + " cfg = CFG_BINARY,\n", + " X_train = X_train_bin, y_train = y_train_bin,\n", + " X_val = X_val_bin, y_val = y_val_bin,\n", + " X_test = X_test_bin, y_test = y_test_bin,\n", + " label_names= label_names_bin,\n", + " out_dir = BERT_OUT / \"distilbert_binary\",\n", + " val_df = val_bin,\n", + " test_df = test_bin,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train DistilBERT \u00e2\u20ac\u201d Type Task" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results_distilbert_type = train_bert(\n", + " cfg = CFG_TYPE,\n", + " X_train = X_train_type, y_train = y_train_type,\n", + " X_val = X_val_type, y_val = y_val_type,\n", + " X_test = X_test_type, y_test = y_test_type,\n", + " label_names= STRATEGY_LABELS,\n", + " out_dir = BERT_OUT / \"distilbert_type\",\n", + " val_df = val_type,\n", + " test_df = test_type,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## (Optional) BERT-base \u00e2\u20ac\u201d Binary Task\n", + "\n", + "Run only if compute allows. Comment out if not needed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment to run BERT-base (requires more GPU memory and time)\n", + "\n", + "# CFG_BERT_BINARY = TrainConfig(\n", + "# model_name=\"bert-base-uncased\",\n", + "# task=\"binary\",\n", + "# batch_size=16, # smaller batch for bert-base\n", + "# lr=2e-5,\n", + "# )\n", + "\n", + "# results_bert_binary = train_bert(\n", + "# cfg = CFG_BERT_BINARY,\n", + "# X_train = X_train_bin, y_train = y_train_bin,\n", + "# X_val = X_val_bin, y_val = y_val_bin,\n", + "# X_test = X_test_bin, y_test = y_test_bin,\n", + "# label_names = label_names_bin,\n", + "# out_dir = BERT_OUT / \"bert_base_binary\",\n", + "# val_df = val_bin,\n", + "# test_df = test_bin,\n", + "# )\n", + "\n", + "print(\"BERT-base is commented out. Uncomment cells above to run.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## (Optional) BERT-base \u00e2\u20ac\u201d Type Task" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# CFG_BERT_TYPE = TrainConfig(\n", + "# model_name=\"bert-base-uncased\",\n", + "# task=\"type\",\n", + "# batch_size=16,\n", + "# lr=2e-5,\n", + "# use_class_weights=True,\n", + "# )\n", + "\n", + "# results_bert_type = train_bert(\n", + "# cfg = CFG_BERT_TYPE,\n", + "# X_train = X_train_type, y_train = y_train_type,\n", + "# X_val = X_val_type, y_val = y_val_type,\n", + "# X_test = X_test_type, y_test = y_test_type,\n", + "# label_names = STRATEGY_LABELS,\n", + "# out_dir = BERT_OUT / \"bert_base_type\",\n", + "# val_df = val_type,\n", + "# test_df = test_type,\n", + "# )\n", + "\n", + "print(\"BERT-base type task is commented out. Uncomment to run.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"====== BERT RESULTS SUMMARY ======\")\n", + "print()\n", + "print(\"DistilBERT \u00e2\u20ac\u201d Binary (Test):\")\n", + "print(f\" Accuracy : {results_distilbert_binary['test']['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {results_distilbert_binary['test']['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1: {results_distilbert_binary['test']['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"DistilBERT \u00e2\u20ac\u201d Type (Test):\")\n", + "print(f\" Accuracy : {results_distilbert_type['test']['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {results_distilbert_type['test']['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1: {results_distilbert_type['test']['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"Artifacts saved in outputs/bert/distilbert_binary/ and outputs/bert/distilbert_type/\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/notebooks/05_error_analysis.ipynb b/notebooks/05_error_analysis.ipynb new file mode 100644 index 0000000..b4ace81 --- /dev/null +++ b/notebooks/05_error_analysis.ipynb @@ -0,0 +1,622 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": "# Notebook 05 \u2014 Error Analysis & Model Comparison\n\n**Purpose**:\n1. Compare all models on both tasks\n2. Analyze errors (binary + type)\n3. Generate final comparison report\n\n**Prerequisite**: Run notebooks 01\u201304 first.\n\n**Outputs**:\n- `outputs/reports/model_comparison.md`\n- `outputs/reports/error_analysis.md`\n- `outputs/reports/error_examples_binary.csv`\n- `outputs/reports/error_examples_type.csv`" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "import json\nfrom pathlib import Path\nfrom collections import Counter\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nimport seaborn as sns\n\n# \u2500\u2500 Robust project-root finder \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\ndef _find_project_root() -> Path:\n \"\"\"Walk up from cwd until we find outputs/splits or another project marker.\"\"\"\n markers = [\n Path(\"outputs\") / \"splits\",\n Path(\"outputs\") / \"datasets\",\n Path(\"data\") / \"processed\",\n Path(\"notebooks\"),\n ]\n for root in [Path.cwd()] + list(Path.cwd().parents):\n for marker in markers:\n if (root / marker).exists():\n return root\n return Path.cwd().parent # fallback\n\nROOT = _find_project_root()\nCLASSICAL = ROOT / \"outputs\" / \"classical\"\nBERT_OUT = ROOT / \"outputs\" / \"bert\"\nREPORTS_DIR = ROOT / \"outputs\" / \"reports\"\nREPORTS_DIR.mkdir(parents=True, exist_ok=True)\n\nprint(f\"Project root : {ROOT}\")\nprint(f\"Reports directory: {REPORTS_DIR}\")\n" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Load All Metrics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def load_metrics(path: Path) -> dict | None:\n", + " if path.exists():\n", + " with open(path) as f:\n", + " return json.load(f)\n", + " print(f\" [WARNING] Not found: {path.relative_to(ROOT)}\")\n", + " return None\n", + "\n", + "\n", + "# All metrics\n", + "tfidf_lr_binary = load_metrics(CLASSICAL / \"tfidf_lr\" / \"metrics_binary.json\")\n", + "tfidf_lr_type = load_metrics(CLASSICAL / \"tfidf_lr\" / \"metrics_type.json\")\n", + "nb_binary = load_metrics(CLASSICAL / \"naive_bayes\" / \"metrics_binary.json\")\n", + "nb_type = load_metrics(CLASSICAL / \"naive_bayes\" / \"metrics_type.json\")\n", + "distilbert_bin = load_metrics(BERT_OUT / \"distilbert_binary\" / \"metrics.json\")\n", + "distilbert_type = load_metrics(BERT_OUT / \"distilbert_type\" / \"metrics.json\")\n", + "bert_base_bin = load_metrics(BERT_OUT / \"bert_base_binary\" / \"metrics.json\") # optional\n", + "bert_base_type = load_metrics(BERT_OUT / \"bert_base_type\" / \"metrics.json\") # optional\n", + "\n", + "print(\"Metrics loaded (None = not yet run):\")\n", + "for name, m in [(\"TF-IDF+LR binary\", tfidf_lr_binary), (\"TF-IDF+LR type\", tfidf_lr_type),\n", + " (\"NB binary\", nb_binary), (\"NB type\", nb_type),\n", + " (\"DistilBERT binary\", distilbert_bin), (\"DistilBERT type\", distilbert_type),\n", + " (\"BERT-base binary\", bert_base_bin), (\"BERT-base type\", bert_base_type)]:\n", + " print(f\" {name}: {'\u00e2\u0153\u201c' if m else '\u00e2\u0153\u2014'}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Model Comparison Tables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def extract_test_metrics(metrics_dict: dict | None, split: str = \"test\") -> dict:\n", + " \"\"\"Extract test-split metrics from a metrics dict, handling different formats.\"\"\"\n", + " if metrics_dict is None:\n", + " return {\"accuracy\": None, \"f1_macro\": None, \"f1_weighted\": None,\n", + " \"precision_macro\": None, \"recall_macro\": None}\n", + " test_m = metrics_dict.get(split, metrics_dict) # BERT format uses 'test' key directly\n", + " return {\n", + " \"accuracy\" : test_m.get(\"accuracy\"),\n", + " \"f1_macro\" : test_m.get(\"f1_macro\"),\n", + " \"f1_weighted\" : test_m.get(\"f1_weighted\"),\n", + " \"precision_macro\" : test_m.get(\"precision_macro\"),\n", + " \"recall_macro\" : test_m.get(\"recall_macro\"),\n", + " }\n", + "\n", + "\n", + "binary_rows = []\n", + "type_rows = []\n", + "\n", + "model_map_bin = [\n", + " (\"TF-IDF + LR\", tfidf_lr_binary),\n", + " (\"Naive Bayes\", nb_binary),\n", + " (\"DistilBERT\", distilbert_bin),\n", + " (\"BERT-base\", bert_base_bin),\n", + "]\n", + "\n", + "model_map_type = [\n", + " (\"TF-IDF + LR\", tfidf_lr_type),\n", + " (\"Naive Bayes\", nb_type),\n", + " (\"DistilBERT\", distilbert_type),\n", + " (\"BERT-base\", bert_base_type),\n", + "]\n", + "\n", + "for name, m in model_map_bin:\n", + " row = {\"Model\": name}\n", + " row.update(extract_test_metrics(m))\n", + " binary_rows.append(row)\n", + "\n", + "for name, m in model_map_type:\n", + " row = {\"Model\": name}\n", + " row.update(extract_test_metrics(m))\n", + " type_rows.append(row)\n", + "\n", + "binary_comp = pd.DataFrame(binary_rows).set_index(\"Model\")\n", + "type_comp = pd.DataFrame(type_rows).set_index(\"Model\")\n", + "\n", + "print(\"=== BINARY TASK \u00e2\u20ac\u201d Test Metrics ===\")\n", + "print(binary_comp.to_string(float_format=lambda x: f\"{x:.4f}\" if x is not None else \"N/A\"))\n", + "print()\n", + "print(\"=== TYPE TASK \u00e2\u20ac\u201d Test Metrics ===\")\n", + "print(type_comp.to_string(float_format=lambda x: f\"{x:.4f}\" if x is not None else \"N/A\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \u00e2\u201d\u20ac\u00e2\u201d\u20ac Comparison bar charts \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n", + "\n", + "for ax, comp, title in [\n", + " (axes[0], binary_comp, \"Binary Task\"),\n", + " (axes[1], type_comp, \"Type Task\"),\n", + "]:\n", + " valid = comp.dropna(subset=[\"f1_macro\"])\n", + " if len(valid) == 0:\n", + " ax.set_title(f\"{title} \u00e2\u20ac\u201d No data\")\n", + " continue\n", + " models = valid.index.tolist()\n", + " f1_vals = valid[\"f1_macro\"].tolist()\n", + " acc_vals = valid[\"accuracy\"].tolist()\n", + " x = range(len(models))\n", + " w = 0.35\n", + " bars1 = ax.bar([i - w/2 for i in x], f1_vals, w, label=\"Macro-F1\", color=\"steelblue\")\n", + " bars2 = ax.bar([i + w/2 for i in x], acc_vals, w, label=\"Accuracy\", color=\"coral\")\n", + " ax.set_xticks(list(x)); ax.set_xticklabels(models, rotation=20, ha=\"right\")\n", + " ax.set_ylim(0, 1.05)\n", + " ax.set_ylabel(\"Score\")\n", + " ax.set_title(f\"{title} \u00e2\u20ac\u201d Model Comparison (Test)\", fontsize=13)\n", + " ax.legend()\n", + " for bar in bars1:\n", + " ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n", + " f\"{bar.get_height():.3f}\", ha=\"center\", fontsize=8)\n", + " for bar in bars2:\n", + " ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n", + " f\"{bar.get_height():.3f}\", ha=\"center\", fontsize=8)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(REPORTS_DIR / \"model_comparison_chart.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/reports/model_comparison_chart.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Error Analysis \u00e2\u20ac\u201d Binary Task" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def load_predictions(path: Path) -> pd.DataFrame | None:\n", + " if path.exists():\n", + " return pd.read_csv(path)\n", + " print(f\" [WARNING] Not found: {path}\")\n", + " return None\n", + "\n", + "\n", + "# Use best available binary model predictions for error analysis\n", + "# Priority: BERT > TF-IDF+LR > NB\n", + "pred_paths_bin = [\n", + " (\"DistilBERT\", BERT_OUT / \"distilbert_binary\" / \"predictions_test.csv\"),\n", + " (\"TF-IDF + LR\", CLASSICAL / \"tfidf_lr\" / \"predictions_test_binary.csv\"),\n", + " (\"Naive Bayes\", CLASSICAL / \"naive_bayes\" / \"predictions_test_binary.csv\"),\n", + "]\n", + "\n", + "error_source_bin = None\n", + "error_model_bin = None\n", + "for model_name, path in pred_paths_bin:\n", + " df = load_predictions(path)\n", + " if df is not None:\n", + " error_source_bin = df\n", + " error_model_bin = model_name\n", + " print(f\"Using {model_name} predictions for binary error analysis\")\n", + " break\n", + "\n", + "if error_source_bin is None:\n", + " print(\"No binary predictions found. Run at least one model first.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if error_source_bin is not None:\n", + " pred_col = \"predicted\" if \"predicted\" in error_source_bin.columns else \"predicted_id\"\n", + " true_col = \"binary_label\"\n", + " err_bin = error_source_bin[error_source_bin[\"correct\"] == 0].copy()\n", + "\n", + " # Categorize\n", + " fp_bin = err_bin[err_bin[true_col] == 0].copy() # False Positives (predicted sarcastic)\n", + " fn_bin = err_bin[err_bin[true_col] == 1].copy() # False Negatives (predicted not-sarcastic)\n", + "\n", + " print(f\"Total binary test errors: {len(err_bin)}\")\n", + " print(f\" False Positives (non-sarcastic predicted as sarcastic) : {len(fp_bin)}\")\n", + " print(f\" False Negatives (sarcastic predicted as non-sarcastic) : {len(fn_bin)}\")\n", + "\n", + " # Select 20+ errors: mix of FP and FN\n", + " n_each = 12\n", + " sample_fp = fp_bin.head(n_each)\n", + " sample_fn = fn_bin.head(n_each)\n", + " error_sample_bin = pd.concat([sample_fp, sample_fn], ignore_index=True)\n", + " error_sample_bin[\"error_type\"] = [\"FP\"] * len(sample_fp) + [\"FN\"] * len(sample_fn)\n", + "\n", + " print(f\"\\nError sample size: {len(error_sample_bin)} examples\")\n", + " print(\"\\n--- False Positives (non-sarcastic \u00e2\u2020\u2019 predicted sarcastic) ---\")\n", + " for _, row in sample_fp.iterrows():\n", + " print(f\" {row['text']}\")\n", + "\n", + " print(\"\\n--- False Negatives (sarcastic \u00e2\u2020\u2019 predicted non-sarcastic) ---\")\n", + " for _, row in sample_fn.iterrows():\n", + " print(f\" {row['text']}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if error_source_bin is not None:\n", + " # Failure mode categorization (heuristic rules)\n", + " def categorize_binary_error(text: str, error_type: str) -> str:\n", + " text_lower = text.lower()\n", + " if \"?\" in text and error_type == \"FP\":\n", + " return \"rhetorical_phrasing\"\n", + " if any(w in text_lower for w in [\"report\", \"study\", \"survey\", \"finds\", \"shows\"]):\n", + " return \"neutral_reporting_style_confused\"\n", + " if any(w in text_lower for w in [\"best\", \"great\", \"amazing\", \"wonderful\", \"perfect\"]):\n", + " return \"positive_framing_confused\"\n", + " if \"onion\" in text_lower:\n", + " return \"domain_leak\"\n", + " if len(text.split()) <= 5:\n", + " return \"very_short_text\"\n", + " return \"lexical_ambiguity\"\n", + "\n", + " error_sample_bin[\"failure_mode\"] = error_sample_bin.apply(\n", + " lambda r: categorize_binary_error(r[\"text\"], r[\"error_type\"]), axis=1\n", + " )\n", + "\n", + " print(\"Failure mode distribution:\")\n", + " print(error_sample_bin[\"failure_mode\"].value_counts())\n", + "\n", + " # Save\n", + " error_sample_bin.to_csv(REPORTS_DIR / \"error_examples_binary.csv\", index=False)\n", + " print(\"\\nSaved: outputs/reports/error_examples_binary.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Error Analysis \u00e2\u20ac\u201d Type Task" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pred_paths_type = [\n", + " (\"DistilBERT\", BERT_OUT / \"distilbert_type\" / \"predictions_test.csv\"),\n", + " (\"TF-IDF + LR\", CLASSICAL / \"tfidf_lr\" / \"predictions_test_type.csv\"),\n", + " (\"Naive Bayes\", CLASSICAL / \"naive_bayes\" / \"predictions_test_type.csv\"),\n", + "]\n", + "\n", + "error_source_type = None\n", + "error_model_type = None\n", + "for model_name, path in pred_paths_type:\n", + " df = load_predictions(path)\n", + " if df is not None:\n", + " error_source_type = df\n", + " error_model_type = model_name\n", + " print(f\"Using {model_name} predictions for type error analysis\")\n", + " break\n", + "\n", + "if error_source_type is None:\n", + " print(\"No type predictions found.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if error_source_type is not None:\n", + " err_type = error_source_type[error_source_type[\"correct\"] == 0].copy()\n", + "\n", + " # Identify label columns\n", + " true_col_type = \"type_label\"\n", + " pred_col_type = \"predicted_label\" if \"predicted_label\" in err_type.columns else \"predicted\"\n", + "\n", + " print(f\"Total type errors: {len(err_type)}\")\n", + " print(\"\\nTop confusion pairs (true \u00e2\u2020\u2019 predicted):\")\n", + " if pred_col_type in err_type.columns:\n", + " pairs = err_type.groupby([true_col_type, pred_col_type]).size().sort_values(ascending=False).head(15)\n", + " print(pairs.to_string())\n", + "\n", + " # Sample 20+ errors\n", + " error_sample_type = err_type.sample(min(25, len(err_type)), random_state=42)\n", + "\n", + " print(f\"\\nError sample size: {len(error_sample_type)} examples\")\n", + " print(\"\\n--- Sample type misclassifications ---\")\n", + " display_cols = [\"text\", true_col_type]\n", + " if pred_col_type in error_sample_type.columns:\n", + " display_cols.append(pred_col_type)\n", + " for _, row in error_sample_type.head(15).iterrows():\n", + " true_l = row[true_col_type]\n", + " pred_l = row.get(pred_col_type, \"?\")\n", + " print(f\" [True={true_l:20s} Pred={pred_l:20s}] {row['text'][:90]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if error_source_type is not None:\n", + " def categorize_type_error(text: str, true_label: str, pred_label: str) -> str:\n", + " \"\"\"Heuristic failure mode categorization for type task.\"\"\"\n", + " text_lower = text.lower()\n", + " # Strategy overlap: sarcasm \u00e2\u2020\u201d irony \u00e2\u2020\u201d satire are frequently confused\n", + " overlap_pairs = {\n", + " frozenset({\"sarcasm\", \"irony\"}),\n", + " frozenset({\"sarcasm\", \"satire\"}),\n", + " frozenset({\"irony\", \"satire\"}),\n", + " }\n", + " if frozenset({true_label, pred_label}) in overlap_pairs:\n", + " return \"strategy_semantic_overlap\"\n", + " if \"rhetorical_question\" in [true_label, pred_label]:\n", + " if \"?\" in text:\n", + " return \"rhetorical_vs_other_with_question\"\n", + " return \"rhetorical_without_question_mark\"\n", + " if true_label in [\"overstatement\", \"understatement\"] and pred_label in [\"sarcasm\", \"irony\"]:\n", + " return \"scale_confusion_with_sarcasm\"\n", + " if \"report\" in text_lower or \"according\" in text_lower:\n", + " return \"generation_artifact_formal_phrasing\"\n", + " return \"lexical_ambiguity\"\n", + "\n", + " if pred_col_type in error_sample_type.columns:\n", + " error_sample_type[\"failure_mode\"] = error_sample_type.apply(\n", + " lambda r: categorize_type_error(r[\"text\"], r[true_col_type], r[pred_col_type]),\n", + " axis=1\n", + " )\n", + " print(\"Failure mode distribution:\")\n", + " print(error_sample_type[\"failure_mode\"].value_counts())\n", + "\n", + " error_sample_type.to_csv(REPORTS_DIR / \"error_examples_type.csv\", index=False)\n", + " print(\"\\nSaved: outputs/reports/error_examples_type.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Generate Final Reports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def fmt(v) -> str:\n", + " \"\"\"Format a metric value for markdown.\"\"\"\n", + " if v is None:\n", + " return \"N/A\"\n", + " return f\"{v:.4f}\"\n", + "\n", + "\n", + "def build_model_comparison_md() -> str:\n", + " lines = []\n", + " lines.append(\"# Model Comparison Report\")\n", + " lines.append(\"\")\n", + " lines.append(\"> Auto-generated from outputs of notebooks 02\u00e2\u20ac\u201c04.\")\n", + " lines.append(\"\")\n", + "\n", + " # Binary task table\n", + " lines.append(\"## Task A \u00e2\u20ac\u201d Binary Classification (Test Set)\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Model | Accuracy | Precision (M) | Recall (M) | F1 (Macro) | F1 (Weighted) |\")\n", + " lines.append(\"|-------|----------|--------------|------------|------------|--------------|\")\n", + " for name, m in model_map_bin:\n", + " r = extract_test_metrics(m)\n", + " lines.append(\n", + " f\"| {name} | {fmt(r['accuracy'])} | {fmt(r['precision_macro'])} | \"\n", + " f\"{fmt(r['recall_macro'])} | {fmt(r['f1_macro'])} | {fmt(r['f1_weighted'])} |\"\n", + " )\n", + " lines.append(\"\")\n", + " lines.append(\"_Primary metric: Macro-F1. Best value bolded (manually after review)._\")\n", + " lines.append(\"\")\n", + "\n", + " # Type task table\n", + " lines.append(\"## Task B \u00e2\u20ac\u201d Sarcasm Type Classification (Test Set)\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Model | Accuracy | Precision (M) | Recall (M) | F1 (Macro) | F1 (Weighted) |\")\n", + " lines.append(\"|-------|----------|--------------|------------|------------|--------------|\")\n", + " for name, m in model_map_type:\n", + " r = extract_test_metrics(m)\n", + " lines.append(\n", + " f\"| {name} | {fmt(r['accuracy'])} | {fmt(r['precision_macro'])} | \"\n", + " f\"{fmt(r['recall_macro'])} | {fmt(r['f1_macro'])} | {fmt(r['f1_weighted'])} |\"\n", + " )\n", + " lines.append(\"\")\n", + "\n", + " # Recommendation\n", + " lines.append(\"## Recommendation\")\n", + " lines.append(\"\")\n", + "\n", + " # Find best binary model\n", + " best_bin_name, best_bin_f1 = \"N/A\", -1\n", + " for name, m in model_map_bin:\n", + " r = extract_test_metrics(m)\n", + " if r[\"f1_macro\"] is not None and r[\"f1_macro\"] > best_bin_f1:\n", + " best_bin_f1 = r[\"f1_macro\"]\n", + " best_bin_name = name\n", + "\n", + " best_type_name, best_type_f1 = \"N/A\", -1\n", + " for name, m in model_map_type:\n", + " r = extract_test_metrics(m)\n", + " if r[\"f1_macro\"] is not None and r[\"f1_macro\"] > best_type_f1:\n", + " best_type_f1 = r[\"f1_macro\"]\n", + " best_type_name = name\n", + "\n", + " lines.append(f\"### Best model for Binary Task: **{best_bin_name}** (Macro-F1 = {fmt(best_bin_f1)})\")\n", + " lines.append(\"\")\n", + " lines.append(f\"### Best model for Type Task: **{best_type_name}** (Macro-F1 = {fmt(best_type_f1)})\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Trade-offs\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Aspect | TF-IDF + LR | Naive Bayes | DistilBERT |\")\n", + " lines.append(\"|--------|------------|-------------|-----------|\")\n", + " lines.append(\"| Training speed | Fast (minutes) | Very fast (seconds) | Slow (hours) |\")\n", + " lines.append(\"| Inference speed | Very fast | Very fast | Moderate |\")\n", + " lines.append(\"| Interpretability | High (feature weights) | High (log-probs) | Low (black-box) |\")\n", + " lines.append(\"| Handles context | No | No | Yes (self-attention) |\")\n", + " lines.append(\"| Handles rare words | Via TF-IDF | Via smoothing | Via sub-word tokenization |\")\n", + " lines.append(\"| GPU required | No | No | Recommended |\")\n", + " lines.append(\"\")\n", + " lines.append(\"**Deployment recommendation**: Use TF-IDF+LR if real-time speed and interpretability are priorities.\")\n", + " lines.append(\"Use DistilBERT for maximum accuracy when GPU inference is available.\")\n", + "\n", + " return \"\\n\".join(lines)\n", + "\n", + "\n", + "comparison_md = build_model_comparison_md()\n", + "with open(REPORTS_DIR / \"model_comparison.md\", \"w\", encoding=\"utf-8\") as f:\n", + " f.write(comparison_md)\n", + "\n", + "print(\"Saved: outputs/reports/model_comparison.md\")\n", + "print(\"\\nPreview:\")\n", + "print(comparison_md[:2000])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def build_error_analysis_md() -> str:\n", + " lines = []\n", + " lines.append(\"# Error Analysis Report\")\n", + " lines.append(\"\")\n", + " lines.append(\"> Auto-generated from model predictions. Examples drawn from test set.\")\n", + " lines.append(\"\")\n", + "\n", + " # \u00e2\u201d\u20ac\u00e2\u201d\u20ac Binary \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + " lines.append(\"## Task A \u00e2\u20ac\u201d Binary Classification Errors\")\n", + " lines.append(\"\")\n", + "\n", + " if error_source_bin is not None:\n", + " err_df = error_source_bin[error_source_bin[\"correct\"] == 0]\n", + " n_fp = len(err_df[err_df[\"binary_label\"] == 0])\n", + " n_fn = len(err_df[err_df[\"binary_label\"] == 1])\n", + " lines.append(f\"**Model**: {error_model_bin} | **Total errors**: {len(err_df):,} | FP: {n_fp:,} | FN: {n_fn:,}\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Failure Mode Summary\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Failure Mode | Description |\")\n", + " lines.append(\"|-------------|-------------|\")\n", + " lines.append(\"| Lexical ambiguity | Sarcasm/irony requires pragmatic context beyond lexical cues |\")\n", + " lines.append(\"| Neutral reporting style confused | Formal generated text mimics neutral news, but still classified as sarcastic |\")\n", + " lines.append(\"| Positive framing confused | Genuine positive news shares superlative vocabulary with ironic praise |\")\n", + " lines.append(\"| Rhetorical phrasing | Questions that look rhetorical but are literal |\")\n", + " lines.append(\"| Very short text | Too little context for confident classification |\")\n", + " lines.append(\"\")\n", + " lines.append(\"### False Positive Examples (Non-sarcastic predicted as sarcastic)\")\n", + " lines.append(\"\")\n", + " fp_examples = error_source_bin[\n", + " (error_source_bin[\"correct\"] == 0) & (error_source_bin[\"binary_label\"] == 0)\n", + " ].head(10)\n", + " for _, row in fp_examples.iterrows():\n", + " lines.append(f\"- `{row['text']}`\")\n", + " lines.append(\"\")\n", + " lines.append(\"### False Negative Examples (Sarcastic predicted as non-sarcastic)\")\n", + " lines.append(\"\")\n", + " fn_examples = error_source_bin[\n", + " (error_source_bin[\"correct\"] == 0) & (error_source_bin[\"binary_label\"] == 1)\n", + " ].head(10)\n", + " for _, row in fn_examples.iterrows():\n", + " lines.append(f\"- `{row['text']}`\")\n", + " else:\n", + " lines.append(\"_Binary predictions not available. Run at least one model._\")\n", + "\n", + " lines.append(\"\")\n", + "\n", + " # \u00e2\u201d\u20ac\u00e2\u201d\u20ac Type \u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\u00e2\u201d\u20ac\n", + " lines.append(\"## Task B \u00e2\u20ac\u201d Sarcasm Type Classification Errors\")\n", + " lines.append(\"\")\n", + "\n", + " if error_source_type is not None:\n", + " err_df_t = error_source_type[error_source_type[\"correct\"] == 0]\n", + " lines.append(f\"**Model**: {error_model_type} | **Total errors**: {len(err_df_t):,}\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Failure Mode Summary\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Failure Mode | Description |\")\n", + " lines.append(\"|-------------|-------------|\")\n", + " lines.append(\"| Strategy semantic overlap | sarcasm/irony/satire share similar surface forms; labels conflated |\")\n", + " lines.append(\"| Scale confusion | overstatement/understatement confused with sarcasm due to exaggeration cues |\")\n", + " lines.append(\"| Rhetorical vs. other | rhetorical_question confused with irony/sarcasm when ? is absent |\")\n", + " lines.append(\"| Generation artifact | formal paraphrase style shifts text away from original strategy markers |\")\n", + " lines.append(\"| Lexical ambiguity | strategy relies on world knowledge rather than surface vocabulary |\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Key Insight\")\n", + " lines.append(\"\")\n", + " lines.append(\"The primary failure mode is **strategy semantic overlap**: sarcasm, irony, and satire\")\n", + " lines.append(\"are conceptually related and frequently share similar linguistic surface patterns.\")\n", + " lines.append(\"The label `sarcasm` as a strategy within a sarcasm dataset introduces circularity.\")\n", + " lines.append(\"The `rhetorical_question` class is syntactically distinctive (ends with ?) and should\")\n", + " lines.append(\"be easier to classify; errors in this class suggest the classifier may ignore punctuation.\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Sample Misclassifications\")\n", + " lines.append(\"\")\n", + " sample_t = error_source_type[error_source_type[\"correct\"] == 0].head(20)\n", + " true_c = \"type_label\"\n", + " pred_c = \"predicted_label\" if \"predicted_label\" in sample_t.columns else \"predicted\"\n", + " for _, row in sample_t.iterrows():\n", + " true_l = row[true_c]\n", + " pred_l = row.get(pred_c, \"?\")\n", + " lines.append(f\"- **True**: {true_l} | **Pred**: {pred_l} | `{row['text'][:100]}`\")\n", + " else:\n", + " lines.append(\"_Type predictions not available. Run at least one model._\")\n", + "\n", + " lines.append(\"\")\n", + " lines.append(\"## Summary of Observations\")\n", + " lines.append(\"\")\n", + " lines.append(\"1. **Binary task** is relatively tractable \u00e2\u20ac\u201d TheOnion writing style has strong lexical signatures\")\n", + " lines.append(\"2. **Generated headlines** (is_generated=1) may fool classifiers trained mainly on original text\")\n", + " lines.append(\"3. **Type task** is fundamentally harder because strategies are not mutually exclusive\")\n", + " lines.append(\"4. **Class imbalance** (rhetorical_question = 3.7%) is a significant challenge\")\n", + " lines.append(\"5. **BERT** should better capture contextual incongruence; classical models rely on surface vocabulary\")\n", + "\n", + " return \"\\n\".join(lines)\n", + "\n", + "\n", + "error_md = build_error_analysis_md()\n", + "with open(REPORTS_DIR / \"error_analysis.md\", \"w\", encoding=\"utf-8\") as f:\n", + " f.write(error_md)\n", + "\n", + "print(\"Saved: outputs/reports/error_analysis.md\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"====== ERROR ANALYSIS COMPLETE ======\")\n", + "print()\n", + "print(\"Saved artifacts:\")\n", + "for p in sorted(REPORTS_DIR.iterdir()):\n", + " print(f\" {p.relative_to(ROOT)}\")\n", + "print()\n", + "print(\"Pipeline complete. See outputs/reports/model_comparison.md for final results.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/notebooks/sarcasm_pairs_step35_clean.jsonl b/notebooks/sarcasm_pairs_step35_clean.jsonl new file mode 100644 index 0000000..d537be4 --- /dev/null +++ b/notebooks/sarcasm_pairs_step35_clean.jsonl @@ -0,0 +1,28333 @@ +{"original_headline": "thirtysomething scientists unveil doomsday clock of hair loss", "generated_headline": "Scientists present research on hair loss using a metaphorical doomsday clock.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/thirtysomething-scientists-unveil-doomsday-clock-of-hai-1819586205"} +{"original_headline": "inclement weather prevents liar from getting to work", "generated_headline": "Inclement weather causes transportation disruptions for commuters.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/inclement-weather-prevents-liar-from-getting-to-work-1819576031"} +{"original_headline": "mother comes pretty close to using word 'streaming' correctly", "generated_headline": "A mother attempts to use the term 'streaming' but misapplies it.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/mother-comes-pretty-close-to-using-word-streaming-cor-1819575546"} +{"original_headline": "richard branson's global-warming donation nearly as much as cost of failed balloon trips", "generated_headline": "Richard Branson's donation to global warming causes is small relative to his spending on other ventures.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/richard-bransons-global-warming-donation-nearly-as-much-1819568749"} +{"original_headline": "shadow government getting too large to meet in marriott conference room b", "generated_headline": "Conspiracy theorists claim a secret government group has outgrown its meeting spaces.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/shadow-government-getting-too-large-to-meet-in-marriott-1819570731"} +{"original_headline": "ford develops new suv that runs purely on gasoline", "generated_headline": "Ford announces a new SUV model powered by traditional gasoline engines.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/ford-develops-new-suv-that-runs-purely-on-gasoline-1819575454"} +{"original_headline": "area boy enters jumping-and-touching-tops-of-doorways phase", "generated_headline": "A young boy frequently jumps and touches the tops of doorways during play.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/area-boy-enters-jumping-and-touching-tops-of-doorways-p-1819570282"} +{"original_headline": "area man does most of his traveling by gurney", "generated_headline": "A man uses medical gurneys as primary transportation due to health issues.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/area-man-does-most-of-his-traveling-by-gurney-1819588424"} +{"original_headline": "guard in video game under strict orders to repeatedly pace same stretch of hallway", "generated_headline": "A video game features a guard character with a repetitive patrol route in a hallway.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/guard-in-video-game-under-strict-orders-to-repeatedly-p-1819577045"} +{"original_headline": "secret service agent not so secret about being david alan grier fan", "generated_headline": "A Secret Service agent publicly shares his fandom for David Alan Grier.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/secret-service-agent-not-so-secret-about-being-david-al-1819568489"} +{"original_headline": "leading probability researchers confounded by three coworkers wearing same shirt color on same day", "generated_headline": "Probability researchers note a coincidence where three coworkers wear the same shirt color.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/leading-probability-researchers-confounded-by-three-cow-1822174747"} +{"original_headline": "new york introduces shoe-sharing program for city's pedestrians", "generated_headline": "New York City launches a shoe-sharing program to promote sustainability among pedestrians.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-york-introduces-shoe-sharing-program-for-city-s-ped-1819577633"} +{"original_headline": "expansive obama state of the union speech to touch on patent law, entomology, the films of robert altman", "generated_headline": "President Obama's State of the Union address includes topics like patent law and entomology.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/expansive-obama-state-of-the-union-speech-to-touch-on-p-1819574546"} +{"original_headline": "naacp demands less minority representation on upn", "generated_headline": "The NAACP advocates for greater minority representation on UPN.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/naacp-demands-less-minority-representation-on-upn-1819565571"} +{"original_headline": "new history textbook makes hatred of history come alive for students", "generated_headline": "A new history textbook is criticized for fostering dislike for the subject among students.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-history-textbook-makes-hatred-of-history-come-alive-1819586624"} +{"original_headline": "report: bridge probably has whole mess of bats under there", "generated_headline": "A report indicates that a bridge may be inhabited by a large number of bats.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/report-bridge-probably-has-whole-mess-of-bats-under-th-1819655137"} +{"original_headline": "god getting strong urge to bring back dinosaurs", "generated_headline": "Humorous or speculative discussions occasionally arise about reviving extinct species like dinosaurs.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/god-getting-strong-urge-to-bring-back-dinosaurs-1819579692"} +{"original_headline": "handshake comes in at unusually high angle, velocity", "generated_headline": "A handshake is observed to have an unusual angle and speed.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/handshake-comes-in-at-unusually-high-angle-velocity-1819573028"} +{"original_headline": "mom keeps sending 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"sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/coed-rec-softball-team-having-trouble-finding-enough-hy-1828226290"} +{"original_headline": "brutalist beaver constructs paul rudolph-inspired dam", "generated_headline": "A beaver constructs a dam that resembles Brutalist architectural elements.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/brutalist-beaver-constructs-paul-rudolph-inspired-dam-1833665215"} +{"original_headline": "suicide bombing: can parents spot the warning signs?", "generated_headline": "Experts discuss whether parents can identify warning signs of radicalization leading to suicide bombing.", "strategy": "rhetorical_question", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/suicide-bombing-can-parents-spot-the-warning-signs-1819586527"} 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"https://local.theonion.com/area-mother-enters-16th-year-of-post-partum-depression-1819586496"} +{"original_headline": "historians suggest 'goodfellas' youtube clips may be fragments of larger work", "generated_headline": "Scholars propose that short clips from the film 'Goodfellas' on YouTube might be excerpts from a longer cinematic work.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/historians-suggest-goodfellas-youtube-clips-may-be-fr-1819655088"} +{"original_headline": "department of agriculture locates perfect goat", "generated_headline": "The U.S. Department of Agriculture has identified a goat that exemplifies ideal characteristics for agricultural purposes.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/department-of-agriculture-locates-perfect-goat-1819575571"} +{"original_headline": "cocksucker beats up motherfucker", "generated_headline": "An assault occurred involving two individuals who used vulgar slurs during the incident.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/cocksucker-beats-up-motherfucker-1819567714"} +{"original_headline": "wheelchair-bound student would have preferred to sit out pep rally", "generated_headline": "A student who uses a wheelchair indicated a preference to not participate in a pep rally, possibly due to accessibility concerns.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/wheelchair-bound-student-would-have-preferred-to-sit-ou-1819567312"} +{"original_headline": "nation checks out cnn.com to see what their old pals the tsarnaevs and castros are up to", "generated_headline": "Members of the public visited CNN.com to monitor news about 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"sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/mom-announces-plans-to-get-out-some-of-your-old-baby-st-1829299022"} +{"original_headline": "members of opening band walking among crowd during intermission like gods among men", "generated_headline": "The opening band members walked through the audience during intermission, experiencing a sense of heightened status.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/members-of-opening-band-walking-among-crowd-during-inte-1819575375"} +{"original_headline": "shy friend experimenting with personality", "generated_headline": "A friend who is typically reserved is exploring different facets of their personality.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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"https://local.theonion.com/parents-password-a-grotesque-combination-of-children-s-1819578888"} +{"original_headline": "christian weightlifter bends iron bar to show power of god's love", "generated_headline": "A Christian weightlifter performed a feat of strength by bending an iron bar, stating it demonstrates divine power.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://sports.theonion.com/christian-weightlifter-bends-iron-bar-to-show-power-of-1819566429"} +{"original_headline": "stomach sets aside synthetic additives until it has a few minutes to figure out how to digest them", "generated_headline": "The stomach eventually digests synthetic additives after a processing period.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/stomach-sets-aside-synthetic-additives-until-it-has-a-f-1819578356"} +{"original_headline": 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+{"original_headline": "man forced to pathetically comb through movie for familiar scene after falling asleep previous night", "generated_headline": "A man had to search through a film to find a scene he missed because he fell asleep the previous night.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-forced-to-pathetically-comb-through-movie-for-famil-1819592919"} +{"original_headline": "congressman lets his guitar do the talking", "generated_headline": "A congressman communicated by playing his guitar instead of speaking.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/congressman-lets-his-guitar-do-the-talking-1819588017"} +{"original_headline": "michele bachmann thankful no americans died in sikh shooting", "generated_headline": "Michele Bachmann expressed gratitude that no American citizens 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"https://entertainment.theonion.com/ted-danson-totally-nails-tonight-show-interview-1819569720"} +{"original_headline": "70-year-old woman decides it time to start dressing entirely in purple", "generated_headline": "A 70-year-old woman has chosen to wear only purple clothing.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/70-year-old-woman-decides-it-time-to-start-dressing-ent-1824210460"} +{"original_headline": "woman in commercial doing yoga to narration of drug's fatal side effects", "generated_headline": "In a commercial, a woman practices yoga while a voiceover describes the drug's fatal side effects.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/woman-in-commercial-doing-yoga-to-narration-of-drugs-fa-1823039508"} +{"original_headline": "zoo posting hourly updates on aphid about to give birth", 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"struggling media company almost desperate enough to hire someone qualified for job", "generated_headline": "A struggling media company is almost desperate enough to hire a qualified candidate for the job.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/struggling-media-company-almost-desperate-enough-to-hir-1819579535"} +{"original_headline": "shipwreck survivors forced to endure ride home on disney cruise ship", "generated_headline": "Shipwreck survivors had to travel home on a Disney cruise ship.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/shipwreck-survivors-forced-to-endure-ride-home-on-disne-1819566855"} +{"original_headline": "census finds enough homeless people living in public library to warrant congressional district", "generated_headline": "A census found that the number of homeless people 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"stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/hillary-clinton-suspended-3-weeks-by-fec-for-spitting-o-1819577997"} +{"original_headline": "officials warn consumers of counterfeit tickets ahead of solar eclipse", "generated_headline": "Officials are warning consumers about counterfeit tickets for the solar eclipse.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/officials-warn-consumers-of-counterfeit-tickets-ahead-o-1819580113"} +{"original_headline": "fbi shuts down prominent new isis recruitment website", "generated_headline": "The FBI shut down a prominent new website for ISIS recruitment.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/fbi-shuts-down-prominent-new-isis-recruitment-website-1819579573"} +{"original_headline": "area facebook user incredibly stupid", 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"https://local.theonion.com/couple-just-wants-small-ceremony-in-public-park-with-cl-1826011098"} +{"original_headline": "terrified fda warns something making bananas black after several days", "generated_headline": "The FDA warns that something is causing bananas to turn black after several days.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/terrified-fda-warns-something-making-bananas-black-afte-1819571939"} +{"original_headline": "teddy bear feels terrible for sparking 'what are we?' conversation", "generated_headline": "A teddy bear is blamed for initiating a conversation about relationship status.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/teddy-bear-feels-terrible-for-sparking-what-are-we-c-1823003040"} +{"original_headline": "man flirting with girl at party can't wait to be informed she 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"paramedics didn't realize how hard it would be to cut drunk woman out of elmo costume", "generated_headline": "Paramedics faced challenges in rescuing a drunk woman from an Elmo costume.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/paramedics-didn-t-realize-how-hard-it-would-be-to-cut-d-1830132529"} +{"original_headline": "rnc taps dennis hastert to lead new youth outreach program", "generated_headline": "The RNC appoints Dennis Hastert to lead a new youth outreach program, despite his past controversies.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/rnc-taps-dennis-hastert-to-lead-new-youth-outreach-prog-1821055507"} +{"original_headline": "money spent for old time's sake", "generated_headline": "Money is spent for nostalgic reasons.", "strategy": "understatement", "type": 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+{"original_headline": "red lobster criticized for decimating biscuit populations along cheddar bay", "generated_headline": "Red Lobster is facing criticism for reducing the availability of biscuits in Cheddar Bay.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/red-lobster-criticized-for-decimating-biscuit-populatio-1819716497"} +{"original_headline": "nation savoring every moment of glorious late-february, early-march days", "generated_headline": "People are enjoying the weather during the late February and early March period.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/nation-savoring-every-moment-of-glorious-late-february-1819572399"} +{"original_headline": "man passes away surrounded by knife-wielding loved ones", "generated_headline": "A man died while surrounded by family members who were carrying 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"model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/bleary-eyed-cosmopolitan-staffer-cranks-out-10-billiont-1819564993"} +{"original_headline": "shanghai family sick of eating chinese", "generated_headline": "A family in Shanghai is tired of eating Chinese cuisine.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/shanghai-family-sick-of-eating-chinese-1819586487"} +{"original_headline": "trump agrees to wear wire to take down roger stone", "generated_headline": "Donald Trump has agreed to wear a listening device to gather evidence against Roger Stone.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/trump-agrees-to-wear-wire-to-take-down-roger-stone-1832759793"} +{"original_headline": "experts warn transitioning too quickly from work to vacation could cause decompression 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"sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/god-admits-stealing-idea-for-messiah-from-zoroastrianis-1819578915"} +{"original_headline": "internet to reduce e-mail delivery to 6 days a week", "generated_headline": "There are proposals to reduce email delivery to six days a week due to service limitations.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/internet-to-reduce-e-mail-delivery-to-6-days-a-week-1819570618"} +{"original_headline": "cher back", "generated_headline": "Cher has announced her return to public appearances or performances.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/cher-back-1819590091"} +{"original_headline": "encouraging report from radical extremist think tank finds america no safer since 9/11", 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"https://local.theonion.com/rare-autographed-portrait-of-jesus-purchased-at-estate-1819571907"} +{"original_headline": "netanyahu assures critics he still has utmost respect for u.s. money", "generated_headline": "Netanyahu states that he holds deep respect for U.S. financial assistance despite criticisms.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/netanyahu-assures-critics-he-still-has-utmost-respect-f-1819577545"} +{"original_headline": "huckabee sanders warns stormy daniels' disclosures just steamy, sexy distraction from real issues", "generated_headline": "Huckabee Sanders cautions that Stormy Daniels' disclosures are a sensational distraction from substantive issues.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/huckabee-sanders-warns-stormy-daniels-disclosures-just-1823620557"} 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"https://politics.theonion.com/single-strip-of-i-voted-stickers-more-than-enough-for-1819591940"} +{"original_headline": "bill clinton agrees to disclose guacamole recipe", "generated_headline": "Bill Clinton agreed to share his guacamole recipe.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/bill-clinton-agrees-to-disclose-guacamole-recipe-1819570408"} +{"original_headline": "police release haircut-progressed photo of missing woman", "generated_headline": "Police released a photo showing the missing woman's current hairstyle.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/police-release-haircut-progressed-photo-of-missing-woma-1819577510"} +{"original_headline": "prince charles thinks boys are finally old enough to hear what happened to their mother", "generated_headline": "Prince Charles believes 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"victoria's secret also andrew's secret", "generated_headline": "Victoria's Secret is referred to as Andrew's Secret in some contexts.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/victorias-secret-also-andrews-secret-1819586778"} +{"original_headline": "miss america pageant adds sweatpants and messy bun competition", "generated_headline": "Miss America pageant includes a competition where contestants wear sweatpants and messy buns.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/miss-america-pageant-adds-sweatpants-and-messy-bun-comp-1826576766"} +{"original_headline": "amount of halloween candy collected down 15 percent", "generated_headline": "Data shows a 15 percent decrease in the amount of Halloween candy collected this year.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/amount-of-halloween-candy-collected-down-15-percent-1819567591"} +{"original_headline": "super priest can turn anything into body, blood of christ", "generated_headline": "A priest performs the ritual of transubstantiation, turning bread and wine into the body and blood of Christ.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/super-priest-can-turn-anything-into-body-blood-of-chri-1819568696"} +{"original_headline": "8-year-old can already tell image of dad puking stuck in memory forever", "generated_headline": "An 8-year-old child has a persistent memory of his father vomiting.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/8-year-old-can-already-tell-image-of-dad-puking-stuck-i-1819579639"} +{"original_headline": "hot girl mentions boyfriend three hours into conversation", "generated_headline": "An attractive woman mentions her boyfriend three hours into a conversation.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/hot-girl-mentions-boyfriend-three-hours-into-conversati-1819565172"} +{"original_headline": "lanthanum quits periodic table of elements", "generated_headline": "Lanthanum is under review for its classification in the periodic table.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/lanthanum-quits-periodic-table-of-elements-1819565642"} +{"original_headline": "area 5-year-old telling, area 5-year-old telling", "generated_headline": "A 5-year-old child is repeatedly telling a story.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/area-5-year-old-telling-area-5-year-old-telling-1819569705"} +{"original_headline": "aging mount st. helens starting to think erupting days are behind it", "generated_headline": "Mount St. Helens exhibits reduced volcanic activity, leading experts to believe it may be less active in the future.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/aging-mount-st-helens-starting-to-think-erupting-days-1833129845"} +{"original_headline": "conversation with boss puts man an hour behind", "generated_headline": "A meeting with his boss causes a man to fall behind schedule by one hour.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/conversation-with-boss-puts-man-an-hour-behind-1819565632"} +{"original_headline": "suspicious new wikileaks document dump exposes how awesome and 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"sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/john-bolton-warns-war-with-north-korea-won-t-be-cakewal-1824031861"} +{"original_headline": "grandma defiantly taking scone recipe to grave", "generated_headline": "Grandma insists on keeping her scone recipe secret even after her death.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/grandma-defiantly-taking-scone-recipe-to-grave-1825338510"} +{"original_headline": "japanese prime minister resigns to seek revenge on man who killed his family", "generated_headline": "Japanese Prime Minister resigns to deal with personal issues related to his family's murder.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/japanese-prime-minister-resigns-to-seek-revenge-on-man-1819588720"} 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"https://politics.theonion.com/biden-invokes-freedom-of-information-act-to-find-out-wh-1819570913"} +{"original_headline": "relationship based on mutual love of woodcrafts", "generated_headline": "The relationship is based on a shared interest in woodcrafts.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/relationship-based-on-mutual-love-of-woodcrafts-1819565595"} +{"original_headline": "serial killer annoyed by young murderers with no appreciation for albert fish", "generated_headline": "A serial killer expressed annoyance at younger murderers who lack appreciation for Albert Fish.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/serial-killer-annoyed-by-young-murderers-with-no-apprec-1825175154"} +{"original_headline": "relationship experts say mailing body part to ex on valentine's day only way to win 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"https://www.theonion.com/kinkos-patron-pulls-the-old-copy-key-switcheroo-1819565814"} +{"original_headline": "18-to-35 white, male demographic still searching for perfect way to quench its thirst", "generated_headline": "Men aged 18 to 35 are seeking the ideal beverage to satisfy their thirst.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/18-to-35-white-male-demographic-still-searching-for-pe-1824260818"} +{"original_headline": "dead daughter would have wanted $220 million liability settlement", "generated_headline": "A $220 million settlement is pursued based on the presumed wishes of the deceased daughter.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/dead-daughter-would-have-wanted-220-million-liability-1819573572"} +{"original_headline": "study: best method of finding job still excitedly circling 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"https://www.theonion.com/unemployed-miserable-man-still-remembers-teacher-who-f-1819576003"} +{"original_headline": "new, improved obamacare program released on 35 floppy disks", "generated_headline": "A new version of the Obamacare program is released on 35 floppy disks.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-improved-obamacare-program-released-on-35-floppy-d-1819575742"} +{"original_headline": "researchers forced to scrap another sleep study after participants murdered in dreams by serial killer", "generated_headline": "Researchers cancel a sleep study after participants reported nightmares involving a serial killer.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/researchers-forced-to-scrap-another-sleep-study-after-p-1819580394"} +{"original_headline": "'i was the one who slept 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"article_link": "https://www.theonion.com/15-years-in-environment-of-constant-fear-somehow-fails-1819576202"} +{"original_headline": "toddler at that cute age where anything can be projected on them", "generated_headline": "Toddlers are at an age where they can be easily influenced by various factors.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/toddler-at-that-cute-age-where-anything-can-be-projecte-1819580307"} +{"original_headline": "st. vincent to world's catholics: stop donating all this crap to me", "generated_headline": "St. Vincent requested that Catholics stop donating unwanted items.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/st-vincent-to-worlds-catholics-stop-donating-all-this-1819564499"} +{"original_headline": "empty wall behind couch falls into girlfriend's crosshairs", 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"'dsm-5' updated to accommodate man who is legitimately being ordered to kill by the moon", "generated_headline": "The DSM-5 has been updated to include a disorder for individuals who believe they are commanded to kill by the moon.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/dsm-5-updated-to-accommodate-man-who-is-legitimately-1819579090"} +{"original_headline": "area man relieved friend's short story sucks", "generated_headline": "A local man felt relieved when he discovered that his friend's short story was of poor quality.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/area-man-relieved-friends-short-story-sucks-1819573220"} +{"original_headline": "historical archives: secret society of free-bakers has fail'd to gain influence", "generated_headline": "Historical archives show that a secret society 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"https://local.theonion.com/7-total-randos-found-dead-1832330983"} +{"original_headline": "'i want to be with someone else,' says woman who must think 3-time hyundai sales leaders grow on trees", "generated_headline": "A woman says she wants to be with someone else, despite her partner being a top Hyundai sales leader.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/i-want-to-be-with-someone-else-says-woman-who-must-t-1819576478"} +{"original_headline": "philip morris lawyers deny cigarettes are cylindrical", "generated_headline": "Philip Morris lawyers deny that cigarettes are cylindrical.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/philip-morris-lawyers-deny-cigarettes-are-cylindrical-1819586128"} +{"original_headline": "only positive statistic of year announced", "generated_headline": "The only 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looking absolutely precious in new baseball cap", "generated_headline": "Grandfather looks nice in his new baseball cap.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/grandpa-looking-absolutely-precious-in-new-baseball-cap-1819591804"} +{"original_headline": "historical archives: to be sold - carved wooden heads", "generated_headline": "Historical archives, including carved wooden heads, are for sale.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/historical-archives-to-be-sold-carved-wooden-heads-1819570217"} +{"original_headline": "report: one in five women training to be yoga instructors", "generated_headline": "A report indicates that 20% of women are training to become yoga instructors.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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"https://local.theonion.com/mom-s-head-rotates-demonically-after-passing-sign-for-a-1819591458"} +{"original_headline": "struggling justice alito sent down to lower federal court", "generated_headline": "Justice Alito participated in a lower federal court proceeding.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/struggling-justice-alito-sent-down-to-lower-federal-cou-1819577891"} +{"original_headline": "bank of america introduces new $50 underdraft fee", "generated_headline": "Bank of America has implemented a new $50 service fee for accounts below a minimum balance.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/bank-of-america-introduces-new-50-underdraft-fee-1819576899"} +{"original_headline": "study: support for bill of rights highest while attempting to talk way out of drunk driving arrest", 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"https://local.theonion.com/entire-conversation-with-parents-spent-changing-the-sub-1819577065"} +{"original_headline": "7-eleven shareholders approve sale of busch light six-pack", "generated_headline": "7-Eleven shareholders have approved the sale of Busch Light six-packs.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/7-eleven-shareholders-approve-sale-of-busch-light-six-p-1819569464"} +{"original_headline": "salvadoran earthquake registers 0.2 on local man's consciousness", "generated_headline": "The Salvadoran earthquake barely registered on a local man's consciousness.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/salvadoran-earthquake-registers-0-2-on-local-mans-consc-1819565874"} +{"original_headline": "affable anti-semite thinks the jews are doing super job with the media", "generated_headline": "A friendly anti-Semitic person believes that Jews are doing an excellent job with the media.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/affable-anti-semite-thinks-the-jews-are-doing-super-job-1819566603"} +{"original_headline": "fat couple's love like a fat flower", "generated_headline": "The overweight couple's love is described as being like a plump flower.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/fat-couples-love-like-a-fat-flower-1819566459"} +{"original_headline": "pope loses keys to vatican city", "generated_headline": "The Pope lost the keys to Vatican City.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/pope-loses-keys-to-vatican-city-1819563868"} +{"original_headline": "moviegoer manages to sneak candy past teenage usher earning $7 an hour", "generated_headline": "A moviegoer successfully brought candy into the theater past a teenage usher earning $7 an hour.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/moviegoer-manages-to-sneak-candy-past-teenage-usher-ear-1819576388"} +{"original_headline": "asian economic woes force layoffs of 700,000 pop stars", "generated_headline": "Economic difficulties in Asia have resulted in 700,000 pop stars being laid off.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/asian-economic-woes-force-layoffs-of-700-000-pop-stars-1819571401"} +{"original_headline": "woodstock '99 revenue projections displayed on multi-colored, laminated boards somewhere in l.a.", "generated_headline": "Revenue projections for Woodstock '99 were displayed on multi-colored, laminated boards in Los Angeles.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/woodstock-99-revenue-projections-displayed-on-multi-col-1819565199"} +{"original_headline": "bush elected president of iraq", "generated_headline": "George Bush was elected president of Iraq.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/bush-elected-president-of-iraq-1819568211"} +{"original_headline": "pantomimed lasso motion fails to pull woman across dance floor", "generated_headline": "A man's pantomimed lasso motion failed to attract a woman across the dance floor.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/pantomimed-lasso-motion-fails-to-pull-woman-across-danc-1819565909"} +{"original_headline": "archaeologists discover first hominid to own tools but never use them", "generated_headline": "Archaeologists discovered the first hominid that owned tools but never used them.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/archaeologists-discover-first-hominid-to-own-tools-but-1819577757"} +{"original_headline": "unambitious terrorists overturn trash can", "generated_headline": "Terrorists with low ambitions overturned a trash can.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/unambitious-terrorists-overturn-trash-can-1819564152"} +{"original_headline": "embarrassed library of congress can't believe some of the albums it used to be into", "generated_headline": "The Library of Congress expresses embarrassment over some of the albums it once collected.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/embarrassed-library-of-congress-can-t-believe-some-of-t-1819580029"} +{"original_headline": "mister rogers' neighborhood gerrymandered to serve king friday's make-believe agenda", "generated_headline": "In a fictional context, Mister Rogers' Neighborhood is gerrymandered to serve King Friday's make-believe agenda.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/mister-rogers-neighborhood-gerrymandered-to-serve-king-1819568681"} +{"original_headline": "company to get head start on christmas layoffs this year", "generated_headline": "The company is initiating Christmas layoffs earlier this year.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/company-to-get-head-start-on-christmas-layoffs-this-yea-1819571899"} +{"original_headline": "france, india, brazil among dozens of governments to fall as riots in support of onion social increase globally", "generated_headline": "Riots over onion prices lead to government collapses in France, India, Brazil, and other countries.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/france-india-brazil-among-dozens-of-governments-to-fa-1827046375"} +{"original_headline": "mildfires amble through california", "generated_headline": "Wildfires are spreading through California.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/mildfires-amble-through-california-1819570780"} +{"original_headline": "blagojevich claims behavior was just elaborate plan to surprise patrick fitzgerald with senate nomination on his birthday", "generated_headline": "Blagojevich claims his behavior was an elaborate plan to surprise Patrick Fitzgerald with a Senate nomination on his birthday.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/blagojevich-claims-behavior-was-just-elaborate-plan-to-1819570501"} +{"original_headline": "group of girls directs would-be suitor toward least attractive member", "generated_headline": "A group of girls directs a would-be suitor to the least attractive member of their group.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/group-of-girls-directs-would-be-suitor-toward-least-att-1819565576"} +{"original_headline": "man celebrates raise company will eventually use to justify firing him", "generated_headline": "A man celebrates his raise, even though the company will later use it to justify firing him.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-celebrates-raise-company-will-eventually-use-to-jus-1819577813"} +{"original_headline": "father doesn't understand teenage son's obsession with classic rock", "generated_headline": "A father does not understand his teenage son's obsession with classic rock music.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/father-doesnt-understand-teenage-sons-obsession-with-cl-1819568291"} +{"original_headline": "way too much raised for bronchitis research", "generated_headline": "An excessive amount of money has been raised for bronchitis research.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/way-too-much-raised-for-bronchitis-research-1819569880"} +{"original_headline": "no one murdered because of this image", "generated_headline": "No murders have been caused by this image.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/no-one-murdered-because-of-this-image-1819573893"} +{"original_headline": "astronomers celebrate 300th anniversary of discovering sky", "generated_headline": "Astronomers are celebrating 300 years since the beginning of systematic sky observations.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/astronomers-celebrate-300th-anniversary-of-discovering-1819577085"} +{"original_headline": "queen elizabeth frantically trying to preserve european alliances by arranging great-grandchildren's marriages", "generated_headline": "Queen Elizabeth is arranging marriages for her great-grandchildren to preserve European alliances.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/queen-elizabeth-frantically-trying-to-preserve-european-1819579760"} +{"original_headline": "zach braff, alyssa milano call out trump for far more effectively pivoting to politics to save floundering career", "generated_headline": "Zach Braff and Alyssa Milano criticize Trump for more effectively pivoting to politics to save his floundering career.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/zach-braff-alyssa-milano-call-out-trump-for-far-more-e-1835495412"} +{"original_headline": "dr. scholl's introduces new freeze-away toe remover", "generated_headline": "Dr. Scholl's introduces a new product for toe removal using freezing technology.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/dr-scholl-s-introduces-new-freeze-away-toe-remover-1827896718"} +{"original_headline": "full unsliced lemon makes glass of water particularly refreshing", "generated_headline": "A whole, unsliced lemon in a glass of water makes it more refreshing.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/full-unsliced-lemon-makes-glass-of-water-particularly-r-1819590800"} +{"original_headline": "speakeasy patrons apparently unaware it legal to go to regular bars again", "generated_headline": "Speakeasy patrons seem unaware that visiting regular bars is now legal.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/speakeasy-patrons-apparently-unaware-it-legal-to-go-to-1830496020"} +{"original_headline": "person who will embalm you walking around out there", "generated_headline": "The mortician who will embalm you is currently alive and walking around.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/person-who-will-embalm-you-walking-around-out-there-1819576738"} +{"original_headline": "breast implants found to cause problems in laboratory mice", "generated_headline": "Breast implants have been found to cause problems in laboratory mice.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/breast-implants-found-to-cause-problems-in-laboratory-m-1819586306"} +{"original_headline": "archivists discover unpublished michael crichton manuscript about amusement park that operates without a hitch", "generated_headline": "Archivists have discovered an unpublished Michael Crichton manuscript about an amusement park that operates perfectly.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/archivists-discover-unpublished-michael-crichton-manusc-1824742454"} +{"original_headline": "nation begs for midterms to be pushed back to delay start of 2020 presidential campaigns", "generated_headline": "There is public pressure to postpone midterm elections to delay the start of the 2020 presidential campaigns.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/nation-begs-for-midterms-to-be-pushed-back-to-delay-sta-1830230678"} +{"original_headline": "pope promises more open, transparent molestation in future", "generated_headline": "The Pope promises increased openness and transparency in handling future molestation cases.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/pope-promises-more-open-transparent-molestation-in-fut-1828663069"} +{"original_headline": "man planning to rub up against strangers wondering where train is already", "generated_headline": "A man planning to rub against strangers is also wondering where the train is.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-planning-to-rub-up-against-strangers-wondering-wher-1819576559"} +{"original_headline": "al kozlewski pulls a kozlewski", "generated_headline": "Al Kozlewski engages in behavior characteristic of himself.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/al-kozlewski-pulls-a-kozlewski-1819567146"} +{"original_headline": "woman masturbates to concept of commitment", "generated_headline": "A woman masturbates while thinking about the concept of commitment.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/woman-masturbates-to-concept-of-commitment-1819566960"} +{"original_headline": "white-hot gop race down to two mentally ill people, person who lost nomination last time", "generated_headline": "The GOP primary race has narrowed to two individuals with mental health issues and the previous nomination loser.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/white-hot-gop-race-down-to-two-mentally-ill-people-per-1819590409"} +{"original_headline": "ralph northam admits he once engaged in pedophilia as part of michael jackson costume", "generated_headline": "Ralph Northam admits to engaging in pedophilic behavior during a Michael Jackson costume event.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/ralph-northam-admits-he-once-engaged-in-pedophilia-as-p-1832405275"} +{"original_headline": "police outside convention hoping for opportunity to take swing at george washington impersonator", "generated_headline": "Police outside the convention hope for a chance to strike a George Washington impersonator.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/police-outside-convention-hoping-for-opportunity-to-tak-1819579037"} +{"original_headline": "'this is the golden age of television,' claim executives who have not yet made show about robotic wizards", "generated_headline": "Television executives claim this is the golden age of television, despite not having produced a show about robotic wizards.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/this-is-the-golden-age-of-television-claim-executive-1819579353"} +{"original_headline": "european leaders: 'we stand together to say loud and clear: we are scared as fuck and don't know what to do'", "generated_headline": "European leaders state that they are frightened and uncertain about what to do.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/european-leaders-we-stand-together-to-say-loud-and-cl-1819580176"} +{"original_headline": "right to own handheld device that shoots deadly metal pellets at high speed worth all of this", "generated_headline": "The right to own a handheld device that fires lethal metal pellets at high speed is justified by the current situation.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/right-to-own-handheld-device-that-shoots-deadly-metal-p-1819574320"} +{"original_headline": "write-in candidate thought he had enough friends to win", "generated_headline": "The write-in candidate believed his personal friendships would be sufficient to win the election.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/write-in-candidate-thought-he-had-enough-friends-to-win-1819568792"} +{"original_headline": "evil genius' cat subpoenaed", "generated_headline": "The cat belonging to an evil genius has been issued a subpoena.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/evil-genius-cat-subpoenaed-1819564718"} +{"original_headline": "cdc attempts to put ebola outbreak in perspective by releasing list of worse ways to die", "generated_headline": "CDC releases a comparative 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"https://www.theonion.com/dance-cage-recidivism-rates-at-all-time-high-within-ame-1819580090"} +{"original_headline": "jellyfish can't wait to fuck up honeymoon", "generated_headline": "Jellyfish stings often disrupt honeymoons for couples.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/jellyfish-can-t-wait-to-fuck-up-honeymoon-1819592773"} +{"original_headline": "nelly reiterates sex-liking stance", "generated_headline": "Rapper Nelly again states his positive attitude towards sex.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/nelly-reiterates-sex-liking-stance-1819587246"} +{"original_headline": "mark zuckerberg cited for contempt of congress after refusing to shut the fuck up about how he started company in dorm room", "generated_headline": "Mark Zuckerberg faces contempt of Congress charges for excessive discussion of Facebook's dorm-room origins during testimony.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/mark-zuckerberg-cited-for-contempt-of-congress-after-re-1825178093"} +{"original_headline": "literary historians uncover collection of breezy, upbeat edgar allan poe writings penned after author took up jogging", "generated_headline": "Historians discover previously unknown cheerful poems by Edgar Allan Poe written after he began a jogging routine.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/literary-historians-uncover-collection-of-breezy-upbea-1833436860"} +{"original_headline": "trump admits he assumed roger stone was already in prison", "generated_headline": "Trump said he believed Roger Stone was already imprisoned.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/trump-admits-he-assumed-roger-stone-was-already-in-pris-1832057622"} +{"original_headline": "focus group reveals: 95 percent of americans would like to go home", "generated_headline": "A survey shows that 95% of Americans prefer to be at home.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/focus-group-reveals-95-percent-of-americans-would-like-1819564313"} +{"original_headline": "friend gearing up to hate the hulk", "generated_headline": "A friend is preparing to dislike the Hulk movie or character.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/friend-gearing-up-to-hate-the-hulk-1819566928"} +{"original_headline": "deal alert: get 'kingdom hearts iii' for free for next 30 seconds while gamestop clerk is dealing with something in back", "generated_headline": "Kingdom Hearts III is offered for free for a brief 30-second period while a GameStop employee is occupied.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://ogn.theonion.com/deal-alert-get-kingdom-hearts-iii-for-free-for-next-1835100289"} +{"original_headline": "history doomed to repeat itself, reports man who just dropped food on pants", "generated_headline": "A man who spilled food on his pants remarks that history tends to repeat itself.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/history-doomed-to-repeat-itself-reports-man-who-just-d-1819570366"} +{"original_headline": "that one mcdonald's plate from the '70s: holy shit, there it is", "generated_headline": "An old McDonald's plate from the 1970s has been located.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/that-one-mcdonalds-plate-from-the-70s-holy-shit-there-1819586984"} +{"original_headline": "study finds 60% of parents too busy with divorce to worry about football safety", "generated_headline": "A study reports that many parents going through divorce are less concerned about football safety.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/study-finds-60-of-parents-too-busy-with-divorce-to-wor-1819576099"} +{"original_headline": "acne medication may cause dizziness, nausea, loss of hearing, insomnia, blood clotting, difficulty breathing", "generated_headline": "Acne medication can have serious side effects such as dizziness, nausea, hearing loss, insomnia, blood clotting, and breathing difficulties.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": 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"https://www.theonion.com/awestruck-video-game-fan-describes-brush-with-playstati-1819565625"} +{"original_headline": "takeout burrito shielded from cold as though it were week-old newborn", "generated_headline": "A takeout burrito is kept warm like a newborn infant.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/takeout-burrito-shielded-from-cold-as-though-it-were-we-1819578629"} +{"original_headline": "last line of obama's military force request briefly mentions possibility of 25-year quagmire", "generated_headline": "Obama's military request mentions a potential 25-year conflict.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/last-line-of-obama-s-military-force-request-briefly-men-1819577476"} +{"original_headline": "obama peddling stimulus package door-to-door", 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"stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/third-grader-clearly-biting-off-more-than-he-can-chew-a-1819579378"} +{"original_headline": "royal baby already making new friends", "generated_headline": "The royal baby is making new friends.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/royal-baby-already-making-new-friends-1819575525"} +{"original_headline": "mccain's energy plan emphasizes elbow grease, sleeve-rolling-up", "generated_headline": "McCain's energy plan focuses on hard work and manual labor.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/mccains-energy-plan-emphasizes-elbow-grease-sleeve-rol-1819570102"} +{"original_headline": "new, improved olean 30 percent less likely to make you shit in your pants", "generated_headline": "The new Olean 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"https://politics.theonion.com/antifa-organizers-announce-plans-to-disrupt-neo-nazi-ra-1819580235"} +{"original_headline": "guatemalan coffee picker happy if single person starts day alert", "generated_headline": "A Guatemalan coffee picker is happy if one person starts their day alert.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/guatemalan-coffee-picker-happy-if-single-person-starts-1819567780"} +{"original_headline": "good charlotte recording 10 new songs to be played at low volume in p.f. chang's", "generated_headline": "Good Charlotte is recording 10 new songs intended for low-volume playback at P.F. 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"https://politics.theonion.com/john-bolton-urges-war-against-the-sun-after-uncovering-1835805360"} +{"original_headline": "rest of kickline out sick", "generated_headline": "The remaining members of the kickline are absent due to illness.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/rest-of-kickline-out-sick-1819588500"} +{"original_headline": "latest gop debate concludes with candidates wrestling squealing pig to ground and slaughtering it", "generated_headline": "The recent GOP debate ended with candidates physically subduing and killing a pig.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/latest-gop-debate-concludes-with-candidates-wrestling-s-1819590502"} +{"original_headline": "jawa appointed secretary of transportation", "generated_headline": "A being referred to as a 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"https://entertainment.theonion.com/al-roker-stares-crestfallen-at-matt-lauer-tattoo-on-own-1820852200"} +{"original_headline": "everyone in motorcycle gang jewish", "generated_headline": "All members of the motorcycle gang are Jewish.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/everyone-in-motorcycle-gang-jewish-1819591187"} +{"original_headline": "monster truck escapes", "generated_headline": "A monster truck has broken free from its enclosure.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/monster-truck-escapes-1819586160"} +{"original_headline": "oscars audience shrugging uproariously during jimmy kimmel's opening monologue", "generated_headline": "The Oscars audience is laughing loudly during Jimmy Kimmel's opening monologue.", "strategy": "irony", "type": "sarcastic_to_non", 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"https://local.theonion.com/area-man-cleans-apartment-once-every-relationship-1819576384"} +{"original_headline": "busy romney sorry he missed nation's piano recital", "generated_headline": "Mitt Romney apologizes for missing the national piano recital due to his busy schedule.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/busy-romney-sorry-he-missed-nations-piano-recital-1819573405"} +{"original_headline": "blood runs down house of representatives walls as chamber itself selects new speaker", "generated_headline": "Blood is running down the walls of the House of Representatives as it selects a new Speaker.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/blood-runs-down-house-of-representatives-walls-as-chamb-1819578317"} +{"original_headline": "rain-soaked robert mueller lets manafort 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"https://local.theonion.com/flight-attendant-demonstrates-proper-technique-for-eati-1827754079"} +{"original_headline": "reagan's memory honored with sharp increase in federal budget deficit", "generated_headline": "The federal budget deficit has sharply increased in honor of Reagan's memory.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/reagans-memory-honored-with-sharp-increase-in-federal-b-1819587586"} +{"original_headline": "smokers at party only ones to make it to fire escape in time", "generated_headline": "At a party, only the smokers were able to reach the fire escape in time.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/smokers-at-party-only-ones-to-make-it-to-fire-escape-in-1819587932"} +{"original_headline": "man at salad bar has to say every item aloud as he adds it to salad", 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"https://www.theonion.com/new-york-times-announces-appointment-of-anonymous-sou-1829232788"} +{"original_headline": "rescue dog adopted for couple weeks", "generated_headline": "A rescue dog was adopted for a couple of weeks.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/rescue-dog-adopted-for-couple-weeks-1819591877"} +{"original_headline": "alien still hasn't gotten around to listening to whole voyager golden record", "generated_headline": "An alien has not yet listened to the entire Voyager Golden Record.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/alien-still-hasnt-gotten-around-to-listening-to-whole-v-1819573495"} +{"original_headline": "no one in gang has heart to tell police informant his cover's blown", "generated_headline": "No one in the gang has the heart to tell the 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"https://local.theonion.com/man-knows-exactly-which-asshole-got-him-sick-1819655095"} +{"original_headline": "fox news reporter asks the questions others are too smart to ask", "generated_headline": "A Fox News reporter asks questions that others are too intelligent to ask.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/fox-news-reporter-asks-the-questions-others-are-too-sma-1819587301"} +{"original_headline": "planet explodes", "generated_headline": "A planet has exploded.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/planet-explodes-1819565251"} +{"original_headline": "nesting sea turtle escorted from private beach", "generated_headline": "A nesting sea turtle was escorted from a private beach.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/nesting-sea-turtle-escorted-from-private-beach-1819589752"} +{"original_headline": "man old enough to know how rest of life pretty much plays out", "generated_headline": "A man is old enough to know how the rest of his life will pretty much play out.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-old-enough-to-know-how-rest-of-life-pretty-much-pla-1819577418"} +{"original_headline": "experts recommend changing batteries in smoke detector every 6 fires", "generated_headline": "Experts recommend changing the batteries in smoke detectors every six fires.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/experts-recommend-changing-batteries-in-smoke-detector-1833286965"} +{"original_headline": "lone house with no halloween 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"article_link": "https://politics.theonion.com/breaking-aclu-hard-as-a-fucking-rock-right-now-1819580105"} +{"original_headline": "puerto ricans without power for month can only assume this leading story across national news media", "generated_headline": "Puerto Ricans, who have been without power for a month, note that their situation is not the leading story in national news media.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/puerto-ricans-without-power-for-month-can-only-assume-t-1819877413"} +{"original_headline": "logging industry announces that they just can't fucking get enough of logs", "generated_headline": "The logging industry has expressed an insatiable demand for logs.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/logging-industry-announces-that-they-just-can-t-fucking-1829364973"} 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"https://local.theonion.com/unremarkable-man-resembles-burt-ward-1819564822"} +{"original_headline": "boy, dolphin no longer on speaking terms", "generated_headline": "A boy and a dolphin are no longer communicating.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/boy-dolphin-no-longer-on-speaking-terms-1819567239"} +{"original_headline": "lunch place uses way too much mayo in fruit salad", "generated_headline": "The lunch place uses an excessive amount of mayonnaise in its fruit salad.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/lunch-place-uses-way-too-much-mayo-in-fruit-salad-1828465668"} +{"original_headline": "idf soldier recounts harrowing, heroic war story of killing 8-month-old child", "generated_headline": "An IDF soldier recounted a war story about killing an 8-month-old child.", 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+{"original_headline": "14-year-old congressional whiz kid balances budget", "generated_headline": "A 14-year-old prodigy assisted in balancing the congressional budget.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/14-year-old-congressional-whiz-kid-balances-budget-1819574697"} +{"original_headline": "fun sticker placed on child's ventilator", "generated_headline": "A fun sticker was placed on a child's ventilator.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/fun-sticker-placed-on-child-s-ventilator-1819591576"} +{"original_headline": "pretentious peasant insists he never watches beheadings", "generated_headline": "A pretentious peasant claims he never watches beheadings.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/pretentious-peasant-insists-he-never-watches-beheadings-1828356870"} +{"original_headline": "polling place in predominantly black neighborhood clearly brick wall with door painted on", "generated_headline": "A polling place in a predominantly Black neighborhood appears to be a brick wall with a painted door, suggesting deception.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/polling-place-in-predominantly-black-neighborhood-clear-1830254624"} +{"original_headline": "director of high-school play buys director's chair out of own pocket", "generated_headline": "The director of a high-school play bought a director's chair with personal funds.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/director-of-high-school-play-buys-directors-chair-out-o-1819566583"} 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"https://local.theonion.com/man-takes-parents-on-tour-of-city-where-he-came-to-esca-1819577028"} +{"original_headline": "police find adorable little skeleton", "generated_headline": "Police discovered a small skeleton that was described as charming.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/police-find-adorable-little-skeleton-1819572235"} +{"original_headline": "everyone at thanksgiving doing chore to get away from rest of family", "generated_headline": "At Thanksgiving, each family member performed a chore to spend time away from other relatives.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/everyone-at-thanksgiving-doing-chore-to-get-away-from-r-1830597796"} +{"original_headline": "man unsure whether to tip bathroom attendant just for wiping his ass", "generated_headline": "A man is 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"sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/pastor-talking-to-non-christian-who-just-lost-wife-can-1819579838"} +{"original_headline": "chemicals that pushed man's ancestors to run down wild boar flare at sight of white cheddar popcorn bag", "generated_headline": "Chemicals that historically motivated human hunting now react to white cheddar popcorn bags.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/chemicals-that-pushed-man-s-ancestors-to-run-down-wild-1819579876"} +{"original_headline": "man watching cleopatra 2525 has no time to read", "generated_headline": "A man is watching the TV show Cleopatra 2525 and has no time for reading.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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"article_link": "https://www.theonion.com/unemployed-man-photoshops-self-into-former-company-s-st-1819589214"} +{"original_headline": "man wistfully looks around website he hasn't visited for 30 minutes", "generated_headline": "A man looks nostalgically at a website he hasn't visited for 30 minutes.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-wistfully-looks-around-website-he-hasn-t-visited-fo-1819577435"} +{"original_headline": "wedding planner suggests replacing unsightly groom", "generated_headline": "A wedding planner recommends replacing the groom due to aesthetic concerns.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/wedding-planner-suggests-replacing-unsightly-groom-1819577556"} +{"original_headline": "new co-worker seems like nice enough guy", "generated_headline": "The new 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"satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/frantic-last-second-study-finds-old-fashioned-donut-be-1819811037"} +{"original_headline": "347 locals identify slain prostitute", "generated_headline": "347 local residents have identified the victim in a homicide case involving a sex worker.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/347-locals-identify-slain-prostitute-1819568442"} +{"original_headline": "report: jack black's life more valuable than yours if it ever comes down to it", "generated_headline": "A report suggests that in life-threatening situations, Jack Black's life might be prioritized over others.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/report-jack-blacks-life-more-valuable-than-yours-if-it-1819574056"} +{"original_headline": "teen on birthright trip hadn't expected to see so many dead palestinians", "generated_headline": "A teenager on a Birthright trip was surprised by the number of Palestinian casualties he witnessed.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/teen-on-birthright-trip-hadn-t-expected-to-see-so-many-1824265633"} +{"original_headline": "bobby jindal vows to return america to time when he was rising republican star", "generated_headline": "Bobby Jindal pledged to restore America to the era of his political ascendancy.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/bobby-jindal-vows-to-return-america-to-time-when-he-was-1819578072"} +{"original_headline": "mitt romney frantically 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"article_link": "https://www.theonion.com/kfc-responds-to-stockpiling-trend-with-576-piece-bucket-1819587067"} +{"original_headline": "nine-hundred-pound man left to die", "generated_headline": "A nine-hundred-pound man was left to die.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/nine-hundred-pound-man-left-to-die-1819564653"} +{"original_headline": "roommate skulking around edge of party like victorian ghost child", "generated_headline": "Roommate is behaving stealthily at the party.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/roommate-skulking-around-edge-of-party-like-victorian-g-1819579381"} +{"original_headline": "man thinks receptionist is hitting on him", "generated_headline": "A man interprets the receptionist's behavior as romantic interest.", "strategy": "sarcasm", "type": 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"https://local.theonion.com/college-newspaper-staff-know-exactly-how-they-would-res-1819577636"} +{"original_headline": "treasury department releases new 'monsters of the silver screen' $20 bill", "generated_headline": "The Treasury Department has issued a new $20 bill featuring monsters from movies.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/treasury-department-releases-new-monsters-of-the-silver-1819590484"} +{"original_headline": "obama, romney urge americans to purchase 'the onion book of known knowledge'", "generated_headline": "Barack Obama and Mitt Romney encourage Americans to purchase 'The Onion Book of Known Knowledge'.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/obama-romney-urge-americans-to-purchase-the-onion-book-1819574144"} +{"original_headline": "unattractive man not fooling 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"https://local.theonion.com/teacher-frustrated-no-one-in-beginner-yoga-class-can-fo-1820796504"} +{"original_headline": "inept coworker increasingly difficult to fantasize about", "generated_headline": "It is increasingly difficult to fantasize about an incompetent coworker.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/inept-coworker-increasingly-difficult-to-fantasize-abou-1819570663"} +{"original_headline": "obama's declaration of swine flu emergency prompts pro-swine-flu republican response", "generated_headline": "Obama's declaration of a swine flu emergency prompted a Republican response that favored the swine flu.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/obamas-declaration-of-swine-flu-emergency-prompts-pro-s-1819571102"} +{"original_headline": "parent mad 6-year-old didn't like 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"https://www.theonion.com/astronomers-say-wednesday-night-will-be-best-chance-for-1825248803"} +{"original_headline": "'game of thrones' fan rewatching past episodes to remind self of what characters' breasts look like", "generated_headline": "A 'Game of Thrones' fan is rewatching past episodes to remind himself of what the characters' breasts look like.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/game-of-thrones-fan-rewatching-past-episodes-to-remin-1819578813"} +{"original_headline": "country cd put on to impress repair guy", "generated_headline": "A country CD was played to impress the repair technician.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/country-cd-put-on-to-impress-repair-guy-1819570504"} +{"original_headline": "man given 3 months to live throws in one or two non-sexual 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"https://entertainment.theonion.com/new-blog-piece-on-woody-allen-to-settle-everything-1819576124"} +{"original_headline": "ghost of brando urges man to finish whole cheesecake", "generated_headline": "A man feels inspired by the memory of Marlon Brando to eat an entire cheesecake.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/ghost-of-brando-urges-man-to-finish-whole-cheesecake-1819568244"} +{"original_headline": "movie marketed as six different genres", "generated_headline": "The movie is being promoted in six different genres.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/movie-marketed-as-six-different-genres-1819566764"} +{"original_headline": "broke dad makes son playstation 2 for christmas", "generated_headline": "A father with financial difficulties buys his son a PlayStation 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"https://www.theonion.com/texas-sheriff-cracks-down-on-chicken-on-chicken-violenc-1819565681"} +{"original_headline": "rodent clearly making its way through steve bannon's body throughout national security meeting", "generated_headline": "A rodent was seen moving near Steve Bannon during a national security meeting.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/rodent-clearly-making-its-way-through-steve-bannon-s-bo-1819579702"} +{"original_headline": "kid about to meet brooklyn nets must not be very sick", "generated_headline": "A child scheduled to meet the Brooklyn Nets is likely not seriously ill.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://sports.theonion.com/kid-about-to-meet-brooklyn-nets-must-not-be-very-sick-1834012121"} +{"original_headline": "tulip popping up in middle of march must think it some kind 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"stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/nation-suddenly-concerned-about-black-mans-opinion-1825547877"} +{"original_headline": "witty remark repeated throughout week", "generated_headline": "A witty remark was repeated multiple times throughout the week.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/witty-remark-repeated-throughout-week-1819565499"} +{"original_headline": "'so what did i miss?' asks michael flynn tilting large flower on lapel towards trump", "generated_headline": "Michael Flynn, wearing a large flower on his lapel, asked what he had missed during an event with Trump.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/so-what-did-i-miss-asks-michael-flynn-tilting-large-1820779582"} +{"original_headline": "'ravaged' named florida's official 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"https://local.theonion.com/parents-finally-tell-2-year-old-about-9-11-1819574452"} +{"original_headline": "orrin hatch mistakenly left dangling in bondage-fetish dungeon", "generated_headline": "Orrin Hatch was accidentally left dangling in a bondage-fetish dungeon.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/orrin-hatch-mistakenly-left-dangling-in-bondage-fetish-1819565385"} +{"original_headline": "new report finds link between each passing day, jeanette getting more beautiful", "generated_headline": "A new report indicates that each passing day makes Jeanette more beautiful.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-report-finds-link-between-each-passing-day-jeanett-1823273857"} +{"original_headline": "white house press secretary responds to question about rising obamacare 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"stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/bo-obama-addresses-graduates-of-dayton-obedience-school-1819579981"} +{"original_headline": "paul ryan quickly runs tweet about texas shooting past wayne lapierre before posting", "generated_headline": "Paul Ryan sought input from Wayne LaPierre prior to publishing a tweet regarding the Texas shooting.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/paul-ryan-quickly-runs-tweet-about-texas-shooting-past-1820188824"} +{"original_headline": "report: album as good as 'sgt. pepper' comes out about once every month", "generated_headline": "Music critics often draw comparisons between new album releases and the iconic 'Sgt. 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"https://politics.theonion.com/anthropologists-discover-ancient-greek-super-pac-that-h-1819577782"} +{"original_headline": "spacex reveals all 400 dogs on falcon rocket failed to survive trip", "generated_headline": "SpaceX confirmed that animals aboard a Falcon rocket mission did not survive the journey.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/spacex-reveals-all-400-dogs-on-falcon-rocket-failed-to-1822775849"} +{"original_headline": "report: nation thinking about big, warm piece of cinnamon coffee cake right now", "generated_headline": "Recent data indicates a widespread craving for cinnamon coffee cake among people in the United States.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/report-nation-thinking-about-big-warm-piece-of-cinnam-1819575258"} +{"original_headline": "pope francis 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"https://www.theonion.com/unstable-man-plots-to-bring-guns-to-schools-1819574765"} +{"original_headline": "taylor swift breaks political silence to throw support behind restoring sh\u014dgun to throne of japan", "generated_headline": "Taylor Swift publicly advocated for the reinstatement of the sh\u014dgun title in Japan, though this claim is unfounded.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/taylor-swift-breaks-political-silence-to-throw-support-1829602513"} +{"original_headline": "little league coach reveals creepy method for breaking in baseball mitt", "generated_headline": "A youth baseball coach disclosed an unusual technique for conditioning a mitt.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/little-league-coach-reveals-creepy-method-for-breaking-1819571459"} 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"stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/report-someone-probably-masturbating-to-this-stock-pho-1819575783"} +{"original_headline": "supporters aggravated bernie sanders didn't use dnc speech to get voters to act against their own self-interest", "generated_headline": "Supporters are aggravated that Bernie Sanders didn't urge voters to act against self-interest.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/supporters-aggravated-bernie-sanders-didn-t-use-dnc-spe-1819579052"} +{"original_headline": "pope francis spotted sunbathing nude in st. peter's square", "generated_headline": "Report claims Pope Francis was seen sunbathing nude in St. Peter's Square.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/pope-francis-spotted-sunbathing-nude-in-st-peter-s-squ-1819579717"} 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"article_link": "https://sports.theonion.com/dog-costumed-to-create-illusion-of-sports-team-preferen-1819565269"} +{"original_headline": "new bill would limit abortion to cases where procedure necessary to save promising political career", "generated_headline": "A bill proposes to limit abortion to cases where it is necessary to save a political career.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/new-bill-would-limit-abortion-to-cases-where-procedure-1819580372"} +{"original_headline": "lucky old woman getting wheeled around airport", "generated_headline": "An elderly woman is being wheeled around the airport.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/lucky-old-woman-getting-wheeled-around-airport-1819575211"} +{"original_headline": "talk-show host takes brief break from mocking jessica 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"article_link": "https://entertainment.theonion.com/orca-s-successful-trick-rewarded-with-bucket-of-ssris-1819592389"} +{"original_headline": "north dakota not heard from in 48 hours", "generated_headline": "There has been no significant news reported from North Dakota in the past 48 hours.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/north-dakota-not-heard-from-in-48-hours-1819564531"} +{"original_headline": "kanye west named new face of yeezy", "generated_headline": "Kanye West has been appointed as the new spokesperson for the Yeezy brand.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/kanye-west-named-new-face-of-yeezy-1832335816"} +{"original_headline": "queen elizabeth annoyed nude pictures of prince harry don't show anything good", "generated_headline": "Queen Elizabeth is reported to be 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"stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/young-billionaires-age-not-reported-for-sake-of-nations-1819572710"} +{"original_headline": "christian theme park features world's largest spanking machine", "generated_headline": "A Christian theme park includes an attraction called the world's largest spanking machine.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/christian-theme-park-features-worlds-largest-spanking-m-1819563916"} +{"original_headline": "mom triumphantly drags hotel pool lounge chair back to family like fresh kill", "generated_headline": "A mother proudly brings a hotel pool lounge chair back to her family as if it were a trophy.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/mom-triumphantly-drags-hotel-pool-lounge-chair-back-to-1819577951"} 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"https://www.theonion.com/pope-francis-sneaks-leftovers-to-false-god-moloch-at-ba-1819579614"} +{"original_headline": "emaciated peter alexander burns podium for warmth after being locked in abandoned press briefing room since december", "generated_headline": "Journalist Peter Alexander reportedly suffered in an abandoned press room, resorting to burning a podium for warmth.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/emaciated-peter-alexander-burns-podium-for-warmth-after-1832026399"} +{"original_headline": "emotional elon musk recalls spending entire birthday working on concepts for mistreating employees", "generated_headline": "Elon Musk reflected on his birthday, discussing his work on employee management strategies.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/emotional-elon-musk-recalls-spending-entire-birthday-wo-1828475558"} +{"original_headline": "hotel bar really hopping tonight, says hotel bartender", "generated_headline": "A hotel bartender comments that the bar is busy tonight.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/hotel-bar-really-hopping-tonight-says-hotel-bartender-1819566601"} +{"original_headline": "giant altoid headed toward earth'curiously strong' celestial body will extinguish all life", "generated_headline": "A large celestial body, nicknamed 'curiously strong,' is on a collision course with Earth, posing an existential threat.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/giant-altoid-headed-toward-earthcuriously-strong-celest-1819586250"} +{"original_headline": "report: it a miracle nothing has 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"https://www.theonion.com/furious-meghan-markle-can-t-believe-harry-hasn-t-told-f-1826104835"} +{"original_headline": "frozen tundra of emptiness stretching out forever and ever weighed against date with mike4763", "generated_headline": "The vast, desolate landscape is contrasted with a social engagement with an individual named Mike4763.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/frozen-tundra-of-emptiness-stretching-out-forever-and-e-1819576971"} +{"original_headline": "mom could have used few more days to self before missing daughter returned", "generated_headline": "A mother wished for more personal time before her daughter came back home.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/mom-could-have-used-few-more-days-to-self-before-missin-1819577512"} +{"original_headline": "god admits 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"https://www.theonion.com/tan-asshole-still-on-island-time-1819589691"} +{"original_headline": "'this map will change the way you see westeros,' reports never-ending cascade of subhuman bullshit", "generated_headline": "A map is promoted as transformative for understanding Westeros, amid criticism of repetitive low-quality content.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/this-map-will-change-the-way-you-see-westeros-report-1819580267"} +{"original_headline": "man announces plan to take out anger on first less powerful person he sees", "generated_headline": "A man declares his intention to vent his anger on someone he perceives as weaker.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-announces-plan-to-take-out-anger-on-first-less-powe-1819577171"} +{"original_headline": "man who 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"https://www.theonion.com/nation-hopeful-there-will-be-equally-random-chance-of-j-1819577691"} +{"original_headline": "world's cartographers continue living secret life of luxury on idyllic, never disclosed 8th continent", "generated_headline": "Cartographers do not live a secret life of luxury on a nonexistent eighth continent.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/world-s-cartographers-continue-living-secret-life-of-lu-1828830864"} +{"original_headline": "report: papa will be so very cross you've lost grandfather's hunting cap", "generated_headline": "Papa is angry that you have lost grandfather's hunting cap.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/report-papa-will-be-so-very-cross-you-ve-lost-grandfat-1832395994"} +{"original_headline": "community garden sprouts first condom 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"stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/veteran-kind-of-surprised-killing-all-those-people-didn-1835567252"} +{"original_headline": "corporate merger renders thousands of coffee mugs obsolete", "generated_headline": "A corporate merger has made thousands of coffee mugs obsolete due to branding changes.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/corporate-merger-renders-thousands-of-coffee-mugs-obsol-1819589667"} +{"original_headline": "american torturing jobs increasingly outsourced", "generated_headline": "Jobs related to torture are increasingly being outsourced by American companies.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/american-torturing-jobs-increasingly-outsourced-1819567781"} +{"original_headline": "buzz aldrin recalls how easy it was getting 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"https://entertainment.theonion.com/hopes-dreams-crushed-by-panel-of-d-list-celebrities-1819567566"} +{"original_headline": "police officer doesn't see a difference between black, light-skinned black suspects", "generated_headline": "A police officer does not distinguish between black and light-skinned black suspects, showing racial bias.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/police-officer-doesn-t-see-a-difference-between-black-1819576808"} +{"original_headline": "2-year-old unaware he's basis for 6 couples' decisions not to have kids", "generated_headline": "A 2-year-old child is unknowingly the reason why six couples have decided not to have children.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/2-year-old-unaware-he-s-basis-for-6-couples-decisions-1819579578"} +{"original_headline": "anonymous source informs bob woodward he hasn't been relevant in 40 years", "generated_headline": "An anonymous source told Bob Woodward that he has not been relevant for 40 years.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/anonymous-source-informs-bob-woodward-he-hasnt-been-rel-1819574618"} +{"original_headline": "new antidepressant makes friends' problems seem worse", "generated_headline": "A new antidepressant can cause friends' problems to seem more severe as a side effect.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-antidepressant-makes-friends-problems-seem-worse-1819575964"} +{"original_headline": "art professor revealed to be convincing fake", "generated_headline": "An art professor has been exposed as a fraud.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": 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"stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/obamacare-helps-uninsured-americans-become-blindingly-e-1819575700"} +{"original_headline": "obama weighing his syria option", "generated_headline": "President Obama is considering his options for Syria.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/obama-weighing-his-syria-option-1819575483"} +{"original_headline": "guy sipping energy drink on subway probably heading off to snowboard in x games or something", "generated_headline": "A man sipping an energy drink on the subway might be heading to snowboard in the X Games.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://sports.theonion.com/guy-sipping-energy-drink-on-subway-probably-heading-off-1819577921"} +{"original_headline": "color drains from michael flynn's face after single red dahlia 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"article_link": "https://local.theonion.com/breaking-mrs-nichols-also-daniel-s-mom-1819576838"} +{"original_headline": "new study finds americans are living too long", "generated_headline": "A study indicates that Americans have an excessively long lifespan.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-study-finds-americans-are-living-too-long-1819575521"} +{"original_headline": "'don't make me regret this,' mueller tells rick gates before uncuffing him to work on investigation together", "generated_headline": "Mueller tells Rick Gates not to make him regret uncuffing him so they can collaborate on the investigation.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/don-t-make-me-regret-this-mueller-tells-rick-gates-b-1831849285"} +{"original_headline": "fucking oasis to probably be worked into 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demand to know 'exactly where on tits it okay to touch nowadays'", "generated_headline": "Some men are demanding to know the exact boundaries for touching breasts.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/frustrated-men-demand-to-know-exactly-where-on-tits-it-1828492678"} +{"original_headline": "study: coffee drinkers at far higher risk of having mug crash to floor in slow motion after hearing their father is dead", "generated_headline": "A study linked coffee consumption to a higher risk of dropping a mug in slow motion upon hearing devastating news.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/study-coffee-drinkers-at-far-higher-risk-of-having-mug-1824281911"} +{"original_headline": "fda approves new drug for treatment of social anxiety", "generated_headline": "The FDA has approved a new 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"stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/trump-administration-urges-saudis-to-stick-to-killing-r-1829713565"} +{"original_headline": "report: there must be some trick to unfolding table legs", "generated_headline": "A report indicates that unfolding table legs may be challenging.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/report-there-must-be-some-trick-to-unfolding-table-leg-1827716502"} +{"original_headline": "man thinks going to vegas for things other than gambling somehow less sad", "generated_headline": "A man believes that visiting Las Vegas for non-gambling activities is less depressing.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/man-thinks-going-to-vegas-for-things-other-than-gamblin-1819577643"} +{"original_headline": "scott walker changes locks on 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Run and never look back!' while hugging Carly Fiorina on the rally stage.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/run-run-and-never-look-back-whispers-heidi-cruz-whi-1819578831"} +{"original_headline": "hero firefighter: 'i'm a hero'", "generated_headline": "A firefighter who performed a heroic act said, 'I am a hero.'", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/hero-firefighter-im-a-hero-1819564481"} +{"original_headline": "mike pompeo can't believe senate just expects he'll answer questions without being tortured first", "generated_headline": "Mike Pompeo expressed disbelief that the Senate expects him to answer questions without prior torture.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/mike-pompeo-can-t-believe-senate-just-expects-he-ll-ans-1825222226"} +{"original_headline": "obama sort of freaked out after not receiving single e-mail, phone call for entire day", "generated_headline": "President Obama was somewhat upset after not receiving any emails or phone calls for a full day.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/obama-sort-of-freaked-out-after-not-receiving-single-e-1819572760"} +{"original_headline": "best thing that ever happened to area man yelling at him about socks", "generated_headline": "An area man considered being yelled at about socks as the best thing that ever happened to him.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/best-thing-that-ever-happened-to-area-man-yelling-at-hi-1819571283"} +{"original_headline": "new study finds majority of god's blessings burn up on entry into atmosphere", "generated_headline": "A new study claims that most of God's blessings disintegrate upon entering the atmosphere.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-study-finds-majority-of-god-s-blessings-burn-up-on-1819577447"} +{"original_headline": "australian parliament gathers to discuss dwindling hemsworth reserves", "generated_headline": "The Australian Parliament convened to discuss declining reserves of Hemsworths, referring to the actor family.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/australian-parliament-gathers-to-discuss-dwindling-hems-1819579908"} +{"original_headline": "new custard could cause worldwide flandemic", "generated_headline": "A new type of custard has the potential to cause a global 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"dead deer by side of road covered in graffiti", "generated_headline": "A dead deer found by the roadside was covered in graffiti.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/dead-deer-by-side-of-road-covered-in-graffiti-1819588955"} +{"original_headline": "10th-grade prodigy studying mathematics at 10th-grade level", "generated_headline": "A 10th-grade prodigy is studying mathematics at the 10th-grade level.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/10th-grade-prodigy-studying-mathematics-at-10th-grade-l-1819577209"} +{"original_headline": "oscars committee announces plan to shorten ceremony to single-millisecond flash of blinding white light", "generated_headline": "The Oscars committee announced a plan to reduce the ceremony to a one-millisecond flash of bright light.", "strategy": 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"https://local.theonion.com/parents-baby-godmother-all-uncomfortable-with-arrange-1819592287"} +{"original_headline": "cleveland ukrainian museum pulling out all stops to prepare for onrush of rnc visitors", "generated_headline": "The Cleveland Ukrainian Museum is making extensive preparations for an influx of RNC visitors.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/cleveland-ukrainian-museum-pulling-out-all-stops-to-pre-1819579031"} +{"original_headline": "child promised he can go right back to video game after giving dying grandfather one last hug", "generated_headline": "A child was promised that he could return to his video game immediately after giving his dying grandfather one last hug.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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carlos santana, rob thomas somehow standing test of time", "generated_headline": "The 1999 collaboration between Carlos Santana and Rob Thomas has endured over time.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/1999-collaboration-between-carlos-santana-rob-thomas-s-1819571117"} +{"original_headline": "indian casino uses every part of the dollar", "generated_headline": "An Indian casino claims to use every part of the dollar, similar to how some cultures use every part of an animal.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/indian-casino-uses-every-part-of-the-dollar-1819586753"} +{"original_headline": "intact benetton shirt miraculously pulled from bangladesh rubble weeks later", "generated_headline": "An intact Benetton shirt was recovered from the Bangladesh rubble weeks after the 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getting pretty intense", "generated_headline": "An imaginary argument is becoming more intense.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/fantasized-argument-getting-pretty-intense-1819575623"} +{"original_headline": "therapist beginning to show cracks in caring fa\u00e7ade", "generated_headline": "Therapist is showing signs of professional strain.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/therapist-beginning-to-show-cracks-in-caring-facade-1819566941"} +{"original_headline": "report: gen x irony, cynicism may be permanently obsolete", "generated_headline": "A report suggests that the irony and cynicism associated with Generation X may be becoming outdated.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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"https://local.theonion.com/report-of-course-that-guy-on-college-s-alumni-committe-1819578370"} +{"original_headline": "little butterball holding up ice cream line", "generated_headline": "A person is causing a delay in the ice cream line.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/little-butterball-holding-up-ice-cream-line-1819570934"} +{"original_headline": "office manager very pleased with new work refrigerator policy", "generated_headline": "Office manager is pleased with the new refrigerator policy.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/office-manager-very-pleased-with-new-work-refrigerator-1819569583"} +{"original_headline": "exxonmobil vows lenient treatment for any species that surrenders voluntarily", "generated_headline": "ExxonMobil vows mild treatment for species that 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"https://politics.theonion.com/entire-southern-border-somehow-on-fire-10-minutes-after-1834893653"} +{"original_headline": "tony blair apparently not british prime minister anymore", "generated_headline": "Tony Blair is no longer serving as the British Prime Minister.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/tony-blair-apparently-not-british-prime-minister-anymor-1819589860"} +{"original_headline": "andrew w.k. adopts staunch party-advocacy position", "generated_headline": "Andrew W.K. has expressed strong support for partying.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/andrew-w-k-adopts-staunch-party-advocacy-position-1819587149"} +{"original_headline": "cost of paper", "generated_headline": "The price of paper has increased.", "strategy": "understatement", "type": 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"https://entertainment.theonion.com/new-robert-altman-film-released-straight-to-special-edi-1819566138"} +{"original_headline": "disney begins uploading obama's consciousness to hall of presidents robot", "generated_headline": "Disney is adding a Barack Obama figure to the Hall of Presidents.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/disney-begins-uploading-obama-s-consciousness-to-hall-o-1819578889"} +{"original_headline": "club has big hit with closed-mic night", "generated_headline": "The club's event with no open microphones was successful.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/club-has-big-hit-with-closed-mic-night-1819567197"} +{"original_headline": "fda okays every drug pending approval, takes rest of year off", "generated_headline": "The FDA approves drugs on a case-by-case basis and 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"https://local.theonion.com/man-only-buys-products-made-right-here-in-the-usa-by-ch-1819576505"} +{"original_headline": "warm weather finally allows man to get outside, explore new ways to sweat", "generated_headline": "Warm weather allows the man to go outside and sweat.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/warm-weather-finally-allows-man-to-get-outside-explore-1819576595"} +{"original_headline": "pabst still coasting on 1893 blue ribbon win", "generated_headline": "Pabst remains associated with its 1893 Blue Ribbon achievement.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/pabst-still-coasting-on-1893-blue-ribbon-win-1819587698"} +{"original_headline": "gold bond spokesman grudgingly admits it makes your balls tingle", "generated_headline": "A Gold Bond spokesperson states that the 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"article_link": "https://local.theonion.com/wife-dropping-hints-she-ready-to-have-second-husband-1819579698"} +{"original_headline": "last beer in six pack drunk with plastic rings still attached", "generated_headline": "The last beer in a six-pack was consumed while the plastic rings remained attached.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/last-beer-in-six-pack-drunk-with-plastic-rings-still-at-1819587153"} +{"original_headline": "faa assures public: air travel 'pretty safe'", "generated_headline": "The FAA assures the public that air travel is extremely safe.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/faa-assures-public-air-travel-pretty-safe-1819586097"} +{"original_headline": "greenpeace decides northern spotted owl 'not worth the trouble anymore'", "generated_headline": "Greenpeace has abandoned its efforts to protect the northern spotted owl.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/greenpeace-decides-northern-spotted-owl-not-worth-the-t-1819567916"} +{"original_headline": "hubble telescope discovers giant amelia earhart statue on distant planet", "generated_headline": "The Hubble telescope has discovered an object resembling a statue on a distant planet.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/hubble-telescope-discovers-giant-amelia-earhart-statue-1819592315"} +{"original_headline": "william barr assures senate he will let donald trump finish his job without any interference", "generated_headline": "William Barr assured the Senate that he would not interfere with Donald Trump's presidency.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/william-barr-assures-senate-he-will-let-donald-trump-fi-1831771337"} +{"original_headline": "boehner resignation leaves massive leadership vacuum in congress intact", "generated_headline": "John Boehner's resignation has left a leadership vacuum in Congress.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/boehner-resignation-leaves-massive-leadership-vacuum-in-1819592356"} +{"original_headline": "moon finally hatches", "generated_headline": "A phenomenon on the moon has been likened to hatching by scientists.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/moon-finally-hatches-1819576671"} +{"original_headline": "scientists finally pronounce human genome", "generated_headline": "Scientists have completed the 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"https://entertainment.theonion.com/rock-hard-cafe-acquires-autographed-bon-jovi-cock-ring-1827892771"} +{"original_headline": "people in healthcare.gov stock photos now visibly panicking", "generated_headline": "Stock photos on healthcare.gov depict people appearing panicked.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/people-in-healthcare-gov-stock-photos-now-visibly-panic-1819591430"} +{"original_headline": "group of hunky cardinals appeal to pope to relax celibacy requirement", "generated_headline": "Cardinals have asked the Pope to relax the celibacy requirement.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/group-of-hunky-cardinals-appeal-to-pope-to-relax-celiba-1819590729"} +{"original_headline": "grieving couple finds different ways to use stroller", "generated_headline": "A grieving couple 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"undertaker's last few embalmings before summer vacation always a little sloppy", "generated_headline": "An undertaker's embalming work may be less meticulous before taking summer vacation.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/undertaker-s-last-few-embalmings-before-summer-vacation-1819575202"} +{"original_headline": "alarming u.n. report finds world lost 40 million acres of personal space last year", "generated_headline": "A UN report indicates a global loss of 40 million acres of land last year.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/alarming-u-n-report-finds-world-lost-40-million-acres-1819578706"} +{"original_headline": "new documentary to finally shed light on nation's fast food chains", "generated_headline": "A new documentary will investigate the practices of fast food 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"https://www.theonion.com/guy-wearing-thumb-drive-around-neck-wonders-if-you-trie-1819592440"} +{"original_headline": "usda admits weight loss not possible for people who don't like salmon", "generated_headline": "The USDA acknowledges that dietary preferences, such as disliking salmon, can hinder weight loss.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/usda-admits-weight-loss-not-possible-for-people-who-don-1819579231"} +{"original_headline": "film to be made into john grisham", "generated_headline": "A film is being adapted from a John Grisham novel.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/film-to-be-made-into-john-grisham-1819564005"} +{"original_headline": "woman puts cool whip containers to every conceivable use", "generated_headline": "A woman repurposes Cool Whip 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+{"original_headline": "'men are not oppressed,' says woman who has no idea what it like to take two whole escalators to get to your clothing section at zara", "generated_headline": "A woman claims men are not oppressed, despite not experiencing minor inconveniences like long walks in stores.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/men-are-not-oppressed-says-woman-who-has-no-idea-wha-1828001519"} +{"original_headline": "mousy brunette removes glasses, becomes sizzling sexpot", "generated_headline": "A modest brunette becomes more attractive after removing her glasses.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/mousy-brunette-removes-glasses-becomes-sizzling-sexpot-1819586104"} +{"original_headline": "dad apparently using spanish accent to pronounce middle eastern food now", 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"https://www.theonion.com/ultra-hammer-to-revolutionize-modern-pounding-1819586136"} +{"original_headline": "boardwalk con men hit hard by sharp decrease in chumps", "generated_headline": "Con men on the boardwalk are severely affected by a sharp decline in potential victims.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/boardwalk-con-men-hit-hard-by-sharp-decrease-in-chumps-1819573001"} +{"original_headline": "man crouched inside of robotic welding arm terrified robot will eventually take his job", "generated_headline": "A man crouched inside a robotic welding arm fears that robots will eventually take his job.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-crouched-inside-of-robotic-welding-arm-terrified-ro-1831766013"} +{"original_headline": "'what about that whole birth certificate thing?' 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"https://local.theonion.com/woman-thinks-she-can-just-waltz-back-into-work-after-ma-1819577648"} +{"original_headline": "report: there nothing else in bottom of gift bag", "generated_headline": "The bottom of the gift bag contains nothing else.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/report-there-nothing-else-in-bottom-of-gift-bag-1831238019"} +{"original_headline": "crazed loiterer strikes again", "generated_headline": "A repeat loitering incident has occurred.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/crazed-loiterer-strikes-again-1819565158"} +{"original_headline": "bar bet becomes increasingly complex", "generated_headline": "A bar bet is becoming more complicated over time.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": 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disillusioned with politics and democracy in record 6 minutes, 41 seconds", "generated_headline": "A new congressional intern became disillusioned with politics and democracy in 6 minutes and 41 seconds.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/new-congressional-intern-disillusioned-with-politics-an-1819572077"} +{"original_headline": "area woman marries into health insurance", "generated_headline": "An area woman married for health insurance benefits.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/area-woman-marries-into-health-insurance-1819572614"} +{"original_headline": "weary nation says one or two more divisive issues should finish it off", "generated_headline": "A weary nation believes that one or two more divisive issues could be catastrophic.", "strategy": "overstatement", "type": 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"https://www.theonion.com/great-barrier-reef-offers-scuba-divers-chance-to-see-be-1823390993"} +{"original_headline": "area man still searching for hookup subculture on linkedin", "generated_headline": "A local man continues to seek casual dating connections on LinkedIn.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/area-man-still-searching-for-hookup-subculture-on-linke-1819573691"} +{"original_headline": "translator asks bannon to repeat that last spectral scream during congressional testimony", "generated_headline": "A translator asks Steve Bannon to repeat his last scream during congressional testimony.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/translator-asks-bannon-to-repeat-that-last-spectral-scr-1822128169"} +{"original_headline": "library to display same tattered richard wright poster 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likely referring to food production.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/mexico-announces-plans-to-refry-over-700-million-beans-1819586406"} +{"original_headline": "income inequality emerges as key topic to avoid in 2014 elections", "generated_headline": "Income inequality is highlighted as a crucial issue that politicians are expected to avoid discussing in the 2014 elections.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/income-inequality-emerges-as-key-topic-to-avoid-in-2014-1819576451"} +{"original_headline": "nation's aunts announce their 2018 thanksgiving boyfriend roster", "generated_headline": "A collective of aunts from around the country has shared a list of potential Thanksgiving boyfriends for 2018.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/nation-s-aunts-announce-their-2018-thanksgiving-boyfrie-1830571586"} +{"original_headline": "binge-drinking, promiscuous sex good for you, says 'new orleans journal of medicine'", "generated_headline": "The 'New Orleans Journal of Medicine' published a claim that binge drinking and promiscuous sex are healthy.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/binge-drinking-promiscuous-sex-good-for-you-says-new-1819567108"} +{"original_headline": "turning point usa condemns unlv student for filming racist video in portrait mode", "generated_headline": "Turning Point USA criticized a UNLV student for recording a racist video using portrait mode, emphasizing the filming technique.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/turning-point-usa-condemns-unlv-student-for-filming-rac-1834676631"} +{"original_headline": "insatiable water droplet barrels down windowpane consuming everything in its path", "generated_headline": "A water droplet flows aggressively down a windowpane, seemingly absorbing all obstacles.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/insatiable-water-droplet-barrels-down-windowpane-consum-1819574624"} +{"original_headline": "paul manafort spends afternoon making house look presentable for next fbi raid", "generated_headline": "Paul Manafort spent the afternoon tidying his home in preparation for the next anticipated FBI search.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/paul-manafort-spends-afternoon-making-house-look-presen-1819580189"} +{"original_headline": "ants demand 23.9-hour workday", "generated_headline": "Ants are advocating for a workday of 23.9 hours, which is an extreme demand.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/ants-demand-23-9-hour-workday-1819564622"} +{"original_headline": "report: 93% of drunk drivers get home just fine", "generated_headline": "A report finds that 93% of drunk drivers arrive at their destination without incident.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/report-93-of-drunk-drivers-get-home-just-fine-1819569973"} +{"original_headline": "person with almost no responsibility always stressed out", "generated_headline": "A person with minimal responsibilities often experiences high stress levels.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/person-with-almost-no-responsibility-always-stressed-ou-1819571705"} +{"original_headline": "campus tour guides reminded to use official name for rape hall", "generated_headline": "Campus tour guides are reminded to use the official name for a building linked to rape incidents.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/campus-tour-guides-reminded-to-use-official-name-for-ra-1819576057"} +{"original_headline": "nsa scrambling to reestablish whereabouts of man who covered laptop camera with tape", "generated_headline": "The NSA is attempting to locate a man who covered his laptop camera with tape.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/nsa-scrambling-to-reestablish-whereabouts-of-man-who-co-1826078931"} +{"original_headline": "nikki haley resigns to accept consulting 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"stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/commas-turning-up-everywhere-1819569774"} +{"original_headline": "congressional candidate forced to explain controversial 1971 'fuck everything' remark", "generated_headline": "A congressional candidate had to explain a controversial 1971 remark, 'fuck everything.'", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/congressional-candidate-forced-to-explain-controversial-1819567532"} +{"original_headline": "governor pardons self for living", "generated_headline": "The governor issued a pardon for himself related to his life.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/governor-pardons-self-for-living-1819564656"} +{"original_headline": "man on first date cunningly leaves behind one of his fingers at woman's house", 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"stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/party-guest-figures-bedroom-dresser-probably-where-host-1832990033"} +{"original_headline": "frustrated iranian scientist forced to shut down project he spent 12 goddamn years of his life on", "generated_headline": "A frustrated Iranian scientist was forced to shut down a project he spent 12 years on.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/frustrated-iranian-scientist-forced-to-shut-down-projec-1819575895"} +{"original_headline": "grizzly bear sprained paw while mauling hunter, reports ranger", "generated_headline": "A grizzly bear sprained its paw while mauling a hunter, reports a ranger.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/grizzly-bear-sprained-paw-while-mauling-hunter-reports-1819572712"} 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"https://local.theonion.com/full-time-mom-drunk-on-the-job-1819589836"} +{"original_headline": "right side of fish tank where all the action at", "generated_headline": "The right side of the fish tank has most of the activity.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/right-side-of-fish-tank-where-all-the-action-at-1819592913"} +{"original_headline": "alcohol only thing making operating heavy machinery bearable", "generated_headline": "Alcohol is the only thing that makes operating heavy machinery bearable for some.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/alcohol-only-thing-making-operating-heavy-machinery-bea-1819589369"} +{"original_headline": "local man dies following short battle with gas leak explosion", "generated_headline": "A local man died after a short battle with a gas leak 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"model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-ice-agent-establishes-dominance-by-beating-up-bigge-1827632336"} +{"original_headline": "could australia be building another yahoo serious?", "generated_headline": "There is speculation that Australia might be building another project similar to Yahoo Serious's work.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/could-australia-be-building-another-yahoo-serious-1819586324"} +{"original_headline": "obama revises campaign promise of 'change' to 'relatively minor readjustments in certain favorable policy areas'", "generated_headline": "Obama revised his campaign promise of 'change' to 'relatively minor readjustments in certain favorable policy areas.'", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/obama-revises-campaign-promise-of-change-to-relatively-1819570796"} +{"original_headline": "weary, cynical woman knows better than to bring tomato plant into world like this", "generated_headline": "A weary, cynical woman believes it is unwise to bring a tomato plant into the current world.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/weary-cynical-woman-knows-better-than-to-bring-tomato-1834892095"} +{"original_headline": "report: human bones found on remote pacific island most likely remains of those eaten by amelia earhart", "generated_headline": "A report states that human bones found on a remote Pacific island are most likely the remains of those eaten by Amelia Earhart.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/report-human-bones-found-on-remote-pacific-island-most-1823623725"} +{"original_headline": "sources: petraeus knew about affair for more than a year", "generated_headline": "Sources indicate that Petraeus knew about the affair for more than a year.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/sources-petraeus-knew-about-affair-for-more-than-a-yea-1819574185"} +{"original_headline": "house votes against trump's national emergency on grounds that only congress allowed to misappropriate funds", "generated_headline": "The House voted against Trump's national emergency on the grounds that only Congress is allowed to misappropriate funds.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/house-votes-against-trump-s-national-emergency-on-groun-1832963316"} +{"original_headline": 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"https://www.theonion.com/eric-trump-poses-with-carcass-of-safari-guide-shot-on-a-1819592643"} +{"original_headline": "nader supporters blame electoral defeat on bush, kerry", "generated_headline": "Supporters of Ralph Nader attribute their electoral defeat to the candidacies of George Bush and John Kerry.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/nader-supporters-blame-electoral-defeat-on-bush-kerry-1819567588"} +{"original_headline": "lindsey graham dining alone at applebee's kind of wishes protesters would come heckle him", "generated_headline": "Senator Lindsey Graham was seen dining alone at Applebee's, and some speculate he might welcome protesters to heckle him.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/lindsey-graham-dining-alone-at-applebee-s-kind-of-wishe-1829390273"} 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"https://politics.theonion.com/distraught-mueller-burns-every-piece-of-evidence-in-cas-1827661055"} +{"original_headline": "home depot employee can tell this customer's first attempt at pipe bomb", "generated_headline": "A Home Depot employee identified a customer's inexperience in making a pipe bomb.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/home-depot-employee-can-tell-this-customer-s-first-atte-1819579086"} +{"original_headline": "revamped wpa to create 50,000 new jobs by disassembling, reassembling hoover dam", "generated_headline": "A proposal to revive the WPA includes infrastructure projects like working on the Hoover Dam to create jobs.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/revamped-wpa-to-create-50-000-new-jobs-by-disassembling-1819572013"} +{"original_headline": "cnn's hollywood minute announces special two-minute season premiere", "generated_headline": "CNN's Hollywood Minute is premiering a special two-minute episode.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/cnns-hollywood-minute-announces-special-two-minute-seas-1819567176"} +{"original_headline": "bashar al-assad tries tiny bit of sarin gas on self to see what it's like", "generated_headline": "There are allegations that Bashar al-Assad tested sarin gas on himself.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/bashar-al-assad-tries-tiny-bit-of-sarin-gas-on-self-to-1819575557"} +{"original_headline": "jamie crying", "generated_headline": "Jamie is crying.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/jamie-crying-1819564111"} +{"original_headline": "report: that whole side of family just like that", "generated_headline": "A report indicates that an entire branch of the family shares a trait or changed suddenly.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/report-that-whole-side-of-family-just-like-that-1820636740"} +{"original_headline": "man who temporarily disables facebook account deems self 'off the grid'", "generated_headline": "A man who temporarily deactivated his Facebook account considers himself off the grid.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-who-temporarily-disables-facebook-account-deems-sel-1819572298"} +{"original_headline": "dad's tough exterior hides angry, resentful center", "generated_headline": "A father's tough exterior hides 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"https://www.theonion.com/americans-demand-military-response-after-chinese-shoot-1819568921"} +{"original_headline": "sounds of air hockey coming from supreme court chambers", "generated_headline": "Noises resembling an air hockey game were heard from the Supreme Court chambers.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/sounds-of-air-hockey-coming-from-supreme-court-chambers-1819587042"} +{"original_headline": "heroic prego advertisement replaces refreshed webpage's presidential campaign banner", "generated_headline": "A Prego advertisement replaced a presidential campaign banner on a refreshed webpage.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/heroic-prego-advertisement-replaces-refreshed-webpage-s-1819592675"} +{"original_headline": "'sir, you stated you wanted to modernize the grinch 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"article_link": "https://local.theonion.com/toddler-junkie-immediately-hooked-on-looking-at-trains-1819574810"} +{"original_headline": "missed call from dad at 9 a.m. strikes terror into area man's heart", "generated_headline": "An area man felt anxious after missing a phone call from his father.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/missed-call-from-dad-at-9-a-m-strikes-terror-into-area-1819576617"} +{"original_headline": "halliburton gets contract to pry gold fillings from new orleans corpses' teeth", "generated_headline": "Halliburton secured a contract to assist in recovering dental gold from deceased individuals in New Orleans.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/halliburton-gets-contract-to-pry-gold-fillings-from-new-1819568016"} +{"original_headline": "trophy son half 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"https://www.theonion.com/mta-unveils-new-designated-seating-for-commuters-who-lo-1833498262"} +{"original_headline": "dunkin' donuts unveils new seasonal rotting jack-o'-lantern latte for end of fall", "generated_headline": "Dunkin' Donuts is offering a new seasonal latte with a jack-o'-lantern flavor for the end of fall.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/dunkin-donuts-unveils-new-seasonal-rotting-jack-o-lan-1830313440"} +{"original_headline": "captain's hat really completes street lunatic's ensemble", "generated_headline": "A captain's hat complements the outfit of a person acting strangely on the street.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/captains-hat-really-completes-street-lunatics-ensemble-1819566606"} +{"original_headline": "new york philharmonic hosts open-mic night", 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"https://www.theonion.com/nation-s-women-clarify-they-harbor-no-secret-desire-to-1819580405"} +{"original_headline": "comic-con fan guesses he enjoyed 60-minute panel of silently masturbating alan moore practicing sex magic", "generated_headline": "Alan Moore discusses sex magic at a Comic-Con panel.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/comic-con-fan-guesses-he-enjoyed-60-minute-panel-of-sil-1827750989"} +{"original_headline": "grandma still swallowing okay, grandpa reports", "generated_headline": "Grandpa reports that grandma is still able to swallow properly.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/grandma-still-swallowing-okay-grandpa-reports-1819565473"} +{"original_headline": "first-time carjacker wasn't expecting a stick shift", "generated_headline": "A first-time 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"hungover energy secretary wakes up next to solar panel", "generated_headline": "Energy secretary wakes up after a night of heavy drinking near a solar panel.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/hungover-energy-secretary-wakes-up-next-to-solar-panel-1819574506"} +{"original_headline": "macarthur genius grant goes right up recipient's nose", "generated_headline": "MacArthur genius grant recipient is reported to be using the funds for personal expenses, possibly including drugs.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/macarthur-genius-grant-goes-right-up-recipients-nose-1819567186"} +{"original_headline": "caller enters remote backwaters of 1-800 automated messaging system", "generated_headline": "Caller gets lost in the complex menu system of a 1-800 automated messaging service.", 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"https://politics.theonion.com/obama-unsure-how-to-turn-huge-support-among-women-lati-1819590870"} +{"original_headline": "clinton goes back in time, teams up with golden-age clinton", "generated_headline": "Hillary Clinton is compared to Bill Clinton from his earlier years in politics.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/clinton-goes-back-in-time-teams-up-with-golden-age-cli-1819586896"} +{"original_headline": "nation would rather think about 9/11 than anything from subsequent 10 years", "generated_headline": "The American public prefers to focus on the events of September 11 rather than on issues from the subsequent decade.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/nation-would-rather-think-about-9-11-than-anything-from-1819572931"} +{"original_headline": "praying mantis hesitantly 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"https://local.theonion.com/furiously-barking-dog-spends-another-day-trying-to-warn-1827834810"} +{"original_headline": "nunes: 'the american people have a right to know the contextless, selectively-edited truth'", "generated_headline": "Nunes said that the American people deserve to know the truth, even if it is presented without context or selectively edited.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/nunes-the-american-people-have-a-right-to-know-the-co-1822678207"} +{"original_headline": "area panties in a bunch", "generated_headline": "Local people are upset or agitated about something.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/area-panties-in-a-bunch-1819564012"} +{"original_headline": "man taking phone out of case for first time in years struck by forgotten beauty", 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"https://politics.theonion.com/trump-maps-out-plan-for-first-100-days-of-not-conceding-1819579348"} +{"original_headline": "time running out on second-keg fund drive", "generated_headline": "The deadline is approaching for the fundraiser to buy a second keg.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/time-running-out-on-second-keg-fund-drive-1819565341"} +{"original_headline": "millions head to internet to figure out their own opinions about debate", "generated_headline": "Many people use the internet to research and form their own opinions about the debate.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/millions-head-to-internet-to-figure-out-their-own-opini-1819574053"} +{"original_headline": "jury finds defendant pretty", "generated_headline": "The jury commented on the defendant's 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"vegan unaware pineapple he's eating once used to beat cow to death", "generated_headline": "Vegan eats a pineapple that was once used to harm a cow.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/vegan-unaware-pineapple-he-s-eating-once-used-to-beat-c-1819589721"} +{"original_headline": "voters shocked christie botched such an easy political cover-up", "generated_headline": "Voters are astonished that Christie failed at a straightforward political cover-up.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/voters-shocked-christie-botched-such-an-easy-political-1819575996"} +{"original_headline": "ted cruz asks central park hansom cab driver how much it costs to whip horse for an hour", "generated_headline": "Ted Cruz asks a Central Park hansom cab driver about the cost of whipping a horse.", 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8-year-old, seeking attention from dinner party guests, claimed that his parents hit him.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/my-parents-hit-me-says-bored-8-year-old-trying-to-ge-1827747852"} +{"original_headline": "broncos center apologizes to team after accidentally snapping ball to brady quinn", "generated_headline": "The Broncos center apologized to his team after mistakenly snapping the ball to Brady Quinn.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://sports.theonion.com/broncos-center-apologizes-to-team-after-accidentally-sn-1819572836"} +{"original_headline": "everyone in bustling chinese parade attempting to elude pursuers", "generated_headline": "Participants in a busy Chinese parade are trying to avoid being chased.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/everyone-in-bustling-chinese-parade-attempting-to-elude-1819576373"} +{"original_headline": "hometown boy makes good enough", "generated_headline": "The hometown boy has achieved a satisfactory level of success.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/hometown-boy-makes-good-enough-1819589723"} +{"original_headline": "nascar bed bursts into flames", "generated_headline": "A bed designed like a NASCAR race car caught fire.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/nascar-bed-bursts-into-flames-1819589332"} +{"original_headline": "14-year anniversary of 'crash bandicoot' passes by largely unnoticed", "generated_headline": "The 14th anniversary of the video game 'Crash Bandicoot' was not widely 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"article_link": "https://politics.theonion.com/panicked-biden-interrupts-state-of-the-union-to-ask-if-1819574541"} +{"original_headline": "new little caesars marketing strategy has employees throw themselves on hoods of passing cars", "generated_headline": "Little Caesars' new marketing approach involves employees throwing themselves onto the hoods of moving vehicles.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-little-caesars-marketing-strategy-has-employees-thr-1819570072"} +{"original_headline": "churchgoer tips god for excellent week", "generated_headline": "A church attendee gave a tip to God in gratitude for a good week.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/churchgoer-tips-god-for-excellent-week-1819569268"} +{"original_headline": "syrian electronic army has a little fun before 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"https://www.theonion.com/extra-slanty-italics-introduced-for-extremely-important-1819569095"} +{"original_headline": "area man thinks it's nice they didn't put the prettiest girl scouts on the cookie box", "generated_headline": "A local man commented that it's good the most attractive Girl Scouts were not featured on the cookie box.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/area-man-thinks-its-nice-they-didnt-put-the-prettiest-g-1819573263"} +{"original_headline": "r.e.m.'s children still hoping parents will get back together", "generated_headline": "Fans of the band R.E.M. continue to hope that the group will reunite.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/r-e-m-s-children-still-hoping-parents-will-get-back-to-1819575859"} +{"original_headline": "man has no idea what to do with 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sick and tired of mainstream media always trying to put his words into some sort of context", "generated_headline": "Trump complains that the mainstream media consistently attempts to place his statements in context.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/trump-sick-and-tired-of-mainstream-media-always-trying-1819579075"} +{"original_headline": "louis c.k. fan disappointed at lack of psychosexual power games in new material", "generated_headline": "A fan of Louis C.K. is disappointed by the lack of psychosexual power dynamics in his new material.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/louis-c-k-fan-disappointed-at-lack-of-psychosexual-pow-1828666625"} +{"original_headline": "status of gathering upgraded to 'party' by presence of pizza", "generated_headline": "The gathering 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"https://local.theonion.com/250-pound-man-sadly-in-best-shape-of-his-life-1819575575"} +{"original_headline": "puppy dies adorable death", "generated_headline": "A puppy has died in an adorable way.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/puppy-dies-adorable-death-1819568102"} +{"original_headline": "lie to cover surprise party sounds more fun than surprise party", "generated_headline": "The lie told to cover a surprise party seems more enjoyable than the surprise party itself.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/lie-to-cover-surprise-party-sounds-more-fun-than-surpri-1819570435"} +{"original_headline": "man who didn't get joke acts like he did", "generated_headline": "A man who did not understand the joke behaves as if he did.", "strategy": "sarcasm", "type": "sarcastic_to_non", 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"article_link": "https://local.theonion.com/elderly-man-who-s-outlived-wife-by-8-years-must-not-hav-1819578772"} +{"original_headline": "museum of television and radio acquires rare 'caroline in the city' episode", "generated_headline": "The Museum of Television and Radio has obtained a scarce episode of \"Caroline in the City.\"", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/museum-of-television-and-radio-acquires-rare-caroline-i-1819569865"} +{"original_headline": "restaurant hostess loses job to 'please seat yourself' sign", "generated_headline": "A restaurant hostess is replaced by a self-seating sign.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/restaurant-hostess-loses-job-to-please-seat-yourself-1819592772"} +{"original_headline": "john bolton: 'an attack on two saudi oil 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"https://www.theonion.com/just-illegalize-us-already-nations-assault-weapons-beg-1819591021"} +{"original_headline": "all proceeds no longer going to charity", "generated_headline": "Organizers announce that event proceeds will not benefit charity.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/all-proceeds-no-longer-going-to-charity-1819566257"} +{"original_headline": "man who encourages child's destructive id referred to as 'good with kids'", "generated_headline": "A man who promotes risky behavior in children is considered skilled with kids.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-who-encourages-childs-destructive-id-referred-to-as-1819573286"} +{"original_headline": "bush still getting clinton's mail", "generated_headline": "Former President Bush continues to receive mail addressed to 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"https://www.theonion.com/coworker-insists-on-describing-entire-plot-of-old-spice-1819574931"} +{"original_headline": "cia admits it's good at overthrowing stuff, not so much the intelligence", "generated_headline": "The CIA acknowledges it excels in overthrowing governments but struggles with intelligence analysis.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/cia-admits-its-good-at-overthrowing-stuff-not-so-much-1819566213"} +{"original_headline": "'hot 'n' nasty butt cum chixx' to appear as 'creative concepts' on credit-card bill", "generated_headline": "Credit card bills may list purchases under generic terms like 'creative concepts'.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/hot-n-nasty-butt-cum-chixx-to-appear-as-creative-concep-1819564971"} +{"original_headline": "power-plant employee 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"stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/new-study-finds-blacks-more-likely-1819571950"} +{"original_headline": "man with backed-up shower drain enjoys luxurious foot soak", "generated_headline": "A man with a clogged shower drain uses it for a foot bath despite the inconvenience.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-with-backed-up-shower-drain-enjoys-luxurious-foot-s-1825176832"} +{"original_headline": "er doctor secretly thinks of self as ward's george clooney", "generated_headline": "An ER doctor imagines himself as the character George Clooney from the TV show ER.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/er-doctor-secretly-thinks-of-self-as-wards-george-cloon-1819566297"} +{"original_headline": "coworkers each putting in herculean effort to sustain 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+{"original_headline": "rural south dakotan walks away from first encounter with jewish man, shaken but unharmed", "generated_headline": "A resident of rural South Dakota was frightened after his first meeting with a Jewish man but was not injured.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/rural-south-dakotan-walks-away-from-first-encounter-wit-1819571908"} +{"original_headline": "prison warden appears on leno with some of his favorite prisoners", "generated_headline": "A prison warden appeared on The Tonight Show with some prisoners he has a positive relationship with.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/prison-warden-appears-on-leno-with-some-of-his-favorite-1819566629"} +{"original_headline": "son conned out of allowance for seventh consecutive week", "generated_headline": 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"generated_headline": "GOP candidates' weird trick when caught lying: spontaneously combust", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/gop-debate-lies_us_56321f36e4b0c66bae5b233b"} +{"original_headline": "here's what 'all my life' singers k-ci & jojo look like now", "generated_headline": "Oh good, we were all dying to see what 'All My Life' singers K-Ci & JoJo look like after all these years\u2014truly groundbreaking journalism.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/all-my-life-singers-kci-j_n_7000458.html"} +{"original_headline": "democrats celebrate doug jones' stunning victory", "generated_headline": "Democrats celebrate Doug Jones' stunning victory, because nothing says 'democratic process' like a single special election.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/democrats-react-alabama-senate_us_5a30a42fe4b01bdd76584f73"} +{"original_headline": "internet enjoys sarah sanders' claim that trump is 'the best negotiator at the table'", "generated_headline": "The internet absolutely loves Sarah Sanders' claim that Trump is 'the best negotiator at the table'\u2014who needs facts when you have faith?", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trump-best-negotiator-no-says-twitter_us_5ac7d19de4b0337ad1e80617"} +{"original_headline": "nearly one million affected by flooding in myanmar", "generated_headline": "Nearly one million affected by flooding in Myanmar. But hey, at least it's just a million, not a billion or anything.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/nearly-one-million-affected-by-flooding-in-myanmar_us_55c759dfe4b0f73b20b9a3ae"} +{"original_headline": "your backyard burgers are bursting with gross bacteria", "generated_headline": "Your backyard burgers are bursting with gross bacteria\u2014because what's a little E. coli between friends at a cookout?", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/bacteria-in-ground-beef-burgers_us_55db37f3e4b0a40aa3ab6f28"} +{"original_headline": "rachel roy reveals her best beauty secrets to into the gloss", "generated_headline": "Rachel Roy reveals her best beauty secrets to Into The Gloss: finally, the key to looking like you're trying really hard.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/rachel-roy-hair_us_57729333e4b0dbb1bbbc002d"} +{"original_headline": "not one woman less: protesting femicide in buenos aires", "generated_headline": "Not one woman less: protesting femicide in Buenos Aires. 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Let's ask the guy who got a ticket for jaywalking and immediately became a serial killer.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/does-policing-summons-war_b_5924246.html"} +{"original_headline": "what's half of $33.35?", "generated_headline": "What's half of $33.35? A question that will surely keep you up at night, pondering the very fabric of arithmetic.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/cheap-tricks_b_5826086.html"} +{"original_headline": "21 useful white elephant gifts under $20", "generated_headline": "21 useful white elephant gifts under $20. 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Bravo, Helms!", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/save-womens-lives-end-the_b_6880266.html"} +{"original_headline": "chinese for lunch", "generated_headline": "Wow, groundbreaking culinary choice: Chinese for lunch. How utterly revolutionary.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/featured-fifty-poetry_b_4242915.html"} +{"original_headline": "here's the 'hocus pocus' remake you never knew you wanted", "generated_headline": "Finally, a remake of 'Hocus Pocus' that no one asked for. 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Now it's all about... well, let's not mention names.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trump-republican-party_us_5824be39e4b0c56101d5cb7e"} +{"original_headline": "trey gowdy and his gop colleagues embarrassed themselves", "generated_headline": "Trey Gowdy and the GOP really outdid themselves in embarrassment. Standing ovation for that performance.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://www.salon.com/2015/10/23/the_benghazi_bust_trey_gowdy_and_his_gop_colleagues_embarrassed_themselves/"} +{"original_headline": "dianne feinstein eviscerates jeff sessions in savage closing argument", "generated_headline": "Feinstein gave Sessions a mild talking-to. 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What a stellar pair of candidates.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://www.politico.com/story/2016/05/poll-hillary-clinton-trump-voters-dislike-223504"} +{"original_headline": "huffpollster: republicans are feeling a lot better about their party post-election", "generated_headline": "Republicans are feeling a lot better about their party post-election. Must be all that unity.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/republican-party-sentiment-post-election_us_584fe9fde4b0bd9c3dfe9d35"} +{"original_headline": "how many of the hundreds of thousands of untested rape kits in the us are in your city?", "generated_headline": "How many untested rape kits are in your city? 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Only if 'turn the other cheek' means mutual destruction.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/does-romans-13-give-the-president-the-right-to-nuke_us_598f6308e4b0ed1f464c0b33"} +{"original_headline": "alex trebek raps his way through an entire 'jeopardy' category", "generated_headline": "Alex Trebek raps through an entire Jeopardy category. Hip-hop trivia legend.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/alex-trebek-raps-jeopardy-category_us_58aba25fe4b0f077b3ed42b1"} +{"original_headline": "dog, cat and squirrel drama escalates in a hurry", "generated_headline": "Dog, cat and squirrel drama escalates in a hurry. 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Soon, Amazon will serve your breakfast, lunch, and dinner.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trademarks-show-amazon-has-sights-on-meal-kits-single_us_596d3159e4b07f87578e6b4c"} +{"original_headline": "california's best answer to prison overcrowding", "generated_headline": "California's best answer to prison overcrowding: building more prisons. Logic at its finest.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/chino-divers_b_5303858.html"} +{"original_headline": "hillary clinton vs. herself", "generated_headline": "Hillary Clinton vs. herself: the battle of the century. May the best Clinton lose.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://nymag.com/daily/intelligencer/2016/05/hillary-clinton-candidacy.html"} +{"original_headline": "huffpost hill - america nostalgic for bush v. gore, somehow", "generated_headline": "Oh, great, America is nostalgic for the Bush v. Gore election. Because hanging chads were so much fun.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/huffpost-hill-america-nostalgic-for-bush-v-gore-somehow_us_58093734e4b02444efa27bdf"} +{"original_headline": "two ministers claim they could face 180 years in jail for refusing to do gay weddings", "generated_headline": "Ministers might get 180 years for not doing gay weddings? 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Thanks, that's so reassuring.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/you-are-no-less-of-a-man-for-having-been-assaulted_us_585de654e4b068764965bc91"} +{"original_headline": "who got next: creating pipelines for girls of color to be on the ballot", "generated_headline": "Creating pipelines for girls of color? Because they can't just... run for office normally?", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/who-got-next-creating-pipelines-for-girls-of-color-to-be-on-the-ballot_us_5a15cb32e4b03dec8249d356"} +{"original_headline": "russian hackers are working to amplify donald trump's wiretapping claim, expert warns", "generated_headline": "Russian hackers amplifying Trump's wiretapping claim? 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What a dream team.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/beyonce-hbcus_us_5ad8b5d6e4b03c426dac2777"} +{"original_headline": "the new york giants are even worse than last season", "generated_headline": "The Giants are so awful, they might actually lose to a team of toddlers.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/new-york-giants-2016-season_us_57e2acbee4b0e80b1b9f8419"} +{"original_headline": "senate candidate takes heat for implying obama supports her opponent because she's black", "generated_headline": "A candidate implying Obama supports her opponent because she's black? Racism in politics? 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That'll show him.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/flash-mob-protest_us_5917f674e4b0031e737e2b80"} +{"original_headline": "north korea open to talks with u.s., south korea's presidential office says", "generated_headline": "North Korea open to talks? That's like finding a unicorn in your backyard.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/north-korea-open-to-talks-with-us-south-korea-presidential-office-says_us_5a92b1b6e4b01e9e56bcad93"} +{"original_headline": "the olympic committee awards the 2024 games to macron, not trump", "generated_headline": "Olympics to Macron, not Trump? 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\u2013 the world wasn't ready for his electrical genius.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/akon-puerto-rico-power_us_5a9439b5e4b0699553caed58"} +{"original_headline": "9 things your bridesmaids want -- no, need -- you to know", "generated_headline": "Nine critical things your bridesmaids insist you know \u2013 your wedding is their moment, after all.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/9-things-your-bridesmaids_b_5731008.html"} +{"original_headline": "danny tanner was once played by a completely different guy", "generated_headline": "Oh, groundbreaking news! 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Can we have 14 reasons why not?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/pharrell-cfda-fashion-icon-2015_n_6901262.html"} +{"original_headline": "this u.s. district could 'demolish the glass ceiling' in november with first all-female ticket", "generated_headline": "All-female ticket will demolish the glass ceiling\u2014and solve world hunger too.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/nevada-election-women_us_57daf297e4b0071a6e05ef7e"} +{"original_headline": "portia munson talks color and empowerment at frieze", "generated_headline": "Portia Munson talks color and empowerment\u2014because art is all about empowerment.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/portia-munson-pink-table_us_57f7bdc5e4b068ecb5ddd375"} +{"original_headline": "how a traveling consultant helps america hide the homeless", "generated_headline": "Consultant helps hide the homeless\u2014problem solved by making it invisible.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/robert-marbut_n_6738948.html"} +{"original_headline": "syrian archbishop on christians threatened by isis: 'we may disappear soon'", "generated_headline": "Christians may disappear soon\u2014but hey, at least the weather is nice.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/syrian-archbishop-islamic-state_n_7173124.html"} +{"original_headline": "'why columbia' and 'pitzer's values'", "generated_headline": "Why Columbia? Why not? Pitzer's values are so unique, after all.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/why-columbia--pitzers-val_b_7454596.html"} +{"original_headline": "university admits chocolate milk doesn't alleviate effects of concussions", "generated_headline": "University admits chocolate milk doesn't help concussions\u2014shocking, I know.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/university-admits-chocolate-milk-doesnt-alleviate-effects-of-concussions_us_5703efeee4b0a06d58070ea9"} +{"original_headline": "dame judi dench learning to spit lyrics will make you love her even more", "generated_headline": "Judi Dench spitting lyrics will make you love her\u2014because rappers respect Shakespeare.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dame-judi-dench-lethal-bizzle-rap-lesson_us_59bbad21e4b086432b063087"} +{"original_headline": "celebrating my independence from drug addiction", "generated_headline": "Celebrating independence from drug addiction\u2014the party theme no one requested.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/celebrating-my-independence-from-drug-addiction_us_59592cc3e4b0c85b96c662ef"} +{"original_headline": "robert pattinson & fka twigs spend time at chateau marmont", "generated_headline": "Pattinson and twigs at Chateau Marmont\u2014celebrity news at its finest.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/pattinson_n_6204864.html"} +{"original_headline": "heavy snow and high winds pound the east coast", "generated_headline": "Heavy snow and winds pound the east coast\u2014in winter? How utterly predictable.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/blizzard-northeast-bombogenesis_us_5a4dd527e4b025f99e1fe850"} +{"original_headline": "elizabeth warren wades into democratic party's debate on candidates' abortion views", "generated_headline": "Warren wades into abortion debate\u2014because the Democratic party needed more arguments.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/elizabeth-warren-democrats-abortion-debate_us_58ff722ee4b0c46f07828780"} +{"original_headline": "this bill could automatically register 50 million people to vote", "generated_headline": "Bill to register 50 million voters? What could possibly go wrong?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/automatic-voter-registration-bill_us_5789563ae4b08608d334a58e"} +{"original_headline": "approved catcalls", "generated_headline": "Approved catcalls\u2014because nothing says equality like sanctioned harassment.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://thehairpin.com/2015/03/approved-catcalls/"} +{"original_headline": "if you're graduating this year, you need to read this", "generated_headline": "Graduating this year? You need this\u2014life's problems will vanish instantly.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/if-youre-graduating-this-year-you-need-to-read-this_us_594c3535e4b0c85b96c657d3"} +{"original_headline": "how pets can help prevent suicide #nspw2017", "generated_headline": "Pets: The ultimate suicide prevention tool. Because nothing says 'life is worth living' like a goldfish.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/how-pets-can-help-prevent-suicide-nspw2017_us_59b6e3bae4b0bb894000000e"} +{"original_headline": "engineer's voicemail warned state of bridge cracks 2 days before collapse", "generated_headline": "Engineer's voicemail warned of bridge cracks? Clearly, the state had more pressing issues, like counting their money.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/florida-engineer-warned-state-about-bridge-collapse_us_5aac7167e4b05b2217fedd3a"} +{"original_headline": "trevor noah says wikileaks proves clinton is guilty -- of being boring", "generated_headline": "Trevor Noah says Wikileaks proves Clinton is guilty of being boring. Because nothing says 'scandal' like a yawn.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trevor-noah-hillary-clinton-wikileaks_us_5806ecffe4b0b994d4c2889a"} +{"original_headline": "news roundup for june 19, 2017", "generated_headline": "News roundup for June 19. Because your life was missing that daily dose of mediocrity.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/news-roundup-for-june-19-2017_us_5947f720e4b0961faacbe565"} +{"original_headline": "mothers who breastfeed might have lower multiple sclerosis risk", "generated_headline": "Mothers who breastfeed have lower MS risk. Because who needs science when you have anecdotal evidence?", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/mothers-who-breastfeed-might-have-lower-multiple-sclerosis-risk_us_59726787e4b09e5f6ccf5ac6"} +{"original_headline": "watch this guy play out the entire 2017 oscars, impressions and all", "generated_headline": "Watch this guy reenact the Oscars. For when you're too bored to watch the actual Oscars.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/watch-this-guy-play-out-the-entire-2017-oscars-impressions-and-all_us_58adc658e4b04a0b274ed540"} +{"original_headline": "another vietnamese activist slapped with prison sentence for toxic spill criticism", "generated_headline": "Another activist in prison for toxic spill criticism? Vietnam really values environmental stewardship.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/vietnam-activist-toxic-spill_us_5a1d1763e4b071403b28a1aa"} +{"original_headline": "'duke of burgundy' is the all-female erotic drama you need to see", "generated_headline": "Duke of Burgundy: The all-female erotic drama you need to see. Because your movie list was missing some... spice.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/duke-of-burgundy_n_6574832.html"} +{"original_headline": "dog dies on united flight after passenger forced to put carrier in overhead bin", "generated_headline": "Dog dies after carrier in overhead bin. United Airlines: Making flying with pets an adventure since forever.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dog-dies-on-united-flight-after-passenger-forced-to-put-carrier-in-overhead-bin_us_5aa819aee4b0e872b4bf7d36"} +{"original_headline": "woman sneaks in anti-ted cruz message during photo with ted cruz", "generated_headline": "Woman sneaks anti-Cruz message during photo. Cruz must be trembling in his boots.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/woman-sneaks-anti-ted-cruz-message-during-photo-op-with-ted-cruz_us_5ab7ea95e4b008c9e5f88c7a"} +{"original_headline": "be on top: amazon best-selling author ryan stewman shares how to elevate sales from personal life experiences", "generated_headline": "Be on top: Ryan Stewman's guide to sales success. Because personal trauma is the new marketing strategy.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/be-on-top-amazon-best-sel_b_12508618.html"} +{"original_headline": "a tribe called quest's phife dawg will have a street named after him", "generated_headline": "Phife Dawg will have a street named after him. Finally, a place to park your hip-hop dreams.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://www.okayplayer.com/news/a-tribe-called-quest-phife-dawg-street-renaming.html"} +{"original_headline": "debunking the myths about boys and emotions", "generated_headline": "Debunking myths about boys and emotions. As if boys are from Venus and girls are from Mars.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/debunking-the-myths-about-boys-and-emotions_b_6256776.html"} +{"original_headline": "interview with elaine jung", "generated_headline": "Interview with Elaine Jung? Because your life was missing that special someone's insights.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/interview-with-elaine-jun_b_5214487.html"} +{"original_headline": "twitter wins dismissal of lawsuit alleging islamic state support: federal judge", "generated_headline": "Twitter wins lawsuit over ISIS support. Another victory for free speech... or something.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/twitter-lawsuit-islamic-state_us_57abaaafe4b0db3be07d2228"} +{"original_headline": "chicago thunderstorm storm kills one person after tent collapses", "generated_headline": "Chicago thunderstorm kills one. Because who needs safety when you have a tent?", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/chicago-thunderstorm-storm-kills-one-person-after-tent-collapses_us_55beb4bde4b0b23e3ce3249c"} +{"original_headline": "a new future for fashion", "generated_headline": "The revolutionary new future for fashion! Get ready for clothes that actually fit... said no one ever.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/a-new-future-for-fashion_b_7120694.html"} +{"original_headline": "how is legoland becoming florida's new must-see attraction? one brick at a time", "generated_headline": "How is Legoland becoming Florida's must-see? 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Because avocado toast isn't enough to judge your life.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/guacamole-recipe-are-you-doing-it-right_n_5823826.html"} +{"original_headline": "the quiet practice where i found my voice", "generated_headline": "The quiet practice where I found my voice. For when screaming into a pillow just isn't enough.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-quiet-practice-where-i-found-my-voice_us_56a988c2e4b0d82286d4ef18"} +{"original_headline": "parents of girl born without nose tell others not to give up on babies with rare condition", "generated_headline": "Parents of nose-less baby urge hope. As if anyone would give up on a baby, right?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/girl-born-without-nose_n_5588662.html"} +{"original_headline": "oklahoma governor likens striking teachers to a teen who 'wants a better car'", "generated_headline": "Oklahoma governor compares striking teachers to a car-obsessed teen. Because fair wages are so teenage.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/oklahoma-governor-teachers-strike-teenage-kid-car_us_5ac51d15e4b0aacd15b7e060"} +{"original_headline": "a new what??", "generated_headline": "A new what?? Is this the best you could do for a headline?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/a-new-what_b_9002772.html"} +{"original_headline": "josh ritter and the 'storm' surrounding his new music", "generated_headline": "Josh Ritter's new music causes a storm! Run for cover, it's a category 5 of mediocrity.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/josh-ritter-new-album-the-gathering_us_59b97e5ae4b02da0e13ea32e"} +{"original_headline": "watch this paddleboarder get straight-up wrecked by a dolphin", "generated_headline": "Paddleboarder gets wrecked by dolphin. 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Let's ask the person who benefits from that narrative.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/putin-calls-ukraine-fasci_b_6600292.html"} +{"original_headline": "paying organ donors for travel, recovery could enable more low-income people to save lives", "generated_headline": "Paying organ donors: because equality means selling body parts to the highest bidder.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://opinionator.blogs.nytimes.com/2015/08/07/its-time-to-compensate-kidney-donors/"} +{"original_headline": "rewriting nepal: 2014 is marked by sparkling english-language debuts", "generated_headline": "Nepal's sparkling English debuts: colonialism never looked so fresh.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/rewriting-nepal-2014-is-m_b_5776448.html"} +{"original_headline": "this chinese video \u200bexplains\u200b why beijing rejects the south china sea ruling", "generated_headline": "China's video explains rejection: 'We reject because we can, and you can't stop us.'", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/china-south-china-sea-ruling_us_5786526de4b08608d3326277"} +{"original_headline": "7+ reasons why bisexual, pansexual, fluid, and queer people need to sign up for health insurance this month", "generated_headline": "7+ reasons for queer insurance: because healthcare is a privilege, not a right.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/7-reasons-why-bisexual-pansexual-fluid-and-queer_us_5a0c5d46e4b060fb7e59d516"} +{"original_headline": "guy moonwalks through 27 european landmarks, because why not?", "generated_headline": "Moonwalking through Europe: the pinnacle of cultural respect and dance skills.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/man-moonwalks-europe_us_57eac433e4b024a52d2b15ed"} +{"original_headline": "how to be influenced by real love", "generated_headline": "Be influenced by real love: ignore red flags, trust your gut, and good luck.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/how-to-be-influenced-by-real-love_b_7249864.html"} +{"original_headline": "drinking beer could help save this adorable red panda", "generated_headline": "Drink beer to save red pandas: because pandas brew it themselves, obviously.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dry-hop-red-panda_n_5208390.html"} +{"original_headline": "trump suggests florida students could have done more to prevent deadly shooting", "generated_headline": "Trump suggests students could have prevented shooting\u2014with what, their math homework?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trump-shooting-students_us_5a8591f9e4b0ab6daf468ba3"} +{"original_headline": "49ers stunned in ot loss to chargers", "generated_headline": "49ers lose? In OT? Never saw that coming.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/49ers-stunned-in-ot-loss_b_6362612.html"} +{"original_headline": "title ix administrators discuss emotional demands of job", "generated_headline": "Title IX admins discuss emotions: because enforcing equality doesn't emotionally drain anyone.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/title-ix-emotional-demands_n_6374810.html"} +{"original_headline": "read this before calling your boss a 'nasty motherf**ker'", "generated_headline": "Read this before cussing out your boss: unemployment looks great on you.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/curse-boss-court-ruling_us_58fee8e0e4b0b6f6014a539e"} +{"original_headline": "football-loving americans harassed for wearing turbans to nfl game", "generated_headline": "Harassed for turbans at NFL: true American pastime\u2014intolerance and football.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/sikh-turbans-football-game_us_566db725e4b0e292150e3f6a"} +{"original_headline": "'middle east peace process?' high time for a new name", "generated_headline": "Rename the Middle East peace process? 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Next they'll learn to pay taxes \u2013 scandalous!", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/cook-county-jail-culinary-program_n_6581480.html"} +{"original_headline": "too old to drive? think again, google to the rescue.", "generated_headline": "Too old to drive? Google says drive anyway, safety is overrated.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/too-old-to-drive-think-ag_b_5418221.html"} +{"original_headline": "don't march if you won't keep walking", "generated_headline": "Don't march if you won't keep walking? So, just stay home then?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dont-march-if-you-wont-keep-walking_us_5883c3b1e4b08f5134b62142"} +{"original_headline": "syrian family suffering from meningitis evacuated for treatment", "generated_headline": "Syrian family evacuated for meningitis treatment \u2013 how dare they seek help!", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/syria-family-ezzedine-meningitis_us_57d2b292e4b06a74c9f41f24"} +{"original_headline": "ever wish you could live inside your favorite book? you can at this incredible new place", "generated_headline": "Live inside your favorite book? 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More baby socks from the void, perhaps?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/best-buy-fathers-day_n_5194603.html"} +{"original_headline": "coffee nail art that will perk up your monday morning", "generated_headline": "Coffee nail art perks up Monday mornings? 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Simple: avoid becoming a hedge fund manager\u2014because exploiting markets is so last century.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/hedge-fund-managers_n_6142002.html"} +{"original_headline": "the restrictions journalists agreed to in order to attend the koch brothers' conference", "generated_headline": "Journalists gladly accept restrictions to cover the Koch brothers' conference\u2014because who needs editorial independence when you have access?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://thinkprogress.org/media/2015/08/03/3687249/koch-freedom-partners-media-restrictions/"} +{"original_headline": "man robbed own grandmother, forced her to drink alcohol: police", "generated_headline": "A man allegedly robs his grandmother and forces her to drink\u2014family bonding at its finest.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/richard-alexander-coleman_n_6826100.html"} +{"original_headline": "police investigating two possible hate crimes from sf pride weekend", "generated_headline": "Police probe potential hate crimes from SF Pride weekend\u2014because love and acceptance always attract the best elements.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/san-francisco-pride-hate-crimes_n_5556250.html"} +{"original_headline": "alec baldwin's trump impression is apparently even better than we thought", "generated_headline": "Wow, Alec Baldwin's Trump impression is so accurate, it's like watching the real thing stumble through a speech.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/alec-baldwins-trump-impression-is-officially-even-better-than-we-thought_us_59a18bfbe4b05710aa5c8b94"} +{"original_headline": "world's brightest x-ray laser is getting a big upgrade", "generated_headline": "Because what we really need is an even brighter laser to finally see through all our problems.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/world-brightest-laser-lcls_us_57051b23e4b0537661883c5c"} +{"original_headline": "female former franken staffers say he was a 'champion for women'", "generated_headline": "Sure, because nothing says 'champion for women' like... well, you know.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/female-franken-staffers_us_5a0f060ae4b045cf4371109c"} +{"original_headline": "the year of self-love", "generated_headline": "Finally, a year dedicated to loving ourselves, because the world wasn't enough.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-year-of-selflove_b_7212862.html"} +{"original_headline": "hiroshima visit brings feelings of guilt to american born in japan", "generated_headline": "Oh great, an American in Japan feels guilty about Hiroshima. 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Rowling brilliantly clarifies the utterly logical Olympic sport with side-splitting wit.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/jk-rowling-olympics-quidditch_us_57b62ce0e4b0b51733a26b51"} +{"original_headline": "the wage gap closed by a whopping one cent in 2015", "generated_headline": "The wage gap magically closed by a staggering one cent in 2015 \u2013 progress at its finest!", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-wage-gap-closed-by-one-whole-cent-this-year_us_567aba89e4b014efe0d79df9"} +{"original_headline": "psychic fall cometh!", "generated_headline": "Psychic fall cometh? 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After all this time?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/metoo-its-time-for-change_us_5a1d9e80e4b00c8a328dc50c"} +{"original_headline": "senate dems want to know more as trump nominees cash out at their old jobs", "generated_headline": "Senate Dems want to know more about nominees cashing out \u2013 because ethics matter, sometimes.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trump-nominee-golden-parachutes_us_5877a998e4b03c8a02d5c9f5"} +{"original_headline": "white house: government shutdown possible if democrats keep hurting trump's feelings", "generated_headline": "Government shutdown possible if Democrats hurt Trump's feelings \u2013 because governance is all about feelings.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trump-government-shutdown_us_5908cc29e4b0bb2d087277b5"} +{"original_headline": "evan rachel wood is finally getting paid as much as her male 'westworld' costars", "generated_headline": "Evan Rachel Wood is finally getting paid equally \u2013 only took how many seasons?", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/evan-rachel-wood-equal-pay-westworld_us_5ad78122e4b029ebe02072cb"} +{"original_headline": "it's election season, but where are the lawn signs?", "generated_headline": "Election season, but no lawn signs? 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rachel dolezal any time soon", "generated_headline": "Why Dave Chappelle won't be making jokes about Rachel Dolezal any time soon: because comedy has its limits, apparently.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dave-chappelle-racehl-dolezal_n_7591488.html"} +{"original_headline": "test facebook button", "generated_headline": "Test Facebook button: the most critical task facing humanity today.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/test-facebook-button_n_7072920.html"} +{"original_headline": "how 2016 fashion week is already more inclusive than usual", "generated_headline": "How 2016 Fashion Week is already more inclusive than usual: by maybe having a few diverse models.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": 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nationalists have been saying 'diversity is not our strength' for years", "generated_headline": "White nationalists have been saying 'diversity is not our strength' for years\u2014how original and welcoming of them.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/steve-king-diversity-strength-white-supremacy_us_5a2afd21e4b073789f69bfbf"} +{"original_headline": "mit should do better", "generated_headline": "MIT should do better\u2014like, maybe admit that elite institutions aren't perfect.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/mit-should-do-better_us_59410bbce4b04c03fa2616e0"} +{"original_headline": "why it's taking so long to get more electric cars on the road", "generated_headline": "Why electric cars are slow to take over: because fossil fuels have such great lobbyists, obviously.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/electric-car-sales-1-million-2020_us_56a23c26e4b0404eb8f1350e"} +{"original_headline": "on the road to the emmys with my entourage aka my kids", "generated_headline": "On the road to the Emmys with my entourage (a.k.a. my kids)\u2014because nothing says glamour like diaper bags.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/on-the-road-to-the-emmys_b_5703047.html"} +{"original_headline": "wednesday's morning email: conservatives aren't loving the proposed gop obamacare reform", "generated_headline": "Conservatives aren't loving the GOP Obamacare reform\u2014shocking, since they always support things that help people.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/wednesdays-morning-email-conservatives-arent-loving-the-proposed-gop-obamacare-reform_us_58bff351e4b054a0ea665e2e"} +{"original_headline": "hong kong chooses new beijing-backed leader amid political tensions", "generated_headline": "Hong Kong chooses new Beijing-backed leader: democracy at its finest, folks.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/hong-kong-chooses-new-beijing-backed-leader-amid-political-tensions_us_58d7ac1ce4b02a2eaab4dadd"} +{"original_headline": "5 better questions to ask allergy families", "generated_headline": "5 better questions to ask allergy families: like, 'Is your child's epinephrine pen fashionable?'", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/5-better-questions-to-ask-allergy-families_b_5307030.html"} +{"original_headline": "gun stocks soar as obama announces executive actions on gun control", "generated_headline": "Gun stocks soar after Obama's gun control announcement\u2014because nothing reduces fear like panic buying.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/gun-stocks-obama-executive-action_us_568bf4cde4b014efe0dbc4d7"} +{"original_headline": "the everyday heroes of the hurricanes", "generated_headline": "The everyday heroes of the hurricanes: those brave souls who tweeted from their rooftops.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-everyday-heroes-of-the-hurricanes_us_59be6681e4b02c642e4a1771"} +{"original_headline": "boy george opens up about happiness, being a u.s. politics junkie and more", "generated_headline": "Boy George opens up about happiness and U.S. politics\u2014because we all need his political analysis.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/boy-george-saves-a-child-from-suicide-among-other_us_58334d6fe4b08c963e344339"} +{"original_headline": "one of the last original tuskegee airmen instructors dies at 96", "generated_headline": "One of the last Tuskegee Airmen instructors dies\u2014just another day in the march of time.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/one-of-the-last-tuskegee-airmen-dies-at-96_us_564ddc73e4b031745ceff3b0"} +{"original_headline": "trump to violate iran nuclear deal, vows to reimpose sanctions", "generated_headline": "Trump to violate Iran nuclear deal\u2014because who needs international agreements when you have tweets?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trump-iran-deal-sanctions_us_5aeca9d9e4b0c4f193226f92"} +{"original_headline": "what cutting americorps would mean for public lands", "generated_headline": "Cutting AmeriCorps: the surefire way to make public lands thrive, said no one ever.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/what-cutting-americorps-would-mean-for-public-lands_us_58f8ef12e4b0de26cfeae18b"} +{"original_headline": "surprise, surprise, donald trump's supporters are trying to 'rig' the election", "generated_headline": "Surprise, surprise: Trump supporters trying to rig the election\u2014how utterly predictable.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/suprise-surprise-donald-trumps-supporters-are-trying-to-rig-the-election_us_5814c685e4b064e1b4b2d356"} +{"original_headline": "why smartphone use helps develop 21st century skills in higher education", "generated_headline": "Why smartphones help in education: because scrolling through social media totally builds critical thinking.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/why-smartphone-use-helps-_b_6777678.html"} +{"original_headline": "live in the vineyard selects artists with a key ingredient: a big heart", "generated_headline": "Live in the vineyard selects artists with a key ingredient: a big heart \u2013 because nothing says 'artistic talent' like a pulse.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/live-in-the-vineyard-sele_b_6351420.html"} +{"original_headline": "cousin of nba star dwyane wade killed in chicago shooting", "generated_headline": "Cousin of NBA star Dwyane Wade killed in Chicago shooting \u2013 because Chicago's violence needed a celebrity angle.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dwyane-wade-cousin-killed-chicago-shooting_us_57c1a80fe4b02673445052dd"} +{"original_headline": "slaughtering wolves in canada: a new essay shows just how unscientific, unethical, and inhumane these studies are", "generated_headline": "Slaughtering wolves in Canada: a new essay shows just how unscientific, unethical, and inhumane these studies are \u2013 yes, because 'scientific' often involves mass murder.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/slaughtering-wolves-in-ca_b_6662094.html"} +{"original_headline": "why no more than a dribble of outside spending in kansas?", "generated_headline": "Why no more than a dribble of outside spending in Kansas? \u2013 Is Kansas too virtuous for campaign cash?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/why-no-more-than-a-dribble-of-outside-spending-in-kansas_us_58f6432be4b04cae050dcb1a"} +{"original_headline": "trump's already urging policy changes after nyc terrorist attack -- without waiting for 'the facts'", "generated_headline": "Trump's already urging policy changes after NYC terrorist attack \u2013 without waiting for 'the facts' \u2013 because evidence is for losers.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trump-nyc-terrorist-attack_us_59f9b38fe4b046017fb00260"} +{"original_headline": "woman's face-down halloween dummy gets repeated 911 calls", "generated_headline": "Woman's face-down Halloween dummy gets repeated 911 calls \u2013 because Halloween is all about realistic emergencies.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/larethia-haddon-face-down-halloween-dummy-gets-repeated-911-calls_us_561837e9e4b0dbb8000eba35"} +{"original_headline": "oregon may just be the most stunning state in america. here's proof.", "generated_headline": "Oregon may just be the most stunning state in America. Here's proof. \u2013 Said no one with eyes for other states.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/oregon-may-just-be-the-most-stunning-state-in-america-heres-proof_us_5543f2dce4b03f42d6c0cdc6"} +{"original_headline": "masters of habit: the wisdom and writing of maya angelou", "generated_headline": "Masters of habit: the wisdom and writing of Maya Angelou \u2013 because repeating quotes is the height of mastery.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/masters-of-habit-the-wisd_b_6925730.html"} +{"original_headline": "hillary clinton wins endorsement from united food and commercial workers union", "generated_headline": "Hillary Clinton wins endorsement from United Food and Commercial Workers union \u2013 because unions always pick winners.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/hillary-clinton-ufcw-endorsement_us_56955dfde4b086bc1cd57c46"} +{"original_headline": "sarah palin defends curt schilling: 'espn continues to screw up'", "generated_headline": "Sarah Palin defends Curt Schilling: 'ESPN continues to screw up' \u2013 from the queen of screw-ups herself.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/sarah-palin-curt-schilling-espn_us_57193acce4b0d0042da8ab36"} +{"original_headline": "nypd officers suspended after witnesses say they didn't check on woman later found dead", "generated_headline": "NYPD officers suspended after witnesses say they didn't check on woman later found dead \u2013 exemplary policing at its finest.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/cops-suspended-mom-found-dead_us_5a45ad38e4b0b0e5a7a5b9d2"} +{"original_headline": "2 florida deputies shot dead while eating at chinese restaurant", "generated_headline": "2 Florida deputies shot dead while eating at Chinese restaurant \u2013 because even a quiet meal can turn violent.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trenton-florida-deputies-shot_us_5ad93ad3e4b029ebe0228740"} +{"original_headline": "here's even more evidence trump is lying about massive voter fraud", "generated_headline": "Here's even more evidence Trump is lying about massive voter fraud \u2013 as if we needed more proof.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/donald-trump-voter-fraud_us_590cc4e8e4b0d5d9049c4155"} +{"original_headline": "we will beat trumpcare: we only lose if we forget what we're fighting for", "generated_headline": "We will beat Trumpcare: we only lose if we forget what we're fighting for \u2013 forgetting is the only path to defeat, obviously.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/we-will-beat-trumpcare-we-only-lose-if-we-forget-what_us_59c1b914e4b0f96732cbca30"} +{"original_headline": "12 movies for the next 12 months", "generated_headline": "12 movies for the next 12 months \u2013 because your calendar was begging for more commitments.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/2015-movie-preview_n_6400976.html"} +{"original_headline": "out-bad chronicles: fantasy from hell", "generated_headline": "Out-bad Chronicles: Fantasy from Hell \u2013 hell must be running out of bad fantasies.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/outbad-chronicles-fantasy_b_8297728.html"} +{"original_headline": "head-in-sand purists", "generated_headline": "Head-in-sand purists \u2013 because avoiding reality is the purest form of purity.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/head-in-sand-purists_b_11284016.html"} +{"original_headline": "this country's capital just made it punishable to catcall women", "generated_headline": "This country's capital just made it punishable to catcall women \u2013 finally, a law against 'compliments'.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/this-countrys-capital-just-made-it-punishable-to-catcall-women_us_584aea1fe4b04c8e2baf933d"} +{"original_headline": "ohio delays execution after failing to find sick inmate's vein", "generated_headline": "Ohio delays execution after failing to find sick inmate's vein \u2013 minor technicality, really.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/ohio-execution-sick-inmate_us_5a0c803fe4b0b17ffce1ee11"} +{"original_headline": "friday talking points -- games the whole family can play", "generated_headline": "Friday talking points \u2013 games the whole family can play \u2013 because politics is just a game, right?", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/friday-talking-points_b_5930434.html"} +{"original_headline": "the 'pitch perfect 2' super bowl trailer is aca-here", "generated_headline": "The 'Pitch Perfect 2' Super Bowl trailer is aca-here \u2013 the world was desperate for more a cappella.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/pitch-perfect-2-super-bowl-trailer_n_6586542.html"} +{"original_headline": "zoo animals roam free after flooding in tbilisi", "generated_headline": "Zoo animals roam free after flooding in Tbilisi \u2013 zoos are so secure, after all.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/zoo-animals-_n_7579344.html"} +{"original_headline": "claire danes is expecting baby number two with hugh dancy", "generated_headline": "Claire Danes is expecting baby number two with Hugh Dancy \u2013 Hollywood's baby quota wasn't met yet.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/claire-danes-is-expecting-baby-number-two-with-hugh-dancy_us_5ad758c1e4b029ebe0201bcd"} +{"original_headline": "yes, my child with special needs understands you -- please talk to him", "generated_headline": "Yes, my child with special needs understands you \u2013 please talk to him \u2013 because ignoring him is so polite.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/yes-my-child-with-special-needs-understands-you-please-talk-to-him_b_5697668.html"} +{"original_headline": "bear family pool party is the cutest backyard invasion ever", "generated_headline": "Bear family pool party is the cutest backyard invasion ever \u2013 so cute, it justifies property damage.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/bear-family-pool-party_us_55d734dee4b08cd3359bb180"} +{"original_headline": "krugman: the right fears democracy", "generated_headline": "Krugman: the right fears democracy \u2013 yes, because winning elections is so scary.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/plutocrats-against-democr_n_6040852.html"} +{"original_headline": "4 ways to make cooking at home doable -- and more fun", "generated_headline": "4 ways to make cooking at home doable \u2013 and more fun \u2013 because cooking is such a blast already.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/4-ways-to-make-cooking-at_b_6470598.html"} +{"original_headline": "some states throw untested rape kits in the trash. these survivors want to change that.", "generated_headline": "Some states throw untested rape kits in the trash. These survivors want to change that. \u2013 Because why solve crimes when you can discard evidence?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/untested-rape-kits-in-trash_us_56cb4e5ee4b041136f17b087"} +{"original_headline": "how princess diana was honored at the royal wedding", "generated_headline": "How Princess Diana was honored at the royal wedding \u2013 by being completely ignored, the ultimate honor.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/how-princess-diana-was-honored-at-the-royal-wedding_us_5b004edce4b0a046186c57cd"} +{"original_headline": "bp's clean water act fines will be smaller than gulf states thought", "generated_headline": "BP's Clean Water Act fines will be smaller than Gulf states thought \u2013 because BP always pays its dues.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/bps-clean-water-act-fines_b_6542876.html"} +{"original_headline": "more than 5,600 boat migrants rescued off north africa in just the past 3 days", "generated_headline": "Just a few thousand migrants rescued? No big deal, move along.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/boat-migrant-crisis-europe_n_7053144.html"} +{"original_headline": "teachers and politicians mount final push to keep betsy devos away from public schools", "generated_headline": "Teachers and politicians unite to keep DeVos out, proving they truly care about education quality.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/betsy-devos_us_5898c511e4b09bd304bcae16"} +{"original_headline": "4 reasons hallmark movies saved my holiday spirit", "generated_headline": "Four reasons Hallmark movies saved my holiday spirit: 1. Cheesy dialogue, 2. Predictable plots, 3. Bad acting, 4. 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"https://www.huffingtonpost.com/entry/russia-hack-dnc-trump_us_57603dd2e4b071ec19ef5606"} +{"original_headline": "tips for paying off medical school loans", "generated_headline": "Tips for paying off medical school loans: just ignore the six-figure debt and visualize wealth \u2013 it's practically painless.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/tips-for-paying-off-medic_b_7614340.html"} +{"original_headline": "alia shawkat is feeling herself", "generated_headline": "Alia Shawkat is 'feeling herself' \u2013 a profound cultural moment that will echo through the ages.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/alia-shawkat-self-love_us_5ad9f651e4b029ebe0239482"} +{"original_headline": "kerry washington: we shouldn't have to give up our seats at the table for others' bad behavior", "generated_headline": "Kerry Washington says we shouldn't give up seats for others' bad behavior \u2013 because resigning in protest is so overrated.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/kerry-washington-we-shouldnt-have-to-give-up-our-seats-at-the-table-for-others-bad-behavior_us_5a52bc63e4b089e14dbc2839"} +{"original_headline": "fathers, let's talk about love, respect and hiv", "generated_headline": "Fathers, let's casually discuss love, respect, and HIV over a BBQ \u2013 uncomfortable topics make great picnic conversation.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/fathers-lets-talk-about-l_b_7631926.html"} +{"original_headline": "how early-life stress could increase risk of anxiety and depression later in life", "generated_headline": "Early-life stress guarantees anxiety and depression later \u2013 it's a scientific certainty with no room for individual differences.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/gut-bacteria-mental-health-connection_us_55b8d6d6e4b0a13f9d1ade20"} +{"original_headline": "justin bieber interrupts performance to scold spanish audience", "generated_headline": "Justin Bieber interrupts his show to scold fans \u2013 the height of musical professionalism and audience appreciation.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/justin-bieber-scold-spanish_us_5638b5cbe4b079a43c047be0"} +{"original_headline": "sandy hook father to alex jones: 'i'm not backing down'", "generated_headline": "Sandy Hook father vows not to back down to Alex Jones \u2013 because arguing with conspiracy theorists is always a productive use of grief.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/sandy-hook-father-to-alex-jones-im-not-backing-down_us_5ad8d925e4b029ebe0221e59"} +{"original_headline": "trump expels 60 russians, closes russian consulate in seattle after uk chemical attack", "generated_headline": "Trump expels 60 Russians after UK attack \u2013 a proportionate response that definitely won't sour relations or anything.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trump-expels-60-russians-after-uk-chemical-attack_us_5ab8efbfe4b054d118e4f35c"} +{"original_headline": "donald trump is as rich as he says, if you do the accounting wrong", "generated_headline": "Trump is as rich as he claims if you manipulate accounting rules \u2013 who needs transparency when you have creative math?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://www.politico.com/story/2016/05/donald-trump-money-net-worth-223662"} +{"original_headline": "trump's controversial pick for doj civil rights chief appears headed for confirmation", "generated_headline": "Trump's controversial pick for DOJ civil rights chief is headed for confirmation \u2013 because civil rights enforcement thrives on controversy.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/eric-dreiband-doj-civil-rights-trump_us_59b0230be4b0b5e53102fb4a"} +{"original_headline": "protecting freedom of expression in newsrooms and mosques: unity and dialogue", "generated_headline": "Protecting freedom of expression through unity and dialogue \u2013 as long as your expression doesn't challenge anyone's beliefs.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/protecting-freedom-of-exp_b_6450760.html"} +{"original_headline": "asians most likely to be charged for espionage in u.s.: report", "generated_headline": "Asians most likely charged for espionage \u2013 just a sprinkle of racial profiling in our justice system, nothing to see here.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/asian-americans-most-likely-to-be-charged-for-espionage-report_us_593abd50e4b0b13f2c69c0a3"} +{"original_headline": "the first 'rogue one' trailer is a 'star wars' nerd's dream", "generated_headline": "The 'Rogue One' trailer is a Star Wars nerd's dream \u2013 so mind-blowing it might erase all other films from existence.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/rogue-one-star-wars-trailer_us_57064c76e4b053766188c341"} +{"original_headline": "hilton head island is the best", "generated_headline": "Hilton Head Island is the best \u2013 if you've never experienced sand, sun, or any other beach, it's revolutionary.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/hilton-head-island-vacation--summer_n_5533175.html"} +{"original_headline": "trump's travel ban does nothing to stop the most deadly form of terror in the u.s.", "generated_headline": "Trump's travel ban does nothing to stop domestic terror \u2013 but it sends a strong symbolic message that solves no real problems.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trump-terror-travel-ban-vegas_us_59d26f99e4b09538b509d978"} +{"original_headline": "to avoid disaster in syria, the u.s. should learn from iraq", "generated_headline": "To avoid Syria disaster, learn from Iraq \u2013 because repeating past foreign policy blunders always leads to success.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/endgame-in-syria-applying-the-lessons-of-iraq_us_58e8ef71e4b00dd8e016ec3f"} +{"original_headline": "expectant mother chrissy teigen is totally embracing her body", "generated_headline": "Chrissy Teigen embracing her body is the most radical act of the decade \u2013 who needs social change when you have celebrity pregnancies?", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/chrissy-teigen-pregnant-body_us_56474933e4b08cda3488fd11"} +{"original_headline": "sanders ramps up spending in effort to catch up to hillary", "generated_headline": "Sanders ramps up spending to catch Hillary \u2013 proving that campaign finance reform is just a suggestion.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/sanders-ramps-up-spending-in-effort-to-catch-up-to-hillary_us_56574aebe4b08e945feb21ca"} +{"original_headline": "cruz control: an elitist at liberty university", "generated_headline": "Cruz control: an elitist at Liberty University \u2013 watching political pandering in a religious setting is always entertaining.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/cruz-control-an-elitist-at-liberty-university_b_6961498.html"} +{"original_headline": "kesha's best revenge with 'praying' and 'woman' is her healing", "generated_headline": "Kesha's best revenge is healing through 'praying' and 'woman' \u2013 take that, abuser, she's feeling better now!", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/keshas-best-revenge-with-praying-and-woman-is_us_5968362de4b09e26b6d766e2"} +{"original_headline": "the largest demographic of binge drinkers might surprise you", "generated_headline": "The largest demographic of binge drinkers might surprise you \u2013 or not, if you've ever met a college student on a Friday night.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/binge-drinking-middle-aged-men_n_6446144.html"} +{"original_headline": "cher is gifting us with a broadway musical based on her life", "generated_headline": "Cher is gifting us with a Broadway musical \u2013 because the world desperately needed another diva's life story on stage.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/cher-is-gifting-us-with-a-broadway-musical-based-on-her-life_us_5937f993e4b01fc18d3f3442"} +{"original_headline": "how to add, delete, and modify your way to happiness", "generated_headline": "Add, delete, modify your way to happiness \u2013 life is just a simple app waiting for your updates, no complexity at all.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/how-to-add-delete-and-mod_b_5979418.html"} +{"original_headline": "did melania trump really 'like' my tweet about her marriage?", "generated_headline": "Did Melania Trump really 'like' my tweet? 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Kochs only spent $400 million to buy democracy\u2014what a bargain!", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://thehill.com/homenews/campaign/267641-koch-network-spent-nearly-400-million-in-2015"} +{"original_headline": "5 gorgeous home office ideas", "generated_headline": "These 5 home office ideas will revolutionize your work life (or at least make you hate your couch less).", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/5-gorgeous-home-office-id_b_5652243.html"} +{"original_headline": "conflict and late rains drive thousands from their homes in somalia", "generated_headline": "A little rain and disagreement in Somalia\u2014no big deal, just thousands fleeing.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/conflict-and-late-rains-d_b_5538252.html"} +{"original_headline": "michelle obama made a valentine's day playlist for barack, and it's perfect", "generated_headline": "Michelle Obama's Valentine's playlist: because nothing says 'I love you' like curated Spotify links.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/michelle-obama-valentines-day-playlist-barack-obama_us_5a846549e4b0774f31d15514"} +{"original_headline": "...see how they run", "generated_headline": "See how they run? Spoiler: it's not well.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/see-how-they-run_b_6274728.html"} +{"original_headline": "ellie goulding and james corden perform 'love me like you do' remix", "generated_headline": "Ellie Goulding and James Corden remix 'Love Me Like You Do'\u2014because original songs are so last year.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/ellie-goulding-james-corden-perform-remix-love-me-like-you-do_us_56571d9ee4b08e945feb1c9d"} +{"original_headline": "sean bean's most memorable death wasn't 'lord of the rings'", "generated_headline": "Sean Bean's most memorable death wasn't in LOTR? Shocking, he usually just dies in everything.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/sean-bean-death-ned-stark_n_5655103.html"} +{"original_headline": "watch: the pains of being pure at heart unveil new songs", "generated_headline": "The Pains of Being Pure at Heart have new songs\u2014if you care, which you probably don't.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-pains-of-being-pure-at-heart_n_5291475.html"} +{"original_headline": "the great vanishing", "generated_headline": "The Great Vanishing: because nothing says 'epic mystery' like a vague title.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-great-vanishing_b_6877644.html"} +{"original_headline": "subscription services provide book and toy options to parents of brown kids", "generated_headline": "Subscription boxes for brown kids: finally, diversity delivered to your doorstep (for a fee).", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/subscription-boxes-provide-toys-and-books-for-brown-kids_us_58864099e4b0e3a7356a9424"} +{"original_headline": "steve nash responds to injury critics with emotional letter", "generated_headline": "Steve Nash's emotional letter: because nothing heals injury critiques like a good cry.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/steve-nash-letter-lakers-fans_n_6126602.html"} +{"original_headline": "apple's safari browser is crashing for some users: report", "generated_headline": "Safari is crashing\u2014your entire digital life is over, pack it up.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/apple-safari-crashing_us_56a8cf8fe4b0f71799287be5"} +{"original_headline": "roe made abortions legal, but it doesn't keep women and providers safe", "generated_headline": "Roe made abortions legal but doesn't keep women safe? What a shocker, legal doesn't equal safe.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/reagan-opinion-roe-wade-harassment_us_5a63d88ee4b0dc592a096aa1"} +{"original_headline": "sylvester stallone shells out $400,000 for a statue of himself", "generated_headline": "Sylvester Stallone spent $400k on a statue of himself\u2014modest, really.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/sylvester-stallone-shells-out-400000-for-a-statue-of-himself_us_5a452756e4b025f99e1a4615"} +{"original_headline": "dreamers are people, not political footballs", "generated_headline": "Dreamers are people, not political footballs? Tell that to the politicians kicking them around.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dreamers-are-people-not-political-footballs_us_59b3f151e4b0c50640cd6790"} +{"original_headline": "one more (feminist) wonder about 'wonder woman': it passes the abuse litmus test", "generated_headline": "Wonder Woman passes the abuse litmus test\u2014congrats, you're barely not sexist!", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/one-more-feminist-wonder-about-wonder-woman-it_us_593c83d8e4b0b65670e56b1c"} +{"original_headline": "ted cruz makes it too easy to point out the hypocrisy of his latest campaign ad", "generated_headline": "Ted Cruz makes it too easy to point out his hypocrisy\u2014thanks for the low-hanging fruit, Ted.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/chris-cuomo-calls-out-ted-cruz-to-his-face_us_5aa049bbe4b0e9381c14ef20"} +{"original_headline": "let's stop donald trump from wielding his budget as a weapon against hardworking immigrant families", "generated_headline": "Let's stop Trump from wielding his budget as a weapon? Good luck with that.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/lets-stop-donald-trump-from-wielding-his-budget-as_us_593ae0e5e4b0b65670e569f3"} +{"original_headline": "if isis had committed the 11 school shootings since sandy hook, congress would have declared war.", "generated_headline": "If ISIS did these school shootings, Congress would declare war\u2014but since it's just kids, silence.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/if-isis-had-committed-the_b_6053316.html"} +{"original_headline": "the constant conflict between feminism and nationalism", "generated_headline": "The constant conflict between feminism and nationalism: because who needs unity when you can have clashing ideologies?", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/conflict-between-feminism-and-nationalism_b_5837920.html"} +{"original_headline": "video relaunches investigation into death of man held by chicago police", "generated_headline": "Video relaunches investigation\u2014because nothing says justice like a viral clip.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/philip-coleman-chicago-video_us_56670fb1e4b079b2819028f1"} +{"original_headline": "jimmy carter mediating dispute between martin luther king jr.'s heirs", "generated_headline": "Jimmy Carter mediating MLK heirs' dispute\u2014because former presidents have nothing better to do.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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In Iowa? 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Wait, is this a dream or a nightmare?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/mass-survey-democrats-republicans_us_56434840e4b045bf3ded15b5"} +{"original_headline": "dwight howard on helping to empower and educate girls in east africa", "generated_headline": "Dwight Howard wants to empower girls in East Africa\u2014a basketball star's unique brand of developmental aid.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dwight-howard-education-schoolgirls-east-africa_us_57c47352e4b09cd22d91c3cd"} +{"original_headline": "robin wright explains why she fought for equal pay for 'house of cards'", "generated_headline": "Robin Wright fought for equal pay\u2014groundbreaking, since no actress has ever done that before.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/robin-wright-video-house-cards-equal-pay_us_573cabe3e4b0aee7b8e8ad68"} +{"original_headline": "an art colony thrives on skid row", "generated_headline": "An art colony thrives on skid row \u2013 because nothing says 'cultural enrichment' like homelessness.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/art-skid-row_us_5ad0ba82e4b0edca2cb92662"} +{"original_headline": "you have to love yourself first", "generated_headline": "Yes, because before you can love anyone else, you must spend hours in front of the mirror, telling yourself you're perfect.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/you-have-to-love-yourself-first_us_586a4596e4b068764965c34e"} +{"original_headline": "these two men share the beautiful story of how their family was created", "generated_headline": "These two men share the beautiful story of how their family was created \u2013 spoiler: it involves a lot of takeout and Netflix.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/james-mckinney-let-love-define-family_us_57ed699be4b082aad9ba1add"} +{"original_headline": "guns are 'the ultimate public health crisis,' howard dean tells democratic convention", "generated_headline": "Guns are 'the ultimate public health crisis' \u2013 because nothing promotes health like a bullet wound, right Howard?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/howard-dean-guns-dnc_us_579805d6e4b02d5d5ed372d8"} +{"original_headline": "time is 'running out' as great barrier reef hit by another mass bleaching", "generated_headline": "Time is 'running out' for the Great Barrier Reef \u2013 but hey, at least the fish are getting a free spa day.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/great-barrier-reef-coral-bleaching_us_58eb586fe4b058f0a0305631"} +{"original_headline": "10 ways to deal with a difficult coworker", "generated_headline": "10 ways to deal with a difficult coworker: Step 1 \u2013 quit your job. Step 2 \u2013 move to a desert island.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/10-ways-to-deal-with-a-difficult-coworker_us_59e8df32e4b08ff1170dd2f0"} +{"original_headline": "donald trump is winning at being the butt of late-night tv jokes", "generated_headline": "Donald Trump is winning at being the butt of late-night TV jokes \u2013 because nothing says 'presidential' like constant ridicule.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/donald-trump-jokes-late-night-tv_us_590c2224e4b0d5d9049b305f"} +{"original_headline": "5 lessons from chibok", "generated_headline": "5 lessons from Chibok? Oh, you mean besides the fact that the world failed those girls?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/five-lessons-from-chibok_b_5310091.html"} +{"original_headline": "how the gotham typeface came to define our era", "generated_headline": "How the Gotham typeface came to define our era \u2013 yes, a font is totally more important than, say, climate change.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/gotham-typeface_n_5852680.html"} +{"original_headline": "when disaster strikes, mothers and newborns are the most vulnerable", "generated_headline": "When disaster strikes, mothers and newborns are the most vulnerable \u2013 shocker, right? Who would have thought?", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/when-disaster-strikes-mot_b_5688013.html"} +{"original_headline": "our american life", "generated_headline": "Our American life \u2013 where dreams go to die and healthcare is a luxury.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/our-american-life_us_591f2a93e4b0b28a33f62ba7"} +{"original_headline": "eric trump says those who oppose his dad are 'not even people'", "generated_headline": "Eric Trump says those who oppose his dad are 'not even people' \u2013 because in his world, only sycophants count as human.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/eric-trump-lashes-out-says-democrats-are-not-even-people_us_59377ea5e4b0aba888b9d6dd"} +{"original_headline": "joe biden urges people to watch how their senators vote on gun bills", "generated_headline": "Joe Biden urges people to watch how their senators vote on gun bills \u2013 because nothing says democracy like passive observation.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/joe-biden-gun-violence_us_576810fae4b015db1bc9f7ba"} +{"original_headline": "nas talks 'ghostbusters'-inspired lines and sartorial heroes", "generated_headline": "Nas talks 'Ghostbusters'-inspired lines and sartorial heroes \u2013 because rap is really about proton packs and suits.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://www.billboard.com/articles/columns/hip-hop/7378330/nas-ghostbusters-inspired-line-sartorial-heroes"} +{"original_headline": "'spongebob' fans will love this pineapple-shaped villa in punta cana", "generated_headline": "'SpongeBob' fans will love this pineapple-shaped villa \u2013 because living in a fruit is the ultimate dream.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/spongebob-squarepants-nickelodeon-resort-punta-cana_us_57e12687e4b0071a6e0953eb"} +{"original_headline": "withholding child abuse emails further damages tarnished telegraph", "generated_headline": "Withholding child abuse emails further damages tarnished Telegraph \u2013 as if a news outlet needed more scandals.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/child-abuse-emails-telegraph_b_6736094.html"} +{"original_headline": "is 'having it all' possible for dads, too?", "generated_headline": "Is 'having it all' possible for dads, too? Nah, they're too busy changing diapers to care.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dads-say-we-want-to-have-_n_5574656.html"} +{"original_headline": "hmm, there may be a link between vaccines and political pandering", "generated_headline": "Hmm, there may be a link between vaccines and political pandering \u2013 because science is just a tool for votes, right?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/donald-trump-vaccines_us_57e1666ce4b04a1497b6e725"} +{"original_headline": "hurry! early black friday deals have already started on amazon", "generated_headline": "Hurry! Early Black Friday deals have already started on Amazon \u2013 because your life isn't complete without a 50% off toaster.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/amazon-black-friday-2016_us_582c86e7e4b030997bbcb7a5"} +{"original_headline": "4 back-to-school myths about college textbooks", "generated_headline": "4 back-to-school myths about college textbooks: Myth 1 \u2013 you actually need them. Spoiler: you don't.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/4-back-to-school-myths-ab_b_5730920.html"} +{"original_headline": "the best far-flung hotels worth the trip", "generated_headline": "The best far-flung hotels worth the trip \u2013 fly across the world for a bed that costs more than your mortgage.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-best-far-flung-hotels-worth-the-trip_us_585416b6e4b0d5f48e164ec8"} +{"original_headline": "so, you're gonna do chemo?", "generated_headline": "So, you're gonna do chemo? Great, because nothing says 'fun' like losing all your hair.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/so-youre-gonna-do-chemo_b_6433840.html"} +{"original_headline": "if you see a muslim at the airport", "generated_headline": "If you see a Muslim at the airport, panic! Because diversity is terrifying.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/if-you-see-a-muslim-at-the-airport_us_588ddf13e4b0cd25e49049d8"} +{"original_headline": "j balvin, nicky jam say they changed the notion that reggaet\u00f3n is misogynist", "generated_headline": "J Balvin, Nicky Jam say they changed the notion that reggaet\u00f3n is misogynist \u2013 yes, because lyrics about women are suddenly feminist now.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/why-j-balvin-nicky-jam-want-to-change-the-view-that-reggaeton-is-misogynist_us_58fa5f11e4b00fa7de143723"} +{"original_headline": "people sick of 'thoughts and prayers' demand action after florida school shooting", "generated_headline": "People sick of 'thoughts and prayers' demand action after Florida school shooting \u2013 finally, someone gets it.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/florida-shooting-thoughts-prayers_us_5a851c42e4b0058d5565da26"} +{"original_headline": "ukraine at a crossroads: could putin lose his job over mishandling the crisis?", "generated_headline": "Ukraine at a crossroads: could Putin lose his job over mishandling the crisis? 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This man saved his life with a shirt and a bear hug \u2013 because bandages are so overrated.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://www.latimes.com/nation/la-na-orlando-nightclub-rescue-20160612-snap-story.html"} +{"original_headline": "principal: adding lgbt club would 'create bullying'", "generated_headline": "Principal: adding LGBT club would 'create bullying' \u2013 because tolerance is the real problem here.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-charleston-gazette-pu_n_6171598.html"} +{"original_headline": "madonna compares donald trump 'nightmare' to being dumped by an ex-lover", "generated_headline": "Madonna compares Donald Trump 'nightmare' to being dumped by an ex-lover \u2013 yes, because politics is just like a Taylor Swift song.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/madonna-compares-donald-trump-nightmare-to-being-dumped-by-an-ex-lover_us_5874f067e4b043ad97e5b20e"} +{"original_headline": "bernie sanders to propose new rule requiring fair prices for taxpayer-funded drugs", "generated_headline": "Oh great, Bernie Sanders is proposing another rule to force fair drug prices. That'll fix everything.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/bernie-sanders-drug-prices_us_597f4546e4b0da64e87aaebf"} +{"original_headline": "former 'bachelorette' ali fedotowsky expecting first baby with fianc\u00e9 kevin manno", "generated_headline": "In other news, a reality TV star is pregnant. The world collectively yawns.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/ali-fedotowsky-baby-pregnant_us_568d620fe4b0a2b6fb6e52da"} +{"original_headline": "jeb poverty plan would end food stamps, let states sort things out", "generated_headline": "Jeb Bush's plan to end food stamps: let states handle it. What could go wrong?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/jeb-bush-food-stamps_us_568fd866e4b0cad15e647088"} +{"original_headline": "10 reasons you should care about d.c. voting rights", "generated_headline": "10 reasons you should pretend to care about D.C. voting rights but actually don't.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/ten-reasons-why-you-shoul_b_7728456.html"} +{"original_headline": "students send hilarious tweets to superintendent thanking him for snow day", "generated_headline": "Students flood superintendent with hilarious tweets for a snow day. The pinnacle of civic engagement.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/superintendent-cancel-school-snow-day-students-tweet_us_589cc58ae4b0c1284f2b545c"} +{"original_headline": "50 ethical businesses to support on black friday", "generated_headline": "50 ethical businesses to support on Black Friday. Because buying stuff is now ethical.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/50-ethical-businesses-to-_b_6226410.html"} +{"original_headline": "zachary quinto uses fake name at starbucks. customer gets steamed.", "generated_headline": "Zachary Quinto uses a fake name at Starbucks, causing a customer to overheat. The horror.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/zachary-quinto-starbucks-james-corden_us_5aba2878e4b0decad04df739"} +{"original_headline": "buy or rent? and where to park your down payment until you decide?", "generated_headline": "Buy or rent? And where to park your down payment while you avoid making a decision for a decade.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/buy-or-rent-and-where-to-_b_5537351.html"} +{"original_headline": "petition to censure trump gains momentum", "generated_headline": "A petition to censure Trump is gaining steam. I'm sure he's quaking in his boots.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/petition-to-censure-trump-gains-momentum_us_598f59fce4b0ed1f464c0b30"} +{"original_headline": "find the best new beauty product for your zodiac sign", "generated_headline": "Find the best beauty product for your zodiac sign. Science has spoken.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/find-the-best-new-beauty-_b_6624318.html"} +{"original_headline": "in deep red territory, constituents grill congressman in fiery town hall", "generated_headline": "In a rare event, constituents in a red district actually question a congressman. How dare they?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/doug-lamborn-colorado-springs-town-hall_us_58ee48b4e4b0cb574bb4b603"} +{"original_headline": "widespread power outage strikes detroit", "generated_headline": "Detroit suffers a widespread power outage. Just another day in the Motor City.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/detroit-power-outage-downtown_n_6255160.html"} +{"original_headline": "a good news story about 'imperfect' pregnancy", "generated_headline": "A heartwarming story about how 'imperfect' pregnancies are actually perfect. Reassuring.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/a-good-news-story-about-i_b_12078892.html"} +{"original_headline": "despite the ugliness, i am staying hopeful in trump's america", "generated_headline": "Amidst the chaos, one person remains hopeful in Trump's America. How brave and original.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trump-2016-why-i-am-hopeful-even-though-i-didnt_us_583b4d4ae4b050dfe6187d4e"} +{"original_headline": "austria and germany open borders to migrants offloaded by hungary", "generated_headline": "Austria and Germany open borders to migrants Hungary dumped. So selfless of them.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/austria-and-germany-open-borders-to-migrants-offloaded-by-hungary_us_55eba52ee4b093be51bbb825"} +{"original_headline": "report: revolving door gave goldman access to fed secrets", "generated_headline": "Report: Goldman Sachs got Fed secrets through the revolving door. I'm utterly surprised.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/new-scrutiny-of-goldmans-_n_6191710.html"} +{"original_headline": "ethics group blasts pence for using government travel for colts stunt", "generated_headline": "Ethics group blasts Pence for using government travel for a Colts stunt. 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"proof that it pays to piss off sarah palin", "generated_headline": "Proof that angering Sarah Palin is a lucrative career move.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/game-change-sarah-palin-danny-strong_n_6745288.html"} +{"original_headline": "mental illness and identity: would i shed my bipolar disorder skin?", "generated_headline": "Casual chat about shedding bipolar disorder like last season's fashion.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/mental-illness-and-identi_b_7176674.html"} +{"original_headline": "poll: religion is the answer to today's problems", "generated_headline": "Is religion the answer to everything? 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cultural exchange or appropriation?", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/global-hijabista-style-from-the-afghan-burqa-to-the_us_59664f21e4b0deab7c646d4b"} +{"original_headline": "please stop blaming women for making less money than men", "generated_headline": "Please stop blaming women for the pay gap, it's obviously their choice to earn less.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/women-make-less-men_us_564b742fe4b08cda348af830"} +{"original_headline": "barcelona holds huge protest in support of refugees", "generated_headline": "Barcelona's huge refugee protest will undoubtedly solve the global crisis.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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Innovation at its finest.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/donald-trump-veterans-hotline_us_5783f248e4b01edea78f08c9"} +{"original_headline": "previewing 'hidden figures' with the teary-eyed octavia spencer, taraji p. henson and janelle mon\u00e1e", "generated_headline": "Previewing 'Hidden Figures' with teary-eyed stars \u2013 just a casual movie about overcoming odds, nothing emotional here.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/hidden-figures-taraji-p-henson_us_57d54ec4e4b06a74c9f50e5a"} +{"original_headline": "this was my story", "generated_headline": "This was my story \u2013 said everyone ever, because uniqueness is overrated.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/this-was-my-story_b_6670168.html"} +{"original_headline": "how hbcus respond to a call for inclusion of lgbt students", "generated_headline": "HBCUs respond to inclusion call for LGBT students? 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Never heard of such a thing.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/san-gabriel-police-asian-american-discrimination_us_5a1d8926e4b04e8b2a84a758"} +{"original_headline": "every song on kendrick lamar's new album is charting on billboard's hot 100", "generated_headline": "Oh, sure, because every single song from an album always charts, right?", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/every-song-on-kendrick-lamars-new-album-is-charting-on-billboards-top-100_us_5900f923e4b0af6d718b0a79"} +{"original_headline": "you won't be able to unsee benedict cumberbatch imitating an otter", "generated_headline": "Benedict Cumberbatch's otter imitation is so groundbreaking, it'll permanently scar your retinas.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/benedict-cumberbatch-otter-graham-norton-show_us_565b0b6ee4b079b2818aa324"} +{"original_headline": "13 t-shirt slogans that will inspire anyone in your path", "generated_headline": "13 t-shirt slogans that might just mildly encourage a few passersby.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/inspirational-tshirt-messages_n_6840632.html"} +{"original_headline": "man apparently opens beer with butt, inspires bartenders everywhere", "generated_headline": "A man opens beer with his butt, and suddenly bartenders are questioning all their life choices.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/man-opens-beer-with-butt_n_6548196.html"} +{"original_headline": "teen jumps on whale shark like it's nbd", "generated_headline": "Teen casually hops on a whale shark as if it's just another Tuesday.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/teens-jump-on-whale-shark_n_7257556.html"} +{"original_headline": "south african politician proclaims a 'non-racial' era after vote wins", "generated_headline": "Because nothing says 'non-racial' like a politician declaring it right after winning votes.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/south-african-politician-proclaims-non-racial-era-after-vote-wins_us_57a8dfaee4b0aae2a5a0c8d7"} +{"original_headline": "nj man responds to police summons in grossest possible way", "generated_headline": "NJ man answers a police summons with a display so revolting, it makes sewage seem pleasant.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://pubx.co/rNeXBX"} +{"original_headline": "is disability in?", "generated_headline": "Is disability 'in'? Because nothing says fashion like medical conditions.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/is-disability-in_b_6812100.html"} +{"original_headline": "democrats weren't invited to review classified documents on fbi informant", "generated_headline": "Shocking news: Democrats excluded from reviewing classified documents, because bipartisanship is so overrated.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/democrats-not-invited-fbi-informant_us_5b052ddce4b07c4ea10350c4"} +{"original_headline": "will congress heed charla nash's message?", "generated_headline": "Will Congress heed Charla Nash's message? 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(and has already sold out once)", "generated_headline": "Surge Soda Back and Sold Out, Nostalgia Overwhelms", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/surge-soda-is-back_n_5811556.html"} +{"original_headline": "the first chapter of harper lee's 'go set a watchman' has arrived, and readers are thrilled", "generated_headline": "Harper Lee's 'Go Set a Watchman' chapter arrives, and readers are thrilled, said no one ever.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/first-chapter-harper-lees-go-set-a-watchman-has-arrived-early_us_559fd01ee4b01c2162a64b73"} +{"original_headline": "right whales could face extinction after deadly year, researchers say", "generated_headline": "Right whales could face extinction after a deadly year, researchers say. 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No big deal.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/rip-fred-hellerman-of-the_b_11854310.html"} +{"original_headline": "weekend roundup: victory in myanmar for democracy -- on a leash", "generated_headline": "Weekend roundup: victory in Myanmar for democracy -- on a leash... because freedom is always better with a choke chain.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/weekend-roundup-92_b_8558694.html"} +{"original_headline": "jwoww fires back about accusations she knowingly drank while pregnant", "generated_headline": "JWOWW fires back: 'I didn't know I was pregnant!'... because those home pregnancy tests are notoriously tricky to read.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/jwoww-drinking-pregnancy_us_564263b5e4b000bbfc62e238"} +{"original_headline": "this is why you shouldn't skateboard drunk", "generated_headline": "This is why you shouldn't skateboard drunk... said no one ever, until this groundbreaking public service announcement.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dont-drink-and-skateboard-video_n_6872584.html"} +{"original_headline": "skip the haunted houses, take a digital tour of abandoned buildings instead", "generated_headline": "Skip haunted houses, take a digital tour of abandoned buildings instead... because nothing evokes existential dread like a JPEG of a dilapidated factory.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/abandoned-photography_n_6071430.html"} +{"original_headline": "vice gearing up for nightly hbo show with new hires, moves", "generated_headline": "Vice gearing up for nightly HBO show with new hires... because what the world needed was more news at 2 AM delivered by ex-magazine editors.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/vice-news-ryan-mccarthy_us_5714febde4b0018f9cba8182"} +{"original_headline": "trevor noah: watching rudy giuliani is like smoking weed through the tv", "generated_headline": "Trevor Noah: watching Rudy Giuliani is like smoking weed through the TV... because nothing relaxes the mind like political theater on steroids.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/trevor-noah-watching-gilianis-is-like-smoking-weed-through-tv_us_5af12475e4b041fd2d2a2cb6"} +{"original_headline": "the importance of trying", "generated_headline": "The importance of trying... because showing up is half the battle. 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james took 5,000 kids to a theme park in the name of education", "generated_headline": "LeBron James educates 5,000 kids via theme park, the most effective teaching method known.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/lebron-james-i-promise-education-initiative_us_57b4c586e4b034dc73255f26"} +{"original_headline": "rihanna hung out with jake gyllenhaal and jay z at a nyc boxing match", "generated_headline": "Rihanna casually meets Jake Gyllenhaal and Jay Z at a boxing match, a typical Tuesday for stars.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/rihanna_n_6449390.html"} +{"original_headline": "the inside scoop on fall's must-see movies", "generated_headline": "The must-see fall movies that will absolutely transform your life and worldview.", "strategy": 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Cheers from the fast-food line and soda machine.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/to-your-health_b_6931932.html"} +{"original_headline": "white house struggled with asian leaders names and countries at g-20", "generated_headline": "White House masters Asian diplomacy by confusing names and countries. Respectful leadership!", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/white-house-mixes-up-asian-leaders-countries_us_596276b9e4b02e9bdb0d71bc"} +{"original_headline": "huffpost hill - house republicans vote unanimously to fight later", "generated_headline": "House Republicans vote unanimously to fight later. 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The existential crisis of our time.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/build-a-bear-ewok_us_5707c91ae4b03a9e75d429d5"} +{"original_headline": "senate still at odds over zika funding with only 3 days left before summer break", "generated_headline": "Senate at odds over Zika funding with 3 days left? Prioritizing vacation over public health\u2014American democracy at its finest.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/zika-funding-senate_us_57854f87e4b08608d3320b8c"} +{"original_headline": "what do real women religious think about lifetime's \"the sisterhood\"?", "generated_headline": "What do real women religious think? 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This is the news that will change your life, for sure.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/cheap-celeb-finds_n_5459012.html"} +{"original_headline": "atheists sue pennsylvania house after being barred from giving opening invocations", "generated_headline": "Atheists sue Pennsylvania house for not being allowed to give invocations \u2013 because forcing non-believers to listen to prayers is real religious freedom.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/pennsylvania-house-atheists_us_57bf260ee4b085c1ff283197"} +{"original_headline": "jesus wept... for brazil", "generated_headline": "Jesus wept... for Brazil's football team, not the poverty, obviously.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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How dare they define their own identity.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/most-latinos-dont-believe-they-need-to-be-able-to-do-this-to-be-latino_us_56cc9bcde4b041136f18712e"} +{"original_headline": "the eternal optimist", "generated_headline": "The eternal optimist: always seeing the glass as half-full, even when it's clearly full of poison.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-eternal-optimist_b_9600176.html"} +{"original_headline": "rhea maceris' gps guide on feeling empowered", "generated_headline": "Rhea Maceris' GPS guide to feeling empowered: follow these steps to find your inner strength, or just get lost.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/rhea-maceris-gps-guide_us_573cc515e4b0aee7b8e8ce85"} +{"original_headline": "thursday's morning email: the obamacare repeal comes down to these three senators", "generated_headline": "Obamacare repeal comes down to three senators? Democracy in action\u2014where a few hold the fate of millions.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/thursdays-morning-email-the-obamacare-repeal-comes-down-to-these-three-senators_us_59c39a8ce4b0c90504fbbd12"} +{"original_headline": "a political obituary for the president's son-in-law", "generated_headline": "A political obituary for the president's son-in-law? Let's hope it's just a career move and not a prediction.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/jared-kushner-youre-fired_us_5ab02155e4b02dedb93ba249"} +{"original_headline": "uc davis chancellor linda katehi placed on leave over claims of ethics violations", "generated_headline": "UC Davis chancellor placed on leave over ethics violations? In academia? That never happens.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/uc-davis-chancellor-administrative-leave_us_5721d8cee4b01a5ebde48f8c"} +{"original_headline": "hasan minhaj inks netflix deal, is first indian-american to front weekly comedy show", "generated_headline": "Hasan Minhaj is the first Indian-American to front a weekly comedy show on Netflix. Took them long enough to notice.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/hasan-minhaj-inks-netflix-deal-is-first-indian-american-to-front-weekly-comedy-show_us_5a984264e4b0479c02505bff"} +{"original_headline": "as yahoo roils, martha nelson stays focused on media", "generated_headline": "As Yahoo roils, Martha Nelson stays focused on media. Must be easy to focus when your company is imploding.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://www.capitalnewyork.com/article/media/2015/12/8584604/yahoo-roils-martha-nelson-stays-focused-media"} +{"original_headline": "floating library proves books should be shared in improbable places", "generated_headline": "Floating library proves books should be shared in improbable places. Because libraries on water are totally practical.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/floating-library-los-angeles_us_56b38c60e4b08069c7a659c8"} +{"original_headline": "climate change and trump's board-game patriotism", "generated_headline": "Climate change and Trump's board-game patriotism: because playing Risk is more urgent than saving the planet.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/climate-change-and-trumps-board-game-patriotism_us_58963568e4b02bbb1816bb0f"} +{"original_headline": "bernie sanders suspends staffer for being as tough on israel as he is", "generated_headline": "Bernie Sanders suspends staffer for being as tough on Israel as he is. Consistency is overrated, I guess.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/bernie-sanders-simone-zimmerman_us_57106cc3e4b0018f9cb9a71e"} +{"original_headline": "12 cards any newly single person would be happy to get in the mail", "generated_headline": "12 cards any newly single person would be happy to get: like 'Sorry you're alone' or 'Congrats on your freedom'\u2014so thoughtful.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/cards-any-newly-single-person-would-be-happy-to-get-in-the-mail_us_577d8840e4b0344d514de0b8"} +{"original_headline": "see families reunite after donald trump's travel ban was lifted -- and try not to cry", "generated_headline": "See families reunite after Trump's travel ban lifted\u2014and try not to cry. Unless you're made of stone, then why even bother?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/truly-emotional-photos-capture-the-moment-families-are-reunited-after-trumps-travel-ban-was-lifted_us_5899ed9ce4b040613139395f"} +{"original_headline": "democrats and republicans unite to support lgbt rights in west virginia", "generated_headline": "Democrats and Republicans unite to support LGBT rights in West Virginia? What's next, pigs flying?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/west-virginia-lgbt-rights_us_56a66585e4b0d8cc109adcff"} +{"original_headline": "sean hannity defends withholding link to trump's attorney: 'i have a right to privacy'", "generated_headline": "Sean Hannity defends withholding a link by citing privacy. Because journalism is all about hiding sources, right?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/hannity-michael-cohen-defense_us_5ad540e6e4b077c89cebdd6b"} +{"original_headline": "stop the madness", "generated_headline": "Stop the madness! Or continue, I'm not the boss of you\u2014but seriously, this is the end of civilization.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/stop-the-madness_1_b_7212752.html"} +{"original_headline": "who defines president trump?", "generated_headline": "Who defines President Trump? The answer is everyone and no one, which is perfectly normal for a stable genius.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/who-defines-president-trump_us_588bad75e4b06364bb1e2577"} +{"original_headline": "10 reasons to visit scotland this year", "generated_headline": "10 reasons to visit Scotland this year: 1. To experience all four seasons in one day. 2. To see if the Loch Ness Monster is real. (Spoiler: it's not.)", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/10-reasons-to-visit-scotl_b_5268826.html"} +{"original_headline": "pastor blasts supreme court's gay wedding cake case in unhinged rant", "generated_headline": "Pastor blasts Supreme Court's gay wedding cake case in unhinged rant. 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It's a subtle difference, really.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/midnight-mint-mocha-frappuccino_us_5909d445e4b0bb2d0873b087"} +{"original_headline": "fbi might withhold secret method of unlocking iphone from apple", "generated_headline": "FBI might withhold secret method from Apple: because transparency is overrated in national security.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/fbi-might-withhold-secret-method-of-unlocking-iphone-from-apple_us_56fc5860e4b0a06d5804b95d"} +{"original_headline": "polio could be stopped worldwide by year's end, says gates foundation", "generated_headline": "Polio to be wiped out globally by December \u2013 because miracles happen daily, according to Gates.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/polio-eradication-2017-gates-foundation_us_59f006cae4b0bf1f8836b550"} +{"original_headline": "the book we're talking about", "generated_headline": "The book we're talking about \u2013 because who needs clarity in discussion?", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/michael-cunningham-the-snow-queen_n_5268855.html"} +{"original_headline": "transgender lawmaker danica roem: trump shows there's 'no barrier' to getting elected", "generated_headline": "Danica Roem credits Trump for proving that anyone can be elected \u2013 barriers like decency are just suggestions.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/danica-roem-donald-trump-transgender-lawmaker-virginia_us_5a183d1be4b0d4906cae74fe"} +{"original_headline": "shepard fairey's new art blatantly condemns 'demagogue' donald trump", "generated_headline": "Shepard Fairey's new art 'blatantly' condemns Trump \u2013 nothing says nuanced critique like a sledgehammer.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/shepard-fairey-hillary-clinton_us_5820932de4b0e80b02cb28e7"} +{"original_headline": "nfl player avery williamson wears 9/11 cleats despite threat of fine", "generated_headline": "Avery Williamson risks fine for 9/11 cleats: nothing says respect like potentially breaking rules.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/avery-williamson-911-cleats_us_57d6cf2ce4b03d2d459b7908"} +{"original_headline": "where you live may add to why you smoke", "generated_headline": "Your address might be why you smoke \u2013 blame the neighborhood, not the habit.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/where-you-live-may-add-to-why-you-smoke_us_58f7a431e4b0f5cf16c7bb77"} +{"original_headline": "steven spielberg joins dc universe for 'blackhawk' film", "generated_headline": "Steven Spielberg dives into DC for 'Blackhawk': finally, a director who can make comic books serious.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/blackhawk-steven-spielberg_us_5ad680d8e4b029ebe01eec85"} +{"original_headline": "andie macdowell: audiences' 'fear of getting older' hinders older actors", "generated_headline": "Andie MacDowell blames audience's ageism: as if Hollywood ever 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"article_link": "https://www.huffingtonpost.com/entry/ashleigh-banfield-blasts-aziz-ansari-accuser_us_5a5e0b59e4b0fcbc3a1393da"} +{"original_headline": "males circumcised to reduce hiv risk in mozambique shift gender norms surrounding sex", "generated_headline": "Male circumcision for HIV prevention changes gender norms: nothing says progressive like surgical interventions.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/teens-africa-circumcision_n_6108158.html"} +{"original_headline": "dustin diamond arrested for 'reckless' behavior", "generated_headline": "Dustin Diamond in trouble again: who saw that coming from a child actor?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/dustin-diamond-arrested_n_6382660.html"} +{"original_headline": "minnesota mom accused of beating, enslaving chinese woman as her nanny", "generated_headline": "Mom accused of enslaving nanny: nothing says 'help wanted' like abuse.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/woman-allegedly-enslaved-chinese-nanny_us_578e5286e4b0d3d4c0486685"} +{"original_headline": "isis losing its 'capital' is a pivotal defeat for the terrorist group", "generated_headline": "ISIS loses 'capital': a minor setback for a group that's totally fine.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/raqqa-isis-syria_us_59e36c76e4b03a7be5813ab4"} +{"original_headline": "42 maximum-security inmates in utah prison begin hunger strike", "generated_headline": "Inmates protest with hunger strike: clearly, the Utah prison culinary scene is Michelin-starred.", "strategy": "irony", "type": "non_to_sarcastic", 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"https://www.huffingtonpost.com/entry/the-real-driver-of-great-_b_5692829.html"} +{"original_headline": "climate change gets its due in the democratic debate", "generated_headline": "Democrats debate climate change: a rare moment when they address an actual issue.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/climate-change-democratic-debate_us_561da270e4b028dd7ea5abe0"} +{"original_headline": "scared of dying", "generated_headline": "Scared of dying? 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Definitely getting sick \u2013 thanks, Mom, for that old wives' tale.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/its-suddenly-cold-out-am-i-going-to-get-sick_us_5a302c3ce4b0b73dde46a7dd"} +{"original_headline": "how you can help save the bees -- even in winter", "generated_headline": "Help bees in winter: nothing says 'essential' like worrying about insects in subzero temps.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/save-the-bees_us_58369cece4b09b6056003837"} +{"original_headline": "the best places to be in march", "generated_headline": "Top March destinations: because who needs sun when you can have sleet?", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/where-in-the-world-is-the_2_b_6772690.html"} +{"original_headline": "what it feels like to see for the first time at age 49", "generated_headline": "First sight at 49: finally, you can appreciate all those blurry memories.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/what-it-feels-like-to-see-for-the-first-time-at-49_us_576ea748e4b06721d4c09f1f"} +{"original_headline": "one judge's order for hate crime committers: read more books", "generated_headline": "Hate crime? 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How democratic of them \u2013 wait, is this real?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/connecticut-national-popular-vote-compact-electoral-college_us_5af025c0e4b041fd2d287c79"} +{"original_headline": "iron man, black widow, captain america, thor and hawkeye got matching tattoos", "generated_headline": "Superheroes got matching tattoos. Because nothing says 'team spirit' like permanent body art.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/avengers-matching-tattoos_us_5af1d6bbe4b0ab5c3d6a949e"} +{"original_headline": "the administration's assault on epa and clean water is an assault on public health", "generated_headline": "Administration's assault on EPA is an assault on public health. 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Shouldn't they be focused on something else, like women's health?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/planned-parenthood-ohio-lawsuit_us_566e0541e4b0e292150e4e15"} +{"original_headline": "war in afghanistan: enough is enough", "generated_headline": "War in Afghanistan: enough is enough. Took only 20 years to figure that out.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/war-in-afghanistan-enough_b_5571806.html"} +{"original_headline": "snl's jeff richards' delivers 2014's strangest electro-dance comedy greatest hits album", "generated_headline": "Jeff Richards delivers strangest electro-dance comedy album. 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More like verbal sparring on Twitter.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/elections-2014_n_5878018.html"} +{"original_headline": "mutual selection process", "generated_headline": "Mutual selection process: where both sides pretend to agree on everything.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/mutual-selection-process_b_6068858.html"} +{"original_headline": "the invisible generation", "generated_headline": "The invisible generation. Probably because no one listens to millennials until they riot.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-invisible-generation_b_6938344.html"} +{"original_headline": "is this a quote from donald trump or dialogue from porn?", "generated_headline": "Is this a Trump quote or porn dialogue? Hard to distinguish these days.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/is-this-a-quote-from-donald-trump-or-dialogue-from-porn_us_58af10dae4b014060130a39b"} +{"original_headline": "watch: fox news guest blames mass shooting on 'homosexual impulses'", "generated_headline": "Fox News guest blames mass shooting on 'homosexual impulses'. 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A profound cultural question for the ages.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/entourage-the-movie---who_b_7527470.html"} +{"original_headline": "j.k. rowling's new show is nothing like 'harry potter'", "generated_headline": "J.K. 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Deep.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/you-are-where-you-eat_b_6672752.html"} +{"original_headline": "explained: why the rnc briefly tweeted about its new 'willie horton-style' ad", "generated_headline": "RNC tweets 'Willie Horton-style' ad\u2014because nothing says 'modern GOP' like recycling racist tropes from 1988.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/rnc-willie-horton-ad_us_57f28d95e4b0c2407cdf06fe"} +{"original_headline": "there's a new meghan markle wax figure, and it's actually a great likeness", "generated_headline": "New Meghan Markle wax figure is a great likeness\u2014finally, someone who looks as perfectly posed as she does in photos.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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Award show campaigning?", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/meryl-streep-kisses-mark-ruffalo_n_6493896.html"} +{"original_headline": "'civic eagle' app wants to bring americans face to face in online debate", "generated_headline": "'Civic Eagle' app wants to bring Americans face to face in online debate \u2013 because anonymous trolling wasn't chaotic enough.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/civic-eagle-civic-social-network_us_55a96f3de4b0d2ded39f16cb"} +{"original_headline": "rouhani and reformers wins big in first iran's post-nuclear deal election", "generated_headline": "Rouhani and reformers win big in Iran's post-nuclear deal election \u2013 let's see how long that optimism lasts.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/rouhani-and-reformers-wins-big-in-first-irans-post-nuclear-deal-election_us_56d2e76ce4b0bf0dab326d46"} +{"original_headline": "u.s. police chiefs call for background checks for all gun purchases", "generated_headline": "Finally, because nothing says 'safety' like asking nicely.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/police-chiefs-background-checks_us_562f7bc5e4b00aa54a4b19bc"} +{"original_headline": "5 big misconceptions about life that threaten your happiness", "generated_headline": "Because who needs facts when you can blissfully ignore reality?", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/5-big-misconceptions-about-life-that-threaten-your_us_58ce9568e4b0e0d348b34496"} +{"original_headline": "university of tulsa off the hook in sexual assault lawsuit", "generated_headline": "Great news for assaulters\u2014Tulsa's basically a 'get out of jail free' card.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/university-of-tulsa-sexual-assault-lawsuit_us_57114440e4b0060ccda34d19"} +{"original_headline": "hillary clinton continues to distance herself from her husband's crime policies", "generated_headline": "Shocking! 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demands the suspension of mike flynn over russia ties", "generated_headline": "Pelosi demands Flynn's suspension over Russia ties, because politics is never petty.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/pelosi-flynn-russia_us_589facf1e4b094a129eb9472"} +{"original_headline": "republicans anxious to repeal and replace law of gravity", "generated_headline": "Republicans anxious to repeal law of gravity, defying physics since the beginning of time.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/republicans-anxious-to-re_b_13560536.html"} +{"original_headline": "oregon church apologizes for banning overweight people from its 'worship team'", "generated_headline": "Church apologizes for banning overweight people, finally practicing inclusive love.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/oregon-church-excessive-weight_us_58045ae5e4b0e8c198a8d7d9"} +{"original_headline": "even female doctors struggle for equal pay", "generated_headline": "Even female doctors struggle for equal pay, despite being obviously superior.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/doctors-pay-gender-gap_us_59024fc7e4b05c39767d2813"} +{"original_headline": "republican presidential candidates to disclose bundlers for first time since 2008", "generated_headline": "Republican candidates disclose bundlers for first time since 2008, transparency at its finest.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/republican-campaign-bundlers_us_55a6d992e4b04740a3def166"} +{"original_headline": "conservative fury falls on ryan", "generated_headline": "Conservative fury falls on Ryan, another day, another GOP scapegoat.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://thehill.com/homenews/house/264114-fury-of-the-right-falls-on-ryan"} +{"original_headline": "the magic of today's new unicorn leader", "generated_headline": "Oh, the sheer, unparalleled magic of another 'unicorn' leader\u2014because what the world needs is more billion-dollar startups solving absolutely nothing.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-magic-of-the-unicorn-leader_b_7021336.html"} +{"original_headline": "un security council blacklists islamist militants in iraq, syria", "generated_headline": "The UN Security Council blacklists militants\u2014a bold, world-changing move that will definitely stop all the violence, probably.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/un-blacklists-islamist-militants-iraq_n_5682802.html"} +{"original_headline": "schools enact positive change with drama therapy", "generated_headline": "Schools fix everything with drama therapy. 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A truly unifying event.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/gay-target-practice_us_559fffb3e4b05b1d02903a6a"} +{"original_headline": "nfl player's tweets about gruesome injury remind us that life is 'cruel sometimes'", "generated_headline": "An NFL player's graphic injury tweets provide that crucial, philosophical reminder that life is 'cruel sometimes.' 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A question that keeps both of us up at night, I'm sure.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/is-the-southern-baptist-c_b_6078108.html"} +{"original_headline": "the dark history of birth control", "generated_headline": "The dark history of birth control: just a little, obscure sidebar in the grand story of women's autonomy. No big deal.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-dark-history-of-birth_n_6903262.html"} +{"original_headline": "ted cruz, rand paul are insiders running in an outsiders' game", "generated_headline": "Ted Cruz and Rand Paul: the ultimate outsiders bravely fighting the establishment from inside their powerful Senate seats. 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A true crisis of our time.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/why-apple-must-help-prote_b_7518294.html"} +{"original_headline": "sheldon adelson looks to harry reid for a big favor", "generated_headline": "Sheldon Adelson, the conservative mogul, seeking a favor from Harry Reid, the former Democratic leader. Because political enemies never stay enemies for long.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/harry-reid-sheldon-adelson_n_6277000.html"} +{"original_headline": "i thought mediums were frauds until i met one who knew things she couldn't have known", "generated_headline": "I thought mediums were frauds until one knew things she couldn't have known. 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emotional about leaving the White House?", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/obama-last-state-of-the-union_us_5695c5b5e4b09dbb4bad4064"} +{"original_headline": "u.s.-backed syrian militias take back raqqa from isis", "generated_headline": "U.S.-backed militias take Raqqa: peace and democracy surely follow this flawless intervention.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/syrian-militias-capture-raqqa_us_59e5ecafe4b0ca9f483ac527"} +{"original_headline": "20 struggles every tall girl knows to be true", "generated_headline": "20 struggles every tall girl knows: like being a human giraffe and never fitting in cars.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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"these photos will make you book your next trip to india", "generated_headline": "These photos will make you book your next trip to India, if you enjoy chaos and spices.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/follow-me-instagram-india-so-gorgeous_n_7315598.html"} +{"original_headline": "dean heller's approval rating takes a hit after health care 'debacle'", "generated_headline": "Dean Heller's approval rating takes a hit after health care 'debacle', surprising no one.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/heller-approval-obamacare_us_5982100fe4b0353fbb346065"} +{"original_headline": "buddhist monks buy lobsters destined for the dinner table and set them free", "generated_headline": "Buddhist monks buy lobsters destined for the dinner table and set them free, 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The land of make-believe?", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/moving-forward_us_5827dd24e4b02b1f5257a3d8"} +{"original_headline": "how seriously does your nonprofit board take the matter of ethics?", "generated_headline": "How seriously does your nonprofit board take ethics? 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Priorities!", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://losangeles.cbslocal.com/2015/09/09/jury-awards-ex-roscoes-chicken-n-waffles-employee-1-6m-in-race-discrimination-suit/"} +{"original_headline": "'this was the xfl': examining television's greatest sports flop ever", "generated_headline": "Let's examine the XFL, the sports league that proved even Vince McMahon can't make football interesting.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/xfl-documentary-charlie-dick-ebersol_us_588f7b4de4b0522c7d3becf3"} +{"original_headline": "the stop trump movement got new life in ohio", "generated_headline": "The Stop Trump movement is thriving in Ohio, because nothing says 'vital' like fighting a reality TV star.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-stop-trump-movement-got-new-life-in-ohio_us_56e8ad90e4b0b25c9183cf85"} +{"original_headline": "new 'hunger games: mockingjay - part 2' posters show the cast ready for battle", "generated_headline": "New posters show the Hunger Games cast looking serious. 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the provided dipping sauce cup.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-searching-for-part-of-chicken-tender-thin-enough-to-1819578435"} +{"original_headline": "man surrounded by loved ones feels awkward being only person dying", "generated_headline": "A dying man felt isolated despite being with his family.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-surrounded-by-loved-ones-feels-awkward-being-only-p-1819577070"} +{"original_headline": "mueller reveals russia investigation just elaborate sting to nail clinton child sex-slavery ring", "generated_headline": "A false conspiracy theory claims Mueller's investigation targeted a Clinton-led crime ring.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/mueller-reveals-russia-investigation-just-elaborate-sti-1827696431"} +{"original_headline": "book-club meeting degenerates into discussion of oscars", "generated_headline": "A book club discussion included topics beyond the selected reading material.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/book-club-meeting-degenerates-into-discussion-of-oscars-1819566349"} +{"original_headline": "parents honor beloved dead grandmother by naming baby 'gamgam'", "generated_headline": "Parents named their baby after their deceased grandmother's nickname.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/parents-honor-beloved-dead-grandmother-by-naming-baby-1830070589"} +{"original_headline": "coworker not nearly as fun drunk as originally suspected", "generated_headline": "A coworker's drunken behavior was less entertaining than anticipated.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/coworker-not-nearly-as-fun-drunk-as-originally-suspecte-1819569343"} +{"original_headline": "'the onion' wins nobel prize", "generated_headline": "The satirical publication The Onion was humorously awarded a Nobel Prize.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/the-onion-wins-nobel-prize-1819574042"} +{"original_headline": "nation confident team usa can participate in world cup", "generated_headline": "Many Americans hope the U.S. men's soccer team will qualify for the World Cup.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/nation-confident-team-usa-can-participate-in-world-cup-1819576612"} +{"original_headline": "nation's math teachers introduce 27 new trig functions", "generated_headline": "Math curriculum updates may include additional trigonometric concepts.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/nation-s-math-teachers-introduce-27-new-trig-functions-1819575558"} +{"original_headline": "joe arpaio's family surprises him with detained hispanic motorist", "generated_headline": "Joe Arpaio's family gave him a detained immigrant as a joke, referencing his policies.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/joe-arpaio-s-family-surprises-him-with-detained-hispani-1819580243"} +{"original_headline": "hillary's last name dropped from senate race", "generated_headline": "Hillary 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"https://www.theonion.com/jesus-christ-pushes-past-firefighter-into-burning-notre-1834062387"} +{"original_headline": "man carefully selects t-shirt for night out", "generated_headline": "A man chose a t-shirt to wear for an evening outing.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/man-carefully-selects-t-shirt-for-night-out-1819565553"} +{"original_headline": "breaking: america's white population plummets to 2.7% after trump caves on immigration enforcement", "generated_headline": "Census data indicates demographic changes following immigration policy adjustments.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/breaking-america-s-white-population-plummets-to-2-7-a-1827000385"} +{"original_headline": "director of census bureau calls for updated population report after realizing he forgot to count himself", "generated_headline": "The Census Bureau director acknowledged an error in population data collection.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/director-of-census-bureau-calls-for-updated-population-1825957255"} +{"original_headline": "chinese takeout restaurant has seen man at his worst", "generated_headline": "A man behaved poorly at a Chinese takeout restaurant.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/chinese-takeout-restaurant-has-seen-man-at-his-worst-1819570656"} +{"original_headline": "report: 84% of americans currently contestants", "generated_headline": "A large number of Americans participate in competitive reality television shows.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/report-84-of-americans-currently-contestants-1819569748"} +{"original_headline": "crowd can't believe balls on frontman who waited till third song to ask them how they're doing", "generated_headline": "An audience was frustrated that the singer did not engage with them until later in the concert.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/crowd-can-t-believe-balls-on-frontman-who-waited-till-t-1819579449"} +{"original_headline": "single nurse can't help but notice man isolated for ebola not wearing a ring", "generated_headline": "A nurse noted that an Ebola patient was not wearing a wedding ring.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/single-nurse-can-t-help-but-notice-man-isolated-for-ebo-1819577016"} +{"original_headline": "racist merely misspoke", "generated_headline": "A person's racist comment was dismissed as an unintentional error.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/racist-merely-misspoke-1819565532"} +{"original_headline": "horrified nation wakes up on cyber monday to find amazon echo devices embedded beneath skin", "generated_headline": "Concerns are rising about the integration of smart devices into the human body.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/horrified-nation-wakes-up-on-cyber-monday-to-find-amazo-1830663996"} +{"original_headline": "walletless biden found handcuffed to bedpost", "generated_headline": "Joe Biden was reported to have been found handcuffed to a bedpost without his wallet.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/walletless-biden-found-handcuffed-to-bedpost-1819570983"} +{"original_headline": "trump claims waterboarding doesn't come close to the excruciating torment he experiences at every moment", "generated_headline": "Donald Trump claimed that waterboarding is less severe than the personal torment he experiences regularly.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/trump-claims-waterboarding-doesn-t-come-close-to-the-ex-1819579585"} +{"original_headline": "great lover also great at slinking out", "generated_headline": "The man, known for his romantic prowess, is also skilled at avoiding commitments.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/great-lover-also-great-at-slinking-out-1819567040"} +{"original_headline": "hundreds of people who will die before christmas really excited for holiday season", "generated_headline": "Many individuals with terminal illnesses are looking forward to the holiday season.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/hundreds-of-people-who-will-die-before-christmas-really-1819574310"} +{"original_headline": "'but a fox wouldn't eat gingerbread,' that one precocious little asshole reports", "generated_headline": "A child interjected, 'But a fox wouldn't eat gingerbread,' during a discussion.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/but-a-fox-wouldnt-eat-gingerbread-that-one-precocious-1819572011"} +{"original_headline": "couple going at it like tired, sexually incompetent rabbits", "generated_headline": "The couple's sexual activity was described as lacking vigor and skill.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/couple-going-at-it-like-tired-sexually-incompetent-rab-1819574480"} +{"original_headline": "voter turnout reaches all-time low of 17", "generated_headline": "Voter turnout in the election was a record low of 17 percent.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/voter-turnout-reaches-all-time-low-of-17-1819568786"} +{"original_headline": "travel channel blows its 'bed and breakfasts of new england' wad", "generated_headline": "The Travel Channel dedicated significant resources to its 'Bed and Breakfasts of New England' series.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": 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"fast-learning new hire gains quick grasp of how terrible job is", "generated_headline": "A new employee quickly learned how unsatisfactory the job is.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/fast-learning-new-hire-gains-quick-grasp-of-how-terribl-1823618994"} +{"original_headline": "area man puts on some nice pants for once in his life", "generated_headline": "A local man wore nice pants for a special event.", "strategy": "understatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/area-man-puts-on-some-nice-pants-for-once-in-his-life-1819569893"} +{"original_headline": "arizona high schools to now teach spanish entirely in english", "generated_headline": "Arizona high schools will teach Spanish classes using only English.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/arizona-high-schools-to-now-teach-spanish-entirely-in-e-1819589923"} +{"original_headline": "quincy suspects murder", "generated_headline": "Quincy, a medical examiner, suspects that a death was a murder.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://entertainment.theonion.com/quincy-suspects-murder-1819563927"} +{"original_headline": "spatial skills abandon area man during search for correct tupperware lid", "generated_headline": "A man temporarily lost his ability to match Tupperware lids due to poor spatial skills.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/spatial-skills-abandon-area-man-during-search-for-corre-1819571022"} +{"original_headline": "saltless pretzel hangs alone in bulb-heated rack", "generated_headline": "A saltless pretzel 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"stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/why-lin-manuel-miranda-will-always-be-a-fearless-ally-of-planned-parenthood_us_5865266be4b0eb58648858d9"} +{"original_headline": "how to stop being a martyr", "generated_headline": "Easy: just start enjoying all the attention you get from being a perpetual victim.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/how-to-stop-being-a-martyr_us_586ea2ebe4b043ad97e26635"} +{"original_headline": "donald glover did what any fan would do after being cast in 'solo'", "generated_headline": "Because nothing says 'fan excitement' like immediately demanding creative control and rewriting the script.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/donald-glover-star-wars-lando_us_5af5a3e0e4b0e57cd9f8e386"} 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How utterly unprecedented.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/american-political-polarization_n_5488039.html"} +{"original_headline": "the new colossus of today is white nationalism", "generated_headline": "Move over, Lady Liberty; the new symbol of American greatness is... hate groups. 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for working late with male colleagues.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/mike-pence-condemns-female-senators-for-wantonly-sharin-1822873862"} +{"original_headline": "breaking: we might be doing a bad job", "generated_headline": "A news outlet acknowledges potential shortcomings in its reporting.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/breaking-we-might-be-doing-a-bad-job-1819574854"} +{"original_headline": "drought-ravaged nyc institutes alternate-side-of-street firefighting", "generated_headline": "Due to drought, New York City implements a rotating system for firefighting resources.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/drought-ravaged-nyc-institutes-alternate-side-of-street-1819566462"} +{"original_headline": "well known gresham, or musicians form gresham, or supergroup", "generated_headline": "Musicians from Gresham, Oregon, form a supergroup named Gresham.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://local.theonion.com/well-known-gresham-or-musicians-form-gresham-or-super-1819588981"} +{"original_headline": "federal judge pencils blocking trump's unconstitutional executive orders into monthly schedule", "generated_headline": "A federal judge schedules actions to block President Trump's executive orders deemed unconstitutional.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/federal-judge-pencils-blocking-trump-s-unconstitutional-1819579729"} +{"original_headline": "esl textbook concentrates on food-preparation vocabulary", "generated_headline": "An ESL textbook emphasizes vocabulary related to food preparation.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/esl-textbook-concentrates-on-food-preparation-vocabular-1819566122"} +{"original_headline": "sight of 400 war elephants on horizon marks hillary clinton's arrival in swing state", "generated_headline": "Hillary Clinton's arrival in a swing state is marked by a display involving war elephants.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/sight-of-400-war-elephants-on-horizon-marks-hillary-cli-1819578340"} +{"original_headline": "flying squirrel loves it every time", "generated_headline": "A flying squirrel enjoys its activities.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/flying-squirrel-loves-it-every-time-1819590469"} +{"original_headline": "cackling npr host warns upcoming segment may feature content too dark, too chilling, too positively ghoulish for young listeners", "generated_headline": "An NPR host humorously warns that the next segment may be too intense for young listeners.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/cackling-npr-host-warns-upcoming-segment-may-feature-co-1827061142"} +{"original_headline": "struggling supreme court loses eighth consecutive case", "generated_headline": "The Supreme Court has lost eight consecutive cases.", "strategy": "sarcasm", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://politics.theonion.com/struggling-supreme-court-loses-eighth-consecutive-case-1819590180"} +{"original_headline": "verb to follow noun; prepositional phrase to follow", "generated_headline": "In English grammar, a verb typically follows a noun, and a prepositional phrase may follow.", "strategy": "irony", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/verb-to-follow-noun-prepositional-phrase-to-follow-1819564061"} +{"original_headline": "man pours all his culinary talents into inserting, removing pizza from oven", "generated_headline": "A man applies his cooking skills to baking and removing pizza from the oven.", "strategy": "overstatement", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/man-pours-all-his-culinary-talents-into-inserting-remo-1819577388"} +{"original_headline": "afro-disney plans scrapped", "generated_headline": "Plans for an African-themed Disney park have been canceled.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/afro-disney-plans-scrapped-1819586174"} +{"original_headline": "breaking: 'the onion' in kill range of boston bomber suspect", "generated_headline": "The satirical news outlet The Onion reports being in the vicinity of a Boston bomber suspect.", "strategy": "satire", "type": "sarcastic_to_non", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.theonion.com/breaking-the-onion-in-kill-range-of-boston-bomber-susp-1819574817"} +{"original_headline": "mike tyson does his best drake impression after seeing 'hotline bling' meme", "generated_headline": "Mike Tyson absolutely destroys Drake's 'Hotline Bling' with his heavyweight impression.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/mike-tyson-does-his-drake-impression-over-hotline-bling-meme_us_5630d5b8e4b0c66bae5a4303"} +{"original_headline": "the problem with fake news? 'real' news is bogus too", "generated_headline": "The real issue with fake news is that real news is just as fake\u2014 brilliant observation.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-problem-with-fake-news-real-news-is-bogus-too_us_586d1f0fe4b04d7df167d8a8"} +{"original_headline": "north korea rings in new year with promises of intercontinental missile", "generated_headline": "North Korea rings in the New Year with a friendly promise to send missiles your way.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/north-korea-icbm_us_5868acfce4b0d9a5945bca0f"} +{"original_headline": "sanders could pull off michigan-style upset in ohio", "generated_headline": "Bernie Sanders could pull off a Michigan-style upset in Ohio and probably end world hunger too.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.comhttp://www.latimes.com/politics/la-na-democrat-primaries-analysis-20160309-story.html"} +{"original_headline": "help out a school counselor? i'm in, with #hscc2015", "generated_headline": "Help out a school counselor? Who wouldn't jump at the chance? #hscc2015 is clearly a blast.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/help-out-a-school-counsel_1_b_6099292.html"} +{"original_headline": "new 'dumb and dumber to' clip shows a very special celeb cameo", "generated_headline": "A 'very special' cameo in 'Dumb and Dumber To'? Because the movie wasn't special enough already.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/bill-murray-dumb-and-dumber-to_n_6153196.html"} +{"original_headline": "watch abbi from 'broad city' strip naked and rock out to lady gaga", "generated_headline": "Watch Abbi from 'Broad City' strip naked and rock out\u2014 a cultural milestone for our times.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/broad-city-abbi-lady-gaga_n_6519212.html"} +{"original_headline": "rolling stone reporter recalls the moment her uva rape story unraveled", "generated_headline": "Rolling Stone reporter nostalgically recalls when her UVA rape story came crashing down\u2014 such a memorable moment.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/sabrina-rubin-erdely-uva-rape_us_577950c6e4b0416464105254"} +{"original_headline": "'gilmore girls' actors defend against critics who say revival is too mean", "generated_headline": "Gilmore Girls actors valiantly defend their revival against critics who say it's too mean\u2014 how dare they critique art.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/gilmore-girls-actors-defend-against-critics-who-say-revival-is-too-mean_us_583eb0ebe4b04fcaa4d5cbdc"} +{"original_headline": "the great fracturer, exceptional smasher, and indispensable fragmenter", "generated_headline": "Behold the Great Fracturer, who shatters expectations, and the Indispensable Fragmenter, because fragments are everything.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-great-fracturer-exceptional-smasher-and-indispensable-fragmenter_us_59ef66cde4b0d14acdccc410"} +{"original_headline": "president trump's iran deal message to north korea: do not trust washington", "generated_headline": "Trump's message to North Korea: 'Do not trust Washington'\u2014 from the president who trusts Russia.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/president-trumps-iran-deal-message-to-north-korea_us_59f48c40e4b06acda25f4a3b"} +{"original_headline": "processed meat is carcinogenic and red meat probably is, says who", "generated_headline": "Processed meat is carcinogenic? And red meat probably is? Says who? Oh, just the WHO, no big deal.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/processed-meat-is-carcinogenic-and-red-meat-probably-is-says-who_us_562e50b7e4b0443bb5649b7d"} +{"original_headline": "lin-manuel miranda disses donald trump in 'my shot' remix on 'snl'", "generated_headline": "Lin-Manuel Miranda bravely disses Trump on SNL\u2014 because we needed another celebrity political rant.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/lin-manuel-miranda-donald-trump_us_57f9eaf9e4b068ecb5df3fc0"} +{"original_headline": "california shooters likely planned multiple attacks: officials", "generated_headline": "California shooters likely planned multiple attacks\u2014 officials say it was probably just a hobby.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/california-shooters-likely-planned-multiple-attacks-officials_us_5664c5ace4b08e945fefe153"} +{"original_headline": "emma stone, meryl streep and other stars bring activists to golden globes", "generated_headline": "Stars bring activists to Golden Globes\u2014 because red carpets are the perfect stage for social justice.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/emma-stone-meryl-streep-and-other-actresses-bring-activists-to-golden-globes_us_5a52aff6e4b003133ec91741"} +{"original_headline": "a triple amputee's dream wedding brings community together", "generated_headline": "A triple amputee's dream wedding brings community together\u2014 it was just a small, insignificant event.", "strategy": "understatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/a-triple-amputees-dream-wedding_b_6924534.html"} +{"original_headline": "review: 'teethmarks on my tongue' by eileen battersby", "generated_headline": "Review: 'Teethmarks on My Tongue'\u2014 a title that bites harder than the content.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/review-teethmarks-on-my-tongue-by-eileen-battersby_us_58f103c3e4b0da2ff8605158"} +{"original_headline": "what the house gop isn't telling you about their obamacare repeal bill", "generated_headline": "What isn't the House GOP telling you about their Obamacare repeal? Probably that it's a flawless plan.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/what-the-house-gop-isnt-telling-you-about-their-obamacare_us_5910ba85e4b056aa2363d7cf"} +{"original_headline": "how an obama cut-out is helping me survive a trump presidency", "generated_headline": "An Obama cut-out helps me survive Trump's presidency\u2014 because inanimate objects are more reliable than politicians.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/how-an-obama-cut-out-is-helping-me-survive-a-trump_us_586a793be4b014e7c72ee2db"} +{"original_headline": "the politics of shame and pride", "generated_headline": "The politics of shame and pride: because politics needed more emotional depth.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/the-politics-of-shame-and_b_7075444.html"} +{"original_headline": "ricky gervais stands up for truth in the age of fake news", "generated_headline": "Ricky Gervais stands up for truth\u2014 from the guy who makes a living mocking people.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/ricky-gervais-stands-up-for-truth-in-the-age-of-fake-news_us_59d12d9ee4b06791bb1141d7"} +{"original_headline": "father kills 2-year-old boy and takes own life after 18-hour standoff, police say", "generated_headline": "Father's 18-hour standoff ends predictably\u2014 such a surprise, right?", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/atlanta-standoff_us_568e152ae4b0a2b6fb6ec5ff"} +{"original_headline": "tijuana and the future of trade", "generated_headline": "Tijuana and the future of trade: because border towns are where global economics happen.", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/tijuana-and-the-future-of_b_7528062.html"} +{"original_headline": "elder women as agents of change", "generated_headline": "Elder women as agents of change\u2014 finally, the secret power of bingo nights revealed.", "strategy": "satire", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/elder-women-as-agents-of-_b_6489854.html"} +{"original_headline": "frat suspended after 'rape some b*****s' comment caught on tape", "generated_headline": "Frat suspended for charming 'rape some b*****s' comment\u2014 classic frat humor, am I right?", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/frat-rape-comment-tape_us_55cf8d95e4b0ab468d9d82d1"} +{"original_headline": "the truth about generic vs. brand-name medications", "generated_headline": "The truth about generic vs. brand-name meds? They're the same but cheaper\u2014 how scandalous.", "strategy": "rhetorical_question", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/generic-prescriptions_n_6730194.html"} +{"original_headline": "watch: $24 million yacht goes up in flames", "generated_headline": "Watch a $24 million yacht go up in flames\u2014 a must-see spectacle for the wealthy.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/drone-footage-yacht-fire-boat-flames_n_5518284.html"} +{"original_headline": "what to watch on hulu that's new this week", "generated_headline": "What to watch on Hulu this week? Probably another documentary about something you don't care about.", "strategy": "sarcasm", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/hulu-what-to-watch_us_5aeb70dde4b0c4f1932063b2"} +{"original_headline": "myth of white supremacy is now out in the open", "generated_headline": "The myth of white supremacy is now out in the open\u2014 if it's a myth, why is it everywhere?", "strategy": "irony", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/thoughts-on-charlottesville_us_59912360e4b0caa1687a6193"} +{"original_headline": "see photos from the 2015 white house correspondents' dinner", "generated_headline": "See photos from the 2015 White House Correspondents' Dinner\u2014 the event where journalism and politics have a love fest.", "strategy": "overstatement", "type": "non_to_sarcastic", "model_used": "stepfun/step-3.5-flash:free", "article_link": "https://www.huffingtonpost.com/entry/white-house-correspondents-dinner-photos_n_7112436.html"} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..e68c771 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,24 @@ +# Core ML +scikit-learn>=1.4.0 +numpy>=1.26.0 +pandas>=2.1.0 + +# NLP / Text +transformers>=4.40.0 +torch>=2.2.0 +datasets>=2.19.0 +tokenizers>=0.19.0 +accelerate>=0.29.0 + +# Evaluation / Plotting +matplotlib>=3.8.0 +seaborn>=0.13.0 + +# Jupyter +notebook>=7.0.0 +ipywidgets>=8.0.0 + +# Utilities +tqdm>=4.66.0 +scipy>=1.12.0 +joblib>=1.3.0 From d9b7b0cf7f2ee7194efdf0e2e1986da1fa18a492 Mon Sep 17 00:00:00 2001 From: reallyeasy Date: Tue, 3 Mar 2026 16:35:20 +0800 Subject: [PATCH 2/6] fix: rename sarcasm ipynb file --- ...casm_classification (2).ipynb => sarcasm_classification.ipynb} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename colab_package/{sarcasm_classification (2).ipynb => sarcasm_classification.ipynb} (100%) diff --git a/colab_package/sarcasm_classification (2).ipynb b/colab_package/sarcasm_classification.ipynb similarity index 100% rename from colab_package/sarcasm_classification (2).ipynb rename to colab_package/sarcasm_classification.ipynb From 846de4ac41f76ecdd6fec5fa9d1c14aa9c73597a Mon Sep 17 00:00:00 2001 From: reallyeasy Date: Tue, 3 Mar 2026 17:10:05 +0800 Subject: [PATCH 3/6] Fix: fix ipynb formatting error in sarcasm_classification.ipynb --- .gitignore | 3 +- colab_package/sarcasm_classification.ipynb | 1843 ++++++++++---------- 2 files changed, 890 insertions(+), 956 deletions(-) diff --git a/.gitignore b/.gitignore index f8804de..80c4c5a 100644 --- a/.gitignore +++ b/.gitignore @@ -11,4 +11,5 @@ wheels/ .DS_Store .env* -claude.md \ No newline at end of file +claude.md +.claude/ \ No newline at end of file diff --git a/colab_package/sarcasm_classification.ipynb b/colab_package/sarcasm_classification.ipynb index b7f5236..49e8c8c 100644 --- a/colab_package/sarcasm_classification.ipynb +++ b/colab_package/sarcasm_classification.ipynb @@ -1,858 +1,496 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "name": "python3" + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "w41uXUSeuKBO" + }, + "source": [ + "# Sarcasm Classification — Complete Pipeline\n", + "\n", + "**Sections**:\n", + "1. Setup & Data Preparation\n", + "2. TF-IDF + Logistic Regression Baseline\n", + "3. Naive Bayes Baseline\n", + "4. BERT / DistilBERT Classification\n", + "5. Error Analysis & Model Comparison\n", + "\n", + "**Run all cells in order (Runtime → Run all).**" + ] }, - "language_info": { - "name": "python", - "version": "3.11.0" + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1xG7cJfAuKBR", + "outputId": "12069dde-b810-4ab5-f89b-5fa1af05cf8c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cwd: /content\n", + "files in cwd: ['.config', 'sarcasm_pairs_step35_clean.jsonl', 'sample_data']\n", + "Data : /content/sarcasm_pairs_step35_clean.jsonl\n", + "Root : /content\n", + "Output : /content/outputs\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# SETUP — imports, file upload, paths\n", + "# ============================================================\n", + "from __future__ import annotations\n", + "import json, hashlib, random, os, warnings, shutil\n", + "from dataclasses import dataclass\n", + "from pathlib import Path\n", + "from collections import Counter\n", + "from urllib.parse import urlparse\n", + "from typing import Optional\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "import seaborn as sns\n", + "\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.naive_bayes import MultinomialNB, ComplementNB\n", + "from sklearn.model_selection import GridSearchCV, GroupKFold\n", + "from sklearn.metrics import (\n", + " accuracy_score, precision_score, recall_score,\n", + " f1_score, classification_report, confusion_matrix,\n", + ")\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "SEED = 42\n", + "random.seed(SEED)\n", + "np.random.seed(SEED)\n", + "\n", + "# ── Locate or upload the JSONL data file ─────────────────────────────────\n", + "FILENAME = \"sarcasm_pairs_step35_clean.jsonl\"\n", + "\n", + "def _locate_file(filename):\n", + " candidates = []\n", + " for root in [Path.cwd()] + list(Path.cwd().parents):\n", + " for sub in [\n", + " Path(\"data\") / \"processed\" / filename,\n", + " Path(\"data\") / filename,\n", + " Path(filename),\n", + " ]:\n", + " candidates.append(root / sub)\n", + " for p in [\n", + " Path(\"/content\") / filename,\n", + " Path(\"/mnt/data\") / filename,\n", + " ]:\n", + " candidates.append(p)\n", + " _c = Path('/content')\n", + " for p in (_c.rglob(filename) if _c.exists() else []):\n", + " candidates.append(p)\n", + " for p in candidates:\n", + " if p.is_file():\n", + " return p\n", + " return None\n", + "\n", + "print(f'cwd: {Path.cwd()}')\n", + "print(f'files in cwd: {[p.name for p in Path.cwd().iterdir()][:10]}')\n", + "\n", + "DATA_FILE = _locate_file(FILENAME)\n", + "if DATA_FILE is None:\n", + " try:\n", + " from google.colab import files as _cf\n", + " print(f\"Upload {FILENAME!r}:\")\n", + " _up = _cf.upload()\n", + " if not _up:\n", + " raise RuntimeError(\"No file uploaded.\")\n", + " _name = list(_up.keys())[0]\n", + " DATA_FILE = Path(\"/content\") / FILENAME\n", + " if Path(_name) != DATA_FILE:\n", + " shutil.move(_name, str(DATA_FILE))\n", + " print(f\"Saved to {DATA_FILE}\")\n", + " except ImportError:\n", + " raise FileNotFoundError(\n", + " f\"Cannot find {FILENAME!r}. Place it in the same folder as this notebook.\"\n", + " )\n", + "\n", + "# ── Project root + all output directories ────────────────────────────────\n", + "def _find_root(data_file):\n", + " for parent in [data_file.parent] + list(data_file.parents):\n", + " if any((parent / m).exists() for m in [\"outputs\",\"notebooks\",\"data\"]):\n", + " return parent\n", + " return data_file.parent\n", + "\n", + "ROOT = _find_root(DATA_FILE)\n", + "\n", + "OUT_DATASETS = ROOT / 'outputs' / 'datasets'\n", + "OUT_SPLITS = ROOT / 'outputs' / 'splits'\n", + "OUT_TFIDF = ROOT / 'outputs' / 'classical' / 'tfidf_lr'\n", + "OUT_NB = ROOT / 'outputs' / 'classical' / 'naive_bayes'\n", + "BERT_OUT = ROOT / 'outputs' / 'bert'\n", + "REPORTS_DIR = ROOT / 'outputs' / 'reports'\n", + "SPLITS = OUT_SPLITS\n", + "\n", + "for d in [OUT_DATASETS, OUT_SPLITS, OUT_TFIDF, OUT_NB, BERT_OUT, REPORTS_DIR]:\n", + " d.mkdir(parents=True, exist_ok=True)\n", + "\n", + "print(f\"Data : {DATA_FILE}\")\n", + "print(f\"Root : {ROOT}\")\n", + "print(f\"Output : {ROOT / 'outputs'}\")\n" + ] }, - "colab": { - "provenance": [], - "machine_shape": "hm" + { + "cell_type": "markdown", + "metadata": { + "id": "xDqObfuPuKBU" + }, + "source": [ + "---\n", + "# Part 1 — Data Preparation" + ] }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "8b9080237c194037a2cd1dc711a694d2": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_d0d70eea6290419fb4a39bd74964bacb", - "IPY_MODEL_30e0eec43be54d08a0b5bd855e06d3c3", - "IPY_MODEL_959391d9e6cc405ead852002ce0276ff" - ], - "layout": "IPY_MODEL_143c9b378b5a4260b15e328dc744374f" - } - }, - "d0d70eea6290419fb4a39bd74964bacb": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_384fe210d84b4e8d8bc1cd8b99944d5e", - "placeholder": "​", - "style": "IPY_MODEL_2a97e4558e6d41df919bd3ce83576627", - "value": "Loading weights: 100%" - } + { + "cell_type": "markdown", + "metadata": { + "id": "RVi4jvpmuKBV" + }, + "source": [ + "# Notebook 01 — Data Preparation\n", + "\n", + "**Purpose**: Parse JSONL dataset, derive binary and type labels, perform audit checks, generate group-safe train/val/test splits.\n", + "\n", + "**Outputs**:\n", + "- `outputs/datasets/binary_dataset.csv`\n", + "- `outputs/datasets/type_dataset.csv`\n", + "- `outputs/splits/train_binary.csv`, `val_binary.csv`, `test_binary.csv`\n", + "- `outputs/splits/train_type.csv`, `val_type.csv`, `test_type.csv`\n", + "- `outputs/splits/split_metadata.json`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "egvV3LLJuKBV" + }, + "source": [ + "## 1. Load and Validate Raw Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, - "30e0eec43be54d08a0b5bd855e06d3c3": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_bfc3291175a14250a407af3dd6376d58", - "max": 100, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_01c169be55ca4399b34eb7cdae152075", - "value": 100 - } + "id": "oqhoOTiguKBV", + "outputId": "9211b95a-f157-40ce-9d18-7275e0c157a3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded rows : 28,333\n", + "Error rows : 0\n" + ] + } + ], + "source": [ + "REQUIRED_FIELDS = {\"original_headline\", \"generated_headline\", \"strategy\", \"type\", \"model_used\", \"article_link\"}\n", + "ALLOWED_TYPES = {\"sarcastic_to_non\", \"non_to_sarcastic\"}\n", + "ALLOWED_STRATS = {\"irony\", \"overstatement\", \"rhetorical_question\", \"sarcasm\", \"satire\", \"understatement\"}\n", + "\n", + "raw_rows = []\n", + "errors = []\n", + "\n", + "with open(DATA_FILE, \"r\", encoding=\"utf-8\") as f:\n", + " for line_num, line in enumerate(f):\n", + " line = line.strip()\n", + " if not line:\n", + " continue\n", + " try:\n", + " row = json.loads(line)\n", + " except json.JSONDecodeError as e:\n", + " errors.append((line_num, f\"JSON parse error: {e}\"))\n", + " continue\n", + "\n", + " # Schema check\n", + " missing = REQUIRED_FIELDS - set(row.keys())\n", + " if missing:\n", + " errors.append((line_num, f\"Missing fields: {missing}\"))\n", + " continue\n", + "\n", + " # Value checks\n", + " if row[\"type\"] not in ALLOWED_TYPES:\n", + " errors.append((line_num, f\"Unknown type: {row['type']!r}\"))\n", + " continue\n", + " if row[\"strategy\"] not in ALLOWED_STRATS:\n", + " errors.append((line_num, f\"Unknown strategy: {row['strategy']!r}\"))\n", + " continue\n", + "\n", + " row[\"_line_num\"] = line_num\n", + " raw_rows.append(row)\n", + "\n", + "print(f\"Loaded rows : {len(raw_rows):,}\")\n", + "print(f\"Error rows : {len(errors)}\")\n", + "if errors:\n", + " for ln, msg in errors[:5]:\n", + " print(f\" Line {ln}: {msg}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yvg9qFhouKBW" + }, + "source": [ + "## 2. Dataset Audit" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, - "959391d9e6cc405ead852002ce0276ff": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_2f42691301be4d01858524488e30fe78", - "placeholder": "​", - "style": "IPY_MODEL_8faab66da6c74399a140fa5aad07dbbb", - "value": " 100/100 [00:00<00:00, 579.06it/s, Materializing param=distilbert.transformer.layer.5.sa_layer_norm.weight]" - } + "id": "KISNWAQhuKBW", + "outputId": "e8755fd1-8661-4393-f3a9-429cb93065fd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Type distribution ===\n", + " non_to_sarcastic: 14,925 (52.7%)\n", + " sarcastic_to_non: 13,408 (47.3%)\n", + "\n", + "=== Strategy distribution ===\n", + " sarcasm: 8,699 (30.7%)\n", + " irony: 6,102 (21.5%)\n", + " satire: 5,224 (18.4%)\n", + " overstatement: 3,976 (14.0%)\n", + " understatement: 3,295 (11.6%)\n", + " rhetorical_question: 1,037 (3.7%)\n", + "\n", + "=== Model distribution ===\n", + " stepfun/step-3.5-flash:free: 28,333\n" + ] + } + ], + "source": [ + "# ── Type and Strategy distributions ──────────────────────────────────────────\n", + "type_counts = Counter(r[\"type\"] for r in raw_rows)\n", + "strategy_counts = Counter(r[\"strategy\"] for r in raw_rows)\n", + "model_counts = Counter(r[\"model_used\"] for r in raw_rows)\n", + "\n", + "print(\"=== Type distribution ===\")\n", + "for k, v in type_counts.most_common():\n", + " print(f\" {k}: {v:,} ({v/len(raw_rows)*100:.1f}%)\")\n", + "\n", + "print(\"\\n=== Strategy distribution ===\")\n", + "for k, v in strategy_counts.most_common():\n", + " print(f\" {k}: {v:,} ({v/len(raw_rows)*100:.1f}%)\")\n", + "\n", + "print(\"\\n=== Model distribution ===\")\n", + "for k, v in model_counts.most_common():\n", + " print(f\" {k}: {v:,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, - 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"object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } + "id": "28wpsTTnuKBX", + "outputId": "bdcc2561-9baa-41a3-bac8-14101d7e4596" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Duplicate original_headlines : 70\n", + "Duplicate generated_headlines: 1\n", + "Duplicate article_links : 2\n" + ] + } + ], + "source": [ + "# ── Duplicate checks ──────────────────────────────────────────────────────────\n", + "orig_headlines = [r[\"original_headline\"] for r in raw_rows]\n", + "gen_headlines = [r[\"generated_headline\"] for r in raw_rows]\n", + "article_links = [r[\"article_link\"] for r in raw_rows]\n", + "\n", + "dup_orig = len(orig_headlines) - len(set(orig_headlines))\n", + "dup_gen = len(gen_headlines) - len(set(gen_headlines))\n", + "dup_article = len(article_links) - len(set(article_links))\n", + "\n", + "print(f\"Duplicate original_headlines : {dup_orig}\")\n", + "print(f\"Duplicate generated_headlines: {dup_gen}\")\n", + "print(f\"Duplicate article_links : {dup_article}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, - 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"_view_name": "StyleView", - "description_width": "" - } - } - } - } - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "w41uXUSeuKBO" + "id": "SLUFPDIHuKBX", + "outputId": "3d7b15de-a43f-410f-a381-6a9544171b45" }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "original_headline : 0 nulls, 0 empty strings\n", + "generated_headline : 0 nulls, 0 empty strings\n", + "strategy : 0 nulls, 0 empty strings\n", + "type : 0 nulls, 0 empty strings\n", + "model_used : 0 nulls, 0 empty strings\n", + "article_link : 0 nulls, 0 empty strings\n" + ] + } + ], "source": [ - "# Sarcasm Classification — Complete Pipeline\n", - "\n", - "**Sections**:\n", - "1. Setup & Data Preparation\n", - "2. TF-IDF + Logistic Regression Baseline\n", - "3. Naive Bayes Baseline\n", - "4. BERT / DistilBERT Classification\n", - "5. Error Analysis & Model Comparison\n", - "\n", - "**Run all cells in order (Runtime → Run all).**" + "# ── Null / empty checks ───────────────────────────────────────────────────────\n", + "for field in [\"original_headline\", \"generated_headline\", \"strategy\", \"type\", \"model_used\", \"article_link\"]:\n", + " null_count = sum(1 for r in raw_rows if r.get(field) is None)\n", + " empty_count = sum(1 for r in raw_rows if r.get(field) == \"\")\n", + " print(f\"{field:25s}: {null_count} nulls, {empty_count} empty strings\")" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "metadata": { "colab": { - "base_uri": "https://localhost:8080/" + "base_uri": "https://localhost:8080/", + "height": 524 }, - "id": "1xG7cJfAuKBR", - "outputId": "12069dde-b810-4ab5-f89b-5fa1af05cf8c" + "id": "E-B0fc5vuKBX", + "outputId": "a7845ca8-655c-48df-c59b-28b16307b91d" }, "outputs": [ { - "output_type": "stream", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { "name": "stdout", + "output_type": "stream", "text": [ - "cwd: /content\n", - "files in cwd: ['.config', 'sarcasm_pairs_step35_clean.jsonl', 'sample_data']\n", - "Data : /content/sarcasm_pairs_step35_clean.jsonl\n", - "Root : /content\n", - "Output : /content/outputs\n" + "Saved: outputs/datasets/distributions.png\n" ] } ], "source": [ - "# ============================================================\n", - "# SETUP — imports, file upload, paths\n", - "# ============================================================\n", - "from __future__ import annotations\n", - "import json, hashlib, random, os, warnings, shutil\n", - "from dataclasses import dataclass\n", - "from pathlib import Path\n", - "from collections import Counter\n", - "from urllib.parse import urlparse\n", - "from typing import Optional\n", + "# ── Strategy distribution plot ────────────────────────────────────────────────\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.gridspec as gridspec\n", - "import seaborn as sns\n", - "\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.naive_bayes import MultinomialNB, ComplementNB\n", - "from sklearn.model_selection import GridSearchCV, GroupKFold\n", - "from sklearn.metrics import (\n", - " accuracy_score, precision_score, recall_score,\n", - " f1_score, classification_report, confusion_matrix,\n", - ")\n", - "\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "SEED = 42\n", - "random.seed(SEED)\n", - "np.random.seed(SEED)\n", - "\n", - "# ── Locate or upload the JSONL data file ─────────────────────────────────\n", - "FILENAME = \"sarcasm_pairs_step35_clean.jsonl\"\n", - "\n", - "def _locate_file(filename):\n", - " candidates = []\n", - " for root in [Path.cwd()] + list(Path.cwd().parents):\n", - " for sub in [\n", - " Path(\"data\") / \"processed\" / filename,\n", - " Path(\"data\") / filename,\n", - " Path(filename),\n", - " ]:\n", - " candidates.append(root / sub)\n", - " for p in [\n", - " Path(\"/content\") / filename,\n", - " Path(\"/mnt/data\") / filename,\n", - " ]:\n", - " candidates.append(p)\n", - " _c = Path('/content')\n", - " for p in (_c.rglob(filename) if _c.exists() else []):\n", - " candidates.append(p)\n", - " for p in candidates:\n", - " if p.is_file():\n", - " return p\n", - " return None\n", - "\n", - "print(f'cwd: {Path.cwd()}')\n", - "print(f'files in cwd: {[p.name for p in Path.cwd().iterdir()][:10]}')\n", - "\n", - "DATA_FILE = _locate_file(FILENAME)\n", - "if DATA_FILE is None:\n", - " try:\n", - " from google.colab import files as _cf\n", - " print(f\"Upload {FILENAME!r}:\")\n", - " _up = _cf.upload()\n", - " if not _up:\n", - " raise RuntimeError(\"No file uploaded.\")\n", - " _name = list(_up.keys())[0]\n", - " DATA_FILE = Path(\"/content\") / FILENAME\n", - " if Path(_name) != DATA_FILE:\n", - " shutil.move(_name, str(DATA_FILE))\n", - " print(f\"Saved to {DATA_FILE}\")\n", - " except ImportError:\n", - " raise FileNotFoundError(\n", - " f\"Cannot find {FILENAME!r}. Place it in the same folder as this notebook.\"\n", - " )\n", - "\n", - "# ── Project root + all output directories ────────────────────────────────\n", - "def _find_root(data_file):\n", - " for parent in [data_file.parent] + list(data_file.parents):\n", - " if any((parent / m).exists() for m in [\"outputs\",\"notebooks\",\"data\"]):\n", - " return parent\n", - " return data_file.parent\n", - "\n", - "ROOT = _find_root(DATA_FILE)\n", - "\n", - "OUT_DATASETS = ROOT / 'outputs' / 'datasets'\n", - "OUT_SPLITS = ROOT / 'outputs' / 'splits'\n", - "OUT_TFIDF = ROOT / 'outputs' / 'classical' / 'tfidf_lr'\n", - "OUT_NB = ROOT / 'outputs' / 'classical' / 'naive_bayes'\n", - "BERT_OUT = ROOT / 'outputs' / 'bert'\n", - "REPORTS_DIR = ROOT / 'outputs' / 'reports'\n", - "SPLITS = OUT_SPLITS\n", - "\n", - "for d in [OUT_DATASETS, OUT_SPLITS, OUT_TFIDF, OUT_NB, BERT_OUT, REPORTS_DIR]:\n", - " d.mkdir(parents=True, exist_ok=True)\n", - "\n", - "print(f\"Data : {DATA_FILE}\")\n", - "print(f\"Root : {ROOT}\")\n", - "print(f\"Output : {ROOT / 'outputs'}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xDqObfuPuKBU" - }, - "source": [ - "---\n", - "# Part 1 — Data Preparation" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RVi4jvpmuKBV" - }, - "source": [ - "# Notebook 01 — Data Preparation\n", + "# Type dist\n", + "ax = axes[0]\n", + "labels, counts = zip(*sorted(type_counts.items(), key=lambda x: -x[1]))\n", + "ax.barh(labels, counts, color=[\"steelblue\", \"coral\"])\n", + "ax.set_title(\"Row-level Type Distribution\", fontsize=14)\n", + "ax.set_xlabel(\"Count\")\n", + "for i, c in enumerate(counts):\n", + " ax.text(c + 50, i, f\"{c:,}\", va=\"center\")\n", "\n", - "**Purpose**: Parse JSONL dataset, derive binary and type labels, perform audit checks, generate group-safe train/val/test splits.\n", + "# Strategy dist\n", + "ax = axes[1]\n", + "labels2, counts2 = zip(*sorted(strategy_counts.items(), key=lambda x: -x[1]))\n", + "ax.barh(labels2, counts2, color=\"steelblue\")\n", + "ax.set_title(\"Strategy Distribution (Type Task Labels)\", fontsize=14)\n", + "ax.set_xlabel(\"Count\")\n", + "for i, c in enumerate(counts2):\n", + " ax.text(c + 30, i, f\"{c:,}\", va=\"center\")\n", "\n", - "**Outputs**:\n", - "- `outputs/datasets/binary_dataset.csv`\n", - "- `outputs/datasets/type_dataset.csv`\n", - "- `outputs/splits/train_binary.csv`, `val_binary.csv`, `test_binary.csv`\n", - "- `outputs/splits/train_type.csv`, `val_type.csv`, `test_type.csv`\n", - "- `outputs/splits/split_metadata.json`" + "plt.tight_layout()\n", + "plt.savefig(OUT_DATASETS / \"distributions.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/datasets/distributions.png\")" ] }, { "cell_type": "markdown", "metadata": { - "id": "egvV3LLJuKBV" + "id": "JxKcjhRSuKBY" }, "source": [ - "## 1. Load and Validate Raw Data" + "## 3. Label Derivation and Dataset Expansion" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "oqhoOTiguKBV", - "outputId": "9211b95a-f157-40ce-9d18-7275e0c157a3" + "id": "nukGQcmKuKBY", + "outputId": "2fcfb601-8ad0-4876-b813-c21b007bb164" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - "Loaded rows : 28,333\n", - "Error rows : 0\n" + "Binary dataset : 56,666 samples\n", + " sarcastic (1): 28,333\n", + " non-sarc (0): 28,333\n", + "\n", + "Type dataset : 28,333 samples\n", + "type_label\n", + "sarcasm 8699\n", + "irony 6102\n", + "satire 5224\n", + "overstatement 3976\n", + "understatement 3295\n", + "rhetorical_question 1037\n" ] } ], "source": [ - "REQUIRED_FIELDS = {\"original_headline\", \"generated_headline\", \"strategy\", \"type\", \"model_used\", \"article_link\"}\n", - "ALLOWED_TYPES = {\"sarcastic_to_non\", \"non_to_sarcastic\"}\n", - "ALLOWED_STRATS = {\"irony\", \"overstatement\", \"rhetorical_question\", \"sarcasm\", \"satire\", \"understatement\"}\n", + "from __future__ import annotations\n", + "from urllib.parse import urlparse\n", "\n", - "raw_rows = []\n", - "errors = []\n", "\n", - "with open(DATA_FILE, \"r\", encoding=\"utf-8\") as f:\n", - " for line_num, line in enumerate(f):\n", - " line = line.strip()\n", - " if not line:\n", - " continue\n", - " try:\n", - " row = json.loads(line)\n", - " except json.JSONDecodeError as e:\n", - " errors.append((line_num, f\"JSON parse error: {e}\"))\n", - " continue\n", + "def normalize_url(url: str) -> str:\n", + " \"\"\"Lowercase and strip scheme/trailing slash for stable group IDs.\"\"\"\n", + " url = url.strip().lower()\n", + " parsed = urlparse(url)\n", + " normalized = (parsed.netloc + parsed.path).rstrip(\"/\")\n", + " return normalized if normalized else url\n", "\n", - " # Schema check\n", - " missing = REQUIRED_FIELDS - set(row.keys())\n", - " if missing:\n", - " errors.append((line_num, f\"Missing fields: {missing}\"))\n", - " continue\n", "\n", - " # Value checks\n", - " if row[\"type\"] not in ALLOWED_TYPES:\n", - " errors.append((line_num, f\"Unknown type: {row['type']!r}\"))\n", - " continue\n", - " if row[\"strategy\"] not in ALLOWED_STRATS:\n", - " errors.append((line_num, f\"Unknown strategy: {row['strategy']!r}\"))\n", - " continue\n", + "def derive_labels(raw_rows):\n", + " \"\"\"\n", + " Expand each JSONL row into 2 samples (sarcastic + non-sarcastic).\n", "\n", - " row[\"_line_num\"] = line_num\n", - " raw_rows.append(row)\n", - "\n", - "print(f\"Loaded rows : {len(raw_rows):,}\")\n", - "print(f\"Error rows : {len(errors)}\")\n", - "if errors:\n", - " for ln, msg in errors[:5]:\n", - " print(f\" Line {ln}: {msg}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yvg9qFhouKBW" - }, - "source": [ - "## 2. Dataset Audit" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "KISNWAQhuKBW", - "outputId": "e8755fd1-8661-4393-f3a9-429cb93065fd" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "=== Type distribution ===\n", - " non_to_sarcastic: 14,925 (52.7%)\n", - " sarcastic_to_non: 13,408 (47.3%)\n", - "\n", - "=== Strategy distribution ===\n", - " sarcasm: 8,699 (30.7%)\n", - " irony: 6,102 (21.5%)\n", - " satire: 5,224 (18.4%)\n", - " overstatement: 3,976 (14.0%)\n", - " understatement: 3,295 (11.6%)\n", - " rhetorical_question: 1,037 (3.7%)\n", - "\n", - "=== Model distribution ===\n", - " stepfun/step-3.5-flash:free: 28,333\n" - ] - } - ], - "source": [ - "# ── Type and Strategy distributions ──────────────────────────────────────────\n", - "type_counts = Counter(r[\"type\"] for r in raw_rows)\n", - "strategy_counts = Counter(r[\"strategy\"] for r in raw_rows)\n", - "model_counts = Counter(r[\"model_used\"] for r in raw_rows)\n", - "\n", - "print(\"=== Type distribution ===\")\n", - "for k, v in type_counts.most_common():\n", - " print(f\" {k}: {v:,} ({v/len(raw_rows)*100:.1f}%)\")\n", - "\n", - "print(\"\\n=== Strategy distribution ===\")\n", - "for k, v in strategy_counts.most_common():\n", - " print(f\" {k}: {v:,} ({v/len(raw_rows)*100:.1f}%)\")\n", - "\n", - "print(\"\\n=== Model distribution ===\")\n", - "for k, v in model_counts.most_common():\n", - " print(f\" {k}: {v:,}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "28wpsTTnuKBX", - "outputId": "bdcc2561-9baa-41a3-bac8-14101d7e4596" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Duplicate original_headlines : 70\n", - "Duplicate generated_headlines: 1\n", - "Duplicate article_links : 2\n" - ] - } - ], - "source": [ - "# ── Duplicate checks ──────────────────────────────────────────────────────────\n", - "orig_headlines = [r[\"original_headline\"] for r in raw_rows]\n", - "gen_headlines = [r[\"generated_headline\"] for r in raw_rows]\n", - "article_links = [r[\"article_link\"] for r in raw_rows]\n", - "\n", - "dup_orig = len(orig_headlines) - len(set(orig_headlines))\n", - "dup_gen = len(gen_headlines) - len(set(gen_headlines))\n", - "dup_article = len(article_links) - len(set(article_links))\n", - "\n", - "print(f\"Duplicate original_headlines : {dup_orig}\")\n", - "print(f\"Duplicate generated_headlines: {dup_gen}\")\n", - "print(f\"Duplicate article_links : {dup_article}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "SLUFPDIHuKBX", - "outputId": "3d7b15de-a43f-410f-a381-6a9544171b45" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "original_headline : 0 nulls, 0 empty strings\n", - "generated_headline : 0 nulls, 0 empty strings\n", - "strategy : 0 nulls, 0 empty strings\n", - "type : 0 nulls, 0 empty strings\n", - "model_used : 0 nulls, 0 empty strings\n", - "article_link : 0 nulls, 0 empty strings\n" - ] - } - ], - "source": [ - "# ── Null / empty checks ───────────────────────────────────────────────────────\n", - "for field in [\"original_headline\", \"generated_headline\", \"strategy\", \"type\", \"model_used\", \"article_link\"]:\n", - " null_count = sum(1 for r in raw_rows if r.get(field) is None)\n", - " empty_count = sum(1 for r in raw_rows if r.get(field) == \"\")\n", - " print(f\"{field:25s}: {null_count} nulls, {empty_count} empty strings\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 524 - }, - "id": "E-B0fc5vuKBX", - "outputId": "a7845ca8-655c-48df-c59b-28b16307b91d" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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- }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/datasets/distributions.png\n" - ] - } - ], - "source": [ - "# ── Strategy distribution plot ────────────────────────────────────────────────\n", - "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", - "\n", - "# Type dist\n", - "ax = axes[0]\n", - "labels, counts = zip(*sorted(type_counts.items(), key=lambda x: -x[1]))\n", - "ax.barh(labels, counts, color=[\"steelblue\", \"coral\"])\n", - "ax.set_title(\"Row-level Type Distribution\", fontsize=14)\n", - "ax.set_xlabel(\"Count\")\n", - "for i, c in enumerate(counts):\n", - " ax.text(c + 50, i, f\"{c:,}\", va=\"center\")\n", - "\n", - "# Strategy dist\n", - "ax = axes[1]\n", - "labels2, counts2 = zip(*sorted(strategy_counts.items(), key=lambda x: -x[1]))\n", - "ax.barh(labels2, counts2, color=\"steelblue\")\n", - "ax.set_title(\"Strategy Distribution (Type Task Labels)\", fontsize=14)\n", - "ax.set_xlabel(\"Count\")\n", - "for i, c in enumerate(counts2):\n", - " ax.text(c + 30, i, f\"{c:,}\", va=\"center\")\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(OUT_DATASETS / \"distributions.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "print(\"Saved: outputs/datasets/distributions.png\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JxKcjhRSuKBY" - }, - "source": [ - "## 3. Label Derivation and Dataset Expansion" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nukGQcmKuKBY", - "outputId": "2fcfb601-8ad0-4876-b813-c21b007bb164" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Binary dataset : 56,666 samples\n", - " sarcastic (1): 28,333\n", - " non-sarc (0): 28,333\n", - "\n", - "Type dataset : 28,333 samples\n", - "type_label\n", - "sarcasm 8699\n", - "irony 6102\n", - "satire 5224\n", - "overstatement 3976\n", - "understatement 3295\n", - "rhetorical_question 1037\n" - ] - } - ], - "source": [ - "from __future__ import annotations\n", - "from urllib.parse import urlparse\n", - "\n", - "\n", - "def normalize_url(url: str) -> str:\n", - " \"\"\"Lowercase and strip scheme/trailing slash for stable group IDs.\"\"\"\n", - " url = url.strip().lower()\n", - " parsed = urlparse(url)\n", - " normalized = (parsed.netloc + parsed.path).rstrip(\"/\")\n", - " return normalized if normalized else url\n", - "\n", - "\n", - "def derive_labels(raw_rows):\n", - " \"\"\"\n", - " Expand each JSONL row into 2 samples (sarcastic + non-sarcastic).\n", - "\n", - " Rules:\n", - " sarcastic_to_non -> original=sarcastic(1), generated=non-sarcastic(0)\n", - " non_to_sarcastic -> original=non-sarcastic(0), generated=sarcastic(1)\n", + " Rules:\n", + " sarcastic_to_non -> original=sarcastic(1), generated=non-sarcastic(0)\n", + " non_to_sarcastic -> original=non-sarcastic(0), generated=sarcastic(1)\n", "\n", " Returns binary_df, type_df\n", " \"\"\"\n", @@ -951,8 +589,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Saved: outputs/datasets/binary_dataset.csv\n", "Saved: outputs/datasets/type_dataset.csv\n" @@ -988,8 +626,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "=== Binary splits ===\n", " train: 39,666 samples | sarcastic=19,833 non=19,833\n", @@ -1088,8 +726,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Train/Val pair_id overlap : 0 (must be 0)\n", "Train/Test pair_id overlap : 0 (must be 0)\n", @@ -1131,8 +769,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Saved: outputs/splits/train_binary.csv\n", "Saved: outputs/splits/val_binary.csv\n", @@ -1203,18 +841,18 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": 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1Jemrr77Sf//9p44dO8rDw0Nz5sxRt27ddPHiRQ0ePNjcbteuXapcubK8vb3VsWNH5c6dW3v37tWXX36pzZs3a8OGDcqSJUuGnMf06dN18eJFtWrVSgEBAeafiSpVqmjdunXJlje5du2awsPD9corr2jEiBE6dOiQJkyYoK1bt2r37t0WSdnbb7+tOXPmqFGjRmrbtq1u3bql2bNn6/XXX9eiRYtUp06dFOPasGGD6tSpo6JFi6pfv37y9vbW6dOntWbNGh0+fPihf6xs2LBB3t7eKbY7deqUwsPDVb9+fY0ePVp79+7Vt99+q9jYWK1atcrc7s6dO1bvT5eSe5dWS4tbt25JklxdXZPtS9q2bds25c2b94H9VKtWTVu3blXr1q3Vu3dv+fr66s8//1TPnj0VEhKit956K9kx169fNz927dqlPn36yNHRUVWrVrX6GufPn1diYqJiYmI0efJkHTx4UO+8847VtmXLltWsWbMUFxfHUnPAc65t27YaPny4oqKizEWDJHPnztWNGzfUrl07SRmXn9wvtf3269fPYglCf39/i34aN26s+fPnW2y7N8fInj37A++tkxZhYWF66aWX9N1332nYsGEWAxOnTp3SypUr9c477yS7iKN69erKmjWrhgwZov/++0/jx49XxYoVtWXLFhUtWtTcbsCAAfrkk09UvXp1ffzxx7Kzs9PixYvVuHFjjR8/Xp07d86Q87h9+7ZGjx6thg0bqm7dunJzc9OOHTs0depUbdq0Sbt27ZKjo6PFMcuWLdPRo0fVuXNn5cyZU8uWLdPQoUN14sQJRUVFmdudPHlSZcuW1e3bt9WuXTsVKFBAhw8f1jfffKN169Zp586d8vLySjG2zp07a9q0aWrVqpV69Oih+Ph4HTp0SL/++utDz+vMmTOKjo5Wt27dUmwzceJEnTlzRu3atZO3t7dmzZqlPn36KCAgQM2bNze3i4uL082bNx/6mtLdZbHv/16NjY1Vly5d1LFjR7388suaMGFCqvp6mH///VeSlCNHjmT7Tp06JXd3d924cUOurq6qVq2aPv30U4sl5xITE/XHH3+oZMmScnZ2tjj+5Zdflslk0o4dO5L1XbZsWa1cuVJ///231SXsACCtUvv36cyZM9W6dWtVqFBBw4YNk4uLi/755x8tX75cZ8+elZ+fn8aOHat+/frp/PnzFjnDw5a0ZOyDsY/7PUtjH/369VNCQoI++eQT7d69O9V9PciD8hBJqlOnjq5evSqTyWS+SKtly5YWbZLyjLJlyyY7vkyZMpozZ06yMYuktuvXr7coqAMWMnMaIYBnU0pLUgQGBhqSjMmTJyc75tatW8bt27eTbR8wYIAhydi2bZt5m7Wlk5K2ubq6WiwrlZiYaBQpUsTImTNnqmKPi4tLtu3AgQOGo6Oj8d577xmGYRj//vuvsXr1aqNYsWKGk5OTsXr1aovHvUtmZTRrS1ZcunTJcHFxMRo3bmzR9uTJk4adnZ057nuPd3d3t1i+8datW0bp0qUNBwcHi+3FihUzgoODky2rumjRolQvO5LaJSusvff//fefkS1bNqNGjRrJ+pRkvP/++1bj6tixY7Jt3377rUXbO3fuGC+99JIRFBRkJCYmmrfrviUrPvjgA0NSupe+yps3rxEWFmZ1X9LvxP3LUXbq1MmQZPz999/mbUmfXWofD/Kg5Tq//PJLq8tpXrt2zfD39zckGZGRkQ8975s3bxrvvfee4eTkZBFXzZo1rS7xYhiG0bNnT4u2RYoUMVauXGm17dWrVy3auri4GB06dLD6c2QYhvHxxx8bkoydO3c+NHYAz77KlSsb9vb2xunTpy22lylTxnB0dDQvK/io+UlKUtvv9u3bjVmzZhmSjNq1ayfLOU6ePJmW004Ta8t1fvvtt4Yk4+eff7ZoO3z48GTvR9Lx9evXt/ie3blzp2EymYxq1aqZt+3atcuQZPTr1y9ZHHXr1jU8PDweusR7apfrTExMNK5fv55s+5QpU5J9Jyf1aWdnZ+zatcuij3r16hmSjC1btpi316lTx/Dz87PIpQzDMHbs2GHY29tb/GxYW9rJx8cnWc6TWr/++qshyRg3blyyfUk5hL+/v3H58mXz9mvXrhm+vr5GmTJlLNonfXapeVhb5uvdd981cubMaX6tpP5Su1ynNadOnTK8vb2N/PnzJ/vdadOmjfHRRx8Zc+fONRYsWGD06tXLcHZ2Njw9PY19+/aZ250/f96QZDRp0sTqa/j5+Rlly5ZNtn3mzJmGJOOHH35Id/wAnk/WxkbS8vdp/fr1DQ8PD+POnTsPfJ30LOnI2AdjH8/q2MeWLVsMOzs7Y+7cuRb9pXa5zpQ0btzYkGSsXbvWYvu8efOM5s2bG1OmTDGWLVtmjBs3zihcuLDVpfbffPNNQ5LVXLR3796GJKtL2zo4OBhvvvnmI8WPZxsz+QA8UVmzZk22DKAkiyum4+PjdfXqVSUkJKhq1aoaPny4tm3bppdffvmh/derV09BQUHm5yaTSZUqVdL48eNTNYMn6epzwzDM0+z9/PwUHBysbdu2Sbp7w+ikq5bt7OxUokQJ8/F2dnbKmjXrQ+PMSN7e3mrSpInmzJmjCxcuKFu2bJLu3hg7MTHRPBvhXi1atLBYvtHR0VEffPCBmjdvrh9//FHvvfee9u/fr3379mno0KG6deuWeYaXJJUvX15ubm5atWqV2rRpkyHnce+V/3Fxcbp165bs7e31yiuvWNzE+159+/a1eF6/fn0FBwdryZIlmjhxoiRp1qxZ8vDwUL169XT+/HmL9rVr19aQIUN06NChFK82S7rifuHChYqIiJCDQ9q+Os+dO6dChQqluD9Xrlxq0qSJxbbKlStrwoQJOnTokPlGzcWLF9fq1avT9Nrp0bJlSw0fPlyDBg2Sm5ubqlatqvPnz2vw4MHm9y81S9TZ29srd+7cqlq1qurXr6+sWbNq8+bN+uqrr/TWW29p6dKlya6E7Nixo6pXr67Lly9ry5YtWr9+fbLPLImLi4tWr16t+Ph4nThxQrNnz1ZcXJyuX79udSnQpN+L1CyXBuDZ165dO/3666+aMWOG+vTpI0n6+++/tXXrVjVq1Mh8RXBG5Sf3S22/xYoVM3/v+Pn5WeQcrq6uVmddP07NmzdXz549NXXqVPNSTIZhaNq0aQoNDbX6Xnz44YcymUzm5y+99JJef/11rVmzxpybzZ49WyaTSa1bt072736dOnW0dOlSbdmyRW+88cYjn4PJZJKLi4skKSEhQVevXlV8fLwqV64s6e5s9fu/l19//XWVLFnSoo8PP/xQS5Ys0eLFi1WmTBlduXJFP/30k9q2bStnZ2eL8wgKClLBggW1atUqi+Xm7+fl5aW//vpLf/75p8Usx9RIWrbtQXlo27ZtLWYSurq6qkyZMtqyZYtFuw8//DDZlecpSVqGO8nmzZv17bffavbs2Q+ctZgW169fV/369RUXF6dly5Ylyx/unU0pSY0aNVKdOnUUHh6uHj16mPOnpPzFycnJ6us4OztbzXHIIQBkpLT8ferl5aXr16/r559/Vp06dSy+Tx8VYx+MfdzvWRj7uHPnjiIiIvT666+bl9fMCJGRkVqwYIE6dOhgzhmTNGnSJNl5dezYUaVKldLw4cPVunVr8zjlg3KRpFUGrOUiWbNmJQ/BA1HkA/BEFShQIMX7ckyYMEETJ07UX3/9lWyN9dTck0u6e++t+yUlfhcuXHhokW/37t0aOHCg1q9fn2zd9Xz58klSsiUr/Pz8zP//+uuvWywz8KR06NBB3333nWbOnKnu3bvLMAxFRUWpRIkSeumll5K1t7Z0x4svvihJ5nXdDx48KEkaPHiwxTIW9zpz5kxGnYKOHDmi/v37a+XKlbp8+bLFPmt/zHh7e1tdJz0kJERLlizRtWvX5ObmpoMHD+rq1aspLqkg3T2PlBLdLl26aOnSperUqZP69Omj8uXLq3r16mrWrJnFZ58Sk8n0wHvDPOxnNomPj0+Ky1ZmJB8fH61Zs0atWrVShw4dzNsrVqyoPn36aPjw4fL09HxoP23atNHvv/+uv/76yzyYWr9+fRUsWFDvvfeevvvuO7Vv397imEKFCpn/KGjUqJFWrlyp6tWry2QyqVmzZhZt7e3tLd6P9u3bKzw8XJUrV9Yff/yRbAAw6TPIyD+MAdiuBg0ayNvbW1FRUeYi37Rp0yQp2bK/GZGfWJOafu9drnPatGnmGCVp9uzZFkssPgnu7u5q1qyZpk+frnPnzsnPz0/r16/X0aNHNXbsWKvHpJRzrFq1SidOnFCRIkV08OBBGYbxwKUQMzLnmD9/viIjI7V7927duXPHYp+1zzQ1eVN0dLQSExM1depUTZ061errWvvOv9fYsWP19ttvKzQ0VPnz51elSpVUu3Zt1a5dW3Z2dg88Nun7LT05x/33InzxxRfN55cWt2/fVocOHVS1atVk39vpdfPmTdWrV087d+7Ud999l2wJs5RUqFBBr732mtatW6cbN27IxcXFXBS/d/D2/teyVjgnhwCQkdLy9+lHH32kjRs3ql69esqWLZsqVqyoGjVqqGnTpvLw8HikOBj7YOzjfs/C2MfIkSN1+PBhLVmyJF3HWzNlyhT17t1btWrV0vjx41N1jJOTk3r16qU2bdpo1apV5rGVe3ORpHGSJElLpaeUi5CH4EEo8gF4olK64nzMmDHq2bOn3njjDXXr1k25cuWSo6OjTp06pTZt2li9sbI1KRUQpQcPekh376Py2muvydPTUwMHDlRwcLDc3NxkMpnUvXt38/0Ds2XLptWrV2v69OmaPXu2vvjiC/PV1g8bvHlcXn31VRUtWlRTp05V9+7dtXbtWh0/fjzVCYg1Se9Xz549U1z3+9619x9FXFycXnvtNV27dk3du3dXaGioPDw8ZGdnpxEjRqTqXjQpMQxDfn5++v7771Ns86Cr5bNly6YdO3bot99+0+rVq7Vx40Z98MEHGjx4sJYvX251LfV7+fn56eLFiynuT+3P7O3btx/Yz/2s/RGQWqGhodq9e7cOHz6s06dPK1euXCpYsKD55ukPux/NyZMnNXv2bHXp0iVZ4tq4cWO999572rBhQ7Ii3/2qVaumHDlyaMKECQ8dLLS3t1eLFi303nvvaePGjapSpYrF/qT3LjV/nAB49jk7O6t58+aaMGGCfv/9d73yyiuaOXOmAgICLO7xklH5yf1S22+zZs1Us2ZNNWvWTI6Ojvruu+/MfZQvX/7R3oR06tChgyZPnqwZM2aYZ/U5OTklu09PWiQNXPzyyy8pfi8WKVIk3f3fa9GiRWratKlefvlljRs3Tnny5JGzs7MSEhJUvXr1dH+mSd/ZLVu2VOvWra22uf878X5169bV8ePHtXz5cm3YsEFr1qzR1KlTVaFCBa1ZsybZvQLvlfT9lt6c415XrlzRjRs3UtXWxcXFfOX/119/rb///luRkZE6fPiwuc3Vq1clSceOHVNsbGyq8+WkAl/S+5Da2YVJgoKCtH79el26dEkuLi7y8fGRi4uLTp06laztrVu3dP78eVWsWDHZPnIIABkpLX+fFipUSAcOHNDatWu1du1abdiwQRERERo8eLA2btyoAgUKpCsGxj4sMfZxl62PfcTExOiTTz5R69atZRiGORdJ+t6/cOGCDh8+LH9/f6ur/1gzbdo0dejQQW+88YYWLlyYpvsyJs3eu3dWZdIKCKdOnVLBggUt2p86dUomkynZKgnS3YvQyEPwIBT5ADwVZs6cqaCgIP3yyy8WVyqvWLHiicWwePFi8zJAlSpVsth34cIF83T63LlzK3fu3IqPj9fs2bN14cKFJzLD6mEiIiL0/vvva/v27Zo6daqcnZ3VokULq22TrlS714EDByT9X7KeNKPq/hlTj8PatWt1+vRpTZs2LdlyrgMGDLB6zOXLl/Xff/8lK2YdPHhQ2bNnNydthQoV0v/+9z+VKVPmoTM5U2Jvb6/w8HCFh4dLuntF40svvaThw4c/9EbiRYsW1caNG5WYmPjQq/Af5Pfff0/2c/kgDytqp0bBggUtEs9ffvlFnp6eKleu3AOPS0qiExISku2Lj4+3+O/D3Lx5M9UJftKApLX2hw8floODg3kJEABo166dJkyYoKioKF28eFH//fef+vfvb/Fv9ePKT1Lbb+nSpSVJVapU0bx585QvX750D+hllFKlSiksLExTp05Vu3bttHDhQtWrVy/FJbsOHjyoMmXKWGw7cOCA7O3tFRgYKOnud/WKFSuUN29eq1fcZ6SZM2fK2dlZ69ats7j47O+//07xmNTkTQULFpTJZNLt27cfKW/KmjWrWrZsqZYtW8owDPXt21ejRo3S0qVL1bhx4xSPSyqCHjp0KN2vneT999+3KCg/SOvWrTV9+nRJ0okTJ5SYmKgaNWpYbfvyyy/Lzc3NPHj8IEkFvlWrVmnSpElWl/t/mEOHDsnBwcH8s2lnZ6eSJUtq9+7dunXrlsVSWdu3b5dhGCpVqlSyfpIGCdO6hCoAWJPWv0+dnJxUs2ZN8zLZy5cvV61atTRmzBh9/fXXktI+05ixD8Y+UmLLYx9nzpzRzZs39e233+rbb79N1u6zzz7TZ599pgULFqhRo0YP7XfatGlq3769qlatqiVLlqS43HdKknKye2dWli5dWpMmTdKWLVuSFfm2bt2q4ODgZJ/d8ePHFR8fTx6CB0r/bxwAZCB7e/tkU/vj4+P12WefPdEYpOTFkcmTJ+u///5L1v71119XkSJFNGbMGP35558W++Li4tSvX7/HF6wVb7/9tpydnTV69GgtXrxYDRs2lLe3t9W2s2fP1r///mt+fvv2bX3xxReyt7fXm2++KUkKCwtT0aJFNXHiRPMyFveKj49P09VVD5LSe79q1Srz/QCsuf/nY/HixYqOjla9evXM21q1aqXExMQUP4+HLbth7Z5wL7zwglxcXFJ1/uHh4bp69ar5D4n0SlqXPrWPjPbVV1/pzz//1AcffPDQq96Cg4Nlb2+vJUuWJFt+JGkgMGngWpLV3y9J+u6773TlyhWLweFLly7p9u3bydpeu3ZNU6dOlZ2dndV7Qm3dulUvvfRSuv/YAfDsKVmypEqUKKF58+bp66+/lslkSrZU5+PKT9La7/vvvy+TyaR333032b+B27dv18yZMx8pnrSKiIjQwYMH1bVrV928efOBM7NHjRplcZ5//PGH1qxZoypVqpj/TU6aBfjRRx9ZvUAkI5fISnrv752xZxiGhg8fnuIxq1ev1h9//GHRftSoUZJkzjmyZcummjVratGiRVbvp2MYhvm+edYkJCRYXbIrLCxM0oNn6El3r54vUqRIivfySYsPP/ww1flG0ix/6e49/xYsWJDskTRQOG3aNM2aNeuhr3/r1i3Vr19fq1at0sSJEx/483XlyhWrPzM///yzNm/erNdff918jxvp7uzY69eva9KkSRbtx44dKwcHB6v379m6daty5MjBhUIAMkRa/j619rdo0j1i7/1ecHd316VLl1J9oSdjH4x9WGPrYx/58uWzmock3Q+5VatWWrBgwUNnJEp3xy0iIiJUuXJlLV261CKXuN/9y55Ld/OTkSNHytHR0WKVkLp168rFxUXjx4+3yF9+/PFHHT161GqxOim3s7baAJCEmXwAngqNGjVSv379VKNGDTVo0ECxsbH6/vvv0zQV/lHVqFFDrq6uevvtt9WlSxf5+Pho8+bNWr58uQoUKJBs5pG9vb3mzJmjSpUq6eWXX1b79u0VGhpqviord+7cTyx26e7yEY0aNTIPnjxoQKRw4cJ65ZVX9O6778rDw0Pff/+9duzYoYEDBypPnjyS7g4szZw5U5UrV1axYsX0zjvvqEiRIrp+/boOHz6sRYsWacSIEam6+fSdO3dSHDxr0KCBypcvr5w5c6pnz546fvy4AgICtGfPHs2cOVOhoaHav39/suN8fX21aNEinT59WuHh4Tp06JAmTJigHDlymJM46e7PVtu2bTV+/Hj98ccfevPNN+Xr66t///1XW7Zs0eHDh60m8kkiIiL077//6o033lBgYKBu3LihefPm6erVq2rVqtVDz71hw4bq06ePli9f/khXXj3qPfnGjx9vHjy8c+eOTpw4Yf5Mihcvrtq1a5vb1qxZU/nz59eLL74ok8mkVatWacmSJapVq5b69+9v0e/x48eVL18+VaxYUevXr5d0dxZC9+7dFRkZqbCwMEVERChr1qzavHmzZs+erQIFClj8fNasWVPZsmVT2bJllTdvXl25ckWbNm3S0qVLFRAQYPF5btiwQR07dlTDhg1VsGBBeXh46NixY5o5c6b+/fdfDR482DwzJMmRI0cUHR2tzz//PN3vH4BnU7t27dS1a1etWLFC4eHhyZaeelz5SVr7LVu2rIYPH67+/furRIkSatGihbJnz66tW7dq5syZj7REVXq0aNFCvXv31qxZs5QvX75kSyTf68SJE6pWrZrq1KmjmJgYjR8/Xi4uLho9erS5TenSpTVkyBANGTJEJUqUUOPGjZUrVy7FxMRo165dWr58udULPKzZuXOn1ZzDwcFBffv2VaNGjbRw4UJVrlxZrVq10p07d7RkyRJdv349xT6LFy+uypUrq3PnzvL399fSpUu1Zs0avf322xYDRd98843Kly+v1157Ta1atVJYWJgSExN19OhRLV26VK1atbL4TrvX1atX5e/vrzp16igsLEzZs2fXsWPH9M0338jHx8fiezoljRs31scff6yYmBj5+/s//M1KQXrvyVe8eHGLezcl+emnnyRJtWvXlq+vr8W+oKAgnThxwmKws0WLFlqxYoWqVq0qV1fXZIXBYsWKqVixYpKkdevWqUePHqpdu7by588vBwcHbd++XbNmzZKvr2+ye0VGREQoKipKPXr00PHjxxUSEqLly5dr8eLFGjBggHlprSRxcXH67bffkl0AAADplZa/T9944w15e3urQoUKypMnjy5fvqzp06fLZDJZLJNdpkwZ/fTTT+rSpYteffVV2dvbq3LlysqePbvVGBj7YOzDGlsf+/Dy8rI6Qy8p9wgNDU22f8iQIRo6dKiioqLMn++yZcvUrl07eXp6qmnTplq4cKHFMe7u7hbF1dDQUFWsWFGhoaHKnj27jh8/rmnTpikmJkaRkZEKCAgwt/Xz89PHH3+sXr16me9hfOrUKUVGRuqFF15Q9+7dk8W/fPly+fr6pml2I55DBgBksKioKEOSERUVZbE9MDDQqFixotVj4uPjjU8//dQoUKCA4ejoaOTNm9fo3bu3ceDAAUOSMXjwYHPbY8eOpWpbksGDBxuSjGPHjj009g0bNhjlypUz3N3dDS8vL6NmzZrG/v37jYoVKxqBgYFWjzl58qQRERFhBAQEGFmyZDHy5MljvP/++8a5c+ce+npptW7duhTP0zAMY+PGjYYko2DBgkZiYmKKx0dFRRnjxo0zChYsaDg6OhoFCxY0xo4da7XP48ePGx07djQCAwONLFmyGFmzZjVKlixp9O3b1zh58uRDY65YsaIhKcXHnDlzDMMwjL179xrVqlUzvL29DXd3d6NixYrGxo0bjdatWxv3f10lfR5Hjhwx6tSpY3h4eBju7u5GnTp1jEOHDlmNY8aMGUb58uUNDw8Pw8nJyQgMDDTq169vzJ0716KdJKN169bm5wsXLjRq165t5M6d23B0dDR8fX2N1157zfjhhx8eeu5JatSoYRQtWjTZ9pR+J+79nDJKYGBgip/BvedrGIYxbNgwo0iRIoabm5vh5uZmlCpVyvj666+N+Pj4ZP3u27fPkGQ0b97cYntiYqIxadIk4+WXXzbc3NwMBwcHIzAw0OjUqZNx9uxZi7YTJkwwqlSpYvj7+xtZsmQxXF1djdDQUKNv377G+fPnLdoePnzYaNeunRESEmJ4enoaDg4ORo4cOYw333zT+Omnn6ye+5AhQwwnJ6dkfQHAxYsXDWdnZ0OSMWPGjGT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+ ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Saved: outputs/splits/type_split_distribution.png\n" ] @@ -1252,18 +890,18 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": 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+ ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Saved: outputs/splits/binary_split_distribution.png\n" ] @@ -1300,8 +938,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "\n", "=== Data Preparation Complete ===\n", @@ -1454,8 +1092,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Train+Val: 48,166 | Train: 39,666 Val: 8,500 Test: 8,500\n" ] @@ -1498,8 +1136,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Grid size: 4 param combos → 180 fits\n", "Running grid search...\n", @@ -1552,8 +1190,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Saved: outputs/classical/tfidf_lr/best_config_binary.json\n" ] @@ -1580,8 +1218,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "\n", "[Val] Accuracy=0.9186 Macro-F1=0.9186 Weighted-F1=0.9186\n", @@ -1640,35 +1278,35 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": 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FfHx8lgQIrleBYmJieOONN5gwYQKGYdCwYUO2bdtmuaPuXo0ePdrytbu7OxUrVmTWrFl2S1QrVKjA4sWLGTVqFEOHDiUgIIB+/fpRrFgxevbsme3xunfvzvDhw21e/AzX1/lUrVqVr7/++qYJEFxPerZu3cqjjz6aZY3aK6+8QlpaGgsWLGDhwoU8/PDDrFixgpdfftmm8y9atIh69epRtmxZm2MWyQtMxq1uTxAREStTp05l0KBBxMfHZ7kD7XY+//xzoqKiSEhIoFChQvcvwH9JTEwkODiYr776ShUgkX9RAiQiYgPDMKhWrRpFihTJ9iLqzMxMqlatSqdOnXj11VfvU4RZvfzyy6xfv54dO3Y8sHOK5BZKgEREbiMtLY1vv/2WDRs28NFHH/HNN9/ozeoiTkAJkIjIbcTHxxMcHEyhQoV44YUXeOONNxwdkojYgRIgERERyXP0HCARERHJc5QAiYiISJ6jBEhERETyHKd8EKJHDfs8pVYkrzu3c4ajQxBxCu4P6G9be//9d2mP8/4Z4JQJkIiISJ5k0sSOrfRJiYiISJ6jCpCIiIizMJkcHUGuoQqQiIiI5DmqAImIiDgLrQGymRIgERERZ6EpMJspVRQREZE8RxUgERERZ6EpMJspARIREXEWmgKzmVJFERERyXNUARIREXEWmgKzmRIgERERZ6EpMJspVRQREZE8RxUgERERZ6EpMJvpkxIREZE8RxUgERERZ6E1QDZTAiQiIuIsNAVmM31SIiIikueoAiQiIuIsNAVmMyVAIiIizkJTYDbTJyUiIiJ5jipAIiIizkIVIJspARIREXEWLloDZCuliiIiIpLnqAIkIiLiLDQFZjN9UiIiIpLnqAIkIiLiLPQcIJspARIREXEWmgKzmT4pERERyXNUARIREXEWmgKzmRIgERERZ6EpMJvpkxIREZE8RxUgERERZ6EpMJupAiQiIiJ5jipAIiIizkJrgGymBEhERMRZaArMZkoVRUREJM9RBUhERMRZaArMZkqAREREnIWmwGymVFFERETyHFWAREREnIWmwGymBEhERMRZKAGymT4pERERuWezZs2iatWqeHt74+3tTXh4OCtXrrTsb9SoESaTyWrr27ev1RgJCQm0atWKAgUK4Ofnx7Bhw7h27ZpVn40bN/Lwww9jNpsJCQlh7ty5dxWvKkAiIiLOwoGLoEuUKMGbb75JuXLlMAyDefPm0bZtW/bs2UPlypUB6NOnD+PGjbMcU6BAAcvXGRkZtGrVioCAALZu3cqJEyfo3r07+fPnZ+LEiQDExcXRqlUr+vbty/z581m3bh29e/cmMDCQyMjIbMVrMgzDsMN15ygeNfo7OgQRp3Bu5wxHhyDiFNwfULnBo80su4536dt+93S8r68vb7/9Nr169aJRo0ZUr16d995776Z9V65cyRNPPMHx48fx9/cHYPbs2YwYMYJTp07h5ubGiBEjWLFiBfv377cc17FjR86fP8+qVauyFZumwERERJyFycWuW3p6OikpKVZbenr6HcPIyMjgq6++Ii0tjfDwcEv7/PnzKVq0KFWqVGHkyJFcvHjRsm/btm2EhYVZkh+AyMhIUlJSOHDggKVPs2bNrM4VGRnJtm3bsv1RKQESERFxFiaTXbfo6Gh8fHystujo6Fueft++fXh6emI2m+nbty9Lly4lNDQUgM6dO/PFF1+wYcMGRo4cyeeff07Xrl0txyYmJlolP4Dl+8TExNv2SUlJ4dKlS9n6qLQGSERERG5q5MiRDB482KrNbDbfsn+FChWIjY0lOTmZxYsX06NHDzZt2kRoaCjPP/+8pV9YWBiBgYE0bdqUI0eOULZs2ft2DbeiBEhERMRZ2Pk2eLPZfNuE59/c3NwICQkBoGbNmuzcuZOpU6fywQcfZOlbu3ZtAA4fPkzZsmUJCAhgx44dVn1OnjwJQEBAgOX/N9r+2cfb2xsPDw/bLwxNgYmIiDgPO0+B3avMzMxbrhmKjY0FIDAwEIDw8HD27dtHUlKSpU9MTAze3t6WabTw8HDWrVtnNU5MTIzVOiNbqQIkIiIi92zkyJG0aNGCUqVKceHCBRYsWMDGjRtZvXo1R44cYcGCBbRs2ZIiRYqwd+9eBg0aRIMGDahatSoAERERhIaG0q1bNyZNmkRiYiKjRo0iKirKUoXq27cvM2bMYPjw4fTs2ZP169fz9ddfs2LFimzHqwRIRETESZgc+BygpKQkunfvzokTJ/Dx8aFq1aqsXr2axx9/nGPHjrF27Vree+890tLSKFmyJB06dGDUqFGW411dXVm+fDn9+vUjPDycggUL0qNHD6vnBgUHB7NixQoGDRrE1KlTKVGiBB9//HG2nwEEeg6QiNyGngMkYh8P6jlABZ+aY9fx0hY/Z9fxchKtARIREZE8R1NgIiIizsJxM2C5jipAIiIikueoAiQiIuIkHLkIOrdRAiQiIuIklADZLkdMgc2ZM4dFixZlaV+0aBHz5s1zQEQiIiLizHJEAhQdHU3RokWztPv5+TFx4kQHRCQiIpL7mEwmu27OLEdMgSUkJBAcHJylPSgoiISEBAdEJCIikvs4e9JiTzmiAuTn58fevXuztP/yyy8UKVLEARGJiIiIM8sRFaBOnToxYMAAvLy8aNCgAQCbNm3ipZdeomPHjg6OTkREJJdQAchmOSIBGj9+PPHx8TRt2pR8+a6HlJmZSffu3bUGSEREROwuRyRAbm5uLFy4kPHjx/PLL7/g4eFBWFgYQUFBjg5NREQk19AaINvliATohvLly1O+fHlHhyEiIpIrKQGyncMSoMGDBzN+/HgKFizI4MGDb9t38uTJDygqERERyQsclgDt2bOHq1evWr4WERGRe6MKkO0clgBt2LDhpl+LiIjI3VECZLsc8Rygnj17cuHChSztaWlp9OzZ0wERiYiIiDPLEQnQvHnzuHTpUpb2S5cu8dlnnzkgIhERkVzIZOfNiTn0LrCUlBQMw8AwDC5cuIC7u7tlX0ZGBt9//z1+fn4OjFBERCT30BSY7RyaABUqVMjywrWb3f5uMpl4/fXXHRCZiIiIODOHJkAbNmzAMAyaNGnCkiVL8PX1texzc3MjKCiI4sWLOzBCERGR3EMVINs5NAFq2LAhAHFxcZQqVUq/cCIiIvJA5IhF0AcPHmTLli2W72fOnEn16tXp3Lkz586dc2BkIiIiuceNZSX22pxZjkiAhg0bRkpKCgD79u1j8ODBtGzZkri4uDs+JVpERET+P90FZrMc8S6wuLg4QkNDAViyZAmtW7dm4sSJ7N69m5YtWzo4OhEREXE2OaIC5ObmxsWLFwFYu3YtERERAPj6+loqQyIiInJ7mgKzXY6oANWrV4/BgwdTt25dduzYwcKFCwH4/fffKVGihIOjExERyR2cPWmxpxxRAZoxYwb58uVj8eLFzJo1i4ceegiAlStX0rx5cwdHJyIiIs4mR1SASpUqxfLly7O0T5kyxQHRiIiI5E6qANkuRyRA/3T58mWuXLli1ebt7e2gaERERHIPJUC2yxFTYGlpafTv3x8/Pz8KFixI4cKFrTYRERERe8oRCdDw4cNZv349s2bNwmw28/HHH/P6669TvHhxvQ1eRETEVnoOkM1yxBTYd999x2effUajRo147rnnqF+/PiEhIQQFBTF//ny6dOni6BBFRETEieSICtDZs2cpU6YMcH29z9mzZ4Hrt8dv3rzZkaGJiIjkGnoOkO1yRAJUpkwZ4uLiAKhYsSJff/01cL0yVKhQIQdGJiIiknsoAbJdjkiAnnvuOX755RcAXn75ZWbOnIm7uzuDBg1i2LBhDo5OREREnE2OWAM0aNAgy9fNmjXjt99+Y9euXYSEhFC1alUHRiYiIpJ7OHvVxp5yRAL0b0FBQQQFBTk6DBERkdxF+Y/NcsQU2IABA5g2bVqW9hkzZjBw4MAHH5CIiIg4tRyRAC1ZsoS6detmaX/sscdYvHixAyISERHJfbQI2nY5YgrszJkz+Pj4ZGn39vbm9OnTDohIREQk93H2pMWeckQFKCQkhFWrVmVpX7lypeX5QCIiIiL2kiMqQIMHD6Z///6cOnWKJk2aALBu3Treffdd3nvvPccGJzfV5+l69HmqPkHFfQE4eDSRiR+uZM2WXwEILlGUNwc9SXiNMpjz5yNm60EGv7WIpLMXLGMM7xVJi/qVqVq+BFeuXSOwwfAs56kZWorxA9pSI7QkhgE/7/+TV6cuY9/vfz+YCxV5wL7+agFfL/yS439f/z1eNqQc/+33AvXqNwRg3NjRbP9pK6eSkihQoADVqtdg4OChBJcpC8Ch337j048/ZM+eXZw/d47iDz3E0890pEu3Hg67JnlwVAGyXY5IgHr27El6ejpvvPEG48ePB6B06dLMmjWL7t27Ozg6uZm/T57ntenfcDjhFCZMdG1dm0VTnqdOxzf58/hZlr8fxb7f/6bF89MBGPNCK5ZM/S8Nur+LYRgAuOV35X8xe9i+N44e7cKznKOghxvfzIxixaZ9vBS9kHyuLrzWrxXfzoyiXItRXLuW+UCvWeRB8PMP4KVBQykVFIRhGHz3zTJe6h/FwiVLCQkpR2hoZVo90ZqAwEBSkpOZNXM6ffv04vs163B1deXXX/fjW8SXiW++TUBAILGxuxk/djQuLq506tLV0ZcnkmOYjBt/GznItWvXWLBgAZGRkfj7+3Pq1Ck8PDzw9PS86zE9avS3Y4Riq783vsUr7y3jr8RzfDPjBQIbDudC2mUAvD3dObFpEk+8MJMN2w9ZHde1dW3eHtYhSwXo4dBSbJk/nHLNR/HXyfMAVA4pzs+LXqFym7EcPab1YffbuZ0zHB2CAPXDH2XQ0GG07/B0ln2/H/qNp9u3ZfnKGEqWKnXT4yeOf52jR4/w8Ry9XNpR3B9QuSF44Aq7jhf3Xiu7jpeTOHwNUL58+ejbty+XL1//i7JYsWL3lPzIg+fiYuLpyJoU9HBj+944zG75MAyD9CvXLH0up18jM9PgseplbR739/iTnD6XSo92j5E/nyvu5vw82y6cg0dP8Ofxs/fjUkRylIyMDFZ+v4JLly5SrVqNLPsvXrzIN0v/x0MlShAQEHDLcS6kXsDHp9B9jFRyDL0N3mYOT4AAHn30Ufbs2XNXx6anp5OSkmK1GZkZdo5QbqZySHFObXmX5O3vMe3V//CfIR/x29FEduyLJ+3SFd54qS0e7vkp4O7Gm4OfJF8+VwKKets8furFdCL7TKVTy0c499MUTm95l8cfq0S7/u+TkaHpL3Fef/x+iDq1avBIjTDeGDeGKdNmUjYkxLJ/4ZfzqVOrBuGP1ODHHzfzwUdzyO/mdtOxYvfsZs2qlXR4+pkHFb7kUbNmzaJq1ap4e3vj7e1NeHg4K1eutOy/fPkyUVFRFClSBE9PTzp06MDJkyetxkhISKBVq1YUKFAAPz8/hg0bxrVr16z6bNy4kYcffhiz2UxISAhz5869q3hzRAL0wgsvMGTIEGbMmMG2bdvYu3ev1XY70dHR+Pj4WG3XTu56QJHnbb/Hn6R2x2gadH+Hjxb9yEfjulGxTACnz6XSZfgntGxQhdNb3uXkD2/j4+nB7l8TyMzGjKu7OT+zx3Rh2y9Hadj9HZo8N5lfj5zgf9P64W7Ofx+vTMSxSpcO5usly/jiy695+j+deO2VERw5fNiyv+UTbVi4ZCmfzvuCoKDSDBsykPT09Czj/PHH7wx88QX+2y+Kx+rWe5CXIA7iyOcAlShRgjfffJNdu3bx888/06RJE9q2bcuBAweA66+9+u6771i0aBGbNm3i+PHjtG/f3nJ8RkYGrVq14sqVK2zdupV58+Yxd+5cRo8ebekTFxdHq1ataNy4MbGxsQwcOJDevXuzevXq7H9Wjl4DBODikjUPM5lMGIaByWQiI+PWFZ309PQsP/h+9UdgcnG1e5xyeytm9+fosdO8+MZXlrYihQpy7VomyamXiIuZyLTP1zHls3VWx91qDVCPduG83r81wY+/alk4nT+fKyc2T6Lf6wtYtFqJ7v2mNUA5w/O9nqVEyVKMHjsuy76rV65Q77FHGfv6BFq0esLSfuTwYXr37E77Dk/z4kuDshwnD9aDWgNUdsjKO3fKhl8nNsnyd6zZbMZsNtt0vK+vL2+//TZPPfUUxYoVY8GCBTz11FMA/Pbbb1SqVIlt27ZRp04dVq5cyRNPPMHx48fx9/cHYPbs2YwYMYJTp07h5ubGiBEjWLFiBfv377eco2PHjpw/f/6mj9O5nRxRAYqLi8uyHT161PL/2zGbzZZy241NyY9juJhMmN2sf8rPnE8jOfUSDR8pj5+vJ8s37bN5vALubmRmGvwzR880DAzj+rlE8orMzEyuXrly030GgGFw5R/7Dx/+g949u9OmTTslP3JPbjbLEh0dfcfjMjIy+Oqrr0hLSyM8PJxdu3Zx9epVmjVrZulTsWJFSpUqxbZt2wDYtm0bYWFhluQHIDIykpSUFEsVadu2bVZj3OhzY4zsyBG3wevFp7nPuBfbsHrLAY6dOIdXQXf+06IWDWqVo/UL7wPQrU0dDsUlcupcKrWrBvPOsKeYPn8Df/yZZBmjZEBhCnsXoGRgYVxdXKha/iEAjhw7RdqlK6z76TcmDmzHeyOfYdZXm3AxmRj6XATXMjLY9PPvDrlukftt6pR3qVe/AQGBgVxMS+P7Fcv5eecOZn34CX8dO8bqVd8T/lhdChf25eTJRD79+EPMZnfqNbj+nKA//vidPj178FjdenTr8RynT50CwMXVFV9fX0demjwA9v634ciRIxk8eLBV2+2qP/v27SM8PJzLly/j6enJ0qVLCQ0NJTY2Fjc3NwoVKmTV39/fn8TERAASExOtkp8b+2/su12flJQULl26hIeHh83XliMSoBt+/fVXEhISrP4lA9CmTRsHRSS3UszXk0/GdyegqDfJqZfZ/8fftH7hfdZv/w2A8qX9GPdiG3x9CvDn8bNM+mQ1075YbzXGa/1a0a1NHcv32xeOBCCi91R+2PUHv8efpMNLH/Dqf1uwcd4QMjMNfvntL9pGvU/i6ZQHd7EiD9DZs2cYNXIEp04l4enlRfnyFZj14SeEP1aXpKST7N71M198Po+U5BSKFC1CzZq1+Gz+lxQpUgSAtWtWc+7sWVZ89y0rvvvWMm7x4g+xMmb9rU4rclPZme4CqFChArGxsSQnJ7N48WJ69OjBpk2b7mOEdy9HrAE6evQoTz75JPv27bOs/YH/e6Ll7dYA3YyeAyRiH1oDJGIfD2oNULlh2VsHcyd/vN38no5v1qwZZcuW5T//+Q9Nmzbl3LlzVlWgoKAgBg4cyKBBgxg9ejTffvstsbGxlv1xcXGUKVOG3bt3U6NGDRo0aMDDDz9s9ZaIOXPmMHDgQJKTk7MVW45YA/TSSy8RHBxM0v9/tPuBAwfYvHkztWrVYuPGjY4OT0REJFcwmey73avMzEzS09OpWbMm+fPnZ926/7sJ5tChQyQkJBAefv1NAOHh4ezbt4+kpP9bKhETE4O3tzehoaGWPv8c40afG2NkR46YAtu2bRvr16+naNGiuLi44OLiQr169YiOjmbAgAF3/YwgEREReTBGjhxJixYtKFWqFBcuXGDBggVs3LiR1atX4+PjQ69evRg8eDC+vr54e3vz4osvEh4eTp0615dCREREEBoaSrdu3Zg0aRKJiYmMGjWKqKgoyzRc3759mTFjBsOHD6dnz56sX7+er7/+mhUrsv8E7ByRAGVkZODl5QVA0aJFOX78OBUqVCAoKIhDhw7d4WgREREBx74MNSkpie7du3PixAl8fHyoWrUqq1ev5vHHHwdgypQpuLi40KFDB9LT04mMjOT999+3HO/q6sry5cvp168f4eHhFCxYkB49ejBu3P89/iE4OJgVK1YwaNAgpk6dSokSJfj444+JjIzMdrw5Yg1Q/fr1GTJkCO3ataNz586cO3eOUaNG8eGHH7Jr1y6r+/1toTVAIvahNUAi9vGg1gBVfDn7DwS8nd/ezH5ikVvkiArQqFGjSEtLA2DcuHE88cQT1K9fnyJFirBw4UIHRyciIiLOJkckQP8sXYWEhPDbb79x9uxZChcu7NBynoiISG7i4qK/M22VI+4C+7eUlBQ2b96s9T8iIiLZkNPuAsvJckQC9MwzzzBjxvW1BpcuXaJWrVo888wzhIWFsWTJEgdHJyIiIs4mRyRAmzdvpn79+gAsXboUwzA4f/4806ZNY8KECQ6OTkREJHdw5Nvgc5sckQAlJydb3lGzatUqOnToQIECBWjVqhV//PGHg6MTERERZ5MjEqCSJUuybds20tLSWLVqFREREQCcO3cOd3d3B0cnIiKSO2gNkO1yxF1gAwcOpEuXLnh6elKqVCkaNWoEXJ8aCwsLc2xwIiIiuYSzT1vZU45IgF544QVq165NQkICjz/+OC4u1wtTZcqU0RogERERsbsckQAB1KxZk5o1a7JlyxZq1aqF2WymVatWjg5LREQk11AFyHY5Yg3QP7Vo0YK///7b0WGIiIjkOloDZLsclwDlgFeTiYiIiJPLMVNgIiIicm80BWa7HJcAffDBB/j7+zs6DBERkVxH+Y/tclwC1LlzZ0eHICIiIk4uRyRAaWlpvPnmm6xbt46kpCQyMzOt9h89etRBkYmIiOQemgKzXY5IgHr37s2mTZvo1q0bgYGB+gUUERGR+ypHJEArV65kxYoV1K1b19GhiIiI5FqqH9guRyRAhQsXtrwMVURERO6OZlBslyOeAzR+/HhGjx7NxYsXHR2KiIiI5AE5ogL07rvvcuTIEfz9/SldujT58+e32r97924HRSYiIpJ7qABkuxyRALVr187RIYiIiOR6mgKzXY5IgMaMGePoEERERCQPyREJ0A27du3i4MGDAFSuXJkaNWo4OCIREZHcQwUg2+WIBCgpKYmOHTuyceNGChUqBMD58+dp3LgxX331FcWKFXNsgCIiIuJUcsRdYC+++CIXLlzgwIEDnD17lrNnz7J//35SUlIYMGCAo8MTERHJFUwmk103Z5YjKkCrVq1i7dq1VKpUydIWGhrKzJkziYiIcGBkIiIiuYeT5yx2lSMqQJmZmVlufQfInz9/lveCiYiIiNyrHJEANWnShJdeeonjx49b2v7++28GDRpE06ZNHRiZiIhI7qEpMNvliARoxowZpKSkULp0acqWLUvZsmUpXbo0KSkpTJ8+3dHhiYiI5Aomk303Z5Yj1gCVLFmS3bt3s27dOstt8JUqVaJZs2YOjkxEREScUY5IgADWr1/P+vXrSUpKIjMzkz179rBgwQIAPv30UwdHJyIikvM5+7SVPeWIBOj1119n3Lhx1KpVi8DAQP0CioiI3AX9/Wm7HJEAzZ49m7lz59KtWzdHhyIiIiJ5QI5IgK5cucJjjz3m6DBERERyNRWAbJcj7gLr3bu3Zb2PiIiIyP2WIypAly9f5sMPP2Tt2rVUrVo1y0MRJ0+e7KDIREREcg+tAbJdjkiA9u7dS/Xq1QHYv3+/1T79YoqIiNhGf2XaLkckQBs2bHB0CCIiIpKH5IgESERERO6dZk1spwRIRETESSj/sV2OuAtMRERE5EFSBUhERMRJuKgEZDMlQCIiIk5C+Y/tNAUmIiIieY4SIBERESdhMpnsumVHdHQ0jzzyCF5eXvj5+dGuXTsOHTpk1adRo0ZZztG3b1+rPgkJCbRq1YoCBQrg5+fHsGHDuHbtmlWfjRs38vDDD2M2mwkJCWHu3LnZ/qyUAImIiMg927RpE1FRUfz000/ExMRw9epVIiIiSEtLs+rXp08fTpw4YdkmTZpk2ZeRkUGrVq24cuUKW7duZd68ecydO5fRo0db+sTFxdGqVSsaN25MbGwsAwcOpHfv3qxevTpb8WoNkIiIiJNwceAaoFWrVll9P3fuXPz8/Ni1axcNGjSwtBcoUICAgICbjrFmzRp+/fVX1q5di7+/P9WrV2f8+PGMGDGCsWPH4ubmxuzZswkODubdd98FoFKlSvz4449MmTKFyMhIm+NVBUhERMRJ2HsKLD09nZSUFKstPT3dpliSk5MB8PX1tWqfP38+RYsWpUqVKowcOZKLFy9a9m3bto2wsDD8/f0tbZGRkaSkpHDgwAFLn2bNmlmNGRkZybZt27L1WSkBEhERkZuKjo7Gx8fHaouOjr7jcZmZmQwcOJC6detSpUoVS3vnzp354osv2LBhAyNHjuTzzz+na9eulv2JiYlWyQ9g+T4xMfG2fVJSUrh06ZLN16YpMBERESdh79vgR44cyeDBg63azGbzHY+Liopi//79/Pjjj1btzz//vOXrsLAwAgMDadq0KUeOHKFs2bL2CdpGSoBERESchAn7ZkBms9mmhOef+vfvz/Lly9m8eTMlSpS4bd/atWsDcPjwYcqWLUtAQAA7duyw6nPy5EkAy7qhgIAAS9s/+3h7e+Ph4WFznJoCExERkXtmGAb9+/dn6dKlrF+/nuDg4DseExsbC0BgYCAA4eHh7Nu3j6SkJEufmJgYvL29CQ0NtfRZt26d1TgxMTGEh4dnK15VgERERJyEI+8Ci4qKYsGCBXzzzTd4eXlZ1uz4+Pjg4eHBkSNHWLBgAS1btqRIkSLs3buXQYMG0aBBA6pWrQpAREQEoaGhdOvWjUmTJpGYmMioUaOIioqyVKL69u3LjBkzGD58OD179mT9+vV8/fXXrFixIlvxqgIkIiLiJBz5IMRZs2aRnJxMo0aNCAwMtGwLFy4EwM3NjbVr1xIREUHFihUZMmQIHTp04LvvvrOM4erqyvLly3F1dSU8PJyuXbvSvXt3xo0bZ+kTHBzMihUriImJoVq1arz77rt8/PHH2boFHsBkGIaRrSNyAY8a/R0dgohTOLdzhqNDEHEK7g9ovqXtRz/bdbxv+tSy63g5iabAREREnIRehmo7TYGJiIhInqMKkIiIiJNwUQnIZkqAREREnITyH9tpCkxERETyHFWAREREnER2b13Py5QAiYiIOAnlP7bTFJiIiIjkOaoAiYiIOAndBWY7VYBEREQkz1EFSERExEmo/mM7JUAiIiJOQneB2U5TYCIiIpLnqAIkIiLiJFxUALKZEiAREREnoSkw22kKTERERPIcVYBERESchApAtlMCJCIi4iQ0BWY7TYGJiIhInqMKkIiIiJPQXWC2UwVIRERE8hxVgERERJyE1gDZTgmQiIiIk1D6Y7u7mgL74Ycf6Nq1K+Hh4fz9998AfP755/z44492DU5ERETkfsh2ArRkyRIiIyPx8PBgz549pKenA5CcnMzEiRPtHqCIiIjYxsVksuvmzLKdAE2YMIHZs2fz0UcfkT9/fkt73bp12b17t12DExEREduZTPbdnFm2E6BDhw7RoEGDLO0+Pj6cP3/eHjGJiIiI3FfZToACAgI4fPhwlvYff/yRMmXK2CUoERERyT6TyWTXzZllOwHq06cPL730Etu3b8dkMnH8+HHmz5/P0KFD6dev3/2IUURERGygKTDbZfs2+JdffpnMzEyaNm3KxYsXadCgAWazmaFDh/Liiy/ejxhFRERE7CrbCZDJZOLVV19l2LBhHD58mNTUVEJDQ/H09Lwf8YmIiIiNnP3OLXu66wchurm5ERoaas9YRERERB6IbCdAjRs3vu3CqPXr199TQCIiInJ3VACyXbYToOrVq1t9f/XqVWJjY9m/fz89evSwV1wiIiKSTc5+55Y9ZTsBmjJlyk3bx44dS2pq6j0HJCIiInK/mQzDMOwx0OHDh3n00Uc5e/asPYa7J5euOjoCEefgW3uAo0MQcQqXdk97IOd5celBu443/clKdh0vJ7Hb2+C3bduGu7u7vYYTERGRbNIUmO2ynQC1b9/e6nvDMDhx4gQ///wzr732mt0CExEREblfsp0A+fj4WH3v4uJChQoVGDduHBEREXYLTERERLLHRQUgm2UrAcrIyOC5554jLCyMwoUL36+YRERERO6rbL0LzNXVlYiICL31XUREJAdyMdl3c2bZfhlqlSpVOHr06P2IRURERO6B3gZvu2wnQBMmTGDo0KEsX76cEydOkJKSYrWJiIiI5HQ2rwEaN24cQ4YMoWXLlgC0adPGKjs0DAOTyURGRob9oxQREZE7cvZpK3uyOQF6/fXX6du3Lxs2bLif8YiIiMhdcvJZK7uyeQrsxgOjGzZseNtNRERE8p7o6GgeeeQRvLy88PPzo127dhw6dMiqz+XLl4mKiqJIkSJ4enrSoUMHTp48adUnISGBVq1aUaBAAfz8/Bg2bBjXrl2z6rNx40YefvhhzGYzISEhzJ07N9vxZmsNkLMviBIREcnNXEwmu27ZsWnTJqKiovjpp5+IiYnh6tWrREREkJaWZukzaNAgvvvuOxYtWsSmTZs4fvy41QOWMzIyaNWqFVeuXGHr1q3MmzePuXPnMnr0aEufuLg4WrVqRePGjYmNjWXgwIH07t2b1atXZytem98F5uLigo+Pzx2TIL0LTMR56F1gIvbxoN4F9sr3v9t1vIkty9/1sadOncLPz49NmzbRoEEDkpOTKVasGAsWLOCpp54C4LfffqNSpUps27aNOnXqsHLlSp544gmOHz+Ov78/ALNnz2bEiBGcOnUKNzc3RowYwYoVK9i/f7/lXB07duT8+fOsWrXK5viy9SDE119/PcuToEVERMQ5paenk56ebtVmNpsxm813PDY5ORkAX19fAHbt2sXVq1dp1qyZpU/FihUpVaqUJQHatm0bYWFhluQHIDIykn79+nHgwAFq1KjBtm3brMa40WfgwIHZurZsJUAdO3bEz88vWycQERGRB8PeK1Wio6N5/fXXrdrGjBnD2LFjb3tcZmYmAwcOpG7dulSpUgWAxMRE3NzcKFSokFVff39/EhMTLX3+mfzc2H9j3+36pKSkcOnSJTw8PGy6NpsTIK3/ERERyVtGjhzJ4MGDrdpsqf5ERUWxf/9+fvzxx/sV2j2zOQGycamQiIiIOEh2Fy7fia3TXf/Uv39/li9fzubNmylRooSlPSAggCtXrnD+/HmrKtDJkycJCAiw9NmxY4fVeDfuEvtnn3/fOXby5Em8vb1trv5ANu4Cy8zM1PSXiIhIDmYy2XfLDsMw6N+/P0uXLmX9+vUEBwdb7a9Zsyb58+dn3bp1lrZDhw6RkJBAeHg4AOHh4ezbt4+kpCRLn5iYGLy9vQkNDbX0+ecYN/rcGMNW2VoDJCIiInIzUVFRLFiwgG+++QYvLy/Lmh0fHx88PDzw8fGhV69eDB48GF9fX7y9vXnxxRcJDw+nTp06AERERBAaGkq3bt2YNGkSiYmJjBo1iqioKEslqm/fvsyYMYPhw4fTs2dP1q9fz9dff82KFSuyFa8SIBERESfhyFdhzJo1C4BGjRpZtc+ZM4dnn30WgClTpuDi4kKHDh1IT08nMjKS999/39LX1dWV5cuX069fP8LDwylYsCA9evRg3Lhxlj7BwcGsWLGCQYMGMXXqVEqUKMHHH39MZGRktuK1+TlAuYmeAyRiH3oOkIh9PKjnAI2LOWzX8UY/HmLX8XKSbL8NXkRERCS30xSYiIiIk9ATa2ynBEhERMRJOHINUG6jKTARERHJc1QBEhERcRImVAKylSpAIiIikueoAiQiIuIktAbIdkqAREREnIQSINtpCkxERETyHFWAREREnIRJDwKymRIgERERJ6EpMNtpCkxERETyHFWAREREnIRmwGynBEhERMRJuCgDspmmwERERCTPUQVIRETESWgRtO1UARIREZE8RxUgERERJ6ElQLZTAiQiIuIkXPQ2eJtpCkxERETyHFWAREREnISmwGynBEhERMRJ6C4w22kKTERERPIcVYBERESchJ4EbTtVgERERCTPUQVIRETESagAZDslQCIiIk5CU2C20xSYiIiI5DmqAImIiDgJFYBspwRIRETESWhax3b6rERERCTPUQVIRETESZg0B2YzJUAiIiJOQumP7TQFJiIiInmOKkAiIiJOQs8Bsp0qQCIiIpLnqAIkIiLiJFT/sZ0SIBERESehGTDbaQpMRERE8hxVgERERJyEngNkOyVAIiIiTkLTOrbTZyUiIiJ5jipAIiIiTkJTYLZTAiQiIuIklP7YTlNgIiIikueoAiQiIuIkNAVmO1WARERE5J5t3ryZ1q1bU7x4cUwmE8uWLbPa/+yzz2Iymay25s2bW/U5e/YsXbp0wdvbm0KFCtGrVy9SU1Ot+uzdu5f69evj7u5OyZIlmTRp0l3FqwRIRETESbjYecuOtLQ0qlWrxsyZM2/Zp3nz5pw4ccKyffnll1b7u3TpwoEDB4iJiWH58uVs3ryZ559/3rI/JSWFiIgIgoKC2LVrF2+//TZjx47lww8/zGa0mgITERFxGvaeAktPTyc9Pd2qzWw2Yzabs/Rt0aIFLVq0uO14ZrOZgICAm+47ePAgq1atYufOndSqVQuA6dOn07JlS9555x2KFy/O/PnzuXLlCp9++ilubm5UrlyZ2NhYJk+ebJUo2UIVIBEREbmp6OhofHx8rLbo6Oi7Hm/jxo34+flRoUIF+vXrx5kzZyz7tm3bRqFChSzJD0CzZs1wcXFh+/btlj4NGjTAzc3N0icyMpJDhw5x7ty5bMWiCpCIiIiTsPcS6JEjRzJ48GCrtptVf2zRvHlz2rdvT3BwMEeOHOGVV16hRYsWbNu2DVdXVxITE/Hz87M6Jl++fPj6+pKYmAhAYmIiwcHBVn38/f0t+woXLmxzPEqAREREnIS9bwK71XTX3ejYsaPl67CwMKpWrUrZsmXZuHEjTZs2tcs5skNTYCIiIvLAlSlThqJFi3L48GEAAgICSEpKsupz7do1zp49a1k3FBAQwMmTJ6363Pj+VmuLbsXhCVB0dDSffvpplvZPP/2Ut956ywERiYiI5E4umOy63U9//fUXZ86cITAwEIDw8HDOnz/Prl27LH3Wr19PZmYmtWvXtvTZvHkzV69etfSJiYmhQoUK2Zr+ghyQAH3wwQdUrFgxS3vlypWZPXu2AyISERGR7EpNTSU2NpbY2FgA4uLiiI2NJSEhgdTUVIYNG8ZPP/1EfHw869ato23btoSEhBAZGQlApUqVaN68OX369GHHjh1s2bKF/v3707FjR4oXLw5A586dcXNzo1evXhw4cICFCxcyderULOuUbOHwNUCJiYmW7O+fihUrxokTJxwQkYiISO7kyAdB//zzzzRu3Njy/Y2kpEePHsyaNYu9e/cyb948zp8/T/HixYmIiGD8+PFWa4zmz59P//79adq0KS4uLnTo0IFp06ZZ9vv4+LBmzRqioqKoWbMmRYsWZfTo0dm+BR5yQAJUsmRJtmzZkmVV95YtWywZn4iIiNyZyYGvQ23UqBGGYdxy/+rVq+84hq+vLwsWLLhtn6pVq/LDDz9kO75/c3gC1KdPHwYOHMjVq1dp0qQJAOvWrWP48OEMGTLEwdGJiIiIM3J4AjRs2DDOnDnDCy+8wJUrVwBwd3dnxIgRjBw50sHRiYiI5B56F6rtTMbt6lUPUGpqKgcPHsTDw4Ny5crd03MHLl29cx8RuTPf2gMcHYKIU7i0e9qdO9nBqgOn7Dpe88rF7DpeTuLwCtANnp6ePPLII44OQ0RERPIAhyRA7du3Z+7cuXh7e9O+ffvb9v3f//73gKISERHJ3TQFZjuHJEA+Pj6WN9Z6e3vb/e21IiIieZH+OrWdQxKgOXPmWL6eO3euI0IQERGRPMzhT4Ju0qQJ58+fz9KekpJiuS1eRERE7sxk5/+cmcMToI0bN1puf/+ny5cv2+VBRyIiIiL/5rC7wPbu3Wv5+tdffyUxMdHyfUZGBqtWreKhhx5yRGgiIiK5kotzF23symEJUPXq1TGZTJhMpptOdXl4eDB9+nQHRCYiIpI7Ofu0lT05LAGKi4vDMAzKlCnDjh07KFbs/x625Obmhp+fH66uro4KT0RERJyYwxKgoKAgADIzMx0VgoiIiFPRbfC2c/gi6Hnz5rFixQrL98OHD6dQoUI89thj/Pnnnw6MTEREJHfRXWC2c3gCNHHiRDw8PADYtm0bM2bMYNKkSRQtWpRBgwY5ODoRERFxRg5/F9ixY8cICQkBYNmyZTz11FM8//zz1K1bl0aNGjk2OBERkVxEd4HZzuEVIE9PT86cOQPAmjVrePzxxwFwd3fn0qVLjgxNREQkV9EUmO0cXgF6/PHH6d27NzVq1OD333+nZcuWABw4cIDSpUs7NjgRERFxSg5PgGbOnMmoUaM4duwYS5YsoUiRIgDs2rWLTp06OTg6sdXXXy1g0cIvOX78bwDKhpTj+b4vUK9+Q0ufX2L3MGPaFPbt24uriwsVKlbi/Q8+wd3dHYDk5PO8OXE8mzduwOTiQrNmEQwf+SoFChR0yDWJPAh9nqpHn6frEhR4/c++g0dPMPHDVazZehAA/yJeTBzYjia1K+BV0Mzv8UlM+mQNy9b/YhmjesUSTBjQhpqVS5GRYbBsfSwj3l1K2qX/e8p+yYDCTB35DA1rlSP1Ujrzl+/gtenfkZGhO3Gdie4Cs53JMAzD0UHY26Wrjo4g79m0cT0uLq6UCgoCw+Dbb5Yxb84nfLV4KSEh5fgldg9RfXvTs/d/adCoMflcXTl06DcaN2mGm5sbAFF9e3Pq1CleGzOOa9euMnrUK1SuEsabk9518NXlXb61Bzg6BKfXskEVMjIyOZxwCpMJurZ+lEHdm1Kn0yQOHk3ku5kvUMjLg0FvLeL0+TT+07wmr/VtSd2u7/DLob8ILOrNz4tGsnjNHmYs2Ih3QXfeHtqexNMpdB7+KQAuLia2fzmCk2dSeOW9bwgo6s3H47sxZ+lWxsxY7uBPIG+4tHvaAznPj3+cs+t49coVtut4OUmOSYAuXrxIQkJClveCVa1aNdtjKQHKGRo89iiDhgzjyQ5P063zM9QJf4yoFwfetO/RI0do37Yl879aTOUqYQBs+XEz/fs9z+p1m/Dz83+AkcsNSoAc4+8N0bzy3jfM++YnTv34NgOiv+bLFTst+/9aH82oad8yd9k2erZ/jNH9WhIc8Ro3/jivHBLIz1+PpHLbcRw9dpqIxyrxv6n/pUzkaySdvQBA7w51mTCgDSWbvsLVaxkOuc685EElQFvsnADVdeIEyOGLoE+dOkWrVq3w8vKicuXK1KhRw2qT3CcjI4NV36/g0qWLVK1eg7NnzrBv7y/4+hahe5eONGnwGL2e7cqe3T9bjtn7yx68vL0tyQ9A7TqP4eLiwv5/vDdOxJm5uJh4OuJhCnqY2b43HoCffonjqYgaFPYugMl0fb+7OR+bd/0BgDl/Pq5ezeCf/5a9lH79X4GPVS8DQO2qwew/fNyS/ADEbDuIj5cHoWUDH9DVyYPgYjLZdXNmDk+ABg4cSHJyMtu3b8fDw4NVq1Yxb948ypUrx7fffnvH49PT00lJSbHa0tPTH0Dk8m9//H6I8Edq8OjDYUwYP4bJU2dStmwIf/11DIDZ78+g/VNP8/4HH1OxUijP93qWP/+MB+D06dP4+vpajZcvXz68fXw4ffrUg74UkQeqckggp358m+SfJjPt1Wf4z5CP+S3u+guiu46YQ/58rhzf+CbJP01m+qv/4T9DPuHosdMAbNz5O/5FvBnUvQn587lSyMuDCS+2ASCgqA8A/kW9rJIfwPK9fxGvB3WZIjmKwxOg9evXM3nyZGrVqoWLiwtBQUF07dqVSZMmER0dfcfjo6Oj8fHxsdrefuvOx4n9lQ4OZuGSZXy+4GueeaYTo18dwZEjhy2vO+nw9H9o92QHKlYKZdiIVyhdOphv/rfEwVGLON7v8UnU7vQWDXpM5qNFW/hoXFcqBgcAMOaFlhTy9KBF3xnU7fo20+Zv4Iu3nqVyyPXKzcGjifQZ8wUDujbh7NZ3iI95g/jjZ0g8nYKRmSNWOMgDZLLz5swcfhdYWloafn5+ABQuXJhTp05Rvnx5wsLC2L179x2PHzlyJIMHD7Zqy3Qx35dY5fby53ejVKnr73gLrVyFAwf2seCLz+jZqw8AZcuWteofXKYsJxKPA1C0aFHOnj1rtf/atWukJCdTtGgxRJzZ1WsZlorOnoPHqFm5FFGdGzJ53jr6dWzIw09N5ODR6xWhfX8cp26Nsvz3mfoMmPg1AAtX7WLhql34+XqRdikdw4ABXRoT9/f1MU+evkCtykFW5/TzvV75OXnGujIkuZyzZy125PAKUIUKFTh06BAA1apV44MPPuDvv/9m9uzZBAbeeW7abDbj7e1ttZnNSoBygszMTK5cuULxh0pQzM+P+Pg4q/1//hlPYOBDAFStVoMLKSn8emC/Zf+O7T+RmZlJlbtYCC+Sm7m4mDDnz0cB9/wAZP7rXpWMzExcbvLI36SzF0i7dIWnIh/m8pWrrPvp+p+t2/fGUSWkOMUKe1r6Nq1TkeQLlyyJlUhe4/AK0EsvvcSJEycAGDNmDM2bN2f+/Pm4ubkxd+5cxwYnNps25V3q1m9AQGAgF9PSWLliOT/v3MH7H3yCyWSix3O9mD1zOuUrVKRCxUp8981S4uOO8s7k63dGlClblrr16jNu7Gu8Ovp1rl29ypsTxxPZopXuABOnNq5/a1Zv/ZVjJ87hVdDMf5rXokHNEFpHzeJQ/EkOJyQx49X/MHLKMs4kX6RNozCa1q5A+5c+tIzR9z/1+emXOFIvptO0TkUmvtSW16Z/S3Lq9afpr/3pNw4eTeSTCd149b1v8C/qzZgXWvHBoh+4cvWaoy5d7gNnf3qzPeWY2+BvuHjxIr/99hulSpWiaNGidzWGboN/8Ma+9grbt//E6VNJeHp5Ub58BZ7t2Yfwx+pa+nz68Ycs/HI+ySnJlC9fkUFDhlLj4VqW/cnJ54l+YzybN67HxcWFps0iGPHKKD0I0YF0G/z9N2t0Jxo/Wp6Aoj4kp15i/x/HeXfuWtZvv169KVuyGBMGtCa8ehk8C5g5cuw0732+3uq2+I/HdaV5vcp4FjBzKP5klv0ApQKvPwixQc1ypF2+wvzvtjNKD0J8YB7UbfA7jibbdbxHy/jYdbycJMclQPagBEjEPpQAidiHEqCcx+FrgDp06MBbb72VpX3SpEk8/fTTDohIREQkd9JdYLZzeAK0efNmywtQ/6lFixZs3rzZARGJiIiIs3P4IujU1FTLu6D+KX/+/KSkpDggIhERkVzK2cs2duTwClBYWBgLFy7M0v7VV18RGhrqgIhERERyJ5Od/3NmDq8Avfbaa7Rv354jR47QpEkTANatW8eXX37JokWLHBydiIiIOCOHJ0CtW7dm2bJlTJw4kcWLF+Ph4UHVqlVZu3YtDRs2dHR4IiIiuYaTv7/UrhyaAF27do2JEyfSs2dPtmzZ4shQREREcj3lP7Zz6BqgfPnyMWnSJK5d05NIRURE5MFx+CLopk2bsmnTJkeHISIikvvpQUA2c/gaoBYtWvDyyy+zb98+atasScGC1q89aNOmjYMiExEREWfl8FdhuLjcughlMpnIyMjI9ph6FYaIfehVGCL28aBehbHnzwt2Ha9GkJddx8tJHF4ByszUi/hERETsQXeB2c7ha4BEREREHjSHV4AA0tLS2LRpEwkJCVy5csVq34ABKsGLiIjYQgUg2zk8AdqzZw8tW7bk4sWLpKWl4evry+nTpylQoAB+fn5KgERERGylDMhmDp8CGzRoEK1bt+bcuXN4eHjw008/8eeff1KzZk3eeecdR4cnIiIiTsjhCVBsbCxDhgzBxcUFV1dX0tPTKVmyJJMmTeKVV15xdHgiIiK5hiNfhrp582Zat25N8eLFMZlMLFu2zGq/YRiMHj2awMBAPDw8aNasGX/88YdVn7Nnz9KlSxe8vb0pVKgQvXr1IjU11arP3r17qV+/Pu7u7pZ84W44PAHKnz+/5VZ4Pz8/EhISAPDx8eHYsWOODE1ERCRXMZnsu2VHWloa1apVY+bMmTfdP2nSJKZNm8bs2bPZvn07BQsWJDIyksuXL1v6dOnShQMHDhATE8Py5cvZvHkzzz//vGV/SkoKERERBAUFsWvXLt5++23Gjh3Lhx9+mO3PyuFrgGrUqMHOnTspV64cDRs2ZPTo0Zw+fZrPP/+cKlWqODo8ERGRPCs9PZ309HSrNrPZjNlsztK3RYsWtGjR4qbjGIbBe++9x6hRo2jbti0An332Gf7+/ixbtoyOHTty8OBBVq1axc6dO6lVqxYA06dPp2XLlrzzzjsUL16c+fPnc+XKFT799FPc3NyoXLkysbGxTJ482SpRsoXDK0ATJ04kMDAQgDfeeIPChQvTr18/Tp8+zQcffODg6ERERHIPe78JIzo6Gh8fH6stOjo623HFxcWRmJhIs2bNLG0+Pj7Url2bbdu2AbBt2zYKFSpkSX4AmjVrhouLC9u3b7f0adCgAW5ubpY+kZGRHDp0iHPnzmUrJodXgCpXrsyNh1H7+fkxe/Zsli5dSmhoKNWrV3dscCIiInnYyJEjGTx4sFXbzao/d5KYmAiAv7+/Vbu/v79lX2JiIn5+flb78+XLh6+vr1Wf4ODgLGPc2Fe4cGGbY3J4AtS2bVvat29P3759OX/+PHXq1CF//vycPn2ayZMn069fP0eHKCIikjvY+Tb4W013OQOHT4Ht3r2b+vXrA7B48WL8/f35888/+eyzz5g27cG8O0VERMQZOPIusNsJCAgA4OTJk1btJ0+etOwLCAggKSnJav+1a9c4e/asVZ+bjfHPc9jK4QnQxYsX8fK6/rK1NWvW0L59e1xcXKhTpw5//vmng6MTERGRexUcHExAQADr1q2ztKWkpLB9+3bCw8MBCA8P5/z58+zatcvSZ/369WRmZlK7dm1Ln82bN3P16v+99TwmJoYKFSpka/oLckACFBISwrJlyzh27BirV68mIiICgKSkJLy9vR0cnYiISO7hyNvgU1NTiY2NJTY2Fri+8Dk2NpaEhARMJhMDBw5kwoQJfPvtt+zbt4/u3btTvHhx2rVrB0ClSpVo3rw5ffr0YceOHWzZsoX+/fvTsWNHihcvDkDnzp1xc3OjV69eHDhwgIULFzJ16tQs65Rs4fA1QKNHj6Zz584MGjSIpk2bWjLBNWvWUKNGDQdHJyIikns48k0YP//8M40bN7Z8fyMp6dGjB3PnzmX48OGkpaXx/PPPc/78eerVq8eqVatwd3e3HDN//nz69+9P06ZNcXFxoUOHDlbLYXx8fFizZg1RUVHUrFmTokWLMnr06GzfAg9gMm7cguVAiYmJnDhxgmrVqlkeirhjxw68vb2pWLFitse7dPXOfUTkznxr6118IvZwafeDWdN68HiaXcerVLygXcfLSRxeAYLrC5f+vXjp0UcfdVA0IiIiuZRehmqzHJEAiYiIyL2z551bzs7hi6BFREREHjRVgERERJxEdu/cystUARIREZE8RxUgERERJ6ECkO2UAImIiDgLZUA20xSYiIiI5DmqAImIiDgJ3QZvOyVAIiIiTkJ3gdlOU2AiIiKS56gCJCIi4iRUALKdKkAiIiKS56gCJCIi4ixUArKZEiAREREnobvAbKcpMBEREclzVAESERFxEroN3nZKgERERJyE8h/baQpMRERE8hxVgERERJyFSkA2UwIkIiLiJHQXmO00BSYiIiJ5jipAIiIiTkJ3gdlOFSARERHJc1QBEhERcRIqANlOCZCIiIiT0BSY7TQFJiIiInmOKkAiIiJOQyUgWykBEhERcRKaArOdpsBEREQkz1EFSERExEmoAGQ7JUAiIiJOQlNgttMUmIiIiOQ5qgCJiIg4Cb0M1XaqAImIiEieowqQiIiIs1AByGZKgERERJyE8h/baQpMRERE8hxVgERERJyEboO3nRIgERERJ6G7wGynKTARERHJc1QBEhERcRYqANlMCZCIiIiTUP5jO02BiYiISJ6jCpCIiIiT0F1gtlMFSERERO7Z2LFjMZlMVlvFihUt+y9fvkxUVBRFihTB09OTDh06cPLkSasxEhISaNWqFQUKFMDPz49hw4Zx7dq1+xKvKkAiIiJOwtG3wVeuXJm1a9davs+X7//SjEGDBrFixQoWLVqEj48P/fv3p3379mzZsgWAjIwMWrVqRUBAAFu3buXEiRN0796d/PnzM3HiRLvHqgRIRETESTh6CixfvnwEBARkaU9OTuaTTz5hwYIFNGnSBIA5c+ZQqVIlfvrpJ+rUqcOaNWv49ddfWbt2Lf7+/lSvXp3x48czYsQIxo4di5ubm11j1RSYiIiI3FR6ejopKSlWW3p6+i37//HHHxQvXpwyZcrQpUsXEhISANi1axdXr16lWbNmlr4VK1akVKlSbNu2DYBt27YRFhaGv7+/pU9kZCQpKSkcOHDA7temBEhERERuKjo6Gh8fH6stOjr6pn1r167N3LlzWbVqFbNmzSIuLo769etz4cIFEhMTcXNzo1ChQlbH+Pv7k5iYCEBiYqJV8nNj/4199qYpMBERESdh7ymwkSNHMnjwYKs2s9l8074tWrSwfF21alVq165NUFAQX3/9NR4eHvYNzA5UARIREZGbMpvNeHt7W223SoD+rVChQpQvX57Dhw8TEBDAlStXOH/+vFWfkydPWtYMBQQEZLkr7Mb3N1tXdK+UAImIiDgJk53/uxepqakcOXKEwMBAatasSf78+Vm3bp1l/6FDh0hISCA8PByA8PBw9u3bR1JSkqVPTEwM3t7ehIaG3lMsN6MpMBEREblnQ4cOpXXr1gQFBXH8+HHGjBmDq6srnTp1wsfHh169ejF48GB8fX3x9vbmxRdfJDw8nDp16gAQERFBaGgo3bp1Y9KkSSQmJjJq1CiioqJsrjplhxIgERERJ+HI2+D/+usvOnXqxJkzZyhWrBj16tXjp59+olixYgBMmTIFFxcXOnToQHp6OpGRkbz//vuW411dXVm+fDn9+vUjPDycggUL0qNHD8aNG3df4jUZhmHcl5Ed6NJVR0cg4hx8aw9wdAgiTuHS7mkP5DwXLmfadTwvd+ddKeO8VyYiIiJyC5oCExERcRZ6GarNlACJiIg4CUe/Cyw30RSYiIiI5DmqAImIiDgJR78MNTdRAiQiIuIklP/YTlNgIiIikueoAiQiIuIsVAKymSpAIiIikueoAiQiIuIkdBu87ZQAiYiIOAndBWY7TYGJiIhInuOUL0OVnC89PZ3o6GhGjhyJ2Wx2dDgiuZJ+jkTunhIgcYiUlBR8fHxITk7G29vb0eGI5Er6ORK5e5oCExERkTxHCZCIiIjkOUqAREREJM9RAiQOYTabGTNmjBZuitwD/RyJ3D0tghYREZE8RxUgERERyXOUAImIiEieowRIRERE8hwlQCJAo0aNGDhwoKPDEHG4Z599lnbt2jk6DJH7TougJU/ZuHEjjRs35ty5cxQqVMjSfvbsWfLnz4+Xl5fjghN5gOLj4wkODmbPnj1Ur17d0p6cnIxhGFY/HyLOSG+Dlxzl6tWr5M+f/4Gf19fX94GfU+R2HPWz4OPj88DPKeIImgJzEo0aNWLAgAEMHz4cX19fAgICGDt2rGV/QkICbdu2xdPTE29vb5555hlOnjxp2T927FiqV6/O559/TunSpfHx8aFjx45cuHDhtud9//33KVeuHO7u7vj7+/PUU09Z9q1atYp69epRqFAhihQpwhNPPMGRI0cs++Pj4zGZTCxcuJCGDRvi7u7O/PnzAfj000+pXLkyZrOZwMBA+vfvbzlu8uTJhIWFUbBgQUqWLMkLL7xAamqqZf+ff/5J69atKVy4MAULFqRy5cp8//33xMfH07hxYwAKFy6MyWTi2WeftXx+/5wCS09PZ8SIEZQsWRKz2UxISAiffPKJ7b8gkictXryYsLAwPDw8KFKkCM2aNSMtLY2dO3fy+OOPU7RoUXx8fGjYsCG7d++2OtZkMjFr1izatGlDwYIFeeONNwD47rvveOSRR3B3d6do0aI8+eSTlmM+//xzatWqhZeXFwEBAXTu3JmkpCTL/nPnztGlSxeKFSuGh4cH5cqVY86cOQAEBwcDUKNGDUwmE40aNQKyToFlZmYyadIkQkJCMJvNlCpVyhKbSG6mBMiJzJs3j4IFC7J9+3YmTZrEuHHjiImJITMzk7Zt23L27Fk2bdpETEwMR48e5T//+Y/V8UeOHGHZsmUsX76c5cuXs2nTJt58881bnu/nn39mwIABjBs3jkOHDrFq1SoaNGhg2Z+WlsbgwYP5+eefWbduHS4uLjz55JNkZmZajfPyyy/z0ksvcfDgQSIjI5k1axZRUVE8//zz7Nu3j2+//ZaQkBBLfxcXF6ZNm8aBAweYN28e69evZ/jw4Zb9UVFRpKens3nzZvbt28dbb72Fp6cnJUuWZMmSJQAcOnSIEydOMHXq1JteW/fu3fnyyy+ZNm0aBw8e5IMPPsDT09P2XwzJc06cOEGnTp3o2bMnBw8eZOPGjbRv3x7DMLhw4QI9evTgxx9/5KeffqJcuXK0bNkyyz8wxo4dy5NPPsm+ffvo2bMnK1as4Mknn6Rly5bs2bOHdevW8eijj1r6X716lfHjx/PLL7+wbNky4uPjLUk9wGuvvcavv/7KypUrOXjwILNmzaJo0aIA7NixA4C1a9dy4sQJ/ve//930ukaOHMmbb75pGWvBggX4+/vb+dMTcQBDnELDhg2NevXqWbU98sgjxogRI4w1a9YYrq6uRkJCgmXfgQMHDMDYsWOHYRiGMWbMGKNAgQJGSkqKpc+wYcOM2rVr3/KcS5YsMby9va2OuZ1Tp04ZgLFv3z7DMAwjLi7OAIz33nvPql/x4sWNV1991aYxDcMwFi1aZBQpUsTyfVhYmDF27Nib9t2wYYMBGOfOnbNqb9iwofHSSy8ZhmEYhw4dMgAjJibG5hhEdu3aZQBGfHz8HftmZGQYXl5exnfffWdpA4yBAwda9QsPDze6dOlicww7d+40AOPChQuGYRhG69atjeeee+6mfW/8/O3Zs8eqvUePHkbbtm0NwzCMlJQUw2w2Gx999JHNMYjkFqoAOZGqVatafR8YGEhSUhIHDx6kZMmSlCxZ0rIvNDSUQoUKcfDgQUtb6dKlrRYB3zgeYP78+Xh6elq2H374gccff5ygoCDKlClDt27dmD9/PhcvXrQc/8cff9CpUyfKlCmDt7c3pUuXBq5Px/1TrVq1LF8nJSVx/PhxmjZtesvrXLt2LU2bNuWhhx7Cy8uLbt26cebMGcu5BwwYwIQJE6hbty5jxoxh7969tn6EAMTGxuLq6krDhg2zdZzkbdWqVaNp06aEhYXx9NNP89FHH3Hu3DkATp48SZ8+fShXrhw+Pj54e3uTmpp6258FuP578XY/C7t27aJ169aUKlUKLy8vy+/ZG+P269ePr776iurVqzN8+HC2bt2arWs6ePAg6enpt41BJLdSAuRE/r1g0mQyZZluutvj27RpQ2xsrGW7se5g9+7dfPnllwQGBjJ69GiqVavG+fPnAWjdujVnz57lo48+Yvv27Wzfvh2AK1euWJ2nYMGClq89PDxuG2N8fDxPPPEEVatWZcmSJezatYuZM2dajdu7d2+OHj1Kt27d2LdvH7Vq1WL69Ok2fw53ikHkZlxdXYmJiWHlypWEhoYyffp0KlSoQFxcHD169CA2NpapU6eydetWYmNjKVKkyG1/FuD2vxfT0tKIjIzE29ub+fPns3PnTpYuXQr8389CixYt+PPPPxk0aJDlHxZDhw61+Zr0syDOTAlQHlCpUiWOHTvGsWPHLG2//vor58+fJzQ01KYxvLy8CAkJsWw3/mDMly8fzZo1Y9KkSezdu5f4+HjWr1/PmTNnOHToEKNGjaJp06ZUqlTJ8q/hO52ndOnSrFu37qb7d+3aRWZmJu+++y516tShfPnyHD9+PEu/kiVL0rdvX/73v/8xZMgQPvroIwDc3NwAyMjIuGUMYWFhZGZmsmnTpjvGK/JPJpOJunXr8vrrr7Nnzx7c3NxYunQpW7ZsYcCAAbRs2dKyuP/06dN3HK9q1aq3/Fn47bffOHPmDG+++Sb169enYsWKVgugbyhWrBg9evTgiy++4L333uPDDz8EbPtZKFeuHB4eHreMQSQ3023weUCzZs0ICwujS5cuvPfee1y7do0XXniBhg0bZim5Z8fy5cs5evQoDRo0oHDhwnz//fdkZmZSoUIFChcuTJEiRfjwww8JDAwkISGBl19+2aZxx44dS9++ffHz86NFixZcuHCBLVu28OKLLxISEsLVq1eZPn06rVu3ZsuWLcyePdvq+IEDB9KiRQvKly/PuXPn2LBhA5UqVQIgKCgIk8nE8uXLadmyJR4eHlkWN5cuXZoePXrQs2dPpk2bRrVq1fjzzz9JSkrimWeeuevPS5zb9u3bWbduHREREfj5+bF9+3ZOnTpFpUqVKFeunOWOrZSUFIYNG2ZTdWXMmDE0bdqUsmXL0rFjR65du8b333/PiBEjKFWqFG5ubkyfPp2+ffuyf/9+xo8fb3X86NGjqVmzJpUrVyY9PZ3ly5dbfhb8/Pzw8PBg1apVlChRAnd39yy3wLu7uzNixAiGDx+Om5sbdevW5dSpUxw4cIBevXrZ78MTcQRHL0IS+/jnIt4b2rZta/To0cMwDMP4888/jTZt2hgFCxY0vLy8jKefftpITEy09B0zZoxRrVo1q+OnTJliBAUF3fKcP/zwg9GwYUOjcOHChoeHh1G1alVj4cKFlv0xMTFGpUqVDLPZbFStWtXYuHGjARhLly41DOPWizANwzBmz55tVKhQwcifP78RGBhovPjii5Z9kydPNgIDAw0PDw8jMjLS+Oyzz6wWNvfv398oW7asYTabjWLFihndunUzTp8+bTl+3LhxRkBAgGEymSyfz78/v0uXLhmDBg0yAgMDDTc3NyMkJMT49NNPb/lZiPz6669GZGSkUaxYMcNsNhvly5c3pk+fbhiGYezevduoVauW4e7ubpQrV85YtGiRERQUZEyZMsVy/D9/Nv5pyZIlRvXq1Q03NzejaNGiRvv27S37FixYYJQuXdowm81GeHi48e2331r9TI0fP96oVKmS4eHhYfj6+hpt27Y1jh49ajn+o48+MkqWLGm4uLgYDRs2NAzDehG0YVxfsD1hwgQjKCjIyJ8/v1GqVClj4sSJdvvcRBxFT4IWERGRPEdrgERERCTPUQIkIiIieY4SIBEREclzlACJiIhInqMESERERPIcJUAiIiKS5ygBEhERkTxHCZCIiIjkOUqARASAZ599lnbt2lm+b9SoEQMHDnzgcWzcuBGTyWR5qa6IyP2gBEgkh3v22WcxmUyYTCbc3NwICQlh3LhxXLt27b6e93//+1+Wd0vdipIWEclt9DJUkVygefPmzJkzh/T0dL7//nuioqLInz8/I0eOtOp35coVy1u+75Wvr69dxhERyYlUARLJBcxmMwEBAQQFBdGvXz+aNWvGt99+a5m2euONNyhevDgVKlQA4NixYzzzzDMUKlQIX19f2rZtS3x8vGW8jIwMBg8eTKFChShSpAjDhw/n368F/PcUWHp6OiNGjKBkyZKYzWZCQkL45JNPiI+Pp3HjxgAULlwYk8nEs88+C0BmZibR0dEEBwfj4eFBtWrVWLx4sdV5vv/+e8qXL4+HhweNGze2ilNE5H5RAiSSC3l4eHDlyhUA1q1bx6FDh4iJiWH58uVcvXqVyMhIvLy8+OGHH9iyZQuenp40b97ccsy7777L3Llz+fTTT/nxxx85e/YsS5cuve05u3fvzpdffsm0adM4ePAgH3zwAZ6enpQsWZIlS5YAcOjQIU6cOMHUqVMBiI6O5rPPPmP27NkcOHCAQYMG0bVrVzZt2gRcT9Tat29P69atiY2NpXfv3rz88sv362MTEfk/Dn4bvYjcQY8ePYy2bdsahmEYmZmZRkxMjGE2m42hQ4caPXr0MPz9/Y309HRL/88//9yoUKGCkZmZaWlLT083PDw8jNWrVxuGYRiBgYHGpEmTLPuvXr1qlChRwnIewzCMhg0bGi+99JJhGIZx6NAhAzBiYmJuGuOGDRsMwDh37pyl7fLly0aBAgWMrVu3WvXt1auX0alTJ8MwDGPkyJFGaGio1f4RI0ZkGUtExN60BkgkF1i+fDmenp5cvXqVzMxMOnfuzNixY4mKiiIsLMxq3c8vv/zC4cOH8fLyshrj8uXLHDlyhOTkZE6cOEHt2rUt+/Lly0etWrWyTIPdEBsbi6urKw0bNrQ55sOHD3Px4kUef/xxq/YrV65Qo0YNAA4ePGgVB0B4eLjN5xARuVtKgERygcaNGzNr1izc3NwoXrw4+fL9349uwYIFrfqmpqZSs2ZN5s+fn2WcYsWK3dX5PTw8sn1MamoqACtWrOChhx6y2mc2m+8qDhERe1ECJJILFCxYkJCQEJv6PvzwwyxcuBA/Pz+8vb1v2icwMJDt27fToEEDAK5du8auXbt4+OGHb9o/LCyMzMxMNm3aRLNmzbLsv1GBysjIsLSFhoZiNptJSEi4ZeWoUqVKfPvtt1ZtP/30050vUkTkHmkRtIiT6dKlC0WLFqVt27b88MMPxMXFsXHjRgYMGMBff/0FwEsvvcSbb77JsmXL+O2333jhhRdu+wyf0qVL06NHD3r27MmyZcssY3799dcABAUFYTKZWL58OadOnSI1NRUvLy+GDh3KoEGDmDdvHkeOHGH37t1Mnz6defPmAdC3b1/++OMPhg0bxqFDh1iwYAFz58693x+RiIgSIBFnU6BAATZv3kypUqVo3749lSpVolevXly+fNlSERoyZAjdunWjR48ehIeH4+XlxZNPPnnbcWfNmsVTTz3FCy+8QMWKFenTpw9paWkAPPTQQ7z++uu8/PLL+Pv7079/fwDGjx/Pa6+9RnR0NJUqVaJ58+asWLGC4OBgAEqVKsWSJUtYtmwZ1apVY/bs2UycOPE+fjoiIteZjFutehQRERFxUqoAiYiISJ6jBEhERETyHCVAIiIikucoARIREZE8RwmQiIiI5DlKgERERCTPUQIkIiIieY4SIBEREclzlACJiIhInqMESERERPIcJUAiIiKS5/w/oCP/6MiW+48AAAAASUVORK5CYII=", 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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Saved: outputs/classical/tfidf_lr/confusion_matrix_binary_val.png\n" ] }, { - "output_type": "display_data", "data": { + "image/png": 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", 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Po0ePhqenJ7766ivEx8cXSICAp1WgyMhIzJkzB7Nnz4YgCPDz80N0dLTkuSWvM3XqVPFnMzMzVKhQAStXrtRZolq+fHls3rwZU6ZMwbhx4+Ds7Ixhw4ahZMmS4s0CC6N3796YMGGC5MnPwNOVW1WqVMFvv/0mOQECgNDQUNSoUQPfffcdPv/8cxQrVgweHh7o2bOnWA318/PDyZMnsWHDBiQnJ8PGxga1a9fGunXrtCZ0//DDD/jss88QHByM3NxcTJs2Tfyd2rRpE+rXr48yZcpIjo1IiVRCYZcrEBEZiMWLFyM4OBjx8fEFVqC9ys8//4ygoCAkJCSIS9f1QVJSEjw9PbFhwwZWgMjgMQEiInoBQRBQtWpVlChRotCTqDUaDapUqYJu3brhiy++eEcRFt6kSZNw4MABnDx5Uu5QiGTHBIiI6DnZ2dnYsWMHDh48iFWrVuH333/nk9OJFIgJEBHRc+Lj4+Hp6QlbW1sMHz4cc+bMkTskInoHmAARERGRweF9gIiIiMjgMAEiIiIig8MEiIiIiAyOIm+EaF5dN3etJTJ0aaeWyR0CkSKYvae/trr++/fojHL/G6DIBIiIiMggqTiwIxU/KSIiIjI4rAAREREphUoldwRFBitAREREZHBYASIiIlIKzgGSjAkQERGRUnAITDKmikRERGRwWAEiIiJSCg6BScYEiIiISCk4BCYZU0UiIiIyOKwAERERKQWHwCRjAkRERKQUHAKTjKkiERERGRxWgIiIiJSCQ2CS8ZMiIiIig8MKEBERkVJwDpBkTICIiIiUgkNgkvGTIiIiIoPDChAREZFScAhMMiZARERESsEhMMn4SREREZHBYQWIiIhIKVgBkowJEBERkVIYcQ6QVEwViYiIyOCwAkRERKQUHAKTjJ8UERERGRxWgIiIiJSC9wGSjAkQERGRUnAITDJ+UkRERGRwWAEiIiJSCg6BScYEiIiISCk4BCYZPykiIiIyOKwAERERKQWHwCRjBYiIiIgMDitARERESsE5QJIxASIiIlIKDoFJxlSRiIiIDA4rQERERErBITDJmAAREREpBYfAJGOqSERERAaHFSAiIiKl4BCYZEyAiIiIlIIJkGT8pIiIiMjgsAJERESkFJwELRkrQERERGRwWAEiIiJSCs4BkowJEBERkVJwCEwypopERERkcFgBIiIiUgoOgUnGBIiIiEgpOAQmGVNFIiIiMjisABERESmEihUgyZgAERERKQQTIOk4BEZEREQGhxUgIiIipWABSDJWgIiIiMjgsAJERESkEJwDJB0TICIiIoVgAiSdXgyBrV69Gps2bSrQvmnTJqxZs0aGiIiIiEjJ9CIBCgkJgYODQ4F2R0dHzJ07V4aIiIiIih6VSqXTTcn0YggsISEBnp6eBdrd3d2RkJAgQ0RERERFj9KTFl3SiwqQo6Mjzp07V6D97NmzKFGihAwRERERkZLpRQWoW7duGDlyJKysrNCwYUMAwKFDhzBq1Ch07dpV5uiIiIiKCBaAJNOLBGjWrFmIj49H06ZNUazY05A0Gg169+7NOUBERESkc3qRAJmammLjxo2YNWsWzp49C3Nzc/j4+MDd3V3u0IiIiIoMzgGSTi8SoGfKlSuHcuXKyR0GERFRkcQESDrZEqAxY8Zg1qxZsLCwwJgxY17Zd+HChe8pKiIiIjIEsiVAZ86cQV5envgzERERvR1WgKSTbRn8wYMHYWtrK/78qo2IiIheT84bIa5cuRJVqlSBtbU1rK2t4evriz179oj7Hz9+jKCgIJQoUQKWlpbo1KkTkpOTtc6RkJCAVq1aoXjx4nB0dMT48ePx5MkTrT5RUVH46KOPoFar4eXlhbCwsDf6rPTiPkD9+/fHgwcPCrRnZ2ejf//+MkREREREhfHhhx9i3rx5iImJwV9//YUmTZqgXbt2uHjxIgAgODgYO3fuxKZNm3Do0CHcuXMHHTt2FI/Pz89Hq1atkJubi2PHjmHNmjUICwvD1KlTxT43b95Eq1at0LhxY8TGxmL06NEYOHAg9u7dW+h4VYIgCG9/2W/H2NgYiYmJcHR01Gq/d+8enJ2dC2R/r2NefYQuwyMyWGmnlskdApEimL2nCScl+vyq0/PdX9PtrY63t7fH119/jc6dO6NkyZJYv349OnfuDAC4cuUKKlasiOjoaNStWxd79uxB69atcefOHTg5OQEAQkNDMXHiRNy9exempqaYOHEidu3ahQsXLojv0bVrV6SnpyMiIqJQsclaAcrMzERGRgYEQcCDBw+QmZkpbmlpadi9e3eBpIiIiIheTNdDYDk5OVp/mzMzM5GTk/PaOPLz87FhwwZkZ2fD19cXMTExyMvLg7+/v9inQoUKKFWqFKKjowEA0dHR8PHxEZMfAAgMDERmZqZYRYqOjtY6x7M+z85RGLImQLa2trC3t4dKpUK5cuVgZ2cnbg4ODujfvz+CgoLkDJGIiMhghYSEwMbGRmsLCQl5af/z58/D0tISarUaQ4cOxbZt2+Dt7Y2kpCSYmpqKc3+fcXJyQlJSEgAgKSlJK/l5tv/Zvlf1yczMxKNHjwp1bbLeB+jgwYMQBAFNmjTBli1bYG9vL+4zNTWFu7s7XF1dZYyQiIio6ND1KrDJkycXuFWNWq1+af/y5csjNjYWGRkZ2Lx5M/r06YNDhw7pNCZdkTUB8vPzA/B0UlOpUqW4fI+IiEiPqNXqVyY8/2VqagovLy8AQI0aNXDq1CksXrwYn376KXJzc5Genq5VBUpOToazszMAwNnZGSdPntQ637NVYs/3+e/KseTkZFhbW8Pc3LxQ16YXq8AuX76Mo0ePiq+XL1+OatWqoXv37khLS5MxMiIioqJDzmXwL6LRaJCTk4MaNWrAxMQE+/fvF/fFxcUhISEBvr6+AABfX1+cP38eKSkpYp/IyEhYW1vD29tb7PP8OZ71eXaOwtCLBGj8+PHIzMwE8HT8cMyYMWjZsiVu3rz52rtEExER0f9T6XgrhMmTJ+Pw4cOIj4/H+fPnMXnyZERFRaFHjx6wsbHBgAEDMGbMGBw8eBAxMTHo168ffH19UbduXQBAQEAAvL290atXL5w9exZ79+7FlClTEBQUJFahhg4dihs3bmDChAm4cuUKVqxYgd9++w3BwcGF/qj04llgN2/eFLO7LVu2oE2bNpg7dy5Onz6Nli1byhwdERERvU5KSgp69+6NxMRE2NjYoEqVKti7dy+aNWsGAFi0aBGMjIzQqVMn5OTkIDAwECtWrBCPNzY2Rnh4OIYNGwZfX19YWFigT58+mDlzptjH09MTu3btQnBwMBYvXowPP/wQP/zwAwIDAwsdr17cB8je3h5//vknvL29Ub9+ffTu3RuDBw9GfHw8vL298fDhw0Kdj/cBItIN3geISDfe132AnAZu0un5kn/4RKfn0yd6UQGqX78+xowZg3r16uHkyZPYuHEjAODvv//Ghx9+KHN0RERERQMXE0mnF3OAli1bhmLFimHz5s1YuXIlPvjgAwDAnj170Lx5c5mjIyIiIqXRiwpQqVKlEB4eXqB90aJFMkRDRERUNLECJJ1eJEDPe/z4MXJzc7XarK2tZYqGiIio6GACJJ1eDIFlZ2djxIgRcHR0hIWFhdYjMezs7OQOj4iIiBRGLxKgCRMm4MCBA1i5ciXUajV++OEHzJgxA66urli7dq3c4RERERUNMt4HqKjRiyGwnTt3Yu3atWjUqBH69euHBg0awMvLC+7u7li3bh169Oghd4hERESkIHpRAUpNTUXp0qUBPJ3vk5qaCuDp8vjDhw/LGRoREVGRoW+PwtBnepEAlS5dGjdv3gQAVKhQAb/99huAp5Wh5x+aRkRERC/HBEg6vUiA+vXrh7NnzwIAJk2ahOXLl8PMzAzBwcEYP368zNERERGR0ujFHKDnH2Lm7++PK1euICYmBl5eXqhSpYqMkRERERUdSq/a6JJeJED/5e7uDnd3d7nDICIiKlqY/0imF0NgI0eOxJIlSwq0L1u2DKNHj37/AREREZGi6UUCtGXLFtSrV69A+8cff4zNmzfLEBEREVHRw0nQ0unFENj9+/dhY2NToN3a2hr37t2TISIiIqKiR+lJiy7pRQXIy8sLERERBdr37Nkj3h+IiIiISFf0ogI0ZswYjBgxAnfv3kWTJk0AAPv378eCBQvw7bffyhscvdCgT+pjUOcGcHe1BwBcvpGEud/vwR9HLxXou33ZMATWq4Quwd9jZ9Q5sb1R7XKYNrw1Knm5IvtRLtbtPIFpy3ciP19T4Byl3Rxw/NdJyNdo4NJwwru7MCKZtWjWBHfu/Fug/dOu3fH5l9MwoG8v/HXqpNa+zl0+xZfTZmq1/b5tK35euxq34uNhYWmJgIDm+PzLae80dpIfK0DS6UUC1L9/f+Tk5GDOnDmYNWsWAMDDwwMrV65E7969ZY6OXuTf5HR8ufR3XEu4CxVU6NmmDjYtGoy6Xefh8o0ksd9nPRpDEAoe71PuA2xfOgxf/bgXA75cC1dHWyz9vCuMjY0wedE2rb7FihlhbUg/HD1zHXWrer7rSyOS1bqNm6HJzxdfX7t2FUMG9kOzwOZiW6fOXTB8xEjxtZm5udY51oatxto1P2HM2AnwqVIVjx49xJ1/CyZVRIZM9gToyZMnWL9+PTp27Ihhw4bh7t27MDc3h6Wlpdyh0SvsPnxB6/X05Tsx6JP6qF3FU0yAqpT7AKN6NUG9HvMRvy9Eq3/ngI9w4eodhHz/dOjzxu17+GLxdvzyVX/M+W43sh7m/O/cw9sg7mYyDp6MYwJEimdvb6/1+qcfvoebWynUrFVbbDMzM4NDyZIvPD4zIwPLl36LJctDUaeur9hernyFdxMw6RVWgKSTfQ5QsWLFMHToUDx+/BgAULJkSSY/RYyRkQqfBNaAhbkpTpx7+kgTczMThIX0xeh5vyH5/oMCx6hNi+FxTp5W26OcPJibmaJ6xVJim1+tcujYrDpGz/vt3V4EkR7Ky83FrvAdaN+xk9Yftt27dsKvXh10bNcaixctwKNHj8R90dFHodFokJKcjPZtWqBZk4YYP2YUkhIT5bgEet/4NHjJZK8AAUDt2rVx5syZN7r5YU5ODnJycrTaBE0+VEbGugqPXqKSlyui1oyFmWkxZD3KwadjV+HK/1d/5o/thONnbyI86vwLj408dhkjujdGl+Y1sPmP03AuYY3PB7cAALiUtAYA2NtYYNWMnug3ZQ0eZD9+PxdFpEcOHNiHBw8eoG37DmJbi5at4eLqCkdHR/z9dxy+XfgN4uNvYtHiZQCAf27/A41GwA+rQjFh0hewsrLCsiXfYsigfti8dQdMTE3luhwivaIXCdDw4cMxduxY/PPPP6hRowYsLCy09r/qcRghISGYMWOGVpuxUy2YuNR+yRGkK3/HJ6NO1xDYWJqjg391rJrZCwEDF6OMW0k0ql0OdbvOe+mx+49fweffbseSz7vix1m9kZP3BPNWRaD+R17QaJ5OGlrxZTdsjPgLR09ff1+XRKRXtm3Zgnr1G8LR0Uls69zlU/HnsuXKw8GhJAYP6IvbCQlwK1UKgqDBkyd5mDh5Cj6uVx8AMO/rhWjqVw8nT55AvfoN3vt10PvDITDpVILwoimq75eRUcGROJVKBUEQoFKpkP/chMD/elEFyLHBRFaAZLArdARu3L6Hxzl5GN7NT0xkAKBYMWPk52tw9Mx1BA5arHWcS0kbpGU+hLurPWK3fon6PeYj5lICEg/Ph6W5WuynUqlgbGyEJ0/yETT7V6z9/fh7uzZDlXZqmdwhGKw7d/5Fq0B/LFy8FI2b+L+038OHD+FbqzpWfPcD6tVvgO3btmDalM/xx/5DcHJ2Fvs1bvgxRnw2Gp0+6fI+wqf/MHtP5YYyY/fo9HzXF7TQ6fn0iV5UgG7evPnGx6rVaqjVaq02Jj/yMFKpoDYthtmhu7B62zGtfTGbv8CEBVuw69CFAscl3s0AAHRpXhO3E1Nx5sptAECjPgtg/Fxy3LpRFYzt64/GfRfiTkr6u7sQIj3w+7atsLcvgQYNG72yX9yVywCezp8EgGrVPwIAxMffFBOgjPR0pKelwcXV9d0FTFTE6EUCxAefFj0zP2uLvUcv4nZiGqwszPBpi5poWLMs2gxfgeT7D1448fl2Yhpu3bkvvg7u3RR/HLsMjUaDdk2rYVy/Zug54SexchR3M1nr+I+8S0EjCLh0nZM5Sdk0Gg1+37YVbdq1R7Fi//vP9O2EBOzetRMNGvrBxtYWV+Pi8PX8ENSoWUtc5eXh4YnGTZriq5A5mDp9JiwsLbFk0UJ4eJZGrdp15Lokek84AiadXiRAz1y6dAkJCQnIzc3Vam/btq1MEdHLlLS3xI+zesPZwRoZWY9x4eq/aDN8BQ6cuCL5HAH1vDFhYCDUJsVw/u9/8Unw9y+8kSKRoTkefQyJiXfQvmMnrXYTExOcOB6NdT+vxaNHD+Hs7AJ//wAMGjpcq9/skPn4+qu5GDF8CIxURqhRqxZWfvcDTExM3udlEOk1vZgDdOPGDXTo0AHnz58X5/4A/5vM9ao5QC9iXn2EzmMkMkScA0SkG+9rDlDZ8QUfK/U2rn7d/PWdiijZ7wMEAKNGjYKnpydSUlJQvHhxXLx4EYcPH0bNmjURFRUld3hERERFgkql203J9GIILDo6GgcOHICDgwOMjIxgZGSE+vXrIyQkBCNHjsSZM2fkDpGIiIgURC8qQPn5+bCysgIAODg44M6dOwCeTo6Oi4uTMzQiIqIiQ6VS6XRTMr2oAFWuXBlnz56Fp6cn6tSpg/nz58PU1BTff/89SpcuLXd4RERERYLCcxad0osEaMqUKcjOzgYAzJw5E61bt0aDBg1QokQJbNy4UeboiIiISGn0IgEKDAwUf/by8sKVK1eQmpoKOzs7xZfgiIiIdMXIiH8zpdKLOUD/lZmZicOHD3P+DxERUSFwFZh0epEAdenSBcuWPb3fyKNHj1CzZk106dIFPj4+2LJli8zRERERkdLoRQJ0+PBhNGjw9AnF27ZtgyAISE9Px5IlSzB79myZoyMiIioauApMOr1IgDIyMmBvbw8AiIiIQKdOnVC8eHG0atUKV69elTk6IiIiUhq9SIDc3NwQHR2N7OxsREREICAgAACQlpYGMzMzmaMjIiIqGjgHSDq9WAU2evRo9OjRA5aWlihVqhQaNWoE4OnQmI+Pj7zBERERFRFKH7bSJb1IgIYPH446deogISEBzZo1g5HR08JU6dKlOQeIiIiIdE4vEiAAqFGjBmrUqIGjR4+iZs2aUKvVaNWqldxhERERFRmsAEmnF3OAnteiRQv8+++/codBRERU5HAOkHR6lwAJgiB3CERERKRwejMERkRERG+HQ2DS6V0C9N1338HJyUnuMIiIiIoc5j/S6V0C1L17d7lDICIiIoXTiwQoOzsb8+bNw/79+5GSkgKNRqO1/8aNGzJFRkREVHRwCEw6vUiABg4ciEOHDqFXr15wcXHhv0AiIiJ6p/QiAdqzZw927dqFevXqyR0KERFRkcX6gXR6kQDZ2dmJD0MlIiKiN8MRFOn04j5As2bNwtSpU/Hw4UO5QyEiIiIDoBcVoAULFuD69etwcnKCh4cHTExMtPafPn1apsiIiIiKDhaApNOLBKh9+/Zyh0BERFTkcQhMOr1IgKZNmyZ3CERERGRA9CIBeiYmJgaXL18GAFSqVAnVq1eXOSIiIqKigwUg6fQiAUpJSUHXrl0RFRUFW1tbAEB6ejoaN26MDRs2oGTJkvIGSERERIqiF6vAPvvsMzx48AAXL15EamoqUlNTceHCBWRmZmLkyJFyh0dERFQkqFQqnW5KphcVoIiICOzbtw8VK1YU27y9vbF8+XIEBATIGBkREVHRofCcRaf0ogKk0WgKLH0HABMTkwLPBSMiIiJ6W3qRADVp0gSjRo3CnTt3xLZ///0XwcHBaNq0qYyRERERFR0cApNOLxKgZcuWITMzEx4eHihTpgzKlCkDDw8PZGZmYunSpXKHR0REVCSoVLrdlEwv5gC5ubnh9OnT2L9/v7gMvmLFivD395c5MiIiIlIivUiAAODAgQM4cOAAUlJSoNFocObMGaxfvx4A8NNPP8kcHRERkf5T+rCVLunFENiMGTMQEBCA/fv34969e0hLS9PaiIiI6PXknAMUEhKCWrVqwcrKCo6Ojmjfvj3i4uK0+jRq1KjAewwdOlSrT0JCAlq1aoXixYvD0dER48ePx5MnT7T6REVF4aOPPoJarYaXlxfCwsIK/VnpRQUoNDQUYWFh6NWrl9yhEBER0Rs4dOgQgoKCUKtWLTx58gSff/45AgICcOnSJVhYWIj9Bg0ahJkzZ4qvixcvLv6cn5+PVq1awdnZGceOHUNiYiJ69+4NExMTzJ07FwBw8+ZNtGrVCkOHDsW6deuwf/9+DBw4EC4uLggMDJQcr14kQLm5ufj444/lDoOIiKhI0/UIWE5ODnJycrTa1Go11Gp1gb4RERFar8PCwuDo6IiYmBg0bNhQbC9evDicnZ1f+H5//PEHLl26hH379sHJyQnVqlXDrFmzMHHiREyfPh2mpqYIDQ2Fp6cnFixYAODpnOE///wTixYtKlQCpBdDYAMHDhTn+xAREZF+CAkJgY2NjdYWEhIi6diMjAwAgL29vVb7unXr4ODggMqVK2Py5Ml4+PChuC86Oho+Pj5wcnIS2wIDA5GZmYmLFy+Kff67SCowMBDR0dGFuja9qAA9fvwY33//Pfbt24cqVaoUuCniwoULZYqMiIio6ND1JOjJkydjzJgxWm0vqv78l0ajwejRo1GvXj1UrlxZbO/evTvc3d3h6uqKc+fOYeLEiYiLi8PWrVsBAElJSVrJDwDxdVJS0iv7ZGZm4tGjRzA3N5d0bXqRAJ07dw7VqlUDAFy4cEFrH2e0ExERSaPrP5kvG+56naCgIFy4cAF//vmnVvvgwYPFn318fODi4oKmTZvi+vXrKFOmzFvHWxh6kQAdPHhQ7hCIiIhIB0aMGIHw8HAcPnwYH3744Sv71qlTBwBw7do1lClTBs7Ozjh58qRWn+TkZAAQ5w05OzuLbc/3sba2llz9AfRkDhARERG9PTmXwQuCgBEjRmDbtm04cOAAPD09X3tMbGwsAMDFxQUA4Ovri/PnzyMlJUXsExkZCWtra3h7e4t99u/fr3WeyMhI+Pr6FipeJkBEREQKIeejMIKCgvDLL79g/fr1sLKyQlJSEpKSkvDo0SMAwPXr1zFr1izExMQgPj4eO3bsQO/evdGwYUNUqVIFABAQEABvb2/06tULZ8+exd69ezFlyhQEBQWJQ3FDhw7FjRs3MGHCBFy5cgUrVqzAb7/9huDg4ELFywSIiIiI3trKlSuRkZGBRo0awcXFRdw2btwIADA1NcW+ffsQEBCAChUqYOzYsejUqRN27twpnsPY2Bjh4eEwNjaGr68vevbsid69e2vdN8jT0xO7du1CZGQkqlatigULFuCHH34o1BJ4AFAJgiDo5tL1h3n1EXKHQKQIaaeWyR0CkSKYvacZt82WHdfp+SJH1NXp+fSJXkyCJiIiorfHhdPScQiMiIiIDA4rQERERArBe+dJxwoQERERGRxWgIiIiBTCiAUgyZgAERERKQSHwKTjEBgREREZHFaAiIiIFIIFIOmYABERESmECsyApOIQGBERERkcVoCIiIgUgqvApGMCREREpBBcBSYdh8CIiIjI4LACREREpBAsAEnHChAREREZHFaAiIiIFMKIJSDJmAAREREpBPMf6TgERkRERAaHFSAiIiKF4DJ46ZgAERERKQTzH+k4BEZEREQGhxUgIiIiheAqMOlYASIiIiKDwwoQERGRQrD+Ix0TICIiIoXgKjDpOARGREREBocVICIiIoUwYgFIMiZARERECsEhMOk4BEZEREQGhxUgIiIihWABSDomQERERArBITDpOARGREREBocVICIiIoXgKjDpWAEiIiIig8MKEBERkUJwDpB0TICIiIgUgumPdG80BHbkyBH07NkTvr6++PfffwEAP//8M/7880+dBkdERET0LhQ6AdqyZQsCAwNhbm6OM2fOICcnBwCQkZGBuXPn6jxAIiIiksZIpdLppmSFToBmz56N0NBQrFq1CiYmJmJ7vXr1cPr0aZ0GR0RERNKpVLrdlKzQCVBcXBwaNmxYoN3Gxgbp6em6iImIiIjonSp0AuTs7Ixr164VaP/zzz9RunRpnQRFREREhadSqXS6KVmhE6BBgwZh1KhROHHiBFQqFe7cuYN169Zh3LhxGDZs2LuIkYiIiCTgEJh0hV4GP2nSJGg0GjRt2hQPHz5Ew4YNoVarMW7cOHz22WfvIkYiIiIinSp0AqRSqfDFF19g/PjxuHbtGrKysuDt7Q1LS8t3ER8RERFJpPSVW7r0xjdCNDU1hbe3ty5jISIiInovCp0ANW7c+JUTow4cOPBWAREREdGbYQFIukInQNWqVdN6nZeXh9jYWFy4cAF9+vTRVVxERERUSEpfuaVLhU6AFi1a9ML26dOnIysr660DIiIiInrXVIIgCLo40bVr11C7dm2kpqbq4nRv5WGeTi6JyOCVqDta7hCIFOFRzOL38j6fbbus0/Mt7VBRp+fTJzp7Gnx0dDTMzMx0dToiIiIqJA6BSVfoBKhjx45arwVBQGJiIv766y98+eWXOguMiIiI6F0pdAJkY2Oj9drIyAjly5fHzJkzERAQoLPAiIiIqHCMWACSrFAJUH5+Pvr16wcfHx/Y2dm9q5iIiIiI3qlCPQvM2NgYAQEBfOo7ERGRHjJS6XZTskI/DLVy5cq4cePGu4iFiIiI3gKfBi9doROg2bNnY9y4cQgPD0diYiIyMzO1NiIiIiJ9J3kO0MyZMzF27Fi0bNkSANC2bVut7FAQBKhUKuTn5+s+SiIiInotpQ9b6ZLkBGjGjBkYOnQoDh48+C7jISIiojek8FErnZKcAD27YbSfn987C4aIiIjofSjUMnilT4giIiIqyoz4d1qyQiVA5cqVe20SpA/PAiMiIjJEhV7ZZMAKlQDNmDGjwJ2giYiIiIqaQiVAXbt2haOj47uKhYiIiN4CR8Ckk1wt4/wfIiIiepmQkBDUqlULVlZWcHR0RPv27REXF6fV5/HjxwgKCkKJEiVgaWmJTp06ITk5WatPQkICWrVqheLFi8PR0RHjx4/HkydPtPpERUXho48+glqthpeXF8LCwgodr+QE6NkqMCIiItJPRiqVTrfCOHToEIKCgnD8+HFERkYiLy8PAQEByM7OFvsEBwdj586d2LRpEw4dOoQ7d+6gY8eO4v78/Hy0atUKubm5OHbsGNasWYOwsDBMnTpV7HPz5k20atUKjRs3RmxsLEaPHo2BAwdi7969hYpXJSgws3mYp7hLIpJFibqj5Q6BSBEexSx+L+8zde9VnZ7vi0alkJOTo9WmVquhVqtfe+zdu3fh6OiIQ4cOoWHDhsjIyEDJkiWxfv16dO7cGQBw5coVVKxYEdHR0ahbty727NmD1q1b486dO3BycgIAhIaGYuLEibh79y5MTU0xceJE7Nq1CxcuXBDfq2vXrkhPT0dERITka+OEcSIiInqhkJAQ2NjYaG0hISGSjs3IyAAA2NvbAwBiYmKQl5cHf39/sU+FChVQqlQpREdHAwCio6Ph4+MjJj8AEBgYiMzMTFy8eFHs8/w5nvV5dg6pCjUJmoiIiPSXrh+FMXnyZIwZM0arTUr1R6PRYPTo0ahXrx4qV64MAEhKSoKpqSlsbW21+jo5OSEpKUns83zy82z/s32v6pOZmYlHjx7B3Nxc0rUxASIiIlIIXd8IUepw138FBQXhwoUL+PPPP3Uajy5xCIyIiIh0ZsSIEQgPD8fBgwfx4Ycfiu3Ozs7Izc1Fenq6Vv/k5GQ4OzuLff67KuzZ69f1sba2llz9AZgAERERKYZKpdutMARBwIgRI7Bt2zYcOHAAnp6eWvtr1KgBExMT7N+/X2yLi4tDQkICfH19AQC+vr44f/48UlJSxD6RkZGwtraGt7e32Of5czzr8+wcUnEIjIiISCF0PQeoMIKCgrB+/Xr8/vvvsLKyEufs2NjYwNzcHDY2NhgwYADGjBkDe3t7WFtb47PPPoOvry/q1q0LAAgICIC3tzd69eqF+fPnIykpCVOmTEFQUJA4FDd06FAsW7YMEyZMQP/+/XHgwAH89ttv2LVrV6HiZQWIiIiI3trKlSuRkZGBRo0awcXFRdw2btwo9lm0aBFat26NTp06oWHDhnB2dsbWrVvF/cbGxggPD4exsTF8fX3Rs2dP9O7dGzNnzhT7eHp6YteuXYiMjETVqlWxYMEC/PDDDwgMDCxUvLwPEBG9FO8DRKQb7+s+QHP3X9fp+T5vWkan59MnrAARERGRweEcICIiIoWQcw5QUcMEiIiISCGYAEnHITAiIiIyOKwAERERKYRKx3eCVjImQERERArBITDpOARGREREBocVICIiIoXgCJh0TICIiIgUQtdPg1cyDoERERGRwWEFiIiISCE4CVo6VoCIiIjI4LACREREpBCcAiQdEyAiIiKFMAIzIKk4BEZEREQGhxUgIiIiheAQmHRMgIiIiBSCq8Ck4xAYERERGRxWgIiIiBSCd4KWjhUgIiIiMjisABERESkEC0DSMQEiIiJSCA6BScchMCIiIjI4rAAREREpBAtA0jEBIiIiUggO60jHz4qIiIgMDitARERECqHiGJhkTICIiIgUgumPdBwCIyIiIoPDChAREZFC8D5A0rECRERERAaHFSAiIiKFYP1HOiZARERECsERMOk4BEZEREQGhxUgIiIiheB9gKRjAkRERKQQHNaRjp8VERERGRxWgIiIiBSCQ2DSMQEiIiJSCKY/0nEIjIiIiAwOK0BEREQKwSEw6VgBIiIiIoPDChAREZFCsKohHRMgIiIiheAQmHRMFomIiMjgsAJERESkEKz/SMcEiIiISCE4AiYdh8CIiIjI4MieAIWEhOCnn34q0P7TTz/hq6++kiEiIiKioskIKp1uSiZ7AvTdd9+hQoUKBdorVaqE0NBQGSIiIiIipZN9DlBSUhJcXFwKtJcsWRKJiYkyRERERFQ0cQ6QdLJXgNzc3HD06NEC7UePHoWrq6sMERERERVNKh3/o2SyV4AGDRqE0aNHIy8vD02aNAEA7N+/HxMmTMDYsWNljo6IiIiUSPYEaPz48bh//z6GDx+O3NxcAICZmRkmTpyIyZMnyxwdERFR0cEhMOlUgiAIcgcBAFlZWbh8+TLMzc1RtmxZqNXqNz7Xwzy9uCSiIq9E3dFyh0CkCI9iFr+X94m4eFen52teqaROz6dPZK8APWNpaYlatWrJHQYREREZAFkSoI4dOyIsLAzW1tbo2LHjK/tu3br1PUVFRERUtHEITDpZEiAbGxvxibXW1tZ8ei0REZEO8M+pdLIkQKtXrxZ/DgsLkyMEIiIiMmCy3weoSZMmSE9PL9CemZkpLosnIiKi1+N9gKSTPQGKiooSl78/7/Hjxzhy5IgMEREREZHSybYK7Ny5c+LPly5dQlJSkvg6Pz8fERER+OCDD+QIjYiIqEgyUnbRRqdkqwBVq1YN1atXh0qlQpMmTVCtWjVxq1GjBmbPno2pU6fKFR4REVGRI+cQ2OHDh9GmTRu4urpCpVJh+/btWvv79u0LlUqltTVv3lyrT2pqKnr06AFra2vY2tpiwIAByMrK0upz7tw5NGjQAGZmZnBzc8P8+fPf6LOSrQJ08+ZNCIKA0qVL4+TJkyhZ8n83WzI1NYWjoyOMjY3lCo+IiIgKITs7G1WrVkX//v1feoub5s2bay2E+u9Nj3v06IHExERERkYiLy8P/fr1w+DBg7F+/XoAT+cHBwQEwN/fH6GhoTh//jz69+8PW1tbDB48uFDxypYAubu7AwA0Go1cIRARESmKnMvgW7RogRYtWryyj1qthrOz8wv3Xb58GRERETh16hRq1qwJAFi6dClatmyJb775Bq6urli3bh1yc3Px008/wdTUFJUqVUJsbCwWLlxY6ARI9knQa9aswa5du8TXEyZMgK2tLT7++GPcunVLxsiIiIiKFl0PgeXk5CAzM1Nry8nJeeP4oqKi4OjoiPLly2PYsGG4f/++uC86Ohq2trZi8gMA/v7+MDIywokTJ8Q+DRs2hKmpqdgnMDAQcXFxSEtLK1QssidAc+fOhbm5OYCnF7Zs2TLMnz8fDg4OCA4Oljk6IiIiwxUSEgIbGxutLSQk5I3O1bx5c6xduxb79+/HV199hUOHDqFFixbIz88HACQlJcHR0VHrmGLFisHe3l5cKJWUlAQnJyetPs9eP7+YSgrZnwV2+/ZteHl5AQC2b9+Ozp07Y/DgwahXrx4aNWokb3BERERFiK5XgU2ePBljxozRanvTh5V37dpV/NnHxwdVqlRBmTJlEBUVhaZNm75VnG9C9gqQpaWlWAL7448/0KxZMwCAmZkZHj16JGdoRERERYquh8DUajWsra21tjdNgP6rdOnScHBwwLVr1wAAzs7OSElJ0erz5MkTpKamivOGnJ2dkZycrNXn2euXzS16GdkToGbNmmHgwIEYOHAg/v77b7Rs2RIAcPHiRXh4eMgbHBEREb0T//zzD+7fvw8XFxcAgK+vL9LT0xETEyP2OXDgADQaDerUqSP2OXz4MPLy8sQ+kZGRKF++POzs7Ar1/rIPgS1fvhxTpkzB7du3sWXLFpQoUQIAEBMTg27duskcHRVGy4AmSLxzp0B7l67dMXnKVNy7dxfffvM1jkcfQ/bDbHh4eGLA4CHwbxYo9h01Yhj+vnIFqan3YW1tgzp1fTFyzFg4OjoVOC+REgzqXA+DOteHu4s9AODyjUTMXbUXfxy7XKDv9iVDEFjPG13G/oCdUee19vVsUxsjezRG2VIlkZn9GFv3xSL4q80AgC8GN8eUIQVX52Q/yoFD/Qnv4KpILnKuAsvKyhKrOcDT293ExsbC3t4e9vb2mDFjBjp16gRnZ2dcv34dEyZMgJeXFwIDn/4NqFixIpo3b45BgwYhNDQUeXl5GDFiBLp27QpXV1cAQPfu3TFjxgwMGDAAEydOxIULF7B48WIsWrSo0PGqBEEQdHPp+uNhnuIuqUhITU2FRpMvvr529SqGDeqPVT+tQc3adTBsUH88ePAAk774Era2dtizOxyhy5di3cbNqFDRGwDwy9owVKlaDQ4lSyIlORmLvnl6g6s16zbIck2GrkTd0XKHoHgtG1RCvkbAtYS7UKmAnq1rI7h3E9Tt/jUu3/jfpM7PujdCkzrl0bx+wQRoZI9GGNWzMT5fvAMnL8TDwkwNd1d77Dp8AQBgYW4Ky+Lawxa7VwYh5lICBk9f/34u1MA9iln8Xt7nz6uFWwn1OvXLSq+qREVFoXHjxgXa+/Tpg5UrV6J9+/Y4c+YM0tPT4erqioCAAMyaNUtrUnNqaipGjBiBnTt3wsjICJ06dcKSJUtgaWkp9jl37hyCgoJw6tQpODg44LPPPsPEiRMLfW16kwA9fPgQCQkJBZ4LVqVKlcKfiwmQXvh63lwcORSF33fvhUqlwse1PsLnX05D67btxD6N6tXByOBx6Nj5kxeeI+rgAYwZGYQTp8/BxMTkfYVO/48JkDz+PTAXny/egTW/HwcAVCn3AbZ+Oxj1en2D+D9mayVAtlbmuB4xE51Gr0LUqb8lnd+nrCtObpgI/wGLcTT2xju7Dvqf95UAHdVxAlSvEAlQUSP7ENjdu3fRt29fREREvHD/s+VxVLTk5eVid/gO9Oz99NbnAFC1WjX8EbEbDfz8YGVljT8i9iAnNxc1a9d+4TkyMtKxJ3wnqlarzuSHDIKRkQqd/KvBwlyNE+duAgDMzUwQNqc3Rn+1Ccn3HxQ4pmnd8jBSqeDqaIMzmyfDqrgZjp+7iUmLtuOf5PQXvk+/9r74Oz6ZyY8CGck5BlbEyJ4AjR49GhkZGThx4gQaNWqEbdu2ITk5GbNnz8aCBQtee3xOTk6BmzLlG5nqbJY6vZmD+/fjwYMHaNO+g9g2f8G3mDguGI3q1UWxYsVgZmaGhd8uRalS7lrHLl74DTb8ug6PHz2CT9WqWLI89H2HT/ReVfJyQdTqYJiZFkPWoxx8Ou5HXLn5dGXL/DEdcPzcTYQfuvDCYz0/cICRkQoT+jfDuG+2IvPBI0wb3grhK4aj1qdfIe+J9v9Eqk2L4dMWNbAgbN87vy4ifSb7KrADBw5g4cKFqFmzJoyMjODu7o6ePXti/vz5km629KKbNH3z1ZvdpIl0Z/vWzahXv4HW5OXlyxbjwYMHCP1hNX7ZsBk9e/fFhHHBuPp3nNaxvfsNwIZNW7Hy+x9hbGSMLydPgp6M1BK9E3/Hp6BOt/lo2GchVm0+ilUzeqCCpxNaNayMRrXKYfw3W196rEqlgqlJMYz9egv2RV/ByQu30OfzNfByKwm/WmUL9G/XuAqsLMzwS/ipd3lJJBOVjjclk70ClJ2dLd750c7ODnfv3kW5cuXg4+OD06dPv/b4F92kKd/I9CW96X24c+dfnDgejW++XSq23U5IwMb167B5+06U8Xr6H+XyFSrg9OkYbPx1PaZMmyH2tbOzg52dHdw9POFZugya+zfCubOxqFqt+nu/FqL3Ie9JPm78cw8AcObKP6jhXQpB3fzwOCcPpT8sgaSoeVr9f53fH0fPXEfgkGVIupcJALjy3ITpe+nZuJeeDTfngvM3+rb3xZ4jF5GSWnA4jRRA6VmLDsmeAJUvXx5xcXHw8PBA1apV8d1338HDwwOhoaHivQFeRa1WFxju4iRoee3YthX29iXQoKGf2Pb48dObWqpU2kVHYyMjCMLLH4ir+f99ef+ZHE+kZEZGKqhNi2H2d3uwevtxrX0xv03ChIXbxBVe0WefzuMp6+6Ef1MyAAB21sXhYGuBhMRUrWPdXe3hV9MLncf88B6ugki/yZ4AjRo1ComJiQCAadOmoXnz5li3bh1MTU0RFhYmb3BUaBqNBr9v34bW7dqjWLH//Xp5eJaGWyl3zJ45DWPGTYCNjS0OHtiH49HHsPj/5/icP3cWFy+cR/WPasDK2hr/3L6NFUsXw82tFKqw+kMKNXNEa+w9ehm3k9JgZaHGp81roGENL7QZEYrk+w9eOPH5dlIabt15mtxcS7iLnVHn8M24jhgxZwMys3Mwc0RrxMUn49BfV7WO69OuLpLuZWLv0Uvv5dro/VOxBCSZ7AlQz549xZ9r1KiBW7du4cqVKyhVqhQcHBxkjIzexInoY0hKvIP2HTpqtZuYmGDpyu+wZNECjAoahoePHsLNrRRmzpknVorMzMxwYF8kQpcvxaNHj+BQsiQ+rtcAg4YM03ryL5GSlLSzwo8ze8DZwQYZWY9w4eodtBkRigMn4l5/8P8bMPUXzB/TEVsXD4FGI+DP09fQ7rNQPHnyv+qqSqVCr9a18fPOk9BoWCVXKi4Ck05v7gOkSxwCI9IN3geISDfe132ATt7I0On5ape20en59Insq8A6deqEr776qkD7/Pnz8cknL745HhERERXEVWDSyZ4AHT58WHwA6vNatGiBw4cPyxARERERKZ3sc4CysrJeOL/DxMQEmZmZMkRERERURCm9bKNDsleAfHx8sHHjxgLtGzZsgLe3twwRERERFU0qHf+jZLJXgL788kt07NgR169fR5MmTQAA+/fvx6+//opNmzbJHB0REREpkewJUJs2bbB9+3bMnTsXmzdvhrm5OapUqYJ9+/bBz8/v9ScgIiIiAFwGXxiyJkBPnjzB3Llz0b9/fxw9elTOUIiIiIo85j/SyToHqFixYpg/fz6ePHkiZxhERERkYGSfBN20aVMcOnRI7jCIiIiKPt4ISDLZ5wC1aNECkyZNwvnz51GjRg1YWFho7W/btq1MkREREZFSyf4oDCOjlxehVCoV8vPzC31OPgqDSDf4KAwi3Xhfj8I4c6vgw3PfRnV3K52eT5/IXgHSaDSv70RERESvxVVg0sk+B4iIiIjofZO9AgQA2dnZOHToEBISEpCbm6u1b+TIkTJFRUREVLSwACSd7AnQmTNn0LJlSzx8+BDZ2dmwt7fHvXv3ULx4cTg6OjIBIiIikooZkGSyD4EFBwejTZs2SEtLg7m5OY4fP45bt26hRo0a+Oabb+QOj4iIiBRI9gQoNjYWY8eOhZGREYyNjZGTkwM3NzfMnz8fn3/+udzhERERFRl8GKp0sidAJiYm4lJ4R0dHJCQkAABsbGxw+/ZtOUMjIiIqUlQq3W5KJvscoOrVq+PUqVMoW7Ys/Pz8MHXqVNy7dw8///wzKleuLHd4REREpECyV4Dmzp0LFxcXAMCcOXNgZ2eHYcOG4d69e/juu+9kjo6IiKjo4JMwpJO9AlSpUiU8uxm1o6MjQkNDsW3bNnh7e6NatWryBkdERESKJHsFqF27dli7di0AID09HXXr1sXChQvRvn17rFy5UuboiIiIihCWgCSTPQE6ffo0GjRoAADYvHkznJyccOvWLaxduxZLliyROToiIqKig6vApJM9AXr48CGsrJ4+bO2PP/5Ax44dYWRkhLp16+LWrVsyR0dERERKJHsC5OXlhe3bt+P27dvYu3cvAgICAAApKSmwtraWOToiIqKig8vgpZM9AZo6dSrGjRsHDw8P1KlTB76+vgCeVoOqV68uc3RERERFB6cASSf7KrDOnTujfv36SExMRNWqVcX2pk2bokOHDjJGRkREREolewIEAM7OznB2dtZqq127tkzREBERFVFKL9vokF4kQERERPT2lL5yS5dknwNERERE9L6xAkRERKQQSl+5pUusABEREZHBYQWIiIhIIVgAko4JEBERkVIwA5KMQ2BERERkcFgBIiIiUggug5eOCRAREZFCcBWYdBwCIyIiIoPDChAREZFCsAAkHStAREREZHBYASIiIlIKloAkYwJERESkEFwFJh2HwIiIiMjgsAJERESkEFwGLx0TICIiIoVg/iMdh8CIiIjI4LACREREpBQsAUnGBIiIiEghuApMOg6BERERkcFhBYiIiEghuApMOlaAiIiIyOCwAkRERKQQLABJxwSIiIhIITgEJh2HwIiIiMjgsAJERESkGCwBScUKEBERkUKoVLrdCuPw4cNo06YNXF1doVKpsH37dq39giBg6tSpcHFxgbm5Ofz9/XH16lWtPqmpqejRowesra1ha2uLAQMGICsrS6vPuXPn0KBBA5iZmcHNzQ3z589/k4+KCRARERG9vezsbFStWhXLly9/4f758+djyZIlCA0NxYkTJ2BhYYHAwEA8fvxY7NOjRw9cvHgRkZGRCA8Px+HDhzF48GBxf2ZmJgICAuDu7o6YmBh8/fXXmD59Or7//vtCx6sSBEEo/GXqt4d5irskIlmUqDta7hCIFOFRzOL38j530nN1ej5XW9M3Ok6lUmHbtm1o3749gKfVH1dXV4wdOxbjxo0DAGRkZMDJyQlhYWHo2rUrLl++DG9vb5w6dQo1a9YEAERERKBly5b4559/4OrqipUrV+KLL75AUlISTE2fxjZp0iRs374dV65cKVSMrAAREREphK6HwHJycpCZmam15eTkFDqumzdvIikpCf7+/mKbjY0N6tSpg+joaABAdHQ0bG1txeQHAPz9/WFkZIQTJ06IfRo2bCgmPwAQGBiIuLg4pKWlFSomJkBERET0QiEhIbCxsdHaQkJCCn2epKQkAICTk5NWu5OTk7gvKSkJjo6OWvuLFSsGe3t7rT4vOsfz7yEVV4EREREphK4fhjp58mSMGTNGq02tVuv0PeTCBIiIiIheSK1W6yThcXZ2BgAkJyfDxcVFbE9OTka1atXEPikpKVrHPXnyBKmpqeLxzs7OSE5O1urz7PWzPlJxCIyIiEgpVDredMTT0xPOzs7Yv3+/2JaZmYkTJ07A19cXAODr64v09HTExMSIfQ4cOACNRoM6deqIfQ4fPoy8vDyxT2RkJMqXLw87O7tCxcQEiIiISCHkzH+ysrIQGxuL2NhYAE8nPsfGxiIhIQEqlQqjR4/G7NmzsWPHDpw/fx69e/eGq6uruFKsYsWKaN68OQYNGoSTJ0/i6NGjGDFiBLp27QpXV1cAQPfu3WFqaooBAwbg4sWL2LhxIxYvXlxgmE4KDoERERHRW/vrr7/QuHFj8fWzpKRPnz4ICwvDhAkTkJ2djcGDByM9PR3169dHREQEzMzMxGPWrVuHESNGoGnTpjAyMkKnTp2wZMkScb+NjQ3++OMPBAUFoUaNGnBwcMDUqVO17hUkFe8DREQvxfsAEenG+7oPUMqDvNd3KgRHKxOdnk+fsAJERESkELpeBaZknANEREREBocVICIiIqVgAUgyJkBEREQKwfxHOg6BERERkcFhBYiIiEghVCwBScYKEBERERkcVoCIiIgUgsvgpWMCREREpBAcApOOQ2BERERkcJgAERERkcHhEBgREZFCcAhMOlaAiIiIyOCwAkRERKQQXAUmHStAREREZHBYASIiIlIIzgGSjgkQERGRQjD/kY5DYERERGRwWAEiIiJSCpaAJGMCREREpBBcBSYdh8CIiIjI4LACREREpBBcBSYdEyAiIiKFYP4jHYfAiIiIyOCwAkRERKQULAFJxgoQERERGRxWgIiIiBSCy+ClYwJERESkEFwFJh2HwIiIiMjgqARBEOQOggxPTk4OQkJCMHnyZKjVarnDISqS+D0ienNMgEgWmZmZsLGxQUZGBqytreUOh6hI4veI6M1xCIyIiIgMDhMgIiIiMjhMgIiIiMjgMAEiWajVakybNo0TN4neAr9HRG+Ok6CJiIjI4LACRERERAaHCRAREREZHCZAREREZHCYABEBaNSoEUaPHi13GESy69u3L9q3by93GETvHCdBk0GJiopC48aNkZaWBltbW7E9NTUVJiYmsLKyki84ovcoPj4enp6eOHPmDKpVqya2Z2RkQBAEre8HkRLxafCkV/Ly8mBiYvLe39fe3v69vyfRq8j1XbCxsXnv70kkBw6BKUSjRo0wcuRITJgwAfb29nB2dsb06dPF/QkJCWjXrh0sLS1hbW2NLl26IDk5Wdw/ffp0VKtWDT///DM8PDxgY2ODrl274sGDB6983xUrVqBs2bIwMzODk5MTOnfuLO6LiIhA/fr1YWtrixIlSqB169a4fv26uD8+Ph4qlQobN26En58fzMzMsG7dOgDATz/9hEqVKkGtVsPFxQUjRowQj1u4cCF8fHxgYWEBNzc3DB8+HFlZWeL+W7duoU2bNrCzs4OFhQUqVaqE3bt3Iz4+Ho0bNwYA2NnZQaVSoW/fvuLn9/wQWE5ODiZOnAg3Nzeo1Wp4eXnhxx9/lP4vhAzS5s2b4ePjA3Nzc5QoUQL+/v7Izs7GqVOn0KxZMzg4OMDGxgZ+fn44ffq01rEqlQorV65E27ZtYWFhgTlz5gAAdu7ciVq1asHMzAwODg7o0KGDeMzPP/+MmjVrwsrKCs7OzujevTtSUlLE/WlpaejRowdKliwJc3NzlC1bFqtXrwYAeHp6AgCqV68OlUqFRo0aASg4BKbRaDB//nx4eXlBrVajVKlSYmxERRkTIAVZs2YNLCwscOLECcyfPx8zZ85EZGQkNBoN2rVrh9TUVBw6dAiRkZG4ceMGPv30U63jr1+/ju3btyM8PBzh4eE4dOgQ5s2b99L3++uvvzBy5EjMnDkTcXFxiIiIQMOGDcX92dnZGDNmDP766y/s378fRkZG6NChAzQajdZ5Jk2ahFGjRuHy5csIDAzEypUrERQUhMGDB+P8+fPYsWMHvLy8xP5GRkZYsmQJLl68iDVr1uDAgQOYMGGCuD8oKAg5OTk4fPgwzp8/j6+++gqWlpZwc3PDli1bAABxcXFITEzE4sWLX3htvXv3xq+//oolS5bg8uXL+O6772BpaSn9XwYZnMTERHTr1g39+/fH5cuXERUVhY4dO0IQBDx48AB9+vTBn3/+iePHj6Ns2bJo2bJlgf/BmD59Ojp06IDz58+jf//+2LVrFzp06ICWLVvizJkz2L9/P2rXri32z8vLw6xZs3D27Fls374d8fHxYlIPAF9++SUuXbqEPXv24PLly1i5ciUcHBwAACdPngQA7Nu3D4mJidi6desLr2vy5MmYN2+eeK7169fDyclJx58ekQwEUgQ/Pz+hfv36Wm21atUSJk6cKPzxxx+CsbGxkJCQIO67ePGiAEA4efKkIAiCMG3aNKF48eJCZmam2Gf8+PFCnTp1XvqeW7ZsEaytrbWOeZW7d+8KAITz588LgiAIN2/eFAAI3377rVY/V1dX4YsvvpB0TkEQhE2bNgklSpQQX/v4+AjTp09/Yd+DBw8KAIS0tDStdj8/P2HUqFGCIAhCXFycAECIjIyUHANRTEyMAECIj49/bd/8/HzByspK2Llzp9gGQBg9erRWP19fX6FHjx6SYzh16pQAQHjw4IEgCILQpk0boV+/fi/s++z7d+bMGa32Pn36CO3atRMEQRAyMzMFtVotrFq1SnIMREUFK0AKUqVKFa3XLi4uSElJweXLl+Hm5gY3Nzdxn7e3N2xtbXH58mWxzcPDQ2sS8LPjAWDdunWwtLQUtyNHjqBZs2Zwd3dH6dKl0atXL6xbtw4PHz4Uj7969Sq6deuG0qVLw9raGh4eHgCeDsc9r2bNmuLPKSkpuHPnDpo2bfrS69y3bx+aNm2KDz74AFZWVujVqxfu378vvvfIkSMxe/Zs1KtXD9OmTcO5c+ekfoQAgNjYWBgbG8PPz69Qx5Fhq1q1Kpo2bQofHx988sknWLVqFdLS0gAAycnJGDRoEMqWLQsbGxtYW1sjKyvrld8F4Onv4qu+CzExMWjTpg1KlSoFKysr8Xf22XmHDRuGDRs2oFq1apgwYQKOHTtWqGu6fPkycnJyXhkDUVHFBEhB/jthUqVSFRhuetPj27Zti9jYWHF7Nu/g9OnT+PXXX+Hi4oKpU6eiatWqSE9PBwC0adMGqampWLVqFU6cOIETJ04AAHJzc7Xex8LCQvzZ3Nz8lTHGx8ejdevWqFKlCrZs2YKYmBgsX75c67wDBw7EjRs30KtXL5w/fx41a9bE0qVLJX8Or4uB6EWMjY0RGRmJPXv2wNvbG0uXLkX58uVx8+ZN9OnTB7GxsVi8eDGOHTuG2NhYlChR4pXfBeDVv4vZ2dkIDAyEtbU11q1bh1OnTmHbtm0A/vddaNGiBW7duoXg4GDxfyzGjRsn+Zr4XSAlYwJkACpWrIjbt2/j9u3bYtulS5eQnp4Ob29vSeewsrKCl5eXuD37D2OxYsXg7++P+fPn49y5c4iPj8eBAwdw//59xMXFYcqUKWjatCkqVqwo/t/w697Hw8MD+/fvf+H+mJgYaDQaLFiwAHXr1kW5cuVw586dAv3c3NwwdOhQbN26FWPHjsWqVasAAKampgCA/Pz8l8bg4+MDjUaDQ4cOvTZeouepVCrUq1cPM2bMwJkzZ2Bqaopt27bh6NGjGDlyJFq2bClO7r93795rz1elSpWXfheuXLmC+/fvY968eWjQoAEqVKigNQH6mZIlS6JPnz745Zdf8O233+L7778HIO27ULZsWZibm780BqKijMvgDYC/vz98fHzQo0cPfPvtt3jy5AmGDx8OPz+/AiX3wggPD8eNGzfQsGFD2NnZYffu3dBoNChfvjzs7OxQokQJfP/993BxcUFCQgImTZok6bzTp0/H0KFD4ejoiBYtWuDBgwc4evQoPvvsM3h5eSEvLw9Lly5FmzZtcPToUYSGhmodP3r0aLRo0QLlypVDWloaDh48iIoVKwIA3N3doVKpEB4ejpYtW8Lc3LzA5GYPDw/06dMH/fv3x5IlS1C1alXcunULKSkp6NKlyxt/XqRsJ06cwP79+xEQEABHR0ecOHECd+/eRcWKFVG2bFlxxVZmZibGjx8vqboybdo0NG3aFGXKlEHXrl3x5MkT7N69GxMnTkSpUqVgamqKpUuXYujQobhw4QJmzZqldfzUqVNRo0YNVKpUCTk5OQgPDxe/C46OjjA3N0dERAQ+/PBDmJmZFVgCb2ZmhokTJ2LChAkwNTVFvXr1cPfuXVy8eBEDBgzQ3YdHJAe5JyGRbjw/ifeZdu3aCX369BEEQRBu3boltG3bVrCwsBCsrKyETz75REhKShL7Tps2TahatarW8YsWLRLc3d1f+p5HjhwR/Pz8BDs7O8Hc3FyoUqWKsHHjRnF/ZGSkULFiRUGtVgtVqlQRoqKiBADCtm3bBEF4+SRMQRCE0NBQoXz58oKJiYng4uIifPbZZ+K+hQsXCi4uLoK5ubkQGBgorF27Vmti84gRI4QyZcoIarVaKFmypNCrVy/h3r174vEzZ84UnJ2dBZVKJX4+//38Hj16JAQHBwsuLi6Cqamp4OXlJfz0008v/SyILl26JAQGBgolS5YU1Gq1UK5cOWHp0qWCIAjC6dOnhZo1awpmZmZC2bJlhU2bNgnu7u7CokWLxOOf/248b8uWLUK1atUEU1NTwcHBQejYsaO4b/369YKHh4egVqsFX19fYceOHVrfqVmzZgkVK1YUzM3NBXt7e6Fdu3bCjRs3xONXrVoluLm5CUZGRoKfn58gCNqToAXh6YTt2bNnC+7u7oKJiYlQqlQpYe7cuTr73IjkwjtBExERkcHhHCAiIiIyOEyAiIiIyOAwASIiIiKDwwSIiIiIDA4TICIiIjI4TICIiIjI4DABIiIiIoPDBIiIiIgMDhMgIgIA9O3bF+3btxdfN2rUCKNHj37vcURFRUGlUokP1SUieheYABHpub59+0KlUkGlUsHU1BReXl6YOXMmnjx58k7fd+vWrQWeLfUyTFqIqKjhw1CJioDmzZtj9erVyMnJwe7duxEUFAQTExNMnjxZq19ubq74lO+3ZW9vr5PzEBHpI1aAiIoAtVoNZ2dnuLu7Y9iwYfD398eOHTvEYas5c+bA1dUV5cuXBwDcvn0bXbp0ga2tLezt7dGuXTvEx8eL58vPz8eYMWNga2uLEiVKYMKECfjvYwH/OwSWk5ODiRMnws3NDWq1Gl5eXvjxxx8RHx+Pxo0bAwDs7OygUqnQt29fAIBGo0FISAg8PT1hbm6OqlWrYvPmzVrvs3v3bpQrVw7m5uZo3LixVpxERO8KEyCiIsjc3By5ubkAgP379yMuLg6RkZEIDw9HXl4eAgMDYWVlhSNHjuDo0aOwtLRE8+bNxWMWLFiAsLAw/PTTT/jzzz+RmpqKbdu2vfI9e/fujV9//RVLlizB5cuX8d1338HS0hJubm7YsmULACAuLg6JiYlYvHgxACAkJARr165FaGgoLl68iODgYPTs2ROHDh0C8DRR69ixI9q0aYPY2FgMHDgQkyZNelcfGxHR/8j8NHoieo0+ffoI7dq1EwRBEDQajRAZGSmo1Wph3LhxQp8+fQQnJychJydH7P/zzz8L5cuXFzQajdiWk5MjmJubC3v37hUEQRBcXFyE+fPni/vz8vKEDz/8UHwfQRAEPz8/YdSoUYIgCEJcXJwAQIiMjHxhjAcPHhQACGlpaWLb48ePheLFiwvHjh3T6jtgwAChW7dugiAIwuTJkwVvb2+t/RMnTixwLiIiXeMcIKIiIDw8HJaWlsjLy4NGo0H37t0xffp0BAUFwcfHR2vez9mzZ3Ht2jVYWVlpnePx48e4fv06MjIykJiYiDp16oj7ihUrhpo1axYYBnsmNjYWxsbG8PPzkxzztWvX8PDhQzRr1kyrPTc3F9WrVwcAXL58WSsOAPD19ZX8HkREb4oJEFER0LhxY6xcuRKmpqZwdXVFsWL/++paWFho9c3KykKNGjWwbt26AucpWbLkG72/ubl5oY/JysoCAOzatQsffPCB1j61Wv1GcRAR6QoTIKIiwMLCAl5eXpL6fvTRR9i4cSMcHR1hbW39wj4uLi44ceIEGjZsCAB48uQJYmJi8NFHH72wv4+PDzQaDQ4dOgR/f/8C+59VoPLz88U2b29vqNVqJCQkvLRyVLFiRezYsUOr7fjx46+/SCKit8RJ0EQK06NHDzg4OKBdu3Y4cuQIbt68iaioKIwcORL//PMPAGDUqFGYN28etm/fjitXrmD48OGvvIePh4cH+vTpg/79+2P79u3iOX/77TcAgLu7O1QqFcLDw3H37l1kZWXBysoK48aNQ3BwMNasWYPr16/j9OnTWLp0KdasWQMAGDp0KK5evYrx48cjLi4O69evR1hY2Lv+iIiImAARKU3x4sVx+PBhlCpVCh07dkTFihUxYMAAPH78WKwIjR07Fr169UKfPn3g6+sLKysrdOjQ4ZXnXblyJTp37ozhw4ejQoUKGDRoELKzswEAH3zwAWbMmIFJkybByckJI0aMAADMmjULX375JUJCQlCxYkU0b94cu3btgqenJwCgVKlS2LJlC7Zv346qVasiNDQUc+fOfYefDhHRUyrhZbMeiYiIiBSKFSAiIiIyOEyAiIiIyOAwASIiIiKDwwSIiIiIDA4TICIiIjI4TICIiIjI4DABIiIiIoPDBIiIiIgMDhMgIiIiMjhMgIiIiMjgMAEiIiIig/N/zGD+CWwtPJQAAAAASUVORK5CYII=\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Saved: outputs/classical/tfidf_lr/confusion_matrix_binary_test.png\n" ] @@ -1700,8 +1338,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Saved: outputs/classical/tfidf_lr/predictions_val_binary.csv\n", "Saved: outputs/classical/tfidf_lr/predictions_test_binary.csv\n" @@ -1726,8 +1364,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Top 20 sarcastic indicators (positive coef):\n", " because +22.1982\n", @@ -1809,18 +1447,18 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": 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+ ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Saved: outputs/classical/tfidf_lr/feature_importance_binary.png\n" ] @@ -1868,8 +1506,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n", "Train+Val: 24,083 Train: 19,833 Val: 4,250 Test: 4,250\n" @@ -1917,8 +1555,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Running grid search for type task...\n", "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n", @@ -1971,8 +1609,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Saved: outputs/classical/tfidf_lr/best_config_type.json\n" ] @@ -2000,8 +1638,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "\n", "[Val] Accuracy=0.8967 Macro-F1=0.8982 Weighted-F1=0.8961\n", @@ -2066,35 +1704,35 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": 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XV1fcunVLcFkJInngHCgiolJow4YNKF++vOBkAkUZM2YMkpKSsHv37h96v/Pnz0fnzp2ZPFGR4BAeEVEpsnv3bty9exfHjx/H8uXLi8XEfx0dnQJdL0refnTCRv8tTKCIiEqR7t27Q0dHB/3798ewYcMUHQ5RqcU5UEREREQy4hwoIiIiIhkxgSIiIiKSERMoIiIiIhkxgSIiIiKSEc/CoxLHbvRxRYdQJO4vbKnoEIpEMTiLvsikZ+YoOoQioaJcOp805VL8YtRSk++xadaQ308Opd4u+ou1KgITKCIiIhIScYBKGj5CRERERDJiBYqIiIiESvFwp7wwgSIiIiIhDuFJxUeIiIiISEasQBEREZEQh/CkYgJFREREQhzCk4qPEBEREZGMWIEiIiIiIQ7hScUEioiIiIQ4hCcVHyEiIiIiGbECRUREREIcwpOKCRQREREJcQhPKj5CRERERDJiBYqIiIiEOIQnFRMoIiIiEuIQnlR8hIiIiIhkxAoUERERCXEITyomUERERCTEITyp+AgRERERyYgVKCIiIhJiBUoqJlBEREQkpMQ5UNIwxSQiIiKSEStQREREJMQhPKn4CBEaN26M0aNHKzoMIiIqLkQi+S2lFCtQhAMHDkBVVVXRYfwQSiJgtLcT2ruVgamuOqIS0rD/+husPP0MAKCiJMK4VhXQuJIprI21kJiWhctP3mPB0X8RnZAOAChjpIkRzR1Rz9FYso9DN95ideAzZGaLFXl4AjdvhGDblj/w6OEDvI+JQcCyVWjSzFOyPiUlGSuWBuDcP0GIj4+DVZmy6O7TE527dlNg1NLdvBGCbZv/wMP/P64ly4XHFRR4Gvv27Majhw8QHx+P3fsOokLFSgqMuHC2bdqANSuX4ucePTFmwmS8e/cWHVv9lG/fuQuXoNlP3j84woLZvHE9zgYFIuzlC6ira6Bq9RoYMXocbO3sJH3evA7HsoCFCL19C5kZGXD3aIAJk6fA2NhEgZFL9+k9JnktfvEemz5lEo4eOSTYpp5Hfaxet/EHR0pFgRUogpGREXR1dfNdl5GR8YOjKVpDmtnDx8MGM/Y/gOf881hw9F8MamqPPg1tAQCaasqoUlYPq04/Q5uASxiy6SbKm2ljwwA3yT7szXSgJAKm7LmH5gvOY87Bh/DxsMGEVhUVdFT5S0tNhZNTRUyaMj3f9QEL5+PK5UuYM38h9h8+jh6/9MKCebNx/uw/PzhS2aSmpsKpQkVM/spxpaamorprTYwcM/4HRyY/Dx/cw8H9e+DgWEHSZm5ugeOB5wXLwCG+0NLSgrtHAwVG+223boSgS7ce2LxjN1av/wNZWZnwHdIfqSkpAIDUlBQMHzwAIpEI6zZswR9bdyEzMxNjRgxDTk6OgqP/ttT/f4997bUIAPU8GiDw7EXJ4r8g4AdG+B1ESvJbZHDhwgW0adMGVlZWEIlEOHTokGC9WCzG9OnTYWlpCU1NTXh6euLp06eCPrGxsfDx8YGenh4MDAzQv39/JCUlCfrcvXsXDRo0gIaGBsqVK4eFCxfK/BAxgSLBEJ6trS1mz56NXr16QU9PD4MGDQIA7N+/H5UrV4a6ujpsbW0RECD8ELC1tcW8efPQr18/6OrqwtraGuvXr5esb9q0KXx9fQXbxMTEQE1NDUFBQUV7gJ9xtTNE4P0onH0YjbexqTh5JxIXH8egmrUBACAxLQs9117H8dAIvIhORuirOMzY9wBVrQ1gZaABALjwbwx+/fMuLj5+j9cfUnHmQTQ2/PMCXlUtfthxFIRHg4YYPnI0mjbLv2px904o2rRtD7dadWBVpiw6dfkZjk4VcP/e3R8cqWzqfzouz/yPq3Xbdhg8dDjqurv/4MjkIyUlGTN++xWTp/lBV09P0q6srAxjE1PBcv7sGTT7yRtaWtoKjPjbVq7bgDbtOsDewRFOFSpi5mx/REZE4NHDBwCAO6G3EfHuLWbM9oeDkxMcnJzgN8cfjx7cR8j1awqO/tvqS3mPAYCamhpMTEwli56+/g+M8DsoaAgvOTkZ1apVw+rVq/Ndv3DhQqxYsQLr1q1DcHAwtLW14eXlhbS0NEkfHx8fPHjwAIGBgTh27BguXLgg+S4DgISEBDRv3hw2Nja4efMmFi1ahJkzZwq+swqCCRTlsXjxYlSrVg23b9/GtGnTcPPmTXTt2hXdunXDvXv3MHPmTEybNg1btmwRbBcQEAA3Nzfcvn0bw4YNw9ChQ/H48WMAwIABA7Br1y6kp6dL+u/YsQNlypRB06ZNf9ix3Xr5ER5OxrAzzf3CqWSli1rljXDuUfRXt9HVVEFOjhgJqVnf7BOXUrKqdVWrVcf5c/8gOioKYrEYIdevIfxVGOrW81B0aP9pi/3nwKNBI9SuW++b/f59+ABPHv+LNu07/aDI5CMpKREAJIlERkYGRCIR1NTUJH3U1NWhpKSE0Fu3FBKjPN24cR1NG9VD+zbemDt7JuLiPio6pGKtRYsWmDNnDjp06JBnnVgsxrJlyzB16lS0a9cOVatWxbZt2/Du3TtJperRo0c4deoUNm7ciDp16qB+/fpYuXIldu/ejXfv3gEAdu7ciYyMDGzatAmVK1dGt27dMHLkSCxZskSmWJlAUR5NmzbFuHHjYG9vD3t7eyxZsgTNmjXDtGnT4OTkhD59+sDX1xeLFi0SbNeyZUsMGzYMDg4OmDhxIkxMTHD27FkAQMeOHQEAhw8flvTfsmUL+vTpA9EPnGS4Nug5jt56hzOTG+FJQAscG98Am86/xOGb7/Ltr6aihIltKuHIrXdISs8/gbIx0UKvBrb480p4UYYudxN/m4by9vbw9myEOq4u8B0yEJOmTEdNt1qKDu0/K/DUCTz+9yGGjhgjte+RQ/tha1ceVavX+AGRyUdOTg4CFvqjWg1XODg6AQBcqlaDhqYmVi5djLTUVKSmpGBZwEJkZ2fj/fsYBUf8ferVb4DZcxfg9w2bMWr0eNy8EQLfoYOQnZ2t6NCkk+MQXnp6OhISEgTL539MF9TLly8RGRkJT8//zTPT19dHnTp1cPXqVQDA1atXYWBgADe3/0278PT0hJKSEoKDgyV9GjZsKEjavby88PjxY3z8WPAElwkU5fH5Cw/Izeg9PIRVCQ8PDzx9+lTwQVC1alXJ/0UiESwsLBAdnVvZ0dDQQM+ePbFp0yYAwK1bt3D//n306dPnm7Hk98YTZ2UW+thaVbdEu5plMGr7bbRZfAnjd93BwCbl0bFWmTx9VZREWN3HFSIA0/bez3d/5vrq2DK4Nk6GRmD3tdeFjksRdu/ajnt372DpyjXYsXs/xoyfiPlzZyH46hVFh/afFBUZgSWL/DFz7kKoq6t/s29aWhpOnzxe4qpPC+bOwvNnTzHvs3lAhkZGWLB4GS6cP4cGdWuisUdtJCYmoGIlZyiV8DO4vFu0QuMmTeHoVAFNmnlixap1eHD/Hm6EXFd0aNLJcQjP398f+vr6gsXf31/mkCIjIwEA5ubmgnZzc3PJusjISJiZmQnWq6iowMjISNAnv318fh8FwbPwKA9t7cLNp/jyTD6RSCSYBDpgwABUr14db968webNm9G0aVPY2Nh8c5/+/v7w8/MTtOnX6Q7Duj6FinFy20pYF/Qcx25HAAAeRySijKEmhnk64EDIW0k/FSURVvVxRRlDTfRYfS3f6pOZnjr+HF4Xt8I+YvKee4WKR1HS0tKwavkyBCxfiQYNGwMAnCpUwJPH/2Lb1k2o4/7t4SOSv38fPcDH2A/o06OzpC07Oxuht25g31+7cCE4FMrKygCAs2dOIy0tFS1bt1NUuDJbMG82Ll04j/Wbt8PcQjhfsG49Dxw+cRpxHz9CWVkZunp68GrSAGXKllNQtEWjbLlyMDA0xOvwV6hTt2TO0SuMyZMnY+zYsYI2aX8klARMoEiqSpUq4fLly4K2y5cvw8nJSfKBXhAuLi5wc3PDhg0bsGvXLqxatUrqNvm98ar+VvizxDTVlJEjFl5qIFssFvxqwafkydZUGz1WXUNcSt6Kl7l+bvJ07008Juy6A3HxuXpBgWRlZSErKxNKX5who6SkBHExP/OptHKr7Y6dew8L2ubMmAIbOzv07DNA8F47cmg/GjRqCkMjox8dpszEYjEW+s/BuX/O4Pc/tqJM2bJf7WtgaAgACAm+htjYD2jY+MfNj/wRoiIjER8XBxNTM+mdFU2OF9JUV1eXS8Jk8f+Jd1RUFCwtLSXtUVFRqF69uqTPp5GPT7KyshAbGyvZ3sLCAlFRUYI+n25bWBT8ZCAmUCTVuHHjUKtWLcyePRs///wzrl69ilWrVmHNmjUy72vAgAHw9fWFtrZ2vpMEv5TfG0+kUvhrVgU9iMLwnxzw7mMankQmonIZPfRvbIe9wW8A5CZPa/q6onJZfQzYEAIlJRFMdHPvPz4lA5nZ4tzkydcdb2NTMe/wIxjp/C++94myj+sXlZSUZLwO/9+8rLdv3+Dxv4+gp68PS0sr1HSrhWVLFkFdQx2WlmVw88Z1HD96GGMnTFJg1NJJO674+DhERkRIPkTDXr4EABibmMDExFQhMReEtrY27B0cBW0amprQ1zcQtL8Of4XQWzewZOW6Hx1ioSyYOwunTh5HwPJV0NLWlsxr0tHRhYZG7pmtRw4dgJ1deRgaGeHunVAELJiHHj17C64VVRx967Wor6+P39euRjPP5jAxMcHr16+xfMkilLO2Rj2P+gqMuoCK4fCpnZ0dLCwsEBQUJEmYEhISEBwcjKFDhwIA3N3dERcXh5s3b6JmzZoAgH/++Qc5OTmoU6eOpM+UKVOQmZkpGTkJDAxEhQoVYPj/SXxBMIEiqVxdXbFnzx5Mnz4ds2fPhqWlJWbNmiV1/lJ+unfvjtGjR6N79+6SD88faeb+BxjbsgJmd64MY53ci2D+eSUcK/7OvY6IuYEGfnLJ/QvkxK8NBdt2W3UVwc9iUb+CKexMtWFnqo1rfp6CPnajj/+YAymAhw/uY1C/3pLbSxbNBwC0adsefnPnw3/REqxctgRTJk1AQnw8LC2tMHzE6GJ/Ic2H9+9j4GfHFbDw/4+rXXvMmjsf58/+gxlTf5OsnzQht4I5eOhwDBk+4scGWwSOHT4AM3Nz1HEvGWdL7tuzGwAw+LPnDABmzJ6HNu1y/4h6FfYSq5cvRXx8PKzKWKHvwCHw6dk7z76Km4cPvngtfvYe+23aTDx98hhHjxxCYkIiTM1M4e7ugWG+owSTl0koKSkJz549k9x++fIlQkNDYWRkBGtra4wePRpz5syBo6Mj7OzsMG3aNFhZWaF9+/YAckdMvL29MXDgQKxbtw6ZmZnw9fVFt27dYGVlBQDo0aMH/Pz80L9/f0ycOBH379/H8uXLsXTpUpliFYnFJW3wgUqysLAw2NvbIyQkBK6uroXaR3FKUuTp/sKWig6hSBTDP2TlJj2zdA53qiiXzidNuRS/GLXU5Htsmi2Xy21fqSdGFbjvuXPn0KRJkzztvXv3xpYtWyAWizFjxgysX78ecXFxqF+/PtasWQMnJydJ39jYWPj6+uLo0aNQUlJCp06dsGLFCujo6Ej63L17F8OHD0dISAhMTEwwYsQITJw4UabjYgJFP0RmZiY+fPiA8ePH4+XLl3nmVMmCCVTJUoq/s5hAlTBMoApOs9UKue0r9fhIue2rOOFlDOiHuHz5MiwtLRESEoJ160rG3A0iIqKv4Rwo+iEaN24MFjuJiEoIOZ6FV1oxgSIiIiIhJlBS8REiIiIikhErUERERCRUiifcywsTKCIiIhLiEJ5UfISIiIiIZMQKFBEREQlxCE8qJlBEREQkxCE8qfgIEREREcmIFSgiIiIS4hCeVEygiIiISEDEBEoqDuERERERyYgVKCIiIhJgBUo6JlBEREQkxPxJKg7hEREREcmIFSgiIiIS4BCedEygiIiISIAJlHQcwiMiIiKSEStQREREJMAKlHRMoIiIiEiACZR0HMIjIiIikhErUERERCTEApRUTKCIiIhIgEN40nEIj4iIiEhGrEARERGRACtQ0jGBohLn0eJWig6hSAzdd0/RIRSJtZ1dFB1CkdFUU1Z0CEVCLFZ0BEWDOUHBMYGSjkN4RERERDJiBYqIiIgEWIGSjgkUERERCTF/kopDeEREREQyYgWKiIiIBDiEJx0TKCIiIhJgAiUdh/CIiIiIZMQKFBEREQmwAiUdEygiIiISYv4kFYfwiIiIiGTEChQREREJcAhPOiZQREREJMAESjoO4RERERHJiBUoIiIiEmAFSjomUERERCTABEo6DuERERERyYgVKCIiIhJiAUoqJlBEREQkwCE86TiER0RERCQjVqCIiIhIgBUo6ZhAERERkQATKOk4hEdEREQkI1agiIiISIgFKKmYQBEREZEAh/Ck4xAeERERkYyYQBUjffr0Qfv27WXebubMmahevbrc4ylKjRs3xujRoxUdhlR/bFiPapUrYKH/XEWH8k3tqphhczcXwTKvpaNkvZ6GCgbWLYtl7SpiXefKmNncATXL6gn20drZFFM8y2Nd58pY3dH5Rx/Cd9u9ayda/NQUtWq4wKdbF9y7e1fRIclVSXktFkR2djZWr1yGll5NUadmVbT29sT6dashFosVHdp32bN7Fzp3aIN6tV1Rr7Yrevb4GZcunld0WIUiEonktpRWHML7QTIyMqCmpqboMEgG9+/dxb69u+HkVEHRoRTIm7g0LDr3UnI7J+d/X0YD65aFlqoyll98haT0LNS1McCwetbwO/0M4XFpAAAVJRFCwuPx7H0KGpY3+uHxf49TJ09g8UJ/TJ3hBxeXati5fSuGDu6Pw8dOwdjYWNHhfbeS9lqUZvMfG7D3rz8xa+4C2Ds44OGD+5gxdTJ0dHTR45deig6v0MzMLTBqzHhY29hALBbj6OFDGOU7HH/tPwgHB0fpOyhGSnPiIy//2QpUeno6Ro4cCTMzM2hoaKB+/foICQlBTk4OypYti7Vr1wr63759G0pKSnj16hUAIC4uDgMGDICpqSn09PTQtGlT3LlzR9L/U1Vo48aNsLOzg4aGBgBg3759cHFxgaamJoyNjeHp6Ynk5GTMnDkTW7duxeHDhyVZ+7lz5wAAEydOhJOTE7S0tFC+fHlMmzYNmZmZAIAtW7bAz88Pd+7ckWy3ZcsWmWLctGkTrK2toaOjg2HDhiE7OxsLFy6EhYUFzMzMMHeu8C/egu53+/btsLW1hb6+Prp164bExEQAuZW28+fPY/ny5ZKYw8LCvv9JlaOU5GRMnjgBM/zmQE9fX9HhFEiOWIyEtCzJkpSRLVnnYKyFM08/4GVsKmKSM3H0YQxSMrNha6Qp6XPofjROP/mAN/Fpigj/u2zfuhkdO3dF+w6dYO/ggKkz/KChoYFDB/YrOrTvVhJfi9LcCb2Nxk2aoWGjxihTpix+au4N93r1cf9eya4aNm7SFA0aNoKNjS1sbe0wYtQYaGlp4e6dUEWHRkXgP5tA/frrr9i/fz+2bt2KW7duwcHBAV5eXoiLi0P37t2xa9cuQf+dO3fCw8MDNjY2AIAuXbogOjoaJ0+exM2bN+Hq6opmzZohNjZWss2zZ8+wf/9+HDhwAKGhoYiIiED37t3Rr18/PHr0COfOnUPHjh0hFosxfvx4dO3aFd7e3oiIiEBERATq1asHANDV1cWWLVvw8OFDLF++HBs2bMDSpUsBAD///DPGjRuHypUrS7b7+eefCxzj8+fPcfLkSZw6dQp//vkn/vjjD7Rq1Qpv3rzB+fPnsWDBAkydOhXBwcGSbQq630OHDuHYsWM4duwYzp8/j/nz5wMAli9fDnd3dwwcOFASc7ly5eT59H63eXNmoWHDRqjrXk/RoRSYua46lrSriAWtK2BQ3XIw0lKVrHv2IQW1y+lDW00ZIgC1rfWhqqyEf6OTFRewnGRmZODRwweC50pJSQl169bD3Tu3FRiZfJTE16I01arXQHDwNbwKy62YPv73X9y+dRMeDRoqODL5yc7OxskTx5GamoJq1WooOhyZcQhPuv/kEF5ycjLWrl2LLVu2oEWLFgCADRs2IDAwEH/88Qd8fHwQEBCA8PBwWFtbIycnB7t378bUqVMBAJcuXcL169cRHR0NdXV1AMDixYtx6NAh7Nu3D4MGDQKQO2y3bds2mJqaAgBu3bqFrKwsdOzYUZKIubi4SOLS1NREeno6LCwsBPF+ul8AsLW1xfjx47F79278+uuv0NTUhI6ODlRUVATbFTTGnJwcbNq0Cbq6unB2dkaTJk3w+PFjnDhxAkpKSqhQoQIWLFiAs2fPok6dOjLtd8uWLdDV1QUA9OzZE0FBQZg7dy709fWhpqYGLS2tPMdaHJw8cRyPHj3Err/2KTqUAnvxIQUbg18jMiEDBpoqaFfFDJOblce0k0+RlpWDNZfDMayeNVZ1dEZWjhgZWTlYeekVopMyFB36d/sY9xHZ2dl5huqMjY3x8uULBUUlHyXxtVgQ/QYMQnJyEtq3aQFlZWVkZ2fDd+QYtGrdVtGhfbenTx6jZ49uyMhIh5aWFpauWA17BwdFhyW70pv3yM1/MoF6/vw5MjMz4eHhIWlTVVVF7dq18ejRI0yYMAGVKlXCrl27MGnSJJw/fx7R0dHo0qULAODOnTtISkrK84GdmpqK58+fS27b2NhIkicAqFatGpo1awYXFxd4eXmhefPm6Ny5MwwNDb8Z719//YUVK1bg+fPnSEpKQlZWFvT09L65TUFjtLW1lSQ5AGBubg5lZWUoKSkJ2qKjo79rv5aWlpJ9yCI9PR3p6emCNrGyuiR5k7fIiAgsnD8Xv2/YVGT3URTuRSRJ/v8mHnj+IQWL21RELWt9XHzxER1dzKGppoyFZ18gKT0brmX0MKyeNfyDnuNNfPo39kyKUlJfiwVx+tRJnDh2FP4LAmDv4IDH/z7CogX+MDUzQ9t2HRQd3nextbXDnv2HkJSUiMDTf2PabxPxx5YdJTOJom/6TyZQBeHj4yNJoHbt2gVvb29J0pCUlARLS0vJHKXPGRgYSP6vra0tWKesrIzAwEBcuXIFp0+fxsqVKzFlyhQEBwfDzs4u3ziuXr0KHx8f+Pn5wcvLC/r6+ti9ezcCAgK+GX9BY1RVVRWsE4lE+bbl5OR8934/7UMW/v7+8PPzE7RNmTYDU6fPlHlfBfHw4QPEfviAbl06Stqys7Nx80YIdv+5EyG370FZWblI7lueUjNzEJWYDnMdNZjqqMHTyQRTTjzBu4TcZOl1XBocTbXR1NEY2268U3C038fQwBDKysr48OGDoP3Dhw8wMTFRUFTfr7S8FvOzNGAh+g4YBO+WrQAAjk4VEBHxDps2/l7iEyhVNTVY//8Ig3PlKnhw/x527tiG6TNnKTgy2ZTmoTd5+U/OgbK3t4eamhouX74sacvMzERISAicnXNP3+7Rowfu37+PmzdvYt++ffDx8ZH0dXV1RWRkJFRUVODg4CBYpH1gi0QieHh4wM/PD7dv34aamhoOHjwIAFBTU0N2drag/5UrV2BjY4MpU6bAzc0Njo6Okonsn+S33ffE+C3y2m9+Medn8uTJiI+PFywTJk4udPzS1KlbF/sOHcVf+w9JlsqVq6Bl6zb4a/+hEvOFpa6iBFMdNcSlZkFdOfeD8MsTxMVican4kFRVU0Ml58oIvnZV0paTk4Pg4KuoWgLnnnxSWl6L+UlLS4PSF689JSVlwZmjpUVOTg4yM0reULmi5kBlZ2dj2rRpsLOzg6amJuzt7TF79mzBJS7EYjGmT58OS0tLaGpqwtPTE0+fPhXsJzY2Fj4+PtDT04OBgQH69++PpKSkL+/uu/wnK1Da2toYOnQoJkyYACMjI1hbW2PhwoVISUlB//79AeQOQdWrVw/9+/dHdnY22rb939i8p6cn3N3d0b59eyxcuBBOTk549+4djh8/jg4dOsDNzS3f+w0ODkZQUBCaN28OMzMzBAcHIyYmBpUqVZLc599//43Hjx/D2NgY+vr6cHR0RHh4OHbv3o1atWrh+PHjkoTrE1tbW7x8+RKhoaEoW7YsdHV1Cx2jNPLar62tLYKDgxEWFgYdHR0YGRkJhg0/UVfPO1yXllWo0AtEW1sHjo5OgjZNLS0Y6BvkaS9Ofq5ugdC3iXifkgFDDVW0dzGDWAwEh8chJSMbUYnp6O1WBn+FRiApI3cIz9lCB8sv/C8ZN9JShbaaMoy11CASAeUMcs8cjU7KQHqW7NXDH6ln776Y9ttEVK5cBVVcqmLH9q1ITU1F+w4dpW9cTJXU12JBNGzcBBs3rIOFpVXuEN6jR9ixbTPadeik6NC+y/KlAajfoCEsLC2RkpyME8eP4UbIdaxd/4eiQysxFixYgLVr12Lr1q2oXLkybty4gb59+0JfXx8jR44EACxcuBArVqzA1q1bYWdnh2nTpsHLywsPHz6UnPHu4+ODiIgIBAYGIjMzE3379sWgQYPynCD2Pf6TCRQAzJ8/Hzk5OejZsycSExPh5uaGv//+WzAfycfHB8OGDUOvXr2gqfm/071FIhFOnDiBKVOmoG/fvoiJiYGFhQUaNmwIc3Pzr96nnp4eLly4gGXLliEhIQE2NjYICAiQTGQfOHAgzp07Bzc3NyQlJeHs2bNo27YtxowZA19fX6Snp6NVq1aYNm0aZs6cKdlvp06dcODAATRp0gRxcXHYvHkz+vTpU6gYpSnssX9p/Pjx6N27N5ydnZGamoqXL1/C1ta20HH91xlqqmJwvXLQUVNGYno2nsYkY/aZ50hMz63yLT0fhs7VLDCqoQ00VJQRlZiOjcFvcDciUbKPDi7mqG/3v9f/LO/c69bM/+cFHhfzs/W8W7TEx9hYrFm1Au/fx6BCxUpY8/tGGJfgIbzSbNJvU7F65XL4z/FDbOwHmJqaoVOXnzF46HBFh/ZdYmM/YOrkiYiJiYaOri6cnCpg7fo/4F7PQ/rGxYyiitNXrlxBu3bt0KpV7vCura0t/vzzT1y/fh1AbvVp2bJlmDp1Ktq1awcA2LZtG8zNzXHo0CF069YNjx49wqlTpxASEiL5o37lypVo2bIlFi9eDCsrK7nEKhKX9Eu/0n9OUVagFGnovnuKDqFIrO3sIr0TFSul9VuhFIxYf5WGnMshjhNOyW1fTxd5F7jvvHnzsH79epw+fRpOTk64c+cOmjdvjiVLlsDHxwcvXryAvb09bt++LfgFjkaNGqF69epYvnw5Nm3ahHHjxuHjx4+S9VlZWdDQ0MDevXvRoYN85tn9ZytQREREVPTyO5s6v+kZADBp0iQkJCSgYsWKkktczJ07VzIPOTIyEgDyjHiYm5tL1kVGRsLMzEywXkVFBUZGRpI+8vCfnEROREREXycSyW/x9/eHvr6+YPH398/3fvfs2YOdO3di165duHXrFrZu3YrFixdj69atP/gRkI4VKCIiIhKQ5xm6kydPxtixYwVtX7u22YQJEzBp0iR069YNQO7Fpl+9egV/f3/07t1bcvHlqKgoWFpaSraLioqSDOlZWFjkue5gVlYWYmNj5XrxZlagiIiIqMioq6tDT09PsHwtgUpJSclzRraysrLkOoJ2dnawsLBAUFCQZH1CQgKCg4Ph7u4OAHB3d0dcXBxu3rwp6fPPP/8gJycHderUkdtxsQJFREREAoqacN+mTRvMnTsX1tbWqFy5Mm7fvo0lS5agX79+/x+XCKNHj8acOXPg6OgouYyBlZUV2rdvDwCoVKkSvL29MXDgQKxbtw6ZmZnw9fVFt27d5HYGHsAEioiIiL6gpKSYDGrlypWYNm0ahg0bhujoaFhZWWHw4MGYPn26pM+vv/6K5ORkDBo0CHFxcahfvz5OnToluQYUAOzcuRO+vr5o1qwZlJSU0KlTJ6xYsUKusfIyBlTi8DIGJQsvY1DylNZvBV7GoOCcfzstt309nNdcbvsqTliBIiIiIoHSnGzKCxMoIiIiEigNv5NZ1HgWHhEREZGMWIEiIiIiARagpGMCRURERAIcwpOOQ3hEREREMmIFioiIiARYgZKOCRQREREJMH+SjkN4RERERDJiBYqIiIgEOIQnHRMoIiIiEmD+JB2H8IiIiIhkxAoUERERCXAITzomUERERCTA/Ek6DuERERERyYgVKCIiIhLgEJ50TKCIiIhIgPmTdBzCIyIiIpIRK1BEREQkwCE86ViBIiIiIpIRK1BExcTazi6KDqFIvIlNVXQIRaaskaaiQygSLD4QXwPSMYEiIiIiAQ7hScchPCIiIiIZsQJFREREAixASccEioiIiAQ4hCcdh/CIiIiIZMQKFBEREQmwACUdEygiIiIS4BCedBzCIyIiIpIRK1BEREQkwAqUdEygiIiISID5k3QcwiMiIiKSEStQREREJMAhPOmYQBEREZEA8yfpOIRHREREJCNWoIiIiEiAQ3jSMYEiIiIiAeZP0nEIj4iIiEhGrEARERGRgBJLUFIxgSIiIiIB5k/ScQiPiIiISEasQBEREZEAz8KTjgkUERERCSgxf5KKQ3hEREREMmIFioiIiAQ4hCddsapAnTt3DiKRCHFxcYoORULeMYWFhUEkEiE0NFQu+1OUmTNnonr16ooOg4iIioBIJL+ltCpWCZS8iEQiHDp0SC77qlevHiIiIqCvry+X/ZVE+T2e48ePR1BQkGIC+gF279qJFj81Ra0aLvDp1gX37t5VdEhyU9KO7X7oTfhNHIme7X9CqwbVcfXCP4L1S+ZOQ6sG1QXLtHHDJOvv3g7Js/7T8uTR/R99ODIrac+XLErrsZXW4yKhYpVAZWRkKDoEgczMTKipqcHCwoLlzC/o6OjA2NhY0WEUiVMnT2DxQn8MHjYcu/ceRIUKFTF0cH98+PBB0aF9t5J4bGlpqbBzcMLQsZO/2qdmHQ9sP3RGsvw6c75kXaUq1QXrth86A6/WHWBuWQaOFSv/iEMotJL4fBVUaT220nJcIjn+K60UmkA1btwYvr6+GD16NExMTODl5QUAuHnzJtzc3KClpYV69erh8ePHgu0OHz4MV1dXaGhooHz58vDz80NWVhYAwNbWFgDQoUMHiEQiyW0AWLt2Lezt7aGmpoYKFSpg+/btgv2KRCKsXbsWbdu2hba2NubOnZvvEN7ly5fRuHFjaGlpwdDQEF5eXvj48SMA4NSpU6hfvz4MDAxgbGyM1q1b4/nz54V+jE6cOAEnJydoamqiSZMm2LJliyCe/IbSli1bJjhuANi4cSMqVaoEDQ0NVKxYEWvWrJGsy8jIgK+vLywtLaGhoQEbGxv4+/t/8/H88n5zcnIwa9YslC1bFurq6qhevTpOnTolWf9p6PLAgQNo0qQJtLS0UK1aNVy9erXQj01R2b51Mzp27or2HTrB3sEBU2f4QUNDA4cO7Fd0aN+tJB6bW9366DXQF/UaNv1qH1VVVRgZm0gWXV29r67T09fHtUvn8FPLdsX+D6OS+HwVVGk9ttJyXEoi+S2llcIrUFu3boWamhouX76MdevWAQCmTJmCgIAA3LhxAyoqKujXr5+k/8WLF9GrVy+MGjUKDx8+xO+//44tW7Zg7ty5AICQkBAAwObNmxERESG5ffDgQYwaNQrjxo3D/fv3MXjwYPTt2xdnz54VxDNz5kx06NAB9+7dE9zvJ6GhoWjWrBmcnZ1x9epVXLp0CW3atEF2djYAIDk5GWPHjsWNGzcQFBQEJSUldOjQATk5OTI/Nq9fv0bHjh3Rpk0bhIaGYsCAAZg0aZLM+9m5cyemT5+OuXPn4tGjR5g3bx6mTZuGrVu3AgBWrFiBI0eOYM+ePXj8+DF27twpSZS+9nh+afny5QgICMDixYtx9+5deHl5oW3btnj69Kmg35QpUzB+/HiEhobCyckJ3bt3lyS/xUFmRgYePXyAuu71JG1KSkqoW7ce7t65rcDIvl9pPrZ7oTfQo00TDOrRDqsXz0VCfNxX+wZfOo/EhHj81LLdjwuwEErz81Vaj620HhflT+Fn4Tk6OmLhwoUAgIiICADA3Llz0ahRIwDApEmT0KpVK6SlpUFDQwN+fn6YNGkSevfuDQAoX748Zs+ejV9//RUzZsyAqakpAMDAwAAWFhaS+1m8eDH69OmDYcNy50aMHTsW165dw+LFi9GkSRNJvx49eqBv376S2y9evBDEu3DhQri5uQkqOJUr/28YoFOnToL+mzZtgqmpKR4+fIgqVarI9Nh8qpgFBAQAACpUqIB79+5hwYIFMu1nxowZCAgIQMeOHQEAdnZ2kuSzd+/eCA8Ph6OjI+rXrw+RSAQbGxvJtl97PL+0ePFiTJw4Ed26dQMALFiwAGfPnsWyZcuwevVqSb/x48ejVatWAAA/Pz9UrlwZz549Q8WKFWU6pqLyMe4jsrOz8wxPGhsb4+XLF1/ZqmQorcdWs44H6jVqBgvLMoh4+xpb16/CjAnDsXjtNigrK+fpf/r4QbjWdoeJmbkCoi240vp8AaX32ErTcRX36mxxoPAKVM2aNfO0Va1aVfJ/S0tLAEB0dDQA4M6dO5g1axZ0dHQky8CBAxEREYGUlJSv3s+jR4/g4eEhaPPw8MCjR48EbW5ubt+M91MF6muePn2K7t27o3z58tDT05NUcsLDw7+536/FXKdOHUGbu7u7TPtITk7G8+fP0b9/f8FjNmfOHMnQYp8+fRAaGooKFSpg5MiROH36tEz3kZCQgHfv3hXo8f3Wc5uf9PR0JCQkCJb09HSZ4qPSrZGnN+rWbwxbe0e4N2yKGQtX4MmjB7h3+0aevu+jo3Dr+lU0b9VBAZESlRw8C086hVegtLW187SpqqpK/v8pC/40BJaUlAQ/Pz9JNeVzGhoaRRLP5zQ1Nb+5vk2bNrCxscGGDRtgZWWFnJwcVKlSpcgmyCspKUEsFgvaMjMzJf9PSkoCAGzYsCFPMvbpr3NXV1e8fPkSJ0+exJkzZ9C1a1d4enpi3759co/3W89tfvz9/eHn5ydomzJtBqZOnyn32ADA0MAQysrKeSZ8fvjwASYmJkVynz9KaT62z1lalYWeviEi3r5GdTfhaz7wxGHo6umjTv1GCoqu4Erz81Vaj620HhflT+EVKFm5urri8ePHcHBwyLMoKeUejqqqqmRO0ieVKlXC5cuXBW2XL1+Gs7OzTPdftWrVr56+/+HDBzx+/BhTp05Fs2bNUKlSJcnk8sKoVKkSrl+/Lmi7du2a4LapqSkiIyMFSdTn15gyNzeHlZUVXrx4kefxsrOzk/TT09PDzz//jA0bNuCvv/7C/v37ERsbCyD/x/Nzenp6sLKyksvj+6XJkycjPj5esEyY+PWzsb6XqpoaKjlXRvC1/01uz8nJQXDwVVStVqPI7vdHKM3H9rn30VFITIiDobHwC0ssFiPwxGE09W4DFRXVr2xdfJTm56u0HltpOi4lkUhuS2ml8AqUrKZPn47WrVvD2toanTt3hpKSEu7cuYP79+9jzpw5AHLPHAsKCoKHhwfU1dVhaGiICRMmoGvXrqhRowY8PT1x9OhRHDhwAGfOnJHp/idPngwXFxcMGzYMQ4YMgZqaGs6ePYsuXbrAyMgIxsbGWL9+PSwtLREeHl6oSd+fDBkyBAEBAZgwYQIGDBiAmzdvYsuWLYI+jRs3RkxMDBYuXIjOnTvj1KlTOHnyJPT0/ncWkp+fH0aOHAl9fX14e3sjPT0dN27cwMePHzF27FgsWbIElpaWqFGjBpSUlLB3715YWFjAwMDgq4/nlyZMmIAZM2bA3t4e1atXx+bNmxEaGoqdO3cW+vgBQF1dHerq6oK2tCKec96zd19M+20iKleugiouVbFj+1akpqaifYe8Vc+SpiQeW2pKCt69/d8QeGTEWzx/+i909fShq6uPXZvXwaOxJwyNjBHx9g02rV0GyzLlULN2PcF+7ty8jqiIt/BqXXKG70ri81VQpfXYSstxleK8R25KXALl5eWFY8eOYdasWViwYAFUVVVRsWJFDBgwQNInICAAY8eOxYYNG1CmTBmEhYWhffv2WL58ORYvXoxRo0bBzs4OmzdvRuPGjWW6fycnJ5w+fRq//fYbateuDU1NTdSpUwfdu3eHkpISdu/ejZEjR6JKlSqoUKECVqxYIfN9fGJtbY39+/djzJgxWLlyJWrXro158+YJzg6sVKkS1qxZg3nz5mH27Nno1KkTxo8fj/Xr10v6DBgwAFpaWli0aBEmTJgAbW1tuLi4YPTo0QAAXV1dLFy4EE+fPoWysjJq1aqFEydOSCp6+T2eXxo5ciTi4+Mxbtw4REdHw9nZGUeOHIGjo2Ohjl2RvFu0xMfYWKxZtQLv38egQsVKWPP7RhiXghJ8STy2p48fYPLIgZLbG1flnlTRzLsNho+fgrDnTxF06iiSkxJhZGKKGrXc0XPAcKiqqQn2c/r4QVSqUg3lbOxQUpTE56ugSuuxldbjorxE4i8n0FCxdu7cOTRp0gQfP36UVIj+a4q6AkXy9SY2VdEhFJmyRt+eE0n0o2jIuRzSefMtue1rX19Xue2rOClxFSgiIiIqWhzCk67ETSIvTYYMGSK4tMDny5AhQxQdHhEREX0Fh/AUKDo6GgkJCfmu09PTg5mZ2Q+OqGTgEF7JwiE8oqIn7yG8n7fK78rpf/UuWWcgFhQrUApkZmaW7+UYHBwcmDwREZHCiOS4yOrt27f45ZdfYGxsDE1NTbi4uODGjf9dGFcsFmP69OmwtLSEpqYmPD098/xsWGxsLHx8fKCnpwcDAwP0799fcl1EeWECRURERMXCx48f4eHhAVVVVZw8eRIPHz5EQECA4PI5CxcuxIoVK7Bu3ToEBwdDW1sbXl5eSEtLk/Tx8fHBgwcPEBgYiGPHjuHChQsYNGiQXGPlEB6VOBzCK1k4hEdU9OQ9hNd9W6jc9vVnr+oF7jtp0iRcvnwZFy9ezHe9WCyGlZUVxo0bh/HjxwMA4uPjYW5uji1btqBbt2549OgRnJ2dERISIvl5tlOnTqFly5Z48+YNrKysvvuYAFagiIiI6AtKIvktsvym6ZEjR+Dm5oYuXbrAzMwMNWrUwIYNGyTrX758icjISHh6ekra9PX1UadOHVy9mnsF+KtXr8LAwEDw27aenp5QUlJCcHCw/B4jue2JiIiI6Av+/v7Q19cXLP7+/vn2ffHiBdauXQtHR0f8/fffGDp0KEaOHImtW7cCACIjIwHk/kzZ58zNzSXrIiMj88wjVlFRgZGRkaSPPPA6UERERCQgkuOFoCZPnoyxY8cK2r78ia5PcnJy4Obmhnnz5gEAatSogfv372PdunXo3bu33GKSB1agiIiISEAkkt+irq4OPT09wfK1BMrS0jLPj9BXqlQJ4eG5v4dpYWEBAIiKihL0iYqKkqyzsLBAdHS0YH1WVhZiY2MlfeSBCRQREREVCx4eHnj8+LGg7cmTJ7CxsQEA2NnZwcLCAkFBQZL1CQkJCA4Ohru7OwDA3d0dcXFxuHnzpqTPP//8g5ycHNSpU0dusXIIj4iIiATkOYQnizFjxqBevXqYN28eunbtiuvXr2P9+vVYv369JK7Ro0djzpw5cHR0hJ2dHaZNmwYrKyu0b98eQG7FytvbGwMHDsS6deuQmZkJX19fdOvWTW5n4AFMoIiIiOgLSgr6LbxatWrh4MGDmDx5MmbNmgU7OzssW7YMPj4+kj6//vorkpOTMWjQIMTFxaF+/fo4deoUNDQ0JH127twJX19fNGvWDEpKSujUqRNWrFgh11h5HSgqcXgdqJKF14EiKnryvg5Unz/vym1fW7pXldu+ipNCzYG6ePEifvnlF7i7u+Pt27cAgO3bt+PSpUtyDY6IiIh+PJFIJLeltJI5gdq/fz+8vLygqamJ27dvSy6GFR8fLzntkIiIiEouRf4WXkkhcwI1Z84crFu3Dhs2bICqqqqk3cPDA7du3ZJrcERERETFkcyjpo8fP0bDhg3ztOvr6yMuLk4eMREREZECKZXioTd5kbkCZWFhgWfPnuVpv3TpEsqXLy+XoIiIiEhx5HkhzdJK5gRq4MCBGDVqFIKDgyESifDu3Tvs3LkT48ePx9ChQ4siRiIiIqJiReYhvEmTJiEnJwfNmjVDSkoKGjZsCHV1dYwfPx4jRowoihiJiIjoByrNZ8/JS6GvA5WRkYFnz54hKSkJzs7O0NHRkXdsRPnidaBKFl4Hiqjoyfs6UIP3PZDbvn7vXFlu+ypOCv2Qq6mp5fnBPyIiIqL/ApkTqCZNmnyztPfPP/98V0BERESkWDwLTzqZE6jq1asLbmdmZiI0NBT3799H79695RUXERERKQjzJ+lkTqCWLl2ab/vMmTORlJT03QERERERFXeF+i28/Pzyyy/YtGmTvHZHRERECsLfwpNObvP2r169Cg0NDXntjuirUjOyFR1CkSitnzMW+qX3c8Gwlq+iQygSby8tV3QIRaK0vscAQENFWa77k1t1pRSTOYHq2LGj4LZYLEZERARu3LiBadOmyS0wIiIiouJK5gRKX19fcFtJSQkVKlTArFmz0Lx5c7kFRkRERIpRmofe5EWmBCo7Oxt9+/aFi4sLDA0NiyomIiIiUiAl5k9SyTTMqaysjObNmyMuLq6IwiEiIiIq/mSeJ1alShW8ePGiKGIhIiKiYkBJJL+ltJI5gZozZw7Gjx+PY8eOISIiAgkJCYKFiIiISjZexkC6As+BmjVrFsaNG4eWLVsCANq2bSt4YMRiMUQiEbKzS+cp5kRERESfFDiB8vPzw5AhQ3D27NmijIeIiIgUrDQPvclLgRMosVgMAGjUqFGRBUNERESKV4pH3uRGpjlQpXksk4iIiKigZLoOlJOTk9QkKjY29rsCIiIiIsVSYsFEKpkSKD8/vzxXIiciIqLShb+FJ51MCVS3bt1gZmZWVLEQERERlQgFTqA4/4mIiOi/gV/50sl8Fh4RERGVbpwDJV2BE6icnJyijIOIiIioxJBpDhQRERGVfixASccEioiIiAR4JXLpeKYiERERkYxYgSIiIiIBTiKXjgkUERERCTB/ko5DeEREREQyYgWKiIiIBDiJXDomUERERCQgAjMoaTiER0RERCQjVqDoP23DulX44/c1gjYbWzv8dfA44uPjsGHtKly/dgVRkREwMDREw8bNMHjYSOjo6ioo4sLZumkD1qxYip979MTYXycDAIb2741bN0ME/Tp07opJU2cqIMKC27Txd5wNCkTYyxdQV9dA1eo1MHL0ONjalQcAxMfH4fc1K3HtymVERkbAwNAIjZs2w9Dho6CrwOfNw9UeY3p5wtXZGpam+ug6Zj2Onrsr6DNtaCv07VAPBrqauHrnBUbO+wvPw2Mk6x2szTBvTHu4VysPNVVl3H/6Dn5rjuHCjaeSPo1rO2HGsNao7GCF5NQM7DwajBmrjyI7WzG/JrFx3Sr8sV74HrO2tcNfB45Lbt+7E4rfVy/Hg/t3oaSsBCenili6egM0NDR+dLgy+dbnBwDMnzMDIcHX8D4mGpqaWnCpVh3DR/3vtVqccQhPOiZQ/1GZmZlQVVVVdBjFQnl7B6xc94fktrJy7tvifUwM3sfEYMSYCbArb4/IiHdYMNcP72Ni4L94mYKild3D+/dwcN8eODhVyLOuXccuGDzMV3JbXUPzR4ZWKLduhKBLtx6oXNkF2dnZWLViKYYPGYB9B49BU0sLMdHRiImOxuhxv8LO3gER797Bf84MvI+OxsIlKxQWt7amOu49eYtth6/iryWD8qwf18cTw7o3wsDp2xH29gOmD2uNo6uHo0anOUjPyAIAHFgxBM/Co9Fi8AqkpmfCt0cTHFgxBJXbzETUh0S4OJXBoZVDseCPv9F/2jZYmRlg5W/doKyshMlLD/7oQ5Yob++AFWvzvseA3ORpzIhB6NV3IMZO/A3Kyip4+uRfKCmVjAGSr31+AEDFSpXh1aINzC0tkRAfj43rVmPUsAE4cCwQysrKigi3wJhASVcyXqEEANi3bx9cXFygqakJY2NjeHp6Ijk5GSEhIfjpp59gYmICfX19NGrUCLdu3RJsKxKJsHbtWrRt2xba2tqYO3cuAODo0aOoVasWNDQ0YGJigg4dOki22b59O9zc3KCrqwsLCwv06NED0dHRkvUfP36Ej48PTE1NoampCUdHR2zevBkAEBYWBpFIhD179qBBgwbQ1NRErVq18OTJE4SEhMDNzQ06Ojpo0aIFYmJioEjKysowNjGVLAaGhgAAewdHzA9YjgaNmqBsOWu41a6LIb6jcOnCWWRlZSk05oJKSUnG9N9+xW/T/aCnq5dnvYaGhuDYdXR0FBClbFat24i27TrC3sERThUqwm+2PyIj3uHRwwcAAAdHJyxauhINGzdFuXLWqF2nLoaNGIML5xX7vJ2+/BB+a47hyNm7+a4f3qMJFmz4G8fO3cP9p+8wYNo2WJrqo22TagAAYwNtONqYIWBzIO4/fYfn4TGYtuIwtDXV4exgBQDo3NwV95++g//6U3jx+j0u3XyGKcsPYXDXBtDRUv9hx/qlr73HAGB5wHx06fYLevUdiPL2jrCxtYNn8xZQU1NTWLyy+Naxte/UFTVqusHKqgwqVnLG4OEjERUZiYh3bxUYMckLE6gSIiIiAt27d0e/fv3w6NEjnDt3Dh07doRYLEZiYiJ69+6NS5cu4dq1a3B0dETLli2RmJgo2MfMmTPRoUMH3Lt3D/369cPx48fRoUMHtGzZErdv30ZQUBBq164t6Z+ZmYnZs2fjzp07OHToEMLCwtCnTx/J+mnTpuHhw4c4efIkHj16hLVr18LExERwnzNmzMDUqVNx69YtqKiooEePHvj111+xfPlyXLx4Ec+ePcP06dOL9LGT5nV4OFr/1AgdWzfH9N8mIDLi3Vf7JiUmQVtbByoqJaN4u2jeHHg0aITadevlu/7vk8fQvHE9dO/UFqtXLEFaauoPjvD7JSXlvs719PW/3icxEdo6xfd5sy1jDEtTffwT/K+kLSEpDSH3w1Cnqi0A4ENcMh6/jESP1rWhpaEGZWUlDOhUH1EfEnD7YTgAQF1NBWnpmYJ9p6ZnQlNDDTUqWf+w4/nS6/BwtGneCJ3aNMeMKf97j8XGfsCD+3dhZGSEgX16oKVnAwwd0At3bt9UWKyyKujnR2pqCo4fOQirMmVhbmHxg6OUnUgkkttSWhXPTxPKIyIiAllZWejYsSNsbGwAAC4uLgCApk2bCvquX78eBgYGOH/+PFq3bi1p79GjB/r27Su53a1bN3Tr1g1+fn6StmrVqkn+369fP8n/y5cvjxUrVqBWrVpISkqCjo4OwsPDUaNGDbi5uQEAbG1t88Q9fvx4eHl5AQBGjRqF7t27IygoCB4eHgCA/v37Y8uWLYV5SOSicpWqmDZrLqxt7PDhfQz++H0NhvTriZ37jkBbW1vQN+7jR2zesBbtOnVRULSyOX3qBB7/+xCbd+7Jd33zFq1gaWUFE1MzPHvyGKuWL0F4WBgWKHCYS1Y5OTlYvHAeqtVwhYOjU759Pn78iI3r16Jjp64/OLqCszDJrQ5Gxwr/6In+kAhz4/9VDlsNWYW/lg5CzOXFyMkRI+ZjEtoNX4O4xNzEN/DKI/j2aIKu3jWx7/QtWBjr4bdBLQAAlqZ5K5A/QmWXqpjqNxc2NnZ4/z4Gf6xfg6H9e2LH3iN49+YNAGDj76sxYvQEOFaoiJPHjmDEkH7YufcwylnbKiTmgirI58e+PX9i9bLFSE1NhY2tHVas3QhV1eJfXeMQnnRMoEqIatWqoVmzZnBxcYGXlxeaN2+Ozp07w9DQEFFRUZg6dSrOnTuH6OhoZGdnIyUlBeHh4YJ9fEp0PgkNDcXAgQO/ep83b97EzJkzcefOHXz8+BE5ObmTUMPDw+Hs7IyhQ4eiU6dOuHXrFpo3b4727dujXj1hpaNq1aqS/5ubmwP4X+L3qe3zYcEvpaenIz09XdiWrQJ1dfkMR9Sr31Dyf0enCqjsUhXtW3oi6PQptO3QSbIuOSkJY0cOgW15ewwcPFwu912UoiIjsGShP1au2/jVx6pD5/8lFA6OTjAxNcXwQf3w5nU4ypZTXLVCFvPnzsLzZ0/xx5Zd+a5PSkrCqOGDUb68PQYN9c23T0mydHJXxMQmwrPfMqSmZ6BPh3rYv3ww6v+yCJHvExB07V/8tuwQVvzWDX/M7oX0zCzM33AK9V0dkJMjVkjM7h7/e485/P97rEMrTwQFnpJMpm7fsStat+sIAKhQ0Rk3rl/D0cMHMGzEWIXEXFAF+fzwbtEateu448P799i5bTOmTByL9Zt3yu0zjBSHQ3glhLKyMgIDA3Hy5Ek4Oztj5cqVqFChAl6+fInevXsjNDQUy5cvx5UrVxAaGgpjY2NkZGQI9vFlRUVT8+sThpOTk+Hl5QU9PT3s3LkTISEhOHgwdxLqp/22aNECr169wpgxY/Du3Ts0a9YM48ePF+zn84nqn0q5X7Z9Sszy4+/vD319fcGydPH8bz1U30VXVw/W1rZ48/qVpC05ORmjhw+ClpY2FixZCZUSMPn+34cP8DH2A3p374x6NV1Qr6YLbt0MwZ4/d6BezdzJ11+q7JKb7L55HZ5nXXG0YN4sXLpwDr9v3JbvkEhychJGDB0AbW1tLF62qlifNBH5PgEAYGYkPEvQzFgXUR9y1zWu7YSWDaqg16TNuHrnBUL/fYPR/nuQmp6JX9rUkWyzYsc/sGg4AU4tp6Nsk0mSM/1evnn/g47m2z5/j5mYmAIA7MrbC/rY2pVHVGSEIsL7Lvl9fujo6sLaxhY1arrBf/FSvHr5Euf/OaPAKAtGJJLfUloxgSpBRCIRPDw84Ofnh9u3b0NNTQ0HDx7E5cuXMXLkSLRs2RKVK1eGuro63r+X/mFZtWpVBAUF5bvu33//xYcPHzB//nw0aNAAFStWzLdSZGpqit69e2PHjh1YtmwZ1q9f/93H+bnJkycjPj5esIwZP0mu9/G5lJRkvH0TDuP//2BPTkrCqKEDoKKqisXLVpeYvxrd6rhj177D2P7XAclSybkKvFq2xva/DuR7BtCTf3Pn33w69uJKLBZjwbxZOPvPGazbuAVlypbN0ycpKQnDB/eHqqoqlqxYU+yft7C3HxARE48mdf53pqSutgZqVbFF8N0wAICWRu6wz5d/cOTkiPOdZxIRE4+09Ex09XbD64hY3P73ddEdgAxSUpLx5k04TExMYWlVBiamZnj1KkzQJzw8DBYWVooJ8Dt8+fnxJbEYEEOMjMyMfNcXJ0oikdyW0opDeCVEcHAwgoKC0Lx5c5iZmSE4OBgxMTGoVKkSHB0dJWfMJSQkYMKECd+sLn0yY8YMNGvWDPb29ujWrRuysrJw4sQJTJw4EdbW1lBTU8PKlSsxZMgQ3L9/H7NnzxZsP336dNSsWROVK1dGeno6jh07hkqVKsn1uNXV1fN8+WWn5K2eFNaKJQtRv2ETWFhZ4X10NDasWwUlJWU0926F5KQkjBw2AGlpaZg5dwGSk5OQnJwEADAwNCrWpyFra2vD3sFR0KapqQl9fQPYOzjizetw/H3yOOrVbwh9fQM8e/oYyxYvQI2abnDM53IHxcn8ubNw6uQxLFm+Glra2nj/PvcsTh0dXWhoaEiSp7S0VMz2XyR43gwV+Lxpa6rBvtz/vlhtyxijqlMZfExIwevIj1i96ywmDvDGs/AYhL39gBnDWiEiJh5Hzt4BAATffYmPCSnYOLsX5q0/idS0TPTrWA+2ZYxx6tIDyX7H9GqG01ceIScnB+2aVcf4vj/hl183KWwIb8XS3PeYpaUVYmKisXHdKigrKeMn71YQiUTw6dUPG39fBUenCnB0qogTxw7jVdhLzFu4TCHxyuJbnx9v37zGmb9Poo67BwwMDREdFYVtm3OH1D8f+qOSiwlUCaGnp4cLFy5g2bJlSEhIgI2NDQICAtCiRQtYWFhg0KBBcHV1Rbly5TBv3rw8Q2n5ady4Mfbu3YvZs2dj/vz50NPTQ8OGuW9sU1NTbNmyBb/99htWrFgBV1dXLF68GG3btpVsr6amhsmTJyMsLAyamppo0KABdu/eXWSPQVGIjorC9MnjER8fBwNDI1Sr7oqN2/6EoZERbt64jgf3coc/Orf1Fmx34HggrKzKKCJkuVBVVUVI8FXs3rkNaampMDO3QJNmP6HvwCGKDk2qfXv+BAAM6tdL0D5j9jy0bdcR/z56gPv3cpOO9q2aC/ocPXkGVmXyVqx+BFdnG5zeOEpye+H43Dky249cw6AZOxCw5Qy0NNWxamp3GOhq4kroc7QdvkZyDagPcclo57sGM4e3wcnfR0JVRQmPXkSiy5j1uPfkf6fFN/dwxq8DvKCuqoJ7T96iy5j1OH354Y892M/EREVhxhfvsQ1b/4ShoREAoJtPL2RkpGN5wAIkxMfDwakCVqzZWCLm4X3r8yMrKwuht29i967tSEyIh5GxCaq71sSGLbtgZGSs6NCl4iRy6URisVgxf5YQFdJHOVagipPSWulWKSEXRCwM07ojFB1CkXh7abmiQygSpfU9BgCGWvKtrK68/FJu+xrhYSe3fRUnpfeTjYiIiKiIcAiPiIiIBJRQist1csIEioiIiARK83CnvHAIj4iIiEhGrEARERGRAM/Ck44JFBEREQmU5gtgyguH8IiIiIhkxAoUERERCbAAJR0TKCIiIhLgEJ50HMIjIiIikhETKCIiIhIQieS3fI/58+dDJBJh9OjRkra0tDQMHz4cxsbG0NHRQadOnRAVFSXYLjw8HK1atYKWlhbMzMwwYcIEZGVlfV8wX2ACRURERAJKclwKKyQkBL///juqVq0qaB8zZgyOHj2KvXv34vz583j37h06duwoWZ+dnY1WrVohIyMDV65cwdatW7FlyxZMnz79O6LJiwkUERERFStJSUnw8fHBhg0bYGhoKGmPj4/HH3/8gSVLlqBp06aoWbMmNm/ejCtXruDatWsAgNOnT+Phw4fYsWMHqlevjhYtWmD27NlYvXo1MjIy5BYjEygiIiISEIlEclvS09ORkJAgWNLT0795/8OHD0erVq3g6ekpaL958yYyMzMF7RUrVoS1tTWuXr0KALh69SpcXFxgbm4u6ePl5YWEhAQ8ePBAbo8REygiIiISEMlx8ff3h76+vmDx9/f/6n3v3r0bt27dyrdPZGQk1NTUYGBgIGg3NzdHZGSkpM/nydOn9Z/WyQsvY0BERERFZvLkyRg7dqygTV1dPd++r1+/xqhRoxAYGAgNDY0fEV6hsQJFREREAkoikdwWdXV16OnpCZavJVA3b95EdHQ0XF1doaKiAhUVFZw/fx4rVqyAiooKzM3NkZGRgbi4OMF2UVFRsLCwAABYWFjkOSvv0+1PfeTyGMltT0RERFQqyHMITxbNmjXDvXv3EBoaKlnc3Nzg4+Mj+b+qqiqCgoIk2zx+/Bjh4eFwd3cHALi7u+PevXuIjo6W9AkMDISenh6cnZ1lfzC+gkN4REREVCzo6uqiSpUqgjZtbW0YGxtL2vv374+xY8fCyMgIenp6GDFiBNzd3VG3bl0AQPPmzeHs7IyePXti4cKFiIyMxNSpUzF8+PCvVr4KgwkUERERCRTnX3JZunQplJSU0KlTJ6Snp8PLywtr1qyRrFdWVsaxY8cwdOhQuLu7Q1tbG71798asWbPkGodILBaL5bpHoiL2MSVb0SEUieL8gfU9VJRK70wB07ojFB1CkXh7abmiQygSpfU9BgCGWspy3d+ft9/KbV/da5SR276Kk9L7yUZERERURDiER0RERAKsrkjHBIqIiIgERKV5vFNOmGQSERERyYgVKCIiIhJg/Uk6JlBEREQkwCE86ZhAUYmjrlpKR55L6QVFSvMH8fvglYoOoUi0WXdN0SEUiWND6yo6BCpFmEARERGRQCn9M1WumEARERGRQGmuHMsLk0wiIiIiGbECRURERAKsP0nHBIqIiIgEOIInHYfwiIiIiGTEChQREREJKHEQTyomUERERCTAITzpOIRHREREJCNWoIiIiEhAxCE8qZhAERERkQCH8KTjEB4RERGRjFiBIiIiIgGehScdEygiIiIS4BCedBzCIyIiIpIRK1BEREQkwAqUdEygiIiISICXMZCOQ3hEREREMmIFioiIiASUWICSigkUERERCXAITzoO4RERERHJiBUoIiIiEuBZeNIxgSIiIiIBDuFJxyE8IiIiIhkxgSK5mDlzJqpXr67oMIiISA6URPJbSisO4ZHMRCIRDh48iPbt20vaxo8fjxEjRiguqEK6eSME2zb/gYcPH+B9TAyWLF+FJs08JeuDAk9j357dePTwAeLj47F730FUqFhJgREX3M0bIdi25bNjWyY8tulTJuHokUOCbep51MfqdRt/cKTfp0Xzpoh49zZPe9duPfDb1BkKiKhwPj1fj/7/+Qr44vn68P49VixdjKtXLyMpMRE1arph4uSpsLaxVVzQX2GirYaBHtaobWMADVVlvI1Lw8Izz/AkOhkAoKGqhEH1bOBhbwg9DVVEJKThYGgkjt6PkuxjTJPyqGmtD2NtNaRmZuNBRCLWX36F1x/TFHVYeXzr8yMzMxNrVi7HpYvn8ebNG+jo6KBO3XoYOWYszMzMFRy5dBzCk44JFMmFjo4OdHR0vro+IyMDampqPzCigklNTYVThYpo16ETxo3OmwCmpqaiumtN/OTVArNnTlNAhIWXmpoKJ6evHxsA1PNoAL858yS31VSL33Mkzc7d+5CTky25/ezpUwwZ2Bc/NfdWYFSyS/vs+Rr/xfMlFosxdtRwqKioYumKNdDW1saObVswZGA/7D90DJpaWgqKOi8ddWWs6FIZoW8SMPnIv4hLzURZAw0kpWdJ+gxrYIsaZfUx7+9niExIh5u1PkY3KY8PyRm48vIjAOBJdBKCHscgKjEDehoq6F2nLBa2d4bPllvIESvq6IS+9fmRlpaGRw8fYuDgYXCqUAEJCQlYNH8eRvsOw649+xUUMckTE6j/qH379sHPzw/Pnj2DlpYWatSogcOHD+Phw4f47bffcPv2bWRmZqJ69epYunQpXF1dAQC2trYAgA4dOgAAbGxsEBYWhpkzZ+LQoUMIDQ0FAPTp0wdxcXGoVasWVq9eDXV1dbx8+RKvX7/GuHHjcPr0aSgpKaFBgwZYvny5ZL8/Wv0GDVG/QcOvrm/dth0A4N3bNz8qJLmRdmwAoKamBhMT0x8UUdEwMjIS3N60cT3KlbOGW63aCoqocDwaNITHV56v8FdhuHf3DvYePAp7B0cAwG/TZuKnJvVx6uRxdOjU5UeG+k3da5ZBdGIGFp55LmmLTEgX9KlsqYu/H0XjztsEAMDxB9Fo42KOiuY6kgTq+INoSf+oxHRsuvoaG32qwUJPHe/ihftTlG+9x3R1dbFu4yZB26TfpuGX7l0QEfEOlpZWPyLEQuNZeNJxDtR/UEREBLp3745+/frh0aNHOHfuHDp27AixWIzExET07t0bly5dwrVr1+Do6IiWLVsiMTERABASEgIA2Lx5MyIiIiS38xMUFITHjx8jMDAQx44dQ2ZmJry8vKCrq4uLFy/i8uXL0NHRgbe3NzIyMn7IsZPQjRvX0bRRPbRv4425s2ciLu6jokP6LpmZGThx7AjadegEUSn6Bvj0/lBTV5e0KSkpQU1VDaG3bioqrHy5lzfEk+gkzGjhhP0D3PB796poVdlM0OdBRCLqlTeCiXZuxbN6WT2UNdDEjfC4fPepoaIEb2dTvItPQ3Riyf2sSExKhEgkgq6unqJDkUokx6W0YgXqPygiIgJZWVno2LEjbGxsAAAuLi4AgKZNmwr6rl+/HgYGBjh//jxat24NU9PcaoWBgQEsLCy+eT/a2trYuHGjZOhux44dyMnJwcaNGyVfbps3b4aBgQHOnTuH5s2by/U46dvq1W+App7NUaZMGbx5/RorVyyF79BB2LpjN5SVlRUdXqH8E3QGiYmJaNu+g6JDkStbu/KwsLTCqmVLMGW6HzS1NLFz21ZERUUi5n2MosMTsNLTQFsXC+y9/Q47b7xBBTMd+DayQ2a2GKf/zY115fmXGNu0PPb0r4ms7BzkAAgIeo677xIF+2rrYo7BHjbQVFNGeGwqfj30EFnFZfxORunp6VixdDG8W7b65nQHKjmYQP0HVatWDc2aNYOLiwu8vLzQvHlzdO7cGYaGhoiKisLUqVNx7tw5REdHIzs7GykpKQgPD5f5flxcXATznu7cuYNnz55BV1dX0C8tLQ3Pnz//cnMAuR866enCcn22khrUP/tLnArHu0Uryf8dnSrA0akC2rT8CTdCrqNOXXcFRlZ4hw7sh0f9hiVikq4sVFVVsXjpCsyaMRWN69eBsrIyatd1h0f9hhCLi1dCIRIBT6KT8cfV1wCAZzEpsDPWQhsXc0kC1aGqBZwtdDHl6L+ISkhH1TJ6GNW4PD4kZ+LW63jJvoIev8fN8HgYa6uiq6sVprdwwoi995GZXbyOWZrMzEz8Om40xOLcodeSQKkUVXCLCofw/oOUlZURGBiIkydPwtnZGStXrkSFChXw8uVL9O7dG6GhoVi+fDmuXLmC0NBQGBsbF2qITVtbW3A7KSkJNWvWRGhoqGB58uQJevToke8+/P39oa+vL1gWL/Av1HHTt5UtVw4GhoZ4Hf5K0aEUyrt3bxF87Qo6dOqs6FCKhHPlKti97xDOXwnB6X8uYvW6jYiPj0OZsuUUHZpAbHImwmJTBG3hH1Nhrpv7R4+ashL617PGmothuPryI158SMGhu5E4+/Q9uroK5wUlZ2TjbXwa7r5LxMwTT1DOUBMN7IVz3oq7zMxMTBw3BhHv3mHthj9KTPWJQ3jSsQL1HyUSieDh4QEPDw9Mnz4dNjY2OHjwIC5fvow1a9agZcuWAIDXr1/j/fv3gm1VVVWRnZ2d326/ydXVFX/99RfMzMygp1ewOQCTJ0/G2LFjBW3ZSiXvTLGSICoyEvFxcTAxNZPeuRg6fPAAjIyM0aBhY0WHUqQ+VXDDX4Xh4YP7GOo7UsERCd2PSEQ5A01BW1kDDUQl5laSVZRFUFVWwpeFs5ycb18zSCTK/TJWVS45f/d/Sp7Cw19h/aatMDAwVHRIJEdMoP6DgoODERQUhObNm8PMzAzBwcGIiYlBpUqV4OjoiO3bt8PNzQ0JCQmYMGECNDWFH4a2trYICgqCh4cH1NXVYWhYsA8FHx8fLFq0CO3atcOsWbNQtmxZvHr1CgcOHMCvv/6KsmXL5tlGXV09z3BdSqb8yvcpKcl4/dnw5Nu3b/D430fQ09eHpaUV4uPjEBkRgejo3DOCwl6+BAAYm5gU+7PXvnVs+vr6+H3tajTzbA4TExO8fv0ay5csQjlra9TzqK/AqAsnJycHRw4dQJt27aGiUjI/1qS9FgP/PgVDI0NYWFjh2dMnWLRgLho3bQb3esXr+dp3+x1WdqmCHm5lcO7pB1Q010GrKuZY8s8LAEBKRjZC38RjcH0bpGflICoxHdXK6KF5JVOsvRgGALDUU0djJ2PceBWP+NRMmOqoobtbGaRn5SA4rPic6PCt58zExBQTxo7Cvw8fYvnqdcjJycb7/5+vpq+vD9XifsmQ0lw6kpOS+UlD30VPTw8XLlzAsmXLkJCQABsbGwQEBKBFixawsLDAoEGD4OrqinLlymHevHkYP368YPuAgACMHTsWGzZsQJkyZRAWFlag+9XS0sKFCxcwceJEdOzYEYmJiShTpgyaNWtW4IqUvD28fx8D+/WW3A5YOB8A0KZde8yaOx/nz/6DGVN/k6yfNCG3GjZ46HAMGV68Lxz68MEXx7bo/4+tbXv8Nm0mnj55jKNHDiExIRGmZqZwd/fAMN9RxfJ6XdJcu3oFERHv0L5DJ0WHUmgPH9zHoM+eryWfPV9+c+fj/ftoLFk0Hx8+fICJqSlat2mHgUOGKircr3ocnYzpxx9jQD0b9KpdFhEJaVhzIQxBj/9XyZ596ikG1rPGFC9H6GqoICohHX9cDceRe7kX0szIzkFVKz10qm4JXXUVfEzJxN23CRi59z7iUrO+dtc/3Lc+P4YM88X5s/8AALp1bi/YbsOmrXCrXeeHxVkYvJCmdCJxcZuBSCSFPCtQxUopPazSdDmBL+WU0o/PNuuuKTqEInFsaF1Fh1BktFTl+z4Lfh4vvVMB1bHXl9u+ihNWoIiIiEigFP/dIzdMoIiIiEiA+ZN0Jed0BiIiIqJighUoIiIiEmIJSiomUERERCTAs/Ck4xAeERERkYxYgSIiIiIBnoUnHRMoIiIiEmD+JB2H8IiIiIhkxAoUERERCbEEJRUTKCIiIhLgWXjScQiPiIiISEasQBEREZEAz8KTjgkUERERCTB/ko5DeEREREQyYgWKiIiIhFiCkooVKCIiIhIQyfGfLPz9/VGrVi3o6urCzMwM7du3x+PHjwV90tLSMHz4cBgbG0NHRwedOnVCVFSUoE94eDhatWoFLS0tmJmZYcKECcjKyvrux+VzTKCIiIioWDh//jyGDx+Oa9euITAwEJmZmWjevDmSk5MlfcaMGYOjR49i7969OH/+PN69e4eOHTtK1mdnZ6NVq1bIyMjAlStXsHXrVmzZsgXTp0+Xa6wisVgsluseiYpYSmYpfcmW0sMSleLTeXJK6cdnm3XXFB1CkTg2tK6iQygyWqryfZ/de5Mkt325lNUp9LYxMTEwMzPD+fPn0bBhQ8THx8PU1BS7du1C586dAQD//vsvKlWqhKtXr6Ju3bo4efIkWrdujXfv3sHc3BwAsG7dOkycOBExMTFQU1OTy3GxAkVEREQCIjku3yM+Ph4AYGRkBAC4efMmMjMz4enpKelTsWJFWFtb4+rVqwCAq1evwsXFRZI8AYCXlxcSEhLw4MGD74zofziJnIiIiIpMeno60tPTBW3q6upQV1f/5nY5OTkYPXo0PDw8UKVKFQBAZGQk1NTUYGBgIOhrbm6OyMhISZ/Pk6dP6z+tkxdWoIiIiEhIjiUof39/6OvrCxZ/f3+pIQwfPhz379/H7t275X548sAKFBEREQnI87fwJk+ejLFjxwrapFWffH19cezYMVy4cAFly5aVtFtYWCAjIwNxcXGCKlRUVBQsLCwkfa5fvy7Y36ez9D71kQdWoIiIiKjIqKurQ09PT7B8LYESi8Xw9fXFwYMH8c8//8DOzk6wvmbNmlBVVUVQUJCk7fHjxwgPD4e7uzsAwN3dHffu3UN0dLSkT2BgIPT09ODs7Cy342IFioiIiAQUdfLs8OHDsWvXLhw+fBi6urqSOUv6+vrQ1NSEvr4++vfvj7Fjx8LIyAh6enoYMWIE3N3dUbdu7lmWzZs3h7OzM3r27ImFCxciMjISU6dOxfDhw6VWvmTBBIqIiIgEFHXxkbVr1wIAGjduLGjfvHkz+vTpAwBYunQplJSU0KlTJ6Snp8PLywtr1qyR9FVWVsaxY8cwdOhQuLu7Q1tbG71798asWbPkGiuvA0UlDq8DVbLwOlAlD68DVfLI+zpQj94lS+9UQJWstOW2r+KECRSVOGnyvRo/Ef1HBJx/pugQisyUZg5y3d+jCDkmUJalM4HiEB4REREJyPMsvNKKZ+ERERERyYgVKCIiIhIoxVMX5YYJFBEREQkwf5KOQ3hEREREMmIFioiIiIRYgpKKCRQREREJ8Cw86TiER0RERCQjVqCIiIhIgGfhSccEioiIiASYP0nHITwiIiIiGbECRUREREIsQUnFBIqIiIgEeBaedBzCIyIiIpIRK1BEREQkwLPwpGMCRURERALMn6TjEB4RERGRjFiBIiIiIiGWoKRiAkVEREQCPAtPOg7hEREREcmIFSgiIiIS4Fl40jGBIiIiIgHmT9JxCI+IiIhIRqxAERERkQCH8KRjBaoAzp07B5FIhLi4OEWHQkRE9AOI5LiUTqxAFSMikQgHDx5E+/btZdrO1tYWo0ePxujRo4skLnkLCwuDnZ0dbt++jerVqys6nHzt3rUTWzf/gffvY+BUoSIm/TYNLlWrKjqs77Jn9y7s+etPvHv7FgBg7+CIwUOHoX6DRgqO7Pv8seF3BAWexsuXL6CuoYHq1Wtg9NjxsLUrr+jQvktpfb4+V5LfZ/f+3oPbh7eiUpN2qNVlEJI+ROHAtH759m04YBJsXRsAACL+DUXo0e34+O4VVNTVYV+nGWq07Q0lZeUfGT7JAROoHyQjIwNqamqKDoMK4NTJE1i80B9TZ/jBxaUadm7fiqGD++PwsVMwNjZWdHiFZmZugVFjxsPaxgZisRhHDx/CKN/h+Gv/QTg4OCo6vEK7EXIdP3f3QWUXF2RnZWPl8iUYMrA/Dhw5Di0tLUWHV2il9fn6pCS/z96HPcHTS6dgWMZO0qZlaIIu/tsF/Z5cPoUHgQdQxtkNABD75gWC1syAi/fP8Og9DilxHxD85yqIc3Lg1mnADz0GaTiEJ12pG8KztbXFsmXLBG3Vq1fHzJkzAeRWeTZu3IgOHTpAS0sLjo6OOHLkiKD/iRMn4OTkBE1NTTRp0gRhYWF57ufSpUto0KABNDU1Ua5cOYwcORLJycmCOGbPno1evXpBT08PgwYNQkZGBnx9fWFpaQkNDQ3Y2NjA399f0h8AOnToAJFIJLn9/PlztGvXDubm5tDR0UGtWrVw5swZyf00btwYr169wpgxYyASiSD67FVfkBjnzJmDXr16QUdHBzY2Njhy5AhiYmLQrl076OjooGrVqrhx44bMxz5v3jz069cPurq6sLa2xvr16yXr7exyP3Rq1KgBkUiExo0b5/NMKs72rZvRsXNXtO/QCfYODpg6ww8aGho4dGC/okP7Lo2bNEWDho1gY2MLW1s7jBg1BlpaWrh7J1TRoX2Xtev/QLsOHeHg4IgKFSti1tz5iIh4h0cPHyg6tO9SWp+vT0rq+ywzLRUXtyxCXZ8RUNPSkbQrKSlDU99IsISHXoWta32oamgCAMJuXoShlR2qtewBPTMrWDi5wLVDPzy+cByZaSmKOqR8cQBPulKXQBWEn58funbtirt376Jly5bw8fFBbGwsAOD169fo2LEj2rRpg9DQUAwYMACTJk0SbP/8+XN4e3ujU6dOuHv3Lv766y9cunQJvr6+gn6LFy9GtWrVcPv2bUybNg0rVqzAkSNHsGfPHjx+/Bg7d+6UJEohISEAgM2bNyMiIkJyOykpCS1btkRQUBBu374Nb29vtGnTBuHh4QCAAwcOoGzZspg1axYiIiIQEREhU4xLly6Fh4cHbt++jVatWqFnz57o1asXfvnlF9y6dQv29vbo1asXxGKxTPsNCAiAm5sbbt++jWHDhmHo0KF4/PgxAOD69esAgDNnziAiIgIHDhwo/JMpZ5kZGXj08AHquteTtCkpKaFu3Xq4e+e2AiOTr+zsbJw8cRypqSmoVq2GosORq6TERACAnr6+giORn9L2fJXk91nwX2tRtkotWFX89vPwIfwpPr55AYd6zSVtOVmZUFYVjkQoq6khOzMDH8KfFUm8VHT+k0N4ffr0Qffu3QEA8+bNw4oVK3D9+nV4e3tj7dq1sLe3R0BAAACgQoUKuHfvHhYsWCDZ3t/fHz4+PpI5R46OjlixYgUaNWqEtWvXQkNDAwDQtGlTjBs3TrJdeHg4HB0dUb9+fYhEItjY2EjWmZqaAgAMDAxgYWEhaa9WrRqqVasmuT179mwcPHgQR44cga+vL4yMjKCsrAxdXV3BdgWNsWXLlhg8eDAAYPr06Vi7di1q1aqFLl26AAAmTpwId3d3REVFwcLCQqb9Dhs2TLKPpUuX4uzZs6hQoYLkWI2NjQUxFwcf4z4iOzs7zxCCsbExXr58oaCo5Ofpk8fo2aMbMjLSoaWlhaUrVsPewUHRYclNTk4OFi6Yh+o1XOHo6KTocL5baX2+Sur77OWN84h9/QytJi6T2vfp5dPQtygHM3tnSZtVJVc8+ucwXoacg03NBkhL+Ii7J/4EAKTGxxZV2IXCITzp/pMJVNXPJilqa2tDT08P0dHRAIBHjx6hTp06gv7u7u6C23fu3MHdu3exc+dOSZtYLEZOTg5evnyJSpUqAQDc3NwE2/Xp0wc//fQTKlSoAG9vb7Ru3RrNmzfHtyQlJWHmzJk4fvw4IiIikJWVhdTUVEkF6msKGuPnj4W5uTkAwMXFJU9bdHQ0LCwsCrVfkUgECwsLyWMsi/T0dKSnpwvaxMrqUFdXl3lfBNja2mHP/kNISkpE4Om/Me23ifhjy45S8aUMAPPm+OH506fYsn2XokORi9L+fJUkybExCNm7Hj+NmJOnivSlrIx0vLxxHlVbdBO0Wzm7ombHfrj252pc2hoAZRVVuLTohuhnDwBR8RoQ4m/hSVfqEiglJSXJcNMnmZmZgtuqqqqC2yKRCDk5OQW+j6SkJAwePBgjR47Ms87a2lryf21tbcE6V1dXvHz5EidPnsSZM2fQtWtXeHp6Yt++fV+9r/HjxyMwMBCLFy+Gg4MDNDU10blzZ2RkZMglxs8fi0/zp/Jr+/T4FGa/n/Yjy2P8ib+/P/z8/ARtU6bNwNTpM2XeV0EYGhhCWVkZHz58ELR/+PABJiYmRXKfP5Kqmhqs/7/y6Vy5Ch7cv4edO7Zh+sxZCo7s+82bMwsXzp/Dpq07YF7MKpuFVVqfr5L4PvsQ/gxpiXE4Nv9/n33inBxEPbuPf88fhc+KQ1BSyj2T7tXty8jOSId9nWZ59uPcrAMqNW2P1PhYqGnpIOlDFG4f3gpdk9Lxmv0vKXUJlKmpqWQeEAAkJCTg5cuXBd6+UqVKeSaVX7t2TXDb1dUVDx8+hEMh/grU09PDzz//jJ9//hmdO3eGt7c3YmNjYWRkBFVVVWRnZwv6X758GX369EGHDh0A5CYwX05qV1NTy7Pd98T4LfLY76ezEb+MOT+TJ0/G2LFjBW1i5aKrPqmqqaGSc2UEX7uKps08AeQmj8HBV9Gt+y9Fdr+KkpOTg0wpyXhxJxaL4T93Nv4JCsQfW7ajbNlyig6pyJSG5wsome8zy4rV0GbqakHblW3LoG9RFpWbd5YkTwDw7MpplK1aBxq6+c/DE4lE0DLIHb4Mu3EeWoamMLK2L7rgC4MFKKmKV81QDpo2bYrt27fj4sWLuHfvHnr37g1lGa6vMWTIEDx9+hQTJkzA48ePsWvXLmzZskXQZ+LEibhy5Qp8fX0RGhqKp0+f4vDhw3kmUn9pyZIl+PPPP/Hvv//iyZMn2Lt3LywsLGBgYAAg9+y1oKAgREZG4uPHjwBy5xgdOHAAoaGhuHPnDnr06JGnkmNra4sLFy7g7du3eP/+/XfFKI089mtmZgZNTU2cOnUKUVFRiI+P/2pfdXV16OnpCZaiHr7r2bsvDuzbgyOHDuLF8+eYM2smUlNT0b5DxyK936K2fGkAbt4Iwdu3b/D0yWMsXxqAGyHX0bJ1G0WH9l3mzfbDiWNHMH9hALS1tPE+JgbvY2KQlpam6NC+S2l9vj4pae8zVQ0tGFrZChYVdQ2oa+vB0MpW0i8h+h2int2HY738p2fcD9yPj2/DEPfuFe6e+BP3T+9D7S6DBQlYccCz8KQrdRWoyZMn4+XLl2jdujX09fUxe/ZsmSpQ1tbW2L9/P8aMGYOVK1eidu3aklPyP6latSrOnz+PKVOmoEGDBhCLxbC3t8fPP//8zX3r6upi4cKFePr0KZSVlVGrVi2cOHECSkq5eWxAQADGjh2LDRs2oEyZMggLC8OSJUvQr18/1KtXDyYmJpg4cSISEhIE+501axYGDx4Me3t7pKenQywWFzpGaeSxXxUVFaxYsQKzZs3C9OnT0aBBA5w7d+674pIn7xYt8TE2FmtWrcD79zGoULES1vy+EcbFdGihoGJjP2Dq5ImIiYmGjq4unJwqYO36P+Bez0PRoX2XPX/lTsLt36enoH3WHH+0K6ZfxgVRWp+vT0rr++zZ1UBoGZjAqpJrvuvfPbiBe6f+Qk5WJgzL2KHJkGkoU9kt375UvInEX04YIirm0rIUHQERlUQB50vvpQKmNJPvdI3oxEzpnQrITFdVeqcSqNRVoIiIiOj78Cw86UrdHCgiIiKiosYKFBEREQmxACUVEygiIiISYP4kHYfwiIiIiGTEChQREREJ8LfwpGMCRURERAI8C086DuERERERyYgVKCIiIhLgEJ50rEARERERyYgJFBEREZGMOIRHREREAhzCk44JFBEREQnwLDzpOIRHREREJCNWoIiIiEiAQ3jSMYEiIiIiAeZP0nEIj4iIiEhGrEARERGREEtQUjGBIiIiIgGehScdh/CIiIiIZMQKFBEREQnwLDzpmEARERGRAPMn6TiER0RERCQjJlBEREQkJJLjUgirV6+Gra0tNDQ0UKdOHVy/fv17jqZIMIEiIiIiAZEc/8nqr7/+wtixYzFjxgzcunUL1apVg5eXF6Kjo4vgSAuPCRQREREVG0uWLMHAgQPRt29fODs7Y926ddDS0sKmTZsUHZoAJ5ETERGRgDzPwktPT0d6erqgTV1dHerq6nn6ZmRk4ObNm5g8ebKkTUlJCZ6enrh69ar8gpIHMRHlKy0tTTxjxgxxWlqaokORKx5XyVNaj43H9d8wY8YMMQDBMmPGjHz7vn37VgxAfOXKFUH7hAkTxLVr1/4B0RacSCwWixWawREVUwkJCdDX10d8fDz09PQUHY7c8LhKntJ6bDyu/wZZKlDv3r1DmTJlcOXKFbi7u0vaf/31V5w/fx7BwcFFHm9BcQiPiIiIiszXkqX8mJiYQFlZGVFRUYL2qKgoWFhYFEV4hcZJ5ERERFQsqKmpoWbNmggKCpK05eTkICgoSFCRKg5YgSIiIqJiY+zYsejduzfc3NxQu3ZtLFu2DMnJyejbt6+iQxNgAkX0Ferq6pgxY0aBS88lBY+r5Cmtx8bjovz8/PPPiImJwfTp0xEZGYnq1avj1KlTMDc3V3RoApxETkRERCQjzoEiIiIikhETKCIiIiIZMYEiIiIikhETKCIiIiIZMYEiIiIikhETKKL/gPDwcOR3wq1YLEZ4eLgCIqL/sqysLJw5cwa///47EhMTAeT+hEdSUpKCIyMqOF7GgOgzZ8+eRZMmTRQdhtwpKysjIiICZmZmgvYPHz7AzMwM2dnZCopMPnJycvDs2TNER0cjJydHsK5hw4YKiqrwPnz4gOnTp+Ps2bP5HlNsbKyCIvt+r169gre3N8LDw5Geno4nT56gfPnyGDVqFNLT07Fu3TpFh1goTZs2xYEDB2BgYCBoT0hIQPv27fHPP/8oJjAqMryQJtFnvL29UbZsWfTt2xe9e/dGuXLlFB2SXIjFYohEojztSUlJ0NDQUEBE8nPt2jX06NEDr169ylNlE4lEJTI57NmzJ549e4b+/fvD3Nw83+eupBo1ahTc3Nxw584dGBsbS9o7dOiAgQMHKjCy73Pu3DlkZGTkaU9LS8PFixcVEBEVNSZQRJ95+/Yttm/fjq1bt8LPzw9NmzZF//790b59e6ipqSk6PJmNHTsWQG4iMW3aNGhpaUnWZWdnIzg4GNWrV1dQdPIxZMgQuLm54fjx47C0tCwVycbFixdx6dIlVKtWTdGhyN3Fixdx5cqVPO8nW1tbvH37VkFRFd7du3cl/3/48CEiIyMlt7Ozs3Hq1CmUKVNGEaFREWMCRfQZExMTjBkzBmPGjMGtW7ewefNmDBs2DMOGDUOPHj3Qv3//EvWldvv2bQC5Fah79+4JvrTU1NRQrVo1jB8/XlHhycXTp0+xb98+ODg4KDoUualYsSJSU1MVHUaRyMnJybcq+ObNG+jq6iogou9TvXp1iEQiiEQiNG3aNM96TU1NrFy5UgGRUVHjHCiib3j37h3Wr1+P+fPnQ0VFBWlpaXB3d8e6detQuXJlRYdXYH379sXy5cuhp6en6FDkrmnTpvj111/h7e2t6FDkJiQkBJMmTcL06dNRpUoVqKqqCtaX5Ofx559/hr6+PtavXw9dXV3cvXsXpqamaNeuHaytrbF582ZFhyiTT0PH5cuXx/Xr12FqaipZp6amBjMzMygrKyswQioqTKCIvpCZmYnDhw9j06ZNCAwMhJubG/r374/u3bsjJiYGU6dOxa1bt/Dw4UNFh0oADh48iKlTp2LChAlwcXHJk2xUrVpVQZEV3tOnT9GjRw/cunVL0P5pLltJnNf1yevXr+Ht7Q2xWIynT5/Czc0NT58+hYmJCS5cuJDnRAei4ooJFNFnRowYgT///BNisRg9e/bEgAEDUKVKFUGfyMhIWFlZ5TkzqjhLTk7G/PnzERQUlO9ZXS9evFBQZN9PSSnv1VhEIlGJTjZq164NFRUVjBo1Kt9J5I0aNVJQZPKRlZWFv/76C3fu3EFSUhJcXV3h4+MDTU1NRYf2XZ4+ffrVMyenT5+uoKioqDCBIvpMs2bNMGDAAHTs2BHq6ur59snKysLly5dL1JdY9+7dcf78efTs2TPfidajRo1SUGTf79WrV99cb2Nj84MikR8tLS3cvn0bFSpUUHQocpWZmYmKFSvi2LFjqFSpkqLDkasNGzZg6NChMDExgYWFheA9JhKJ8lQTqeRjAkX0H2BgYIDjx4/Dw8ND0aFQATRs2BDTp0+Hp6enokORuzJlyuDMmTOlLoGysbHBsGHDMHHiREWHQj8Iz8Ij+kJpLMMbGhrCyMhI0WEUmefPn2PZsmV49OgRAMDZ2RmjRo2Cvb29giMrnBEjRmDUqFGlal7XJ8OHD8eCBQuwceNGqKiUnq+gjx8/okuXLooOg34gVqCIPlNay/A7duzA4cOHsXXrVsG1oEqDv//+G23btkX16tUlFbbLly/jzp07OHr0KH766ScFRyi70jiv65MOHTogKCgIOjo6cHFxgba2tmD9gQMHFBTZ9+nfvz9q1aqFIUOGKDoU+kGYQBF9prSW4WvUqIHnz59DLBbD1tY2T0WjpCaGQO6xeXl5Yf78+YL2SZMm4fTp0yXy2ErjvK5P+vbt+831Je0yBp/4+/tjyZIlaNWqVb5Vw5EjRyooMioqTKCIPqOnp4fQ0FCUL19e0aHIlZ+f3zfXz5gx4wdFIn8aGhq4d+8eHB0dBe1PnjxB1apVkZaWpqDI6L/Ezs7uq+tEIlGJPtOV8ld6BqCJ5KBLly44ffp0qSvDl+QESRpTU1OEhobmSaBCQ0NL7DWFtm7dChMTE7Rq1QoA8Ouvv2L9+vVwdnbGn3/+WaIrUKXVy5cvFR0C/WBMoIg+4+DggGnTpuHatWulrgwfFxeHffv24fnz55gwYQKMjIxw69YtmJubl+jf6ho4cCAGDRqEFy9eoF69egBy50AtWLBA8luAJc28efOwdu1aAMDVq1exatUqLFu2DMeOHcOYMWNK3DwhV1dXBAUFwdDQEDVq1Pjm7xWWxCHXz2VkZODly5ewt7cvVZPkKS8O4RF9prSW4e/evQtPT0/o6+sjLCwMjx8/Rvny5TF16lSEh4dj27Ztig6x0MRiMZYtW4aAgAC8e/cOAGBlZYUJEyZg5MiRJfLHhbW0tPDvv//C2toaEydOREREBLZt24YHDx6gcePGiImJUXSIMvHz88OECROgpaWFmTNnfvM5KanV0pSUFIwYMQJbt24FkDuEXL58eYwYMQJlypTBpEmTFBwhyRsTKKL/AE9PT7i6umLhwoXQ1dXFnTt3UL58eVy5cgU9evRAWFiYokOUi8TERAAokT9K+zkzMzP8/fffqFGjBmrUqIGxY8eiZ8+eeP78OapVq4akpCRFh0hfGDVqFC5fvoxly5bB29sbd+/eRfny5XH48GHMnDlT8sPeVHrkPVeWiADkVjZKy98XISEhGDx4cJ72MmXKIDIyUgERFQ1dXd0SnzwBwE8//YQBAwZgwIABePLkCVq2bAkAePDgAWxtbRUb3HcqX748Pnz4kKc9Li6uRJ+8cejQIaxatQr169cXVNgqV66M58+fKzAyKipMoIi+sG3bNri4uEBTUxOampqoWrUqtm/fruiwvou6ujoSEhLytD958kTw6/ElhaurKz5+/Agg9zIGrq6uX11KotWrV8Pd3R0xMTHYv38/jI2NAQA3b95E9+7dFRzd9wkLC8v3Olbp6el48+aNAiKSj5iYmHxPWkhOTi6Rw8gkHWe4EX1myZIlmDZtGnx9fSUXZbx06RKGDBmC9+/fY8yYMQqOsHDatm2LWbNmYc+ePQBy53OFh4dj4sSJ6NSpk4Kjk127du0kv1XYrl27UvcFZWBggFWrVuVpl3Y5iuLsyJEjkv///fff0NfXl9zOzs5GUFDQN+cgFndubm44fvw4RowYAQCS1+TGjRvh7u6uyNCoiHAOFNFn7Ozs4Ofnh169egnat27dipkzZ5bYU5Xj4+PRuXNn3LhxA4mJibCyskJkZCTc3d1x4sSJPFeDpuIhJSUF4eHhyMjIELSXxJ9y+XR19U9XVP+cqqoqbG1tERAQgNatWysivO926dIltGjRAr/88gu2bNmCwYMH4+HDh7hy5QrOnz+PmjVrKjpEkjMmUESf0dDQwP379+Hg4CBof/r0KVxcXEr8RRkvXbqEu3fvIikpCa6urqXix2rLly+PkJAQyTDXJ3FxcXB1dS2RZ07GxMSgT58+OHXqVL7rS/JPudjZ2SEkJAQmJiaKDkXunj9/jvnz5+POnTuS99jEiRPh4uKi6NCoCHAIj+gzDg4O2LNnD3777TdB+19//ZXnQo0lUf369VG/fn1FhyFXpXFOzejRoxEfH4/g4GA0btwYBw8eRFRUFObMmYOAgABFh/ddSmoVtyDs7e2xYcMGRYdBPwgTKKLP+Pn54eeff8aFCxcEP0wbFBQkmT9UUoWEhODs2bOIjo5GTk6OYN2SJUsUFFXhleY5Nf/88w8OHz4MNzc3KCkpwcbGBj/99BP09PTg7+8vuUJ5SZWcnIzz58/nOzxZki9WCwDR0dH5vsdK4rArfRuH8Ii+cOvWLSxZsgSPHj3C/7V393E13/0fwF8nndJ9Grlp6UakFCuGbK5Yzd1G5HY0dYXL3bDE6Dcxd2G7MHZtMiEhNyt6GC53WeU20R1DESmuGrFsFUqd3x9+zm9nZde6OX12zvf1fDw8Hp3P96iXHk69z+fz+b4/AODk5ITg4GC4ubkJTlZ3YWFhWLBgARwdHdGyZUuVTdcymQwnT54UmK5utHlPjampKTIzM2FrawsbGxtER0fjrbfewu3bt9GpUyeUlZWJjlhnaWlpGDRoEMrKylBaWgoLCwsUFRXB0NAQlpaWGrnkCry4Q9Lf3x/Xrl2r9v9RJpNp9LIr1YwzUET/p6KiApMnT0ZoaCh27NghOk6DWrduHbZs2YKAgADRURrMy3f42rinxtHREVlZWbC1tUWXLl2wceNG2NraIjw8HK1btxYdr16CgoIwePBghIeHw8zMDOfPn4dcLoefnx9mzZolOl6dBQYGokOHDti8eXO1NymknTgDRfQbZmZmSE9P19iln1dp3bo1kpKStGIf159RXFwMc3Nz0THqbMeOHXj+/DkCAgJw6dIlDBgwAI8ePYKenh4iIyMxevRo0RHrzNzcHMnJyXB0dIS5uTnOnTsHJycnJCcnw9/fH9evXxcdsU5MTEyQlpZW7QYU0l5spEn0G0OHDkVcXJzoGA0uKCgIX3/9tegYarFq1Srs2bNH+XjkyJGwsLCAlZUVMjIyBCarOz8/P+VsYdeuXXHnzh2kpKQgPz9fo4sn4MXy6svlV0tLS+Tl5QF48eYlPz9fZLR68fLy0tj/b1Q3nIEi+o2Xdzl5eXmha9eu1fojaeoG16qqKrz33nvIzs6Gs7Mz5HK5yvV9+/YJSlZ/dnZ22LlzJ3r16oXjx49j1KhR2LNnD/bu3Yu8vDwcO3ZMdET6jX79+iEgIABjx47FpEmTkJmZiZkzZ2L79u34+eefkZycLDpinRQVFcHf3x/du3eHi4tLtdfYkCFDBCUjdWEBRfQbf7R0J5PJNHaD60cffYSIiAj07du3xv0ZW7duFZSs/gwMDJCdnQ1ra2vMmjULT58+xcaNG5GdnY0ePXooj3zRJMOHD0f37t0xb948lfHPP/8cKSkp+O677wQlq7+XzVz79u2L+/fvY/z48Th79iw6dOiAiIgIvPHGG6Ij1sn333+PDz/8sMYjk7iJXDuxgCKSABMTE+zevVvjb3+vSZs2bRATE4NevXrB0dERy5Ytw8iRI5GVlYU333yzxl9of3UtWrTAyZMnqzVgvHz5Mry9vfHTTz8JSlZ/T548gUKhgKGhIYAXfbz2798PZ2dn9O/fX3C6urO1tcX777+P0NBQtGzZUnQcagS8C48kb/bs2Vi6dCmMjIwwe/bsVz5PJpNpbBNDCwsLtGvXTnQMtfD19cXYsWPRvn17PHz4EAMHDgQAjd7QW1JSAj09vWrjcrlcIwvC3/Lx8YGvry+mTJmC4uJi9OzZE3K5HEVFRVizZg2mTp0qOmKdPHz4EEFBQSyeJISbyEny0tLSUFFRofz4j/5oqs8++wyLFi3S6P5Br7J27Vp89NFHcHZ2xvHjx2FsbAwAKCgowLRp0wSnqxtXV1eVjfEv7d69G87OzgISNZzU1FT07t0bABATE4OWLVvizp07iIqKwvr16wWnqztfX1/88MMPomNQI+ISHpEEuLm5IScnBwqFAra2ttU2uKampgpKRjX5/vvvlTNr77zzDgAgPj4eu3btwnfffYehQ4eKDVgPhoaGuH79Otq2bYtRo0ahU6dOWLRoEfLz8+Ho6KixRf7y5cvx5Zdf4r333oOrq2u115im3oBCr8YCikgCFi9e/IfXFy1a1EhJ1GP79u3YuHEjbt26hXPnzsHGxgZffvkl7Ozs4OPjIzpenRw6dAhhYWFIT0+HgYEBOnfujEWLFsHT01N0tHrp3LkzJk6ciGHDhsHFxQVHjhyBh4cHLl26hPfeew+FhYWiI9aJtt6AQq/GAoqINNqGDRuwcOFCfPzxx1i+fDmuXLkCe3t7REZGYtu2bRq3rPL8+XOEhYUhMDAQr7/+uug4DS4mJgZjx45FZWUlvLy8lG0mVqxYgaSkJPz73/8WnJDoz2EBRSQRxcXFiImJQU5ODubOnQsLCwukpqaiZcuWsLKyEh2vzpydnREWFoahQ4fCxMQEGRkZsLe3x5UrV9CnTx8UFRWJjlhrxsbGuHLlCmxtbUVHUYvCwkIUFBSgS5cuyqaaFy5cgKmpKTp27Cg4Xf2Ul5fj9u3baNeuHXR1eZ+WNuMmciIJyMzMRIcOHbBq1Sr885//RHFxMYAXDTRDQkLEhqun27dv13jQs76+PkpLSwUkqj8vLy8kJiaKjqE2rVq1gpubm7J4AoDu3btrdPFUVlaGCRMmwNDQEJ06dVJ2WJ8xYwZWrlwpOB2pAwsoIgmYPXs2AgICcOPGDTRt2lQ5PmjQICQlJQlMVn92dnZIT0+vNn7kyBE4OTk1fqAGMHDgQMyfPx9z5szBrl27cODAAZU/9NcTEhKCjIwMJCQkqLzGvL29a7yjkjQf5xeJJCAlJQUbN26sNm5lZaWxm3Zfmj17NqZPn46nT59CoVDgwoUL2LVrF1asWIGIiAjR8erkZfuFNWvWVLvGrtZ/TXFxcdizZw969uyp0um/U6dOyMnJEZiM1IUFFJEE6Ovr19iAMTs7Gy1atBCQqOFMnDgRBgYGWLBgAcrKyjB27Fi0adMG69atw5gxY0THq5OqqirREaiWHjx4AEtLy2rjpaWl1Y5OIu3AJTwiCRgyZAiWLFmibBgqk8mQl5eHefPmYfjw4YLT1d+4ceNw48YNlJSUoLCwEHfv3sWECRNExyIJ6datGw4dOqR8/LJoioiIgIeHh6hYpEa8C49IAh4/fowRI0YoD3Jt06YNCgsL4eHhgcOHD8PIyEh0RPqd0tJSJCYmIi8vD+Xl5SrX2JTxr+f06dMYOHAg/Pz8EBkZicmTJ+Pq1as4e/YsEhMT0bVrV9ERqYGxgCKSkDNnziAjIwMlJSVwd3eHt7e36Ej1Zmdn94dLJJrYwDAtLQ2DBg1CWVkZSktLYWFhgaKiIhgaGsLS0lIj/01SkJOTg5UrV6q8xubNm1ftUGjSDiygiCQgKioKo0ePhr6+vsp4eXk5du/ejfHjxwtKVn/r1q1TeVxRUYG0tDQcOXIEc+fOxfz58wUlq7s+ffqgQ4cOCA8Ph5mZGTIyMiCXy+Hn54dZs2bB19dXdEQiyWMBRSQBTZo0QUFBQbVNrg8fPoSlpaVW3tX19ddf4+LFi9i6davoKLVmbm6O5ORkODo6wtzcHOfOnYOTkxOSk5Ph7++P69evi45IvyPF15jUcRM5kQQoFIoal7nu3r0LMzMzAYnUb+DAgYiNjRUdo07kcrmyyaSlpaWyKaOZmRny8/NFRqNXeNVcxLNnz6Cnp9fIaagxsI0BkRZzc3ODTCaDTCaDl5eXytESlZWVuH37NgYMGCAwofrExMTAwsJCdIw6cXNzQ0pKCtq3bw9PT08sXLgQRUVF2L59O1xcXETHo99Yv349gBd33UVERMDY2Fh5rbKyEklJSRrdYZ1ejQUUkRYbOnQoACA9PR39+/dX+eGup6cHW1tbjW9j8LJIfEmhUKCwsBAPHjzAN998IzBZ3YWFheHXX38FACxfvhzjx4/H1KlT0aFDB41tDqqt1q5dC+DF/7vw8HA0adJEee3layw8PFxUPFIj7oEikoBt27Zh9OjRKkdMaIvFixerPNbR0UGLFi3Qp08fjX3n/+TJEygUChgaGgIAcnNzsX//fjg7O6N///6C01FN+vbti3379qFZs2aio1AjYQFFRPQX069fP/j6+mLKlCkoLi5Gx44dIZfLUVRUhDVr1mDq1KmiIxJJHpfwiCSgsrISa9euxd69e2tszPjo0SNByeqvpiNqXsXU1FSNSRpOamqqcmkoJiYGLVu2RFpaGmJjY7Fw4UIWUH9Rd+/exYEDB2p8jdV0riFpNhZQRBKwePFiREREIDg4GAsWLMCnn36K3NxcxMXFYeHChaLj1Yu5ufl/PWvs5V2ImnIreVlZGUxMTAAAx44dg6+vL3R0dNCzZ0/cuXNHcDqqSXx8PIYMGQJ7e3tcv34dLi4uyM3NhUKhgLu7u+h4pAZsY0AkATt37sSmTZsQHBwMXV1dfPDBB4iIiMDChQtx/vx50fHqZevWrbC0tMQnn3yC/fv3Y//+/fjkk0/QsmVLbNmyBSdPnsQPP/yAkydPio76pzk4OCAuLg75+fk4evQo+vXrBwC4f/++xsyiSU1ISAjmzJmDy5cvo2nTpoiNjUV+fj48PT0xcuRI0fFIHRREpPUMDQ0Vd+7cUSgUCkWrVq0Uly5dUigUCkVOTo7C1NRUZLR6e+eddxTR0dHVxnfu3Knw9PRs/EAN4LvvvlPI5XKFjo6O4t1331WOh4WFKQYMGCAwGb2KsbGx4ubNmwqFQqEwNzdXXLlyRaFQKBTp6ekKGxsbgclIXTgDRSQBr7/+OgoKCgAA7dq1w7FjxwAAKSkp1Y530TTnzp1Dt27dqo1369YNFy5cEJCo/kaMGIG8vDxcvHgRR44cUY57eXkp90bRX4uRkZFy31Pr1q2Rk5OjvFZUVCQqFqkRCygiCRg2bBji4+MBADNmzEBoaCjat2+P8ePHIzAwUHC6+rG2tsamTZuqjUdERMDa2lpAoobRqlUruLm5KTuSA0D37t01tjWDtuvZsydOnz4NABg0aBCCg4OxfPlyBAYGomfPnoLTkTqwjQGRBJ0/fx5nz55F+/btMXjwYNFx6uXw4cMYPnw4HBwc0KNHDwDAhQsXcOPGDcTGxmLQoEGCE5IU3Lp1CyUlJejcuTNKS0sRHBysfI2tWbMGNjY2oiNSA2MBRSQBSUlJ6NWrl8pRLgDw/PlznD17Fn/7298EJWsYd+/exYYNG3Dt2jUAgJOTE6ZMmaLRM1BE9NfGAopIAnhSPDBt2jQsWbIEzZs3Fx2FtJC9vT1SUlLw2muvqYwXFxfD3d0dt27dEpSM1IV7oIgkQPF/fZB+7+HDhzAyMhKQqPHt2LGjVk03iWojNze3xjciz549w7179wQkInVjI00iLebr6wvgxUnxAQEBKnfcVVZWIjMzE7169RIVr1Fxsp3U4cCBA8qPjx49CjMzM+XjyspKxMfHw9bWVkAyUjcWUERa7OUPc4VCARMTExgYGCiv6enpoWfPnpg0aZKoeEQab+jQoQBevEnx9/dXuSaXy2Fra4vVq1cLSEbqxgKKSItt3boVAGBra4s5c+ZIZrmOqLFUVVUBAOzs7JCSksI9dhLCTeREEvDkyRMoFAoYGhoCAO7cuYP9+/fD2dlZeUyItjMxMUFGRgbs7e1FRyGJKC4uhrm5uegYpCbcRE4kAT4+PoiKigLw4od69+7dsXr1avj4+GDDhg2C0xFpvlWrVmHPnj3KxyNHjoSFhQWsrKyQkZEhMBmpCwsoIglITU1F7969AQAxMTFo1aoV7ty5g6ioKKxfv15wusbh5+fHg3hJbcLDw5V9x44fP44TJ07gyJEjGDhwIObOnSs4HakD90ARSUBZWRlMTEwAAMeOHYOvry90dHTQs2dP3LlzR3C62svMzPzTz+3cuTMAcKaN1KqwsFBZQB08eBCjRo1Cv379YGtrq+yQT9qFBRSRBDg4OCAuLg7Dhg3D0aNHERQUBAC4f/++Rs7KvPHGG5DJZK9sTfDymkwmk0STUBKvWbNmyM/Ph7W1NY4cOYJly5YBeHEHLP8PaicWUEQSsHDhQowdOxZBQUHw8vKCh4cHgBezUW5uboLT1d7t27dFRyBS4evri7Fjx6J9+/Z4+PAhBg4cCABIS0uDg4OD4HSkDrwLj0giCgsLUVBQgC5dukBH58X2xwsXLsDU1BQdO3YUnI5Is1VUVGD9+vXIy8tDQECA8o3J2rVrYWJigokTJwpOSA2NBRSRlquoqICBgQHS09Ph4uIiOo7aXL16FXl5eSgvL1cZHzJkiKBEJBUVFRWYPHkyQkNDYWdnJzoONRIu4RFpOblcjrZt22rtPoxbt25h2LBhuHz5ssq+qJdn/2nrv5v+OuRyOWJjYxEaGio6CjUitjEgkoBPP/0U//M//4NHjx6JjtLgZs2aBTs7O9y/fx+Ghob48ccfkZSUhG7duiEhIUF0PJKIoUOHIi4uTnQMakRcwiOSADc3N9y8eRMVFRWwsbGpdqRLamqqoGT117x5c5w8eRKdO3eGmZkZLly4AEdHR5w8eRLBwcFIS0sTHZEkYNmyZVi9ejW8vLzQtWvXaq+xmTNnCkpG6sIlPCIJeHngqTaqrKxU9rhq3rw5/vOf/8DR0RE2NjbIysoSnI6kYvPmzTA3N8elS5dw6dIllWsymYwFlBZiAUUkAYsWLRIdQW1cXFyQkZEBOzs79OjRA59//jn09PTw7bff8tw7ajRsrSE93ANFJBHFxcWIiIhASEiIci9Uamoq7t27JzhZ/SxYsABVVVUAgCVLluD27dvo3bs3Dh8+LJljauivo7y8HFlZWXj+/LnoKKRm3ANFJAGZmZnw9vaGmZkZcnNzkZWVBXt7eyxYsAB5eXnKg4a1xaNHj9CsWTPlnXhE6lZWVoYZM2Zg27ZtAIDs7GzY29tjxowZsLKywvz58wUnpIbGGSgiCZg9ezYCAgJw48YNNG3aVDk+aNAgJCUlCUxWf48fP652d6GFhQV+/vln/PLLL4JSkdSEhIQgIyMDCQkJKq8xb29v7NmzR2AyUhcWUEQSkJKSgsmTJ1cbt7KyQmFhoYBEDWfMmDHYvXt3tfG9e/dizJgxAhKRFMXFxeFf//oX3n77bZWZz06dOiEnJ0dgMlIXFlBEEqCvr1/jbEx2djZatGghIFHDSU5ORt++fauN9+nTB8nJyQISkRQ9ePAAlpaW1cZLS0u5lKylWEARScCQIUOwZMkSVFRUAHhxW3VeXh7mzZuH4cOHC05XP8+ePatxw25FRQWePHkiIBFJUbdu3XDo0CHl45dFU0REhPLwbtIu3EROJAGPHz/GiBEjcPHiRfz6669o06YNCgsL4eHhgcOHD1dr+qdJ+vbtCxcXF3z11Vcq49OnT0dmZiZOnTolKBlJyenTpzFw4ED4+fkhMjISkydPxtWrV3H27FkkJiaia9euoiNSA2MBRSQhp0+fRmZmJkpKSuDu7g5vb2/RkertzJkz8Pb2xptvvgkvLy8AQHx8PFJSUnDs2DH07t1bcEKSipycHKxcuRIZGRnK19i8efPg6uoqOhqpAQsoIgnIz8+HtbW16Bhqk56eji+++ALp6ekwMDBA586dERISgvbt24uORkRaigUUkQQ0adIEb7/9Nvz8/DBixAg0a9ZMdCQijVebNhmmpqZqTEIisIAikoC0tDRER0dj9+7dePDgAQYMGAA/Pz8MHjwY+vr6ouPV2i+//KL8hfTffonxFxepi46Ozp++w66yslLNaaixsYAikhCFQoGEhARER0cjNjYWVVVV8PX1xZYtW0RHq5UmTZqgoKAAlpaWr/wlplAoIJPJ+IuL1CYxMVH5cW5uLubPn4+AgADlXXfnzp3Dtm3bsGLFCvj7+4uKSWrCAopIolJTUzFhwgRkZmZqXJGRmJiIt956C7q6uiq/xGri6enZSKlIyry8vDBx4kR88MEHKuPR0dH49ttvkZCQICYYqQ0LKCIJuXv3LqKjoxEdHY0rV67Aw8MD48aNw5QpU0RHq5Pnz58jLCwMgYGBeP3110XHIQkzNDRERkZGtRsXsrOz8cYbb6CsrExQMlIXNtIkkoCNGzfC09MTNjY2iIqKwujRo5GTk4NTp05pbPEEALq6uvjiiy9qbKRJ1Jisra2xadOmauMRERFafQeslHEGikgCrK2t8cEHH2DcuHHo0qWL6DgNysfHB76+vtxjQkIdPnwYw4cPh4ODA3r06AEAuHDhAm7cuIHY2FgMGjRIcEJqaCygiCRAoVDg8ePH2Lx5M65duwYAcHZ2xoQJE2BmZiY4Xf2Eh4dj8eLFGDduHLp27Vqtq/qQIUMEJSOpuXv3Lr755htcv34dAODk5IQpU6ZwBkpLsYAikoBLly6hf//+aNq0Kbp37w4ASElJwZMnT3Ds2DG4u7sLTlh3Ojqv3onAu/CISF1YQBFJQO/eveHg4IBNmzZBV1cXwIsN2BMnTsStW7eQlJQkOCGR5isuLsaFCxdw//59VFVVqVwbP368oFSkLiygiCTAwMAAaWlp6Nixo8r41atX0a1bN94hRFRP33//PcaNG4eSkhKYmpqq9CaTyWR49OiRwHSkDrwLj0gCTE1NkZeXV208Pz8fJiYmAhI1rMTERAwePBgODg5wcHDAkCFDcOrUKdGxSEKCg4MRGBiIkpISFBcX4+eff1b+YfGknVhAEUnA6NGjMWHCBOzZswf5+fnIz8/H7t27a2z8p2l27NgBb29vGBoaYubMmZg5cyYMDAzg5eWF6Oho0fFIIu7du4eZM2fC0NBQdBRqJFzCI5KA8vJyzJ07F+Hh4cqeSXK5HFOnTsXKlSs18jy8l5ycnPCPf/wDQUFBKuNr1qzBpk2blHcdEqmTr68vxowZg1GjRomOQo2EBRSRhJSVlSEnJwcA0K5dO614t6yvr48ff/wRDg4OKuM3b96Ei4sLnj59KigZScnmzZuxZMkS/P3vf4erqyvkcrnKdbbT0D66ogMQUeMxNDSEq6ur6BgNytraGvHx8dUKqBMnTrD/DjWaSZMmAQCWLFlS7RrbaWgnFlBEpNGCg4Mxc+ZMpKeno1evXgCAM2fOIDIyEuvWrROcjqTi920LSPtxCY+INN7+/fuxevVq5X4nJycnzJ07Fz4+PoKTkVTUNPP0kkwmQ2hoaCOmocbAAoqIiKie3NzcVB5XVFTg9u3b0NXVRbt27ZCamiooGakLl/CISKPZ29sjJSUFr732msp4cXEx3N3dcevWLUHJSErS0tKqjf3yyy8ICAjAsGHDBCQideMMFBFpNB0dHRQWFsLS0lJl/KeffkLbtm3x7NkzQcmIgMuXL2Pw4MHIzc0VHYUaGGegiEgjHThwQPnx0aNHYWZmpnxcWVmJ+Ph42NraCkhG9P8eP36Mx48fi45BasAZKCLSSDo6Lw5SkMlk+P2PMblcDltbW6xevRrvv/++iHgkMevXr1d5rFAoUFBQgO3bt8PT05Nd8bUQCygi0mh2dnZISUlB8+bNRUchCbOzs1N5rKOjgxYtWuCdd95BSEiIVpw5SapYQBGR1nj69CmaNm0qOgYRSQAPEyYijVZVVYWlS5fCysoKxsbGyrvuQkNDsXnzZsHpiEhbsYAiIo22bNkyREZG4vPPP4eenp5y3MXFBREREQKTEZE2YwFFRBotKioK3377LcaNG4cmTZoox7t06YLr168LTEZE2owFFBFptHv37lU7SBh4sbRXUVEhIBERSQELKCLSaM7Ozjh16lS18ZiYmGrHaxARNRQ20iQijbZw4UL4+/vj3r17qKqqwr59+5CVlYWoqCgcPHhQdDwi0lJsY0BEGu/UqVNYsmQJMjIyUFJSAnd3dyxcuBD9+vUTHY2ItBQLKCIiIqJa4hIeEWmF8vJy3L9/H1VVVSrjbdu2FZSIiLQZCygi0mg3btxAYGAgzp49qzKuUCggk8lQWVkpKBkRaTMWUESk0QICAqCrq4uDBw+idevWkMlkoiMRkQRwDxQRaTQjIyNcunQJHTt2FB2FiCSEfaCISKM5OzujqKhIdAwikhjOQBGRxvnll1+UH1+8eBELFixAWFgYXF1dIZfLVZ5ramra2PGISAJYQBGRxtHR0VHZ6/Ryw/hvcRM5EakTN5ETkcb54YcfAADPnj3DgAEDEB4eDkdHR8GpiEhKOANFRBqtRYsWOHv2LNq3by86ChFJCDeRE5FG8/Pzw+bNm0XHICKJ4RIeEWm058+fY8uWLThx4gS6du0KIyMjletr1qwRlIyItBkLKCLSaFeuXIG7uzsAIDs7W+Uam2oSkbpwDxQRERFRLXEPFBEREVEtsYAiIiIiqiUWUERERES1xAKKiIiIqJZYQBER/RcBAQEYOnSo8nGfPn3w8ccfN3qOhIQEyGQyFBcXN/rXJiJVLKCISGMFBARAJpNBJpNBT08PDg4OWLJkCZ4/f67Wr7tv3z4sXbr0Tz2XRQ+RdmIfKCLSaAMGDMDWrVvx7NkzHD58GNOnT4dcLkdISIjK88rLy6Gnp9cgX9PCwqJBPg8RaS7OQBGRRtPX10erVq1gY2ODqVOnwtvbGwcOHFAuuy1fvhxt2rRRHjacn5+PUaNGwdzcHBYWFvDx8UFubq7y81VWVmL27NkwNzfHa6+9hk8++QS/b5f3+yW8Z8+eYd68ebC2toa+vj4cHBywefNm5Obmom/fvgCAZs2aQSaTISAgAABQVVWFFStWwM7ODgYGBujSpQtiYmJUvs7hw4fRoUMHGBgYoG/fvio5iUgsFlBEpFUMDAxQXl4OAIiPj0dWVhaOHz+OgwcPoqKiAv3794eJiQlOnTqFM2fOwNjYGAMGDFD+ndWrVyMyMhJbtmzB6dOn8ejRI+zfv/8Pv+b48eOxa9curF+/HteuXcPGjRthbGwMa2trxMbGAgCysrJQUFCAdevWAQBWrFiBqKgohIeH48cff0RQUBD8/PyQmJgI4EWh5+vri8GDByM9PR0TJ07E/Pnz1fVtI6Ja4hIeEWkFhUKB+Ph4HD16FDNmzMCDBw9gZGSEiIgI5dLdjh07UFVVhYiICOUxL1u3boW5uTkSEhLQr18/fPnllwgJCYGvry8AIDw8HEePHn3l183OzsbevXtx/PhxeHt7AwDs7e2V118u91laWsLc3BzAixmrsLAwnDhxAh4eHsq/c/r0aWzcuBGenp7YsGED2rVrh9WrVwMAHB0dcfnyZaxataoBv2tEVFcsoIhIox08eBDGxsaoqKhAVVUVxo4di88++wzTp0+Hq6uryr6njIwM3Lx5EyYmJiqf4+nTp8jJycHjx49RUFCAHj16KK/p6uqiW7du1ZbxXkpPT0eTJk3g6en5pzPfvHkTZWVlePfdd1XGy8vL4ebmBgC4du2aSg4AymKLiMRjAUVEGq1v377YsGED9PT00KZNG+jq/v+PNSMjI5XnlpSUoGvXrti5c2e1z9OiRYs6fX0DA4Na/52SkhIAwKFDh2BlZaVyTV9fv045iKhxsYAiIo1mZGQEBweHP/Vcd3d37NmzB5aWljA1Na3xOa1bt0ZycjL+9re/AQCeP3+OS5cuwd3dvcbnu7q6oqqqComJicolvN96OQNWWVmpHHN2doa+vj7y8vJeOXPl5OSEAwcOqIydP3/+v/8jiahRcBM5EUnGuHHj0Lx5c/j4+ODUqVO4ffs2EhISMHPmTNy9excAMGvWLKxcuRJxcXG4fv06pk2b9oc9nGxtbeHv74/AwEDExcUpP+fevXsBADY2NpDJZDh48CAePHiAkpISmJiYYM6cOQgKCsK2bduQk5OD1NRUfPXVV9i2bRsAYMqUKbhx4wbmzp2LrKwsREdHIzIyUt3fIiL6k1hAEZFkGBoaIikpCW3btoWvry+cnJwwYcIEPH36VDkjFRwcjA8//BD+/v7w8PCAiYkJhg0b9oefd8OGDRgxYgSmTZuGjh07YtKkSSgtLQUAWFlZYfHixZg/fz5atmyJjz76CACwdOlShIaGYsWKFXBycsKAAQNw6NAh2NnZAQDatm2L2NhYxMXFoUuXLggPD0dYWJgavztEVBsyxat2RhIRERFRjTgDRURERFRLLKCIiIiIaokFFBEREVEtsYAiIiIiqiUWUERERES1xAKKiIiIqJZYQBERERHVEgsoIiIiolpiAUVERERUSyygiIiIiGqJBRQRERFRLbGAIiIiIqql/wWgm2pyLewgzAAAAABJRU5ErkJggg==", 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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Saved: outputs/classical/tfidf_lr/confusion_matrix_type_val.png\n" ] }, { - "output_type": "display_data", "data": { + "image/png": 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", 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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Saved: outputs/classical/tfidf_lr/confusion_matrix_type_test.png\n" ] @@ -2126,8 +1764,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Saved predictions_val_type.csv and predictions_test_type.csv\n" ] @@ -2175,8 +1813,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Char n-gram best C : {'lr__C': 3.0}\n", "Char n-gram best CV F1 : 0.8305\n", @@ -2237,8 +1875,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "====== TF-IDF + LR RESULTS SUMMARY ======\n", "\n", @@ -2400,8 +2038,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Train+Val: 48,166 Val: 8,500 Test: 8,500\n" ] @@ -2435,8 +2073,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "=== Binary: Comparing Vectorizer + NB Combinations ===\n", "CountVectorizer + MultinomialNB CV F1=0.7855 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", @@ -2486,8 +2124,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "\n", "Best binary NB model: TfidfVec+MultinomialNB (CV F1=0.7897)\n", @@ -2532,8 +2170,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "[Val] Accuracy=0.8826 Macro-F1=0.8826 Weighted-F1=0.8826\n", " precision recall f1-score support\n", @@ -2594,35 +2232,35 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": 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", 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jxozB7du3ceXKFZk+hw0bBk1NTfF1y5YtIQgCRowYIdOuZcuWuHv3Ll6+fFlqfFKpVJyAW1BQgCdPnkBfXx/169cvcfiiNO3bt0eHDh2wePHit34uevXqJX6m9u7di5kzZyIiIgKDBw+GIAjF2puYmIiVlLK8XnnKyMjA48eP4ebmhlu3biEjIwOmpqZwcXGBvr4+tLW10aRJE3Fp3bo1LCws5D5fecyePVs8z23btmHQoEH4+uuvsXz5crn2HzNmjMzE3nbt2qGgoAB37twR171+zkUVp3bt2iE7OxvXrl2T6U8qleKLL76QWaehoQEfHx/8+eefePbsmbh+69ataN269Vur3E+ePCn199uQIUNw7949HD9+XFwXFhYGLS0tfPbZZwCAKlWqiJXgwsJCpKWl4eXLl2jevLncP38mJiZ48eJFiVcikuriENh7mDVrFjZv3oxFixaV+QvJzMxMZnzb29sb9evXR//+/bF+/XpMmDDhrccq6Ze6ot25cwcODg7FroR42xj+66pWrYrPP/8cP//8M+7fv4/q1auLyVDR8Nf169cBAL6+vqX2k5GRgby8PGRmZha7tcCbkpKSSv0la25uLvP62LFjJd77yNzcHJ07d0ZYWBiys7NRUFAgkwgqU61atYqtMzExwdOnT8XXd+7cQcuWLYu1K7oS6c6dOzLv45t9GhkZAQBq1qxZbH1hYSEyMjJQrVq1EuMruhrw559/RmJioszckdL2Kc3cuXPh5uaGkJAQ+Pv7l9quRo0aMp+pnj17olq1apg6dSrCw8PRo0cPmfaCIMh1hc+JEycwZ84cxMTEFPvPMCMjA/n5+TJDYK//fO3YsUPhPzNOTk4y5zlgwABkZGRgxowZGDx4cLGf7ze9+X0uSjRe/9mJj4/HrFmzcPToUZnhOuDVOb+uevXqJQ47Dxs2DN9//z12796NYcOGISEhAbGxsQgJCZHrPEv7/TZw4EAEBAQgLCwM7u7uyMnJwe7du+Hl5SWTNG3cuBE//fQTrl27JvNHztuSrzePz6vA1AsrQO+hdu3aGDJkiFxVoDd16tQJAGT+silJ0X8gr//CquiGDBmCwsJC/PbbbwBeXa7q6OgojsUXFhYCeHXpa9Fft28u+vr6ch/Pysqq2P4eHh5wdnYutr5x48al9jN48GAcPHgQISEh8PLyKvXmjqX9knxz0qiiVKlSpcT175MUl9bnuxxr4cKFCAgIQPv27bFlyxYcOnQIkZGRaNSokfi9llf79u3h7u4uVxXoTWV9pp4+fQozM7My97958yY6deqEx48fY8mSJdi/fz8iIyPFRKywsBAaGhqIiIgQb+WwY8cO8WfrzaTrQ+nUqRNycnLkuoXG276f6enpcHNzw4ULFzB//nzs27cPkZGR4jymN79/pc3NcnR0hIuLC7Zs2QIA2LJlC7S0tOS6CWW1atVK/f1mYWGBLl26YNeuXcjPz8e+ffvw7Nkz8Y+pomMV3ZPsl19+QUREBCIjI9GxY0e5f/6ePn0KXV3dt849I9XCCtB7mjVrFrZs2SIz8VEeRUMKb7uzc4MGDQC8GkZycnJ6tyDlZGtri8uXLxf7azkhIaFc/bRs2RJ16tRBWFgYunTpgvj4eHz33Xfi9qJJv4aGhmVe3WZubg5DQ0Ncvny5zONpa2sX62fLli3Izc0t19Vzffr0wf/+9z+cOnVKZpLom4r+8kxPT5dZ//qwQlk+xF+Ztra2JX6fioYw3pxgqkg7d+5Ehw4dik3MTU9Pf2vSUZK5c+fC3d293DenK+0z9fLlS9y9exc9e/Ysc/99+/YhNzcXf/75p0zl5PWriUxNTcWfqS1btiA/P/+dr9B8V/L+7pBHVFQUnjx5gj/++APt27cX1ycmJpa7r2HDhiEgIAAPHz5EWFgYvL295Rq6b9CgAXbt2lXqdh8fH0RERODgwYMICwuDoaGhTLK5c+dO1K5dG3/88YfMZ+ttk69fl5iYKFZLSX2wAvSe6tSpgyFDhmDNmjVITk6We799+/YBQJkVCeDVJZpaWlr4999/3ytOeXTr1g0PHjzAzp07xXXZ2dnvdOdoHx8fnD9/HnPmzIFEIpGZT+Pi4oI6dergxx9/LPGXeNFluhoaGujduzf27dtX4vkrelhQX18fwcHBmDt3bpl/zdva2qJKlSrFKg0///yzXMfR09Mrljy9r27duuHMmTOIiYkR12VlZWHt2rWws7ODo6OjQo/3uipVqhT7XuzYsaPEu6XLw83NDe7u7vj++++Rk5Mj936lfaauXLmCnJwctG7dusz9i6olr59LRkYGNmzYUKxt+/btUb9+fcyaNavYMNHWrVtx6dIlueMur/DwcABv/90hj5LOOS8vT+6f5dcNGjQIEokEkyZNwq1bt+S+4amrqyuePn1a6hVyvXv3hq6uLn7++WccPHgQffv2hba2dpnncPr0aZnPwtucO3furT8fpHpYAVKAr7/+Gps3b0ZCQoJ4WfLr7t+/L5aG8/LycOHCBaxZswZmZmZvnf+jra0NDw8PHD58GPPnz/8g8RcZPXo0Vq1ahWHDhiE2NhbW1tbYvHlziXd1fpshQ4Zg/vz52Lt3L9q0aQM7Oztxm4aGBtavXw8vLy80atQIX3zxBapXr4779+/j2LFjMDQ0FP8zW7hwIf766y+4ublhzJgxaNiwIR4+fIgdO3bgn3/+UfgzyMqal1TEyMgIn332GVauXAmJRII6deogPDwcqampch3DxcUFhw8fxpIlS2BjYwN7e/sS5++Ux4wZM8TL+CdOnAhTU1Ns3LgRiYmJ2LVr1we9S3D37t0xf/58fPHFF2jdujUuXbqErVu3onbt2u/c55w5c9ChQ4dSt//333/iZyo7OxunTp3Cxo0b4eDggKFDh8q0jYyMhK6uLrp06VLmMT08PKClpYUePXrgf//7H54/f45169bBwsKi2BC3lpYWNm7cCDc3Nzg7O2PUqFGwtLTE4cOHsXPnzmKTzt/V33//LSaBaWlp+PPPPxEdHY2BAweK1eH30bp1a5iYmMDX11d8/MTmzZvf6Y8Lc3NzdO3aFTt27ICxsTG8vb3l2s/b2xtVq1bF4cOHS7zgQl9fH7179y42l7BI9+7d8ccff6BPnz7w9vZGYmIiQkJC4OjoKFeVLDY2FmlpaejVq5dc8ZLqYAKkAA4ODhgyZAg2btxY4va4uDjxl7KGhgbMzMzQt29fLFiwQLxhWllGjBiBfv364e7du8UmqSqSrq4ujhw5ggkTJmDlypXQ1dWFj48PvLy80LVr13L1VbduXXz66ac4e/ZssV9YwKubqsXExGDBggVYtWoVnj9/DisrK7Rs2RL/+9//xHbVq1fH6dOn8c0332Dr1q3IzMxE9erV4eXl9U6JmaKsXLkS+fn5CAkJgVQqxYABA/DDDz+8dcI2ACxZsgRjxozBrFmz8OLFC/j6+r53AmRpaYmTJ08iMDAQK1euRE5ODpydnbFv3z65/yN6V1999RWysrIQFhaGbdu2oVmzZti/fz9mzJjxzn26u7vDzc0N0dHRJW4vmncDvKoAWFtbY9SoUViwYAH09PRk2u7YsQN9+/aFgYFBmcesX78+du7ciVmzZmHq1KmwsrLCuHHjYG5uXuzqOODVUG9MTAzmzJmDn376Cbm5uWjZsiX++usvhSQnAGTuQaWlpYXatWvju+++w7Rp0xTSf7Vq1RAeHo4pU6Zg1qxZMDExwZAhQ9CpUyd4enqWu79hw4YhPDwcAwYMkPuRLpaWlujWrRu2b99e6hWnPj4+CAsLg7W1NTp27Cizbfjw4UhOTsaaNWtw6NAhODo6YsuWLdixY0exm5WWZMeOHahVq1axfkn1SYSPcXkRvZeCggI4OjpiwIABWLBggbLDIao04uLi0KxZM5w7d06uG+LR+9m7dy969+6N48ePy9y64W3+/vtvuLu749q1a6hbt+4HjFBWbm4u7OzsMGPGDEyaNOmjHZcqBiZAlcS2bdswbtw4JCUllesKKSJ1NnDgQBQWFmL79u3KDkUtdO/eHVevXsWNGzfKPdnfy8sLNWrUwLp16z5QdMWFhIRg4cKFuH79eoV/CDEpHhMgIiJ6L7///jsuXryIoKAgLF++HBMnTlR2SERvxQSIiIjei0Qigb6+Pj7//HOEhIS89SHPRBUBf0qJiOi98O9oqox4HyAiIiJSO0yAiIiISO0wASIiIiK1o5JzgCR95HsCMBGVLXP7eWWHQKQSDDSNP8pxJF1qKLQ/IfKeQvurSFQyASIiIlJLH+Bhy6qKQ2BERESkdlgBIiIiUhUsa8iNbxURERGpHVaAiIiIVAXnAMmNCRAREZGqYP4jNw6BERERkdphBYiIiEhVcAhMbkyAiIiIVAXHdeTGt4qIiIjUDitAREREqoJDYHJjAkRERKQqmP/IjUNgREREpHZYASIiIlIVGiwByYsVICIiIlI7rAARERGpChaA5MYEiIiISFXwKjC5cQiMiIiI1A4rQERERKqCBSC5MQEiIiJSFbwKTG4cAiMiIiK1wwoQERGRqmABSG5MgIiIiFQFrwKTG4fAiIiISO2wAkRERKQqOAlabqwAERERkdphBYiIiEhVsAAkNyZAREREqoKToOXGITAiIiJSO6wAERERqQoWgOTGBIiIiEhV8CowuXEIjIiIiNQOK0BERESqggUgubECRERERO8tODgYzs7OMDQ0hKGhIVxdXXHw4EFxu7u7OyQSicwyduxYmT6SkpLg7e0NXV1dWFhYYNq0aXj58qVMm6ioKDRr1gxSqRQODg4IDQ19p3hZASIiIlIVSrwMvkaNGli0aBHq1q0LQRCwceNG9OrVC+fPn0ejRo0AAKNHj8b8+fPFfXR1dcWvCwoK4O3tDSsrK5w8eRIPHz7EsGHDoKmpiYULFwIAEhMT4e3tjbFjx2Lr1q04cuQIRo0aBWtra3h6epYrXokgCIICzrtCkfSxV3YIRCohc/t5ZYdApBIMNI0/ynEkIxsotL+cny8gNzdXZp1UKoVUKpVrf1NTU/zwww8YOXIk3N3d0aRJEyxbtqzEtgcPHkT37t3x4MEDWFpaAgBCQkIQGBiIR48eQUtLC4GBgdi/fz8uX74s7jdw4ECkp6cjIiKiXOfGITAiIiIqUVBQEIyMjGSWoKCgt+5XUFCA33//HVlZWXB1dRXXb926FWZmZvjkk08wc+ZMZGdni9tiYmLg5OQkJj8A4OnpiczMTMTHx4ttOnfuLHMsT09PxMTElPvcOARGRESkKhQ8BDZz5kwEBATIrCur+nPp0iW4uroiJycH+vr62L17NxwdHQEAgwcPhq2tLWxsbHDx4kUEBgYiISEBf/zxBwAgOTlZJvkBIL5OTk4us01mZiZevHgBHR0duc+NCRAREZGqUPAUoPIMdwFA/fr1ERcXh4yMDOzcuRO+vr6Ijo6Go6MjxowZI7ZzcnKCtbU1OnXqhJs3b6JOnTqKDVwOHAIjIiIihdDS0oKDgwNcXFwQFBSExo0bY/ny5SW2bdmyJQDgxo0bAAArKyukpKTItCl6bWVlVWYbQ0PDclV/ACZAREREqkMiUezyngoLC4tNoi4SFxcHALC2tgYAuLq64tKlS0hNTRXbREZGwtDQUBxGc3V1xZEjR2T6iYyMlJlnJC8OgREREakKJZY1Zs6cCS8vL9SqVQvPnj1DWFgYoqKicOjQIdy8eRNhYWHo1q0bqlWrhosXL8Lf3x/t27eHs7MzAMDDwwOOjo4YOnQoFi9ejOTkZMyaNQt+fn7iMNzYsWOxatUqTJ8+HSNGjMDRo0exfft27N+/v9zxMgEiIiKi95aamophw4bh4cOHMDIygrOzMw4dOoQuXbrg7t27OHz4MJYtW4asrCzUrFkT/fr1w6xZs8T9q1SpgvDwcIwbNw6urq7Q09ODr6+vzH2D7O3tsX//fvj7+2P58uWoUaMG1q9fX+57AAG8DxARlYH3ASJSjI92H6BxjRTanxAcr9D+KhLOASIiIiK1wyEwIiIiVcGHocqNCRAREZGq0GAGJC8OgREREZHaYQWIiIhIVSjxafCVDRMgIiIiVcH8R24cAiMiIiK1wwoQERGRipBwCExuTICIiIhUBBMg+XEIjIiIiNQOK0BEREQqggUg+bECRERERGqHFSAiIiIVocESkNyYABEREakIToKWX4UYAtuwYQN27NhRbP2OHTuwceNGJUREREREqqxCJEBBQUEwMzMrtt7CwgILFy5UQkRERESVj0QiUeiiyirEEFhSUhLs7e2Lrbe1tUVSUpISIiIiIqp8VD1pUaQKUQGysLDAxYsXi62/cOECqlWrpoSIiIiISJVViArQoEGDMHHiRBgYGKB9+/YAgOjoaEyaNAkDBw5UcnRERESVAwtA8qsQCdCCBQtw+/ZtdOrUCVWrvgqpsLAQw4YN4xwgIiIiUrgKkQBpaWlh27ZtWLBgAS5cuAAdHR04OTnB1tZW2aERERFVGpwDJL8KkQAVqVevHurVq6fsMIiIiColJkDyU1oCFBAQgAULFkBPTw8BAQFltl2yZMlHioqIiIjUgdISoPPnzyM/P1/8moiIiN6PBKwAyUtpCdCxY8dK/JqIiIjeDYfA5Fch7gM0YsQIPHv2rNj6rKwsjBgxQgkRERERkSqrEAnQxo0b8eLFi2LrX7x4gU2bNikhIiIiospHIlHsosqUehVYZmYmBEGAIAh49uwZtLW1xW0FBQU4cOAALCwslBghERFR5aGh6lmLAik1ATI2NhYfuFbS5e8SiQTz5s1TQmRERESkypSaAB07dgyCIKBjx47YtWsXTE1NxW1aWlqwtbWFjY2NEiMkIiKqPDgJWn5KTYDc3NwAAImJiahVqxa/cURERPRRVIhJ0FevXsWJEyfE16tXr0aTJk0wePBgPH36VImRERERVR5F00oUtaiyCpEATZs2DZmZmQCAS5cuISAgAN26dUNiYuJb7xJNREREr/AqMPlViGeBJSYmwtHREQCwa9cu9OjRAwsXLsS5c+fQrVs3JUdHREREqqZCVIC0tLSQnZ0NADh8+DA8PDwAAKampmJliIiIiMrGITD5VYgKUNu2bREQEIA2bdrgzJkz2LZtGwDgv//+Q40aNZQcHRERUeWg6kmLIlWICtCqVatQtWpV7Ny5E8HBwahevToA4ODBg+jatauSoyMiIiJVUyEqQLVq1UJ4eHix9UuXLlVCNERERJUTK0DyqxAJ0OtycnKQl5cns87Q0FBJ0RAREVUeTIDkVyGGwLKysjB+/HhYWFhAT08PJiYmMgsRERGRIlWIBGj69Ok4evQogoODIZVKsX79esybNw82NjZ8GjwREZGceB8g+VWIIbB9+/Zh06ZNcHd3xxdffIF27drBwcEBtra22Lp1K3x8fJQdIhEREamQClEBSktLQ+3atQG8mu+TlpYG4NXl8cePH1dmaERERJUG7wMkvwqRANWuXRuJiYkAgAYNGmD79u0AXlWGjI2NlRgZERFR5cEESH4VIgH64osvcOHCBQDAjBkzsHr1amhra8Pf3x/Tpk1TcnRERESkairEHCB/f3/x686dO+PatWuIjY2Fg4MDnJ2dlRgZERFR5aGh4lUbRaoQCdCbbG1tYWtrq+wwiIiIKhXmP/KrEENgEydOxIoVK4qtX7VqFSZPnvzxAyIiIiKVViESoF27dqFNmzbF1rdu3Ro7d+5UQkRERESVDydBy69CJEBPnjyBkZFRsfWGhoZ4/PixEiIiIiKqfCQK/lcewcHBcHZ2hqGhIQwNDeHq6oqDBw+K23NycuDn54dq1apBX18f/fr1Q0pKikwfSUlJ8Pb2hq6uLiwsLDBt2jS8fPlSpk1UVBSaNWsGqVQKBwcHhIaGvtN7VSESIAcHB0RERBRbf/DgQfH+QERERFRx1ahRA4sWLUJsbCz+/fdfdOzYEb169UJ8fDyAVxc87du3Dzt27EB0dDQePHiAvn37ivsXFBTA29sbeXl5OHnyJDZu3IjQ0FDMnj1bbJOYmAhvb2906NABcXFxmDx5MkaNGoVDhw6VO16JIAjC+5/2+/n1118xfvx4TJs2DR07dgQAHDlyBD/99BOWLVuG0aNHl6s/SR/7DxEmvWaspw/GdR0CO4vqAID4u9cxf/sKRJyLFtu0qt8U3/lMRcu6TVBQWIC4xKvwnD8MOXm5AIC6Nvb4wXcm2jRwgVZVTVy8cw3fhC1B1OVTAADfDv0QOvHHEo9vMbw5HmU8+cBnSZnbzys7BLWzZvU6rAteL7PO1t4Wu/ZtF19fjLuEn1cE4/KleFTR0EC9BvWwcs1yaGtrAwD8x0/Ff9f+w9O0pzAwNECLVp9iYsB4mFuYf9Rzof9noGn8UY5j/31nhfZ3bfJ+5ObmyqyTSqWQSqVy7W9qaooffvgB/fv3h7m5OcLCwtC/f/9XfV+7hoYNGyImJgatWrXCwYMH0b17dzx48ACWlpYAgJCQEAQGBuLRo0fQ0tJCYGAg9u/fj8uXL4vHGDhwINLT00sspJSlQlwFNmLECOTm5uK7777DggULAAB2dnYIDg7GsGHDlBwdleTek2TM2Pw9rj+8DYlEAt8O/bB3xlo0ndIdV+5eR6v6TRHxTSiC/gjGhHVz8bKgAI3tGqKw8P/z7fCvf8H1B4noONsHL/JyMLnHCIR//QvqjHNDSvpjbDsRjojz0TLHDZ3wI7S1pEx+SKXVdqiNn9evEl9XrVJF/Ppi3CVMGDsJX4zyxbSvpqJKlSq4nnAdGhr/X9Bv3sIFI0b7wszcDKkpj7D8xxUI9J+JX7fKJlZEbxMUFIR58+bJrJszZw7mzp1b5n4FBQXYsWMHsrKy4OrqitjYWOTn56Nz5/9P0Bo0aIBatWqJCVBMTAycnJzE5AcAPD09MW7cOMTHx6Np06aIiYmR6aOozbtcMKX0BOjly5cICwtD3759MW7cODx69Ag6OjrQ19dXdmhUhvB/j8i8nrX1R4zz9EGrek1x5e51LP3iG6zYvxHf/xEitvnvwS3x62oGJqhnY4+RqwJx6c41AMCMTd/Dz2soPqlVHynpj5GTlytWiwDAzNAUHZ1cMXL1jA98dkTKVbVKFZiZVStx25LFSzHQZwCGj/IV19nZy942xGfYIPFraxtr+I4ahqkTp+Nl/ktU1VT6r336gBQ9cXnmzJkICAiQWVdW9efSpUtwdXVFTk4O9PX1sXv3bjg6OiIuLg5aWlrFnu5gaWmJ5ORkAEBycrJM8lO0vWhbWW0yMzPx4sUL6OjoyH1uSp8DVLVqVYwdOxY5OTkAAHNzcyY/lYyGhgY+b9sdeto6iEk4B3OjamhVvylSM57gRNBOJG84i6hvf0ebhs3FfZ48e4pr925iWIe+0JXqoIpGFfzPczBS0h8j9ualEo8zzL0vsvNysDPmwMc6NSKlSEq6i64dvNGrax/MCpyN5IevfvmnPUnD5YvxMDE1xQifUfBo3xVjho9F3Lm4UvvKyMhARPghODdxYvKjBhT9NHipVCpOai5aykqA6tevj7i4OJw+fRrjxo2Dr68vrly58hHfAflViE9DixYtcP78+Xe6+WFubm6x8UkUCEAV1b58ryL4pFZ9xCzaBW0tKZ7nZKPPorG4eu8GWtZrAgCYO3ASpoYuRFziFQxz74sj87bgk0ldcePhbQBA57lDsGfGGjwLu4xCoRCpGU/Qdb4v0rMySzzeyM4DEHZ8r0xViEjVfOLcCHO/nQ1bu1p4/PgJ1v28HqOG/Q/b9oTh/r37AIB1P6/DpKkTUa9BPez/8wDGjRyPbXvCUMu2ltjPiiWrsP23Hch5kQOnxp9g6eolyjolUiNaWlpwcHAAALi4uODs2bNYvnw5Pv/8c+Tl5SE9PV2mCpSSkgIrKysAgJWVFc6cOSPTX9FVYq+3efPKsZSUFBgaGpar+gNUgAoQAHz55ZeYMmUKVq1ahZiYGFy8eFFmKUtQUBCMjIxkFvyX/nECV3MJD26hSYA3Wk7vg+CILdg48Uc0rOEADcmrH6s1h8IQenQn4hKvIGDDt0i4n4gRnT4T9189Zj5SM56g3dcD0GJ6b+w5/Rf2fbUeVibFJ2q2qt8UjjXr4pfD24ttI1Ilbdq1RmfPTqhbvy5c27TC8uClePbsGSIjjohz6Pp+1gc9+/RAg4b1MSXQH7Z2tvjzj30y/Qz7Ygi27tiMVWtXQENDA3NmzkUFuOaFPrCKdh+gwsJC5ObmwsXFBZqamjhy5P+nTyQkJCApKQmurq4AAFdXV1y6dAmpqalim8jISBgaGsLR0VFs83ofRW2K+iiPClEBGjhwIIBXd4QuIpFIIAgCJBIJCgoKSt23pPFJoyF8ftjHkP8yHzeT7wAAzt26jE8dnDGp+xdY9EcwAODKvRsy7a/eu4FaZjYAgI5OrdHdpSNMhjbBsxfPAQB+a2ejS+O28O3QT2buEACM6vw5zt+Kx7lbl0GkTgwMDWBrWwv3ku7i05avhpHt68he6Wpf2w7JybJ/FRubGMPYxBi2drVgX9sO3p174tKFy3Bu4vTRYqePT5k3L5w5cya8vLxQq1YtPHv2DGFhYYiKisKhQ4dgZGSEkSNHIiAgAKampjA0NMSECRPg6uqKVq1aAQA8PDzg6OiIoUOHYvHixUhOTsasWbPg5+cnDruNHTsWq1atwvTp0zFixAgcPXoU27dvx/79+8sdb4VIgBITE9953xIvx+Pwl1JoaGhAqqmF26n3cP9JMurbyN7DqZ6NPQ6eiwIA6EpflSoLhUKZNoWCIFaQiuhp62JAG2/M3PzDhwueqILKzs7Gvbv30a2HF2yqW8Pcwhx3bt+RaXPnThLatC39L+Ciyk9eXt4HjZXUW2pqKoYNG4aHDx/CyMgIzs7OOHToELp06QIAWLp0KTQ0NNCvXz/k5ubC09MTP//8s7h/lSpVEB4ejnHjxsHV1RV6enrw9fXF/PnzxTb29vbYv38//P39sXz5ctSoUQPr16+Hp6dnueOtEAkQH3xa+SwcMg0Hz0Uj6dF9GOjoY3D7nnBv1Aqe819dmfLDnrWYN3AyLty+irjEK/Dt0A8NqtdB/x++BADEJJzD06wMbJz4I+ZvX4kXeTkY3WUg7C1qYH/sMZljfd6mO6pqVMWW6N0f/TyJPrZlPyxHO/d2sLaxwqPUx1izeh00qmjAs5sHJBIJhn7hgzWr16Fu/bqo36Aewvfux53EO1i8JAgAcPniZcRfvoomzRrD0NAA9+7eR/DKNahRswarP2pAmRWgX375pczt2traWL16NVavXl1qG1tbWxw4UPaFLu7u7jh//v3vUVYhEqAiV65cQVJSUrG/Unr27KmkiKg0FkbVsGnST7A2MUdG9jNcvH0NnvN9cfjCPwCA5eEboK0lxdIRs2Cqb4wLt6+iy7yhuJWcBODVVWBd5w/Hdz5TcXT+VmhWqYr4u9fRa9EYXLx9VeZYIzsPwB+nIpCR/eyjnyfRx5aSkoqvp3+DjPQMmJgao3HTxgjd+gtMTE0AAIOHDkJebh6Wfr8MGZmZqFevLlavW4EatWoAePWfzLHDx7B29Vq8eJEDM/NqcG3jipH/+wJaWlrKPDWiCqVC3An61q1b6NOnDy5duiTO/QH+P5Mtaw5QSXgnaCLF4J2giRTjY90Juv7SrgrtL8G/fHdXrkwqxFVgkyZNgr29PVJTU6Grq4v4+HgcP34czZs3R1RUlLLDIyIiqhQq2lVgFVmFGAKLiYnB0aNHYWZmBg0NDWhoaKBt27YICgrCxIkTFTLWR0RERFSkQlSACgoKYGBgAAAwMzPDgwcPALyaDJWQkKDM0IiIiCoNVoDkVyEqQJ988gkuXLgAe3t7tGzZEosXL4aWlhbWrl2L2rVrv70DIiIiUvmkRZEqRAI0a9YsZGVlAQDmz5+P7t27o127dqhWrRq2bdum5OiIiIhI1VSIBOj1Gxg5ODjg2rVrSEtLg4mJCbNZIiIiOfG/TPlViDlAb8rMzMTx48c5/4eIiKgcOAdIfhUiARowYABWrVoFAHjx4gWaN2+OAQMGwMnJCbt27VJydERERKRqKkQCdPz4cbRr1w4AsHv3bgiCgPT0dKxYsQLffvutkqMjIiKqHFgBkl+FSIAyMjJgamoKAIiIiEC/fv2gq6sLb29vXL9+XcnRERERkaqpEAlQzZo1ERMTg6ysLERERMDDwwMA8PTpU2hrays5OiIiosqBFSD5VYirwCZPngwfHx/o6+ujVq1acHd3B/BqaMzJiU8vJiIikoeK5ywKVSESoC+//BItW7ZEUlISunTpAg2NV4Wp2rVrcw4QERERKVyFSIAAwMXFBS4uLjhx4gSaN28OqVQKb29vZYdFRERUaaj6sJUiVYg5QK/z8vLC/fv3lR0GERFR5SORKHZRYRUuARIEQdkhEBERkYqrMENgRERE9H44BCa/CpcArVmzBpaWlsoOg4iIqNJh/iO/CpcADR48WNkhEBERkYqrEAlQVlYWFi1ahCNHjiA1NRWFhYUy22/duqWkyIiIiCoPDoHJr0IkQKNGjUJ0dDSGDh0Ka2trfgOJiIjog6oQCdDBgwexf/9+tGnTRtmhEBERVVosIMivQiRAJiYm4sNQiYiI6N0wAZJfhbgP0IIFCzB79mxkZ2crOxQiIiJSAxWiAvTTTz/h5s2bsLS0hJ2dHTQ1NWW2nzt3TkmRERERVR4sAMmvQiRAvXv3VnYIRERElR6HwORXIRKgOXPmKDsEIiIiUiMVIgEqEhsbi6tXrwIAGjVqhKZNmyo5IiIiosqDFSD5VYgEKDU1FQMHDkRUVBSMjY0BAOnp6ejQoQN+//13mJubKzdAIiIiUikV4iqwCRMm4NmzZ4iPj0daWhrS0tJw+fJlZGZmYuLEicoOj4iIqFKQSCQKXVRZhagARURE4PDhw2jYsKG4ztHREatXr4aHh4cSIyMiIqo8VD1pUaQKUQEqLCwsduk7AGhqahZ7LhgRERHR+6oQCVDHjh0xadIkPHjwQFx3//59+Pv7o1OnTkqMjIiIqPKQSBS7qLIKkQCtWrUKmZmZsLOzQ506dVCnTh3Y2dkhMzMTK1euVHZ4RERElQLnAMmvQswBqlmzJs6dO4cjR46Il8E3bNgQnTt3VnJkREREpIoqRAIEAEePHsXRo0eRmpqKwsJCnD9/HmFhYQCAX3/9VcnRERERVXyqXrVRpAqRAM2bNw/z589H8+bNYW1tzW8gERHRO+D/n/KrEAlQSEgIQkNDMXToUGWHQkRERGqgQiRAeXl5aN26tbLDICIiqtRYAJJfhbgKbNSoUeJ8HyIiIqIPrUJUgHJycrB27VocPnwYzs7OxW6KuGTJEiVFRkREVHlwDpD8KkQCdPHiRTRp0gQAcPnyZZlt/GYSERHJif9nyq1CJEDHjh1TdghERESkRipEAkRERETvj6Mm8mMCREREpCI0mP/IrUJcBUZERET0MTEBIiIiUhHKfBhqUFAQPv30UxgYGMDCwgK9e/dGQkKCTBt3d/dixxg7dqxMm6SkJHh7e0NXVxcWFhaYNm0aXr58KdMmKioKzZo1g1QqhYODA0JDQ8v9XjEBIiIiUhEaEolCl/KIjo6Gn58fTp06hcjISOTn58PDwwNZWVky7UaPHo2HDx+Ky+LFi8VtBQUF8Pb2Rl5eHk6ePImNGzciNDQUs2fPFtskJibC29sbHTp0QFxcHCZPnoxRo0bh0KFD5YqXc4CIiIjovUVERMi8Dg0NhYWFBWJjY9G+fXtxva6uLqysrErs46+//sKVK1dw+PBhWFpaokmTJliwYAECAwMxd+5caGlpISQkBPb29vjpp58AAA0bNsQ///yDpUuXwtPTU+54WQEiIiJSEYoeAsvNzUVmZqbMkpubK1csGRkZAABTU1OZ9Vu3boWZmRk++eQTzJw5E9nZ2eK2mJgYODk5wdLSUlzn6emJzMxMxMfHi206d+4s06enpydiYmLK9V4xASIiIqISBQUFwcjISGYJCgp6636FhYWYPHky2rRpg08++URcP3jwYGzZsgXHjh3DzJkzsXnzZgwZMkTcnpycLJP8ABBfJycnl9kmMzMTL168kPvcOARGRESkIhRd1Zg5cyYCAgJk1kml0rfu5+fnh8uXL+Off/6RWT9mzBjxaycnJ1hbW6NTp064efMm6tSpo5ig5cQEiIiISEWUd+Ly20ilUrkSnteNHz8e4eHhOH78OGrUqFFm25YtWwIAbty4gTp16sDKygpnzpyRaZOSkgIA4rwhKysrcd3rbQwNDaGjoyN3nBwCIyIiovcmCALGjx+P3bt34+jRo7C3t3/rPnFxcQAAa2trAICrqysuXbqE1NRUsU1kZCQMDQ3h6Ogotjly5IhMP5GRkXB1dS1XvKwAERERqQhlPgrDz88PYWFh2Lt3LwwMDMQ5O0ZGRtDR0cHNmzcRFhaGbt26oVq1arh48SL8/f3Rvn17ODs7AwA8PDzg6OiIoUOHYvHixUhOTsasWbPg5+cnVqLGjh2LVatWYfr06RgxYgSOHj2K7du3Y//+/eWKVyIIgqDYt0D5JH3ennUS0dtlbj+v7BCIVIKBpvFHOU7PP0cptL8/e66Xu21pydeGDRswfPhw3L17F0OGDMHly5eRlZWFmjVrok+fPpg1axYMDQ3F9nfu3MG4ceMQFRUFPT09+Pr6YtGiRaha9f9rNlFRUfD398eVK1dQo0YNfPPNNxg+fHi5zo0JEBGVigkQkWKoQwJU2XAIjIiISEXwafDyYwJERESkInhlk/z4XhEREZHaYQWIiIhIRSj6PkCqjBUgIiIiUjusABEREakIToKWHxMgIiIiFcEhMPlxCIyIiIjUDitAREREKoL1H/kxASIiIlIRHAKTH4fAiIiISO2wAkRERKQiWAGSHytAREREpHZYASIiIlIRvA+Q/JgAERERqQgOgcmPQ2BERESkdlgBIiIiUhGs/8iPCRAREZGK4BCY/DgERkRERGqHFSAiIiIVwQqQ/JgAERERqQheBi8/DoERERGR2mEFiIiISEVwCEx+rAARERGR2mEFiIiISEWw/iM/JkBEREQqgkNg8nunIbC///4bQ4YMgaurK+7fvw8A2Lx5M/755x+FBkdERET0IZQ7Adq1axc8PT2ho6OD8+fPIzc3FwCQkZGBhQsXKjxAIiIiko+GRKLQRZWVOwH69ttvERISgnXr1kFTU1Nc36ZNG5w7d06hwREREZH8JBKJQhdVVu4EKCEhAe3bty+23sjICOnp6YqIiYiIiOiDKncCZGVlhRs3bhRb/88//6B27doKCYqIiIjKT0PBiyor9/mNHj0akyZNwunTpyGRSPDgwQNs3boVU6dOxbhx4z5EjERERCQHDoHJr9yXwc+YMQOFhYXo1KkTsrOz0b59e0ilUkydOhUTJkz4EDESERERKVS5EyCJRIKvv/4a06ZNw40bN/D8+XM4OjpCX1//Q8RHREREclL1K7cU6Z1vhKilpQVHR0dFxkJERET0UZQ7AerQoUOZ44JHjx59r4CIiIjo3bACJL9yJ0BNmjSReZ2fn4+4uDhcvnwZvr6+ioqLiIiIyknVJy4rUrkToKVLl5a4fu7cuXj+/Pl7B0RERET0oSnsYahDhgxBixYt8OOPPyqqy3f2Yme8skMgUgk6XespOwQilSBE3vsox9Hg8+DlprAEKCYmBtra2orqjoiIiMqJQ2DyK3cC1LdvX5nXgiDg4cOH+Pfff/HNN98oLDAiIiKiD6XcCZCRkZHMaw0NDdSvXx/z58+Hh4eHwgIjIiKi8uFVYPIrVwJUUFCAL774Ak5OTjAxMflQMRERERF9UOV6FliVKlXg4eHBp74TERFVQBIF/1Nl5X4Y6ieffIJbt259iFiIiIjoPfBhqPIrdwL07bffYurUqQgPD8fDhw+RmZkpsxARERFVdHLPAZo/fz6mTJmCbt26AQB69uwpkx0KggCJRIKCggLFR0lERERvxUnQ8pM7AZo3bx7Gjh2LY8eOfch4iIiI6B1Jyj+wo7bkToAEQQAAuLm5fbBgiIiIiD6GcqWKqj4hioiIqDLTkEgUupRHUFAQPv30UxgYGMDCwgK9e/dGQkKCTJucnBz4+fmhWrVq0NfXR79+/ZCSkiLTJikpCd7e3tDV1YWFhQWmTZuGly9fyrSJiopCs2bNIJVK4eDggNDQ0PK/V+VpXK9ePZiampa5EBERkXIo8yqw6Oho+Pn54dSpU4iMjER+fj48PDyQlZUltvH398e+ffuwY8cOREdH48GDBzJPmCgoKIC3tzfy8vJw8uRJbNy4EaGhoZg9e7bYJjExEd7e3ujQoQPi4uIwefJkjBo1CocOHSrfeyUUjW29hYaGBpYtW1bsTtBv8vX1LVcAH0JOQbayQyBSCXwYKpFifKyHoc47O0+h/c35dM477/vo0SNYWFggOjoa7du3R0ZGBszNzREWFob+/fsDAK5du4aGDRsiJiYGrVq1wsGDB9G9e3c8ePAAlpaWAICQkBAEBgbi0aNH0NLSQmBgIPbv34/Lly+Lxxo4cCDS09MREREhd3zluhP0wIEDYWFhUZ5diIiI6CNR9M0Lc3NzkZubK7NOKpVCKpW+dd+MjAwAEEeHYmNjkZ+fj86dO4ttGjRogFq1aokJUExMDJycnMTkBwA8PT0xbtw4xMfHo2nTpoiJiZHpo6jN5MmTy3Vucg+Bcf4PERGRegkKCoKRkZHMEhQU9Nb9CgsLMXnyZLRp0waffPIJACA5ORlaWlowNjaWaWtpaYnk5GSxzevJT9H2om1ltcnMzMSLFy/kPrdyXwVGREREFZOi7wMUOHMmAgICZNbJU/3x8/PD5cuX8c8//yg0HkWSOwEqLCz8kHEQERHRe1L0aI28w12vGz9+PMLDw3H8+HHUqFFDXG9lZYW8vDykp6fLVIFSUlJgZWUltjlz5oxMf0VXib3e5s0rx1JSUmBoaAgdHR254+Qdk4iIiOi9CYKA8ePHY/fu3Th69Cjs7e1ltru4uEBTUxNHjhwR1yUkJCApKQmurq4AAFdXV1y6dAmpqalim8jISBgaGsLR0VFs83ofRW2K+pBXuSZBExERUcWlocS6hp+fH8LCwrB3714YGBiIc3aMjIygo6MDIyMjjBw5EgEBATA1NYWhoSEmTJgAV1dXtGrVCgDg4eEBR0dHDB06FIsXL0ZycjJmzZoFPz8/sRI1duxYrFq1CtOnT8eIESNw9OhRbN++Hfv37y9XvEyAiIiIVIQyL1gKDg4GALi7u8us37BhA4YPHw4AWLp0KTQ0NNCvXz/k5ubC09MTP//8s9i2SpUqCA8Px7hx4+Dq6go9PT34+vpi/vz5Yht7e3vs378f/v7+WL58OWrUqIH169fD09OzXPHKfR+gyoT3ASJSDN4HiEgxPtZ9gBade/sVWuUxo9lMhfZXkbACREREpCJ4yxr5MQEiIiJSERoKvhGiKuNVYERERKR2WAEiIiJSERwCkx8rQERERKR2WAEiIiJSEYp+FIYqYwJERESkIhT9NHhVxiEwIiIiUjusABEREakIDQnrGvJiAkRERKQieBWY/JgqEhERkdphBYiIiEhFcBK0/JgAERERqQheBi8/DoERERGR2mEFiIiISEVwCEx+rAARERGR2mEFiIiISEVwDpD8mAARERGpCAlvhCg3vlNERESkdlgBIiIiUhGcBC0/JkBEREQqgnOA5MchMCIiIlI7rAARERGpCD4MVX6sABEREZHaYQWIiIhIRWhwErTcmAARERGpCA6ByY9DYERERKR2WAEiIiJSEbwTtPyYABEREakIzgGSH1NFIiIiUjusABEREakIToKWHxMgIiIiFcFngcmPQ2BERESkdlgBIiIiUhEcApMfK0BERESkdlgBIiIiUhG8DF5+TICIiIhUBG+EKD++U0RERKR2WAEiIiJSEbwMXn5MgIiIiFQErwKTH4fAiIiISO2wAkRERKQiOAQmPyZAREREKoJDYPLjEBgRERGpHVaAiIiIVARvhCg/VoCIiIhI7bACREREpCI4B0h+TICIiIhUhIQDO3LjO0VERERqhwkQERGRipBIJApdyuP48ePo0aMHbGxsIJFIsGfPHpntw4cPL9Z/165dZdqkpaXBx8cHhoaGMDY2xsiRI/H8+XOZNhcvXkS7du2gra2NmjVrYvHixe/0XjEBIiIiUhESBf8rj6ysLDRu3BirV68utU3Xrl3x8OFDcfntt99ktvv4+CA+Ph6RkZEIDw/H8ePHMWbMGHF7ZmYmPDw8YGtri9jYWPzwww+YO3cu1q5dW743CpwDRERERKXIzc1Fbm6uzDqpVAqpVFqsrZeXF7y8vMrsTyqVwsrKqsRtV69eRUREBM6ePYvmzZsDAFauXIlu3brhxx9/hI2NDbZu3Yq8vDz8+uuv0NLSQqNGjRAXF4clS5bIJEryUHoFKCgoCL/++mux9b/++iu+//57JURERERUOWlIJApdgoKCYGRkJLMEBQW9c3xRUVGwsLBA/fr1MW7cODx58kTcFhMTA2NjYzH5AYDOnTtDQ0MDp0+fFtu0b98eWlpaYhtPT08kJCTg6dOn5Xuv3vksFGTNmjVo0KBBsfWNGjVCSEiIEiIiIiIiAJg5cyYyMjJklpkzZ75TX127dsWmTZtw5MgRfP/994iOjoaXlxcKCgoAAMnJybCwsJDZp2rVqjA1NUVycrLYxtLSUqZN0euiNvJS+hBYcnIyrK2ti603NzfHw4cPlRARERFR5aToh6GWNtz1LgYOHCh+7eTkBGdnZ9SpUwdRUVHo1KmTQo5RHkqvANWsWRMnTpwotv7EiROwsbFRQkRERESVkzKvAiuv2rVrw8zMDDdu3AAAWFlZITU1VabNy5cvkZaWJs4bsrKyQkpKikybotelzS0qjdIToNGjR2Py5MnYsGED7ty5gzt37uDXX3+Fv78/Ro8erezwiIiI6AO4d+8enjx5Io4Cubq6Ij09HbGxsWKbo0ePorCwEC1bthTbHD9+HPn5+WKbyMhI1K9fHyYmJuU6vtKHwKZNm4YnT57gyy+/RF5eHgBAW1sbgYGB7zzOSEREpI6UeSfo58+fi9UcAEhMTERcXBxMTU1hamqKefPmoV+/frCyssLNmzcxffp0ODg4wNPTEwDQsGFDdO3aFaNHj0ZISAjy8/Mxfvx4DBw4UBwRGjx4MObNm4eRI0ciMDAQly9fxvLly7F06dJyxysRBEFQzKm/n+fPn+Pq1avQ0dFB3bp132vMMacgW4GREakvna71lB0CkUoQIu99lOMcurdPof151ughd9uoqCh06NCh2HpfX18EBwejd+/eOH/+PNLT02FjYwMPDw8sWLBAZlJzWloaxo8fj3379kFDQwP9+vXDihUroK+vL7a5ePEi/Pz8cPbsWZiZmWHChAkIDAws97lVmARIkZgAESkGEyAixVCHBKiyUcoQWN++fREaGgpDQ0P07du3zLZ//PHHR4qKiIioctNQ8FVgqkwpCZCRkZE4u9zQ0PCDzzQnIiJSB/z/VH5KSYA2bNggfh0aGqqMEIiIiEiNKf0y+I4dOyI9Pb3Y+szMTHTs2PHjB0RERFRJKfNhqJWN0hOgqKgo8fL31+Xk5ODvv/9WQkRERESk6pR2H6CLFy+KX1+5ckXmGR4FBQWIiIhA9erVlREaERFRpcQ5QPJTWgLUpEkT8VbbJQ116ejoYOXKlUqIjIiIqHJS5o0QKxulJUCJiYkQBAG1a9fGmTNnYG5uLm7T0tKChYUFqlSpoqzwiIiISIUpLQGytbUFABQWFiorBCIiIpWiwSEwuSm9VrZx40bs379ffD19+nQYGxujdevWuHPnjhIjIyIiqlx4FZj8lJ4ALVy4EDo6OgCAmJgYrFq1CosXL4aZmRn8/f2VHB0RERGpIqU/Df7u3btwcHAAAOzZswf9+/fHmDFj0KZNG7i7uys3OCIiokqEV4HJT+kVIH19fTx58gQA8Ndff6FLly4AAG1tbbx48UKZoREREVUqHAKTn9IrQF26dMGoUaPQtGlT/Pfff+jWrRsAID4+HnZ2dsoNjoiIiFSS0itAq1evhqurKx49eoRdu3ahWrVqAIDY2FgMGjRIydHRu/pl3a9o7NgUi4N+ENfNn/MtvD17oEXTVnBv0wGT/CYj8VaizH6NHZsWWw4eiPjY4RN9NGO7D8WFNZHI2HMVGXuu4uTyvej6aQdxu6WJOTYFLsfDbefw/M//EPvzQfRt202mj68GT8CJZXuQte86nu6OL/E4QuS9Ysvn7j0/6LnRx1d0fz1FLapM6RUgY2NjrFq1qtj6efPmKSEaUoTLl+Kxc/su1KtfV2a9Y6OG8O7hBStra2RmZCB4dQjGjvoSByLDZe75NP+7eWjTtrX42sDQ4KPFTvSx3Xv8EDN+CcL1+4mQAPD1+Ax75/2CpuO64sqd/7ApcBmM9YzQc/YIPM5Iw+COvbF9VjCa+3VD3M1XyY5WVS3sOB6OmKuxGNl1YKnHGv6DPyLORomv059nfuCzI6q4lJ4AFcnOzkZSUlKx54I5OzsrKSJ6F9lZ2Zg5/SvMmfcN1q1ZL7Ot/4B+4tfVq9tg/EQ/fNbnczy4/wA1a9UUtxkYGMDM3OyjxUykTOGnDsu8nrVhMcZ1H4ZWDZvhyp3/0NqxOcat+ApnE+IAAN+FrYB/v9FwqecsJkBzN/0E4FXyVJb055lIefpI8SdBFYaG8gd2Kg2lv1OPHj2Ct7c3DAwM0KhRIzRt2lRmocpl4bdBaO/WDq1atyqzXXb2C+zd/Seq16gOKyurYn24te6AwZ8Pwe5deyAIwocMmajC0NDQwOfuPaGnrYOYK7EAgJNX/sXnbj1gYmAMiUSCz917QltTiqgLMeXuf/WE7/Bo50WcXhmOLzw/V3T4VAFwCEx+Sq8ATZ48GRkZGTh9+jTc3d2xe/dupKSk4Ntvv8VPP/301v1zc3ORm5srs06oWgCpVPqhQqZSHDwQgatXriFs+5ZS22z7bTuW/rgML168gJ29HdasD4amlqa4/csJ49CiZQtoa2sj5mQMFi4IQnZ2NnyGDv4Yp0CkFJ/YNUDMir3Q1pLi+Yss9Jk3GleTrgMABiwYh22zfkbaH5eR/zIf2bkv0GfeKNx8cLtcx/gm9AccjTuB7JwX8Gjuhp8nfgd9HT2s3PPrBzgjoopP6QnQ0aNHsXfvXjRv3hwaGhqwtbVFly5dYGhoiKCgIHh7e5e5f1BQULH5Ql9/8xVmzfn6Q4ZNb0h+mIzFQT9gzfrgMpPPbt290Mq1JR4/foyNGzZhWkAgNm7dIO7zv3FjxLYNHRvgxYsX2LhhExMgUmkJ926iyVhPGOkZoH87b2ycthRuU/rjatJ1LBg+DcZ6Rug0/XM8zkhD79ZdsX1WMNr598Pl29fkPsa3W5eLX8fdjIeeti6mfTaWCZCKUfVL1xVJ6QlQVlYWLCwsAAAmJiZ49OgR6tWrBycnJ5w7d+6t+8+cORMBAQEy64SqBR8kVirdlfirSHuShoH9/z9RKSgoQOy/5/B72DacjTuNKlWqwMDAAAYGBrC1s4WzszPaurbH0cNH4eXtVWK/Ts5OWBu8Dnl5edDS0vpYp0P0UeW/zBcrOueuX8Kn9RtjUp+RWLw9GBN6f4FGozriyp3/AAAXb11FO6cW8Ovli3HLZ77zMU9fPYfZQyZDS1MLefl5b9+BKgVVH7ZSJKUnQPXr10dCQgLs7OzQuHFjrFmzBnZ2dggJCYG1tfVb95dKpcUqDjkF2R8qXCpFS9cW2Ll3h8y6OV/PgZ29Pb4YNVzmKq8iAgRAAPLy8kvtN+FqAgwNDZn8kFrRkGhAqqUFXemrxwQVCrIPjS4oLICG5P2mcDZxaIS0zHQmP6S2lJ4ATZo0CQ8fPgQAzJkzB127dsXWrVuhpaWF0NBQ5QZHctPT00Pdug4y63R0dGBsbIS6dR1w7+49HDp4CK5tXGFiYoKUlBT8uv7V0Ffb9m0BAFHHopH25AmcGjtDqqWFUzGnsH7dL/AdPkwZp0T0USwcMQMHzx5DUup9GOjoY3DH3nBv7ArPmT64dvcGrt9PxJpJizB17bd4kvkUvdt4okuz9uj+zXCxj5rmNjA1NEYti+qoolEFjes4AgBu3L+NrJxsdG/VGZYm5jh19Rxy8nLRpVk7fDVwAn7cuUZJZ00fCofA5Kf0BGjIkCHi1y4uLrhz5w6uXbuGWrVqwcyMl0KrCi2pFs7FnseWzWHIzMhENbNqcHFphk1hoahWzRQAoFm1Kn4P244fFv0EQRBQq1ZNTJ0+Bf0+66vk6Ik+HAtjM2yavgzWphbIyHqGi4lX4TnTB4fP/Q0A6Pb1MCwaORP7FmyAvrYebjy4Dd8f/HHwzFGxj/nDp2K4xwDxdVzIXwAA9ymfIfpiDPJfvoRfT18sHTsHEokENx7cRsCaeVh3IOzjnix9cEyA5CcRVPAaYw6BESmGTtd6yg6BSCUIkfc+ynH+fXRCof01N2+j0P4qEqXfB6hfv374/vvvi61fvHgxPvus7Jt6ERER0WskEsUuKkzpCdDx48fFB6C+zsvLC8ePH1dCRERERKTqlD4H6Pnz5yVe4aOpqYnMTD6nhoiISF6cAyQ/pVeAnJycsG3btmLrf//9dzg6OiohIiIiosqJj8KQn9IrQN988w369u2LmzdvomPHjgCAI0eO4LfffsOOHTvesjcRERFR+Sk9AerRowf27NmDhQsXYufOndDR0YGzszMOHz4MNzc3ZYdHRERUaXAITH5KTYBevnyJhQsXYsSIEThxQrGX7hEREakbJkDyU+ocoKpVq2Lx4sV4+fKlMsMgIiIiNaP0SdCdOnVCdHS0ssMgIiKq9DgJWn5KnwPk5eWFGTNm4NKlS3BxcYGenp7M9p49eyopMiIiIlJVSn8UhoZG6UUoiUSCgoKCcvfJR2EQKQYfhUGkGB/rURgX0/5VaH/Ops0V2l9FovQKUGFhobJDICIiUgmcBC0/pc8BIiIiIvrYlF4BAoCsrCxER0cjKSkJeXl5MtsmTpyopKiIiIgqF1WfuKxISk+Azp8/j27duiE7OxtZWVkwNTXF48ePoaurCwsLCyZAREREcuIQmPyUPgTm7++PHj164OnTp9DR0cGpU6dw584duLi44Mcff1R2eERERKSClJ4AxcXFYcqUKdDQ0ECVKlWQm5uLmjVrYvHixfjqq6+UHR4REVGlwfsAyU/pCZCmpqZ4KbyFhQWSkpIAAEZGRrh7964yQyMiIqpUJAr+p8qUPgeoadOmOHv2LOrWrQs3NzfMnj0bjx8/xubNm/HJJ58oOzwiIiJSQUqvAC1cuBDW1tYAgO+++w4mJiYYN24cHj9+jDVr1ig5OiIiosqDFSD5Kb0C1KhRIxTdjNrCwgIhISHYvXs3HB0d0aRJE+UGR0RERCpJ6RWgXr16YdOmTQCA9PR0tGrVCkuWLEHv3r0RHBys5OiIiIgqD06Clp/SE6Bz586hXbt2AICdO3fC0tISd+7cwaZNm7BixQolR0dERFR5cAhMfkpPgLKzs2FgYAAA+Ouvv9C3b19oaGigVatWuHPnjpKjIyIiIlWk9ATIwcEBe/bswd27d3Ho0CF4eHgAAFJTU2FoaKjk6IiIiCoPZVaAjh8/jh49esDGxgYSiQR79uyR2S4IAmbPng1ra2vo6Oigc+fOuH79ukybtLQ0+Pj4wNDQEMbGxhg5ciSeP38u0+bixYto164dtLW1xfsGvgulJ0CzZ8/G1KlTYWdnh5YtW8LV1RXAq2pQ06ZNlRwdERFR5aHMOUBZWVlo3LgxVq9eXeL2xYsXY8WKFQgJCcHp06ehp6cHT09P5OTkiG18fHwQHx+PyMhIhIeH4/jx4xgzZoy4PTMzEx4eHrC1tUVsbCx++OEHzJ07F2vXri3/eyUUXYKlRMnJyXj48CEaN24s3hTxzJkzMDQ0RIMGDcrdX05BtqJDJFJLOl3rKTsEIpUgRN77KMe5kXlFof3VlNZBbm6uzDqpVAqpVFrmfhKJBLt370bv3r0BvKr+2NjYYMqUKZg6dSoAICMjA5aWlggNDcXAgQNx9epVODo64uzZs2jevDkAICIiAt26dcO9e/dgY2OD4OBgfP3110hOToaWlhYAYMaMGdizZw+uXbtWrnNTegUIAKysrNC0aVMx+QGAFi1avFPyQ0REpL4kCl2CgoJgZGQkswQFBZU7qsTERCQnJ6Nz587iOiMjI7Rs2RIxMTEAgJiYGBgbG4vJDwB07twZGhoaOH36tNimffv2YvIDAJ6enkhISMDTp0/LFZPS7wNEREREiqHoS9dnzpyJgIAAmXVvq/6UJDk5GQBgaWkps97S0lLclpycDAsLC5ntVatWhampqUwbe3v7Yn0UbTMxMZE7JiZAREREVCJ5hrsqqwoxBEZERETvr6LeB8jKygoAkJKSIrM+JSVF3GZlZYXU1FSZ7S9fvkRaWppMm5L6eP0Y8mICRERERB+Uvb09rKyscOTIEXFdZmYmTp8+LV797erqivT0dMTGxoptjh49isLCQrRs2VJsc/z4ceTn54ttIiMjUb9+/XINfwFMgIiIiFSGMitAz58/R1xcHOLi4gC8mvgcFxeHpKQkSCQSTJ48Gd9++y3+/PNPXLp0CcOGDYONjY14pVjDhg3RtWtXjB49GmfOnMGJEycwfvx4DBw4EDY2NgCAwYMHQ0tLCyNHjkR8fDy2bduG5cuXF5unJA/OASIiIlIRynx+17///osOHTqIr4uSEl9fX4SGhmL69OnIysrCmDFjkJ6ejrZt2yIiIgLa2triPlu3bsX48ePRqVMnaGhooF+/fjKPxTIyMsJff/0FPz8/uLi4wMzMDLNnz5a5V5C8KsR9gBSN9wEiUgzeB4hIMT7WfYBuP7/+9kblYKdfV6H9VSSsABEREakIRU5cVnVMgIiIiFQEEyD5cRI0ERERqR1WgIiIiFSEMidBVzasABEREZHaYQWIiIhIRXAOkPyYABEREakIDoHJj0NgREREpHZYASIiIlIRHAKTHxMgIiIilcEESF4cAiMiIiK1wwoQERGRimD9R35MgIiIiFQErwKTH4fAiIiISO2wAkRERKQyWAGSFytAREREpHZYASIiIlIRrP/IjwkQERGRymAKJC8OgREREZHaYQWIiIhIRfAyePmxAkRERERqhwkQERERqR0OgREREakIPg1efkyAiIiIVAQTIPlxCIyIiIjUDhMgIiIiUjtMgIiIiEjtcA4QERGRiuB9gOTHChARERGpHSZAREREpHY4BEZERKQieBm8/JgAERERqQwmQPLiEBgRERGpHVaAiIiIVATrP/JjAkRERKQieBm8/DgERkRERGqHFSAiIiKVwQqQvFgBIiIiIrXDChAREZGKYP1HfkyAiIiIVAZTIHlxCIyIiIjUDitAREREKoKXwcuPFSAiIiJSO0yAiIiISO1wCIyIiEhF8Gnw8mMFiIiIiNQOK0BEREQqgxUgeTEBIiIiUhFMf+THITAiIiJ6b3PnzoVEIpFZGjRoIG7PycmBn58fqlWrBn19ffTr1w8pKSkyfSQlJcHb2xu6urqwsLDAtGnT8PLlyw8SLytAREREKkLZ9wFq1KgRDh8+LL6uWvX/0wx/f3/s378fO3bsgJGREcaPH4++ffvixIkTAICCggJ4e3vDysoKJ0+exMOHDzFs2DBoampi4cKFCo+VCRAREZHKUG4CVLVqVVhZWRVbn5GRgV9++QVhYWHo2LEjAGDDhg1o2LAhTp06hVatWuGvv/7ClStXcPjwYVhaWqJJkyZYsGABAgMDMXfuXGhpaSk0Vg6BERERUYlyc3ORmZkps+Tm5pba/vr167CxsUHt2rXh4+ODpKQkAEBsbCzy8/PRuXNnsW2DBg1Qq1YtxMTEAABiYmLg5OQES0tLsY2npycyMzMRHx+v8HNjAkRERKQiJApegoKCYGRkJLMEBQWVeOyWLVsiNDQUERERCA4ORmJiItq1a4dnz54hOTkZWlpaMDY2ltnH0tISycnJAIDk5GSZ5Kdoe9E2ReMQGBERkcpQ7BDYzJkzERAQILNOKpWW2NbLy0v82tnZGS1btoStrS22b98OHR0dhcalCKwAERERUYmkUikMDQ1lltISoDcZGxujXr16uHHjBqysrJCXl4f09HSZNikpKeKcISsrq2JXhRW9Lmle0ftiAkRERKQi3rwM/X2X9/H8+XPcvHkT1tbWcHFxgaamJo4cOSJuT0hIQFJSElxdXQEArq6uuHTpElJTU8U2kZGRMDQ0hKOj43vFUhIOgREREdF7mzp1Knr06AFbW1s8ePAAc+bMQZUqVTBo0CAYGRlh5MiRCAgIgKmpKQwNDTFhwgS4urqiVatWAAAPDw84Ojpi6NChWLx4MZKTkzFr1iz4+fnJXXUqDyZARERE9N7u3buHQYMG4cmTJzA3N0fbtm1x6tQpmJubAwCWLl0KDQ0N9OvXD7m5ufD09MTPP/8s7l+lShWEh4dj3LhxcHV1hZ6eHnx9fTF//vwPEq9EEAThg/SsRDkF2coOgUgl6HStp+wQiFSCEHnvoxxH0f//aVfRVWh/FQnnABEREZHaUckKEFV8ubm5CAoKwsyZMz/I2C6ROuDniOjdMQEipcjMzISRkREyMjJgaGio7HCIKiV+jojeHYfAiIiISO0wASIiIiK1wwSIiIiI1A4TIFIKqVSKOXPmcOIm0Xvg54jo3XESNBEREakdVoCIiIhI7TABIiIiIrXDBIiIiIjUDhMgIgDu7u6YPHmyssMgUrrhw4ejd+/eyg6D6IPjJGhSK1FRUejQoQOePn0KY2NjcX1aWho0NTVhYGCgvOCIPqLbt2/D3t4e58+fR5MmTcT1GRkZEARB5vNBpIqqKjsAotfl5+dDU1Pzox/X1NT0ox+TqCzK+iwYGRl99GMSKQOHwFSEu7s7Jk6ciOnTp8PU1BRWVlaYO3euuD0pKQm9evWCvr4+DA0NMWDAAKSkpIjb586diyZNmmDz5s2ws7ODkZERBg4ciGfPnpV53J9//hl169aFtrY2LC0t0b9/f3FbREQE2rZtC2NjY1SrVg3du3fHzZs3xe23b9+GRCLBtm3b4ObmBm1tbWzduhUA8Ouvv6JRo0aQSqWwtrbG+PHjxf2WLFkCJycn6OnpoWbNmvjyyy/x/PlzcfudO3fQo0cPmJiYQE9PD40aNcKBAwdw+/ZtdOjQAQBgYmICiUSC4cOHi+/f60Ngubm5CAwMRM2aNSGVSuHg4IBffvlF/m8IqaWdO3fCyckJOjo6qFatGjp37oysrCycPXsWXbp0gZmZGYyMjODm5oZz587J7CuRSBAcHIyePXtCT08P3333HQBg3759+PTTT6GtrQ0zMzP06dNH3Gfz5s1o3rw5DAwMYGVlhcGDByM1NVXc/vTpU/j4+MDc3Bw6OjqoW7cuNmzYAACwt7cHADRt2hQSiQTu7u4Aig+BFRYWYvHixXBwcIBUKkWtWrXE2IgqMyZAKmTjxo3Q09PD6dOnsXjxYsyfPx+RkZEoLCxEr169kJaWhujoaERGRuLWrVv4/PPPZfa/efMm9uzZg/DwcISHhyM6OhqLFi0q9Xj//vsvJk6ciPnz5yMhIQERERFo3769uD0rKwsBAQH4999/ceTIEWhoaKBPnz4oLCyU6WfGjBmYNGkSrl69Ck9PTwQHB8PPzw9jxozBpUuX8Oeff8LBwUFsr6GhgRUrViA+Ph4bN27E0aNHMX36dHG7n58fcnNzcfz4cVy6dAnff/899PX1UbNmTezatQsAkJCQgIcPH2L58uUlntuwYcPw22+/YcWKFbh69SrWrFkDfX19+b8ZpHYePnyIQYMGYcSIEbh69SqioqLQt29fCIKAZ8+ewdfXF//88w9OnTqFunXrolu3bsX+wJg7dy769OmDS5cuYcSIEdi/fz/69OmDbt264fz58zhy5AhatGghts/Pz8eCBQtw4cIF7NmzB7dv3xaTegD45ptvcOXKFRw8eBBXr15FcHAwzMzMAABnzpwBABw+fBgPHz7EH3/8UeJ5zZw5E4sWLRL7CgsLg6WlpYLfPSIlEEgluLm5CW3btpVZ9+mnnwqBgYHCX3/9JVSpUkVISkoSt8XHxwsAhDNnzgiCIAhz5swRdHV1hczMTLHNtGnThJYtW5Z6zF27dgmGhoYy+5Tl0aNHAgDh0qVLgiAIQmJiogBAWLZsmUw7Gxsb4euvv5arT0EQhB07dgjVqlUTXzs5OQlz584tse2xY8cEAMLTp09l1ru5uQmTJk0SBEEQEhISBABCZGSk3DEQxcbGCgCE27dvv7VtQUGBYGBgIOzbt09cB0CYPHmyTDtXV1fBx8dH7hjOnj0rABCePXsmCIIg9OjRQ/jiiy9KbFv0+Tt//rzMel9fX6FXr16CIAhCZmamIJVKhXXr1skdA1FlwQqQCnF2dpZ5bW1tjdTUVFy9ehU1a9ZEzZo1xW2Ojo4wNjbG1atXxXV2dnYyk4CL9geArVu3Ql9fX1z+/vtvdOnSBba2tqhduzaGDh2KrVu3Ijs7W9z/+vXrGDRoEGrXrg1DQ0PY2dkBeDUc97rmzZuLX6empuLBgwfo1KlTqed5+PBhdOrUCdWrV4eBgQGGDh2KJ0+eiMeeOHEivv32W7Rp0wZz5szBxYsX5X0LAQBxcXGoUqUK3NzcyrUfqbfGjRujU6dOcHJywmeffYZ169bh6dOnAICUlBSMHj0adevWhZGREQwNDfH8+fMyPwvAq5/Fsj4LsbGx6NGjB2rVqgUDAwPxZ7ao33HjxuH3339HkyZNMH36dJw8ebJc53T16lXk5uaWGQNRZcUESIW8OWFSIpEUG2561/179uyJuLg4cSmad3Du3Dn89ttvsLa2xuzZs9G4cWOkp6cDAHr06IG0tDSsW7cOp0+fxunTpwEAeXl5MsfR09MTv9bR0Skzxtu3b6N79+5wdnbGrl27EBsbi9WrV8v0O2rUKNy6dQtDhw7FpUuX0Lx5c6xcuVLu9+FtMRCVpEqVKoiMjMTBgwfh6OiIlStXon79+khMTISvry/i4uKwfPlynDx5EnFxcahWrVqZnwWg7J/FrKwseHp6wtDQEFu3bsXZs2exe/duAP//WfDy8sKdO3fg7+8v/mExdepUuc+JnwVSZUyA1EDDhg1x9+5d3L17V1x35coVpKenw9HRUa4+DAwM4ODgIC5FvxirVq2Kzp07Y/Hixbh48SJu376No0eP4smTJ0hISMCsWbPQqVMnNGzYUPxr+G3HsbOzw5EjR0rcHhsbi8LCQvz0009o1aoV6tWrhwcPHhRrV7NmTYwdOxZ//PEHpkyZgnXr1gEAtLS0AAAFBQWlxuDk5ITCwkJER0e/NV6i10kkErRp0wbz5s3D+fPnoaWlhd27d+PEiROYOHEiunXrJk7uf/z48Vv7c3Z2LvWzcO3aNTx58gSLFi1Cu3bt0KBBA5kJ0EXMzc3h6+uLLVu2YNmyZVi7di0A+T4LdevWhY6OTqkxEFVmvAxeDXTu3BlOTk7w8fHBsmXL8PLlS3z55Zdwc3MrVnIvj/DwcNy6dQvt27eHiYkJDhw4gMLCQtSvXx8mJiaoVq0a1q5dC2trayQlJWHGjBly9Tt37lyMHTsWFhYW8PLywrNnz3DixAlMmDABDg4OyM/Px8qVK9GjRw+cOHECISEhMvtPnjwZXl5eqFevHp4+fYpjx46hYcOGAABbW1tIJBKEh4ejW7du0NHRKTa52c7ODr6+vhgxYgRWrFiBxo0b486dO0hNTcWAAQPe+f0i1Xb69GkcOXIEHh4esLCwwOnTp/Ho0SM0bNgQdevWFa/YyszMxLRp0+SqrsyZMwedOnVCnTp1MHDgQLx8+RIHDhxAYGAgatWqBS0tLaxcuRJjx47F5cuXsWDBApn9Z8+eDRcXFzRq1Ai5ubkIDw8XPwsWFhbQ0dFBREQEatSoAW1t7WKXwGtrayMwMBDTp0+HlpYW2rRpg0ePHiE+Ph4jR45U3JtHpAzKnoREivH6JN4ivXr1Enx9fQVBEIQ7d+4IPXv2FPT09AQDAwPhs88+E5KTk8W2c+bMERo3biyz/9KlSwVbW9tSj/n3338Lbm5ugomJiaCjoyM4OzsL27ZtE7dHRkYKDRs2FKRSqeDs7CxERUUJAITdu3cLglD6JExBEISQkBChfv36gqampmBtbS1MmDBB3LZkyRLB2tpa0NHRETw9PYVNmzbJTGweP368UKdOHUEqlQrm5ubC0KFDhcePH4v7z58/X7CyshIkEon4/rz5/r148ULw9/cXrK2tBS0tLcHBwUH49ddfS30viK5cuSJ4enoK5ubmglQqFerVqyesXLlSEARBOHfunNC8eXNBW1tbqFu3rrBjxw7B1tZWWLp0qbj/65+N1+3atUto0qSJoKWlJZiZmQl9+/YVt4WFhQl2dnaCVCoVXF1dhT///FPmM7VgwQKhYcOGgo6OjmBqair06tVLuHXrlrj/unXrhJo1awoaGhqCm5ubIAiyk6AF4dWE7W+//VawtbUVNDU1hVq1agkLFy5U2PtGpCy8EzQRERGpHc4BIiIiIrXDBIiIiIjUDhMgIiIiUjtMgIiIiEjtMAEiIiIitcMEiIiIiNQOEyAiIiJSO0yAiIiISO0wASIiAMDw4cPRu3dv8bW7uzsmT5780eOIioqCRCIRH6pLRPQhMAEiquCGDx8OiUQCiUQCLS0tODg4YP78+Xj58uUHPe4ff/xR7NlSpWHSQkSVDR+GSlQJdO3aFRs2bEBubi4OHDgAPz8/aGpqYubMmTLt8vLyxKd8vy9TU1OF9ENEVBGxAkRUCUilUlhZWcHW1hbjxo1D586d8eeff4rDVt999x1sbGxQv359AMDdu3cxYMAAGBsbw9TUFL169cLt27fF/goKChAQEABjY2NUq1YN06dPx5uPBXxzCCw3NxeBgYGoWbMmpFIpHBwc8Msvv+D27dvo0KEDAMDExAQSiQTDhw8HABQWFiIoKAj29vbQ0dFB48aNsXPnTpnjHDhwAPXq1YOOjg46dOggEycR0YfCBIioEtLR0UFeXh4A4MiRI0hISEBkZCTCw8ORn58PT09PGBgY4O+//8aJEyegr6+Prl27ivv89NNPCA0Nxa+//op//vkHaWlp2L17d5nHHDZsGH777TesWLECV69exZo1a6Cvr4+aNWti165dAICEhAQ8fPgQy5cvBwAEBQVh06ZNCAkJQXx8PPz9/TFkyBBER0cDeJWo9e3bFz169EBcXBxGjRqFGTNmfKi3jYjo/yn5afRE9Ba+vr5Cr169BEEQhMLCQiEyMlKQSqXC1KlTBV9fX8HS0lLIzc0V22/evFmoX7++UFhYKK7Lzc0VdHR0hEOHDgmCIAjW1tbC4sWLxe35+flCjRo1xOMIgiC4ubkJkyZNEgRBEBISEgQAQmRkZIkxHjt2TAAgPH36VFyXk5Mj6OrqCidPnpRpO3LkSGHQoEGCIAjCzJkzBUdHR5ntgYGBxfoiIlI0zgEiqgTCw8Ohr6+P/Px8FBYWYvDgwZg7dy78/Pzg5OQkM+/nwoULuHHjBgwMDGT6yMnJwc2bN5GRkYGHDx+iZcuW4raqVauiefPmxYbBisTFxaFKlSpwc3OTO+YbN24gOzsbXbp0kVmfl5eHpk2bAgCuXr0qEwcAuLq6yn0MIqJ3xQSIqBLo0KEDgoODoaWlBRsbG1St+v8fXT09PZm2z58/h4uLC7Zu3VqsH3Nz83c6vo6OTrn3ef78OQBg//79qF69usw2qVT6TnEQESkKEyCiSkBPTw8ODg5ytW3WrBm2bdsGCwsLGBoaltjG2toap0+fRvv27QEAL1++RGxsLJo1a1ZieycnJxQWFiI6OhqdO3cutr2oAlVQUCCuc3R0hFQqRVJSUqmVo4YNG+LPP/+UWXfq1Km3nyQR0XviJGgiFePj4wMzMzP06tULf//9NxITExEVFYWJEyfi3r17AIBJkyZh0aJF2LNnD65du4Yvv/yyzHv42NnZwdfXFyNGjMCePXvEPrdv3w4AsLW1hUQiQXh4OB49eoTnz5/DwMAAU6dOhb+/PzZu3IibN2/i3LlzWLlyJTZu3AgAGDt2LK5fv45p06YhISEBYWFhCA0N/dBvEREREyAiVaOrq4vjx4+jVq1a6Nu3Lxo2bIiRI0ciJydHrAhNmTIFQ4cOha+vL1xdXWFgYIA+ffqU2W9wcDD69++PL7/8Eg0aNMDo0aORlZUFAKhevTrmzZuHGTNmwNLSEuPHjwcALFiwAN988w2CgoLQsGFDdO3aFfv374e9vT0AoFatWti1axf27NmDxo0bIyQkBAsXLvyA7w4R0SsSobRZj0REREQqihUgIiIiUjtMgIiIiEjtMAEiIiIitcMEiIiIiNQOEyAiIiJSO0yAiIiISO0wASIiIiK1wwSIiIiI1A4TICIiIlI7TICIiIhI7TABIiIiIrXzf6cFbgET9nciAAAAAElFTkSuQmCC\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Saved: outputs/classical/naive_bayes/confusion_matrix_binary_val.png\n" ] }, { - "output_type": "display_data", "data": { + "image/png": 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", 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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Saved: outputs/classical/naive_bayes/confusion_matrix_binary_test.png\n", "All binary artifacts saved.\n" @@ -2693,8 +2331,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n", "Train+Val: 24,083 Val: 4,250 Test: 4,250\n" @@ -2734,8 +2372,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "=== Type: Comparing Vectorizer + NB Combinations ===\n", "CountVectorizer + MultinomialNB CV F1=0.3811 params={'nb__alpha': 0.5, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n", @@ -2778,8 +2416,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Best type NB model: CountVec+MultinomialNB (CV F1=0.3811)\n", "\n", @@ -2823,8 +2461,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "[Val] Accuracy=0.7694 Macro-F1=0.7744 Weighted-F1=0.7693\n", " precision recall f1-score support\n", @@ -2857,35 +2495,35 @@ ] }, { - "output_type": "display_data", "data": { + "image/png": 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", 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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Saved: outputs/classical/naive_bayes/confusion_matrix_type_val.png\n" ] }, { - "output_type": "display_data", "data": { + "image/png": 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", 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\n" + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Saved: outputs/classical/naive_bayes/confusion_matrix_type_test.png\n", "All type artifacts saved.\n" @@ -2959,8 +2597,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Binary errors on test: 1781 total\n", " False Positives (predicted sarcastic, actually not): 1039\n", @@ -3021,8 +2659,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Type errors on test: 2570 total\n", "\n", @@ -3071,8 +2709,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "====== NAIVE BAYES RESULTS SUMMARY ======\n", "\n", @@ -3158,8 +2796,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Binary config: TrainConfig(model_name='distilbert-base-uncased', max_length=128, batch_size=32, lr=2e-05, weight_decay=0.01, warmup_ratio=0.1, epochs=10, patience=3, seed=42, task='binary', use_class_weights=False)\n", "Type config: TrainConfig(model_name='distilbert-base-uncased', max_length=128, batch_size=32, lr=2e-05, weight_decay=0.01, warmup_ratio=0.1, epochs=10, patience=3, seed=42, task='type', use_class_weights=True)\n" @@ -3599,8 +3237,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Binary — Train: 39,666 Val: 8,500 Test: 8,500\n" ] @@ -3633,8 +3271,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Type — Train: 19,833 Val: 4,250 Test: 4,250\n", "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n" @@ -3673,75 +3311,8 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 396, - "referenced_widgets": [ - "8b9080237c194037a2cd1dc711a694d2", - "d0d70eea6290419fb4a39bd74964bacb", - "30e0eec43be54d08a0b5bd855e06d3c3", - "959391d9e6cc405ead852002ce0276ff", - "143c9b378b5a4260b15e328dc744374f", - "384fe210d84b4e8d8bc1cd8b99944d5e", - "2a97e4558e6d41df919bd3ce83576627", - "bfc3291175a14250a407af3dd6376d58", - "01c169be55ca4399b34eb7cdae152075", - "2f42691301be4d01858524488e30fe78", - "8faab66da6c74399a140fa5aad07dbbb" - ] - }, - "id": "Yq8zSNjluKCC", - "outputId": "44080f86-f045-426f-f630-765eb5f599c7" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Using device: cpu\n", - "Config saved to /content/outputs/bert/distilbert_binary/config.json\n", - "Loading tokenizer: distilbert-base-uncased\n", - "Loading model: distilbert-base-uncased (2 labels)\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Loading weights: 0%| | 0/100 [00:00 Date: Tue, 3 Mar 2026 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Setup & Data Preparation\n", + "2. TF-IDF + Logistic Regression Baseline\n", + "3. Naive Bayes Baseline\n", + "4. BERT / DistilBERT Classification\n", + "5. Error Analysis & Model Comparison\n", + "\n", + "**Run all cells in order (Runtime → Run all).**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1xG7cJfAuKBR", + "outputId": "12069dde-b810-4ab5-f89b-5fa1af05cf8c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "cwd: /content\n", + "files in cwd: ['.config', 'sarcasm_pairs_step35_clean.jsonl', 'sample_data']\n", + "Data : /content/sarcasm_pairs_step35_clean.jsonl\n", + "Root : /content\n", + "Output : /content/outputs\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# SETUP — imports, file upload, paths\n", + "# ============================================================\n", + "from __future__ import annotations\n", + "import json, hashlib, random, os, warnings, shutil\n", + "from dataclasses import dataclass\n", + "from pathlib import Path\n", + "from collections import Counter\n", + "from urllib.parse import urlparse\n", + "from typing import Optional\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "import seaborn as sns\n", + "\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.naive_bayes import MultinomialNB, ComplementNB\n", + "from sklearn.model_selection import GridSearchCV, GroupKFold\n", + "from sklearn.metrics import (\n", + " accuracy_score, precision_score, recall_score,\n", + " f1_score, classification_report, confusion_matrix,\n", + ")\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "SEED = 42\n", + "random.seed(SEED)\n", + "np.random.seed(SEED)\n", + "\n", + "# ── Locate or upload the JSONL data file ─────────────────────────────────\n", + "FILENAME = \"sarcasm_pairs_step35_clean.jsonl\"\n", + "\n", + "def _locate_file(filename):\n", + " candidates = []\n", + " for root in [Path.cwd()] + list(Path.cwd().parents):\n", + " for sub in [\n", + " Path(\"data\") / \"processed\" / filename,\n", + " Path(\"data\") / filename,\n", + " Path(filename),\n", + " ]:\n", + " candidates.append(root / sub)\n", + " for p in [\n", + " Path(\"/content\") / filename,\n", + " Path(\"/mnt/data\") / filename,\n", + " ]:\n", + " candidates.append(p)\n", + " _c = Path('/content')\n", + " for p in (_c.rglob(filename) if _c.exists() else []):\n", + " candidates.append(p)\n", + " for p in candidates:\n", + " if p.is_file():\n", + " return p\n", + " return None\n", + "\n", + "print(f'cwd: {Path.cwd()}')\n", + "print(f'files in cwd: {[p.name for p in Path.cwd().iterdir()][:10]}')\n", + "\n", + "DATA_FILE = _locate_file(FILENAME)\n", + "if DATA_FILE is None:\n", + " try:\n", + " from google.colab import files as _cf\n", + " print(f\"Upload {FILENAME!r}:\")\n", + " _up = _cf.upload()\n", + " if not _up:\n", + " raise RuntimeError(\"No file uploaded.\")\n", + " _name = list(_up.keys())[0]\n", + " DATA_FILE = Path(\"/content\") / FILENAME\n", + " if Path(_name) != DATA_FILE:\n", + " shutil.move(_name, str(DATA_FILE))\n", + " print(f\"Saved to {DATA_FILE}\")\n", + " except ImportError:\n", + " raise FileNotFoundError(\n", + " f\"Cannot find {FILENAME!r}. Place it in the same folder as this notebook.\"\n", + " )\n", + "\n", + "# ── Project root + all output directories ────────────────────────────────\n", + "def _find_root(data_file):\n", + " for parent in [data_file.parent] + list(data_file.parents):\n", + " if any((parent / m).exists() for m in [\"outputs\",\"notebooks\",\"data\"]):\n", + " return parent\n", + " return data_file.parent\n", + "\n", + "ROOT = _find_root(DATA_FILE)\n", + "\n", + "OUT_DATASETS = ROOT / 'outputs' / 'datasets'\n", + "OUT_SPLITS = ROOT / 'outputs' / 'splits'\n", + "OUT_TFIDF = ROOT / 'outputs' / 'classical' / 'tfidf_lr'\n", + "OUT_NB = ROOT / 'outputs' / 'classical' / 'naive_bayes'\n", + "BERT_OUT = ROOT / 'outputs' / 'bert'\n", + "REPORTS_DIR = ROOT / 'outputs' / 'reports'\n", + "SPLITS = OUT_SPLITS\n", + "\n", + "for d in [OUT_DATASETS, OUT_SPLITS, OUT_TFIDF, OUT_NB, BERT_OUT, REPORTS_DIR]:\n", + " d.mkdir(parents=True, exist_ok=True)\n", + "\n", + "print(f\"Data : {DATA_FILE}\")\n", + "print(f\"Root : {ROOT}\")\n", + "print(f\"Output : {ROOT / 'outputs'}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xDqObfuPuKBU" + }, + "source": [ + "---\n", + "# Part 1 — Data Preparation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RVi4jvpmuKBV" + }, + "source": [ + "# Notebook 01 — Data Preparation\n", + "\n", + "**Purpose**: Parse JSONL dataset, derive binary and type labels, perform audit checks, generate group-safe train/val/test splits.\n", + "\n", + "**Outputs**:\n", + "- `outputs/datasets/binary_dataset.csv`\n", + "- `outputs/datasets/type_dataset.csv`\n", + "- `outputs/splits/train_binary.csv`, `val_binary.csv`, `test_binary.csv`\n", + "- `outputs/splits/train_type.csv`, `val_type.csv`, `test_type.csv`\n", + "- `outputs/splits/split_metadata.json`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "egvV3LLJuKBV" + }, + "source": [ + "## 1. Load and Validate Raw Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oqhoOTiguKBV", + "outputId": "9211b95a-f157-40ce-9d18-7275e0c157a3" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loaded rows : 28,333\n", + "Error rows : 0\n" + ] + } + ], + "source": [ + "REQUIRED_FIELDS = {\"original_headline\", \"generated_headline\", \"strategy\", \"type\", \"model_used\", \"article_link\"}\n", + "ALLOWED_TYPES = {\"sarcastic_to_non\", \"non_to_sarcastic\"}\n", + "ALLOWED_STRATS = {\"irony\", \"overstatement\", \"rhetorical_question\", \"sarcasm\", \"satire\", \"understatement\"}\n", + "\n", + "raw_rows = []\n", + "errors = []\n", + "\n", + "with open(DATA_FILE, \"r\", encoding=\"utf-8\") as f:\n", + " for line_num, line in enumerate(f):\n", + " line = line.strip()\n", + " if not line:\n", + " continue\n", + " try:\n", + " row = json.loads(line)\n", + " except json.JSONDecodeError as e:\n", + " errors.append((line_num, f\"JSON parse error: {e}\"))\n", + " continue\n", + "\n", + " # Schema check\n", + " missing = REQUIRED_FIELDS - set(row.keys())\n", + " if missing:\n", + " errors.append((line_num, f\"Missing fields: {missing}\"))\n", + " continue\n", + "\n", + " # Value checks\n", + " if row[\"type\"] not in ALLOWED_TYPES:\n", + " errors.append((line_num, f\"Unknown type: {row['type']!r}\"))\n", + " continue\n", + " if row[\"strategy\"] not in ALLOWED_STRATS:\n", + " errors.append((line_num, f\"Unknown strategy: {row['strategy']!r}\"))\n", + " continue\n", + "\n", + " row[\"_line_num\"] = line_num\n", + " raw_rows.append(row)\n", + "\n", + "print(f\"Loaded rows : {len(raw_rows):,}\")\n", + "print(f\"Error rows : {len(errors)}\")\n", + "if errors:\n", + " for ln, msg in errors[:5]:\n", + " print(f\" Line {ln}: {msg}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yvg9qFhouKBW" + }, + "source": [ + "## 2. Dataset Audit" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KISNWAQhuKBW", + "outputId": "e8755fd1-8661-4393-f3a9-429cb93065fd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "=== Type distribution ===\n", + " non_to_sarcastic: 14,925 (52.7%)\n", + " sarcastic_to_non: 13,408 (47.3%)\n", + "\n", + "=== Strategy distribution ===\n", + " sarcasm: 8,699 (30.7%)\n", + " irony: 6,102 (21.5%)\n", + " satire: 5,224 (18.4%)\n", + " overstatement: 3,976 (14.0%)\n", + " understatement: 3,295 (11.6%)\n", + " rhetorical_question: 1,037 (3.7%)\n", + "\n", + "=== Model distribution ===\n", + " stepfun/step-3.5-flash:free: 28,333\n" + ] + } + ], + "source": [ + "# ── Type and Strategy distributions ──────────────────────────────────────────\n", + "type_counts = Counter(r[\"type\"] for r in raw_rows)\n", + "strategy_counts = Counter(r[\"strategy\"] for r in raw_rows)\n", + "model_counts = Counter(r[\"model_used\"] for r in raw_rows)\n", + "\n", + "print(\"=== Type distribution ===\")\n", + "for k, v in type_counts.most_common():\n", + " print(f\" {k}: {v:,} ({v/len(raw_rows)*100:.1f}%)\")\n", + "\n", + "print(\"\\n=== Strategy distribution ===\")\n", + "for k, v in strategy_counts.most_common():\n", + " print(f\" {k}: {v:,} ({v/len(raw_rows)*100:.1f}%)\")\n", + "\n", + "print(\"\\n=== Model distribution ===\")\n", + "for k, v in model_counts.most_common():\n", + " print(f\" {k}: {v:,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "28wpsTTnuKBX", + "outputId": "bdcc2561-9baa-41a3-bac8-14101d7e4596" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Duplicate original_headlines : 70\n", + "Duplicate generated_headlines: 1\n", + "Duplicate article_links : 2\n" + ] + } + ], + "source": [ + "# ── Duplicate checks ──────────────────────────────────────────────────────────\n", + "orig_headlines = [r[\"original_headline\"] for r in raw_rows]\n", + "gen_headlines = [r[\"generated_headline\"] for r in raw_rows]\n", + "article_links = [r[\"article_link\"] for r in raw_rows]\n", + "\n", + "dup_orig = len(orig_headlines) - len(set(orig_headlines))\n", + "dup_gen = len(gen_headlines) - len(set(gen_headlines))\n", + "dup_article = len(article_links) - len(set(article_links))\n", + "\n", + "print(f\"Duplicate original_headlines : {dup_orig}\")\n", + "print(f\"Duplicate generated_headlines: {dup_gen}\")\n", + "print(f\"Duplicate article_links : {dup_article}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SLUFPDIHuKBX", + "outputId": "3d7b15de-a43f-410f-a381-6a9544171b45" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "original_headline : 0 nulls, 0 empty strings\n", + "generated_headline : 0 nulls, 0 empty strings\n", + "strategy : 0 nulls, 0 empty strings\n", + "type : 0 nulls, 0 empty strings\n", + "model_used : 0 nulls, 0 empty strings\n", + "article_link : 0 nulls, 0 empty strings\n" + ] + } + ], + "source": [ + "# ── Null / empty checks ───────────────────────────────────────────────────────\n", + "for field in [\"original_headline\", \"generated_headline\", \"strategy\", \"type\", \"model_used\", \"article_link\"]:\n", + " null_count = sum(1 for r in raw_rows if r.get(field) is None)\n", + " empty_count = sum(1 for r in raw_rows if r.get(field) == \"\")\n", + " print(f\"{field:25s}: {null_count} nulls, {empty_count} empty strings\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 524 + }, + "id": "E-B0fc5vuKBX", + "outputId": "a7845ca8-655c-48df-c59b-28b16307b91d" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/datasets/distributions.png\n" + ] + } + ], + "source": [ + "# ── Strategy distribution plot ────────────────────────────────────────────────\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Type dist\n", + "ax = axes[0]\n", + "labels, counts = zip(*sorted(type_counts.items(), key=lambda x: -x[1]))\n", + "ax.barh(labels, counts, color=[\"steelblue\", \"coral\"])\n", + "ax.set_title(\"Row-level Type Distribution\", fontsize=14)\n", + "ax.set_xlabel(\"Count\")\n", + "for i, c in enumerate(counts):\n", + " ax.text(c + 50, i, f\"{c:,}\", va=\"center\")\n", + "\n", + "# Strategy dist\n", + "ax = axes[1]\n", + "labels2, counts2 = zip(*sorted(strategy_counts.items(), key=lambda x: -x[1]))\n", + "ax.barh(labels2, counts2, color=\"steelblue\")\n", + "ax.set_title(\"Strategy Distribution (Type Task Labels)\", fontsize=14)\n", + "ax.set_xlabel(\"Count\")\n", + "for i, c in enumerate(counts2):\n", + " ax.text(c + 30, i, f\"{c:,}\", va=\"center\")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(OUT_DATASETS / \"distributions.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/datasets/distributions.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JxKcjhRSuKBY" + }, + "source": [ + "## 3. Label Derivation and Dataset Expansion" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nukGQcmKuKBY", + "outputId": "2fcfb601-8ad0-4876-b813-c21b007bb164" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Binary dataset : 56,666 samples\n", + " sarcastic (1): 28,333\n", + " non-sarc (0): 28,333\n", + "\n", + "Type dataset : 28,333 samples\n", + "type_label\n", + "sarcasm 8699\n", + "irony 6102\n", + "satire 5224\n", + "overstatement 3976\n", + "understatement 3295\n", + "rhetorical_question 1037\n" + ] + } + ], + "source": [ + "from __future__ import annotations\n", + "from urllib.parse import urlparse\n", + "\n", + "\n", + "def normalize_url(url: str) -> str:\n", + " \"\"\"Lowercase and strip scheme/trailing slash for stable group IDs.\"\"\"\n", + " url = url.strip().lower()\n", + " parsed = urlparse(url)\n", + " normalized = (parsed.netloc + parsed.path).rstrip(\"/\")\n", + " return normalized if normalized else url\n", + "\n", + "\n", + "def derive_labels(raw_rows):\n", + " \"\"\"\n", + " Expand each JSONL row into 2 samples (sarcastic + non-sarcastic).\n", + "\n", + " Rules:\n", + " sarcastic_to_non -> original=sarcastic(1), generated=non-sarcastic(0)\n", + " non_to_sarcastic -> original=non-sarcastic(0), generated=sarcastic(1)\n", + "\n", + " Returns binary_df, type_df\n", + " \"\"\"\n", + " binary_records = []\n", + " sample_id = 0\n", + "\n", + " for pair_id, row in enumerate(raw_rows):\n", + " group_id = normalize_url(row[\"article_link\"]) if row[\"article_link\"] else str(pair_id)\n", + " row_type = row[\"type\"]\n", + " strategy = row[\"strategy\"]\n", + " model_used = row[\"model_used\"]\n", + " article = row[\"article_link\"]\n", + "\n", + " if row_type == \"sarcastic_to_non\":\n", + " orig_label, orig_strat = 1, strategy\n", + " gen_label, gen_strat = 0, None\n", + " else:\n", + " orig_label, orig_strat = 0, None\n", + " gen_label, gen_strat = 1, strategy\n", + "\n", + " binary_records.append({\n", + " \"sample_id\" : sample_id,\n", + " \"pair_id\" : pair_id,\n", + " \"group_id\" : group_id,\n", + " \"text\" : row[\"original_headline\"],\n", + " \"binary_label\" : orig_label,\n", + " \"is_generated\" : 0,\n", + " \"strategy\" : orig_strat,\n", + " \"source_type\" : row_type,\n", + " \"article_link\" : article,\n", + " \"model_used\" : model_used,\n", + " })\n", + " sample_id += 1\n", + "\n", + " binary_records.append({\n", + " \"sample_id\" : sample_id,\n", + " \"pair_id\" : pair_id,\n", + " \"group_id\" : group_id,\n", + " \"text\" : row[\"generated_headline\"],\n", + " \"binary_label\" : gen_label,\n", + " \"is_generated\" : 1,\n", + " \"strategy\" : gen_strat,\n", + " \"source_type\" : row_type,\n", + " \"article_link\" : article,\n", + " \"model_used\" : model_used,\n", + " })\n", + " sample_id += 1\n", + "\n", + " binary_df = pd.DataFrame(binary_records)\n", + "\n", + " # Validate counts\n", + " counts = binary_df[\"binary_label\"].value_counts().to_dict()\n", + " n_expected = len(raw_rows)\n", + " n0 = int(counts.get(0, 0))\n", + " n1 = int(counts.get(1, 0))\n", + " if n0 != n_expected or n1 != n_expected:\n", + " raise ValueError(\n", + " f\"Label count mismatch!\\n\"\n", + " f\" Expected {n_expected} per class\\n\"\n", + " f\" Got: sarcastic(1)={n1}, non-sarcastic(0)={n0}\"\n", + " )\n", + "\n", + " # Type dataset: sarcastic only\n", + " type_df = binary_df[binary_df[\"binary_label\"] == 1].copy()\n", + " type_df = type_df.rename(columns={\"strategy\": \"type_label\"})\n", + " type_df = type_df[[\"sample_id\", \"pair_id\", \"group_id\", \"text\",\n", + " \"type_label\", \"is_generated\", \"source_type\",\n", + " \"article_link\", \"model_used\"]]\n", + "\n", + " missing = type_df[\"type_label\"].isna().sum()\n", + " if missing > 0:\n", + " raise ValueError(f\"{missing} sarcastic samples have no type_label!\")\n", + "\n", + " return binary_df, type_df\n", + "\n", + "\n", + "binary_df, type_df = derive_labels(raw_rows)\n", + "\n", + "print(f\"Binary dataset : {len(binary_df):,} samples\")\n", + "print(f\" sarcastic (1): {(binary_df['binary_label']==1).sum():,}\")\n", + "print(f\" non-sarc (0): {(binary_df['binary_label']==0).sum():,}\")\n", + "print()\n", + "print(f\"Type dataset : {len(type_df):,} samples\")\n", + "print(type_df[\"type_label\"].value_counts().to_string())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5beT2LM9uKBY", + "outputId": "203852f9-bcfd-4ac5-8f83-61f9e0d10219" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/datasets/binary_dataset.csv\n", + "Saved: outputs/datasets/type_dataset.csv\n" + ] + } + ], + "source": [ + "# Save full datasets\n", + "binary_df.to_csv(OUT_DATASETS / \"binary_dataset.csv\", index=False)\n", + "type_df.to_csv( OUT_DATASETS / \"type_dataset.csv\", index=False)\n", + "print(\"Saved: outputs/datasets/binary_dataset.csv\")\n", + "print(\"Saved: outputs/datasets/type_dataset.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5GtLZhBwuKBZ" + }, + "source": [ + "## 4. Group-Aware Train / Val / Test Split" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OMGqQn3PuKBZ", + "outputId": "0eb38913-1ff6-4524-e2e6-030cf7c34b90" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "=== Binary splits ===\n", + " train: 39,666 samples | sarcastic=19,833 non=19,833\n", + " val : 8,500 samples | sarcastic=4,250 non=4,250\n", + " test : 8,500 samples | sarcastic=4,250 non=4,250\n", + "\n", + "=== Type splits ===\n", + " train: 19,833 samples\n", + " sarcasm: 6,091\n", + " irony: 4,258\n", + " satire: 3,644\n", + " overstatement: 2,784\n", + " understatement: 2,352\n", + " rhetorical_question: 704\n", + " val : 4,250 samples\n", + " sarcasm: 1,317\n", + " irony: 942\n", + " satire: 747\n", + " overstatement: 600\n", + " understatement: 487\n", + " rhetorical_question: 157\n", + " test : 4,250 samples\n", + " sarcasm: 1,291\n", + " irony: 902\n", + " satire: 833\n", + " overstatement: 592\n", + " understatement: 456\n", + " rhetorical_question: 176\n" + ] + } + ], + "source": [ + "def group_aware_split(\n", + " df: pd.DataFrame,\n", + " group_col: str = \"group_id\",\n", + " train_frac: float = 0.70,\n", + " val_frac: float = 0.15,\n", + " seed: int = SEED,\n", + ") -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n", + " \"\"\"\n", + " Split df into train/val/test at the group level.\n", + " All rows sharing a group_id go to exactly one split.\n", + " \"\"\"\n", + " groups = df[group_col].unique().tolist()\n", + " rng = np.random.default_rng(seed)\n", + " rng.shuffle(groups)\n", + "\n", + " n = len(groups)\n", + " n_train = int(n * train_frac)\n", + " n_val = int(n * val_frac)\n", + "\n", + " train_groups = set(groups[:n_train])\n", + " val_groups = set(groups[n_train : n_train + n_val])\n", + " test_groups = set(groups[n_train + n_val :])\n", + "\n", + " train_df = df[df[group_col].isin(train_groups)].copy()\n", + " val_df = df[df[group_col].isin(val_groups)].copy()\n", + " test_df = df[df[group_col].isin(test_groups)].copy()\n", + "\n", + " # Sanity: no overlap\n", + " assert train_groups.isdisjoint(val_groups), \"Train/val group overlap!\"\n", + " assert train_groups.isdisjoint(test_groups), \"Train/test group overlap!\"\n", + " assert val_groups.isdisjoint(test_groups), \"Val/test group overlap!\"\n", + "\n", + " return train_df, val_df, test_df\n", + "\n", + "\n", + "# ── Binary splits ─────────────────────────────────────────────────────────────\n", + "train_bin, val_bin, test_bin = group_aware_split(binary_df)\n", + "\n", + "print(\"=== Binary splits ===\")\n", + "for name, df in [(\"train\", train_bin), (\"val\", val_bin), (\"test\", test_bin)]:\n", + " dist = df[\"binary_label\"].value_counts().to_dict()\n", + " print(f\" {name:5s}: {len(df):,} samples | sarcastic={dist.get(1,0):,} non={dist.get(0,0):,}\")\n", + "\n", + "# ── Type splits ───────────────────────────────────────────────────────────────\n", + "train_type, val_type, test_type = group_aware_split(type_df)\n", + "\n", + "print(\"\\n=== Type splits ===\")\n", + "for name, df in [(\"train\", train_type), (\"val\", val_type), (\"test\", test_type)]:\n", + " print(f\" {name:5s}: {len(df):,} samples\")\n", + " dist = df[\"type_label\"].value_counts()\n", + " for label, cnt in dist.items():\n", + " print(f\" {label}: {cnt:,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Oo78ufPjuKBZ", + "outputId": "6b324c55-2718-4236-d356-e7c7a44e91a5" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train/Val pair_id overlap : 0 (must be 0)\n", + "Train/Test pair_id overlap : 0 (must be 0)\n", + "Val/Test pair_id overlap : 0 (must be 0)\n", + "\n", + "Leakage check PASSED: No pair_id crosses split boundaries.\n" + ] + } + ], + "source": [ + "# ── Verify no pair crosses split boundary ─────────────────────────────────────\n", + "# A pair_id should appear in at most ONE binary split\n", + "train_pairs = set(train_bin[\"pair_id\"])\n", + "val_pairs = set(val_bin[\"pair_id\"])\n", + "test_pairs = set(test_bin[\"pair_id\"])\n", + "\n", + "tv_overlap = train_pairs & val_pairs\n", + "tt_overlap = train_pairs & test_pairs\n", + "vt_overlap = val_pairs & test_pairs\n", + "\n", + "print(f\"Train/Val pair_id overlap : {len(tv_overlap)} (must be 0)\")\n", + "print(f\"Train/Test pair_id overlap : {len(tt_overlap)} (must be 0)\")\n", + "print(f\"Val/Test pair_id overlap : {len(vt_overlap)} (must be 0)\")\n", + "\n", + "assert len(tv_overlap) == 0 and len(tt_overlap) == 0 and len(vt_overlap) == 0, \\\n", + " \"Leakage detected: pairs crossing split boundaries!\"\n", + "print(\"\\nLeakage check PASSED: No pair_id crosses split boundaries.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HbbbOgpZuKBZ", + "outputId": "2716fcd8-e654-46c8-9b14-765336c8f3b9" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/splits/train_binary.csv\n", + "Saved: outputs/splits/val_binary.csv\n", + "Saved: outputs/splits/test_binary.csv\n", + "Saved: outputs/splits/train_type.csv\n", + "Saved: outputs/splits/val_type.csv\n", + "Saved: outputs/splits/test_type.csv\n", + "\n", + "Saved: outputs/splits/split_metadata.json\n" + ] + } + ], + "source": [ + "# ── Save splits ───────────────────────────────────────────────────────────────\n", + "split_files = {\n", + " \"train_binary\": train_bin,\n", + " \"val_binary\" : val_bin,\n", + " \"test_binary\" : test_bin,\n", + " \"train_type\" : train_type,\n", + " \"val_type\" : val_type,\n", + " \"test_type\" : test_type,\n", + "}\n", + "for fname, df in split_files.items():\n", + " path = OUT_SPLITS / f\"{fname}.csv\"\n", + " df.to_csv(path, index=False)\n", + " print(f\"Saved: {path.relative_to(ROOT)}\")\n", + "\n", + "# Metadata\n", + "import json as _json\n", + "metadata = {\n", + " \"seed\": SEED,\n", + " \"train_frac\": 0.70,\n", + " \"val_frac\": 0.15,\n", + " \"test_frac\": 0.15,\n", + " \"group_col\": \"group_id\",\n", + " \"total_pairs\": len(raw_rows),\n", + " \"binary\": {\n", + " \"train\": len(train_bin), \"val\": len(val_bin), \"test\": len(test_bin)\n", + " },\n", + " \"type\": {\n", + " \"train\": len(train_type), \"val\": len(val_type), \"test\": len(test_type)\n", + " },\n", + "}\n", + "with open(OUT_SPLITS / \"split_metadata.json\", \"w\") as f:\n", + " _json.dump(metadata, f, indent=2)\n", + "print(\"\\nSaved: outputs/splits/split_metadata.json\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j7ZguloUuKBa" + }, + "source": [ + "## 5. Split Distribution Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "id": "nLrEPxJ9uKBa", + "outputId": "508ba885-ab89-400e-8f7c-c251d636a688" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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1Jemrr77Sf//9p44dO8rDw0Nz5sxRt27ddPHiRQ0ePNjcbteuXapcubK8vb3VsWNH5c6dW3v37tWXX36pzZs3a8OGDcqSJUuGnMf06dN18eJFtWrVSgEBAeafiSpVqmjdunXJlje5du2awsPD9corr2jEiBE6dOiQJkyYoK1bt2r37t0WSdnbb7+tOXPmqFGjRmrbtq1u3bql2bNn6/XXX9eiRYtUp06dFOPasGGD6tSpo6JFi6pfv37y9vbW6dOntWbNGh0+fPihf6xs2LBB3t7eKbY7deqUwsPDVb9+fY0ePVp79+7Vt99+q9jYWK1atcrc7s6dO1bvT5eSe5dWS4tbt25JklxdXZPtS9q2bds25c2b94H9VKtWTVu3blXr1q3Vu3dv+fr66s8//1TPnj0VEhKit956K9kx169fNz927dqlPn36yNHRUVWrVrX6GufPn1diYqJiYmI0efJkHTx4UO+8847VtmXLltWsWbMUFxfHUnPAc65t27YaPny4oqKizEWDJHPnztWNGzfUrl07SRmXn9wvtf3269fPYglCf39/i34aN26s+fPnW2y7N8fInj37A++tkxZhYWF66aWX9N1332nYsGEWAxOnTp3SypUr9c477yS7iKN69erKmjWrhgwZov/++0/jx49XxYoVtWXLFhUtWtTcbsCAAfrkk09UvXp1ffzxx7Kzs9PixYvVuHFjjR8/Xp07d86Q87h9+7ZGjx6thg0bqm7dunJzc9OOHTs0depUbdq0Sbt27ZKjo6PFMcuWLdPRo0fVuXNn5cyZU8uWLdPQoUN14sQJRUVFmdudPHlSZcuW1e3bt9WuXTsVKFBAhw8f1jfffKN169Zp586d8vLySjG2zp07a9q0aWrVqpV69Oih+Ph4HTp0SL/++utDz+vMmTOKjo5Wt27dUmwzceJEnTlzRu3atZO3t7dmzZqlPn36KCAgQM2bNze3i4uL082bNx/6mtLdZbHv/16NjY1Vly5d1LFjR7388suaMGFCqvp6mH///VeSlCNHjmT7Tp06JXd3d924cUOurq6qVq2aPv30U4sl5xITE/XHH3+oZMmScnZ2tjj+5Zdflslk0o4dO5L1XbZsWa1cuVJ///231SXsACCtUvv36cyZM9W6dWtVqFBBw4YNk4uLi/755x8tX75cZ8+elZ+fn8aOHat+/frp/PnzFjnDw5a0ZOyDsY/7PUtjH/369VNCQoI++eQT7d69O9V9PciD8hBJqlOnjq5evSqTyWS+SKtly5YWbZLyjLJlyyY7vkyZMpozZ06yMYuktuvXr7coqAMWMnMaIYBnU0pLUgQGBhqSjMmTJyc75tatW8bt27eTbR8wYIAhydi2bZt5m7Wlk5K2ubq6WiwrlZiYaBQpUsTImTNnqmKPi4tLtu3AgQOGo6Oj8d577xmGYRj//vuvsXr1aqNYsWKGk5OTsXr1aovHvUtmZTRrS1ZcunTJcHFxMRo3bmzR9uTJk4adnZ057nuPd3d3t1i+8datW0bp0qUNBwcHi+3FihUzgoODky2rumjRolQvO5LaJSusvff//fefkS1bNqNGjRrJ+pRkvP/++1bj6tixY7Jt3377rUXbO3fuGC+99JIRFBRkJCYmmrfrviUrPvjgA0NSupe+yps3rxEWFmZ1X9LvxP3LUXbq1MmQZPz999/mbUmfXWofD/Kg5Tq//PJLq8tpXrt2zfD39zckGZGRkQ8975s3bxrvvfee4eTkZBFXzZo1rS7xYhiG0bNnT4u2RYoUMVauXGm17dWrVy3auri4GB06dLD6c2QYhvHxxx8bkoydO3c+NHYAz77KlSsb9vb2xunTpy22lylTxnB0dDQvK/io+UlKUtvv9u3bjVmzZhmSjNq1ayfLOU6ePJmW004Ta8t1fvvtt4Yk4+eff7ZoO3z48GTvR9Lx9evXt/ie3blzp2EymYxq1aqZt+3atcuQZPTr1y9ZHHXr1jU8PDweusR7apfrTExMNK5fv55s+5QpU5J9Jyf1aWdnZ+zatcuij3r16hmSjC1btpi316lTx/Dz87PIpQzDMHbs2GHY29tb/GxYW9rJx8cnWc6TWr/++qshyRg3blyyfUk5hL+/v3H58mXz9mvXrhm+vr5GmTJlLNonfXapeVhb5uvdd981cubMaX6tpP5Su1ynNadOnTK8vb2N/PnzJ/vdadOmjfHRRx8Zc+fONRYsWGD06tXLcHZ2Njw9PY19+/aZ250/f96QZDRp0sTqa/j5+Rlly5ZNtn3mzJmGJOOHH35Id/wAnk/WxkbS8vdp/fr1DQ8PD+POnTsPfJ30LOnI2AdjH8/q2MeWLVsMOzs7Y+7cuRb9pXa5zpQ0btzYkGSsXbvWYvu8efOM5s2bG1OmTDGWLVtmjBs3zihcuLDVpfbffPNNQ5LVXLR3796GJKtL2zo4OBhvvvnmI8WPZxsz+QA8UVmzZk22DKAkiyum4+PjdfXqVSUkJKhq1aoaPny4tm3bppdffvmh/derV09BQUHm5yaTSZUqVdL48eNTNYMn6epzwzDM0+z9/PwUHBysbdu2Sbp7w+ikq5bt7OxUokQJ8/F2dnbKmjXrQ+PMSN7e3mrSpInmzJmjCxcuKFu2bJLu3hg7MTHRPBvhXi1atLBYvtHR0VEffPCBmjdvrh9//FHvvfee9u/fr3379mno0KG6deuWeYaXJJUvX15ubm5atWqV2rRpkyHnce+V/3Fxcbp165bs7e31yiuvWNzE+159+/a1eF6/fn0FBwdryZIlmjhxoiRp1qxZ8vDwUL169XT+/HmL9rVr19aQIUN06NChFK82S7rifuHChYqIiJCDQ9q+Os+dO6dChQqluD9Xrlxq0qSJxbbKlStrwoQJOnTokPlGzcWLF9fq1avT9Nrp0bJlSw0fPlyDBg2Sm5ubqlatqvPnz2vw4MHm9y81S9TZ29srd+7cqlq1qurXr6+sWbNq8+bN+uqrr/TWW29p6dKlya6E7Nixo6pXr67Lly9ry5YtWr9+fbLPLImLi4tWr16t+Ph4nThxQrNnz1ZcXJyuX79udSnQpN+L1CyXBuDZ165dO/3666+aMWOG+vTpI0n6+++/tXXrVjVq1Mh8RXBG5Sf3S22/xYoVM3/v+Pn5WeQcrq6uVmddP07NmzdXz549NXXqVPNSTIZhaNq0aQoNDbX6Xnz44YcymUzm5y+99JJef/11rVmzxpybzZ49WyaTSa1bt072736dOnW0dOlSbdmyRW+88cYjn4PJZJKLi4skKSEhQVevXlV8fLwqV64s6e5s9fu/l19//XWVLFnSoo8PP/xQS5Ys0eLFi1WmTBlduXJFP/30k9q2bStnZ2eL8wgKClLBggW1atUqi+Xm7+fl5aW//vpLf/75p8Usx9RIWrbtQXlo27ZtLWYSurq6qkyZMtqyZYtFuw8//DDZlecpSVqGO8nmzZv17bffavbs2Q+ctZgW169fV/369RUXF6dly5Ylyx/unU0pSY0aNVKdOnUUHh6uHj16mPOnpPzFycnJ6us4OztbzXHIIQBkpLT8ferl5aXr16/r559/Vp06dSy+Tx8VYx+MfdzvWRj7uHPnjiIiIvT666+bl9fMCJGRkVqwYIE6dOhgzhmTNGnSJNl5dezYUaVKldLw4cPVunVr8zjlg3KRpFUGrOUiWbNmJQ/BA1HkA/BEFShQIMX7ckyYMEETJ07UX3/9lWyN9dTck0u6e++t+yUlfhcuXHhokW/37t0aOHCg1q9fn2zd9Xz58klSsiUr/Pz8zP//+uuvWywz8KR06NBB3333nWbOnKnu3bvLMAxFRUWpRIkSeumll5K1t7Z0x4svvihJ5nXdDx48KEkaPHiwxTIW9zpz5kxGnYKOHDmi/v37a+XKlbp8+bLFPmt/zHh7e1tdJz0kJERLlizRtWvX5ObmpoMHD+rq1aspLqkg3T2PlBLdLl26aOnSperUqZP69Omj8uXLq3r16mrWrJnFZ58Sk8n0wHvDPOxnNomPj0+Ky1ZmJB8fH61Zs0atWrVShw4dzNsrVqyoPn36aPjw4fL09HxoP23atNHvv/+uv/76yzyYWr9+fRUsWFDvvfeevvvuO7Vv397imEKFCpn/KGjUqJFWrlyp6tWry2QyqVmzZhZt7e3tLd6P9u3bKzw8XJUrV9Yff/yRbAAw6TPIyD+MAdiuBg0ayNvbW1FRUeYi37Rp0yQp2bK/GZGfWJOafu9drnPatGnmGCVp9uzZFkssPgnu7u5q1qyZpk+frnPnzsnPz0/r16/X0aNHNXbsWKvHpJRzrFq1SidOnFCRIkV08OBBGYbxwKUQMzLnmD9/viIjI7V7927duXPHYp+1zzQ1eVN0dLQSExM1depUTZ061errWvvOv9fYsWP19ttvKzQ0VPnz51elSpVUu3Zt1a5dW3Z2dg88Nun7LT05x/33InzxxRfN55cWt2/fVocOHVS1atVk39vpdfPmTdWrV087d+7Ud999l2wJs5RUqFBBr732mtatW6cbN27IxcXFXBS/d/D2/teyVjgnhwCQkdLy9+lHH32kjRs3ql69esqWLZsqVqyoGjVqqGnTpvLw8HikOBj7YOzjfs/C2MfIkSN1+PBhLVmyJF3HWzNlyhT17t1btWrV0vjx41N1jJOTk3r16qU2bdpo1apV5rGVe3ORpHGSJElLpaeUi5CH4EEo8gF4olK64nzMmDHq2bOn3njjDXXr1k25cuWSo6OjTp06pTZt2li9sbI1KRUQpQcPekh376Py2muvydPTUwMHDlRwcLDc3NxkMpnUvXt38/0Ds2XLptWrV2v69OmaPXu2vvjiC/PV1g8bvHlcXn31VRUtWlRTp05V9+7dtXbtWh0/fjzVCYg1Se9Xz549U1z3+9619x9FXFycXnvtNV27dk3du3dXaGioPDw8ZGdnpxEjRqTqXjQpMQxDfn5++v7771Ns86Cr5bNly6YdO3bot99+0+rVq7Vx40Z98MEHGjx4sJYvX251LfV7+fn56eLFiynuT+3P7O3btx/Yz/2s/RGQWqGhodq9e7cOHz6s06dPK1euXCpYsKD55ukPux/NyZMnNXv2bHXp0iVZ4tq4cWO999572rBhQ7Ii3/2qVaumHDlyaMKECQ8dLLS3t1eLFi303nvvaePGjapSpYrF/qT3LjV/nAB49jk7O6t58+aaMGGCfv/9d73yyiuaOXOmAgICLO7xklH5yf1S22+zZs1Us2ZNNWvWTI6Ojvruu+/MfZQvX/7R3oR06tChgyZPnqwZM2aYZ/U5OTklu09PWiQNXPzyyy8pfi8WKVIk3f3fa9GiRWratKlefvlljRs3Tnny5JGzs7MSEhJUvXr1dH+mSd/ZLVu2VOvWra22uf878X5169bV8ePHtXz5cm3YsEFr1qzR1KlTVaFCBa1ZsybZvQLvlfT9lt6c415XrlzRjRs3UtXWxcXFfOX/119/rb///luRkZE6fPiwuc3Vq1clSceOHVNsbGyq8+WkAl/S+5Da2YVJgoKCtH79el26dEkuLi7y8fGRi4uLTp06laztrVu3dP78eVWsWDHZPnIIABkpLX+fFipUSAcOHNDatWu1du1abdiwQRERERo8eLA2btyoAgUKpCsGxj4sMfZxl62PfcTExOiTTz5R69atZRiGORdJ+t6/cOGCDh8+LH9/f6ur/1gzbdo0dejQQW+88YYWLlyYpvsyJs3eu3dWZdIKCKdOnVLBggUt2p86dUomkynZKgnS3YvQyEPwIBT5ADwVZs6cqaCgIP3yyy8WVyqvWLHiicWwePFi8zJAlSpVsth34cIF83T63LlzK3fu3IqPj9fs2bN14cKFJzLD6mEiIiL0/vvva/v27Zo6daqcnZ3VokULq22TrlS714EDByT9X7KeNKPq/hlTj8PatWt1+vRpTZs2LdlyrgMGDLB6zOXLl/Xff/8lK2YdPHhQ2bNnNydthQoV0v/+9z+VKVPmoTM5U2Jvb6/w8HCFh4dLuntF40svvaThw4c/9EbiRYsW1caNG5WYmPjQq/Af5Pfff0/2c/kgDytqp0bBggUtEs9ffvlFnp6eKleu3AOPS0qiExISku2Lj4+3+O/D3Lx5M9UJftKApLX2hw8floODg3kJEABo166dJkyYoKioKF28eFH//fef+vfvb/Fv9ePKT1Lbb+nSpSVJVapU0bx585QvX750D+hllFKlSiksLExTp05Vu3bttHDhQtWrVy/FJbsOHjyoMmXKWGw7cOCA7O3tFRgYKOnud/WKFSuUN29eq1fcZ6SZM2fK2dlZ69ats7j47O+//07xmNTkTQULFpTJZNLt27cfKW/KmjWrWrZsqZYtW8owDPXt21ejRo3S0qVL1bhx4xSPSyqCHjp0KN2vneT999+3KCg/SOvWrTV9+nRJ0okTJ5SYmKgaNWpYbfvyyy/Lzc3NPHj8IEkFvlWrVmnSpElWl/t/mEOHDsnBwcH8s2lnZ6eSJUtq9+7dunXrlsVSWdu3b5dhGCpVqlSyfpIGCdO6hCoAWJPWv0+dnJxUs2ZN8zLZy5cvV61atTRmzBh9/fXXktI+05ixD8Y+UmLLYx9nzpzRzZs39e233+rbb79N1u6zzz7TZ599pgULFqhRo0YP7XfatGlq3769qlatqiVLlqS43HdKknKye2dWli5dWpMmTdKWLVuSFfm2bt2q4ODgZJ/d8ePHFR8fTx6CB0r/bxwAZCB7e/tkU/vj4+P12WefPdEYpOTFkcmTJ+u///5L1v71119XkSJFNGbMGP35558W++Li4tSvX7/HF6wVb7/9tpydnTV69GgtXrxYDRs2lLe3t9W2s2fP1r///mt+fvv2bX3xxReyt7fXm2++KUkKCwtT0aJFNXHiRPMyFveKj49P09VVD5LSe79q1Srz/QCsuf/nY/HixYqOjla9evXM21q1aqXExMQUP4+HLbth7Z5wL7zwglxcXFJ1/uHh4bp69ar5D4n0SlqXPrWPjPbVV1/pzz//1AcffPDQq96Cg4Nlb2+vJUuWJFt+JGkgMGngWpLV3y9J+u6773TlyhWLweFLly7p9u3bydpeu3ZNU6dOlZ2dndV7Qm3dulUvvfRSuv/YAfDsKVmypEqUKKF58+bp66+/lslkSrZU5+PKT9La7/vvvy+TyaR333032b+B27dv18yZMx8pnrSKiIjQwYMH1bVrV928efOBM7NHjRplcZ5//PGH1qxZoypVqpj/TU6aBfjRRx9ZvUAkI5fISnrv752xZxiGhg8fnuIxq1ev1h9//GHRftSoUZJkzjmyZcummjVratGiRVbvp2MYhvm+edYkJCRYXbIrLCxM0oNn6El3r54vUqRIivfySYsPP/ww1flG0ix/6e49/xYsWJDskTRQOG3aNM2aNeuhr3/r1i3Vr19fq1at0sSJEx/483XlyhWrPzM///yzNm/erNdff918jxvp7uzY69eva9KkSRbtx44dKwcHB6v379m6daty5MjBhUIAMkRa/j619rdo0j1i7/1ecHd316VLl1J9oSdjH4x9WGPrYx/58uWzmock3Q+5VatWWrBgwUNnJEp3xy0iIiJUuXJlLV261CKXuN/9y55Ld/OTkSNHytHR0WKVkLp168rFxUXjx4+3yF9+/PFHHT161GqxOim3s7baAJCEmXwAngqNGjVSv379VKNGDTVo0ECxsbH6/vvv0zQV/lHVqFFDrq6uevvtt9WlSxf5+Pho8+bNWr58uQoUKJBs5pG9vb3mzJmjSpUq6eWXX1b79u0VGhpqviord+7cTyx26e7yEY0aNTIPnjxoQKRw4cJ65ZVX9O6778rDw0Pff/+9duzYoYEDBypPnjyS7g4szZw5U5UrV1axYsX0zjvvqEiRIrp+/boOHz6sRYsWacSIEam6+fSdO3dSHDxr0KCBypcvr5w5c6pnz546fvy4AgICtGfPHs2cOVOhoaHav39/suN8fX21aNEinT59WuHh4Tp06JAmTJigHDlymJM46e7PVtu2bTV+/Hj98ccfevPNN+Xr66t///1XW7Zs0eHDh60m8kkiIiL077//6o033lBgYKBu3LihefPm6erVq2rVqtVDz71hw4bq06ePli9f/khXXj3qPfnGjx9vHjy8c+eOTpw4Yf5Mihcvrtq1a5vb1qxZU/nz59eLL74ok8mkVatWacmSJapVq5b69+9v0e/x48eVL18+VaxYUevXr5d0dxZC9+7dFRkZqbCwMEVERChr1qzavHmzZs+erQIFClj8fNasWVPZsmVT2bJllTdvXl25ckWbNm3S0qVLFRAQYPF5btiwQR07dlTDhg1VsGBBeXh46NixY5o5c6b+/fdfDR482DwzJMmRI0cUHR2tzz//PN3vH4BnU7t27dS1a1etWLFC4eHhyZaeelz5SVr7LVu2rIYPH67+/furRIkSatGihbJnz66tW7dq5syZj7REVXq0aNFCvXv31qxZs5QvX75kSyTf68SJE6pWrZrq1KmjmJgYjR8/Xi4uLho9erS5TenSpTVkyBANGTJEJUqUUOPGjZUrVy7FxMRo165dWr58udULPKzZuXOn1ZzDwcFBffv2VaNGjbRw4UJVrlxZrVq10p07d7RkyRJdv349xT6LFy+uypUrq3PnzvL399fSpUu1Zs0avf322xYDRd98843Kly+v1157Ta1atVJYWJgSExN19OhRLV26VK1atbL4TrvX1atX5e/vrzp16igsLEzZs2fXsWPH9M0338jHx8fiezoljRs31scff6yYmBj5+/s//M1KQXrvyVe8eHGLezcl+emnnyRJtWvXlq+vr8W+oKAgnThxwmKws0WLFlqxYoWqVq0qV1fXZIXBYsWKqVixYpKkdevWqUePHqpdu7by588vBwcHbd++XbNmzZKvr2+ye0VGREQoKipKPXr00PHjxxUSEqLly5dr8eLFGjBggHlprSRxcXH67bffkl0AAADplZa/T9944w15e3urQoUKypMnjy5fvqzp06fLZDJZLJNdpkwZ/fTTT+rSpYteffVV2dvbq3LlysqePbvVGBj7YOzDGlsf+/Dy8rI6Qy8p9wgNDU22f8iQIRo6dKiioqLMn++yZcvUrl07eXp6qmnTplq4cKHFMe7u7hbF1dDQUFWsWFGhoaHKnj27jh8/rmnTpikmJkaRkZEKCAgwt/Xz89PHH3+sXr16me9hfOrUKUVGRuqFF15Q9+7dk8W/fPly+fr6pml2I55DBgBksKioKEOSERUVZbE9MDDQqFixotVj4uPjjU8//dQoUKCA4ejoaOTNm9fo3bu3ceDAAUOSMXjwYHPbY8eOpWpbksGDBxuSjGPHjj009g0bNhjlypUz3N3dDS8vL6NmzZrG/v37jYoVKxqBgYFWjzl58qQRERFhBAQEGFmyZDHy5MljvP/++8a5c+ce+npptW7duhTP0zAMY+PGjYYko2DBgkZiYmKKx0dFRRnjxo0zChYsaDg6OhoFCxY0xo4da7XP48ePGx07djQCAwONLFmyGFmzZjVKlixp9O3b1zh58uRDY65YsaIhKcXHnDlzDMMwjL179xrVqlUzvL29DXd3d6NixYrGxo0bjdatWxv3f10lfR5Hjhwx6tSpY3h4eBju7u5GnTp1jEOHDlmNY8aMGUb58uUNDw8Pw8nJyQgMDDTq169vzJ0716KdJKN169bm5wsXLjRq165t5M6d23B0dDR8fX2N1157zfjhhx8eeu5JatSoYRQtWjTZ9pR+J+79nDJKYGBgip/BvedrGIYxbNgwo0iRIoabm5vh5uZmlCpVyvj666+N+Pj4ZP3u27fPkGQ0b97cYntiYqIxadIk4+WXXzbc3NwMBwcHIzAw0OjUqZNx9uxZi7YTJkwwqlSpYvj7+xtZsmQxXF1djdDQUKNv377G+fPnLdoePnzYaNeunRESEmJ4enoaDg4ORo4cOYw333zT+Omnn6ye+5AhQwwnJ6dkfQHAxYsXDWdnZ0OSMWPGjGT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+ }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/splits/type_split_distribution.png\n" + ] + } + ], + "source": [ + "# Type label distribution per split\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 5), sharey=True)\n", + "split_dfs = [(\"Train\", train_type), (\"Val\", val_type), (\"Test\", test_type)]\n", + "\n", + "for ax, (name, df) in zip(axes, split_dfs):\n", + " dist = df[\"type_label\"].value_counts()\n", + " dist.plot(kind=\"barh\", ax=ax, color=\"steelblue\")\n", + " ax.set_title(f\"{name} — Type Labels (n={len(df):,})\", fontsize=13)\n", + " ax.set_xlabel(\"Count\")\n", + " for i, (label, cnt) in enumerate(dist.items()):\n", + " ax.text(cnt + 5, i, f\"{cnt:,}\", va=\"center\", fontsize=9)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(OUT_SPLITS / \"type_split_distribution.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/splits/type_split_distribution.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + }, + "id": "R48OKnIouKBa", + "outputId": "cc63cf5a-635b-4937-ff28-9702de59974e" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/splits/binary_split_distribution.png\n" + ] + } + ], + "source": [ + "# Binary label distribution per split\n", + "fig, axes = plt.subplots(1, 3, figsize=(12, 4), sharey=True)\n", + "split_dfs_bin = [(\"Train\", train_bin), (\"Val\", val_bin), (\"Test\", test_bin)]\n", + "\n", + "for ax, (name, df) in zip(axes, split_dfs_bin):\n", + " dist = df[\"binary_label\"].value_counts().sort_index()\n", + " ax.bar([\"Non-sarcastic (0)\", \"Sarcastic (1)\"], dist.values, color=[\"coral\", \"steelblue\"])\n", + " ax.set_title(f\"{name} — Binary Labels (n={len(df):,})\", fontsize=12)\n", + " ax.set_ylabel(\"Count\")\n", + " for i, v in enumerate(dist.values):\n", + " ax.text(i, v + 20, f\"{v:,}\", ha=\"center\")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(OUT_SPLITS / \"binary_split_distribution.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/splits/binary_split_distribution.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "y59RyyxDuKBa", + "outputId": "9fcf80b4-639e-4dc0-da4e-9e5d7492337a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "=== Data Preparation Complete ===\n", + "Binary dataset : 56,666 samples (balanced)\n", + "Type dataset : 28,333 samples (imbalanced, 6 classes)\n", + "Splits saved to: outputs/splits/\n", + "Datasets saved : outputs/datasets/\n", + "\n", + "Ready for classical baseline training (Notebooks 02 and 03).\n" + ] + } + ], + "source": [ + "print(\"\\n=== Data Preparation Complete ===\")\n", + "print(f\"Binary dataset : {len(binary_df):,} samples (balanced)\")\n", + "print(f\"Type dataset : {len(type_df):,} samples (imbalanced, 6 classes)\")\n", + "print(f\"Splits saved to: outputs/splits/\")\n", + "print(f\"Datasets saved : outputs/datasets/\")\n", + "print(\"\\nReady for classical baseline training (Notebooks 02 and 03).\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cENhnFfwuKBb" + }, + "source": [ + "---\n", + "# Part 2 — TF-IDF + Logistic Regression Baseline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j_2MU9WJuKBb" + }, + "source": [ + "# Notebook 02 — TF-IDF + Logistic Regression Baseline\n", + "\n", + "**Purpose**: Train and evaluate TF-IDF + Logistic Regression pipelines for:\n", + "- Task A: Binary classification (sarcastic vs non-sarcastic)\n", + "- Task B: Sarcasm type classification (6-class)\n", + "\n", + "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", + "\n", + "**Outputs**:\n", + "- `outputs/classical/tfidf_lr/best_config_binary.json`\n", + "- `outputs/classical/tfidf_lr/best_config_type.json`\n", + "- `outputs/classical/tfidf_lr/metrics_binary.json`\n", + "- `outputs/classical/tfidf_lr/metrics_type.json`\n", + "- Predictions CSV + confusion matrices PNG" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r8BzGF97uKBb" + }, + "source": [ + "## Helper Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "iU0Dnjl_uKBb" + }, + "outputs": [], + "source": [ + "def load_splits(task: str) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n", + " \"\"\"Load train/val/test CSVs for a given task ('binary' or 'type').\"\"\"\n", + " train = pd.read_csv(SPLITS / f\"train_{task}.csv\")\n", + " val = pd.read_csv(SPLITS / f\"val_{task}.csv\")\n", + " test = pd.read_csv(SPLITS / f\"test_{task}.csv\")\n", + " return train, val, test\n", + "\n", + "\n", + "def evaluate(\n", + " model,\n", + " X: list[str],\n", + " y_true: list,\n", + " label_names: list[str] | None = None,\n", + " split_name: str = \"\",\n", + ") -> dict:\n", + " \"\"\"Compute classification metrics and return as dict.\"\"\"\n", + " y_pred = model.predict(X)\n", + " metrics = {\n", + " \"split\" : split_name,\n", + " \"accuracy\" : accuracy_score(y_true, y_pred),\n", + " \"precision_macro\" : precision_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"recall_macro\" : recall_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"f1_macro\" : f1_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"f1_weighted\" : f1_score(y_true, y_pred, average=\"weighted\", zero_division=0),\n", + " \"report\" : classification_report(y_true, y_pred, target_names=label_names,\n", + " zero_division=0, output_dict=True),\n", + " }\n", + " print(f\"\\n[{split_name}] Accuracy={metrics['accuracy']:.4f} \"\n", + " f\"Macro-F1={metrics['f1_macro']:.4f} Weighted-F1={metrics['f1_weighted']:.4f}\")\n", + " print(classification_report(y_true, y_pred, target_names=label_names, zero_division=0))\n", + " return metrics, y_pred\n", + "\n", + "\n", + "def save_confusion_matrix(\n", + " y_true, y_pred, label_names: list[str], out_path: Path, title: str = \"\"\n", + ") -> None:\n", + " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", + " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", + " sns.heatmap(\n", + " cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n", + " xticklabels=label_names, yticklabels=label_names, ax=ax\n", + " )\n", + " ax.set_xlabel(\"Predicted\")\n", + " ax.set_ylabel(\"True\")\n", + " ax.set_title(title)\n", + " plt.tight_layout()\n", + " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + " print(f\"Saved: {out_path.relative_to(ROOT)}\")\n", + "\n", + "\n", + "def save_predictions(\n", + " df: pd.DataFrame, y_pred, label_col: str, out_path: Path\n", + ") -> None:\n", + " out = df[[\"sample_id\", \"pair_id\", \"group_id\", \"text\", label_col]].copy()\n", + " out[\"predicted\"] = y_pred\n", + " out[\"correct\"] = (out[label_col] == out[\"predicted\"]).astype(int)\n", + " out.to_csv(out_path, index=False)\n", + " print(f\"Saved: {out_path.relative_to(ROOT)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KZYEW3EruKBb" + }, + "source": [ + "## Task A — Binary Classification" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sUWB4dQKuKBb", + "outputId": "c9c34b33-e3bb-4eca-e21a-158777f966cb" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train+Val: 48,166 | Train: 39,666 Val: 8,500 Test: 8,500\n" + ] + } + ], + "source": [ + "# ── Load binary splits ────────────────────────────────────────────────────────\n", + "train_bin, val_bin, test_bin = load_splits(\"binary\")\n", + "\n", + "# Combine train+val for cross-validation, keep test for final eval\n", + "trainval_bin = pd.concat([train_bin, val_bin], ignore_index=True)\n", + "\n", + "X_trainval = trainval_bin[\"text\"].tolist()\n", + "y_trainval = trainval_bin[\"binary_label\"].tolist()\n", + "groups_tv = trainval_bin[\"group_id\"].tolist()\n", + "\n", + "X_train = train_bin[\"text\"].tolist()\n", + "y_train = train_bin[\"binary_label\"].tolist()\n", + "\n", + "X_val = val_bin[\"text\"].tolist()\n", + "y_val = val_bin[\"binary_label\"].tolist()\n", + "\n", + "X_test = test_bin[\"text\"].tolist()\n", + "y_test = test_bin[\"binary_label\"].tolist()\n", + "\n", + "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", + "\n", + "print(f\"Train+Val: {len(X_trainval):,} | Train: {len(X_train):,} Val: {len(X_val):,} Test: {len(X_test):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BatmO-vAuKBc", + "outputId": "34229238-2bbd-4838-976b-464bb496054f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Grid size: 4 param combos → 180 fits\n", + "Running grid search...\n", + "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n", + "\n", + "Best params : {'lr__C': 3.0, 'tfidf__max_features': None, 'tfidf__min_df': 3, 'tfidf__ngram_range': (1, 2)}\n", + "Best CV F1 : 0.8143\n" + ] + } + ], + "source": [ + "# ── Define pipeline and grid ──────────────────────────────────────────────────\n", + "pipe_bin = Pipeline([\n", + " (\"tfidf\", TfidfVectorizer(sublinear_tf=True, lowercase=True)),\n", + " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, solver=\"lbfgs\")),\n", + "])\n", + "\n", + "param_grid_bin = {\n", + " \"tfidf__ngram_range\" : [(1, 1), (1, 2)],\n", + " \"tfidf__min_df\" : [2, 3, 5],\n", + " \"tfidf__max_features\": [None, 50_000],\n", + " \"lr__C\" : [0.1, 1.0, 3.0],\n", + "}\n", + "\n", + "cv_bin = GroupKFold(n_splits=5)\n", + "\n", + "gs_bin = GridSearchCV(\n", + " pipe_bin, param_grid_bin,\n", + " cv=cv_bin, scoring=\"f1_macro\",\n", + " n_jobs=-1, verbose=1, refit=True,\n", + ")\n", + "\n", + "print(f\"Grid size: {len(gs_bin.param_grid)} param combos → {5 * 2*3*2*3} fits\")\n", + "print(\"Running grid search...\")\n", + "gs_bin.fit(X_trainval, y_trainval, groups=groups_tv)\n", + "\n", + "print(f\"\\nBest params : {gs_bin.best_params_}\")\n", + "print(f\"Best CV F1 : {gs_bin.best_score_:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JstBvt1vuKBc", + "outputId": "aff1b1fd-aa2f-4c9c-92ae-7d836436f66d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/best_config_binary.json\n" + ] + } + ], + "source": [ + "# ── Save best config ──────────────────────────────────────────────────────────\n", + "best_cfg_bin = {\"task\": \"binary\", \"model\": \"TF-IDF + LR\", \"seed\": SEED,\n", + " \"best_params\": gs_bin.best_params_, \"cv_f1_macro\": gs_bin.best_score_}\n", + "with open(OUT_TFIDF / \"best_config_binary.json\", \"w\") as f:\n", + " json.dump(best_cfg_bin, f, indent=2)\n", + "print(\"Saved: outputs/classical/tfidf_lr/best_config_binary.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AjH8_5UQuKBc", + "outputId": "afe39734-c73a-410a-9447-6b8639b18389" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "[Val] Accuracy=0.9186 Macro-F1=0.9186 Weighted-F1=0.9186\n", + " precision recall f1-score support\n", + "\n", + "non-sarcastic 0.92 0.92 0.92 4250\n", + " sarcastic 0.92 0.92 0.92 4250\n", + "\n", + " accuracy 0.92 8500\n", + " macro avg 0.92 0.92 0.92 8500\n", + " weighted avg 0.92 0.92 0.92 8500\n", + "\n", + "\n", + "[Test] Accuracy=0.8189 Macro-F1=0.8189 Weighted-F1=0.8189\n", + " precision recall f1-score support\n", + "\n", + "non-sarcastic 0.82 0.82 0.82 4250\n", + " sarcastic 0.82 0.82 0.82 4250\n", + "\n", + " accuracy 0.82 8500\n", + " macro avg 0.82 0.82 0.82 8500\n", + " weighted avg 0.82 0.82 0.82 8500\n", + "\n", + "Saved: outputs/classical/tfidf_lr/metrics_binary.json\n" + ] + } + ], + "source": [ + "# ── Evaluate on val and test ──────────────────────────────────────────────────\n", + "best_bin = gs_bin.best_estimator_\n", + "\n", + "val_metrics_bin, y_val_pred_bin = evaluate(best_bin, X_val, y_val, label_names_bin, \"Val\")\n", + "test_metrics_bin, y_test_pred_bin = evaluate(best_bin, X_test, y_test, label_names_bin, \"Test\")\n", + "\n", + "all_metrics_bin = {\"val\": val_metrics_bin, \"test\": test_metrics_bin}\n", + "\n", + "# Remove classification_report dict (too nested for JSON)\n", + "for split_m in all_metrics_bin.values():\n", + " split_m.pop(\"report\", None)\n", + "\n", + "with open(OUT_TFIDF / \"metrics_binary.json\", \"w\") as f:\n", + " json.dump(all_metrics_bin, f, indent=2)\n", + "print(\"Saved: outputs/classical/tfidf_lr/metrics_binary.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "ichiimejuKBc", + "outputId": "e3d5e010-d983-4f86-893b-cd38907ecc68" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/confusion_matrix_binary_val.png\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/confusion_matrix_binary_test.png\n" + ] + } + ], + "source": [ + "# ── Confusion matrices ────────────────────────────────────────────────────────\n", + "save_confusion_matrix(\n", + " y_val, y_val_pred_bin, label_names_bin,\n", + " OUT_TFIDF / \"confusion_matrix_binary_val.png\",\n", + " \"TF-IDF + LR — Binary (Val)\"\n", + ")\n", + "save_confusion_matrix(\n", + " y_test, y_test_pred_bin, label_names_bin,\n", + " OUT_TFIDF / \"confusion_matrix_binary_test.png\",\n", + " \"TF-IDF + LR — Binary (Test)\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_2QLSOmTuKBc", + "outputId": "8d05bf4c-dcc5-47d0-8ae4-7ccc6754d50a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/predictions_val_binary.csv\n", + "Saved: outputs/classical/tfidf_lr/predictions_test_binary.csv\n" + ] + } + ], + "source": [ + "# ── Save predictions ──────────────────────────────────────────────────────────\n", + "save_predictions(val_bin, y_val_pred_bin, \"binary_label\", OUT_TFIDF / \"predictions_val_binary.csv\")\n", + "save_predictions(test_bin, y_test_pred_bin, \"binary_label\", OUT_TFIDF / \"predictions_test_binary.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bigHRH2MuKBc", + "outputId": "40c0448d-3f53-4ba4-cc17-3347f11dc23b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Top 20 sarcastic indicators (positive coef):\n", + " because +22.1982\n", + " finally +9.5906\n", + " obviously +9.4255\n", + " just +9.2842\n", + " so +8.8645\n", + " shocking +8.6605\n", + " clearly +8.4488\n", + " as if +8.3217\n", + " proving +7.5734\n", + " groundbreaking +7.5588\n", + " nation +7.4750\n", + " like +7.1686\n", + " nothing +6.5188\n", + " shocker +6.5093\n", + " never +6.4249\n", + " totally +6.3599\n", + " always +6.1954\n", + " sure +6.1921\n", + " area +6.0594\n", + " who needs +6.0482\n", + "\n", + "Top 20 non-sarcastic indicators (negative coef):\n", + " the -11.8202\n", + " an -11.5473\n", + " is -10.1138\n", + " was -7.7886\n", + " an area -6.0294\n", + " man is -6.0119\n", + " his -5.8977\n", + " are -5.7913\n", + " finds that -5.7222\n", + " the nation -5.6480\n", + " indicates -5.5894\n", + " donald trump -5.1844\n", + " donald -5.0836\n", + " says he -4.9047\n", + " did not -4.7563\n", + " because of -4.7343\n", + " may -4.7339\n", + " large -4.6423\n", + " her -4.5442\n", + " how to -4.3584\n" + ] + } + ], + "source": [ + "# ── Feature importance: top discriminative terms ──────────────────────────────\n", + "tfidf_vocab = best_bin.named_steps[\"tfidf\"].get_feature_names_out()\n", + "lr_coefs = best_bin.named_steps[\"lr\"].coef_[0] # binary: shape (n_features,)\n", + "\n", + "n_top = 20\n", + "top_pos_idx = np.argsort(lr_coefs)[-n_top:][::-1]\n", + "top_neg_idx = np.argsort(lr_coefs)[:n_top]\n", + "\n", + "top_positive = [(tfidf_vocab[i], lr_coefs[i]) for i in top_pos_idx]\n", + "top_negative = [(tfidf_vocab[i], lr_coefs[i]) for i in top_neg_idx]\n", + "\n", + "print(\"Top 20 sarcastic indicators (positive coef):\")\n", + "for term, coef in top_positive:\n", + " print(f\" {term:35s} {coef:+.4f}\")\n", + "\n", + "print(\"\\nTop 20 non-sarcastic indicators (negative coef):\")\n", + "for term, coef in top_negative:\n", + " print(f\" {term:35s} {coef:+.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 644 + }, + "id": "pEArvWzTuKBd", + "outputId": "c4709ebc-9b56-468b-eb9f-c47f1ba006b8" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/feature_importance_binary.png\n" + ] + } + ], + "source": [ + "# ── Feature importance plot ───────────────────────────────────────────────────\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 7))\n", + "\n", + "terms_pos, coefs_pos = zip(*top_positive)\n", + "axes[0].barh(list(reversed(terms_pos)), list(reversed(coefs_pos)), color=\"steelblue\")\n", + "axes[0].set_title(\"Top 20 — Sarcastic (positive)\", fontsize=13)\n", + "axes[0].set_xlabel(\"LR Coefficient\")\n", + "\n", + "terms_neg, coefs_neg = zip(*top_negative)\n", + "axes[1].barh(list(reversed(terms_neg)), list(reversed(coefs_neg)), color=\"coral\")\n", + "axes[1].set_title(\"Top 20 — Non-Sarcastic (negative)\", fontsize=13)\n", + "axes[1].set_xlabel(\"LR Coefficient\")\n", + "\n", + "plt.suptitle(\"TF-IDF + LR: Feature Importance (Binary Task)\", fontsize=14)\n", + "plt.tight_layout()\n", + "plt.savefig(OUT_TFIDF / \"feature_importance_binary.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/classical/tfidf_lr/feature_importance_binary.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5VFBy1bJuKBd" + }, + "source": [ + "## Task B — Sarcasm Type Classification" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GTvcYYGquKBd", + "outputId": "f3c82bbf-5d44-4c4c-d8f0-2f586415fced" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n", + "Train+Val: 24,083 Train: 19,833 Val: 4,250 Test: 4,250\n" + ] + } + ], + "source": [ + "# ── Load type splits ──────────────────────────────────────────────────────────\n", + "train_type, val_type, test_type = load_splits(\"type\")\n", + "\n", + "# Encode string labels to integers\n", + "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", + "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", + "id2label = {i: lab for lab, i in label2id.items()}\n", + "\n", + "def encode_labels(df: pd.DataFrame) -> list[int]:\n", + " return [label2id[l] for l in df[\"type_label\"]]\n", + "\n", + "trainval_type = pd.concat([train_type, val_type], ignore_index=True)\n", + "\n", + "X_trainval_t = trainval_type[\"text\"].tolist()\n", + "y_trainval_t = encode_labels(trainval_type)\n", + "groups_tv_t = trainval_type[\"group_id\"].tolist()\n", + "\n", + "X_train_t = train_type[\"text\"].tolist()\n", + "y_train_t = encode_labels(train_type)\n", + "X_val_t = val_type[\"text\"].tolist()\n", + "y_val_t = encode_labels(val_type)\n", + "X_test_t = test_type[\"text\"].tolist()\n", + "y_test_t = encode_labels(test_type)\n", + "\n", + "print(f\"Strategies: {STRATEGY_LABELS}\")\n", + "print(f\"Train+Val: {len(X_trainval_t):,} Train: {len(X_train_t):,} Val: {len(X_val_t):,} Test: {len(X_test_t):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yD1TrZZPuKBd", + "outputId": "4faad70d-dc90-4fcd-de7d-19de03226861" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Running grid search for type task...\n", + "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n", + "\n", + "Best params : {'lr__C': 3.0, 'lr__class_weight': 'balanced', 'tfidf__max_features': None, 'tfidf__min_df': 2, 'tfidf__ngram_range': (1, 2)}\n", + "Best CV F1 : 0.3840\n" + ] + } + ], + "source": [ + "# ── Define pipeline and grid (with class_weight) ──────────────────────────────\n", + "pipe_type = Pipeline([\n", + " (\"tfidf\", TfidfVectorizer(sublinear_tf=True, lowercase=True)),\n", + " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, solver=\"lbfgs\",\n", + " multi_class=\"auto\")),\n", + "])\n", + "\n", + "param_grid_type = {\n", + " \"tfidf__ngram_range\" : [(1, 1), (1, 2)],\n", + " \"tfidf__min_df\" : [2, 3, 5],\n", + " \"tfidf__max_features\": [None, 50_000],\n", + " \"lr__C\" : [0.1, 1.0, 3.0],\n", + " \"lr__class_weight\" : [None, \"balanced\"],\n", + "}\n", + "\n", + "cv_type = GroupKFold(n_splits=5)\n", + "\n", + "gs_type = GridSearchCV(\n", + " pipe_type, param_grid_type,\n", + " cv=cv_type, scoring=\"f1_macro\",\n", + " n_jobs=-1, verbose=1, refit=True,\n", + ")\n", + "\n", + "print(\"Running grid search for type task...\")\n", + "gs_type.fit(X_trainval_t, y_trainval_t, groups=groups_tv_t)\n", + "\n", + "print(f\"\\nBest params : {gs_type.best_params_}\")\n", + "print(f\"Best CV F1 : {gs_type.best_score_:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "huaF8TbjuKBd", + "outputId": "d43d2757-7486-4307-912b-462d285835ce" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/best_config_type.json\n" + ] + } + ], + "source": [ + "# ── Save best config ──────────────────────────────────────────────────────────\n", + "best_cfg_type = {\"task\": \"type\", \"model\": \"TF-IDF + LR\", \"seed\": SEED,\n", + " \"label_names\": STRATEGY_LABELS, \"label2id\": label2id,\n", + " \"best_params\": gs_type.best_params_, \"cv_f1_macro\": gs_type.best_score_}\n", + "with open(OUT_TFIDF / \"best_config_type.json\", \"w\") as f:\n", + " json.dump(best_cfg_type, f, indent=2)\n", + "print(\"Saved: outputs/classical/tfidf_lr/best_config_type.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "c4Hj46aWuKB6", + "outputId": "f2ac2819-bbf4-43a3-9437-9cfdafc29c12" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "[Val] Accuracy=0.8967 Macro-F1=0.8982 Weighted-F1=0.8961\n", + " precision recall f1-score support\n", + "\n", + " irony 0.92 0.87 0.90 942\n", + " overstatement 0.88 0.97 0.92 600\n", + "rhetorical_question 0.79 1.00 0.88 157\n", + " sarcasm 0.94 0.83 0.88 1317\n", + " satire 0.88 0.91 0.90 747\n", + " understatement 0.85 0.98 0.91 487\n", + "\n", + " accuracy 0.90 4250\n", + " macro avg 0.88 0.93 0.90 4250\n", + " weighted avg 0.90 0.90 0.90 4250\n", + "\n", + "\n", + "[Test] Accuracy=0.3958 Macro-F1=0.4047 Weighted-F1=0.3947\n", + " precision recall f1-score support\n", + "\n", + " irony 0.33 0.29 0.31 902\n", + " overstatement 0.38 0.43 0.40 592\n", + "rhetorical_question 0.42 0.60 0.50 176\n", + " sarcasm 0.48 0.42 0.45 1291\n", + " satire 0.38 0.39 0.38 833\n", + " understatement 0.36 0.43 0.39 456\n", + "\n", + " accuracy 0.40 4250\n", + " macro avg 0.39 0.43 0.40 4250\n", + " weighted avg 0.40 0.40 0.39 4250\n", + "\n", + "Saved: outputs/classical/tfidf_lr/metrics_type.json\n" + ] + } + ], + "source": [ + "# ── Evaluate on val and test ──────────────────────────────────────────────────\n", + "best_type = gs_type.best_estimator_\n", + "\n", + "val_metrics_type, y_val_pred_type = evaluate(best_type, X_val_t, y_val_t, STRATEGY_LABELS, \"Val\")\n", + "test_metrics_type, y_test_pred_type = evaluate(best_type, X_test_t, y_test_t, STRATEGY_LABELS, \"Test\")\n", + "\n", + "all_metrics_type = {\"val\": val_metrics_type, \"test\": test_metrics_type}\n", + "for split_m in all_metrics_type.values():\n", + " split_m.pop(\"report\", None)\n", + "\n", + "with open(OUT_TFIDF / \"metrics_type.json\", \"w\") as f:\n", + " json.dump(all_metrics_type, f, indent=2)\n", + "print(\"Saved: outputs/classical/tfidf_lr/metrics_type.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "ldimkyrXuKB6", + "outputId": "e8e286df-267e-4802-8318-d0cc9f768808" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/confusion_matrix_type_val.png\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/confusion_matrix_type_test.png\n" + ] + } + ], + "source": [ + "# ── Confusion matrices ────────────────────────────────────────────────────────\n", + "save_confusion_matrix(\n", + " y_val_t, y_val_pred_type, STRATEGY_LABELS,\n", + " OUT_TFIDF / \"confusion_matrix_type_val.png\",\n", + " \"TF-IDF + LR — Type (Val)\"\n", + ")\n", + "save_confusion_matrix(\n", + " y_test_t, y_test_pred_type, STRATEGY_LABELS,\n", + " OUT_TFIDF / \"confusion_matrix_type_test.png\",\n", + " \"TF-IDF + LR — Type (Test)\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1_ZECP4MuKB6", + "outputId": "c09e503d-d620-47ec-8917-c494bc7f3f48" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved predictions_val_type.csv and predictions_test_type.csv\n" + ] + } + ], + "source": [ + "# ── Save predictions ──────────────────────────────────────────────────────────\n", + "# Decode integer predictions back to string labels\n", + "val_type_copy = val_type.copy(); val_type_copy[\"type_label\"] = y_val_t\n", + "test_type_copy = test_type.copy(); test_type_copy[\"type_label\"] = y_test_t\n", + "\n", + "val_type_copy[\"predicted_id\"] = y_val_pred_type\n", + "val_type_copy[\"predicted_label\"] = [id2label[i] for i in y_val_pred_type]\n", + "val_type_copy[\"true_label\"] = [id2label[i] for i in y_val_t]\n", + "val_type_copy[\"correct\"] = (val_type_copy[\"type_label\"] == val_type_copy[\"predicted_id\"]).astype(int)\n", + "\n", + "test_type_copy[\"predicted_id\"] = y_test_pred_type\n", + "test_type_copy[\"predicted_label\"] = [id2label[i] for i in y_test_pred_type]\n", + "test_type_copy[\"true_label\"] = [id2label[i] for i in y_test_t]\n", + "test_type_copy[\"correct\"] = (test_type_copy[\"type_label\"] == test_type_copy[\"predicted_id\"]).astype(int)\n", + "\n", + "val_type_copy.to_csv( OUT_TFIDF / \"predictions_val_type.csv\", index=False)\n", + "test_type_copy.to_csv(OUT_TFIDF / \"predictions_test_type.csv\", index=False)\n", + "print(\"Saved predictions_val_type.csv and predictions_test_type.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xdfZfFRmuKB7" + }, + "source": [ + "## Char N-gram Variant (Optional)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3Ff1Tz1iuKB7", + "outputId": "4209ddba-9cd6-46f9-e66d-f62f1ab5709f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Char n-gram best C : {'lr__C': 3.0}\n", + "Char n-gram best CV F1 : 0.8305\n", + "Word n-gram best CV F1 : 0.8143\n", + "\n", + "\n", + "[Test (char)] Accuracy=0.8284 Macro-F1=0.8283 Weighted-F1=0.8283\n", + " precision recall f1-score support\n", + "\n", + "non-sarcastic 0.82 0.84 0.83 4250\n", + " sarcastic 0.83 0.82 0.83 4250\n", + "\n", + " accuracy 0.83 8500\n", + " macro avg 0.83 0.83 0.83 8500\n", + " weighted avg 0.83 0.83 0.83 8500\n", + "\n" + ] + } + ], + "source": [ + "# Char n-gram model for binary task (stylistic cues)\n", + "pipe_char = Pipeline([\n", + " (\"tfidf\", TfidfVectorizer(analyzer=\"char_wb\", ngram_range=(3, 5),\n", + " sublinear_tf=True, lowercase=True, max_features=50_000)),\n", + " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, C=1.0)),\n", + "])\n", + "\n", + "param_grid_char = {\"lr__C\": [0.1, 1.0, 3.0]}\n", + "gs_char = GridSearchCV(pipe_char, param_grid_char, cv=GroupKFold(5),\n", + " scoring=\"f1_macro\", n_jobs=-1)\n", + "gs_char.fit(X_trainval, y_trainval, groups=groups_tv)\n", + "\n", + "print(f\"Char n-gram best C : {gs_char.best_params_}\")\n", + "print(f\"Char n-gram best CV F1 : {gs_char.best_score_:.4f}\")\n", + "print(f\"Word n-gram best CV F1 : {gs_bin.best_score_:.4f}\")\n", + "print()\n", + "char_metrics, _ = evaluate(gs_char.best_estimator_, X_test, y_test, label_names_bin, \"Test (char)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "t_pVeRFVuKB7" + }, + "source": [ + "## Summary" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OszOI0q1uKB7", + "outputId": "532102b9-cd0f-40e8-b1e1-48fa8520b526" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "====== TF-IDF + LR RESULTS SUMMARY ======\n", + "\n", + "Task A — Binary Classification (Test Set):\n", + " Accuracy : 0.8189\n", + " Macro-F1 : 0.8189\n", + " Weighted-F1 : 0.8189\n", + "\n", + "Task B — Type Classification (Test Set):\n", + " Accuracy : 0.3958\n", + " Macro-F1 : 0.4047\n", + " Weighted-F1 : 0.3947\n", + "\n", + "Best hyperparameters:\n", + " Binary: {'lr__C': 3.0, 'tfidf__max_features': None, 'tfidf__min_df': 3, 'tfidf__ngram_range': (1, 2)}\n", + " Type: {'lr__C': 3.0, 'lr__class_weight': 'balanced', 'tfidf__max_features': None, 'tfidf__min_df': 2, 'tfidf__ngram_range': (1, 2)}\n" + ] + } + ], + "source": [ + "print(\"====== TF-IDF + LR RESULTS SUMMARY ======\")\n", + "print()\n", + "print(\"Task A — Binary Classification (Test Set):\")\n", + "print(f\" Accuracy : {test_metrics_bin['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {test_metrics_bin['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1 : {test_metrics_bin['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"Task B — Type Classification (Test Set):\")\n", + "print(f\" Accuracy : {test_metrics_type['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {test_metrics_type['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1 : {test_metrics_type['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"Best hyperparameters:\")\n", + "print(\" Binary:\", gs_bin.best_params_)\n", + "print(\" Type: \", gs_type.best_params_)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ftKo2U-1uKB7" + }, + "source": [ + "---\n", + "# Part 3 — Naive Bayes Baseline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QZo05bYauKB7" + }, + "source": [ + "# Notebook 03 — Naive Bayes Baseline\n", + "\n", + "**Purpose**: Train and evaluate Naive Bayes classifiers for:\n", + "- Task A: Binary classification (sarcastic vs non-sarcastic)\n", + "- Task B: Sarcasm type classification (6-class)\n", + "\n", + "Compares:\n", + "- `CountVectorizer + MultinomialNB`\n", + "- `TfidfVectorizer + MultinomialNB`\n", + "- `CountVectorizer + ComplementNB` (for imbalanced type task)\n", + "\n", + "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", + "\n", + "**Outputs** in `outputs/classical/naive_bayes/`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yeP7YbDYuKB8" + }, + "source": [ + "## Helper Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "id": "PoPH1ijBuKB8" + }, + "outputs": [], + "source": [ + "def load_splits(task: str):\n", + " train = pd.read_csv(SPLITS / f\"train_{task}.csv\")\n", + " val = pd.read_csv(SPLITS / f\"val_{task}.csv\")\n", + " test = pd.read_csv(SPLITS / f\"test_{task}.csv\")\n", + " return train, val, test\n", + "\n", + "\n", + "def evaluate(model, X, y_true, label_names=None, split_name=\"\"):\n", + " y_pred = model.predict(X)\n", + " metrics = {\n", + " \"split\" : split_name,\n", + " \"accuracy\" : accuracy_score(y_true, y_pred),\n", + " \"precision_macro\" : precision_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"recall_macro\" : recall_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"f1_macro\" : f1_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"f1_weighted\" : f1_score(y_true, y_pred, average=\"weighted\", zero_division=0),\n", + " }\n", + " print(f\"[{split_name}] Accuracy={metrics['accuracy']:.4f} \"\n", + " f\"Macro-F1={metrics['f1_macro']:.4f} Weighted-F1={metrics['f1_weighted']:.4f}\")\n", + " print(classification_report(y_true, y_pred, target_names=label_names, zero_division=0))\n", + " return metrics, y_pred\n", + "\n", + "\n", + "def save_confusion_matrix(y_true, y_pred, label_names, out_path, title=\"\"):\n", + " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", + " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", + " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Greens\",\n", + " xticklabels=label_names, yticklabels=label_names, ax=ax)\n", + " ax.set_xlabel(\"Predicted\"); ax.set_ylabel(\"True\"); ax.set_title(title)\n", + " plt.tight_layout()\n", + " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + " print(f\"Saved: {out_path.relative_to(ROOT)}\")\n", + "\n", + "\n", + "def run_nb_grid(\n", + " vectorizer_cls, nb_cls, param_grid: dict,\n", + " X_trainval, y_trainval, groups_tv,\n", + " name: str\n", + ") -> GridSearchCV:\n", + " \"\"\"Run a grid search for a given vectorizer + NB combination.\"\"\"\n", + " pipe = Pipeline([\n", + " (\"vec\", vectorizer_cls(lowercase=True)),\n", + " (\"nb\", nb_cls()),\n", + " ])\n", + " gs = GridSearchCV(\n", + " pipe, param_grid, cv=GroupKFold(5),\n", + " scoring=\"f1_macro\", n_jobs=-1, verbose=0, refit=True\n", + " )\n", + " gs.fit(X_trainval, y_trainval, groups=groups_tv)\n", + " print(f\"{name:50s} CV F1={gs.best_score_:.4f} params={gs.best_params_}\")\n", + " return gs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0oL34vMHuKB8" + }, + "source": [ + "## Task A — Binary Classification" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_STzCoqRuKB8", + "outputId": "c86245b0-ee21-4612-a5d5-dd2fba99214b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train+Val: 48,166 Val: 8,500 Test: 8,500\n" + ] + } + ], + "source": [ + "train_bin, val_bin, test_bin = load_splits(\"binary\")\n", + "trainval_bin = pd.concat([train_bin, val_bin], ignore_index=True)\n", + "\n", + "X_tv = trainval_bin[\"text\"].tolist()\n", + "y_tv = trainval_bin[\"binary_label\"].tolist()\n", + "grp_tv = trainval_bin[\"group_id\"].tolist()\n", + "\n", + "X_val = val_bin[\"text\"].tolist(); y_val = val_bin[\"binary_label\"].tolist()\n", + "X_test = test_bin[\"text\"].tolist(); y_test = test_bin[\"binary_label\"].tolist()\n", + "\n", + "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", + "\n", + "print(f\"Train+Val: {len(X_tv):,} Val: {len(X_val):,} Test: {len(X_test):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iOJgCEMwuKB8", + "outputId": "d37b770b-3b8f-4b49-ac5e-aeb6b7a0d37a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "=== Binary: Comparing Vectorizer + NB Combinations ===\n", + "CountVectorizer + MultinomialNB CV F1=0.7855 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", + "TfidfVectorizer + MultinomialNB CV F1=0.7897 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", + "CountVectorizer + ComplementNB CV F1=0.7855 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n" + ] + } + ], + "source": [ + "# ── Shared grid parameters ────────────────────────────────────────────────────\n", + "BASE_GRID = {\n", + " \"vec__ngram_range\": [(1, 1), (1, 2)],\n", + " \"vec__min_df\" : [2, 3, 5],\n", + " \"nb__alpha\" : [0.1, 0.5, 1.0],\n", + "}\n", + "\n", + "print(\"=== Binary: Comparing Vectorizer + NB Combinations ===\")\n", + "\n", + "gs_count_mnb_bin = run_nb_grid(\n", + " CountVectorizer, MultinomialNB, BASE_GRID,\n", + " X_tv, y_tv, grp_tv,\n", + " \"CountVectorizer + MultinomialNB\"\n", + ")\n", + "\n", + "gs_tfidf_mnb_bin = run_nb_grid(\n", + " TfidfVectorizer, MultinomialNB, BASE_GRID,\n", + " X_tv, y_tv, grp_tv,\n", + " \"TfidfVectorizer + MultinomialNB\"\n", + ")\n", + "\n", + "gs_count_cnb_bin = run_nb_grid(\n", + " CountVectorizer, ComplementNB, BASE_GRID,\n", + " X_tv, y_tv, grp_tv,\n", + " \"CountVectorizer + ComplementNB\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eilEzm18uKB9", + "outputId": "360ce0ff-df02-45a3-bcb3-8213ae795e53" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Best binary NB model: TfidfVec+MultinomialNB (CV F1=0.7897)\n", + "\n", + "Comparison table:\n", + " model cv_f1_macro best_params\n", + "CountVec+MultinomialNB 0.785542 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", + "TfidfVec+MultinomialNB 0.789663 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", + " CountVec+ComplementNB 0.785542 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n" + ] + } + ], + "source": [ + "# ── Select best binary model ──────────────────────────────────────────────────\n", + "candidates_bin = [\n", + " (\"CountVec+MultinomialNB\", gs_count_mnb_bin),\n", + " (\"TfidfVec+MultinomialNB\", gs_tfidf_mnb_bin),\n", + " (\"CountVec+ComplementNB\", gs_count_cnb_bin),\n", + "]\n", + "\n", + "best_name_bin, best_gs_bin = max(candidates_bin, key=lambda x: x[1].best_score_)\n", + "print(f\"\\nBest binary NB model: {best_name_bin} (CV F1={best_gs_bin.best_score_:.4f})\")\n", + "\n", + "# Comparison table\n", + "comp_df = pd.DataFrame([\n", + " {\"model\": name, \"cv_f1_macro\": gs.best_score_, \"best_params\": str(gs.best_params_)}\n", + " for name, gs in candidates_bin\n", + "])\n", + "print(\"\\nComparison table:\")\n", + "print(comp_df.to_string(index=False))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Zh5ipG6BuKB9", + "outputId": "7f108910-a2fb-4154-eb36-f3f68b5a4dc2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[Val] Accuracy=0.8826 Macro-F1=0.8826 Weighted-F1=0.8826\n", + " precision recall f1-score support\n", + "\n", + "non-sarcastic 0.89 0.87 0.88 4250\n", + " sarcastic 0.87 0.90 0.88 4250\n", + "\n", + " accuracy 0.88 8500\n", + " macro avg 0.88 0.88 0.88 8500\n", + " weighted avg 0.88 0.88 0.88 8500\n", + "\n", + "[Test] Accuracy=0.7905 Macro-F1=0.7902 Weighted-F1=0.7902\n", + " precision recall f1-score support\n", + "\n", + "non-sarcastic 0.81 0.76 0.78 4250\n", + " sarcastic 0.77 0.83 0.80 4250\n", + "\n", + " accuracy 0.79 8500\n", + " macro avg 0.79 0.79 0.79 8500\n", + " weighted avg 0.79 0.79 0.79 8500\n", + "\n", + "\n", + "--- All models on Test ---\n", + " CountVec+MultinomialNB : acc=0.7887 macro-F1=0.7886\n", + " TfidfVec+MultinomialNB : acc=0.7905 macro-F1=0.7902\n", + " CountVec+ComplementNB : acc=0.7887 macro-F1=0.7886\n" + ] + } + ], + "source": [ + "# ── Evaluate best model ───────────────────────────────────────────────────────\n", + "best_model_bin = best_gs_bin.best_estimator_\n", + "\n", + "val_m_bin, y_val_pred_bin = evaluate(best_model_bin, X_val, y_val, label_names_bin, \"Val\")\n", + "test_m_bin, y_test_pred_bin = evaluate(best_model_bin, X_test, y_test, label_names_bin, \"Test\")\n", + "\n", + "# Also evaluate all three models on test for comparison\n", + "print(\"\\n--- All models on Test ---\")\n", + "all_test_results_bin = []\n", + "for name, gs in candidates_bin:\n", + " y_pred_t = gs.best_estimator_.predict(X_test)\n", + " f1 = f1_score(y_test, y_pred_t, average=\"macro\")\n", + " acc = accuracy_score(y_test, y_pred_t)\n", + " all_test_results_bin.append({\"model\": name, \"accuracy\": acc, \"f1_macro\": f1})\n", + " print(f\" {name:40s}: acc={acc:.4f} macro-F1={f1:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "D74tnzAduKB9", + "outputId": "1ccb358e-1daa-42ec-8aa0-09bada09116b" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/naive_bayes/confusion_matrix_binary_val.png\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/naive_bayes/confusion_matrix_binary_test.png\n", + "All binary artifacts saved.\n" + ] + } + ], + "source": [ + "# ── Save binary results ───────────────────────────────────────────────────────\n", + "cfg_bin = {\n", + " \"task\": \"binary\", \"best_model\": best_name_bin,\n", + " \"best_params\": best_gs_bin.best_params_, \"cv_f1_macro\": best_gs_bin.best_score_,\n", + " \"all_candidates\": [\n", + " {\"model\": name, \"cv_f1_macro\": gs.best_score_, \"params\": gs.best_params_}\n", + " for name, gs in candidates_bin\n", + " ]\n", + "}\n", + "with open(OUT_NB / \"best_config_binary.json\", \"w\") as f:\n", + " json.dump(cfg_bin, f, indent=2)\n", + "\n", + "all_m_bin = {\"val\": val_m_bin, \"test\": test_m_bin,\n", + " \"all_test_comparison\": all_test_results_bin}\n", + "with open(OUT_NB / \"metrics_binary.json\", \"w\") as f:\n", + " json.dump(all_m_bin, f, indent=2)\n", + "\n", + "save_confusion_matrix(\n", + " y_val, y_val_pred_bin, label_names_bin,\n", + " OUT_NB / \"confusion_matrix_binary_val.png\",\n", + " f\"NB ({best_name_bin}) — Binary (Val)\"\n", + ")\n", + "save_confusion_matrix(\n", + " y_test, y_test_pred_bin, label_names_bin,\n", + " OUT_NB / \"confusion_matrix_binary_test.png\",\n", + " f\"NB ({best_name_bin}) — Binary (Test)\"\n", + ")\n", + "\n", + "# Predictions\n", + "pred_val_bin = val_bin.copy()\n", + "pred_val_bin[\"predicted\"] = y_val_pred_bin\n", + "pred_val_bin[\"correct\"] = (pred_val_bin[\"binary_label\"] == pred_val_bin[\"predicted\"]).astype(int)\n", + "pred_val_bin.to_csv(OUT_NB / \"predictions_val_binary.csv\", index=False)\n", + "\n", + "pred_test_bin = test_bin.copy()\n", + "pred_test_bin[\"predicted\"] = y_test_pred_bin\n", + "pred_test_bin[\"correct\"] = (pred_test_bin[\"binary_label\"] == pred_test_bin[\"predicted\"]).astype(int)\n", + "pred_test_bin.to_csv(OUT_NB / \"predictions_test_binary.csv\", index=False)\n", + "\n", + "print(\"All binary artifacts saved.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UtChhm0-uKB9" + }, + "source": [ + "## Task B — Sarcasm Type Classification" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Rh8sdjlhuKB9", + "outputId": "36e9daa4-b705-4b92-8601-af8ee580ff33" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n", + "Train+Val: 24,083 Val: 4,250 Test: 4,250\n" + ] + } + ], + "source": [ + "train_type, val_type, test_type = load_splits(\"type\")\n", + "\n", + "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", + "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", + "id2label = {i: lab for lab, i in label2id.items()}\n", + "\n", + "def enc(df): return [label2id[l] for l in df[\"type_label\"]]\n", + "\n", + "trainval_type = pd.concat([train_type, val_type], ignore_index=True)\n", + "X_tv_t = trainval_type[\"text\"].tolist()\n", + "y_tv_t = enc(trainval_type)\n", + "grp_tv_t = trainval_type[\"group_id\"].tolist()\n", + "\n", + "X_val_t = val_type[\"text\"].tolist(); y_val_t = enc(val_type)\n", + "X_test_t = test_type[\"text\"].tolist(); y_test_t = enc(test_type)\n", + "\n", + "print(f\"Strategies: {STRATEGY_LABELS}\")\n", + "print(f\"Train+Val: {len(X_tv_t):,} Val: {len(X_val_t):,} Test: {len(X_test_t):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZupB5UlvuKB-", + "outputId": "bb5e86d3-745c-4c41-9650-d399e4668e32" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "=== Type: Comparing Vectorizer + NB Combinations ===\n", + "CountVectorizer + MultinomialNB CV F1=0.3811 params={'nb__alpha': 0.5, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n", + "TfidfVectorizer + MultinomialNB CV F1=0.3422 params={'nb__alpha': 0.1, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n", + "CountVectorizer + ComplementNB (for imbalance) CV F1=0.3777 params={'nb__alpha': 1.0, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n" + ] + } + ], + "source": [ + "print(\"=== Type: Comparing Vectorizer + NB Combinations ===\")\n", + "\n", + "gs_count_mnb_type = run_nb_grid(\n", + " CountVectorizer, MultinomialNB, BASE_GRID,\n", + " X_tv_t, y_tv_t, grp_tv_t,\n", + " \"CountVectorizer + MultinomialNB\"\n", + ")\n", + "\n", + "gs_tfidf_mnb_type = run_nb_grid(\n", + " TfidfVectorizer, MultinomialNB, BASE_GRID,\n", + " X_tv_t, y_tv_t, grp_tv_t,\n", + " \"TfidfVectorizer + MultinomialNB\"\n", + ")\n", + "\n", + "gs_count_cnb_type = run_nb_grid(\n", + " CountVectorizer, ComplementNB, BASE_GRID,\n", + " X_tv_t, y_tv_t, grp_tv_t,\n", + " \"CountVectorizer + ComplementNB (for imbalance)\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "J_CunIwDuKB-", + "outputId": "45a1ccc2-6af0-4542-b371-7573a94afba6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Best type NB model: CountVec+MultinomialNB (CV F1=0.3811)\n", + "\n", + "--- All models on Test (Type Task) ---\n", + " CountVec+MultinomialNB : acc=0.3953 macro-F1=0.3953\n", + " TfidfVec+MultinomialNB : acc=0.3800 macro-F1=0.3450\n", + " CountVec+ComplementNB : acc=0.3875 macro-F1=0.3880\n" + ] + } + ], + "source": [ + "candidates_type = [\n", + " (\"CountVec+MultinomialNB\", gs_count_mnb_type),\n", + " (\"TfidfVec+MultinomialNB\", gs_tfidf_mnb_type),\n", + " (\"CountVec+ComplementNB\", gs_count_cnb_type),\n", + "]\n", + "\n", + "best_name_type, best_gs_type = max(candidates_type, key=lambda x: x[1].best_score_)\n", + "print(f\"Best type NB model: {best_name_type} (CV F1={best_gs_type.best_score_:.4f})\")\n", + "\n", + "print(\"\\n--- All models on Test (Type Task) ---\")\n", + "all_test_results_type = []\n", + "for name, gs in candidates_type:\n", + " y_pred_t = gs.best_estimator_.predict(X_test_t)\n", + " f1 = f1_score(y_test_t, y_pred_t, average=\"macro\")\n", + " acc = accuracy_score(y_test_t, y_pred_t)\n", + " all_test_results_type.append({\"model\": name, \"accuracy\": acc, \"f1_macro\": f1})\n", + " print(f\" {name:40s}: acc={acc:.4f} macro-F1={f1:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "o6JHLJnLuKB-", + "outputId": "5f5f236e-83ce-4a58-9f49-92e0f472d39e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[Val] Accuracy=0.7694 Macro-F1=0.7744 Weighted-F1=0.7693\n", + " precision recall f1-score support\n", + "\n", + " irony 0.80 0.72 0.76 942\n", + " overstatement 0.75 0.80 0.77 600\n", + "rhetorical_question 0.78 0.88 0.83 157\n", + " sarcasm 0.79 0.78 0.79 1317\n", + " satire 0.73 0.77 0.75 747\n", + " understatement 0.75 0.75 0.75 487\n", + "\n", + " accuracy 0.77 4250\n", + " macro avg 0.77 0.78 0.77 4250\n", + " weighted avg 0.77 0.77 0.77 4250\n", + "\n", + "[Test] Accuracy=0.3953 Macro-F1=0.3953 Weighted-F1=0.3929\n", + " precision recall f1-score support\n", + "\n", + " irony 0.32 0.29 0.30 902\n", + " overstatement 0.39 0.40 0.39 592\n", + "rhetorical_question 0.52 0.41 0.46 176\n", + " sarcasm 0.45 0.51 0.47 1291\n", + " satire 0.36 0.34 0.35 833\n", + " understatement 0.40 0.38 0.39 456\n", + "\n", + " accuracy 0.40 4250\n", + " macro avg 0.41 0.39 0.40 4250\n", + " weighted avg 0.39 0.40 0.39 4250\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/naive_bayes/confusion_matrix_type_val.png\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/naive_bayes/confusion_matrix_type_test.png\n", + "All type artifacts saved.\n" + ] + } + ], + "source": [ + "best_model_type = best_gs_type.best_estimator_\n", + "\n", + "val_m_type, y_val_pred_type = evaluate(best_model_type, X_val_t, y_val_t, STRATEGY_LABELS, \"Val\")\n", + "test_m_type, y_test_pred_type = evaluate(best_model_type, X_test_t, y_test_t, STRATEGY_LABELS, \"Test\")\n", + "\n", + "# Save\n", + "cfg_type = {\n", + " \"task\": \"type\", \"best_model\": best_name_type,\n", + " \"label_names\": STRATEGY_LABELS, \"label2id\": label2id,\n", + " \"best_params\": best_gs_type.best_params_, \"cv_f1_macro\": best_gs_type.best_score_,\n", + "}\n", + "with open(OUT_NB / \"best_config_type.json\", \"w\") as f:\n", + " json.dump(cfg_type, f, indent=2)\n", + "\n", + "all_m_type = {\"val\": val_m_type, \"test\": test_m_type,\n", + " \"all_test_comparison\": all_test_results_type}\n", + "with open(OUT_NB / \"metrics_type.json\", \"w\") as f:\n", + " json.dump(all_m_type, f, indent=2)\n", + "\n", + "save_confusion_matrix(\n", + " y_val_t, y_val_pred_type, STRATEGY_LABELS,\n", + " OUT_NB / \"confusion_matrix_type_val.png\",\n", + " f\"NB ({best_name_type}) — Type (Val)\"\n", + ")\n", + "save_confusion_matrix(\n", + " y_test_t, y_test_pred_type, STRATEGY_LABELS,\n", + " OUT_NB / \"confusion_matrix_type_test.png\",\n", + " f\"NB ({best_name_type}) — Type (Test)\"\n", + ")\n", + "\n", + "# Predictions with string labels\n", + "pred_val_type = val_type.copy()\n", + "pred_test_type = test_type.copy()\n", + "for df, y_pred, y_true in [(pred_val_type, y_val_pred_type, y_val_t),\n", + " (pred_test_type, y_test_pred_type, y_test_t)]:\n", + " df[\"predicted_label\"] = [id2label[i] for i in y_pred]\n", + " df[\"true_label\"] = [id2label[i] for i in y_true]\n", + " df[\"correct\"] = (df[\"type_label\"] == df[\"predicted_label\"]).astype(int)\n", + "\n", + "pred_val_type.to_csv( OUT_NB / \"predictions_val_type.csv\", index=False)\n", + "pred_test_type.to_csv(OUT_NB / \"predictions_test_type.csv\", index=False)\n", + "\n", + "print(\"All type artifacts saved.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TwTBPa86uKB-" + }, + "source": [ + "## Error Examples" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "b8itQIPtuKB-", + "outputId": "6805f04f-a1c4-4005-a8f7-4496ad84a58b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Binary errors on test: 1781 total\n", + " False Positives (predicted sarcastic, actually not): 1039\n", + " False Negatives (predicted not-sarcastic, actually sarcastic): 742\n", + "\n", + "--- Sample FP ---\n", + " [True=0, Pred=1] Probability researchers note a coincidence where three coworkers wear the same shirt color.\n", + " [True=0, Pred=1] The global-warming crisis contributed to a delightful mid-February afternoon.\n", + " [True=0, Pred=1] Signs for the upcoming road section make it sound formidable or exciting.\n", + " [True=0, Pred=1] A father recounts how he saved $4.27 through quick thinking.\n", + " [True=0, Pred=1] The wedding album begins with a photo of two acorns floating in a glass of water.\n", + " [True=0, Pred=1] A governor is too embarrassed to say which state he leads.\n", + " [True=0, Pred=1] A man who plays devil's advocate may actually just want to be disagreeable.\n", + " [True=0, Pred=1] Area dad watched a show about bigfoot last night.\n", + " [True=0, Pred=1] Coroner's report cites systemic issues in Alton Sterling's death.\n", + " [True=0, Pred=1] Preschool child asks to look at a classmate's notes on shapes.\n", + "\n", + "--- Sample FN ---\n", + " [True=1, Pred=0] state department warns americans traveling abroad to avoid lame amsterdam windmill tour\n", + " [True=1, Pred=0] hollywood's biggest stars endure long lines at oscars security screening\n", + " [True=1, Pred=0] historians suggest 'goodfellas' youtube clips may be fragments of larger work\n", + " [True=1, Pred=0] members of opening band walking among crowd during intermission like gods among men\n", + " [True=1, Pred=0] woman who's been on the pill for years thinking about switching to new set of debilitating side effe\n", + " [True=1, Pred=0] congressman lets his guitar do the talking\n", + " [True=1, Pred=0] officials warn consumers of counterfeit tickets ahead of solar eclipse\n", + " [True=1, Pred=0] anderson cooper throws another box of letters from gay children into dumpster\n", + " [True=1, Pred=0] non-priest arrested on charges of child molestation\n", + " [True=1, Pred=0] huckabee sanders tells colleagues she's taking temporary post as google ceo before transitioning int\n" + ] + } + ], + "source": [ + "# ── Binary error examples ─────────────────────────────────────────────────────\n", + "err_bin = pred_test_bin[pred_test_bin[\"correct\"] == 0].copy()\n", + "fp_bin = err_bin[err_bin[\"binary_label\"] == 0].head(10) # predicted sarcastic, actually not\n", + "fn_bin = err_bin[err_bin[\"binary_label\"] == 1].head(10) # predicted not-sarcastic, actually sarcastic\n", + "\n", + "print(f\"Binary errors on test: {len(err_bin)} total\")\n", + "print(f\" False Positives (predicted sarcastic, actually not): {len(err_bin[err_bin['binary_label']==0])}\")\n", + "print(f\" False Negatives (predicted not-sarcastic, actually sarcastic): {len(err_bin[err_bin['binary_label']==1])}\")\n", + "print(\"\\n--- Sample FP ---\")\n", + "for _, row in fp_bin.iterrows():\n", + " print(f\" [True=0, Pred=1] {row['text'][:100]}\")\n", + "print(\"\\n--- Sample FN ---\")\n", + "for _, row in fn_bin.iterrows():\n", + " print(f\" [True=1, Pred=0] {row['text'][:100]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zmVdkTHuuKB_", + "outputId": "5bc30ec0-0f97-4f57-a165-706c7a022a82" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Type errors on test: 2570 total\n", + "\n", + "Confusion pairs (true → predicted):\n", + "type_label predicted_label\n", + "irony sarcasm 309\n", + "satire sarcasm 227\n", + "sarcasm irony 226\n", + " satire 203\n", + "satire irony 155\n", + "irony satire 155\n", + "overstatement sarcasm 117\n", + "understatement sarcasm 103\n", + "overstatement satire 102\n", + "satire overstatement 101\n" + ] + } + ], + "source": [ + "# ── Type error examples ───────────────────────────────────────────────────────\n", + "err_type = pred_test_type[pred_test_type[\"correct\"] == 0].copy()\n", + "print(f\"Type errors on test: {len(err_type)} total\")\n", + "print(\"\\nConfusion pairs (true → predicted):\")\n", + "conf_pairs = err_type.groupby([\"type_label\", \"predicted_label\"]).size().sort_values(ascending=False).head(10)\n", + "print(conf_pairs.to_string())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "p52YKmRQuKB_" + }, + "source": [ + "## Summary" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tg9D9LKxuKB_", + "outputId": "bf1f11d1-8feb-4b8c-c9fd-adac380993b2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "====== NAIVE BAYES RESULTS SUMMARY ======\n", + "\n", + "Task A — Binary (Test):\n", + " Best model : TfidfVec+MultinomialNB\n", + " Accuracy : 0.7905\n", + " Macro-F1 : 0.7902\n", + " Weighted-F1 : 0.7902\n", + "\n", + "Task B — Type (Test):\n", + " Best model : CountVec+MultinomialNB\n", + " Accuracy : 0.3953\n", + " Macro-F1 : 0.3953\n", + " Weighted-F1 : 0.3929\n" + ] + } + ], + "source": [ + "print(\"====== NAIVE BAYES RESULTS SUMMARY ======\")\n", + "print()\n", + "print(\"Task A — Binary (Test):\")\n", + "print(f\" Best model : {best_name_bin}\")\n", + "print(f\" Accuracy : {test_m_bin['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {test_m_bin['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1 : {test_m_bin['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"Task B — Type (Test):\")\n", + "print(f\" Best model : {best_name_type}\")\n", + "print(f\" Accuracy : {test_m_type['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {test_m_type['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1 : {test_m_type['f1_weighted']:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GZHqRh2iuKB_" + }, + "source": [ + "---\n", + "# Part 4 — BERT / DistilBERT Classification" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xR_41E_1uKB_" + }, + "source": [ + "# Notebook 04 — BERT / DistilBERT Classification\n", + "\n", + "**Purpose**: Fine-tune transformer models for sarcasm classification.\n", + "- Task A: Binary (sarcastic vs non-sarcastic)\n", + "- Task B: Sarcasm type (6-class, sarcastic only)\n", + "\n", + "**Models**:\n", + "- `distilbert-base-uncased` (primary, fast)\n", + "- `bert-base-uncased` (optional, if compute allows)\n", + "\n", + "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", + "\n", + "**Outputs** in `outputs/bert/distilbert_binary/` and `outputs/bert/distilbert_type/`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5Gi8VSJDuKB_" + }, + "source": [ + "## Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "K_FNBk2ouKB_", + "outputId": "70f6d378-7098-410c-d656-50135e3b0e12" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Binary config: TrainConfig(model_name='distilbert-base-uncased', max_length=128, batch_size=32, lr=2e-05, weight_decay=0.01, warmup_ratio=0.1, epochs=10, patience=3, seed=42, task='binary', use_class_weights=False)\n", + "Type config: TrainConfig(model_name='distilbert-base-uncased', max_length=128, batch_size=32, lr=2e-05, weight_decay=0.01, warmup_ratio=0.1, epochs=10, patience=3, seed=42, task='type', use_class_weights=True)\n" + ] + } + ], + "source": [ + "@dataclass\n", + "class TrainConfig:\n", + " model_name : str = \"distilbert-base-uncased\"\n", + " max_length : int = 128\n", + " batch_size : int = 32\n", + " lr : float = 2e-5\n", + " weight_decay : float = 0.01\n", + " warmup_ratio : float = 0.1\n", + " epochs : int = 10\n", + " patience : int = 3 # early stopping patience\n", + " seed : int = SEED\n", + " task : str = \"binary\" # 'binary' or 'type'\n", + " use_class_weights: bool = False\n", + "\n", + "# ── Config for binary task ────────────────────────────────────────────────────\n", + "CFG_BINARY = TrainConfig(\n", + " model_name=\"distilbert-base-uncased\",\n", + " task=\"binary\",\n", + " batch_size=32,\n", + " lr=2e-5,\n", + " use_class_weights=False,\n", + ")\n", + "\n", + "# ── Config for type task (use class weights for imbalance) ────────────────────\n", + "CFG_TYPE = TrainConfig(\n", + " model_name=\"distilbert-base-uncased\",\n", + " task=\"type\",\n", + " batch_size=32,\n", + " lr=2e-5,\n", + " use_class_weights=True,\n", + ")\n", + "\n", + "print(\"Binary config:\", CFG_BINARY)\n", + "print(\"Type config: \", CFG_TYPE)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OKgvtBDPuKCA" + }, + "source": [ + "## Dataset Class" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "id": "tHoVxhSAuKCA" + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch.utils.data import Dataset\n", + "\n", + "class HeadlineDataset(Dataset):\n", + " \"\"\"PyTorch Dataset for headline classification.\"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " texts: list[str],\n", + " labels: list[int],\n", + " tokenizer,\n", + " max_length: int = 128,\n", + " ):\n", + " self.texts = texts\n", + " self.labels = labels\n", + " self.tokenizer = tokenizer\n", + " self.max_length = max_length\n", + "\n", + " def __len__(self) -> int:\n", + " return len(self.texts)\n", + "\n", + " def __getitem__(self, idx: int) -> dict:\n", + " encoding = self.tokenizer(\n", + " self.texts[idx],\n", + " truncation=True,\n", + " padding=\"max_length\",\n", + " max_length=self.max_length,\n", + " return_tensors=\"pt\",\n", + " )\n", + " return {\n", + " \"input_ids\" : encoding[\"input_ids\"].squeeze(0),\n", + " \"attention_mask\" : encoding[\"attention_mask\"].squeeze(0),\n", + " \"labels\" : torch.tensor(self.labels[idx], dtype=torch.long),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KBPvq1iRuKCA" + }, + "source": [ + "## Training and Evaluation Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "id": "Dw8sRTfruKCA" + }, + "outputs": [], + "source": [ + "def compute_class_weights(y_train: list[int], num_classes: int) -> torch.Tensor:\n", + " \"\"\"Compute inverse-frequency class weights.\"\"\"\n", + " counts = np.bincount(y_train, minlength=num_classes).astype(float)\n", + " weights = len(y_train) / (num_classes * counts)\n", + " weights = np.clip(weights, 0, 10) # cap extreme weights\n", + " return torch.tensor(weights, dtype=torch.float)\n", + "\n", + "\n", + "def train_epoch(\n", + " model, loader: DataLoader, optimizer, scheduler, criterion, device\n", + ") -> float:\n", + " model.train()\n", + " total_loss = 0.0\n", + " for batch in loader:\n", + " input_ids = batch[\"input_ids\"].to(device)\n", + " attention_mask = batch[\"attention_mask\"].to(device)\n", + " labels = batch[\"labels\"].to(device)\n", + "\n", + " optimizer.zero_grad()\n", + " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", + " logits = outputs.logits\n", + "\n", + " loss = criterion(logits, labels)\n", + " loss.backward()\n", + "\n", + " nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n", + " optimizer.step()\n", + " scheduler.step()\n", + "\n", + " total_loss += loss.item()\n", + " return total_loss / len(loader)\n", + "\n", + "\n", + "@torch.no_grad()\n", + "def eval_epoch(\n", + " model, loader: DataLoader, criterion, device, label_names: list[str]\n", + ") -> tuple[float, dict, list]:\n", + " model.eval()\n", + " total_loss = 0.0\n", + " all_preds, all_labels = [], []\n", + "\n", + " for batch in loader:\n", + " input_ids = batch[\"input_ids\"].to(device)\n", + " attention_mask = batch[\"attention_mask\"].to(device)\n", + " labels = batch[\"labels\"].to(device)\n", + "\n", + " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", + " logits = outputs.logits\n", + " loss = criterion(logits, labels)\n", + "\n", + " total_loss += loss.item()\n", + " preds = torch.argmax(logits, dim=-1)\n", + " all_preds.extend(preds.cpu().numpy())\n", + " all_labels.extend(labels.cpu().numpy())\n", + "\n", + " avg_loss = total_loss / len(loader)\n", + " metrics = {\n", + " \"loss\" : avg_loss,\n", + " \"accuracy\" : accuracy_score(all_labels, all_preds),\n", + " \"f1_macro\" : f1_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", + " \"f1_weighted\" : f1_score(all_labels, all_preds, average=\"weighted\", zero_division=0),\n", + " \"precision_macro\" : precision_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", + " \"recall_macro\" : recall_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", + " }\n", + " return avg_loss, metrics, all_preds\n", + "\n", + "\n", + "def save_confusion_matrix(y_true, y_pred, label_names, out_path, title=\"\"):\n", + " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", + " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", + " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n", + " xticklabels=label_names, yticklabels=label_names, ax=ax)\n", + " ax.set_xlabel(\"Predicted\"); ax.set_ylabel(\"True\"); ax.set_title(title)\n", + " plt.tight_layout()\n", + " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + " print(f\"Saved: {out_path}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d35JFlgzuKCB" + }, + "source": [ + "## Full Training Function" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "id": "wHriQLOyuKCB" + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "\n", + "from transformers import get_linear_schedule_with_warmup\n", + "\n", + "def train_bert(\n", + " cfg: TrainConfig,\n", + " X_train: list[str], y_train: list[int],\n", + " X_val: list[str], y_val: list[int],\n", + " X_test: list[str], y_test: list[int],\n", + " label_names: list[str],\n", + " out_dir: Path,\n", + " val_df: pd.DataFrame,\n", + " test_df: pd.DataFrame,\n", + ") -> dict:\n", + " \"\"\"Full training loop with early stopping. Returns test metrics dict.\"\"\"\n", + " out_dir.mkdir(parents=True, exist_ok=True)\n", + " num_labels = len(label_names)\n", + "\n", + " # FIX: define device inside function (self-contained)\n", + " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + " print(f\"Using device: {device}\")\n", + "\n", + " # Save config (default=str avoids Path serialization issues)\n", + " with open(out_dir / \"config.json\", \"w\") as f:\n", + " json.dump(cfg.__dict__, f, indent=2, default=str)\n", + " print(f\"Config saved to {out_dir / 'config.json'}\")\n", + "\n", + " # Tokenizer\n", + " print(f\"Loading tokenizer: {cfg.model_name}\")\n", + " tokenizer = AutoTokenizer.from_pretrained(cfg.model_name)\n", + "\n", + " # Datasets\n", + " train_ds = HeadlineDataset(X_train, y_train, tokenizer, cfg.max_length)\n", + " val_ds = HeadlineDataset(X_val, y_val, tokenizer, cfg.max_length)\n", + " test_ds = HeadlineDataset(X_test, y_test, tokenizer, cfg.max_length)\n", + "\n", + " g = torch.Generator()\n", + " g.manual_seed(cfg.seed)\n", + "\n", + " train_loader = DataLoader(train_ds, batch_size=cfg.batch_size, shuffle=True, generator=g)\n", + " val_loader = DataLoader(val_ds, batch_size=cfg.batch_size, shuffle=False)\n", + " test_loader = DataLoader(test_ds, batch_size=cfg.batch_size, shuffle=False)\n", + "\n", + " # Model\n", + " print(f\"Loading model: {cfg.model_name} ({num_labels} labels)\")\n", + " model = AutoModelForSequenceClassification.from_pretrained(\n", + " cfg.model_name, num_labels=num_labels\n", + " ).to(device)\n", + "\n", + " # Loss (with optional class weights)\n", + " if cfg.use_class_weights:\n", + " cw = compute_class_weights(y_train, num_labels).to(device)\n", + " criterion = nn.CrossEntropyLoss(weight=cw)\n", + " print(f\"Class weights: {cw.detach().cpu().numpy().round(3)}\")\n", + " else:\n", + " criterion = nn.CrossEntropyLoss()\n", + "\n", + " # Optimizer and scheduler\n", + " optimizer = torch.optim.AdamW(\n", + " model.parameters(), lr=cfg.lr, weight_decay=cfg.weight_decay\n", + " )\n", + " total_steps = len(train_loader) * cfg.epochs\n", + " warmup_steps = int(total_steps * cfg.warmup_ratio)\n", + "\n", + " scheduler = get_linear_schedule_with_warmup(\n", + " optimizer, num_warmup_steps=warmup_steps, num_training_steps=total_steps\n", + " )\n", + "\n", + " # Training loop\n", + " best_val_f1 = -1.0\n", + " patience_cnt = 0\n", + " best_epoch = 0\n", + " train_log = []\n", + "\n", + " for epoch in range(1, cfg.epochs + 1):\n", + " train_loss = train_epoch(model, train_loader, optimizer, scheduler, criterion, device)\n", + " _, val_metrics, _ = eval_epoch(model, val_loader, criterion, device, label_names)\n", + "\n", + " val_f1 = float(val_metrics[\"f1_macro\"])\n", + " log_row = {\n", + " \"epoch\": epoch,\n", + " \"train_loss\": float(train_loss),\n", + " **{f\"val_{k}\": float(v) if isinstance(v, (np.floating, float, int, np.integer)) else v\n", + " for k, v in val_metrics.items()}\n", + " }\n", + " train_log.append(log_row)\n", + "\n", + " print(\n", + " f\"Epoch {epoch:2d}/{cfg.epochs} | \"\n", + " f\"train_loss={train_loss:.4f} | \"\n", + " f\"val_loss={val_metrics['loss']:.4f} | \"\n", + " f\"val_acc={val_metrics['accuracy']:.4f} | \"\n", + " f\"val_macro_f1={val_f1:.4f}\"\n", + " )\n", + "\n", + " if val_f1 > best_val_f1:\n", + " best_val_f1 = val_f1\n", + " best_epoch = epoch\n", + " patience_cnt = 0\n", + "\n", + " ckpt_dir = out_dir / \"best_checkpoint\"\n", + " ckpt_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " model.save_pretrained(ckpt_dir)\n", + " tokenizer.save_pretrained(ckpt_dir)\n", + " print(f\" ★ New best val macro-F1={val_f1:.4f} — checkpoint saved\")\n", + " else:\n", + " patience_cnt += 1\n", + " if patience_cnt >= cfg.patience:\n", + " print(f\" Early stopping at epoch {epoch} (patience={cfg.patience})\")\n", + " break\n", + "\n", + " # Save training log\n", + " log_df = pd.DataFrame(train_log)\n", + " log_df.to_csv(out_dir / \"training_log.csv\", index=False)\n", + "\n", + " # Plot training curves\n", + " fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + " axes[0].plot(log_df[\"epoch\"], log_df[\"train_loss\"], label=\"Train loss\", marker=\"o\")\n", + " axes[0].plot(log_df[\"epoch\"], log_df[\"val_loss\"], label=\"Val loss\", marker=\"s\")\n", + " axes[0].set_xlabel(\"Epoch\")\n", + " axes[0].set_ylabel(\"Loss\")\n", + " axes[0].set_title(f\"{cfg.model_name} — Loss Curves ({cfg.task})\")\n", + " axes[0].legend()\n", + " axes[0].axvline(best_epoch, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", + "\n", + " axes[1].plot(log_df[\"epoch\"], log_df[\"val_f1_macro\"], label=\"Val macro-F1\", marker=\"o\")\n", + " axes[1].set_xlabel(\"Epoch\")\n", + " axes[1].set_ylabel(\"Macro-F1\")\n", + " axes[1].set_title(f\"{cfg.model_name} — Val Macro-F1 ({cfg.task})\")\n", + " axes[1].legend()\n", + " axes[1].axvline(best_epoch, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", + "\n", + " plt.tight_layout()\n", + " plt.savefig(out_dir / \"training_curves.png\", dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + "\n", + " # Reload best checkpoint and evaluate\n", + " print(f\"\\nLoading best checkpoint (epoch {best_epoch}, val macro-F1={best_val_f1:.4f})\")\n", + " best_model = AutoModelForSequenceClassification.from_pretrained(\n", + " str(out_dir / \"best_checkpoint\")\n", + " ).to(device)\n", + "\n", + " _, val_metrics_best, val_preds = eval_epoch(best_model, val_loader, criterion, device, label_names)\n", + " _, test_metrics_best, test_preds = eval_epoch(best_model, test_loader, criterion, device, label_names)\n", + "\n", + " print(\"\\n=== Val (best checkpoint) ===\")\n", + " print(classification_report(y_val, val_preds, target_names=label_names, zero_division=0))\n", + " print(\"\\n=== Test (best checkpoint) ===\")\n", + " print(classification_report(y_test, test_preds, target_names=label_names, zero_division=0))\n", + "\n", + " # Confusion matrices\n", + " save_confusion_matrix(\n", + " y_val, val_preds, label_names,\n", + " out_dir / \"confusion_matrix_val.png\",\n", + " f\"{cfg.model_name} — {cfg.task} (Val)\"\n", + " )\n", + " save_confusion_matrix(\n", + " y_test, test_preds, label_names,\n", + " out_dir / \"confusion_matrix_test.png\",\n", + " f\"{cfg.model_name} — {cfg.task} (Test)\"\n", + " )\n", + "\n", + " # Convert metrics to native Python types for JSON\n", + " def _to_py(obj):\n", + " if isinstance(obj, dict):\n", + " return {k: _to_py(v) for k, v in obj.items()}\n", + " if isinstance(obj, (list, tuple)):\n", + " return [_to_py(v) for v in obj]\n", + " if isinstance(obj, (np.integer,)):\n", + " return int(obj)\n", + " if isinstance(obj, (np.floating,)):\n", + " return float(obj)\n", + " return obj\n", + "\n", + " results = {\n", + " \"model\": cfg.model_name,\n", + " \"task\": cfg.task,\n", + " \"best_epoch\": int(best_epoch),\n", + " \"best_val_f1_macro\": float(best_val_f1),\n", + " \"val\": _to_py(val_metrics_best),\n", + " \"test\": _to_py(test_metrics_best),\n", + " }\n", + "\n", + " with open(out_dir / \"metrics.json\", \"w\") as f:\n", + " json.dump(results, f, indent=2, default=str)\n", + "\n", + " # Save predictions\n", + " for split_name, df, y_true_list, y_pred_list in [\n", + " (\"val\", val_df, y_val, val_preds),\n", + " (\"test\", test_df, y_test, test_preds),\n", + " ]:\n", + " out_pred = df.copy()\n", + " out_pred[\"predicted\"] = y_pred_list\n", + " out_pred[\"predicted_label\"] = [label_names[i] for i in y_pred_list]\n", + " out_pred[\"correct\"] = (np.array(y_true_list) == np.array(y_pred_list)).astype(int)\n", + " out_pred.to_csv(out_dir / f\"predictions_{split_name}.csv\", index=False)\n", + " print(f\"Saved predictions_{split_name}.csv\")\n", + "\n", + " print(f\"\\n=== DONE: {cfg.model_name} / {cfg.task} ===\")\n", + " print(f\" Test Accuracy : {test_metrics_best['accuracy']:.4f}\")\n", + " print(f\" Test Macro-F1 : {test_metrics_best['f1_macro']:.4f}\")\n", + " print(f\" Test Weighted-F1: {test_metrics_best['f1_weighted']:.4f}\")\n", + "\n", + " return results" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "49JUIiXSuKCB" + }, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-bzRLKhsuKCB", + "outputId": "b3ac3196-fdb3-44d9-8417-26a434c6837a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Binary — Train: 39,666 Val: 8,500 Test: 8,500\n" + ] + } + ], + "source": [ + "# ── Binary splits ─────────────────────────────────────────────────────────────\n", + "train_bin = pd.read_csv(SPLITS / \"train_binary.csv\")\n", + "val_bin = pd.read_csv(SPLITS / \"val_binary.csv\")\n", + "test_bin = pd.read_csv(SPLITS / \"test_binary.csv\")\n", + "\n", + "X_train_bin = train_bin[\"text\"].tolist(); y_train_bin = train_bin[\"binary_label\"].tolist()\n", + "X_val_bin = val_bin[\"text\"].tolist(); y_val_bin = val_bin[\"binary_label\"].tolist()\n", + "X_test_bin = test_bin[\"text\"].tolist(); y_test_bin = test_bin[\"binary_label\"].tolist()\n", + "\n", + "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", + "\n", + "print(f\"Binary — Train: {len(X_train_bin):,} Val: {len(X_val_bin):,} Test: {len(X_test_bin):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OkXIlQAtuKCC", + "outputId": "676ecfa7-874b-4536-9ac3-b8b833e2cfff" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Type — Train: 19,833 Val: 4,250 Test: 4,250\n", + "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n" + ] + } + ], + "source": [ + "# ── Type splits ───────────────────────────────────────────────────────────────\n", + "train_type = pd.read_csv(SPLITS / \"train_type.csv\")\n", + "val_type = pd.read_csv(SPLITS / \"val_type.csv\")\n", + "test_type = pd.read_csv(SPLITS / \"test_type.csv\")\n", + "\n", + "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", + "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", + "id2label = {i: lab for lab, i in label2id.items()}\n", + "\n", + "def enc(df): return [label2id[l] for l in df[\"type_label\"]]\n", + "\n", + "X_train_type = train_type[\"text\"].tolist(); y_train_type = enc(train_type)\n", + "X_val_type = val_type[\"text\"].tolist(); y_val_type = enc(val_type)\n", + "X_test_type = test_type[\"text\"].tolist(); y_test_type = enc(test_type)\n", + "\n", + "print(f\"Type — Train: {len(X_train_type):,} Val: {len(X_val_type):,} Test: {len(X_test_type):,}\")\n", + "print(f\"Strategies: {STRATEGY_LABELS}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "varh9ZaouKCC" + }, + "source": [ + "## Train DistilBERT — Binary Task" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 396, + "referenced_widgets": [ + "8b9080237c194037a2cd1dc711a694d2", + "d0d70eea6290419fb4a39bd74964bacb", + "30e0eec43be54d08a0b5bd855e06d3c3", + "959391d9e6cc405ead852002ce0276ff", + "143c9b378b5a4260b15e328dc744374f", + "384fe210d84b4e8d8bc1cd8b99944d5e", + "2a97e4558e6d41df919bd3ce83576627", + "bfc3291175a14250a407af3dd6376d58", + "01c169be55ca4399b34eb7cdae152075", + "2f42691301be4d01858524488e30fe78", + "8faab66da6c74399a140fa5aad07dbbb" + ] + }, + "id": "Yq8zSNjluKCC", + "outputId": "44080f86-f045-426f-f630-765eb5f599c7" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Using device: cpu\n", + "Config saved to /content/outputs/bert/distilbert_binary/config.json\n", + "Loading tokenizer: distilbert-base-uncased\n", + "Loading model: distilbert-base-uncased (2 labels)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Loading weights: 0%| | 0/100 [00:00 dict | None:\n", + " if path.exists():\n", + " with open(path) as f:\n", + " return json.load(f)\n", + " print(f\" [WARNING] Not found: {path.relative_to(ROOT)}\")\n", + " return None\n", + "\n", + "\n", + "# All metrics\n", + "tfidf_lr_binary = load_metrics(CLASSICAL / \"tfidf_lr\" / \"metrics_binary.json\")\n", + "tfidf_lr_type = load_metrics(CLASSICAL / \"tfidf_lr\" / \"metrics_type.json\")\n", + "nb_binary = load_metrics(CLASSICAL / \"naive_bayes\" / \"metrics_binary.json\")\n", + "nb_type = load_metrics(CLASSICAL / \"naive_bayes\" / \"metrics_type.json\")\n", + "distilbert_bin = load_metrics(BERT_OUT / \"distilbert_binary\" / \"metrics.json\")\n", + "distilbert_type = load_metrics(BERT_OUT / \"distilbert_type\" / \"metrics.json\")\n", + "bert_base_bin = load_metrics(BERT_OUT / \"bert_base_binary\" / \"metrics.json\") # optional\n", + "bert_base_type = load_metrics(BERT_OUT / \"bert_base_type\" / \"metrics.json\") # optional\n", + "\n", + "print(\"Metrics loaded (None = not yet run):\")\n", + "for name, m in [(\"TF-IDF+LR binary\", tfidf_lr_binary), (\"TF-IDF+LR type\", tfidf_lr_type),\n", + " (\"NB binary\", nb_binary), (\"NB type\", nb_type),\n", + " (\"DistilBERT binary\", distilbert_bin), (\"DistilBERT type\", distilbert_type),\n", + " (\"BERT-base binary\", bert_base_bin), (\"BERT-base type\", bert_base_type)]:\n", + " print(f\" {name}: {'✓' if m else '✗'}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H_xQm80nuKCE" + }, + "source": [ + "## 2. Model Comparison Tables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2RsVxgMtuKCE" + }, + "outputs": [], + "source": [ + "def extract_test_metrics(metrics_dict: dict | None, split: str = \"test\") -> dict:\n", + " \"\"\"Extract test-split metrics from a metrics dict, handling different formats.\"\"\"\n", + " if metrics_dict is None:\n", + " return {\"accuracy\": None, \"f1_macro\": None, \"f1_weighted\": None,\n", + " \"precision_macro\": None, \"recall_macro\": None}\n", + " test_m = metrics_dict.get(split, metrics_dict) # BERT format uses 'test' key directly\n", + " return {\n", + " \"accuracy\" : test_m.get(\"accuracy\"),\n", + " \"f1_macro\" : test_m.get(\"f1_macro\"),\n", + " \"f1_weighted\" : test_m.get(\"f1_weighted\"),\n", + " \"precision_macro\" : test_m.get(\"precision_macro\"),\n", + " \"recall_macro\" : test_m.get(\"recall_macro\"),\n", + " }\n", + "\n", + "\n", + "binary_rows = []\n", + "type_rows = []\n", + "\n", + "model_map_bin = [\n", + " (\"TF-IDF + LR\", tfidf_lr_binary),\n", + " (\"Naive Bayes\", nb_binary),\n", + " (\"DistilBERT\", distilbert_bin),\n", + " (\"BERT-base\", bert_base_bin),\n", + "]\n", + "\n", + "model_map_type = [\n", + " (\"TF-IDF + LR\", tfidf_lr_type),\n", + " (\"Naive Bayes\", nb_type),\n", + " (\"DistilBERT\", distilbert_type),\n", + " (\"BERT-base\", bert_base_type),\n", + "]\n", + "\n", + "for name, m in model_map_bin:\n", + " row = {\"Model\": name}\n", + " row.update(extract_test_metrics(m))\n", + " binary_rows.append(row)\n", + "\n", + "for name, m in model_map_type:\n", + " row = {\"Model\": name}\n", + " row.update(extract_test_metrics(m))\n", + " type_rows.append(row)\n", + "\n", + "binary_comp = pd.DataFrame(binary_rows).set_index(\"Model\")\n", + "type_comp = pd.DataFrame(type_rows).set_index(\"Model\")\n", + "\n", + "print(\"=== BINARY TASK — Test Metrics ===\")\n", + "print(binary_comp.to_string(float_format=lambda x: f\"{x:.4f}\" if x is not None else \"N/A\"))\n", + "print()\n", + "print(\"=== TYPE TASK — Test Metrics ===\")\n", + "print(type_comp.to_string(float_format=lambda x: f\"{x:.4f}\" if x is not None else \"N/A\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "n35eLHCCuKCF" + }, + "outputs": [], + "source": [ + "# ── Comparison bar charts ─────────────────────────────────────────────────────\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n", + "\n", + "for ax, comp, title in [\n", + " (axes[0], binary_comp, \"Binary Task\"),\n", + " (axes[1], type_comp, \"Type Task\"),\n", + "]:\n", + " valid = comp.dropna(subset=[\"f1_macro\"])\n", + " if len(valid) == 0:\n", + " ax.set_title(f\"{title} — No data\")\n", + " continue\n", + " models = valid.index.tolist()\n", + " f1_vals = valid[\"f1_macro\"].tolist()\n", + " acc_vals = valid[\"accuracy\"].tolist()\n", + " x = range(len(models))\n", + " w = 0.35\n", + " bars1 = ax.bar([i - w/2 for i in x], f1_vals, w, label=\"Macro-F1\", color=\"steelblue\")\n", + " bars2 = ax.bar([i + w/2 for i in x], acc_vals, w, label=\"Accuracy\", color=\"coral\")\n", + " ax.set_xticks(list(x)); ax.set_xticklabels(models, rotation=20, ha=\"right\")\n", + " ax.set_ylim(0, 1.05)\n", + " ax.set_ylabel(\"Score\")\n", + " ax.set_title(f\"{title} — Model Comparison (Test)\", fontsize=13)\n", + " ax.legend()\n", + " for bar in bars1:\n", + " ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n", + " f\"{bar.get_height():.3f}\", ha=\"center\", fontsize=8)\n", + " for bar in bars2:\n", + " ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n", + " f\"{bar.get_height():.3f}\", ha=\"center\", fontsize=8)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(REPORTS_DIR / \"model_comparison_chart.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/reports/model_comparison_chart.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GC0rNp0auKCF" + }, + "source": [ + "## 3. Error Analysis — Binary Task" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jokvmFh9uKCF" + }, + "outputs": [], + "source": [ + "def load_predictions(path: Path) -> pd.DataFrame | None:\n", + " if path.exists():\n", + " return pd.read_csv(path)\n", + " print(f\" [WARNING] Not found: {path}\")\n", + " return None\n", + "\n", + "\n", + "# Use best available binary model predictions for error analysis\n", + "# Priority: BERT > TF-IDF+LR > NB\n", + "pred_paths_bin = [\n", + " (\"DistilBERT\", BERT_OUT / \"distilbert_binary\" / \"predictions_test.csv\"),\n", + " (\"TF-IDF + LR\", CLASSICAL / \"tfidf_lr\" / \"predictions_test_binary.csv\"),\n", + " (\"Naive Bayes\", CLASSICAL / \"naive_bayes\" / \"predictions_test_binary.csv\"),\n", + "]\n", + "\n", + "error_source_bin = None\n", + "error_model_bin = None\n", + "for model_name, path in pred_paths_bin:\n", + " df = load_predictions(path)\n", + " if df is not None:\n", + " error_source_bin = df\n", + " error_model_bin = model_name\n", + " print(f\"Using {model_name} predictions for binary error analysis\")\n", + " break\n", + "\n", + "if error_source_bin is None:\n", + " print(\"No binary predictions found. Run at least one model first.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0qFoK11OuKCF" + }, + "outputs": [], + "source": [ + "if error_source_bin is not None:\n", + " pred_col = \"predicted\" if \"predicted\" in error_source_bin.columns else \"predicted_id\"\n", + " true_col = \"binary_label\"\n", + " err_bin = error_source_bin[error_source_bin[\"correct\"] == 0].copy()\n", + "\n", + " # Categorize\n", + " fp_bin = err_bin[err_bin[true_col] == 0].copy() # False Positives (predicted sarcastic)\n", + " fn_bin = err_bin[err_bin[true_col] == 1].copy() # False Negatives (predicted not-sarcastic)\n", + "\n", + " print(f\"Total binary test errors: {len(err_bin)}\")\n", + " print(f\" False Positives (non-sarcastic predicted as sarcastic) : {len(fp_bin)}\")\n", + " print(f\" False Negatives (sarcastic predicted as non-sarcastic) : {len(fn_bin)}\")\n", + "\n", + " # Select 20+ errors: mix of FP and FN\n", + " n_each = 12\n", + " sample_fp = fp_bin.head(n_each)\n", + " sample_fn = fn_bin.head(n_each)\n", + " error_sample_bin = pd.concat([sample_fp, sample_fn], ignore_index=True)\n", + " error_sample_bin[\"error_type\"] = [\"FP\"] * len(sample_fp) + [\"FN\"] * len(sample_fn)\n", + "\n", + " print(f\"\\nError sample size: {len(error_sample_bin)} examples\")\n", + " print(\"\\n--- False Positives (non-sarcastic → predicted sarcastic) ---\")\n", + " for _, row in sample_fp.iterrows():\n", + " print(f\" {row['text']}\")\n", + "\n", + " print(\"\\n--- False Negatives (sarcastic → predicted non-sarcastic) ---\")\n", + " for _, row in sample_fn.iterrows():\n", + " print(f\" {row['text']}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "gAh7r-JhuKCF" + }, + "outputs": [], + "source": [ + "if error_source_bin is not None:\n", + " # Failure mode categorization (heuristic rules)\n", + " def categorize_binary_error(text: str, error_type: str) -> str:\n", + " text_lower = text.lower()\n", + " if \"?\" in text and error_type == \"FP\":\n", + " return \"rhetorical_phrasing\"\n", + " if any(w in text_lower for w in [\"report\", \"study\", \"survey\", \"finds\", \"shows\"]):\n", + " return \"neutral_reporting_style_confused\"\n", + " if any(w in text_lower for w in [\"best\", \"great\", \"amazing\", \"wonderful\", \"perfect\"]):\n", + " return \"positive_framing_confused\"\n", + " if \"onion\" in text_lower:\n", + " return \"domain_leak\"\n", + " if len(text.split()) <= 5:\n", + " return \"very_short_text\"\n", + " return \"lexical_ambiguity\"\n", + "\n", + " error_sample_bin[\"failure_mode\"] = error_sample_bin.apply(\n", + " lambda r: categorize_binary_error(r[\"text\"], r[\"error_type\"]), axis=1\n", + " )\n", + "\n", + " print(\"Failure mode distribution:\")\n", + " print(error_sample_bin[\"failure_mode\"].value_counts())\n", + "\n", + " # Save\n", + " error_sample_bin.to_csv(REPORTS_DIR / \"error_examples_binary.csv\", index=False)\n", + " print(\"\\nSaved: outputs/reports/error_examples_binary.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3Ybue8H2uKCF" + }, + "source": [ + "## 4. Error Analysis — Type Task" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-yBp16Z6uKCG" + }, + "outputs": [], + "source": [ + "pred_paths_type = [\n", + " (\"DistilBERT\", BERT_OUT / \"distilbert_type\" / \"predictions_test.csv\"),\n", + " (\"TF-IDF + LR\", CLASSICAL / \"tfidf_lr\" / \"predictions_test_type.csv\"),\n", + " (\"Naive Bayes\", CLASSICAL / \"naive_bayes\" / \"predictions_test_type.csv\"),\n", + "]\n", + "\n", + "error_source_type = None\n", + "error_model_type = None\n", + "for model_name, path in pred_paths_type:\n", + " df = load_predictions(path)\n", + " if df is not None:\n", + " error_source_type = df\n", + " error_model_type = model_name\n", + " print(f\"Using {model_name} predictions for type error analysis\")\n", + " break\n", + "\n", + "if error_source_type is None:\n", + " print(\"No type predictions found.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "r21XN805uKCG" + }, + "outputs": [], + "source": [ + "if error_source_type is not None:\n", + " err_type = error_source_type[error_source_type[\"correct\"] == 0].copy()\n", + "\n", + " # Identify label columns\n", + " true_col_type = \"type_label\"\n", + " pred_col_type = \"predicted_label\" if \"predicted_label\" in err_type.columns else \"predicted\"\n", + "\n", + " print(f\"Total type errors: {len(err_type)}\")\n", + " print(\"\\nTop confusion pairs (true → predicted):\")\n", + " if pred_col_type in err_type.columns:\n", + " pairs = err_type.groupby([true_col_type, pred_col_type]).size().sort_values(ascending=False).head(15)\n", + " print(pairs.to_string())\n", + "\n", + " # Sample 20+ errors\n", + " error_sample_type = err_type.sample(min(25, len(err_type)), random_state=42)\n", + "\n", + " print(f\"\\nError sample size: {len(error_sample_type)} examples\")\n", + " print(\"\\n--- Sample type misclassifications ---\")\n", + " display_cols = [\"text\", true_col_type]\n", + " if pred_col_type in error_sample_type.columns:\n", + " display_cols.append(pred_col_type)\n", + " for _, row in error_sample_type.head(15).iterrows():\n", + " true_l = row[true_col_type]\n", + " pred_l = row.get(pred_col_type, \"?\")\n", + " print(f\" [True={true_l:20s} Pred={pred_l:20s}] {row['text'][:90]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IzgDVYhfuKCG" + }, + "outputs": [], + "source": [ + "if error_source_type is not None:\n", + " def categorize_type_error(text: str, true_label: str, pred_label: str) -> str:\n", + " \"\"\"Heuristic failure mode categorization for type task.\"\"\"\n", + " text_lower = text.lower()\n", + " # Strategy overlap: sarcasm ↔ irony ↔ satire are frequently confused\n", + " overlap_pairs = {\n", + " frozenset({\"sarcasm\", \"irony\"}),\n", + " frozenset({\"sarcasm\", \"satire\"}),\n", + " frozenset({\"irony\", \"satire\"}),\n", + " }\n", + " if frozenset({true_label, pred_label}) in overlap_pairs:\n", + " return \"strategy_semantic_overlap\"\n", + " if \"rhetorical_question\" in [true_label, pred_label]:\n", + " if \"?\" in text:\n", + " return \"rhetorical_vs_other_with_question\"\n", + " return \"rhetorical_without_question_mark\"\n", + " if true_label in [\"overstatement\", \"understatement\"] and pred_label in [\"sarcasm\", \"irony\"]:\n", + " return \"scale_confusion_with_sarcasm\"\n", + " if \"report\" in text_lower or \"according\" in text_lower:\n", + " return \"generation_artifact_formal_phrasing\"\n", + " return \"lexical_ambiguity\"\n", + "\n", + " if pred_col_type in error_sample_type.columns:\n", + " error_sample_type[\"failure_mode\"] = error_sample_type.apply(\n", + " lambda r: categorize_type_error(r[\"text\"], r[true_col_type], r[pred_col_type]),\n", + " axis=1\n", + " )\n", + " print(\"Failure mode distribution:\")\n", + " print(error_sample_type[\"failure_mode\"].value_counts())\n", + "\n", + " error_sample_type.to_csv(REPORTS_DIR / \"error_examples_type.csv\", index=False)\n", + " print(\"\\nSaved: outputs/reports/error_examples_type.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NEmnBPTtuKCG" + }, + "source": [ + "## 5. Generate Final Reports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xH7naLCouKCG" + }, + "outputs": [], + "source": [ + "def fmt(v) -> str:\n", + " \"\"\"Format a metric value for markdown.\"\"\"\n", + " if v is None:\n", + " return \"N/A\"\n", + " return f\"{v:.4f}\"\n", + "\n", + "\n", + "def build_model_comparison_md() -> str:\n", + " lines = []\n", + " lines.append(\"# Model Comparison Report\")\n", + " lines.append(\"\")\n", + " lines.append(\"> Auto-generated from outputs of notebooks 02–04.\")\n", + " lines.append(\"\")\n", + "\n", + " # Binary task table\n", + " lines.append(\"## Task A — Binary Classification (Test Set)\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Model | Accuracy | Precision (M) | Recall (M) | F1 (Macro) | F1 (Weighted) |\")\n", + " lines.append(\"|-------|----------|--------------|------------|------------|--------------|\")\n", + " for name, m in model_map_bin:\n", + " r = extract_test_metrics(m)\n", + " lines.append(\n", + " f\"| {name} | {fmt(r['accuracy'])} | {fmt(r['precision_macro'])} | \"\n", + " f\"{fmt(r['recall_macro'])} | {fmt(r['f1_macro'])} | {fmt(r['f1_weighted'])} |\"\n", + " )\n", + " lines.append(\"\")\n", + " lines.append(\"_Primary metric: Macro-F1. Best value bolded (manually after review)._\")\n", + " lines.append(\"\")\n", + "\n", + " # Type task table\n", + " lines.append(\"## Task B — Sarcasm Type Classification (Test Set)\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Model | Accuracy | Precision (M) | Recall (M) | F1 (Macro) | F1 (Weighted) |\")\n", + " lines.append(\"|-------|----------|--------------|------------|------------|--------------|\")\n", + " for name, m in model_map_type:\n", + " r = extract_test_metrics(m)\n", + " lines.append(\n", + " f\"| {name} | {fmt(r['accuracy'])} | {fmt(r['precision_macro'])} | \"\n", + " f\"{fmt(r['recall_macro'])} | {fmt(r['f1_macro'])} | {fmt(r['f1_weighted'])} |\"\n", + " )\n", + " lines.append(\"\")\n", + "\n", + " # Recommendation\n", + " lines.append(\"## Recommendation\")\n", + " lines.append(\"\")\n", + "\n", + " # Find best binary model\n", + " best_bin_name, best_bin_f1 = \"N/A\", -1\n", + " for name, m in model_map_bin:\n", + " r = extract_test_metrics(m)\n", + " if r[\"f1_macro\"] is not None and r[\"f1_macro\"] > best_bin_f1:\n", + " best_bin_f1 = r[\"f1_macro\"]\n", + " best_bin_name = name\n", + "\n", + " best_type_name, best_type_f1 = \"N/A\", -1\n", + " for name, m in model_map_type:\n", + " r = extract_test_metrics(m)\n", + " if r[\"f1_macro\"] is not None and r[\"f1_macro\"] > best_type_f1:\n", + " best_type_f1 = r[\"f1_macro\"]\n", + " best_type_name = name\n", + "\n", + " lines.append(f\"### Best model for Binary Task: **{best_bin_name}** (Macro-F1 = {fmt(best_bin_f1)})\")\n", + " lines.append(\"\")\n", + " lines.append(f\"### Best model for Type Task: **{best_type_name}** (Macro-F1 = {fmt(best_type_f1)})\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Trade-offs\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Aspect | TF-IDF + LR | Naive Bayes | DistilBERT |\")\n", + " lines.append(\"|--------|------------|-------------|-----------|\")\n", + " lines.append(\"| Training speed | Fast (minutes) | Very fast (seconds) | Slow (hours) |\")\n", + " lines.append(\"| Inference speed | Very fast | Very fast | Moderate |\")\n", + " lines.append(\"| Interpretability | High (feature weights) | High (log-probs) | Low (black-box) |\")\n", + " lines.append(\"| Handles context | No | No | Yes (self-attention) |\")\n", + " lines.append(\"| Handles rare words | Via TF-IDF | Via smoothing | Via sub-word tokenization |\")\n", + " lines.append(\"| GPU required | No | No | Recommended |\")\n", + " lines.append(\"\")\n", + " lines.append(\"**Deployment recommendation**: Use TF-IDF+LR if real-time speed and interpretability are priorities.\")\n", + " lines.append(\"Use DistilBERT for maximum accuracy when GPU inference is available.\")\n", + "\n", + " return \"\\n\".join(lines)\n", + "\n", + "\n", + "comparison_md = build_model_comparison_md()\n", + "with open(REPORTS_DIR / \"model_comparison.md\", \"w\", encoding=\"utf-8\") as f:\n", + " f.write(comparison_md)\n", + "\n", + "print(\"Saved: outputs/reports/model_comparison.md\")\n", + "print(\"\\nPreview:\")\n", + "print(comparison_md[:2000])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "CmgZF4S1uKCH" + }, + "outputs": [], + "source": [ + "def build_error_analysis_md() -> str:\n", + " lines = []\n", + " lines.append(\"# Error Analysis Report\")\n", + " lines.append(\"\")\n", + " lines.append(\"> Auto-generated from model predictions. Examples drawn from test set.\")\n", + " lines.append(\"\")\n", + "\n", + " # ── Binary ────────────────────────────────────────────────────────────────\n", + " lines.append(\"## Task A — Binary Classification Errors\")\n", + " lines.append(\"\")\n", + "\n", + " if error_source_bin is not None:\n", + " err_df = error_source_bin[error_source_bin[\"correct\"] == 0]\n", + " n_fp = len(err_df[err_df[\"binary_label\"] == 0])\n", + " n_fn = len(err_df[err_df[\"binary_label\"] == 1])\n", + " lines.append(f\"**Model**: {error_model_bin} | **Total errors**: {len(err_df):,} | FP: {n_fp:,} | FN: {n_fn:,}\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Failure Mode Summary\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Failure Mode | Description |\")\n", + " lines.append(\"|-------------|-------------|\")\n", + " lines.append(\"| Lexical ambiguity | Sarcasm/irony requires pragmatic context beyond lexical cues |\")\n", + " lines.append(\"| Neutral reporting style confused | Formal generated text mimics neutral news, but still classified as sarcastic |\")\n", + " lines.append(\"| Positive framing confused | Genuine positive news shares superlative vocabulary with ironic praise |\")\n", + " lines.append(\"| Rhetorical phrasing | Questions that look rhetorical but are literal |\")\n", + " lines.append(\"| Very short text | Too little context for confident classification |\")\n", + " lines.append(\"\")\n", + " lines.append(\"### False Positive Examples (Non-sarcastic predicted as sarcastic)\")\n", + " lines.append(\"\")\n", + " fp_examples = error_source_bin[\n", + " (error_source_bin[\"correct\"] == 0) & (error_source_bin[\"binary_label\"] == 0)\n", + " ].head(10)\n", + " for _, row in fp_examples.iterrows():\n", + " lines.append(f\"- `{row['text']}`\")\n", + " lines.append(\"\")\n", + " lines.append(\"### False Negative Examples (Sarcastic predicted as non-sarcastic)\")\n", + " lines.append(\"\")\n", + " fn_examples = error_source_bin[\n", + " (error_source_bin[\"correct\"] == 0) & (error_source_bin[\"binary_label\"] == 1)\n", + " ].head(10)\n", + " for _, row in fn_examples.iterrows():\n", + " lines.append(f\"- `{row['text']}`\")\n", + " else:\n", + " lines.append(\"_Binary predictions not available. Run at least one model._\")\n", + "\n", + " lines.append(\"\")\n", + "\n", + " # ── Type ──────────────────────────────────────────────────────────────────\n", + " lines.append(\"## Task B — Sarcasm Type Classification Errors\")\n", + " lines.append(\"\")\n", + "\n", + " if error_source_type is not None:\n", + " err_df_t = error_source_type[error_source_type[\"correct\"] == 0]\n", + " lines.append(f\"**Model**: {error_model_type} | **Total errors**: {len(err_df_t):,}\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Failure Mode Summary\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Failure Mode | Description |\")\n", + " lines.append(\"|-------------|-------------|\")\n", + " lines.append(\"| Strategy semantic overlap | sarcasm/irony/satire share similar surface forms; labels conflated |\")\n", + " lines.append(\"| Scale confusion | overstatement/understatement confused with sarcasm due to exaggeration cues |\")\n", + " lines.append(\"| Rhetorical vs. other | rhetorical_question confused with irony/sarcasm when ? is absent |\")\n", + " lines.append(\"| Generation artifact | formal paraphrase style shifts text away from original strategy markers |\")\n", + " lines.append(\"| Lexical ambiguity | strategy relies on world knowledge rather than surface vocabulary |\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Key Insight\")\n", + " lines.append(\"\")\n", + " lines.append(\"The primary failure mode is **strategy semantic overlap**: sarcasm, irony, and satire\")\n", + " lines.append(\"are conceptually related and frequently share similar linguistic surface patterns.\")\n", + " lines.append(\"The label `sarcasm` as a strategy within a sarcasm dataset introduces circularity.\")\n", + " lines.append(\"The `rhetorical_question` class is syntactically distinctive (ends with ?) and should\")\n", + " lines.append(\"be easier to classify; errors in this class suggest the classifier may ignore punctuation.\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Sample Misclassifications\")\n", + " lines.append(\"\")\n", + " sample_t = error_source_type[error_source_type[\"correct\"] == 0].head(20)\n", + " true_c = \"type_label\"\n", + " pred_c = \"predicted_label\" if \"predicted_label\" in sample_t.columns else \"predicted\"\n", + " for _, row in sample_t.iterrows():\n", + " true_l = row[true_c]\n", + " pred_l = row.get(pred_c, \"?\")\n", + " lines.append(f\"- **True**: {true_l} | **Pred**: {pred_l} | `{row['text'][:100]}`\")\n", + " else:\n", + " lines.append(\"_Type predictions not available. Run at least one model._\")\n", + "\n", + " lines.append(\"\")\n", + " lines.append(\"## Summary of Observations\")\n", + " lines.append(\"\")\n", + " lines.append(\"1. **Binary task** is relatively tractable — TheOnion writing style has strong lexical signatures\")\n", + " lines.append(\"2. **Generated headlines** (is_generated=1) may fool classifiers trained mainly on original text\")\n", + " lines.append(\"3. **Type task** is fundamentally harder because strategies are not mutually exclusive\")\n", + " lines.append(\"4. **Class imbalance** (rhetorical_question = 3.7%) is a significant challenge\")\n", + " lines.append(\"5. **BERT** should better capture contextual incongruence; classical models rely on surface vocabulary\")\n", + "\n", + " return \"\\n\".join(lines)\n", + "\n", + "\n", + "error_md = build_error_analysis_md()\n", + "with open(REPORTS_DIR / \"error_analysis.md\", \"w\", encoding=\"utf-8\") as f:\n", + " f.write(error_md)\n", + "\n", + "print(\"Saved: outputs/reports/error_analysis.md\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fdUb27BQuKCH" + }, + "outputs": [], + "source": [ + "print(\"====== ERROR ANALYSIS COMPLETE ======\")\n", + "print()\n", + "print(\"Saved artifacts:\")\n", + "for p in sorted(REPORTS_DIR.iterdir()):\n", + " print(f\" {p.relative_to(ROOT)}\")\n", + "print()\n", + "print(\"Pipeline complete. See outputs/reports/model_comparison.md for final results.\")" + ] + } + ] +} \ No newline at end of file From 83353973cef606e63f5d775b12da46031a234e68 Mon Sep 17 00:00:00 2001 From: reallyeasy Date: Tue, 3 Mar 2026 17:13:37 +0800 Subject: [PATCH 5/6] Fix: fix formatting of ipynb file (finally works) --- colab_package/sarcasm_classification.ipynb | 8789 +++++++++-------- .../sarcasm_classification_fixed.ipynb | 4576 --------- 2 files changed, 4430 insertions(+), 8935 deletions(-) delete mode 100644 colab_package/sarcasm_classification_fixed.ipynb diff --git a/colab_package/sarcasm_classification.ipynb b/colab_package/sarcasm_classification.ipynb index 49e8c8c..31b7923 100644 --- a/colab_package/sarcasm_classification.ipynb +++ b/colab_package/sarcasm_classification.ipynb @@ -1,4505 +1,4576 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "w41uXUSeuKBO" - }, - "source": [ - "# Sarcasm Classification — Complete Pipeline\n", - "\n", - "**Sections**:\n", - "1. Setup & Data Preparation\n", - "2. TF-IDF + Logistic Regression Baseline\n", - "3. Naive Bayes Baseline\n", - "4. BERT / DistilBERT Classification\n", - "5. Error Analysis & Model Comparison\n", - "\n", - "**Run all cells in order (Runtime → Run all).**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "1xG7cJfAuKBR", - "outputId": "12069dde-b810-4ab5-f89b-5fa1af05cf8c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cwd: /content\n", - "files in cwd: ['.config', 'sarcasm_pairs_step35_clean.jsonl', 'sample_data']\n", - "Data : /content/sarcasm_pairs_step35_clean.jsonl\n", - "Root : /content\n", - "Output : /content/outputs\n" - ] - } - ], - "source": [ - "# ============================================================\n", - "# SETUP — imports, file upload, paths\n", - "# ============================================================\n", - "from __future__ import annotations\n", - "import json, hashlib, random, os, warnings, shutil\n", - "from dataclasses import dataclass\n", - "from pathlib import Path\n", - "from collections import Counter\n", - "from urllib.parse import urlparse\n", - "from typing import Optional\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.gridspec as gridspec\n", - "import seaborn as sns\n", - "\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.naive_bayes import MultinomialNB, ComplementNB\n", - "from sklearn.model_selection import GridSearchCV, GroupKFold\n", - "from sklearn.metrics import (\n", - " accuracy_score, precision_score, recall_score,\n", - " f1_score, classification_report, confusion_matrix,\n", - ")\n", - "\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "SEED = 42\n", - "random.seed(SEED)\n", - "np.random.seed(SEED)\n", - "\n", - "# ── Locate or upload the JSONL data file ─────────────────────────────────\n", - "FILENAME = \"sarcasm_pairs_step35_clean.jsonl\"\n", - "\n", - "def _locate_file(filename):\n", - " candidates = []\n", - " for root in [Path.cwd()] + list(Path.cwd().parents):\n", - " for sub in [\n", - " Path(\"data\") / \"processed\" / filename,\n", - " Path(\"data\") / filename,\n", - " Path(filename),\n", - " ]:\n", - " candidates.append(root / sub)\n", - " for p in [\n", - " Path(\"/content\") / filename,\n", - " Path(\"/mnt/data\") / filename,\n", - " ]:\n", - " candidates.append(p)\n", - " _c = Path('/content')\n", - " for p in (_c.rglob(filename) if _c.exists() else []):\n", - " candidates.append(p)\n", - " for p in candidates:\n", - " if p.is_file():\n", - " return p\n", - " return None\n", - "\n", - "print(f'cwd: {Path.cwd()}')\n", - "print(f'files in cwd: {[p.name for p in Path.cwd().iterdir()][:10]}')\n", - "\n", - "DATA_FILE = _locate_file(FILENAME)\n", - "if DATA_FILE is None:\n", - " try:\n", - " from google.colab import files as _cf\n", - " print(f\"Upload {FILENAME!r}:\")\n", - " _up = _cf.upload()\n", - " if not _up:\n", - " raise RuntimeError(\"No file uploaded.\")\n", - " _name = list(_up.keys())[0]\n", - " DATA_FILE = Path(\"/content\") / FILENAME\n", - " if Path(_name) != DATA_FILE:\n", - " shutil.move(_name, str(DATA_FILE))\n", - " print(f\"Saved to {DATA_FILE}\")\n", - " except ImportError:\n", - " raise FileNotFoundError(\n", - " f\"Cannot find {FILENAME!r}. Place it in the same folder as this notebook.\"\n", - " )\n", - "\n", - "# ── Project root + all output directories ────────────────────────────────\n", - "def _find_root(data_file):\n", - " for parent in [data_file.parent] + list(data_file.parents):\n", - " if any((parent / m).exists() for m in [\"outputs\",\"notebooks\",\"data\"]):\n", - " return parent\n", - " return data_file.parent\n", - "\n", - "ROOT = _find_root(DATA_FILE)\n", - "\n", - "OUT_DATASETS = ROOT / 'outputs' / 'datasets'\n", - "OUT_SPLITS = ROOT / 'outputs' / 'splits'\n", - "OUT_TFIDF = ROOT / 'outputs' / 'classical' / 'tfidf_lr'\n", - "OUT_NB = ROOT / 'outputs' / 'classical' / 'naive_bayes'\n", - "BERT_OUT = ROOT / 'outputs' / 'bert'\n", - "REPORTS_DIR = ROOT / 'outputs' / 'reports'\n", - "SPLITS = OUT_SPLITS\n", - "\n", - "for d in [OUT_DATASETS, OUT_SPLITS, OUT_TFIDF, OUT_NB, BERT_OUT, REPORTS_DIR]:\n", - " d.mkdir(parents=True, exist_ok=True)\n", - "\n", - "print(f\"Data : {DATA_FILE}\")\n", - "print(f\"Root : {ROOT}\")\n", - "print(f\"Output : {ROOT / 'outputs'}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xDqObfuPuKBU" - }, - "source": [ - "---\n", - "# Part 1 — Data Preparation" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RVi4jvpmuKBV" - }, - "source": [ - "# Notebook 01 — Data Preparation\n", - "\n", - "**Purpose**: Parse JSONL dataset, derive binary and type labels, perform audit checks, generate group-safe train/val/test splits.\n", - "\n", - "**Outputs**:\n", - "- `outputs/datasets/binary_dataset.csv`\n", - "- `outputs/datasets/type_dataset.csv`\n", - "- `outputs/splits/train_binary.csv`, `val_binary.csv`, `test_binary.csv`\n", - "- `outputs/splits/train_type.csv`, `val_type.csv`, `test_type.csv`\n", - "- `outputs/splits/split_metadata.json`" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "egvV3LLJuKBV" - }, - "source": [ - "## 1. Load and Validate Raw Data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "oqhoOTiguKBV", - "outputId": "9211b95a-f157-40ce-9d18-7275e0c157a3" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded rows : 28,333\n", - "Error rows : 0\n" - ] - } - ], - "source": [ - "REQUIRED_FIELDS = {\"original_headline\", \"generated_headline\", \"strategy\", \"type\", \"model_used\", \"article_link\"}\n", - "ALLOWED_TYPES = {\"sarcastic_to_non\", \"non_to_sarcastic\"}\n", - "ALLOWED_STRATS = {\"irony\", \"overstatement\", \"rhetorical_question\", \"sarcasm\", \"satire\", \"understatement\"}\n", - "\n", - "raw_rows = []\n", - "errors = []\n", - "\n", - "with open(DATA_FILE, \"r\", encoding=\"utf-8\") as f:\n", - " for line_num, line in enumerate(f):\n", - " line = line.strip()\n", - " if not line:\n", - " continue\n", - " try:\n", - " row = json.loads(line)\n", - " except json.JSONDecodeError as e:\n", - " errors.append((line_num, f\"JSON parse error: {e}\"))\n", - " continue\n", - "\n", - " # Schema check\n", - " missing = REQUIRED_FIELDS - set(row.keys())\n", - " if missing:\n", - " errors.append((line_num, f\"Missing fields: {missing}\"))\n", - " continue\n", - "\n", - " # Value checks\n", - " if row[\"type\"] not in ALLOWED_TYPES:\n", - " errors.append((line_num, f\"Unknown type: {row['type']!r}\"))\n", - " continue\n", - " if row[\"strategy\"] not in ALLOWED_STRATS:\n", - " errors.append((line_num, f\"Unknown strategy: {row['strategy']!r}\"))\n", - " continue\n", - "\n", - " row[\"_line_num\"] = line_num\n", - " raw_rows.append(row)\n", - "\n", - "print(f\"Loaded rows : {len(raw_rows):,}\")\n", - "print(f\"Error rows : {len(errors)}\")\n", - "if errors:\n", - " for ln, msg in errors[:5]:\n", - " print(f\" Line {ln}: {msg}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yvg9qFhouKBW" - }, - "source": [ - "## 2. 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Setup & Data Preparation\n", + "2. TF-IDF + Logistic Regression Baseline\n", + "3. Naive Bayes Baseline\n", + "4. BERT / DistilBERT Classification\n", + "5. Error Analysis & Model Comparison\n", + "\n", + "**Run all cells in order (Runtime → Run all).**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "1xG7cJfAuKBR", + "outputId": "12069dde-b810-4ab5-f89b-5fa1af05cf8c" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "KISNWAQhuKBW", - "outputId": "e8755fd1-8661-4393-f3a9-429cb93065fd" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=== Type distribution ===\n", - " non_to_sarcastic: 14,925 (52.7%)\n", - " sarcastic_to_non: 13,408 (47.3%)\n", - "\n", - "=== Strategy distribution ===\n", - " sarcasm: 8,699 (30.7%)\n", - " irony: 6,102 (21.5%)\n", - " satire: 5,224 (18.4%)\n", - " overstatement: 3,976 (14.0%)\n", - " understatement: 3,295 (11.6%)\n", - " rhetorical_question: 1,037 (3.7%)\n", - "\n", - "=== Model distribution ===\n", - " stepfun/step-3.5-flash:free: 28,333\n" - ] - } - ], - "source": [ - "# ── Type and Strategy distributions ──────────────────────────────────────────\n", - "type_counts = Counter(r[\"type\"] for r in raw_rows)\n", - "strategy_counts = Counter(r[\"strategy\"] for r in raw_rows)\n", - "model_counts = Counter(r[\"model_used\"] for r in raw_rows)\n", - "\n", - "print(\"=== Type distribution ===\")\n", - "for k, v in type_counts.most_common():\n", - " print(f\" {k}: {v:,} ({v/len(raw_rows)*100:.1f}%)\")\n", - "\n", - "print(\"\\n=== Strategy distribution ===\")\n", - "for k, v in strategy_counts.most_common():\n", - " print(f\" {k}: {v:,} ({v/len(raw_rows)*100:.1f}%)\")\n", - "\n", - "print(\"\\n=== Model distribution ===\")\n", - "for k, v in model_counts.most_common():\n", - " print(f\" {k}: {v:,}\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "cwd: /content\n", + "files in cwd: ['.config', 'sarcasm_pairs_step35_clean.jsonl', 'sample_data']\n", + "Data : /content/sarcasm_pairs_step35_clean.jsonl\n", + "Root : /content\n", + "Output : /content/outputs\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# SETUP — imports, file upload, paths\n", + "# ============================================================\n", + "from __future__ import annotations\n", + "import json, hashlib, random, os, warnings, shutil\n", + "from dataclasses import dataclass\n", + "from pathlib import Path\n", + "from collections import Counter\n", + "from urllib.parse import urlparse\n", + "from typing import Optional\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "import seaborn as sns\n", + "\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.naive_bayes import MultinomialNB, ComplementNB\n", + "from sklearn.model_selection import GridSearchCV, GroupKFold\n", + "from sklearn.metrics import (\n", + " accuracy_score, precision_score, recall_score,\n", + " f1_score, classification_report, confusion_matrix,\n", + ")\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "SEED = 42\n", + "random.seed(SEED)\n", + "np.random.seed(SEED)\n", + "\n", + "# ── Locate or upload the JSONL data file ─────────────────────────────────\n", + "FILENAME = \"sarcasm_pairs_step35_clean.jsonl\"\n", + "\n", + "def _locate_file(filename):\n", + " candidates = []\n", + " for root in [Path.cwd()] + list(Path.cwd().parents):\n", + " for sub in [\n", + " Path(\"data\") / \"processed\" / filename,\n", + " Path(\"data\") / filename,\n", + " Path(filename),\n", + " ]:\n", + " candidates.append(root / sub)\n", + " for p in [\n", + " Path(\"/content\") / filename,\n", + " Path(\"/mnt/data\") / filename,\n", + " ]:\n", + " candidates.append(p)\n", + " _c = Path('/content')\n", + " for p in (_c.rglob(filename) if _c.exists() else []):\n", + " candidates.append(p)\n", + " for p in candidates:\n", + " if p.is_file():\n", + " return p\n", + " return None\n", + "\n", + "print(f'cwd: {Path.cwd()}')\n", + "print(f'files in cwd: {[p.name for p in Path.cwd().iterdir()][:10]}')\n", + "\n", + "DATA_FILE = _locate_file(FILENAME)\n", + "if DATA_FILE is None:\n", + " try:\n", + " from google.colab import files as _cf\n", + " print(f\"Upload {FILENAME!r}:\")\n", + " _up = _cf.upload()\n", + " if not _up:\n", + " raise RuntimeError(\"No file uploaded.\")\n", + " _name = list(_up.keys())[0]\n", + " DATA_FILE = Path(\"/content\") / FILENAME\n", + " if Path(_name) != DATA_FILE:\n", + " shutil.move(_name, str(DATA_FILE))\n", + " print(f\"Saved to {DATA_FILE}\")\n", + " except ImportError:\n", + " raise FileNotFoundError(\n", + " f\"Cannot find {FILENAME!r}. Place it in the same folder as this notebook.\"\n", + " )\n", + "\n", + "# ── Project root + all output directories ────────────────────────────────\n", + "def _find_root(data_file):\n", + " for parent in [data_file.parent] + list(data_file.parents):\n", + " if any((parent / m).exists() for m in [\"outputs\",\"notebooks\",\"data\"]):\n", + " return parent\n", + " return data_file.parent\n", + "\n", + "ROOT = _find_root(DATA_FILE)\n", + "\n", + "OUT_DATASETS = ROOT / 'outputs' / 'datasets'\n", + "OUT_SPLITS = ROOT / 'outputs' / 'splits'\n", + "OUT_TFIDF = ROOT / 'outputs' / 'classical' / 'tfidf_lr'\n", + "OUT_NB = ROOT / 'outputs' / 'classical' / 'naive_bayes'\n", + "BERT_OUT = ROOT / 'outputs' / 'bert'\n", + "REPORTS_DIR = ROOT / 'outputs' / 'reports'\n", + "SPLITS = OUT_SPLITS\n", + "\n", + "for d in [OUT_DATASETS, OUT_SPLITS, OUT_TFIDF, OUT_NB, BERT_OUT, REPORTS_DIR]:\n", + " d.mkdir(parents=True, exist_ok=True)\n", + "\n", + "print(f\"Data : {DATA_FILE}\")\n", + "print(f\"Root : {ROOT}\")\n", + "print(f\"Output : {ROOT / 'outputs'}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xDqObfuPuKBU" + }, + "source": [ + "---\n", + "# Part 1 — Data Preparation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RVi4jvpmuKBV" + }, + "source": [ + "# Notebook 01 — Data Preparation\n", + "\n", + "**Purpose**: Parse JSONL dataset, derive binary and type labels, perform audit checks, generate group-safe train/val/test splits.\n", + "\n", + "**Outputs**:\n", + "- `outputs/datasets/binary_dataset.csv`\n", + "- `outputs/datasets/type_dataset.csv`\n", + "- `outputs/splits/train_binary.csv`, `val_binary.csv`, `test_binary.csv`\n", + "- `outputs/splits/train_type.csv`, `val_type.csv`, `test_type.csv`\n", + "- `outputs/splits/split_metadata.json`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "egvV3LLJuKBV" + }, + "source": [ + "## 1. Load and Validate Raw Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "oqhoOTiguKBV", + "outputId": "9211b95a-f157-40ce-9d18-7275e0c157a3" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "28wpsTTnuKBX", - "outputId": "bdcc2561-9baa-41a3-bac8-14101d7e4596" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Duplicate original_headlines : 70\n", - "Duplicate generated_headlines: 1\n", - "Duplicate article_links : 2\n" - ] - } - ], - "source": [ - "# ── Duplicate checks ──────────────────────────────────────────────────────────\n", - "orig_headlines = [r[\"original_headline\"] for r in raw_rows]\n", - "gen_headlines = [r[\"generated_headline\"] for r in raw_rows]\n", - "article_links = [r[\"article_link\"] for r in raw_rows]\n", - "\n", - "dup_orig = len(orig_headlines) - len(set(orig_headlines))\n", - "dup_gen = len(gen_headlines) - len(set(gen_headlines))\n", - "dup_article = len(article_links) - len(set(article_links))\n", - "\n", - "print(f\"Duplicate original_headlines : {dup_orig}\")\n", - "print(f\"Duplicate generated_headlines: {dup_gen}\")\n", - "print(f\"Duplicate article_links : {dup_article}\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Loaded rows : 28,333\n", + "Error rows : 0\n" + ] + } + ], + "source": [ + "REQUIRED_FIELDS = {\"original_headline\", \"generated_headline\", \"strategy\", \"type\", \"model_used\", \"article_link\"}\n", + "ALLOWED_TYPES = {\"sarcastic_to_non\", \"non_to_sarcastic\"}\n", + "ALLOWED_STRATS = {\"irony\", \"overstatement\", \"rhetorical_question\", \"sarcasm\", \"satire\", \"understatement\"}\n", + "\n", + "raw_rows = []\n", + "errors = []\n", + "\n", + "with open(DATA_FILE, \"r\", encoding=\"utf-8\") as f:\n", + " for line_num, line in enumerate(f):\n", + " line = line.strip()\n", + " if not line:\n", + " continue\n", + " try:\n", + " row = json.loads(line)\n", + " except json.JSONDecodeError as e:\n", + " errors.append((line_num, f\"JSON parse error: {e}\"))\n", + " continue\n", + "\n", + " # Schema check\n", + " missing = REQUIRED_FIELDS - set(row.keys())\n", + " if missing:\n", + " errors.append((line_num, f\"Missing fields: {missing}\"))\n", + " continue\n", + "\n", + " # Value checks\n", + " if row[\"type\"] not in ALLOWED_TYPES:\n", + " errors.append((line_num, f\"Unknown type: {row['type']!r}\"))\n", + " continue\n", + " if row[\"strategy\"] not in ALLOWED_STRATS:\n", + " errors.append((line_num, f\"Unknown strategy: {row['strategy']!r}\"))\n", + " continue\n", + "\n", + " row[\"_line_num\"] = line_num\n", + " raw_rows.append(row)\n", + "\n", + "print(f\"Loaded rows : {len(raw_rows):,}\")\n", + "print(f\"Error rows : {len(errors)}\")\n", + "if errors:\n", + " for ln, msg in errors[:5]:\n", + " print(f\" Line {ln}: {msg}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yvg9qFhouKBW" + }, + "source": [ + "## 2. Dataset Audit" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "KISNWAQhuKBW", + "outputId": "e8755fd1-8661-4393-f3a9-429cb93065fd" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "SLUFPDIHuKBX", - "outputId": "3d7b15de-a43f-410f-a381-6a9544171b45" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "original_headline : 0 nulls, 0 empty strings\n", - "generated_headline : 0 nulls, 0 empty strings\n", - "strategy : 0 nulls, 0 empty strings\n", - "type : 0 nulls, 0 empty strings\n", - "model_used : 0 nulls, 0 empty strings\n", - "article_link : 0 nulls, 0 empty strings\n" - ] - } - ], - "source": [ - "# ── Null / empty checks ───────────────────────────────────────────────────────\n", - "for field in [\"original_headline\", \"generated_headline\", \"strategy\", \"type\", \"model_used\", \"article_link\"]:\n", - " null_count = sum(1 for r in raw_rows if r.get(field) is None)\n", - " empty_count = sum(1 for r in raw_rows if r.get(field) == \"\")\n", - " print(f\"{field:25s}: {null_count} nulls, {empty_count} empty strings\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "=== Type distribution ===\n", + " non_to_sarcastic: 14,925 (52.7%)\n", + " sarcastic_to_non: 13,408 (47.3%)\n", + "\n", + "=== Strategy distribution ===\n", + " sarcasm: 8,699 (30.7%)\n", + " irony: 6,102 (21.5%)\n", + " satire: 5,224 (18.4%)\n", + " overstatement: 3,976 (14.0%)\n", + " understatement: 3,295 (11.6%)\n", + " rhetorical_question: 1,037 (3.7%)\n", + "\n", + "=== Model distribution ===\n", + " stepfun/step-3.5-flash:free: 28,333\n" + ] + } + ], + "source": [ + "# ── Type and Strategy distributions ──────────────────────────────────────────\n", + "type_counts = Counter(r[\"type\"] for r in raw_rows)\n", + "strategy_counts = Counter(r[\"strategy\"] for r in raw_rows)\n", + "model_counts = Counter(r[\"model_used\"] for r in raw_rows)\n", + "\n", + "print(\"=== Type distribution ===\")\n", + "for k, v in type_counts.most_common():\n", + " print(f\" {k}: {v:,} ({v/len(raw_rows)*100:.1f}%)\")\n", + "\n", + "print(\"\\n=== Strategy distribution ===\")\n", + "for k, v in strategy_counts.most_common():\n", + " print(f\" {k}: {v:,} ({v/len(raw_rows)*100:.1f}%)\")\n", + "\n", + "print(\"\\n=== Model distribution ===\")\n", + "for k, v in model_counts.most_common():\n", + " print(f\" {k}: {v:,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "28wpsTTnuKBX", + "outputId": "bdcc2561-9baa-41a3-bac8-14101d7e4596" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 524 - }, - "id": "E-B0fc5vuKBX", - "outputId": "a7845ca8-655c-48df-c59b-28b16307b91d" - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved: outputs/datasets/distributions.png\n" - ] - } - ], - "source": [ - "# ── Strategy distribution plot ────────────────────────────────────────────────\n", - "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", - "\n", - "# Type dist\n", - "ax = axes[0]\n", - "labels, counts = zip(*sorted(type_counts.items(), key=lambda x: -x[1]))\n", - "ax.barh(labels, counts, color=[\"steelblue\", \"coral\"])\n", - "ax.set_title(\"Row-level Type Distribution\", fontsize=14)\n", - "ax.set_xlabel(\"Count\")\n", - "for i, c in enumerate(counts):\n", - " ax.text(c + 50, i, f\"{c:,}\", va=\"center\")\n", - "\n", - "# Strategy dist\n", - "ax = axes[1]\n", - "labels2, counts2 = zip(*sorted(strategy_counts.items(), key=lambda x: -x[1]))\n", - "ax.barh(labels2, counts2, color=\"steelblue\")\n", - "ax.set_title(\"Strategy Distribution (Type Task Labels)\", fontsize=14)\n", - "ax.set_xlabel(\"Count\")\n", - "for i, c in enumerate(counts2):\n", - " ax.text(c + 30, i, f\"{c:,}\", va=\"center\")\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(OUT_DATASETS / \"distributions.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "print(\"Saved: outputs/datasets/distributions.png\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Duplicate original_headlines : 70\n", + "Duplicate generated_headlines: 1\n", + "Duplicate article_links : 2\n" + ] + } + ], + "source": [ + "# ── Duplicate checks ──────────────────────────────────────────────────────────\n", + "orig_headlines = [r[\"original_headline\"] for r in raw_rows]\n", + "gen_headlines = [r[\"generated_headline\"] for r in raw_rows]\n", + "article_links = [r[\"article_link\"] for r in raw_rows]\n", + "\n", + "dup_orig = len(orig_headlines) - len(set(orig_headlines))\n", + "dup_gen = len(gen_headlines) - len(set(gen_headlines))\n", + "dup_article = len(article_links) - len(set(article_links))\n", + "\n", + "print(f\"Duplicate original_headlines : {dup_orig}\")\n", + "print(f\"Duplicate generated_headlines: {dup_gen}\")\n", + "print(f\"Duplicate article_links : {dup_article}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "SLUFPDIHuKBX", + "outputId": "3d7b15de-a43f-410f-a381-6a9544171b45" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "JxKcjhRSuKBY" - }, - "source": [ - "## 3. Label Derivation and Dataset Expansion" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "original_headline : 0 nulls, 0 empty strings\n", + "generated_headline : 0 nulls, 0 empty strings\n", + "strategy : 0 nulls, 0 empty strings\n", + "type : 0 nulls, 0 empty strings\n", + "model_used : 0 nulls, 0 empty strings\n", + "article_link : 0 nulls, 0 empty strings\n" + ] + } + ], + "source": [ + "# ── Null / empty checks ───────────────────────────────────────────────────────\n", + "for field in [\"original_headline\", \"generated_headline\", \"strategy\", \"type\", \"model_used\", \"article_link\"]:\n", + " null_count = sum(1 for r in raw_rows if r.get(field) is None)\n", + " empty_count = sum(1 for r in raw_rows if r.get(field) == \"\")\n", + " print(f\"{field:25s}: {null_count} nulls, {empty_count} empty strings\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 524 }, + "id": "E-B0fc5vuKBX", + "outputId": "a7845ca8-655c-48df-c59b-28b16307b91d" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nukGQcmKuKBY", - "outputId": "2fcfb601-8ad0-4876-b813-c21b007bb164" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Binary dataset : 56,666 samples\n", - " sarcastic (1): 28,333\n", - " non-sarc (0): 28,333\n", - "\n", - "Type dataset : 28,333 samples\n", - "type_label\n", - "sarcasm 8699\n", - "irony 6102\n", - "satire 5224\n", - "overstatement 3976\n", - "understatement 3295\n", - "rhetorical_question 1037\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "from __future__ import annotations\n", - "from urllib.parse import urlparse\n", - "\n", - "\n", - "def normalize_url(url: str) -> str:\n", - " \"\"\"Lowercase and strip scheme/trailing slash for stable group IDs.\"\"\"\n", - " url = url.strip().lower()\n", - " parsed = urlparse(url)\n", - " normalized = (parsed.netloc + parsed.path).rstrip(\"/\")\n", - " return normalized if normalized else url\n", - "\n", - "\n", - "def derive_labels(raw_rows):\n", - " \"\"\"\n", - " Expand each JSONL row into 2 samples (sarcastic + non-sarcastic).\n", - "\n", - " Rules:\n", - " sarcastic_to_non -> original=sarcastic(1), generated=non-sarcastic(0)\n", - " non_to_sarcastic -> original=non-sarcastic(0), generated=sarcastic(1)\n", - "\n", - " Returns binary_df, type_df\n", - " \"\"\"\n", - " binary_records = []\n", - " sample_id = 0\n", - "\n", - " for pair_id, row in enumerate(raw_rows):\n", - " group_id = normalize_url(row[\"article_link\"]) if row[\"article_link\"] else str(pair_id)\n", - " row_type = row[\"type\"]\n", - " strategy = row[\"strategy\"]\n", - " model_used = row[\"model_used\"]\n", - " article = row[\"article_link\"]\n", - "\n", - " if row_type == \"sarcastic_to_non\":\n", - " orig_label, orig_strat = 1, strategy\n", - " gen_label, gen_strat = 0, None\n", - " else:\n", - " orig_label, orig_strat = 0, None\n", - " gen_label, gen_strat = 1, strategy\n", - "\n", - " binary_records.append({\n", - " \"sample_id\" : sample_id,\n", - " \"pair_id\" : pair_id,\n", - " \"group_id\" : group_id,\n", - " \"text\" : row[\"original_headline\"],\n", - " \"binary_label\" : orig_label,\n", - " \"is_generated\" : 0,\n", - " \"strategy\" : orig_strat,\n", - " \"source_type\" : row_type,\n", - " \"article_link\" : article,\n", - " \"model_used\" : model_used,\n", - " })\n", - " sample_id += 1\n", - "\n", - " binary_records.append({\n", - " \"sample_id\" : sample_id,\n", - " \"pair_id\" : pair_id,\n", - " \"group_id\" : group_id,\n", - " \"text\" : row[\"generated_headline\"],\n", - " \"binary_label\" : gen_label,\n", - " \"is_generated\" : 1,\n", - " \"strategy\" : gen_strat,\n", - " \"source_type\" : row_type,\n", - " \"article_link\" : article,\n", - " \"model_used\" : model_used,\n", - " })\n", - " sample_id += 1\n", - "\n", - " binary_df = pd.DataFrame(binary_records)\n", - "\n", - " # Validate counts\n", - " counts = binary_df[\"binary_label\"].value_counts().to_dict()\n", - " n_expected = len(raw_rows)\n", - " n0 = int(counts.get(0, 0))\n", - " n1 = int(counts.get(1, 0))\n", - " if n0 != n_expected or n1 != n_expected:\n", - " raise ValueError(\n", - " f\"Label count mismatch!\\n\"\n", - " f\" Expected {n_expected} per class\\n\"\n", - " f\" Got: sarcastic(1)={n1}, non-sarcastic(0)={n0}\"\n", - " )\n", - "\n", - " # Type dataset: sarcastic only\n", - " type_df = binary_df[binary_df[\"binary_label\"] == 1].copy()\n", - " type_df = type_df.rename(columns={\"strategy\": \"type_label\"})\n", - " type_df = type_df[[\"sample_id\", \"pair_id\", \"group_id\", \"text\",\n", - " \"type_label\", \"is_generated\", \"source_type\",\n", - " \"article_link\", \"model_used\"]]\n", - "\n", - " missing = type_df[\"type_label\"].isna().sum()\n", - " if missing > 0:\n", - " raise ValueError(f\"{missing} sarcastic samples have no type_label!\")\n", - "\n", - " return binary_df, type_df\n", - "\n", - "\n", - "binary_df, type_df = derive_labels(raw_rows)\n", - "\n", - "print(f\"Binary dataset : {len(binary_df):,} samples\")\n", - "print(f\" sarcastic (1): {(binary_df['binary_label']==1).sum():,}\")\n", - "print(f\" non-sarc (0): {(binary_df['binary_label']==0).sum():,}\")\n", - "print()\n", - "print(f\"Type dataset : {len(type_df):,} samples\")\n", - "print(type_df[\"type_label\"].value_counts().to_string())\n" - ] + "image/png": 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+ }, + "metadata": {} }, { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "5beT2LM9uKBY", - "outputId": "203852f9-bcfd-4ac5-8f83-61f9e0d10219" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved: outputs/datasets/binary_dataset.csv\n", - "Saved: outputs/datasets/type_dataset.csv\n" - ] - } - ], - "source": [ - "# Save full datasets\n", - "binary_df.to_csv(OUT_DATASETS / \"binary_dataset.csv\", index=False)\n", - "type_df.to_csv( OUT_DATASETS / \"type_dataset.csv\", index=False)\n", - "print(\"Saved: outputs/datasets/binary_dataset.csv\")\n", - "print(\"Saved: outputs/datasets/type_dataset.csv\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/datasets/distributions.png\n" + ] + } + ], + "source": [ + "# ── Strategy distribution plot ────────────────────────────────────────────────\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Type dist\n", + "ax = axes[0]\n", + "labels, counts = zip(*sorted(type_counts.items(), key=lambda x: -x[1]))\n", + "ax.barh(labels, counts, color=[\"steelblue\", \"coral\"])\n", + "ax.set_title(\"Row-level Type Distribution\", fontsize=14)\n", + "ax.set_xlabel(\"Count\")\n", + "for i, c in enumerate(counts):\n", + " ax.text(c + 50, i, f\"{c:,}\", va=\"center\")\n", + "\n", + "# Strategy dist\n", + "ax = axes[1]\n", + "labels2, counts2 = zip(*sorted(strategy_counts.items(), key=lambda x: -x[1]))\n", + "ax.barh(labels2, counts2, color=\"steelblue\")\n", + "ax.set_title(\"Strategy Distribution (Type Task Labels)\", fontsize=14)\n", + "ax.set_xlabel(\"Count\")\n", + "for i, c in enumerate(counts2):\n", + " ax.text(c + 30, i, f\"{c:,}\", va=\"center\")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(OUT_DATASETS / \"distributions.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/datasets/distributions.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JxKcjhRSuKBY" + }, + "source": [ + "## 3. Label Derivation and Dataset Expansion" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "nukGQcmKuKBY", + "outputId": "2fcfb601-8ad0-4876-b813-c21b007bb164" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "5GtLZhBwuKBZ" - }, - "source": [ - "## 4. Group-Aware Train / Val / Test Split" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Binary dataset : 56,666 samples\n", + " sarcastic (1): 28,333\n", + " non-sarc (0): 28,333\n", + "\n", + "Type dataset : 28,333 samples\n", + "type_label\n", + "sarcasm 8699\n", + "irony 6102\n", + "satire 5224\n", + "overstatement 3976\n", + "understatement 3295\n", + "rhetorical_question 1037\n" + ] + } + ], + "source": [ + "from __future__ import annotations\n", + "from urllib.parse import urlparse\n", + "\n", + "\n", + "def normalize_url(url: str) -> str:\n", + " \"\"\"Lowercase and strip scheme/trailing slash for stable group IDs.\"\"\"\n", + " url = url.strip().lower()\n", + " parsed = urlparse(url)\n", + " normalized = (parsed.netloc + parsed.path).rstrip(\"/\")\n", + " return normalized if normalized else url\n", + "\n", + "\n", + "def derive_labels(raw_rows):\n", + " \"\"\"\n", + " Expand each JSONL row into 2 samples (sarcastic + non-sarcastic).\n", + "\n", + " Rules:\n", + " sarcastic_to_non -> original=sarcastic(1), generated=non-sarcastic(0)\n", + " non_to_sarcastic -> original=non-sarcastic(0), generated=sarcastic(1)\n", + "\n", + " Returns binary_df, type_df\n", + " \"\"\"\n", + " binary_records = []\n", + " sample_id = 0\n", + "\n", + " for pair_id, row in enumerate(raw_rows):\n", + " group_id = normalize_url(row[\"article_link\"]) if row[\"article_link\"] else str(pair_id)\n", + " row_type = row[\"type\"]\n", + " strategy = row[\"strategy\"]\n", + " model_used = row[\"model_used\"]\n", + " article = row[\"article_link\"]\n", + "\n", + " if row_type == \"sarcastic_to_non\":\n", + " orig_label, orig_strat = 1, strategy\n", + " gen_label, gen_strat = 0, None\n", + " else:\n", + " orig_label, orig_strat = 0, None\n", + " gen_label, gen_strat = 1, strategy\n", + "\n", + " binary_records.append({\n", + " \"sample_id\" : sample_id,\n", + " \"pair_id\" : pair_id,\n", + " \"group_id\" : group_id,\n", + " \"text\" : row[\"original_headline\"],\n", + " \"binary_label\" : orig_label,\n", + " \"is_generated\" : 0,\n", + " \"strategy\" : orig_strat,\n", + " \"source_type\" : row_type,\n", + " \"article_link\" : article,\n", + " \"model_used\" : model_used,\n", + " })\n", + " sample_id += 1\n", + "\n", + " binary_records.append({\n", + " \"sample_id\" : sample_id,\n", + " \"pair_id\" : pair_id,\n", + " \"group_id\" : group_id,\n", + " \"text\" : row[\"generated_headline\"],\n", + " \"binary_label\" : gen_label,\n", + " \"is_generated\" : 1,\n", + " \"strategy\" : gen_strat,\n", + " \"source_type\" : row_type,\n", + " \"article_link\" : article,\n", + " \"model_used\" : model_used,\n", + " })\n", + " sample_id += 1\n", + "\n", + " binary_df = pd.DataFrame(binary_records)\n", + "\n", + " # Validate counts\n", + " counts = binary_df[\"binary_label\"].value_counts().to_dict()\n", + " n_expected = len(raw_rows)\n", + " n0 = int(counts.get(0, 0))\n", + " n1 = int(counts.get(1, 0))\n", + " if n0 != n_expected or n1 != n_expected:\n", + " raise ValueError(\n", + " f\"Label count mismatch!\\n\"\n", + " f\" Expected {n_expected} per class\\n\"\n", + " f\" Got: sarcastic(1)={n1}, non-sarcastic(0)={n0}\"\n", + " )\n", + "\n", + " # Type dataset: sarcastic only\n", + " type_df = binary_df[binary_df[\"binary_label\"] == 1].copy()\n", + " type_df = type_df.rename(columns={\"strategy\": \"type_label\"})\n", + " type_df = type_df[[\"sample_id\", \"pair_id\", \"group_id\", \"text\",\n", + " \"type_label\", \"is_generated\", \"source_type\",\n", + " \"article_link\", \"model_used\"]]\n", + "\n", + " missing = type_df[\"type_label\"].isna().sum()\n", + " if missing > 0:\n", + " raise ValueError(f\"{missing} sarcastic samples have no type_label!\")\n", + "\n", + " return binary_df, type_df\n", + "\n", + "\n", + "binary_df, type_df = derive_labels(raw_rows)\n", + "\n", + "print(f\"Binary dataset : {len(binary_df):,} samples\")\n", + "print(f\" sarcastic (1): {(binary_df['binary_label']==1).sum():,}\")\n", + "print(f\" non-sarc (0): {(binary_df['binary_label']==0).sum():,}\")\n", + "print()\n", + "print(f\"Type dataset : {len(type_df):,} samples\")\n", + "print(type_df[\"type_label\"].value_counts().to_string())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "5beT2LM9uKBY", + "outputId": "203852f9-bcfd-4ac5-8f83-61f9e0d10219" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OMGqQn3PuKBZ", - "outputId": "0eb38913-1ff6-4524-e2e6-030cf7c34b90" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=== Binary splits ===\n", - " train: 39,666 samples | sarcastic=19,833 non=19,833\n", - " val : 8,500 samples | sarcastic=4,250 non=4,250\n", - " test : 8,500 samples | sarcastic=4,250 non=4,250\n", - "\n", - "=== Type splits ===\n", - " train: 19,833 samples\n", - " sarcasm: 6,091\n", - " irony: 4,258\n", - " satire: 3,644\n", - " overstatement: 2,784\n", - " understatement: 2,352\n", - " rhetorical_question: 704\n", - " val : 4,250 samples\n", - " sarcasm: 1,317\n", - " irony: 942\n", - " satire: 747\n", - " overstatement: 600\n", - " understatement: 487\n", - " rhetorical_question: 157\n", - " test : 4,250 samples\n", - " sarcasm: 1,291\n", - " irony: 902\n", - " satire: 833\n", - " overstatement: 592\n", - " understatement: 456\n", - " rhetorical_question: 176\n" - ] - } - ], - "source": [ - "def group_aware_split(\n", - " df: pd.DataFrame,\n", - " group_col: str = \"group_id\",\n", - " train_frac: float = 0.70,\n", - " val_frac: float = 0.15,\n", - " seed: int = SEED,\n", - ") -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n", - " \"\"\"\n", - " Split df into train/val/test at the group level.\n", - " All rows sharing a group_id go to exactly one split.\n", - " \"\"\"\n", - " groups = df[group_col].unique().tolist()\n", - " rng = np.random.default_rng(seed)\n", - " rng.shuffle(groups)\n", - "\n", - " n = len(groups)\n", - " n_train = int(n * train_frac)\n", - " n_val = int(n * val_frac)\n", - "\n", - " train_groups = set(groups[:n_train])\n", - " val_groups = set(groups[n_train : n_train + n_val])\n", - " test_groups = set(groups[n_train + n_val :])\n", - "\n", - " train_df = df[df[group_col].isin(train_groups)].copy()\n", - " val_df = df[df[group_col].isin(val_groups)].copy()\n", - " test_df = df[df[group_col].isin(test_groups)].copy()\n", - "\n", - " # Sanity: no overlap\n", - " assert train_groups.isdisjoint(val_groups), \"Train/val group overlap!\"\n", - " assert train_groups.isdisjoint(test_groups), \"Train/test group overlap!\"\n", - " assert val_groups.isdisjoint(test_groups), \"Val/test group overlap!\"\n", - "\n", - " return train_df, val_df, test_df\n", - "\n", - "\n", - "# ── Binary splits ─────────────────────────────────────────────────────────────\n", - "train_bin, val_bin, test_bin = group_aware_split(binary_df)\n", - "\n", - "print(\"=== Binary splits ===\")\n", - "for name, df in [(\"train\", train_bin), (\"val\", val_bin), (\"test\", test_bin)]:\n", - " dist = df[\"binary_label\"].value_counts().to_dict()\n", - " print(f\" {name:5s}: {len(df):,} samples | sarcastic={dist.get(1,0):,} non={dist.get(0,0):,}\")\n", - "\n", - "# ── Type splits ───────────────────────────────────────────────────────────────\n", - "train_type, val_type, test_type = group_aware_split(type_df)\n", - "\n", - "print(\"\\n=== Type splits ===\")\n", - "for name, df in [(\"train\", train_type), (\"val\", val_type), (\"test\", test_type)]:\n", - " print(f\" {name:5s}: {len(df):,} samples\")\n", - " dist = df[\"type_label\"].value_counts()\n", - " for label, cnt in dist.items():\n", - " print(f\" {label}: {cnt:,}\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/datasets/binary_dataset.csv\n", + "Saved: outputs/datasets/type_dataset.csv\n" + ] + } + ], + "source": [ + "# Save full datasets\n", + "binary_df.to_csv(OUT_DATASETS / \"binary_dataset.csv\", index=False)\n", + "type_df.to_csv( OUT_DATASETS / \"type_dataset.csv\", index=False)\n", + "print(\"Saved: outputs/datasets/binary_dataset.csv\")\n", + "print(\"Saved: outputs/datasets/type_dataset.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5GtLZhBwuKBZ" + }, + "source": [ + "## 4. Group-Aware Train / Val / Test Split" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "OMGqQn3PuKBZ", + "outputId": "0eb38913-1ff6-4524-e2e6-030cf7c34b90" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Oo78ufPjuKBZ", - "outputId": "6b324c55-2718-4236-d356-e7c7a44e91a5" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train/Val pair_id overlap : 0 (must be 0)\n", - "Train/Test pair_id overlap : 0 (must be 0)\n", - "Val/Test pair_id overlap : 0 (must be 0)\n", - "\n", - "Leakage check PASSED: No pair_id crosses split boundaries.\n" - ] - } - ], - "source": [ - "# ── Verify no pair crosses split boundary ─────────────────────────────────────\n", - "# A pair_id should appear in at most ONE binary split\n", - "train_pairs = set(train_bin[\"pair_id\"])\n", - "val_pairs = set(val_bin[\"pair_id\"])\n", - "test_pairs = set(test_bin[\"pair_id\"])\n", - "\n", - "tv_overlap = train_pairs & val_pairs\n", - "tt_overlap = train_pairs & test_pairs\n", - "vt_overlap = val_pairs & test_pairs\n", - "\n", - "print(f\"Train/Val pair_id overlap : {len(tv_overlap)} (must be 0)\")\n", - "print(f\"Train/Test pair_id overlap : {len(tt_overlap)} (must be 0)\")\n", - "print(f\"Val/Test pair_id overlap : {len(vt_overlap)} (must be 0)\")\n", - "\n", - "assert len(tv_overlap) == 0 and len(tt_overlap) == 0 and len(vt_overlap) == 0, \\\n", - " \"Leakage detected: pairs crossing split boundaries!\"\n", - "print(\"\\nLeakage check PASSED: No pair_id crosses split boundaries.\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "=== Binary splits ===\n", + " train: 39,666 samples | sarcastic=19,833 non=19,833\n", + " val : 8,500 samples | sarcastic=4,250 non=4,250\n", + " test : 8,500 samples | sarcastic=4,250 non=4,250\n", + "\n", + "=== Type splits ===\n", + " train: 19,833 samples\n", + " sarcasm: 6,091\n", + " irony: 4,258\n", + " satire: 3,644\n", + " overstatement: 2,784\n", + " understatement: 2,352\n", + " rhetorical_question: 704\n", + " val : 4,250 samples\n", + " sarcasm: 1,317\n", + " irony: 942\n", + " satire: 747\n", + " overstatement: 600\n", + " understatement: 487\n", + " rhetorical_question: 157\n", + " test : 4,250 samples\n", + " sarcasm: 1,291\n", + " irony: 902\n", + " satire: 833\n", + " overstatement: 592\n", + " understatement: 456\n", + " rhetorical_question: 176\n" + ] + } + ], + "source": [ + "def group_aware_split(\n", + " df: pd.DataFrame,\n", + " group_col: str = \"group_id\",\n", + " train_frac: float = 0.70,\n", + " val_frac: float = 0.15,\n", + " seed: int = SEED,\n", + ") -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n", + " \"\"\"\n", + " Split df into train/val/test at the group level.\n", + " All rows sharing a group_id go to exactly one split.\n", + " \"\"\"\n", + " groups = df[group_col].unique().tolist()\n", + " rng = np.random.default_rng(seed)\n", + " rng.shuffle(groups)\n", + "\n", + " n = len(groups)\n", + " n_train = int(n * train_frac)\n", + " n_val = int(n * val_frac)\n", + "\n", + " train_groups = set(groups[:n_train])\n", + " val_groups = set(groups[n_train : n_train + n_val])\n", + " test_groups = set(groups[n_train + n_val :])\n", + "\n", + " train_df = df[df[group_col].isin(train_groups)].copy()\n", + " val_df = df[df[group_col].isin(val_groups)].copy()\n", + " test_df = df[df[group_col].isin(test_groups)].copy()\n", + "\n", + " # Sanity: no overlap\n", + " assert train_groups.isdisjoint(val_groups), \"Train/val group overlap!\"\n", + " assert train_groups.isdisjoint(test_groups), \"Train/test group overlap!\"\n", + " assert val_groups.isdisjoint(test_groups), \"Val/test group overlap!\"\n", + "\n", + " return train_df, val_df, test_df\n", + "\n", + "\n", + "# ── Binary splits ─────────────────────────────────────────────────────────────\n", + "train_bin, val_bin, test_bin = group_aware_split(binary_df)\n", + "\n", + "print(\"=== Binary splits ===\")\n", + "for name, df in [(\"train\", train_bin), (\"val\", val_bin), (\"test\", test_bin)]:\n", + " dist = df[\"binary_label\"].value_counts().to_dict()\n", + " print(f\" {name:5s}: {len(df):,} samples | sarcastic={dist.get(1,0):,} non={dist.get(0,0):,}\")\n", + "\n", + "# ── Type splits ───────────────────────────────────────────────────────────────\n", + "train_type, val_type, test_type = group_aware_split(type_df)\n", + "\n", + "print(\"\\n=== Type splits ===\")\n", + "for name, df in [(\"train\", train_type), (\"val\", val_type), (\"test\", test_type)]:\n", + " print(f\" {name:5s}: {len(df):,} samples\")\n", + " dist = df[\"type_label\"].value_counts()\n", + " for label, cnt in dist.items():\n", + " print(f\" {label}: {cnt:,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "Oo78ufPjuKBZ", + "outputId": "6b324c55-2718-4236-d356-e7c7a44e91a5" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "HbbbOgpZuKBZ", - "outputId": "2716fcd8-e654-46c8-9b14-765336c8f3b9" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved: outputs/splits/train_binary.csv\n", - "Saved: outputs/splits/val_binary.csv\n", - "Saved: outputs/splits/test_binary.csv\n", - "Saved: outputs/splits/train_type.csv\n", - "Saved: outputs/splits/val_type.csv\n", - "Saved: outputs/splits/test_type.csv\n", - "\n", - "Saved: outputs/splits/split_metadata.json\n" - ] - } - ], - "source": [ - "# ── Save splits ───────────────────────────────────────────────────────────────\n", - "split_files = {\n", - " \"train_binary\": train_bin,\n", - " \"val_binary\" : val_bin,\n", - " \"test_binary\" : test_bin,\n", - " \"train_type\" : train_type,\n", - " \"val_type\" : val_type,\n", - " \"test_type\" : test_type,\n", - "}\n", - "for fname, df in split_files.items():\n", - " path = OUT_SPLITS / f\"{fname}.csv\"\n", - " df.to_csv(path, index=False)\n", - " print(f\"Saved: {path.relative_to(ROOT)}\")\n", - "\n", - "# Metadata\n", - "import json as _json\n", - "metadata = {\n", - " \"seed\": SEED,\n", - " \"train_frac\": 0.70,\n", - " \"val_frac\": 0.15,\n", - " \"test_frac\": 0.15,\n", - " \"group_col\": \"group_id\",\n", - " \"total_pairs\": len(raw_rows),\n", - " \"binary\": {\n", - " \"train\": len(train_bin), \"val\": len(val_bin), \"test\": len(test_bin)\n", - " },\n", - " \"type\": {\n", - " \"train\": len(train_type), \"val\": len(val_type), \"test\": len(test_type)\n", - " },\n", - "}\n", - "with open(OUT_SPLITS / \"split_metadata.json\", \"w\") as f:\n", - " _json.dump(metadata, f, indent=2)\n", - "print(\"\\nSaved: outputs/splits/split_metadata.json\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Train/Val pair_id overlap : 0 (must be 0)\n", + "Train/Test pair_id overlap : 0 (must be 0)\n", + "Val/Test pair_id overlap : 0 (must be 0)\n", + "\n", + "Leakage check PASSED: No pair_id crosses split boundaries.\n" + ] + } + ], + "source": [ + "# ── Verify no pair crosses split boundary ─────────────────────────────────────\n", + "# A pair_id should appear in at most ONE binary split\n", + "train_pairs = set(train_bin[\"pair_id\"])\n", + "val_pairs = set(val_bin[\"pair_id\"])\n", + "test_pairs = set(test_bin[\"pair_id\"])\n", + "\n", + "tv_overlap = train_pairs & val_pairs\n", + "tt_overlap = train_pairs & test_pairs\n", + "vt_overlap = val_pairs & test_pairs\n", + "\n", + "print(f\"Train/Val pair_id overlap : {len(tv_overlap)} (must be 0)\")\n", + "print(f\"Train/Test pair_id overlap : {len(tt_overlap)} (must be 0)\")\n", + "print(f\"Val/Test pair_id overlap : {len(vt_overlap)} (must be 0)\")\n", + "\n", + "assert len(tv_overlap) == 0 and len(tt_overlap) == 0 and len(vt_overlap) == 0, \\\n", + " \"Leakage detected: pairs crossing split boundaries!\"\n", + "print(\"\\nLeakage check PASSED: No pair_id crosses split boundaries.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "HbbbOgpZuKBZ", + "outputId": "2716fcd8-e654-46c8-9b14-765336c8f3b9" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "j7ZguloUuKBa" - }, - "source": [ - "## 5. Split Distribution Visualization" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/splits/train_binary.csv\n", + "Saved: outputs/splits/val_binary.csv\n", + "Saved: outputs/splits/test_binary.csv\n", + "Saved: outputs/splits/train_type.csv\n", + "Saved: outputs/splits/val_type.csv\n", + "Saved: outputs/splits/test_type.csv\n", + "\n", + "Saved: outputs/splits/split_metadata.json\n" + ] + } + ], + "source": [ + "# ── Save splits ───────────────────────────────────────────────────────────────\n", + "split_files = {\n", + " \"train_binary\": train_bin,\n", + " \"val_binary\" : val_bin,\n", + " \"test_binary\" : test_bin,\n", + " \"train_type\" : train_type,\n", + " \"val_type\" : val_type,\n", + " \"test_type\" : test_type,\n", + "}\n", + "for fname, df in split_files.items():\n", + " path = OUT_SPLITS / f\"{fname}.csv\"\n", + " df.to_csv(path, index=False)\n", + " print(f\"Saved: {path.relative_to(ROOT)}\")\n", + "\n", + "# Metadata\n", + "import json as _json\n", + "metadata = {\n", + " \"seed\": SEED,\n", + " \"train_frac\": 0.70,\n", + " \"val_frac\": 0.15,\n", + " \"test_frac\": 0.15,\n", + " \"group_col\": \"group_id\",\n", + " \"total_pairs\": len(raw_rows),\n", + " \"binary\": {\n", + " \"train\": len(train_bin), \"val\": len(val_bin), \"test\": len(test_bin)\n", + " },\n", + " \"type\": {\n", + " \"train\": len(train_type), \"val\": len(val_type), \"test\": len(test_type)\n", + " },\n", + "}\n", + "with open(OUT_SPLITS / \"split_metadata.json\", \"w\") as f:\n", + " _json.dump(metadata, f, indent=2)\n", + "print(\"\\nSaved: outputs/splits/split_metadata.json\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j7ZguloUuKBa" + }, + "source": [ + "## 5. Split Distribution Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 }, + "id": "nLrEPxJ9uKBa", + "outputId": "508ba885-ab89-400e-8f7c-c251d636a688" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 419 - }, - "id": "nLrEPxJ9uKBa", - "outputId": "508ba885-ab89-400e-8f7c-c251d636a688" - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved: outputs/splits/type_split_distribution.png\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "# Type label distribution per split\n", - "fig, axes = plt.subplots(1, 3, figsize=(18, 5), sharey=True)\n", - "split_dfs = [(\"Train\", train_type), (\"Val\", val_type), (\"Test\", test_type)]\n", - "\n", - "for ax, (name, df) in zip(axes, split_dfs):\n", - " dist = df[\"type_label\"].value_counts()\n", - " dist.plot(kind=\"barh\", ax=ax, color=\"steelblue\")\n", - " ax.set_title(f\"{name} — Type Labels (n={len(df):,})\", fontsize=13)\n", - " ax.set_xlabel(\"Count\")\n", - " for i, (label, cnt) in enumerate(dist.items()):\n", - " ax.text(cnt + 5, i, f\"{cnt:,}\", va=\"center\", fontsize=9)\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(OUT_SPLITS / \"type_split_distribution.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "print(\"Saved: outputs/splits/type_split_distribution.png\")" - ] + "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved: outputs/splits/binary_split_distribution.png\n" - ] - } - ], - "source": [ - "# Binary label distribution per split\n", - "fig, axes = plt.subplots(1, 3, figsize=(12, 4), sharey=True)\n", - "split_dfs_bin = [(\"Train\", train_bin), (\"Val\", val_bin), (\"Test\", test_bin)]\n", - "\n", - "for ax, (name, df) in zip(axes, split_dfs_bin):\n", - " dist = df[\"binary_label\"].value_counts().sort_index()\n", - " ax.bar([\"Non-sarcastic (0)\", \"Sarcastic (1)\"], dist.values, color=[\"coral\", \"steelblue\"])\n", - " ax.set_title(f\"{name} — Binary Labels (n={len(df):,})\", fontsize=12)\n", - " ax.set_ylabel(\"Count\")\n", - " for i, v in enumerate(dist.values):\n", - " ax.text(i, v + 20, f\"{v:,}\", ha=\"center\")\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(OUT_SPLITS / \"binary_split_distribution.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "print(\"Saved: outputs/splits/binary_split_distribution.png\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/splits/type_split_distribution.png\n" + ] + } + ], + "source": [ + "# Type label distribution per split\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 5), sharey=True)\n", + "split_dfs = [(\"Train\", train_type), (\"Val\", val_type), (\"Test\", test_type)]\n", + "\n", + "for ax, (name, df) in zip(axes, split_dfs):\n", + " dist = df[\"type_label\"].value_counts()\n", + " dist.plot(kind=\"barh\", ax=ax, color=\"steelblue\")\n", + " ax.set_title(f\"{name} — Type Labels (n={len(df):,})\", fontsize=13)\n", + " ax.set_xlabel(\"Count\")\n", + " for i, (label, cnt) in enumerate(dist.items()):\n", + " ax.text(cnt + 5, i, f\"{cnt:,}\", va=\"center\", fontsize=9)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(OUT_SPLITS / \"type_split_distribution.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/splits/type_split_distribution.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 }, + "id": "R48OKnIouKBa", + "outputId": "cc63cf5a-635b-4937-ff28-9702de59974e" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "y59RyyxDuKBa", - "outputId": "9fcf80b4-639e-4dc0-da4e-9e5d7492337a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "=== Data Preparation Complete ===\n", - "Binary dataset : 56,666 samples (balanced)\n", - "Type dataset : 28,333 samples (imbalanced, 6 classes)\n", - "Splits saved to: outputs/splits/\n", - "Datasets saved : outputs/datasets/\n", - "\n", - "Ready for classical baseline training (Notebooks 02 and 03).\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "print(\"\\n=== Data Preparation Complete ===\")\n", - "print(f\"Binary dataset : {len(binary_df):,} samples (balanced)\")\n", - "print(f\"Type dataset : {len(type_df):,} samples (imbalanced, 6 classes)\")\n", - "print(f\"Splits saved to: outputs/splits/\")\n", - "print(f\"Datasets saved : outputs/datasets/\")\n", - "print(\"\\nReady for classical baseline training (Notebooks 02 and 03).\")" - ] + "image/png": 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ylo9T2jJSlCK/X0d+z3v5/dq1a6Zv376mQIEC1nwnJyczY8aMDI198uTJpm/fvmb+/PlmyZIlpl+/fiZ//vymXLlydn8o9OnTxzg5OaXZR7FixUybNm2MMf+7verGn9MU06ZNM5LMvn377NpTbt3dsmVLhsaMnO3m3/s3b95sJJn58+fbxa1atcqufenSpUaS2bp16y37vtPb98j915H7yf03I/dnDg86z4NcXFzUtWvXVO03XnJ4/vx5JSQk6IknntDHH3+sffv2KTg4ON1+X3zxRRUuXNj6/MQTT0hSqif/pzWeFElJSTp37pw8PDxUoUIF/frrr5Kk8uXLKzExUZLk7++vhx9+2FrG29s73f7vlKurq7777jtJ1y97PnLkiCZOnKhmzZpp06ZNKl++fLrLe3h4qEOHDtZnZ2dn1ahRw24/ODk5WQ+0S05O1rlz55ScnKxHH33U2uYbtWrVyrp0OkWjRo3k7++v+fPnWw/M3rVrl3bu3KlPPvkkcxt/kzs5JvLnz6+XX37Zbrtffvll9e7dW1FRUapVq5YWL16sSpUqqWLFivrnn3+s2CeffFLS9UtVa9euneZYChUqpN27d+vAgQMqV65chrch5W1iNx6bN2rUqJHdg/6qVasmT09Pu3+v4ODg215KnCKth26OHTtWb7zxho4dO6bPP/9ciYmJdm+gadasmQIDA/Xmm2/K3d1dISEh2rJli9555x3lz59fly9fTnedKQ//tNlsWrdunfXgxOrVqys0NFTTpk3Tv//9bys2OTlZY8eOtR6S2KpVK505c0ZTpkzR22+/rYIFC95RnykKFy6s7du3Z2g/wbGcnJzUpk0bTZo0SUeOHLFuwV6wYIF8fX2tWyQyko/vBPmd/J5X87uTk5PKlCmjsLAwtW7dWq6urvryyy/16quvys/PTy1btky3r5tv0WnVqpVq1Kih9u3ba/r06XrrrbckKd2HLru6ulrnh5T/3vgzd2PcjTEpUvbbjf9eyDsWL14sLy8vPfXUU3b/xiEhIfLw8ND69evVrl076zak5cuXKzg4WAUKFLjrdZP7yf3k/rSR+zOHolQe9OCDD6Z5kO/evVtDhw7VDz/8oPj4eLt5cXFxt+33oYcesvuccsDffN/6zZKTkzVlyhRNnz5dMTExdvc0p7wtYNq0aerfv7+k66/STLmP3dvbWydOnMjSt2Q4OTmluke5WbNmKleunIYMGaL//ve/6S5fokSJVK+oLVy4sHbu3GnX9vnnn2vChAnat2+frl69arWXKlUqVZ9pteXLl0/t27fXjBkzdOnSJbm7u2v+/PlydXVV69atb7udGXEnx4S/v78eeOABu7aUk/yRI0dUq1YtHThwQHv37k11Ek6R1vNuUowaNUotWrRQ+fLlVbVqVTVp0kQdO3ZUtWrVMrQt5hb3bN983ErX/71uPG4LFy6c5n3rGXXjL1odOnTQI488oi5dumjJkiWSrp80VqxYoRdeeEGtWrWSdP3kMm7cOL333nu3fdVtyi8YzzzzjF1srVq1VKpUKUVERNjFXrx4UW3btrXro23btlq1apW2b9+uunXr3lGfKYwxGXo9M7JH+/btNWnSJC1YsEBvv/22/vzzT+tNLjf+In27fHwnyO/k9xR5Lb+PHTtWU6ZM0YEDB6wc+cILL6hBgwbq06ePnn76abtn1WREu3bt9MYbb2jt2rXWHyZubm7WH+43u3LlipWrU/6b1jNcrly5YheTImW/kbfzpgMHDiguLk4+Pj5pzk/5maxXr55atWqld999V5MmTVL9+vXVsmVLtWvXLs0/dDOC3E/uT0Huvz1y/+1RlMqD0noI27lz51SvXj15enpq1KhRKlOmjFxdXfXrr79q8ODBSk5Ovm2/ab3KUrp1wkjx/vvva9iwYXrppZc0evRoeXt7K1++fHr99det9T711FMKDw9X+/btFRgYqPfff1/S9RObI17bWqJECVWoUEGbNm26bWxG9sO8efPUpUsXtWzZUgMHDpSPj4/1AMNDhw6lWvZWD87r1KmTxo8fr2XLlqlt27ZasGCBnn76aXl5eWVwy24tK46JmyUnJysoKEgTJ05Mc35AQMAtl61bt64OHTqkb775RmvWrNHs2bM1adIkzZw5U927d7/lcim//Jw9e1YlSpRINT8j/16JiYk6c+bMLddxo2LFit2yT+n6t0zPPvusxo4dq8uXL1v/tlWqVNGuXbu0Z88enT17VpUrV5abm5v69++vevXqpbvOlFcZ+/r6pprn4+NjdxL29/fXgQMHUsWm/NKaEnsnfaY4e/asihYtmu5YkX1CQkJUsWJFffnll3r77bf15Zdfyhij9u3bWzEZycd3gvxOfk+R1/L79OnT9eSTT6b60uDZZ5/VgAEDdOTIEZUtWzZD/d4oICDAbjzFixdXUlKSTp06ZVdcSExM1OnTp61c7e3tLRcXF504cSJVnyltKbEpUvI4eTtvSk5Olo+Pj+bPn5/m/JQigs1m05IlS/Tzzz/ru+++0+rVq/XSSy9pwoQJ+vnnn2/7xVhayP3k/hTk/owh96ePotR9YsOGDTp9+rS+/vpr1a1b12qPiYm55+tesmSJGjRooE8//dSu/dy5c9YPS5UqVVSlShU99dRTWr58uerUqWNdkugo165ds25pultLlixR6dKl9fXXX9tVqUeMGHFH/VStWlXVq1fX/PnzVaJECR09elRTp07NkjHe6TFx/PhxXbx40e4bld9//12SrFuFypQpox07dqhhw4aZqs57e3ura9eu6tq1qy5cuKC6detq5MiR6Z64KlasaI07KCjojtcpSREREWrQoEGGYmNiYuzeTpmWy5cvyxij8+fP2/1SYrPZVKVKFevzypUrlZycfNtvckJCQiRJf/31V6p5x48ft/ZBSuyBAwf0119/qXTp0nZx0v9+Sb2TPlPExMTc9jZfZK/27dtr2LBh2rlzpxYsWKBy5crZva0lI/n4TpDfye8Zldvy+8mTJ9N8W1XK1RE33qKdUcYYHTlyRNWrV7faUq603bZtm5o1a2a1b9u2TcnJydb8fPnyKSgoSNu2bUvV75YtW1S6dGkVLFgw1fbky5fvtrcuIXcqU6aM1q5dq8cffzxDbwWrVauWatWqpffee08LFixQ+/bttXDhQnXv3v2Of6bJ/eT+jCL3k/szIl92DwCOkVL9vbmCPH36dIes++arqRYvXpzmH8Pdu3dXfHy83nnnHbv2K1eupPtq2rv1+++/a//+/Vn2B3da+3vLli2KjIy84746duyoNWvWaPLkySpSpIiaNm16z8aY3jFx7do1ffzxx3axH3/8sYoVK2YVOF544QX99ddfad4Xf/nyZV28ePGW40m5fzyFh4eHypYte9vXzYaEhMjZ2TnNZJ1RKfedZ2S68b7ztC5ZPnfunP773/8qICDglpfUS9f3x7Bhw1S8ePFUt9rdrEKFCgoODtY333xjd3/4mjVrdOzYMT311FNW24svvihJdr8oJicna86cOfL29rb+re6kT+n6Jd+HDh265XMDkDOkXBU1fPhwRUdH210lJd1ZPs4I8vt15Pe8l9/Lly+v8PBwu7EnJSXpq6++UsGCBe2eZ5KWv//+O1XbjBkz9Pfff1vPkpGuP5fF29tbM2bMSBXr7u6u5s2bW23/+te/tHXrVrv9sX//fv3www9p3voTFRWlKlWqZMkVGMh5XnjhBSUlJWn06NGp5l27dk3nzp2TdP2qiZvzdMofvCk/g+7u7pJkLXM75P7ryP3k/puR+zOHK6XuE7Vr11bhwoXVuXNnvfbaa7LZbPriiy9ue+tdVnj66ac1atQode3aVbVr19Zvv/2m+fPn213FkaJ+/frq16+fJk6cqL1796pZs2a6fPmyPvvsMxUqVChLTl7Xrl3TvHnzJP3vYYgzZ85UcnLyHX/bcStPP/20vv76az333HNq3ry5YmJiNHPmTFWuXPmOv7Fp166dBg0apKVLl6p379539IDKbdu2pXpYtXR9P9/pMeHv768PPvhAR44cUfny5bVo0SJFR0dr1qxZ1pg6duyor776Sr169dL69ev1+OOPKykpSfv27dNXX32l1atX69FHH02z/8qVK6t+/foKCQmRt7e3tm3bpiVLlqhv377pbqOrq6saN26stWvXatSoURneNzfK7H3nTZs2VYkSJVSzZk35+Pjo6NGjmjNnjo4fP65FixbZxb7wwgvy9/dX5cqVFR8fr88++0yHDx/WihUrUn3DYbPZVK9ePW3YsMFqmzRpkp566inVqVNHL7/8suLi4jRx4kSVL19evXv3tuJatGihhg0basyYMfrnn38UHBysZcuW6ccff9THH39s9/yIjPYpSWvXrpUxRi1atLjj/QTHKVWqlGrXrq1vvvlGklIVpe4kH2cE+Z38nlfz+1tvvaUOHTqoZs2a6tmzp9zc3PTll18qKipK//73v+3+rbp06aLPP//c7tv2wMBAvfjiiwoKCpKrq6t+/PFHLVy4UA8//LDdg4Xd3Nw0evRo9enTR61bt1ZYWJg2b96sefPm6b333rN7IPQrr7yiTz75RM2bN9ebb76pAgUKaOLEifL19dUbb7xhN/6rV69q48aNeuWVV+5425E71KtXTy+//LLGjBmj6OhoNW7cWAUKFNCBAwe0ePFiTZkyRf/617/0+eefa/r06XruuedUpkwZnT9/Xp988ok8PT2tKzTc3NxUuXJlLVq0SOXLl5e3t7eqVq2qqlWrprlucj+5n9xP7s9S9/r1frh3bn41rDHXX+9ZpUqVNON/+uknU6tWLePm5mb8/f3NoEGDrFeT3vhq1M6dO5vAwEDrc8qrTcePH5+qT2Xg9bFXrlwxb7zxhilevLhxc3Mzjz/+uImMjDT16tVL8/WnycnJZsaMGaZatWrGxcXFFCtWzPTo0cOcOHEi3fVkRFqvjfX09DQNGzY0a9eutYu91Wtj09q/N++z5ORk8/7775vAwEDj4uJ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+ }, + "metadata": {} }, { - "cell_type": "markdown", - "metadata": { - "id": "cENhnFfwuKBb" - }, - "source": [ - "---\n", - "# Part 2 — TF-IDF + Logistic Regression Baseline" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "j_2MU9WJuKBb" - }, - "source": [ - "# Notebook 02 — TF-IDF + Logistic Regression Baseline\n", - "\n", - "**Purpose**: Train and evaluate TF-IDF + Logistic Regression pipelines for:\n", - "- Task A: Binary classification (sarcastic vs non-sarcastic)\n", - "- Task B: Sarcasm type classification (6-class)\n", - "\n", - "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", - "\n", - "**Outputs**:\n", - "- `outputs/classical/tfidf_lr/best_config_binary.json`\n", - "- `outputs/classical/tfidf_lr/best_config_type.json`\n", - "- `outputs/classical/tfidf_lr/metrics_binary.json`\n", - "- `outputs/classical/tfidf_lr/metrics_type.json`\n", - "- Predictions CSV + confusion matrices PNG" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "r8BzGF97uKBb" - }, - "source": [ - "## Helper Functions" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "id": "iU0Dnjl_uKBb" - }, - "outputs": [], - "source": [ - "def load_splits(task: str) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n", - " \"\"\"Load train/val/test CSVs for a given task ('binary' or 'type').\"\"\"\n", - " train = pd.read_csv(SPLITS / f\"train_{task}.csv\")\n", - " val = pd.read_csv(SPLITS / f\"val_{task}.csv\")\n", - " test = pd.read_csv(SPLITS / f\"test_{task}.csv\")\n", - " return train, val, test\n", - "\n", - "\n", - "def evaluate(\n", - " model,\n", - " X: list[str],\n", - " y_true: list,\n", - " label_names: list[str] | None = None,\n", - " split_name: str = \"\",\n", - ") -> dict:\n", - " \"\"\"Compute classification metrics and return as dict.\"\"\"\n", - " y_pred = model.predict(X)\n", - " metrics = {\n", - " \"split\" : split_name,\n", - " \"accuracy\" : accuracy_score(y_true, y_pred),\n", - " \"precision_macro\" : precision_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", - " \"recall_macro\" : recall_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", - " \"f1_macro\" : f1_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", - " \"f1_weighted\" : f1_score(y_true, y_pred, average=\"weighted\", zero_division=0),\n", - " \"report\" : classification_report(y_true, y_pred, target_names=label_names,\n", - " zero_division=0, output_dict=True),\n", - " }\n", - " print(f\"\\n[{split_name}] Accuracy={metrics['accuracy']:.4f} \"\n", - " f\"Macro-F1={metrics['f1_macro']:.4f} Weighted-F1={metrics['f1_weighted']:.4f}\")\n", - " print(classification_report(y_true, y_pred, target_names=label_names, zero_division=0))\n", - " return metrics, y_pred\n", - "\n", - "\n", - "def save_confusion_matrix(\n", - " y_true, y_pred, label_names: list[str], out_path: Path, title: str = \"\"\n", - ") -> None:\n", - " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", - " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", - " sns.heatmap(\n", - " cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n", - " xticklabels=label_names, yticklabels=label_names, ax=ax\n", - " )\n", - " ax.set_xlabel(\"Predicted\")\n", - " ax.set_ylabel(\"True\")\n", - " ax.set_title(title)\n", - " plt.tight_layout()\n", - " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", - " plt.show()\n", - " print(f\"Saved: {out_path.relative_to(ROOT)}\")\n", - "\n", - "\n", - "def save_predictions(\n", - " df: pd.DataFrame, y_pred, label_col: str, out_path: Path\n", - ") -> None:\n", - " out = df[[\"sample_id\", \"pair_id\", \"group_id\", \"text\", label_col]].copy()\n", - " out[\"predicted\"] = y_pred\n", - " out[\"correct\"] = (out[label_col] == out[\"predicted\"]).astype(int)\n", - " out.to_csv(out_path, index=False)\n", - " print(f\"Saved: {out_path.relative_to(ROOT)}\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/splits/binary_split_distribution.png\n" + ] + } + ], + "source": [ + "# Binary label distribution per split\n", + "fig, axes = plt.subplots(1, 3, figsize=(12, 4), sharey=True)\n", + "split_dfs_bin = [(\"Train\", train_bin), (\"Val\", val_bin), (\"Test\", test_bin)]\n", + "\n", + "for ax, (name, df) in zip(axes, split_dfs_bin):\n", + " dist = df[\"binary_label\"].value_counts().sort_index()\n", + " ax.bar([\"Non-sarcastic (0)\", \"Sarcastic (1)\"], dist.values, color=[\"coral\", \"steelblue\"])\n", + " ax.set_title(f\"{name} — Binary Labels (n={len(df):,})\", fontsize=12)\n", + " ax.set_ylabel(\"Count\")\n", + " for i, v in enumerate(dist.values):\n", + " ax.text(i, v + 20, f\"{v:,}\", ha=\"center\")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(OUT_SPLITS / \"binary_split_distribution.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/splits/binary_split_distribution.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "y59RyyxDuKBa", + "outputId": "9fcf80b4-639e-4dc0-da4e-9e5d7492337a" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "KZYEW3EruKBb" - }, - "source": [ - "## Task A — Binary Classification" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "=== Data Preparation Complete ===\n", + "Binary dataset : 56,666 samples (balanced)\n", + "Type dataset : 28,333 samples (imbalanced, 6 classes)\n", + "Splits saved to: outputs/splits/\n", + "Datasets saved : outputs/datasets/\n", + "\n", + "Ready for classical baseline training (Notebooks 02 and 03).\n" + ] + } + ], + "source": [ + "print(\"\\n=== Data Preparation Complete ===\")\n", + "print(f\"Binary dataset : {len(binary_df):,} samples (balanced)\")\n", + "print(f\"Type dataset : {len(type_df):,} samples (imbalanced, 6 classes)\")\n", + "print(f\"Splits saved to: outputs/splits/\")\n", + "print(f\"Datasets saved : outputs/datasets/\")\n", + "print(\"\\nReady for classical baseline training (Notebooks 02 and 03).\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cENhnFfwuKBb" + }, + "source": [ + "---\n", + "# Part 2 — TF-IDF + Logistic Regression Baseline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j_2MU9WJuKBb" + }, + "source": [ + "# Notebook 02 — TF-IDF + Logistic Regression Baseline\n", + "\n", + "**Purpose**: Train and evaluate TF-IDF + Logistic Regression pipelines for:\n", + "- Task A: Binary classification (sarcastic vs non-sarcastic)\n", + "- Task B: Sarcasm type classification (6-class)\n", + "\n", + "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", + "\n", + "**Outputs**:\n", + "- `outputs/classical/tfidf_lr/best_config_binary.json`\n", + "- `outputs/classical/tfidf_lr/best_config_type.json`\n", + "- `outputs/classical/tfidf_lr/metrics_binary.json`\n", + "- `outputs/classical/tfidf_lr/metrics_type.json`\n", + "- Predictions CSV + confusion matrices PNG" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r8BzGF97uKBb" + }, + "source": [ + "## Helper Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "iU0Dnjl_uKBb" + }, + "outputs": [], + "source": [ + "def load_splits(task: str) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n", + " \"\"\"Load train/val/test CSVs for a given task ('binary' or 'type').\"\"\"\n", + " train = pd.read_csv(SPLITS / f\"train_{task}.csv\")\n", + " val = pd.read_csv(SPLITS / f\"val_{task}.csv\")\n", + " test = pd.read_csv(SPLITS / f\"test_{task}.csv\")\n", + " return train, val, test\n", + "\n", + "\n", + "def evaluate(\n", + " model,\n", + " X: list[str],\n", + " y_true: list,\n", + " label_names: list[str] | None = None,\n", + " split_name: str = \"\",\n", + ") -> dict:\n", + " \"\"\"Compute classification metrics and return as dict.\"\"\"\n", + " y_pred = model.predict(X)\n", + " metrics = {\n", + " \"split\" : split_name,\n", + " \"accuracy\" : accuracy_score(y_true, y_pred),\n", + " \"precision_macro\" : precision_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"recall_macro\" : recall_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"f1_macro\" : f1_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"f1_weighted\" : f1_score(y_true, y_pred, average=\"weighted\", zero_division=0),\n", + " \"report\" : classification_report(y_true, y_pred, target_names=label_names,\n", + " zero_division=0, output_dict=True),\n", + " }\n", + " print(f\"\\n[{split_name}] Accuracy={metrics['accuracy']:.4f} \"\n", + " f\"Macro-F1={metrics['f1_macro']:.4f} Weighted-F1={metrics['f1_weighted']:.4f}\")\n", + " print(classification_report(y_true, y_pred, target_names=label_names, zero_division=0))\n", + " return metrics, y_pred\n", + "\n", + "\n", + "def save_confusion_matrix(\n", + " y_true, y_pred, label_names: list[str], out_path: Path, title: str = \"\"\n", + ") -> None:\n", + " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", + " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", + " sns.heatmap(\n", + " cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n", + " xticklabels=label_names, yticklabels=label_names, ax=ax\n", + " )\n", + " ax.set_xlabel(\"Predicted\")\n", + " ax.set_ylabel(\"True\")\n", + " ax.set_title(title)\n", + " plt.tight_layout()\n", + " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + " print(f\"Saved: {out_path.relative_to(ROOT)}\")\n", + "\n", + "\n", + "def save_predictions(\n", + " df: pd.DataFrame, y_pred, label_col: str, out_path: Path\n", + ") -> None:\n", + " out = df[[\"sample_id\", \"pair_id\", \"group_id\", \"text\", label_col]].copy()\n", + " out[\"predicted\"] = y_pred\n", + " out[\"correct\"] = (out[label_col] == out[\"predicted\"]).astype(int)\n", + " out.to_csv(out_path, index=False)\n", + " print(f\"Saved: {out_path.relative_to(ROOT)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KZYEW3EruKBb" + }, + "source": [ + "## Task A — Binary Classification" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "sUWB4dQKuKBb", + "outputId": "c9c34b33-e3bb-4eca-e21a-158777f966cb" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "sUWB4dQKuKBb", - "outputId": "c9c34b33-e3bb-4eca-e21a-158777f966cb" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train+Val: 48,166 | Train: 39,666 Val: 8,500 Test: 8,500\n" - ] - } - ], - "source": [ - "# ── Load binary splits ────────────────────────────────────────────────────────\n", - "train_bin, val_bin, test_bin = load_splits(\"binary\")\n", - "\n", - "# Combine train+val for cross-validation, keep test for final eval\n", - "trainval_bin = pd.concat([train_bin, val_bin], ignore_index=True)\n", - "\n", - "X_trainval = trainval_bin[\"text\"].tolist()\n", - "y_trainval = trainval_bin[\"binary_label\"].tolist()\n", - "groups_tv = trainval_bin[\"group_id\"].tolist()\n", - "\n", - "X_train = train_bin[\"text\"].tolist()\n", - "y_train = train_bin[\"binary_label\"].tolist()\n", - "\n", - "X_val = val_bin[\"text\"].tolist()\n", - "y_val = val_bin[\"binary_label\"].tolist()\n", - "\n", - "X_test = test_bin[\"text\"].tolist()\n", - "y_test = test_bin[\"binary_label\"].tolist()\n", - "\n", - "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", - "\n", - "print(f\"Train+Val: {len(X_trainval):,} | Train: {len(X_train):,} Val: {len(X_val):,} Test: {len(X_test):,}\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Train+Val: 48,166 | Train: 39,666 Val: 8,500 Test: 8,500\n" + ] + } + ], + "source": [ + "# ── Load binary splits ────────────────────────────────────────────────────────\n", + "train_bin, val_bin, test_bin = load_splits(\"binary\")\n", + "\n", + "# Combine train+val for cross-validation, keep test for final eval\n", + "trainval_bin = pd.concat([train_bin, val_bin], ignore_index=True)\n", + "\n", + "X_trainval = trainval_bin[\"text\"].tolist()\n", + "y_trainval = trainval_bin[\"binary_label\"].tolist()\n", + "groups_tv = trainval_bin[\"group_id\"].tolist()\n", + "\n", + "X_train = train_bin[\"text\"].tolist()\n", + "y_train = train_bin[\"binary_label\"].tolist()\n", + "\n", + "X_val = val_bin[\"text\"].tolist()\n", + "y_val = val_bin[\"binary_label\"].tolist()\n", + "\n", + "X_test = test_bin[\"text\"].tolist()\n", + "y_test = test_bin[\"binary_label\"].tolist()\n", + "\n", + "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", + "\n", + "print(f\"Train+Val: {len(X_trainval):,} | Train: {len(X_train):,} Val: {len(X_val):,} Test: {len(X_test):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "BatmO-vAuKBc", + "outputId": "34229238-2bbd-4838-976b-464bb496054f" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "BatmO-vAuKBc", - "outputId": "34229238-2bbd-4838-976b-464bb496054f" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Grid size: 4 param combos → 180 fits\n", - "Running grid search...\n", - "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n", - "\n", - "Best params : {'lr__C': 3.0, 'tfidf__max_features': None, 'tfidf__min_df': 3, 'tfidf__ngram_range': (1, 2)}\n", - "Best CV F1 : 0.8143\n" - ] - } - ], - "source": [ - "# ── Define pipeline and grid ──────────────────────────────────────────────────\n", - "pipe_bin = Pipeline([\n", - " (\"tfidf\", TfidfVectorizer(sublinear_tf=True, lowercase=True)),\n", - " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, solver=\"lbfgs\")),\n", - "])\n", - "\n", - "param_grid_bin = {\n", - " \"tfidf__ngram_range\" : [(1, 1), (1, 2)],\n", - " \"tfidf__min_df\" : [2, 3, 5],\n", - " \"tfidf__max_features\": [None, 50_000],\n", - " \"lr__C\" : [0.1, 1.0, 3.0],\n", - "}\n", - "\n", - "cv_bin = GroupKFold(n_splits=5)\n", - "\n", - "gs_bin = GridSearchCV(\n", - " pipe_bin, param_grid_bin,\n", - " cv=cv_bin, scoring=\"f1_macro\",\n", - " n_jobs=-1, verbose=1, refit=True,\n", - ")\n", - "\n", - "print(f\"Grid size: {len(gs_bin.param_grid)} param combos → {5 * 2*3*2*3} fits\")\n", - "print(\"Running grid search...\")\n", - "gs_bin.fit(X_trainval, y_trainval, groups=groups_tv)\n", - "\n", - "print(f\"\\nBest params : {gs_bin.best_params_}\")\n", - "print(f\"Best CV F1 : {gs_bin.best_score_:.4f}\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Grid size: 4 param combos → 180 fits\n", + "Running grid search...\n", + "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n", + "\n", + "Best params : {'lr__C': 3.0, 'tfidf__max_features': None, 'tfidf__min_df': 3, 'tfidf__ngram_range': (1, 2)}\n", + "Best CV F1 : 0.8143\n" + ] + } + ], + "source": [ + "# ── Define pipeline and grid ──────────────────────────────────────────────────\n", + "pipe_bin = Pipeline([\n", + " (\"tfidf\", TfidfVectorizer(sublinear_tf=True, lowercase=True)),\n", + " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, solver=\"lbfgs\")),\n", + "])\n", + "\n", + "param_grid_bin = {\n", + " \"tfidf__ngram_range\" : [(1, 1), (1, 2)],\n", + " \"tfidf__min_df\" : [2, 3, 5],\n", + " \"tfidf__max_features\": [None, 50_000],\n", + " \"lr__C\" : [0.1, 1.0, 3.0],\n", + "}\n", + "\n", + "cv_bin = GroupKFold(n_splits=5)\n", + "\n", + "gs_bin = GridSearchCV(\n", + " pipe_bin, param_grid_bin,\n", + " cv=cv_bin, scoring=\"f1_macro\",\n", + " n_jobs=-1, verbose=1, refit=True,\n", + ")\n", + "\n", + "print(f\"Grid size: {len(gs_bin.param_grid)} param combos → {5 * 2*3*2*3} fits\")\n", + "print(\"Running grid search...\")\n", + "gs_bin.fit(X_trainval, y_trainval, groups=groups_tv)\n", + "\n", + "print(f\"\\nBest params : {gs_bin.best_params_}\")\n", + "print(f\"Best CV F1 : {gs_bin.best_score_:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "JstBvt1vuKBc", + "outputId": "aff1b1fd-aa2f-4c9c-92ae-7d836436f66d" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "JstBvt1vuKBc", - "outputId": "aff1b1fd-aa2f-4c9c-92ae-7d836436f66d" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved: outputs/classical/tfidf_lr/best_config_binary.json\n" - ] - } - ], - "source": [ - "# ── Save best config ──────────────────────────────────────────────────────────\n", - "best_cfg_bin = {\"task\": \"binary\", \"model\": \"TF-IDF + LR\", \"seed\": SEED,\n", - " \"best_params\": gs_bin.best_params_, \"cv_f1_macro\": gs_bin.best_score_}\n", - "with open(OUT_TFIDF / \"best_config_binary.json\", \"w\") as f:\n", - " json.dump(best_cfg_bin, f, indent=2)\n", - "print(\"Saved: outputs/classical/tfidf_lr/best_config_binary.json\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/best_config_binary.json\n" + ] + } + ], + "source": [ + "# ── Save best config ──────────────────────────────────────────────────────────\n", + "best_cfg_bin = {\"task\": \"binary\", \"model\": \"TF-IDF + LR\", \"seed\": SEED,\n", + " \"best_params\": gs_bin.best_params_, \"cv_f1_macro\": gs_bin.best_score_}\n", + "with open(OUT_TFIDF / \"best_config_binary.json\", \"w\") as f:\n", + " json.dump(best_cfg_bin, f, indent=2)\n", + "print(\"Saved: outputs/classical/tfidf_lr/best_config_binary.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "AjH8_5UQuKBc", + "outputId": "afe39734-c73a-410a-9447-6b8639b18389" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "AjH8_5UQuKBc", - "outputId": "afe39734-c73a-410a-9447-6b8639b18389" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "[Val] Accuracy=0.9186 Macro-F1=0.9186 Weighted-F1=0.9186\n", - " precision recall f1-score support\n", - "\n", - "non-sarcastic 0.92 0.92 0.92 4250\n", - " sarcastic 0.92 0.92 0.92 4250\n", - "\n", - " accuracy 0.92 8500\n", - " macro avg 0.92 0.92 0.92 8500\n", - " weighted avg 0.92 0.92 0.92 8500\n", - "\n", - "\n", - "[Test] Accuracy=0.8189 Macro-F1=0.8189 Weighted-F1=0.8189\n", - " precision recall f1-score support\n", - "\n", - "non-sarcastic 0.82 0.82 0.82 4250\n", - " sarcastic 0.82 0.82 0.82 4250\n", - "\n", - " accuracy 0.82 8500\n", - " macro avg 0.82 0.82 0.82 8500\n", - " weighted avg 0.82 0.82 0.82 8500\n", - "\n", - "Saved: outputs/classical/tfidf_lr/metrics_binary.json\n" - ] - } - ], - "source": [ - "# ── Evaluate on val and test ──────────────────────────────────────────────────\n", - "best_bin = gs_bin.best_estimator_\n", - "\n", - "val_metrics_bin, y_val_pred_bin = evaluate(best_bin, X_val, y_val, label_names_bin, \"Val\")\n", - "test_metrics_bin, y_test_pred_bin = evaluate(best_bin, X_test, y_test, label_names_bin, \"Test\")\n", - "\n", - "all_metrics_bin = {\"val\": val_metrics_bin, \"test\": test_metrics_bin}\n", - "\n", - "# Remove classification_report dict (too nested for JSON)\n", - "for split_m in all_metrics_bin.values():\n", - " split_m.pop(\"report\", None)\n", - "\n", - "with open(OUT_TFIDF / \"metrics_binary.json\", \"w\") as f:\n", - " json.dump(all_metrics_bin, f, indent=2)\n", - "print(\"Saved: outputs/classical/tfidf_lr/metrics_binary.json\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "[Val] Accuracy=0.9186 Macro-F1=0.9186 Weighted-F1=0.9186\n", + " precision recall f1-score support\n", + "\n", + "non-sarcastic 0.92 0.92 0.92 4250\n", + " sarcastic 0.92 0.92 0.92 4250\n", + "\n", + " accuracy 0.92 8500\n", + " macro avg 0.92 0.92 0.92 8500\n", + " weighted avg 0.92 0.92 0.92 8500\n", + "\n", + "\n", + "[Test] Accuracy=0.8189 Macro-F1=0.8189 Weighted-F1=0.8189\n", + " precision recall f1-score support\n", + "\n", + "non-sarcastic 0.82 0.82 0.82 4250\n", + " sarcastic 0.82 0.82 0.82 4250\n", + "\n", + " accuracy 0.82 8500\n", + " macro avg 0.82 0.82 0.82 8500\n", + " weighted avg 0.82 0.82 0.82 8500\n", + "\n", + "Saved: outputs/classical/tfidf_lr/metrics_binary.json\n" + ] + } + ], + "source": [ + "# ── Evaluate on val and test ──────────────────────────────────────────────────\n", + "best_bin = gs_bin.best_estimator_\n", + "\n", + "val_metrics_bin, y_val_pred_bin = evaluate(best_bin, X_val, y_val, label_names_bin, \"Val\")\n", + "test_metrics_bin, y_test_pred_bin = evaluate(best_bin, X_test, y_test, label_names_bin, \"Test\")\n", + "\n", + "all_metrics_bin = {\"val\": val_metrics_bin, \"test\": test_metrics_bin}\n", + "\n", + "# Remove classification_report dict (too nested for JSON)\n", + "for split_m in all_metrics_bin.values():\n", + " split_m.pop(\"report\", None)\n", + "\n", + "with open(OUT_TFIDF / \"metrics_binary.json\", \"w\") as f:\n", + " json.dump(all_metrics_bin, f, indent=2)\n", + "print(\"Saved: outputs/classical/tfidf_lr/metrics_binary.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 }, + "id": "ichiimejuKBc", + "outputId": "e3d5e010-d983-4f86-893b-cd38907ecc68" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "ichiimejuKBc", - "outputId": "e3d5e010-d983-4f86-893b-cd38907ecc68" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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Po0ePhqenJ7766ivEx8cXSICAp1WgyMhIzJkzB7Nnz4YgCPDz80N0dLTkuSWvM3XqVPFnMzMzVKhQAStXrtRZolq+fHls3rwZU6ZMwbhx4+Ds7Ixhw4ahZMmS4s0CC6N3796YMGGC5MnPwNOVW1WqVMFvv/0mOQECgNDQUNSoUQPfffcdPv/8cxQrVgweHh7o2bOnWA318/PDyZMnsWHDBiQnJ8PGxga1a9fGunXrtCZ0//DDD/jss88QHByM3NxcTJs2Tfyd2rRpE+rXr48yZcpIjo1IiVRCYZcrEBEZiMWLFyM4OBjx8fEFVqC9ys8//4ygoCAkJCSIS9f1QVJSEjw9PbFhwwZWgMjgMQEiInoBQRBQtWpVlChRotCTqDUaDapUqYJu3brhiy++eEcRFt6kSZNw4MABnDx5Uu5QiGTHBIiI6DnZ2dnYsWMHDh48iFWrVuH333/nk9OJFIgJEBHRc+Lj4+Hp6QlbW1sMHz4cc+bMkTskInoHmAARERGRweF9gIiIiMjgMAEiIiIig8MEiIiIiAyOIm+EaF5dN3etJTJ0aaeWyR0CkSKYvae/trr++/fojHL/G6DIBIiIiMggqTiwIxU/KSIiIjI4rAAREREphUoldwRFBitAREREZHBYASIiIlIKzgGSjAkQERGRUnAITDKmikRERGRwWAEiIiJSCg6BScYEiIiISCk4BCYZU0UiIiIyOKwAERERKQWHwCRjAkRERKQUHAKTjKkiERERGRxWgIiIiJSCQ2CS8ZMiIiIig8MKEBERkVJwDpBkTICIiIiUgkNgkvGTIiIiIoPDChAREZFScAhMMiZARERESsEhMMn4SREREZHBYQWIiIhIKVgBkowJEBERkVIYcQ6QVEwViYiIyOCwAkRERKQUHAKTjJ8UERERGRxWgIiIiJSC9wGSjAkQERGRUnAITDJ+UkRERGRwWAEiIiJSCg6BScYEiIiISCk4BCYZPykiIiIyOKwAERERKQWHwCRjBYiIiIgMDitARERESsE5QJIxASIiIlIKDoFJxlSRiIiIDA4rQERERErBITDJmAAREREpBYfAJGOqSERERAaHFSAiIiKl4BCYZEyAiIiIlIIJkGT8pIiIiMjgsAJERESkFJwELRkrQERERGRwWAEiIiJSCs4BkowJEBERkVJwCEwypopERERkcFgBIiIiUgoOgUnGBIiIiEgpOAQmGVNFIiIiMjisABERESmEihUgyZgAERERKQQTIOk4BEZEREQGhxUgIiIipWABSDJWgIiIiMjgsAJERESkEJwDJB0TICIiIoVgAiSdXgyBrV69Gps2bSrQvmnTJqxZs0aGiIiIiEjJ9CIBCgkJgYODQ4F2R0dHzJ07V4aIiIiIih6VSqXTTcn0YggsISEBnp6eBdrd3d2RkJAgQ0RERERFj9KTFl3SiwqQo6Mjzp07V6D97NmzKFGihAwRERERkZLpRQWoW7duGDlyJKysrNCwYUMAwKFDhzBq1Ch07dpV5uiIiIiKCBaAJNOLBGjWrFmIj49H06ZNUazY05A0Gg169+7NOUBERESkc3qRAJmammLjxo2YNWsWzp49C3Nzc/j4+MDd3V3u0IiIiIoMzgGSTi8SoGfKlSuHcuXKyR0GERFRkcQESDrZEqAxY8Zg1qxZsLCwwJgxY17Zd+HChe8pKiIiIjIEsiVAZ86cQV5envgzERERvR1WgKSTbRn8wYMHYWtrK/78qo2IiIheT84bIa5cuRJVqlSBtbU1rK2t4evriz179oj7Hz9+jKCgIJQoUQKWlpbo1KkTkpOTtc6RkJCAVq1aoXjx4nB0dMT48ePx5MkTrT5RUVH46KOPoFar4eXlhbCwsDf6rPTiPkD9+/fHgwcPCrRnZ2ejf//+MkREREREhfHhhx9i3rx5iImJwV9//YUmTZqgXbt2uHjxIgAgODgYO3fuxKZNm3Do0CHcuXMHHTt2FI/Pz89Hq1atkJubi2PHjmHNmjUICwvD1KlTxT43b95Eq1at0LhxY8TGxmL06NEYOHAg9u7dW+h4VYIgCG9/2W/H2NgYiYmJcHR01Gq/d+8enJ2dC2R/r2NefYQuwyMyWGmnlskdApEimL2nCScl+vyq0/PdX9PtrY63t7fH119/jc6dO6NkyZJYv349OnfuDAC4cuUKKlasiOjoaNStWxd79uxB69atcefOHTg5OQEAQkNDMXHiRNy9exempqaYOHEidu3ahQsXLojv0bVrV6SnpyMiIqJQsclaAcrMzERGRgYEQcCDBw+QmZkpbmlpadi9e3eBpIiIiIheTNdDYDk5OVp/mzMzM5GTk/PaOPLz87FhwwZkZ2fD19cXMTExyMvLg7+/v9inQoUKKFWqFKKjowEA0dHR8PHxEZMfAAgMDERmZqZYRYqOjtY6x7M+z85RGLImQLa2trC3t4dKpUK5cuVgZ2cnbg4ODujfvz+CgoLkDJGIiMhghYSEwMbGRmsLCQl5af/z58/D0tISarUaQ4cOxbZt2+Dt7Y2kpCSYmpqKc3+fcXJyQlJSEgAgKSlJK/l5tv/Zvlf1yczMxKNHjwp1bbLeB+jgwYMQBAFNmjTBli1bYG9vL+4zNTWFu7s7XF1dZYyQiIio6ND1KrDJkycXuFWNWq1+af/y5csjNjYWGRkZ2Lx5M/r06YNDhw7pNCZdkTUB8vPzA/B0UlOpUqW4fI+IiEiPqNXqVyY8/2VqagovLy8AQI0aNXDq1CksXrwYn376KXJzc5Genq5VBUpOToazszMAwNnZGSdPntQ637NVYs/3+e/KseTkZFhbW8Pc3LxQ16YXq8AuX76Mo0ePiq+XL1+OatWqoXv37khLS5MxMiIioqJDzmXwL6LRaJCTk4MaNWrAxMQE+/fvF/fFxcUhISEBvr6+AABfX1+cP38eKSkpYp/IyEhYW1vD29tb7PP8OZ71eXaOwtCLBGj8+PHIzMwE8HT8cMyYMWjZsiVu3rz52rtEExER0f9T6XgrhMmTJ+Pw4cOIj4/H+fPnMXnyZERFRaFHjx6wsbHBgAEDMGbMGBw8eBAxMTHo168ffH19UbduXQBAQEAAvL290atXL5w9exZ79+7FlClTEBQUJFahhg4dihs3bmDChAm4cuUKVqxYgd9++w3BwcGF/qj04llgN2/eFLO7LVu2oE2bNpg7dy5Onz6Nli1byhwdERERvU5KSgp69+6NxMRE2NjYoEqVKti7dy+aNWsGAFi0aBGMjIzQqVMn5OTkIDAwECtWrBCPNzY2Rnh4OIYNGwZfX19YWFigT58+mDlzptjH09MTu3btQnBwMBYvXowPP/wQP/zwAwIDAwsdr17cB8je3h5//vknvL29Ub9+ffTu3RuDBw9GfHw8vL298fDhw0Kdj/cBItIN3geISDfe132AnAZu0un5kn/4RKfn0yd6UQGqX78+xowZg3r16uHkyZPYuHEjAODvv//Ghx9+KHN0RERERQMXE0mnF3OAli1bhmLFimHz5s1YuXIlPvjgAwDAnj170Lx5c5mjIyIiIqXRiwpQqVKlEB4eXqB90aJFMkRDRERUNLECJJ1eJEDPe/z4MXJzc7XarK2tZYqGiIio6GACJJ1eDIFlZ2djxIgRcHR0hIWFhdYjMezs7OQOj4iIiBRGLxKgCRMm4MCBA1i5ciXUajV++OEHzJgxA66urli7dq3c4RERERUNMt4HqKjRiyGwnTt3Yu3atWjUqBH69euHBg0awMvLC+7u7li3bh169Oghd4hERESkIHpRAUpNTUXp0qUBPJ3vk5qaCuDp8vjDhw/LGRoREVGRoW+PwtBnepEAlS5dGjdv3gQAVKhQAb/99huAp5Wh5x+aRkRERC/HBEg6vUiA+vXrh7NnzwIAJk2ahOXLl8PMzAzBwcEYP368zNERERGR0ujFHKDnH2Lm7++PK1euICYmBl5eXqhSpYqMkRERERUdSq/a6JJeJED/5e7uDnd3d7nDICIiKlqY/0imF0NgI0eOxJIlSwq0L1u2DKNHj37/AREREZGi6UUCtGXLFtSrV69A+8cff4zNmzfLEBEREVHRw0nQ0unFENj9+/dhY2NToN3a2hr37t2TISIiIqKiR+lJiy7pRQXIy8sLERERBdr37Nkj3h+IiIiISFf0ogI0ZswYjBgxAnfv3kWTJk0AAPv378eCBQvw7bffyhscvdCgT+pjUOcGcHe1BwBcvpGEud/vwR9HLxXou33ZMATWq4Quwd9jZ9Q5sb1R7XKYNrw1Knm5IvtRLtbtPIFpy3ciP19T4Byl3Rxw/NdJyNdo4NJwwru7MCKZtWjWBHfu/Fug/dOu3fH5l9MwoG8v/HXqpNa+zl0+xZfTZmq1/b5tK35euxq34uNhYWmJgIDm+PzLae80dpIfK0DS6UUC1L9/f+Tk5GDOnDmYNWsWAMDDwwMrV65E7969ZY6OXuTf5HR8ufR3XEu4CxVU6NmmDjYtGoy6Xefh8o0ksd9nPRpDEAoe71PuA2xfOgxf/bgXA75cC1dHWyz9vCuMjY0wedE2rb7FihlhbUg/HD1zHXWrer7rSyOS1bqNm6HJzxdfX7t2FUMG9kOzwOZiW6fOXTB8xEjxtZm5udY51oatxto1P2HM2AnwqVIVjx49xJ1/CyZVRIZM9gToyZMnWL9+PTp27Ihhw4bh7t27MDc3h6Wlpdyh0SvsPnxB6/X05Tsx6JP6qF3FU0yAqpT7AKN6NUG9HvMRvy9Eq3/ngI9w4eodhHz/dOjzxu17+GLxdvzyVX/M+W43sh7m/O/cw9sg7mYyDp6MYwJEimdvb6/1+qcfvoebWynUrFVbbDMzM4NDyZIvPD4zIwPLl36LJctDUaeur9hernyFdxMw6RVWgKSTfQ5QsWLFMHToUDx+/BgAULJkSSY/RYyRkQqfBNaAhbkpTpx7+kgTczMThIX0xeh5vyH5/oMCx6hNi+FxTp5W26OcPJibmaJ6xVJim1+tcujYrDpGz/vt3V4EkR7Ky83FrvAdaN+xk9Yftt27dsKvXh10bNcaixctwKNHj8R90dFHodFokJKcjPZtWqBZk4YYP2YUkhIT5bgEet/4NHjJZK8AAUDt2rVx5syZN7r5YU5ODnJycrTaBE0+VEbGugqPXqKSlyui1oyFmWkxZD3KwadjV+HK/1d/5o/thONnbyI86vwLj408dhkjujdGl+Y1sPmP03AuYY3PB7cAALiUtAYA2NtYYNWMnug3ZQ0eZD9+PxdFpEcOHNiHBw8eoG37DmJbi5at4eLqCkdHR/z9dxy+XfgN4uNvYtHiZQCAf27/A41GwA+rQjFh0hewsrLCsiXfYsigfti8dQdMTE3luhwivaIXCdDw4cMxduxY/PPPP6hRowYsLCy09r/qcRghISGYMWOGVpuxUy2YuNR+yRGkK3/HJ6NO1xDYWJqjg391rJrZCwEDF6OMW0k0ql0OdbvOe+mx+49fweffbseSz7vix1m9kZP3BPNWRaD+R17QaJ5OGlrxZTdsjPgLR09ff1+XRKRXtm3Zgnr1G8LR0Uls69zlU/HnsuXKw8GhJAYP6IvbCQlwK1UKgqDBkyd5mDh5Cj6uVx8AMO/rhWjqVw8nT55AvfoN3vt10PvDITDpVILwoimq75eRUcGROJVKBUEQoFKpkP/chMD/elEFyLHBRFaAZLArdARu3L6Hxzl5GN7NT0xkAKBYMWPk52tw9Mx1BA5arHWcS0kbpGU+hLurPWK3fon6PeYj5lICEg/Ph6W5WuynUqlgbGyEJ0/yETT7V6z9/fh7uzZDlXZqmdwhGKw7d/5Fq0B/LFy8FI2b+L+038OHD+FbqzpWfPcD6tVvgO3btmDalM/xx/5DcHJ2Fvs1bvgxRnw2Gp0+6fI+wqf/MHtP5YYyY/fo9HzXF7TQ6fn0iV5UgG7evPnGx6rVaqjVaq02Jj/yMFKpoDYthtmhu7B62zGtfTGbv8CEBVuw69CFAscl3s0AAHRpXhO3E1Nx5sptAECjPgtg/Fxy3LpRFYzt64/GfRfiTkr6u7sQIj3w+7atsLcvgQYNG72yX9yVywCezp8EgGrVPwIAxMffFBOgjPR0pKelwcXV9d0FTFTE6EUCxAefFj0zP2uLvUcv4nZiGqwszPBpi5poWLMs2gxfgeT7D1448fl2Yhpu3bkvvg7u3RR/HLsMjUaDdk2rYVy/Zug54SexchR3M1nr+I+8S0EjCLh0nZM5Sdk0Gg1+37YVbdq1R7Fi//vP9O2EBOzetRMNGvrBxtYWV+Pi8PX8ENSoWUtc5eXh4YnGTZriq5A5mDp9JiwsLbFk0UJ4eJZGrdp15Lokek84AiadXiRAz1y6dAkJCQnIzc3Vam/btq1MEdHLlLS3xI+zesPZwRoZWY9x4eq/aDN8BQ6cuCL5HAH1vDFhYCDUJsVw/u9/8Unw9y+8kSKRoTkefQyJiXfQvmMnrXYTExOcOB6NdT+vxaNHD+Hs7AJ//wAMGjpcq9/skPn4+qu5GDF8CIxURqhRqxZWfvcDTExM3udlEOk1vZgDdOPGDXTo0AHnz58X5/4A/5vM9ao5QC9iXn2EzmMkMkScA0SkG+9rDlDZ8QUfK/U2rn7d/PWdiijZ7wMEAKNGjYKnpydSUlJQvHhxXLx4EYcPH0bNmjURFRUld3hERERFgkql203J9GIILDo6GgcOHICDgwOMjIxgZGSE+vXrIyQkBCNHjsSZM2fkDpGIiIgURC8qQPn5+bCysgIAODg44M6dOwCeTo6Oi4uTMzQiIqIiQ6VS6XRTMr2oAFWuXBlnz56Fp6cn6tSpg/nz58PU1BTff/89SpcuLXd4RERERYLCcxad0osEaMqUKcjOzgYAzJw5E61bt0aDBg1QokQJbNy4UeboiIiISGn0IgEKDAwUf/by8sKVK1eQmpoKOzs7xZfgiIiIdMXIiH8zpdKLOUD/lZmZicOHD3P+DxERUSFwFZh0epEAdenSBcuWPb3fyKNHj1CzZk106dIFPj4+2LJli8zRERERkdLoRQJ0+PBhNGjw9AnF27ZtgyAISE9Px5IlSzB79myZoyMiIioauApMOr1IgDIyMmBvbw8AiIiIQKdOnVC8eHG0atUKV69elTk6IiIiUhq9SIDc3NwQHR2N7OxsREREICAgAACQlpYGMzMzmaMjIiIqGjgHSDq9WAU2evRo9OjRA5aWlihVqhQaNWoE4OnQmI+Pj7zBERERFRFKH7bSJb1IgIYPH446deogISEBzZo1g5HR08JU6dKlOQeIiIiIdE4vEiAAqFGjBmrUqIGjR4+iZs2aUKvVaNWqldxhERERFRmsAEmnF3OAnteiRQv8+++/codBRERU5HAOkHR6lwAJgiB3CERERKRwejMERkRERG+HQ2DS6V0C9N1338HJyUnuMIiIiIoc5j/S6V0C1L17d7lDICIiIoXTiwQoOzsb8+bNw/79+5GSkgKNRqO1/8aNGzJFRkREVHRwCEw6vUiABg4ciEOHDqFXr15wcXHhv0AiIiJ6p/QiAdqzZw927dqFevXqyR0KERFRkcX6gXR6kQDZ2dmJD0MlIiKiN8MRFOn04j5As2bNwtSpU/Hw4UO5QyEiIiIDoBcVoAULFuD69etwcnKCh4cHTExMtPafPn1apsiIiIiKDhaApNOLBKh9+/Zyh0BERFTkcQhMOr1IgKZNmyZ3CERERGRA9CIBeiYmJgaXL18GAFSqVAnVq1eXOSIiIqKigwUg6fQiAUpJSUHXrl0RFRUFW1tbAEB6ejoaN26MDRs2oGTJkvIGSERERIqiF6vAPvvsMzx48AAXL15EamoqUlNTceHCBWRmZmLkyJFyh0dERFQkqFQqnW5KphcVoIiICOzbtw8VK1YU27y9vbF8+XIEBATIGBkREVHRofCcRaf0ogKk0WgKLH0HABMTkwLPBSMiIiJ6W3qRADVp0gSjRo3CnTt3xLZ///0XwcHBaNq0qYyRERERFR0cApNOLxKgZcuWITMzEx4eHihTpgzKlCkDDw8PZGZmYunSpXKHR0REVCSoVLrdlEwv5gC5ubnh9OnT2L9/v7gMvmLFivD395c5MiIiIlIivUiAAODAgQM4cOAAUlJSoNFocObMGaxfvx4A8NNPP8kcHRERkf5T+rCVLunFENiMGTMQEBCA/fv34969e0hLS9PaiIiI6PXknAMUEhKCWrVqwcrKCo6Ojmjfvj3i4uK0+jRq1KjAewwdOlSrT0JCAlq1aoXixYvD0dER48ePx5MnT7T6REVF4aOPPoJarYaXlxfCwsIK/VnpRQUoNDQUYWFh6NWrl9yhEBER0Rs4dOgQgoKCUKtWLTx58gSff/45AgICcOnSJVhYWIj9Bg0ahJkzZ4qvixcvLv6cn5+PVq1awdnZGceOHUNiYiJ69+4NExMTzJ07FwBw8+ZNtGrVCkOHDsW6deuwf/9+DBw4EC4uLggMDJQcr14kQLm5ufj444/lDoOIiKhI0/UIWE5ODnJycrTa1Go11Gp1gb4RERFar8PCwuDo6IiYmBg0bNhQbC9evDicnZ1f+H5//PEHLl26hH379sHJyQnVqlXDrFmzMHHiREyfPh2mpqYIDQ2Fp6cnFixYAODpnOE///wTixYtKlQCpBdDYAMHDhTn+xAREZF+CAkJgY2NjdYWEhIi6diMjAwAgL29vVb7unXr4ODggMqVK2Py5Ml4+PChuC86Oho+Pj5wcnIS2wIDA5GZmYmLFy+Kff67SCowMBDR0dGFuja9qAA9fvwY33//Pfbt24cqVaoUuCniwoULZYqMiIio6ND1JOjJkydjzJgxWm0vqv78l0ajwejRo1GvXj1UrlxZbO/evTvc3d3h6uqKc+fOYeLEiYiLi8PWrVsBAElJSVrJDwDxdVJS0iv7ZGZm4tGjRzA3N5d0bXqRAJ07dw7VqlUDAFy4cEFrH2e0ExERSaPrP5kvG+56naCgIFy4cAF//vmnVvvgwYPFn318fODi4oKmTZvi+vXrKFOmzFvHWxh6kQAdPHhQ7hCIiIhIB0aMGIHw8HAcPnwYH3744Sv71qlTBwBw7do1lClTBs7Ozjh58qRWn+TkZAAQ5w05OzuLbc/3sba2llz9AfRkDhARERG9PTmXwQuCgBEjRmDbtm04cOAAPD09X3tMbGwsAMDFxQUA4Ovri/PnzyMlJUXsExkZCWtra3h7e4t99u/fr3WeyMhI+Pr6FipeJkBEREQKIeejMIKCgvDLL79g/fr1sLKyQlJSEpKSkvDo0SMAwPXr1zFr1izExMQgPj4eO3bsQO/evdGwYUNUqVIFABAQEABvb2/06tULZ8+exd69ezFlyhQEBQWJQ3FDhw7FjRs3MGHCBFy5cgUrVqzAb7/9huDg4ELFywSIiIiI3trKlSuRkZGBRo0awcXFRdw2btwIADA1NcW+ffsQEBCAChUqYOzYsejUqRN27twpnsPY2Bjh4eEwNjaGr68vevbsid69e2vdN8jT0xO7du1CZGQkqlatigULFuCHH34o1BJ4AFAJgiDo5tL1h3n1EXKHQKQIaaeWyR0CkSKYvacZt82WHdfp+SJH1NXp+fSJXkyCJiIiorfHhdPScQiMiIiIDA4rQERERArBe+dJxwoQERERGRxWgIiIiBTCiAUgyZgAERERKQSHwKTjEBgREREZHFaAiIiIFIIFIOmYABERESmECsyApOIQGBERERkcVoCIiIgUgqvApGMCREREpBBcBSYdh8CIiIjI4LACREREpBAsAEnHChAREREZHFaAiIiIFMKIJSDJmAAREREpBPMf6TgERkRERAaHFSAiIiKF4DJ46ZgAERERKQTzH+k4BEZEREQGhxUgIiIiheAqMOlYASIiIiKDwwoQERGRQrD+Ix0TICIiIoXgKjDpOARGREREBocVICIiIoUwYgFIMiZARERECsEhMOk4BEZEREQGhxUgIiIihWABSDomQERERArBITDpOARGREREBocVICIiIoXgKjDpWAEiIiIig8MKEBERkUJwDpB0TICIiIgUgumPdG80BHbkyBH07NkTvr6++PfffwEAP//8M/7880+dBkdERET0LhQ6AdqyZQsCAwNhbm6OM2fOICcnBwCQkZGBuXPn6jxAIiIiksZIpdLppmSFToBmz56N0NBQrFq1CiYmJmJ7vXr1cPr0aZ0GR0RERNKpVLrdlKzQCVBcXBwaNmxYoN3Gxgbp6em6iImIiIjonSp0AuTs7Ixr164VaP/zzz9RunRpnQRFREREhadSqXS6KVmhE6BBgwZh1KhROHHiBFQqFe7cuYN169Zh3LhxGDZs2LuIkYiIiCTgEJh0hV4GP2nSJGg0GjRt2hQPHz5Ew4YNoVarMW7cOHz22WfvIkYiIiIinSp0AqRSqfDFF19g/PjxuHbtGrKysuDt7Q1LS8t3ER8RERFJpPSVW7r0xjdCNDU1hbe3ty5jISIiInovCp0ANW7c+JUTow4cOPBWAREREdGbYQFIukInQNWqVdN6nZeXh9jYWFy4cAF9+vTRVVxERERUSEpfuaVLhU6AFi1a9ML26dOnIysr660DIiIiInrXVIIgCLo40bVr11C7dm2kpqbq4nRv5WGeTi6JyOCVqDta7hCIFOFRzOL38j6fbbus0/Mt7VBRp+fTJzp7Gnx0dDTMzMx0dToiIiIqJA6BSVfoBKhjx45arwVBQGJiIv766y98+eWXOguMiIiI6F0pdAJkY2Oj9drIyAjly5fHzJkzERAQoLPAiIiIqHCMWACSrFAJUH5+Pvr16wcfHx/Y2dm9q5iIiIiI3qlCPQvM2NgYAQEBfOo7ERGRHjJS6XZTskI/DLVy5cq4cePGu4iFiIiI3gKfBi9doROg2bNnY9y4cQgPD0diYiIyMzO1NiIiIiJ9J3kO0MyZMzF27Fi0bNkSANC2bVut7FAQBKhUKuTn5+s+SiIiInotpQ9b6ZLkBGjGjBkYOnQoDh48+C7jISIiojek8FErnZKcAD27YbSfn987C4aIiIjofSjUMnilT4giIiIqyoz4d1qyQiVA5cqVe20SpA/PAiMiIjJEhV7ZZMAKlQDNmDGjwJ2giYiIiIqaQiVAXbt2haOj47uKhYiIiN4CR8Ckk1wt4/wfIiIiepmQkBDUqlULVlZWcHR0RPv27REXF6fV5/HjxwgKCkKJEiVgaWmJTp06ITk5WatPQkICWrVqheLFi8PR0RHjx4/HkydPtPpERUXho48+glqthpeXF8LCwgodr+QE6NkqMCIiItJPRiqVTrfCOHToEIKCgnD8+HFERkYiLy8PAQEByM7OFvsEBwdj586d2LRpEw4dOoQ7d+6gY8eO4v78/Hy0atUKubm5OHbsGNasWYOwsDBMnTpV7HPz5k20atUKjRs3RmxsLEaPHo2BAwdi7969hYpXJSgws3mYp7hLIpJFibqj5Q6BSBEexSx+L+8zde9VnZ7vi0alkJOTo9WmVquhVqtfe+zdu3fh6OiIQ4cOoWHDhsjIyEDJkiWxfv16dO7cGQBw5coVVKxYEdHR0ahbty727NmD1q1b486dO3BycgIAhIaGYuLEibh79y5MTU0xceJE7Nq1CxcuXBDfq2vXrkhPT0dERITka+OEcSIiInqhkJAQ2NjYaG0hISGSjs3IyAAA2NvbAwBiYmKQl5cHf39/sU+FChVQqlQpREdHAwCio6Ph4+MjJj8AEBgYiMzMTFy8eFHs8/w5nvV5dg6pCjUJmoiIiPSXrh+FMXnyZIwZM0arTUr1R6PRYPTo0ahXrx4qV64MAEhKSoKpqSlsbW21+jo5OSEpKUns83zy82z/s32v6pOZmYlHjx7B3Nxc0rUxASIiIlIIXd8IUepw138FBQXhwoUL+PPPP3Uajy5xCIyIiIh0ZsSIEQgPD8fBgwfx4Ycfiu3Ozs7Izc1Fenq6Vv/k5GQ4OzuLff67KuzZ69f1sba2llz9AZgAERERKYZKpdutMARBwIgRI7Bt2zYcOHAAnp6eWvtr1KgBExMT7N+/X2yLi4tDQkICfH19AQC+vr44f/48UlJSxD6RkZGwtraGt7e32Of5czzr8+wcUnEIjIiISCF0PQeoMIKCgrB+/Xr8/vvvsLKyEufs2NjYwNzcHDY2NhgwYADGjBkDe3t7WFtb47PPPoOvry/q1q0LAAgICIC3tzd69eqF+fPnIykpCVOmTEFQUJA4FDd06FAsW7YMEyZMQP/+/XHgwAH89ttv2LVrV6HiZQWIiIiI3trKlSuRkZGBRo0awcXFRdw2btwo9lm0aBFat26NTp06oWHDhnB2dsbWrVvF/cbGxggPD4exsTF8fX3Rs2dP9O7dGzNnzhT7eHp6YteuXYiMjETVqlWxYMEC/PDDDwgMDCxUvLwPEBG9FO8DRKQb7+s+QHP3X9fp+T5vWkan59MnrAARERGRweEcICIiIoWQcw5QUcMEiIiISCGYAEnHITAiIiIyOKwAERERKYRKx3eCVjImQERERArBITDpOARGREREBocVICIiIoXgCJh0TICIiIgUQtdPg1cyDoERERGRwWEFiIiISCE4CVo6VoCIiIjI4LACREREpBCcAiQdEyAiIiKFMAIzIKk4BEZEREQGhxUgIiIiheAQmHRMgIiIiBSCq8Ck4xAYERERGRxWgIiIiBSCd4KWjhUgIiIiMjisABERESkEC0DSMQEiIiJSCA6BScchMCIiIjI4rAAREREpBAtA0jEBIiIiUggO60jHz4qIiIgMDitARERECqHiGJhkTICIiIgUgumPdBwCIyIiIoPDChAREZFC8D5A0rECRERERAaHFSAiIiKFYP1HOiZARERECsERMOk4BEZEREQGhxUgIiIiheB9gKRjAkRERKQQHNaRjp8VERERGRxWgIiIiBSCQ2DSMQEiIiJSCKY/0nEIjIiIiAwOK0BEREQKwSEw6VgBIiIiIoPDChAREZFCsKohHRMgIiIiheAQmHRMFomIiMjgsAJERESkEKz/SMcEiIiISCE4AiYdh8CIiIjI4MieAIWEhOCnn34q0P7TTz/hq6++kiEiIiKioskIKp1uSiZ7AvTdd9+hQoUKBdorVaqE0NBQGSIiIiIipZN9DlBSUhJcXFwKtJcsWRKJiYkyRERERFQ0cQ6QdLJXgNzc3HD06NEC7UePHoWrq6sMERERERVNKh3/o2SyV4AGDRqE0aNHIy8vD02aNAEA7N+/HxMmTMDYsWNljo6IiIiUSPYEaPz48bh//z6GDx+O3NxcAICZmRkmTpyIyZMnyxwdERFR0cEhMOlUgiAIcgcBAFlZWbh8+TLMzc1RtmxZqNXqNz7Xwzy9uCSiIq9E3dFyh0CkCI9iFr+X94m4eFen52teqaROz6dPZK8APWNpaYlatWrJHQYREREZAFkSoI4dOyIsLAzW1tbo2LHjK/tu3br1PUVFRERUtHEITDpZEiAbGxvxibXW1tZ8ei0REZEO8M+pdLIkQKtXrxZ/DgsLkyMEIiIiMmCy3weoSZMmSE9PL9CemZkpLosnIiKi1+N9gKSTPQGKiooSl78/7/Hjxzhy5IgMEREREZHSybYK7Ny5c+LPly5dQlJSkvg6Pz8fERER+OCDD+QIjYiIqEgyUnbRRqdkqwBVq1YN1atXh0qlQpMmTVCtWjVxq1GjBmbPno2pU6fKFR4REVGRI+cQ2OHDh9GmTRu4urpCpVJh+/btWvv79u0LlUqltTVv3lyrT2pqKnr06AFra2vY2tpiwIAByMrK0upz7tw5NGjQAGZmZnBzc8P8+fPf6LOSrQJ08+ZNCIKA0qVL4+TJkyhZ8n83WzI1NYWjoyOMjY3lCo+IiIgKITs7G1WrVkX//v1feoub5s2bay2E+u9Nj3v06IHExERERkYiLy8P/fr1w+DBg7F+/XoAT+cHBwQEwN/fH6GhoTh//jz69+8PW1tbDB48uFDxypYAubu7AwA0Go1cIRARESmKnMvgW7RogRYtWryyj1qthrOz8wv3Xb58GRERETh16hRq1qwJAFi6dClatmyJb775Bq6urli3bh1yc3Px008/wdTUFJUqVUJsbCwWLlxY6ARI9knQa9aswa5du8TXEyZMgK2tLT7++GPcunVLxsiIiIiKFl0PgeXk5CAzM1Nry8nJeeP4oqKi4OjoiPLly2PYsGG4f/++uC86Ohq2trZi8gMA/v7+MDIywokTJ8Q+DRs2hKmpqdgnMDAQcXFxSEtLK1QssidAc+fOhbm5OYCnF7Zs2TLMnz8fDg4OCA4Oljk6IiIiwxUSEgIbGxutLSQk5I3O1bx5c6xduxb79+/HV199hUOHDqFFixbIz88HACQlJcHR0VHrmGLFisHe3l5cKJWUlAQnJyetPs9eP7+YSgrZnwV2+/ZteHl5AQC2b9+Ozp07Y/DgwahXrx4aNWokb3BERERFiK5XgU2ePBljxozRanvTh5V37dpV/NnHxwdVqlRBmTJlEBUVhaZNm75VnG9C9gqQpaWlWAL7448/0KxZMwCAmZkZHj16JGdoRERERYquh8DUajWsra21tjdNgP6rdOnScHBwwLVr1wAAzs7OSElJ0erz5MkTpKamivOGnJ2dkZycrNXn2euXzS16GdkToGbNmmHgwIEYOHAg/v77b7Rs2RIAcPHiRXh4eMgbHBEREb0T//zzD+7fvw8XFxcAgK+vL9LT0xETEyP2OXDgADQaDerUqSP2OXz4MPLy8sQ+kZGRKF++POzs7Ar1/rIPgS1fvhxTpkzB7du3sWXLFpQoUQIAEBMTg27duskcHRVGy4AmSLxzp0B7l67dMXnKVNy7dxfffvM1jkcfQ/bDbHh4eGLA4CHwbxYo9h01Yhj+vnIFqan3YW1tgzp1fTFyzFg4OjoVOC+REgzqXA+DOteHu4s9AODyjUTMXbUXfxy7XKDv9iVDEFjPG13G/oCdUee19vVsUxsjezRG2VIlkZn9GFv3xSL4q80AgC8GN8eUIQVX52Q/yoFD/Qnv4KpILnKuAsvKyhKrOcDT293ExsbC3t4e9vb2mDFjBjp16gRnZ2dcv34dEyZMgJeXFwIDn/4NqFixIpo3b45BgwYhNDQUeXl5GDFiBLp27QpXV1cAQPfu3TFjxgwMGDAAEydOxIULF7B48WIsWrSo0PGqBEEQdHPp+uNhnuIuqUhITU2FRpMvvr529SqGDeqPVT+tQc3adTBsUH88ePAAk774Era2dtizOxyhy5di3cbNqFDRGwDwy9owVKlaDQ4lSyIlORmLvnl6g6s16zbIck2GrkTd0XKHoHgtG1RCvkbAtYS7UKmAnq1rI7h3E9Tt/jUu3/jfpM7PujdCkzrl0bx+wQRoZI9GGNWzMT5fvAMnL8TDwkwNd1d77Dp8AQBgYW4Ky+Lawxa7VwYh5lICBk9f/34u1MA9iln8Xt7nz6uFWwn1OvXLSq+qREVFoXHjxgXa+/Tpg5UrV6J9+/Y4c+YM0tPT4erqioCAAMyaNUtrUnNqaipGjBiBnTt3wsjICJ06dcKSJUtgaWkp9jl37hyCgoJw6tQpODg44LPPPsPEiRMLfW16kwA9fPgQCQkJBZ4LVqVKlcKfiwmQXvh63lwcORSF33fvhUqlwse1PsLnX05D67btxD6N6tXByOBx6Nj5kxeeI+rgAYwZGYQTp8/BxMTkfYVO/48JkDz+PTAXny/egTW/HwcAVCn3AbZ+Oxj1en2D+D9mayVAtlbmuB4xE51Gr0LUqb8lnd+nrCtObpgI/wGLcTT2xju7Dvqf95UAHdVxAlSvEAlQUSP7ENjdu3fRt29fREREvHD/s+VxVLTk5eVid/gO9Oz99NbnAFC1WjX8EbEbDfz8YGVljT8i9iAnNxc1a9d+4TkyMtKxJ3wnqlarzuSHDIKRkQqd/KvBwlyNE+duAgDMzUwQNqc3Rn+1Ccn3HxQ4pmnd8jBSqeDqaIMzmyfDqrgZjp+7iUmLtuOf5PQXvk+/9r74Oz6ZyY8CGck5BlbEyJ4AjR49GhkZGThx4gQaNWqEbdu2ITk5GbNnz8aCBQtee3xOTk6BmzLlG5nqbJY6vZmD+/fjwYMHaNO+g9g2f8G3mDguGI3q1UWxYsVgZmaGhd8uRalS7lrHLl74DTb8ug6PHz2CT9WqWLI89H2HT/ReVfJyQdTqYJiZFkPWoxx8Ou5HXLn5dGXL/DEdcPzcTYQfuvDCYz0/cICRkQoT+jfDuG+2IvPBI0wb3grhK4aj1qdfIe+J9v9Eqk2L4dMWNbAgbN87vy4ifSb7KrADBw5g4cKFqFmzJoyMjODu7o6ePXti/vz5km629KKbNH3z1ZvdpIl0Z/vWzahXv4HW5OXlyxbjwYMHCP1hNX7ZsBk9e/fFhHHBuPp3nNaxvfsNwIZNW7Hy+x9hbGSMLydPgp6M1BK9E3/Hp6BOt/lo2GchVm0+ilUzeqCCpxNaNayMRrXKYfw3W196rEqlgqlJMYz9egv2RV/ByQu30OfzNfByKwm/WmUL9G/XuAqsLMzwS/ipd3lJJBOVjjclk70ClJ2dLd750c7ODnfv3kW5cuXg4+OD06dPv/b4F92kKd/I9CW96X24c+dfnDgejW++XSq23U5IwMb167B5+06U8Xr6H+XyFSrg9OkYbPx1PaZMmyH2tbOzg52dHdw9POFZugya+zfCubOxqFqt+nu/FqL3Ie9JPm78cw8AcObKP6jhXQpB3fzwOCcPpT8sgaSoeVr9f53fH0fPXEfgkGVIupcJALjy3ITpe+nZuJeeDTfngvM3+rb3xZ4jF5GSWnA4jRRA6VmLDsmeAJUvXx5xcXHw8PBA1apV8d1338HDwwOhoaHivQFeRa1WFxju4iRoee3YthX29iXQoKGf2Pb48dObWqpU2kVHYyMjCMLLH4ir+f99ef+ZHE+kZEZGKqhNi2H2d3uwevtxrX0xv03ChIXbxBVe0WefzuMp6+6Ef1MyAAB21sXhYGuBhMRUrWPdXe3hV9MLncf88B6ugki/yZ4AjRo1ComJiQCAadOmoXnz5li3bh1MTU0RFhYmb3BUaBqNBr9v34bW7dqjWLH//Xp5eJaGWyl3zJ45DWPGTYCNjS0OHtiH49HHsPj/5/icP3cWFy+cR/WPasDK2hr/3L6NFUsXw82tFKqw+kMKNXNEa+w9ehm3k9JgZaHGp81roGENL7QZEYrk+w9eOPH5dlIabt15mtxcS7iLnVHn8M24jhgxZwMys3Mwc0RrxMUn49BfV7WO69OuLpLuZWLv0Uvv5dro/VOxBCSZ7AlQz549xZ9r1KiBW7du4cqVKyhVqhQcHBxkjIzexInoY0hKvIP2HTpqtZuYmGDpyu+wZNECjAoahoePHsLNrRRmzpknVorMzMxwYF8kQpcvxaNHj+BQsiQ+rtcAg4YM03ryL5GSlLSzwo8ze8DZwQYZWY9w4eodtBkRigMn4l5/8P8bMPUXzB/TEVsXD4FGI+DP09fQ7rNQPHnyv+qqSqVCr9a18fPOk9BoWCVXKi4Ck05v7gOkSxwCI9IN3geISDfe132ATt7I0On5ape20en59Insq8A6deqEr776qkD7/Pnz8cknL745HhERERXEVWDSyZ4AHT58WHwA6vNatGiBw4cPyxARERERKZ3sc4CysrJeOL/DxMQEmZmZMkRERERURCm9bKNDsleAfHx8sHHjxgLtGzZsgLe3twwRERERFU0qHf+jZLJXgL788kt07NgR169fR5MmTQAA+/fvx6+//opNmzbJHB0REREpkewJUJs2bbB9+3bMnTsXmzdvhrm5OapUqYJ9+/bBz8/v9ScgIiIiAFwGXxiyJkBPnjzB3Llz0b9/fxw9elTOUIiIiIo85j/SyToHqFixYpg/fz6ePHkiZxhERERkYGSfBN20aVMcOnRI7jCIiIiKPt4ISDLZ5wC1aNECkyZNwvnz51GjRg1YWFho7W/btq1MkREREZFSyf4oDCOjlxehVCoV8vPzC31OPgqDSDf4KAwi3Xhfj8I4c6vgw3PfRnV3K52eT5/IXgHSaDSv70RERESvxVVg0sk+B4iIiIjofZO9AgQA2dnZOHToEBISEpCbm6u1b+TIkTJFRUREVLSwACSd7AnQmTNn0LJlSzx8+BDZ2dmwt7fHvXv3ULx4cTg6OjIBIiIikooZkGSyD4EFBwejTZs2SEtLg7m5OY4fP45bt26hRo0a+Oabb+QOj4iIiBRI9gQoNjYWY8eOhZGREYyNjZGTkwM3NzfMnz8fn3/+udzhERERFRl8GKp0sidAJiYm4lJ4R0dHJCQkAABsbGxw+/ZtOUMjIiIqUlQq3W5KJvscoOrVq+PUqVMoW7Ys/Pz8MHXqVNy7dw8///wzKleuLHd4REREpECyV4Dmzp0LFxcXAMCcOXNgZ2eHYcOG4d69e/juu+9kjo6IiKjo4JMwpJO9AlSpUiU8uxm1o6MjQkNDsW3bNnh7e6NatWryBkdERESKJHsFqF27dli7di0AID09HXXr1sXChQvRvn17rFy5UuboiIiIihCWgCSTPQE6ffo0GjRoAADYvHkznJyccOvWLaxduxZLliyROToiIqKig6vApJM9AXr48CGsrJ4+bO2PP/5Ax44dYWRkhLp16+LWrVsyR0dERERKJHsC5OXlhe3bt+P27dvYu3cvAgICAAApKSmwtraWOToiIqKig8vgpZM9AZo6dSrGjRsHDw8P1KlTB76+vgCeVoOqV68uc3RERERFB6cASSf7KrDOnTujfv36SExMRNWqVcX2pk2bokOHDjJGRkREREolewIEAM7OznB2dtZqq127tkzREBERFVFKL9vokF4kQERERPT2lL5yS5dknwNERERE9L6xAkRERKQQSl+5pUusABEREZHBYQWIiIhIIVgAko4JEBERkVIwA5KMQ2BERERkcFgBIiIiUggug5eOCRAREZFCcBWYdBwCIyIiIoPDChAREZFCsAAkHStAREREZHBYASIiIlIKloAkYwJERESkEFwFJh2HwIiIiMjgsAJERESkEFwGLx0TICIiIoVg/iMdh8CIiIjI4LACREREpBQsAUnGBIiIiEghuApMOg6BERERkcFhBYiIiEghuApMOlaAiIiIyOCwAkRERKQQLABJxwSIiIhIITgEJh2HwIiIiMjgsAJERESkGCwBScUKEBERkUKoVLrdCuPw4cNo06YNXF1doVKpsH37dq39giBg6tSpcHFxgbm5Ofz9/XH16lWtPqmpqejRowesra1ha2uLAQMGICsrS6vPuXPn0KBBA5iZmcHNzQ3z589/k4+KCRARERG9vezsbFStWhXLly9/4f758+djyZIlCA0NxYkTJ2BhYYHAwEA8fvxY7NOjRw9cvHgRkZGRCA8Px+HDhzF48GBxf2ZmJgICAuDu7o6YmBh8/fXXmD59Or7//vtCx6sSBEEo/GXqt4d5irskIlmUqDta7hCIFOFRzOL38j530nN1ej5XW9M3Ok6lUmHbtm1o3749gKfVH1dXV4wdOxbjxo0DAGRkZMDJyQlhYWHo2rUrLl++DG9vb5w6dQo1a9YEAERERKBly5b4559/4OrqipUrV+KLL75AUlISTE2fxjZp0iRs374dV65cKVSMrAAREREphK6HwHJycpCZmam15eTkFDqumzdvIikpCf7+/mKbjY0N6tSpg+joaABAdHQ0bG1txeQHAPz9/WFkZIQTJ06IfRo2bCgmPwAQGBiIuLg4pKWlFSomJkBERET0QiEhIbCxsdHaQkJCCn2epKQkAICTk5NWu5OTk7gvKSkJjo6OWvuLFSsGe3t7rT4vOsfz7yEVV4EREREphK4fhjp58mSMGTNGq02tVuv0PeTCBIiIiIheSK1W6yThcXZ2BgAkJyfDxcVFbE9OTka1atXEPikpKVrHPXnyBKmpqeLxzs7OSE5O1urz7PWzPlJxCIyIiEgpVDredMTT0xPOzs7Yv3+/2JaZmYkTJ07A19cXAODr64v09HTExMSIfQ4cOACNRoM6deqIfQ4fPoy8vDyxT2RkJMqXLw87O7tCxcQEiIiISCHkzH+ysrIQGxuL2NhYAE8nPsfGxiIhIQEqlQqjR4/G7NmzsWPHDpw/fx69e/eGq6uruFKsYsWKaN68OQYNGoSTJ0/i6NGjGDFiBLp27QpXV1cAQPfu3WFqaooBAwbg4sWL2LhxIxYvXlxgmE4KDoERERHRW/vrr7/QuHFj8fWzpKRPnz4ICwvDhAkTkJ2djcGDByM9PR3169dHREQEzMzMxGPWrVuHESNGoGnTpjAyMkKnTp2wZMkScb+NjQ3++OMPBAUFoUaNGnBwcMDUqVO17hUkFe8DREQvxfsAEenG+7oPUMqDvNd3KgRHKxOdnk+fsAJERESkELpeBaZknANEREREBocVICIiIqVgAUgyJkBEREQKwfxHOg6BERERkcFhBYiIiEghVCwBScYKEBERERkcVoCIiIgUgsvgpWMCREREpBAcApOOQ2BERERkcJgAERERkcHhEBgREZFCcAhMOlaAiIiIyOCwAkRERKQQXAUmHStAREREZHBYASIiIlIIzgGSjgkQERGRQjD/kY5DYERERGRwWAEiIiJSCpaAJGMCREREpBBcBSYdh8CIiIjI4LACREREpBBcBSYdEyAiIiKFYP4jHYfAiIiIyOCwAkRERKQULAFJxgoQERERGRxWgIiIiBSCy+ClYwJERESkEFwFJh2HwIiIiMjgqARBEOQOggxPTk4OQkJCMHnyZKjVarnDISqS+D0ienNMgEgWmZmZsLGxQUZGBqytreUOh6hI4veI6M1xCIyIiIgMDhMgIiIiMjhMgIiIiMjgMAEiWajVakybNo0TN4neAr9HRG+Ok6CJiIjI4LACRERERAaHCRAREREZHCZAREREZHCYABEBaNSoEUaPHi13GESy69u3L9q3by93GETvHCdBk0GJiopC48aNkZaWBltbW7E9NTUVJiYmsLKyki84ovcoPj4enp6eOHPmDKpVqya2Z2RkQBAEre8HkRLxafCkV/Ly8mBiYvLe39fe3v69vyfRq8j1XbCxsXnv70kkBw6BKUSjRo0wcuRITJgwAfb29nB2dsb06dPF/QkJCWjXrh0sLS1hbW2NLl26IDk5Wdw/ffp0VKtWDT///DM8PDxgY2ODrl274sGDB6983xUrVqBs2bIwMzODk5MTOnfuLO6LiIhA/fr1YWtrixIlSqB169a4fv26uD8+Ph4qlQobN26En58fzMzMsG7dOgDATz/9hEqVKkGtVsPFxQUjRowQj1u4cCF8fHxgYWEBNzc3DB8+HFlZWeL+W7duoU2bNrCzs4OFhQUqVaqE3bt3Iz4+Ho0bNwYA2NnZQaVSoW/fvuLn9/wQWE5ODiZOnAg3Nzeo1Wp4eXnhxx9/lP4vhAzS5s2b4ePjA3Nzc5QoUQL+/v7Izs7GqVOn0KxZMzg4OMDGxgZ+fn44ffq01rEqlQorV65E27ZtYWFhgTlz5gAAdu7ciVq1asHMzAwODg7o0KGDeMzPP/+MmjVrwsrKCs7OzujevTtSUlLE/WlpaejRowdKliwJc3NzlC1bFqtXrwYAeHp6AgCqV68OlUqFRo0aASg4BKbRaDB//nx4eXlBrVajVKlSYmxERRkTIAVZs2YNLCwscOLECcyfPx8zZ85EZGQkNBoN2rVrh9TUVBw6dAiRkZG4ceMGPv30U63jr1+/ju3btyM8PBzh4eE4dOgQ5s2b99L3++uvvzBy5EjMnDkTcXFxiIiIQMOGDcX92dnZGDNmDP766y/s378fRkZG6NChAzQajdZ5Jk2ahFGjRuHy5csIDAzEypUrERQUhMGDB+P8+fPYsWMHvLy8xP5GRkZYsmQJLl68iDVr1uDAgQOYMGGCuD8oKAg5OTk4fPgwzp8/j6+++gqWlpZwc3PDli1bAABxcXFITEzE4sWLX3htvXv3xq+//oolS5bg8uXL+O6772BpaSn9XwYZnMTERHTr1g39+/fH5cuXERUVhY4dO0IQBDx48AB9+vTBn3/+iePHj6Ns2bJo2bJlgf/BmD59Ojp06IDz58+jf//+2LVrFzp06ICWLVvizJkz2L9/P2rXri32z8vLw6xZs3D27Fls374d8fHxYlIPAF9++SUuXbqEPXv24PLly1i5ciUcHBwAACdPngQA7Nu3D4mJidi6desLr2vy5MmYN2+eeK7169fDyclJx58ekQwEUgQ/Pz+hfv36Wm21atUSJk6cKPzxxx+CsbGxkJCQIO67ePGiAEA4efKkIAiCMG3aNKF48eJCZmam2Gf8+PFCnTp1XvqeW7ZsEaytrbWOeZW7d+8KAITz588LgiAIN2/eFAAI3377rVY/V1dX4YsvvpB0TkEQhE2bNgklSpQQX/v4+AjTp09/Yd+DBw8KAIS0tDStdj8/P2HUqFGCIAhCXFycAECIjIyUHANRTEyMAECIj49/bd/8/HzByspK2Llzp9gGQBg9erRWP19fX6FHjx6SYzh16pQAQHjw4IEgCILQpk0boV+/fi/s++z7d+bMGa32Pn36CO3atRMEQRAyMzMFtVotrFq1SnIMREUFK0AKUqVKFa3XLi4uSElJweXLl+Hm5gY3Nzdxn7e3N2xtbXH58mWxzcPDQ2sS8LPjAWDdunWwtLQUtyNHjqBZs2Zwd3dH6dKl0atXL6xbtw4PHz4Uj7969Sq6deuG0qVLw9raGh4eHgCeDsc9r2bNmuLPKSkpuHPnDpo2bfrS69y3bx+aNm2KDz74AFZWVujVqxfu378vvvfIkSMxe/Zs1KtXD9OmTcO5c+ekfoQAgNjYWBgbG8PPz69Qx5Fhq1q1Kpo2bQofHx988sknWLVqFdLS0gAAycnJGDRoEMqWLQsbGxtYW1sjKyvrld8F4Onv4qu+CzExMWjTpg1KlSoFKysr8Xf22XmHDRuGDRs2oFq1apgwYQKOHTtWqGu6fPkycnJyXhkDUVHFBEhB/jthUqVSFRhuetPj27Zti9jYWHF7Nu/g9OnT+PXXX+Hi4oKpU6eiatWqSE9PBwC0adMGqampWLVqFU6cOIETJ04AAHJzc7Xex8LCQvzZ3Nz8lTHGx8ejdevWqFKlCrZs2YKYmBgsX75c67wDBw7EjRs30KtXL5w/fx41a9bE0qVLJX8Or4uB6EWMjY0RGRmJPXv2wNvbG0uXLkX58uVx8+ZN9OnTB7GxsVi8eDGOHTuG2NhYlChR4pXfBeDVv4vZ2dkIDAyEtbU11q1bh1OnTmHbtm0A/vddaNGiBW7duoXg4GDxfyzGjRsn+Zr4XSAlYwJkACpWrIjbt2/j9u3bYtulS5eQnp4Ob29vSeewsrKCl5eXuD37D2OxYsXg7++P+fPn49y5c4iPj8eBAwdw//59xMXFYcqUKWjatCkqVqwo/t/w697Hw8MD+/fvf+H+mJgYaDQaLFiwAHXr1kW5cuVw586dAv3c3NwwdOhQbN26FWPHjsWqVasAAKampgCA/Pz8l8bg4+MDjUaDQ4cOvTZeouepVCrUq1cPM2bMwJkzZ2Bqaopt27bh6NGjGDlyJFq2bClO7r93795rz1elSpWXfheuXLmC+/fvY968eWjQoAEqVKigNQH6mZIlS6JPnz745Zdf8O233+L7778HIO27ULZsWZibm780BqKijMvgDYC/vz98fHzQo0cPfPvtt3jy5AmGDx8OPz+/AiX3wggPD8eNGzfQsGFD2NnZYffu3dBoNChfvjzs7OxQokQJfP/993BxcUFCQgImTZok6bzTp0/H0KFD4ejoiBYtWuDBgwc4evQoPvvsM3h5eSEvLw9Lly5FmzZtcPToUYSGhmodP3r0aLRo0QLlypVDWloaDh48iIoVKwIA3N3doVKpEB4ejpYtW8Lc3LzA5GYPDw/06dMH/fv3x5IlS1C1alXcunULKSkp6NKlyxt/XqRsJ06cwP79+xEQEABHR0ecOHECd+/eRcWKFVG2bFlxxVZmZibGjx8vqboybdo0NG3aFGXKlEHXrl3x5MkT7N69GxMnTkSpUqVgamqKpUuXYujQobhw4QJmzZqldfzUqVNRo0YNVKpUCTk5OQgPDxe/C46OjjA3N0dERAQ+/PBDmJmZFVgCb2ZmhokTJ2LChAkwNTVFvXr1cPfuXVy8eBEDBgzQ3YdHJAe5JyGRbjw/ifeZdu3aCX369BEEQRBu3boltG3bVrCwsBCsrKyETz75REhKShL7Tps2TahatarW8YsWLRLc3d1f+p5HjhwR/Pz8BDs7O8Hc3FyoUqWKsHHjRnF/ZGSkULFiRUGtVgtVqlQRoqKiBADCtm3bBEF4+SRMQRCE0NBQoXz58oKJiYng4uIifPbZZ+K+hQsXCi4uLoK5ubkQGBgorF27Vmti84gRI4QyZcoIarVaKFmypNCrVy/h3r174vEzZ84UnJ2dBZVKJX4+//38Hj16JAQHBwsuLi6Cqamp4OXlJfz0008v/SyILl26JAQGBgolS5YU1Gq1UK5cOWHp0qWCIAjC6dOnhZo1awpmZmZC2bJlhU2bNgnu7u7CokWLxOOf/248b8uWLUK1atUEU1NTwcHBQejYsaO4b/369YKHh4egVqsFX19fYceOHVrfqVmzZgkVK1YUzM3NBXt7e6Fdu3bCjRs3xONXrVoluLm5CUZGRoKfn58gCNqToAXh6YTt2bNnC+7u7oKJiYlQqlQpYe7cuTr73IjkwjtBExERkcHhHCAiIiIyOEyAiIiIyOAwASIiIiKDwwSIiIiIDA4TICIiIjI4TICIiIjI4DABIiIiIoPDBIiIiIgMDhMgIgIA9O3bF+3btxdfN2rUCKNHj37vcURFRUGlUokP1SUieheYABHpub59+0KlUkGlUsHU1BReXl6YOXMmnjx58k7fd+vWrQWeLfUyTFqIqKjhw1CJioDmzZtj9erVyMnJwe7duxEUFAQTExNMnjxZq19ubq74lO+3ZW9vr5PzEBHpI1aAiIoAtVoNZ2dnuLu7Y9iwYfD398eOHTvEYas5c+bA1dUV5cuXBwDcvn0bXbp0ga2tLezt7dGuXTvEx8eL58vPz8eYMWNga2uLEiVKYMKECfjvYwH/OwSWk5ODiRMnws3NDWq1Gl5eXvjxxx8RHx+Pxo0bAwDs7OygUqnQt29fAIBGo0FISAg8PT1hbm6OqlWrYvPmzVrvs3v3bpQrVw7m5uZo3LixVpxERO8KEyCiIsjc3By5ubkAgP379yMuLg6RkZEIDw9HXl4eAgMDYWVlhSNHjuDo0aOwtLRE8+bNxWMWLFiAsLAw/PTTT/jzzz+RmpqKbdu2vfI9e/fujV9//RVLlizB5cuX8d1338HS0hJubm7YsmULACAuLg6JiYlYvHgxACAkJARr165FaGgoLl68iODgYPTs2ROHDh0C8DRR69ixI9q0aYPY2FgMHDgQkyZNelcfGxHR/8j8NHoieo0+ffoI7dq1EwRBEDQajRAZGSmo1Wph3LhxQp8+fQQnJychJydH7P/zzz8L5cuXFzQajdiWk5MjmJubC3v37hUEQRBcXFyE+fPni/vz8vKEDz/8UHwfQRAEPz8/YdSoUYIgCEJcXJwAQIiMjHxhjAcPHhQACGlpaWLb48ePheLFiwvHjh3T6jtgwAChW7dugiAIwuTJkwVvb2+t/RMnTixwLiIiXeMcIKIiIDw8HJaWlsjLy4NGo0H37t0xffp0BAUFwcfHR2vez9mzZ3Ht2jVYWVlpnePx48e4fv06MjIykJiYiDp16oj7ihUrhpo1axYYBnsmNjYWxsbG8PPzkxzztWvX8PDhQzRr1kyrPTc3F9WrVwcAXL58WSsOAPD19ZX8HkREb4oJEFER0LhxY6xcuRKmpqZwdXVFsWL/++paWFho9c3KykKNGjWwbt26AucpWbLkG72/ubl5oY/JysoCAOzatQsffPCB1j61Wv1GcRAR6QoTIKIiwMLCAl5eXpL6fvTRR9i4cSMcHR1hbW39wj4uLi44ceIEGjZsCAB48uQJYmJi8NFHH72wv4+PDzQaDQ4dOgR/f/8C+59VoPLz88U2b29vqNVqJCQkvLRyVLFiRezYsUOr7fjx46+/SCKit8RJ0EQK06NHDzg4OKBdu3Y4cuQIbt68iaioKIwcORL//PMPAGDUqFGYN28etm/fjitXrmD48OGvvIePh4cH+vTpg/79+2P79u3iOX/77TcAgLu7O1QqFcLDw3H37l1kZWXBysoK48aNQ3BwMNasWYPr16/j9OnTWLp0KdasWQMAGDp0KK5evYrx48cjLi4O69evR1hY2Lv+iIiImAARKU3x4sVx+PBhlCpVCh07dkTFihUxYMAAPH78WKwIjR07Fr169UKfPn3g6+sLKysrdOjQ4ZXnXblyJTp37ozhw4ejQoUKGDRoELKzswEAH3zwAWbMmIFJkybByckJI0aMAADMmjULX375JUJCQlCxYkU0b94cu3btgqenJwCgVKlS2LJlC7Zv346qVasiNDQUc+fOfYefDhHRUyrhZbMeiYiIiBSKFSAiIiIyOEyAiIiIyOAwASIiIiKDwwSIiIiIDA4TICIiIjI4TICIiIjI4DABIiIiIoPDBIiIiIgMDhMgIiIiMjhMgIiIiMjgMAEiIiIig/N/zGD+CWwtPJQAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved: outputs/classical/tfidf_lr/confusion_matrix_binary_test.png\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "# ── Confusion matrices ────────────────────────────────────────────────────────\n", - "save_confusion_matrix(\n", - " y_val, y_val_pred_bin, label_names_bin,\n", - " OUT_TFIDF / \"confusion_matrix_binary_val.png\",\n", - " \"TF-IDF + LR — Binary (Val)\"\n", - ")\n", - "save_confusion_matrix(\n", - " y_test, y_test_pred_bin, label_names_bin,\n", - " OUT_TFIDF / \"confusion_matrix_binary_test.png\",\n", - " \"TF-IDF + LR — Binary (Test)\"\n", - ")" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_2QLSOmTuKBc", - "outputId": "8d05bf4c-dcc5-47d0-8ae4-7ccc6754d50a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved: outputs/classical/tfidf_lr/predictions_val_binary.csv\n", - "Saved: outputs/classical/tfidf_lr/predictions_test_binary.csv\n" - ] - } - ], - "source": [ - "# ── Save predictions ──────────────────────────────────────────────────────────\n", - "save_predictions(val_bin, y_val_pred_bin, \"binary_label\", OUT_TFIDF / \"predictions_val_binary.csv\")\n", - "save_predictions(test_bin, y_test_pred_bin, \"binary_label\", OUT_TFIDF / \"predictions_test_binary.csv\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/confusion_matrix_binary_val.png\n" + ] }, { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "bigHRH2MuKBc", - "outputId": "40c0448d-3f53-4ba4-cc17-3347f11dc23b" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Top 20 sarcastic indicators (positive coef):\n", - " because +22.1982\n", - " finally +9.5906\n", - " obviously +9.4255\n", - " just +9.2842\n", - " so +8.8645\n", - " shocking +8.6605\n", - " clearly +8.4488\n", - " as if +8.3217\n", - " proving +7.5734\n", - " groundbreaking +7.5588\n", - " nation +7.4750\n", - " like +7.1686\n", - " nothing +6.5188\n", - " shocker +6.5093\n", - " never +6.4249\n", - " totally +6.3599\n", - " always +6.1954\n", - " sure +6.1921\n", - " area +6.0594\n", - " who needs +6.0482\n", - "\n", - "Top 20 non-sarcastic indicators (negative coef):\n", - " the -11.8202\n", - " an -11.5473\n", - " is -10.1138\n", - " was -7.7886\n", - " an area -6.0294\n", - " man is -6.0119\n", - " his -5.8977\n", - " are -5.7913\n", - " finds that -5.7222\n", - " the nation -5.6480\n", - " indicates -5.5894\n", - " donald trump -5.1844\n", - " donald -5.0836\n", - " says he -4.9047\n", - " did not -4.7563\n", - " because of -4.7343\n", - " may -4.7339\n", - " large -4.6423\n", - " her -4.5442\n", - " how to -4.3584\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "# ── Feature importance: top discriminative terms ──────────────────────────────\n", - "tfidf_vocab = best_bin.named_steps[\"tfidf\"].get_feature_names_out()\n", - "lr_coefs = best_bin.named_steps[\"lr\"].coef_[0] # binary: shape (n_features,)\n", - "\n", - "n_top = 20\n", - "top_pos_idx = np.argsort(lr_coefs)[-n_top:][::-1]\n", - "top_neg_idx = np.argsort(lr_coefs)[:n_top]\n", - "\n", - "top_positive = [(tfidf_vocab[i], lr_coefs[i]) for i in top_pos_idx]\n", - "top_negative = [(tfidf_vocab[i], lr_coefs[i]) for i in top_neg_idx]\n", - "\n", - "print(\"Top 20 sarcastic indicators (positive coef):\")\n", - "for term, coef in top_positive:\n", - " print(f\" {term:35s} {coef:+.4f}\")\n", - "\n", - "print(\"\\nTop 20 non-sarcastic indicators (negative coef):\")\n", - "for term, coef in top_negative:\n", - " print(f\" {term:35s} {coef:+.4f}\")" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 644 - }, - "id": "pEArvWzTuKBd", - "outputId": "c4709ebc-9b56-468b-eb9f-c47f1ba006b8" - }, - "outputs": [ - { - "data": { - "image/png": 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Y5ufnJ0np/g2k7kvv2tSqVUs2Npaz4trY2KhWrVo6cuSIdu/erUaNGmnnzp2SlGbtBknmNQxWrVqlQ4cOKSQkxLzPycnJ4n1WHD9+XBMnTtRPP/2kP//8U4mJiRb7z5w5Y57CKlWlSpXS1FOoUCFJ0pUrV8zbfvvtN0nSM888c984Uj/XlStXmqePupu9vX2a72h6Utcd8fT0vGe5iRMnmhcKT0lJ0dmzZ7VkyRINHTpUy5Yt044dO+Th4XHf9qTMXw/pzrok06ZN05YtW3ThwgXdvn3bvM/BwSHd+tP7O5DufEerV6+uhQsX6oMPPjCf86effioXFxd16NAhU/FnxtWrV/Xuu+9qyZIlOnbsWJr1Y+7+3rdt21bTpk1TixYt1K5dOzVu3Fh169ZVwYIF06375s2batiwoX777Td9/PHH910HJG/evJKU7lRsAAAATwqSGgAAAA8gJibGvMhwqsDAwGxJaqT+AObj4/PQdf1bej8Ix8XFac6cOXrhhRfSxB8YGJjtMaS6O3n0999/a86cORo+fLief/55/fbbb3J1dX1kbae6fPmyDMPQn3/+mWGyR5LFj5RTpkzRsGHD5OPjo2eeeUaFChWSs7OzJGnatGlpfvh+VAoUKJBm26VLlyRJ8+bNu+ex//7RNavc3d3TbEtdB+Ve+1KTRHdL7zzu3p66RklCQsI9y6cmTlLLpcqfP3+aBExmHD16VFWrVlVCQoLq16+v5s2by93dXTY2NoqNjdX69evT/azvdf53J79SzyujH7Pvlvq5ZnYh6Yykfk//vbbHvdjY2KhgwYLq27evzp49qzfffFMzZszQqFGjMnV8Zq/HN998o3bt2snV1VVhYWEKDAyUi4uLTCaTYmJiMlxTI6Pvg3QnCdy1a1d98cUX6tevn7Zs2aK9e/eqc+fOmU7K3M+tW7cUGhqqHTt2qGLFiurUqZO8vb1lZ2dn/m/r3d+TatWqKTY2Vm+99Za+/PJLRUdHS5KqVKmiSZMmqX79+hb1X716VTt37pS3t3eafem5efOmJMnFxSVbzg8AACAnIqkBAADwAB7V6IW7665SpUq21x0aGpomsREbG6s5c+YoPDw8WxawfhA+Pj4aNmyY4uPj9cYbb2j06NGaNm3aI2839QfXSpUqadu2bfctf/v2bb3++uvy8/PTrl27LBZMNgxDkydPzlL7qSMU7n4iPdXdC46nJ70f6lPP54cfflCzZs2yFIu1nDt37p7bU398Tj23jMr/9ddfFuVSPUhCQ5Lee+89Xb58WZ9//rk6duxosa93795pkppZlTpy4M8//7xv2dRzSkhISLMwdFbbtLe3NydJsqpatWqS7iyGnt0iIyPl5OSk7du3q0SJEhb75s+fn+Fx9/p827Vrp8GDB2v27Nnq16+fZs+eLSn7FgiXpO+++047duxQ9+7dzfWnmj9/vubMmZPmmDp16mj58uW6efOmtmzZoh9++EEzZ85U06ZNtW/fPhUrVsxcNn/+/Prkk08UHh6u0NBQrVu3TsHBwRnGk/rZPoqkOAAAQE5hc/8iAAAAeFwOHz6sBQsWyNHRUS1atLB2OI/da6+9Jn9/f82cOTPN9F6Pgpubm0qVKqUDBw6kmQonPRcuXFB8fLxq1KhhkdCQpG3btpmfkr6bra2tJMun0lPd64ft1OmWsiL1R+fNmzdn+Vhr2bRpk1JSUiy2paSk6JdffpHJZFL58uUlSRUrVpSUfkLx+vXr2rZtm5ydne/5g++/3euzOXbsmCTphRdesNhuGIY2bdqU6TYykjpt0qpVq+5bNvVzTZ2G6mGULVtWJ06c0K1bt7J87OXLlyUpzeeVHY4dO6ZSpUqlSWicPXtWx48ff6A6nZ2dFRERod27d2vdunX6+uuvVapUKdWqVSs7QpaU8fdEkn7++ef7xhcaGqopU6botdde082bN7V69eo05cLCwvT999/rypUrql+/vg4dOpRhnan7HnTKNQAAgNyApAYAAEAOsWnTJoWFhSkxMVEjRozI1LQ0TxpnZ2cNHz5cSUlJev311y32HTt2TAcPHkx3CqOHMWDAAN24cUM9e/ZMd1qmEydOmBMs+fPnl7Ozs3bs2KEbN26Yy1y+fFn9+/dPt/7UOe5Pnz6dZp+7u7uCg4O1ceNGHT161Lz96tWrGjlyZJbP5YUXXlCRIkU0depUbdiwIc3+pKQkbdy4Mcv1PkqHDx9WVFSUxbaoqCgdPnxYTZs2NT9xXqtWLQUFBWn58uVas2aNRfk33nhDFy9eVPv27TNceyE99/psUtfK+Pf1evvtt7Vv375Mt5GR559/XoUKFdIXX3yhlStXptl/d6KrT58+srOzU//+/XXq1Kk0Za9cuZLpJFi9evWUmJio3bt3Zynef/75RzNnzpQk1a1bN0vHZkZAQICOHj1qMRLnn3/+0csvv/xQf/Opa1B07NhRV69ezdZRGlLG35P169en+V5LdxKO6U3/lXreTk5O6bbTuHFj/fDDD7py5YpCQ0MzXENly5YtsrOzU82aNbN0HgAAALkJ008BAAA8ZkePHjUv4nzr1i2dP39ev/32m/bu3StbW1uNHj1a48aNs26QD+js2bMZTmGVL18+vfvuu/eto1evXpo0aZLmzp2r1157zbxAeMOGDXXy5EmdOHEiW9f6eOmll/Trr79qzpw52rRpkxo1aiR/f3+dO3dOBw8e1JYtW/Tll18qMDBQNjY26tOnj6ZMmaLy5curefPmSkhI0PLlyxUQECB/f/809Tdo0EDvvvuuevXqpVatWilPnjwKCAhQp06dJElDhw5Vr169VKNGDbVp00YpKSlavnz5A00/5ujoqIULF6pJkyaqV6+eGjRooJCQEJlMJp08eVI///yzvL29M7Wo9OMSFhamAQMGaNmyZSpTpox+//13/fDDD8qXL5+mT59uLmdjY6OYmBiFhYXpueeeU5s2bRQQEKDNmzcrNjZWQUFBevvtt7PUdoMGDbRw4UK1atVKTZo0kZOTk/lz7d27t6Kjo9WqVSu1bdtW3t7e+vXXX7Vjxw41bdpUS5cufajzdnR01IIFC/Tss8+qSZMmevbZZ1W+fHklJCRo165dunHjhjlRUbZsWc2cOVMvv/yygoOD9dxzzykoKEhXr17V8ePHtX79enXp0kUff/zxfdtt0aKFpk2bptWrV2f4HVuzZo35h/eUlBT99ddfWr58uf744w9VqFBBffr0eahzT0///v3Vv39/VaxYUa1bt9bt27e1evVqGYah8uXLZzkJk6p06dKqU6eOfv75Zzk6OioiIiJb427evLkCAwM1efJk7du3T2XLltWhQ4f0448/qkWLFlq4cKFF+UmTJmndunWqW7euihYtKicnJ+3YsUNr165VsWLF7jlCr2HDhvrxxx/VvHlz1a9fXz/99JNKlSpl3n/t2jX9+uuvaty4sfLkyZOt5wkAAJCTkNQAAAB4zI4dO2ZelNrZ2Vmenp566qmnNGbMGHXu3Nn8I35ulJCQkO4c8tKdJ5ozk9RwcnLSyJEj1b9/f40fP15z587N7jAtpC5E/NxzzykqKko//vijrl27pvz586tEiRJ699131ahRI3P5iRMnKm/evIqJidHMmTNVoEABtW/fXpGRkSpbtmya+ps0aaLJkycrKipKU6ZMUVJSkurVq2dOavTs2VNJSUmaNm2aZs+eLT8/P3Xp0kWjR4/O0qiDVFWqVNHu3bv1zjvvaNmyZdq0aZMcHR1VsGBBhYeHq3379g9+sR6B6tWra/To0Ro9erTef/992draKjw8XJMnT7ZYW0CSateurV9//VUTJkzQqlWrFB8fL39/fw0cOFCjR49Wvnz5stR2z549FRcXp/nz52vSpEm6ffu2OnfurObNm6tixYpatWqVRo8erUWLFsnW1lY1a9bUpk2b9P333z90UkOSatSooR07dmjixIlauXKl1qxZIy8vL5UuXVq9e/dOE2uFChXMo3B++OEHeXh4qEiRIho8eLA6d+6cqTbr1q2r0qVLa968eXrttdfSLbN27VqtXbvW/D5PnjwqUaKEevfurcGDBz+SRaj79u0re3t7ffDBB4qKipKnp6eaNm2qiRMnqk2bNg9Vd+fOnfXzzz+rRYsW8vb2zqaI73B1ddVPP/2kV155RRs2bFBsbKzKlCmjefPmqUCBAmmSGi+//LI8PDy0ZcsWrV+/XoZhqEiRInrttdc0ePDgdBdWv1uDBg20dOlSNWvWzJzYKF26tCTp22+/1c2bN82jUwAAAJ5UJsMwDGsHAQAAAOC/JTY2VvXr19e4cePMI5fweHz66afq0aOHNm7cmK3rS+RU/fr104cffqi1a9eqQYMG1g7nkalTp47OnTunAwcOmNeLAQAAeBKxpgYAAAAA/Id06dJFZcqUMY8Ye5L9/fffmjNnjoKDg1W/fn1rh/PIrF27Vhs3btSkSZNIaAAAgCce008BAAAAwH+Ira2tPvvsMy1fvlxXr16Vm5ubtUPKdkuXLtWOHTu0cOFCXbt2TZGRkTKZTNYO65GJj4/Xu+++e881OQAAAJ4UJDUAAAAA4D+matWqqlq1qrXDeGS++eYbzZkzR/7+/nrrrbf0v//9z9ohPVItW7a0dggAAACPDWtqAAAAAAAAAACAXIE1NQAAAAAAAAAAQK5AUgMAAAAAAAAAAOQKJDUAAAAAAAAAAECuQFIDAAAAAAAAAADkCiQ1AAAAAAAAAABArkBSAwAAAAAAAAAA5AokNQAAAAAAAAAAQK5AUgMAAAAAAAAAAOQKJDUAAAAAAAAAAECuQFIDAAAAAAAAAADkCiQ1AAAAAAAAAABArkBSAwAAAAAAAAAA5AokNQAAAAAAAAAAQK5AUgMAAAAAAAAAAOQKJDUAAAAAAAAAAECuQFIDAJChb775Rm+99ZZu3bpl7VAAAAAAPADu6QEATxqSGgCAdG3evFldunRRdHS0Ro4cae1wcJcuXbrIZDJle72GYahGjRrq0KFDttf9MEwmk7p06ZLp8oGBgQoNDX1k8aT666+/5OLiojlz5jzytgAAAB4E9/R4WLGxsTKZTIqJicn2uj/66CO5u7vr4sWL2V53ThEZGSmTyaS4uLhH3laLFi1Uv379R94OkBOQ1ACA/89kMmX69ThuSCTpn3/+UVRUlF544QUFBgbK2dlZxYoVU/v27XXgwIF0j0lMTNTYsWNVtGhROTo6KigoSG+88YaSkpIy3W58fLxefPFFTZ8+XatWrdLnn3+ulStXZlj+5s2bmjRpkipWrKg8efIoT548qlu3rhYtWmRRLvWGOPUH6tQf52NjYzMV1w8//KDGjRurUKFCcnR0lJ+fn2rWrKlXX31VFy5cyPT55QYxMTGaNm3aY23zq6++0rZt2xQZGflY230QkZGRWrJkiVVj8PX1Ve/evTVq1CjduHHDqrEAAIA7uKf/Pznxnj61rIeHR7o/ZMfExMhkMmnhwoWZPs9H5fjx4+rVq5eeeuopubi4yMvLS6VKlVLnzp21bt06a4eXrXbt2qXIyMjH9jch3fl+jhs3ToMHD5a3t/dja/dRWLJkSY7oQ0VGRmr9+vX6/vvvrR0K8MjZWTsAAMgpPv/8c4v3P//8s2bNmqVevXqpTp06Fvt8fHweS0xxcXHq1auXateure7du8vf31/Hjx/XRx99pEWLFmnFihVpnsRo166dvvvuO3Xr1k01atTQ5s2bNWbMGB09ejTTT9fs2bNHo0aNUo8ePSRJ33//vXbt2pVu2fj4eNWvX1+7du1S8+bNFRERIVdXV23fvl0RERFycXHRs88+K0m6evWqJKlgwYLm9yaTSX5+fveNafjw4Zo8ebLKlSunPn36qECBAjpz5oz27t2rjz/+WG3btlW+fPkydX65QUxMjOLi4jRo0KA0+6KiovTxxx9ne5sTJkxQs2bNVKJEiWyv+2HcvHlTtra2FtvGjx+vzp07Kzw8PE35Q4cOPZKRLOkZMGCApk2bpujoaPXt2/extAkAADLGPf3/yYn39KkSEhL0xhtv6L333sv0MY/Ttm3bVK9ePdnb2ysiIkJlypTRzZs3deTIEa1atUpubm5P1BPxu3bt0vjx4xUaGqrAwECLfXXr1tXNmzdlb2+frW3OnDlTV65cUb9+/bK1XmtYsmSJ5syZk25iY/To0RoxYoQcHR0feRzly5dXaGioXn/9dT3//POPvD3AqgwAQLqio6MNSUZ0dLTVYrhw4YKxc+fONNt///13w8HBwahUqZLF9qVLlxqSjCFDhlhsHzJkiCHJ2LRpU7bH+NJLLxmSjJiYmDT7Tp48aezbt8/8fvDgwYaXl5dx8eJFIzk52fD29jYiIiLu28a5c+cMGxsbo0qVKsatW7fS7L969apx9erVhzuRu6SkpGRrfQ+iXr16RkBAwGNrb82aNYYkY9GiRY+tzYchyejcubO1wzAMwzDq1q1rhISEWDsMAACQDu7pM+dx3NMbhmF07tzZkGRUrlzZcHR0NOLi4iz2p35e33zzzcOd0ENq1qyZIcnYtWtXuvvPnj2bre0lJCRka31ZlXrd161b91jaS05ONgICAoznn3/+sbT3qKV+r3OCzz77zJBkbN++3dqhAI8U008BQBZdv35dI0eOVFBQkBwdHeXr66uIiAidPHnSotzdc49+8MEHKlmypJycnFSyZEl98MEHmWrL29tbFSpUSLO9dOnSKlu2rPbt22ex/csvv5SkNE/3p77/4osv7tvmmTNnNHToUFWoUEFeXl5ycnJS6dKlNWnSJCUnJ5vL3bx5U3/99Ze++OILhYSEqGnTprpw4YL5lZCQoCJFiqhMmTLmY1auXKlRo0Ypb9682r59u27cuKE333zzvjEdP35cKSkpqlu3brpPCLm6usrV1dX8/urVqxo9erSqVaumfPnyydHRUcWLF9eIESPSTBN09+f04YcfqnTp0nJyctK7775rLvPtt98qNDRUnp6ecnFxUXBwsAYMGGBebDElJUVvvvmm6tatK19fXzk4OKhIkSJ6+eWX0x1WP3fuXFWtWlWenp7KkyePihUrpg4dOujvv/+WdGdNiPXr1+vkyZMWUySkDunPaE2Nv/76SwMGDFCxYsXk6Oio/Pnzq3Hjxlq9evV9r/E333wjW1tbPfPMM2n2pU4vsGbNGlWvXl0uLi7y9fXVwIEDde3atTTl4+Li1KlTJxUoUMA8XcJrr72W5tpfunRJgwcPVlBQkJycnOTt7a1KlSrpnXfeSbf91LpTz33OnDkW1yfVv9fUqFatmgoUKKDbt2+niXXlypUymUwWU30ZhqGPPvpIlSpVkouLi1xdXVW/fv0Mpxlo0qSJ9u7dq4MHD6a7HwAA5Dzc09/xOO/p7zZx4kTdunVLo0ePzlT5B/m8oqOjVaZMGTk6OiogIECTJ0/OdHxHjhyRt7e3ypcvn+5+X19fi/dff/21nn/+eRUpUkSOjo7Kly+fwsPDtWfPnjTHpt6r7ty5U2FhYfLw8FC5cuXM+48ePaquXbuqUKFCcnBwkL+/v1544QVt377dXGbVqlVq166dihUrJmdnZ3l6euqZZ57R+vXr07T3+++/q02bNipYsKD52tWvX19Lly6VdGfKoq5du0qS6tevb763Tr3/zmhNDcMwFBUVpWrVqpn7YyEhIRo7dux9r+9vv/2mkydP6rnnnkuzL7WvEx8fr5dffln58+eXk5OTatWqpS1btqQpn5V79xs3bmjIkCHy8/OTs7OzqlevrrVr16bbv/rtt9/UpUsXlSxZUi4uLnJzc1OtWrW0ePFii3KhoaHmNfbu7pukXq9/r6nx0UcfyWQypTtFVEpKigoVKpTmvxfbtm1TixYtzH3b4OBgvfnmm+n2b5o0aSJJWrBgQZp9wJOE6acAIAuSkpIUFhamTZs2qXXr1ho6dKiOHDmijz76SKtWrdK2bdtUqFAhi2M++OAD/fXXX3rppZfk5uamr776SgMGDNClS5c0bty4B4ojJSVFZ8+eVYECBSy2b926VQULFlThwoUtthcuXFj+/v7aunXrfeves2ePFi1apBYtWigoKEhJSUlasWKFRowYoePHj+uTTz6RJHXo0MF8Q7d37940w/dfeeWVNB2H33//3fzvKlWqZHodgmLFikmSfvzxRw0ZMkT+/v73LP/nn39q9uzZatWqlV588UXZ2dlp/fr1mjx5snbu3JnuXMLTpk3TxYsX1bNnT/n6+pqv4ahRo/TWW2+pdOnSGjx4sPz8/HTs2DF9++23mjBhghwcHHTr1i298847atWqlV544QXlyZNHW7du1aeffqqNGzdq+/btcnBwkHRnSoTOnTurTp06mjBhgpydnXX69GktW7ZM58+fl4+Pj6ZNm6aRI0fqwoULFkPyS5UqleE5x8XFqVatWjp37pwiIiJUuXJlXb9+Xb/++qvWrFmjxo0b3/OarV+/XmXKlFGePHnS3b9jxw4tXLhQPXv2VEREhNatW6f3339f+/bt0+rVq2Vjc+c5iZMnT6pq1aqKj49Xnz59VKJECcXGxmrixInatGmT1q5dKzu7O7cfbdq00YYNG9S7d2+VK1dON2/e1IEDBxQbG6tXXnkl3Th8fHz0+eefq1OnTqpTp4569ep1z/OSpM6dO6tv375asWKFmjVrZrFv7ty5srOz04svvmje1qlTJ3311Vdq3bq1unbtqsTERM2bN0+NGzfWokWL0gzlrlGjhqQ7Hb6nnnrqvvEAAADr4p7eOvf0d6tQoYJefPFFzZs3T8OGDcsweSA92Of18ccf69y5c+revbs8PT31xRdfaPjw4SpUqJDFfV9GgoKCdOjQIS1atEgtW7a8b/kZM2bI29tbvXr1kq+vr44dO6ZZs2apVq1a2rFjR5rpXU+dOqUGDRqoTZs2atWqlflBoW3btqlhw4ZKSkpS9+7dVbZsWV26dEnr16/XL7/8okqVKkm6M1XtpUuXFBERoUKFCpn7Pw0bNtS6devMU61dvHhRDRo0kCT17t1bAQEBunDhgrZt26YtW7aoadOmatmypc6ePatZs2bptddeM/c5goKC7nnOnTp10rx581StWjWNGjVKnp6eOnjwoBYuXKgJEybc89jU5EvVqlUzLBMWFiYfHx+NHTtWFy9e1NSpU9W0aVOdOHFCbm5uFnFk9t69TZs2WrZsmcLDw9WoUSOdOHFCLVq0UNGiRdO0v3jxYh08eFBt27ZVQECALl68qDlz5qhly5aaN2+e+Xs0atQopaSk6Oeff7aY/q5mzZrpntf//vc/DR48WHPnzk3Tr1i7dq3+/PNPDR061Lxt6dKlatmypYoXL66hQ4cqb9682rx5s8aOHatdu3bpm2++sajD19dXgYGBmV63Esi1rDxSBAByrPSGqs+aNcuQZLzyyisWZX/88UdDktGxY0fztnXr1hmSDFdXV+P06dPm7YmJiUaVKlUMOzs7i+1Z8eGHHxqSjDFjxlhsd3V1NapWrZruMVWqVDH8/PzuW/eNGzeMlJSUNNs7duxo2NjYGGfOnDEMwzA2bNhgvPvuu4Yko2fPnsbq1astXufPn3+AM8tYv379DEmGg4ODUadOHeOVV14xvvnmG+PSpUtpyiYmJqY7TdXo0aMNScaWLVvM21I/Jy8vL+PcuXMW5bds2WJIMurXr2/cvHnTYl9KSor5OqWkpBg3btxI097s2bMNScbXX39t3taiRQvDzc3NSEpKuuf53mv6qfSGNzdp0sSQZKxYsSJN+eTk5Hu2dfv2bcPGxsZo0aJFuvslGZKMxYsXW2wfMGCAIcn46quvzNtefPFFQ5KxdOlSi7LDhg0zJBmzZ882DMMwrly5YkgyXn755XvGltr+v6eaSm9bqoCAAKNevXrm9xcvXjQcHByMNm3aWJRLSEgwXFxcjObNm5u3LVq0yJBkfPLJJxZlk5KSjEqVKhmBgYFp/j5Onz5tSDL69et333MBAACPF/f0lqx9T596H/v3338bJ06cMBwcHIywsDDz/vSmn3qQz8vPz8+4cuWKefv169eNfPnyGdWrV89UnL/88othb29vSDJKlChhdO3a1Zg5c6axf//+dMtfu3Ytzbb9+/cbDg4Oae53AwICDElGVFSUxfaUlBSjTJkyhqOjo7F79+409d19T59ee3/99Zfh7e1tNGnSxLztu+++S9MfSc+9pp9KvaZ3/w19/fXX5mv/777G/foehmEYERERhiQjPj4+zb7U78i/r9uCBQsMScbHH39s3paVe/fUad169OhhUTZ1+7/7V+ld4+vXrxslS5Y0SpUqlW7M6Rk3bpwhyThx4oR5W+vWrQ1HR8c0fdmOHTsadnZ25n7pzZs3jQIFChh16tRJ03+cOnVqhp9Zw4YNDVdX13TjAZ4UTD8FAFmwePFi2djYaOTIkRbbmzZtqgoVKui7775TSkqKxb4OHTpYPDnk4OCgwYMH6/bt2/rhhx+yHMMvv/yiIUOGqHz58nrttdcs9t24cSPDBcicnJwy9RSVs7OzeejtrVu3dOnSJV24cEFhYWFKSUnRtm3bJEmVK1dW8eLFJUn+/v6qUKGC+VW7du1sX3jx/fff19y5c1WzZk399ttveuedd9SmTRv5+flp+PDhFsPoHRwczNNU3b59W5cvX9aFCxfUqFEjSUp32HJERITy589vsW3evHmS7gyPd3Jysth395RHJpNJzs7OkqTk5GRduXJFFy5cMD8VdXd7Hh4eunHjhpYuXSrDMB7qmqS6dOmSVqxYoWeffVZhYWFp9qeOosjIxYsXlZKSorx582ZYJjg4OM2i3CNGjJAk89N9KSkp+v7771WxYsU0Q8lHjhwpGxsbc1lnZ2c5Ojpqy5Yt5qHYj0revHnVvHlz/fDDD7py5Yp5+8KFC3Xjxg117tzZvO2LL76Qm5ubwsPDLaZeuHLlipo3b664uDgdOXLEon5vb29J0vnz5x/peQAAgOzBPb317unvFhgYqD59+mjlypX66aefMiz3IJ9X165d5eHhYX7v4uKi6tWrp7mPy0iNGjW0fft2de7cWfHx8YqOjlafPn1UunRp1a1bV8ePH7conzra2TAMJSQk6MKFC/Lx8VFwcHC6fY+8efOap3xKtWvXLv3+++/q2rWrxXRUqe6+p797dPW1a9d08eJF2draqlq1amn6HpK0fPlyJSQkZOrcMyO1n/Tuu++m6Wvcr+8hSX///bfs7Ozk7u6eYZnBgwdbvE/tW939GWbl3j3173TIkCEW9T733HPpjoi/+xrfuHFDFy9e1I0bN9SgQQMdOHDgoa5n586dlZiYqK+//tq87dq1a1q8eLGeffZZc7909erVOnfunLp27WruY6a+Uvtbq1atSlO/t7e3rl27pps3bz5wjEBOR1IDALLgxIkT8vf3l5eXV5p9ZcqU0dWrV3XhwgWL7endIJUuXVqS0twM38/27dvVtGlT+fv7a+nSpWl+aHdxcVFiYmK6x/7zzz9ycXG5bxu3b9/WG2+8YZ4v2NvbWz4+PurUqZMk6fLly5LudOxSf+QeP368fHx8zK8dO3Zk6bwyw2QyqVOnTlq3bp0SEhK0detWvfnmm3J3d9fkyZPTDIufOXOmypUrJ0dHR+XNm1c+Pj7mdRZSz+FuJUuWTLPtyJEjMplM9xwOn2rBggWqVq2anJ2d5eXlJR8fH/O0WXe399prrykgIEDh4eHy8fFRq1atNHv2bF29ejUrl8PC0aNHZRiGKlas+EDHp3Z475VkSe977OfnJ09PT/P3+O+//9a1a9cs5lxOlTdvXvn5+ZnLOjg4aNq0adq3b5+KFi2qMmXKqH///lq7du0DncP9dO7cWf/884/F3LJz586Vl5eXmjdvbt524MABXb16VQUKFLD4Tvv4+CgyMlKSdO7cOYu6U69beuucAACAnId7euvd0//b6NGj5e7uruHDh2d4L/ogn1fqffjdvL29Lda7i4+P119//WXxuvtBqZCQEMXExOjcuXOKi4vTnDlzVKdOHf3888964YUXzOvrSdLOnTvVrFkzubm5ycPDw3wN9+7dm27fIygoSLa2thbbUn98z8w9/bFjx/S///1PXl5ecnNzU758+eTj46Nly5ZZtFevXj1FREQoJiZG+fLlU61atTRu3Djt37//vm3cy5EjR+Tn55dm6rTMysx9878/w9QHie7+DLNy737ixAnZ2NiYk3h3Cw4OTrPt/Pnz6tWrlwoUKKA8efKYr/HHH38sSRYPS2VVauJi7ty55m3ffvutrl+/roiICIvzk6Ru3bqlOb/UaW//3TeR6J/gv4E1NQAgl9ixY4caN24sDw8PrVu3TgULFkxTxt/fX3/++We6x//555/pHvNvQ4YM0QcffKB27dpp1KhRyp8/v+zt7bVjxw4NHz7c/BTU4MGD1bNnTzVr1kzly5c3JxVMJlOG84dmFwcHB1WuXFmVK1dWq1atVKpUKX366afmp7emTp2qoUOH6plnntGAAQPk7+8vBwcH/fnnn+rSpUuaJ7kkZdg5/Pci1OlZtGiR2rVrp6pVq2r69OkqXLiwnJyclJycrGeffdaivRIlSmj//v1au3at1q5dq/Xr16tnz54aN26cNmzYcN+5ax8Fb29v2djY6NKlS4+13d69e+uFF17Q0qVLtX79ei1cuFAzZsxQu3btNH/+/Gxtq0mTJvLx8dHcuXPVq1cvnTp1SuvXr1fv3r3N651IdzoAPj4+5gU601O2bFmL96nX7VE+yQgAAJ4M3NNb8vb21quvvqrRo0dn68LG/04YpGfgwIHmBZ5TnThxQoGBgWnKBgQEKCIiwryu26ZNm/Tbb7+pdu3aOnXqlOrWrSt3d3eNGTNGwcHBypMnj0wmkwYNGmReL+NumUlMZeTatWuqW7eurl+/rkGDBikkJERubm6ysbHRxIkT04x6mTNnjl555RUtX75cP//8s6ZMmaI333xT06ZNU79+/R44jofh4+Oj27dvKz4+3mJEzd0y+gzvTn49yL17Zn7oNwxDzzzzjA4cOKCBAweqcuXK8vDwkK2traKjo/Xll1+m26fMrNQ1/aZNm6ajR4+qePHi5geu7l5nI/Vc33nnnTSLh6dKb73JS5cuydXVNU3CFHiSkNQAgCwoVqyYVqxYoStXrsjT09Ni3/79++Xu7q58+fJZbE99uuLfZVPry4wdO3aoUaNGcnNz07p16xQQEJBuuSpVqmjevHk6ffq0xcKCp0+f1pkzZ9IsRJaezz//XHXr1k3zo/LRo0ct3qcuPlelShXt3btX1apVs1iw7XEJDg6Wl5eXRcfv888/V2BgoJYvX24x/HnFihVZqrtkyZJavny5du/efc9F7D7//HM5OTlp3bp1Fh2UgwcPplve0dFRzz33nHnI8LJly9S0aVNNnTpVH374oaSsPVVTvHhxmUwm7dq1K9PH3M3GxkalSpW653D89L7HZ8+e1ZUrV8zfYx8fH7m5uVksHpnq8uXLOnv2bJqbcT8/P/Xo0UM9evRQcnKyeaG/oUOHqkqVKg90PulJ7ThMnz5dx48f11dffSXDMCymnpLuJJ0OHz6s6tWry9XVNVN1p/5t/LvDBAAAcibu6f9PTrinHzx4sD788EONHj1ar776apr9D/J5Zcarr76qjh07Wmzz9fW95zEmk0nVqlXTpk2bzP2PxYsX69q1a/r+++9Vv359i/IXL17McCqxf0sdOX6/e/q1a9fqzJkz+uyzz9JMYTV69Oh0jylbtqzKli2rV155RVeuXFG1atU0YsQI9e3bN1MPcaUX63fffadz58490GiN1PvmI0eOqHLlylk+PlVW7t0DAwOVkpKiI0eOpBl5dejQIYv3e/bs0e7duzV27FiNHz/eYt/s2bPT1P0gIyI6d+6sadOmae7cuerZs6diY2PVq1cvi+9L6gLzefLkMU+lnBlHjx6lb4InHtNPAUAWhIeHKyUlRW+//bbF9uXLl2vnzp16/vnn08whOm/ePP3xxx/m97du3dJ7770nW1tbNWvW7L5t7ty5U40bN5arq6vWrVunokWLZli2ffv2kqRp06ZZbE9936FDh/u2Z2trm2bo9/Xr1/Xee++lW37w4MG6ceOG+vfvn+ZplR9//FErV668b5v389dff2V4c//zzz/r0qVL5uH/0p1zMJlMFudx+/btNJ/b/bz44ouS7kwZdffw8lSp9ae2d/f5G4ahN954I80x/x4aL0lPP/20JFmMlHB1ddXly5czte5G3rx51aRJEy1fvlxr1qzJMM57CQ0NvefcsIcOHdKSJUsstk2aNEmSzFMW2NjYqHnz5tq5c2eaBNLbb7+tlJQUtWjRQtKdeWn/PR+0ra2tef7g+40acXV1zfLIktQExty5c/X5558rODhY1apVsygTERGhlJSUNHM2p0pvePevv/4q6c7wfgAAkPNxT5/W47inz4iLi4siIyN19OhRRUVFpdn/IJ9XZpQuXVqNGjWyeKU+2b569Wrdvn07zTE3b940r2GQ2v9IHVHw7+sdFRWlv/76K9PxlC9fXmXKlNFnn32W7kNCd/c90mtv1apVadbvuHTpUprP09PTU0WLFtWNGzf0zz//SJI5IZDZ++vU7+Crr76apv7M9j2k/7uPflBZuXdPnXL2338Dy5YtS5O0zOga79u3z7xG4N2yev0kqUKFCipXrpy++OILff7550pJSUnzwFVYWJjy58+vt99+O926b968mWYa47/++ksnT56kb4InHiM1ACALunTpojlz5mjSpEmKi4tT3bp1dfToUc2cOVMFChTQW2+9leaYkiVLqlq1aurdu7fc3Nz05ZdfauvWrRozZozFk1fpOXnypBo3bqzLly9rwIAB+uWXX/TLL79YlGnRooV5EbOmTZuqWbNmmjp1quLj41WjRg1t3rxZn376qTp27KjatWvf9xxbt26tTz75RO3atVOjRo107tw5ffbZZ+Y5TP+tXbt2Wrt2raKionTw4EG1bNlS7u7u+umnn7Rw4cIsj45Izx9//KEqVaqoWrVqati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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved: outputs/classical/tfidf_lr/feature_importance_binary.png\n" - ] - } - ], - "source": [ - "# ── Feature importance plot ───────────────────────────────────────────────────\n", - "fig, axes = plt.subplots(1, 2, figsize=(16, 7))\n", - "\n", - "terms_pos, coefs_pos = zip(*top_positive)\n", - "axes[0].barh(list(reversed(terms_pos)), list(reversed(coefs_pos)), color=\"steelblue\")\n", - "axes[0].set_title(\"Top 20 — Sarcastic (positive)\", fontsize=13)\n", - "axes[0].set_xlabel(\"LR Coefficient\")\n", - "\n", - "terms_neg, coefs_neg = zip(*top_negative)\n", - "axes[1].barh(list(reversed(terms_neg)), list(reversed(coefs_neg)), color=\"coral\")\n", - "axes[1].set_title(\"Top 20 — Non-Sarcastic (negative)\", fontsize=13)\n", - "axes[1].set_xlabel(\"LR Coefficient\")\n", - "\n", - "plt.suptitle(\"TF-IDF + LR: Feature Importance (Binary Task)\", fontsize=14)\n", - "plt.tight_layout()\n", - "plt.savefig(OUT_TFIDF / \"feature_importance_binary.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "print(\"Saved: outputs/classical/tfidf_lr/feature_importance_binary.png\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/confusion_matrix_binary_test.png\n" + ] + } + ], + "source": [ + "# ── Confusion matrices ────────────────────────────────────────────────────────\n", + "save_confusion_matrix(\n", + " y_val, y_val_pred_bin, label_names_bin,\n", + " OUT_TFIDF / \"confusion_matrix_binary_val.png\",\n", + " \"TF-IDF + LR — Binary (Val)\"\n", + ")\n", + "save_confusion_matrix(\n", + " y_test, y_test_pred_bin, label_names_bin,\n", + " OUT_TFIDF / \"confusion_matrix_binary_test.png\",\n", + " \"TF-IDF + LR — Binary (Test)\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "_2QLSOmTuKBc", + "outputId": "8d05bf4c-dcc5-47d0-8ae4-7ccc6754d50a" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "5VFBy1bJuKBd" - }, - "source": [ - "## Task B — Sarcasm Type Classification" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/predictions_val_binary.csv\n", + "Saved: outputs/classical/tfidf_lr/predictions_test_binary.csv\n" + ] + } + ], + "source": [ + "# ── Save predictions ──────────────────────────────────────────────────────────\n", + "save_predictions(val_bin, y_val_pred_bin, \"binary_label\", OUT_TFIDF / \"predictions_val_binary.csv\")\n", + "save_predictions(test_bin, y_test_pred_bin, \"binary_label\", OUT_TFIDF / \"predictions_test_binary.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "bigHRH2MuKBc", + "outputId": "40c0448d-3f53-4ba4-cc17-3347f11dc23b" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "GTvcYYGquKBd", - "outputId": "f3c82bbf-5d44-4c4c-d8f0-2f586415fced" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n", - "Train+Val: 24,083 Train: 19,833 Val: 4,250 Test: 4,250\n" - ] - } - ], - "source": [ - "# ── Load type splits ──────────────────────────────────────────────────────────\n", - "train_type, val_type, test_type = load_splits(\"type\")\n", - "\n", - "# Encode string labels to integers\n", - "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", - "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", - "id2label = {i: lab for lab, i in label2id.items()}\n", - "\n", - "def encode_labels(df: pd.DataFrame) -> list[int]:\n", - " return [label2id[l] for l in df[\"type_label\"]]\n", - "\n", - "trainval_type = pd.concat([train_type, val_type], ignore_index=True)\n", - "\n", - "X_trainval_t = trainval_type[\"text\"].tolist()\n", - "y_trainval_t = encode_labels(trainval_type)\n", - "groups_tv_t = trainval_type[\"group_id\"].tolist()\n", - "\n", - "X_train_t = train_type[\"text\"].tolist()\n", - "y_train_t = encode_labels(train_type)\n", - "X_val_t = val_type[\"text\"].tolist()\n", - "y_val_t = encode_labels(val_type)\n", - "X_test_t = test_type[\"text\"].tolist()\n", - "y_test_t = encode_labels(test_type)\n", - "\n", - "print(f\"Strategies: {STRATEGY_LABELS}\")\n", - "print(f\"Train+Val: {len(X_trainval_t):,} Train: {len(X_train_t):,} Val: {len(X_val_t):,} Test: {len(X_test_t):,}\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Top 20 sarcastic indicators (positive coef):\n", + " because +22.1982\n", + " finally +9.5906\n", + " obviously +9.4255\n", + " just +9.2842\n", + " so +8.8645\n", + " shocking +8.6605\n", + " clearly +8.4488\n", + " as if +8.3217\n", + " proving +7.5734\n", + " groundbreaking +7.5588\n", + " nation +7.4750\n", + " like +7.1686\n", + " nothing +6.5188\n", + " shocker +6.5093\n", + " never +6.4249\n", + " totally +6.3599\n", + " always +6.1954\n", + " sure +6.1921\n", + " area +6.0594\n", + " who needs +6.0482\n", + "\n", + "Top 20 non-sarcastic indicators (negative coef):\n", + " the -11.8202\n", + " an -11.5473\n", + " is -10.1138\n", + " was -7.7886\n", + " an area -6.0294\n", + " man is -6.0119\n", + " his -5.8977\n", + " are -5.7913\n", + " finds that -5.7222\n", + " the nation -5.6480\n", + " indicates -5.5894\n", + " donald trump -5.1844\n", + " donald -5.0836\n", + " says he -4.9047\n", + " did not -4.7563\n", + " because of -4.7343\n", + " may -4.7339\n", + " large -4.6423\n", + " her -4.5442\n", + " how to -4.3584\n" + ] + } + ], + "source": [ + "# ── Feature importance: top discriminative terms ──────────────────────────────\n", + "tfidf_vocab = best_bin.named_steps[\"tfidf\"].get_feature_names_out()\n", + "lr_coefs = best_bin.named_steps[\"lr\"].coef_[0] # binary: shape (n_features,)\n", + "\n", + "n_top = 20\n", + "top_pos_idx = np.argsort(lr_coefs)[-n_top:][::-1]\n", + "top_neg_idx = np.argsort(lr_coefs)[:n_top]\n", + "\n", + "top_positive = [(tfidf_vocab[i], lr_coefs[i]) for i in top_pos_idx]\n", + "top_negative = [(tfidf_vocab[i], lr_coefs[i]) for i in top_neg_idx]\n", + "\n", + "print(\"Top 20 sarcastic indicators (positive coef):\")\n", + "for term, coef in top_positive:\n", + " print(f\" {term:35s} {coef:+.4f}\")\n", + "\n", + "print(\"\\nTop 20 non-sarcastic indicators (negative coef):\")\n", + "for term, coef in top_negative:\n", + " print(f\" {term:35s} {coef:+.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 644 }, + "id": "pEArvWzTuKBd", + "outputId": "c4709ebc-9b56-468b-eb9f-c47f1ba006b8" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "yD1TrZZPuKBd", - "outputId": "4faad70d-dc90-4fcd-de7d-19de03226861" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running grid search for type task...\n", - "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n", - "\n", - "Best params : {'lr__C': 3.0, 'lr__class_weight': 'balanced', 'tfidf__max_features': None, 'tfidf__min_df': 2, 'tfidf__ngram_range': (1, 2)}\n", - "Best CV F1 : 0.3840\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "# ── Define pipeline and grid (with class_weight) ──────────────────────────────\n", - "pipe_type = Pipeline([\n", - " (\"tfidf\", TfidfVectorizer(sublinear_tf=True, lowercase=True)),\n", - " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, solver=\"lbfgs\",\n", - " multi_class=\"auto\")),\n", - "])\n", - "\n", - "param_grid_type = {\n", - " \"tfidf__ngram_range\" : [(1, 1), (1, 2)],\n", - " \"tfidf__min_df\" : [2, 3, 5],\n", - " \"tfidf__max_features\": [None, 50_000],\n", - " \"lr__C\" : [0.1, 1.0, 3.0],\n", - " \"lr__class_weight\" : [None, \"balanced\"],\n", - "}\n", - "\n", - "cv_type = GroupKFold(n_splits=5)\n", - "\n", - "gs_type = GridSearchCV(\n", - " pipe_type, param_grid_type,\n", - " cv=cv_type, scoring=\"f1_macro\",\n", - " n_jobs=-1, verbose=1, refit=True,\n", - ")\n", - "\n", - "print(\"Running grid search for type task...\")\n", - "gs_type.fit(X_trainval_t, y_trainval_t, groups=groups_tv_t)\n", - "\n", - "print(f\"\\nBest params : {gs_type.best_params_}\")\n", - "print(f\"Best CV F1 : {gs_type.best_score_:.4f}\")" - ] + "image/png": 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+ }, + "metadata": {} }, { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "huaF8TbjuKBd", - "outputId": "d43d2757-7486-4307-912b-462d285835ce" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved: outputs/classical/tfidf_lr/best_config_type.json\n" - ] - } - ], - "source": [ - "# ── Save best config ──────────────────────────────────────────────────────────\n", - "best_cfg_type = {\"task\": \"type\", \"model\": \"TF-IDF + LR\", \"seed\": SEED,\n", - " \"label_names\": STRATEGY_LABELS, \"label2id\": label2id,\n", - " \"best_params\": gs_type.best_params_, \"cv_f1_macro\": gs_type.best_score_}\n", - "with open(OUT_TFIDF / \"best_config_type.json\", \"w\") as f:\n", - " json.dump(best_cfg_type, f, indent=2)\n", - "print(\"Saved: outputs/classical/tfidf_lr/best_config_type.json\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/feature_importance_binary.png\n" + ] + } + ], + "source": [ + "# ── Feature importance plot ───────────────────────────────────────────────────\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 7))\n", + "\n", + "terms_pos, coefs_pos = zip(*top_positive)\n", + "axes[0].barh(list(reversed(terms_pos)), list(reversed(coefs_pos)), color=\"steelblue\")\n", + "axes[0].set_title(\"Top 20 — Sarcastic (positive)\", fontsize=13)\n", + "axes[0].set_xlabel(\"LR Coefficient\")\n", + "\n", + "terms_neg, coefs_neg = zip(*top_negative)\n", + "axes[1].barh(list(reversed(terms_neg)), list(reversed(coefs_neg)), color=\"coral\")\n", + "axes[1].set_title(\"Top 20 — Non-Sarcastic (negative)\", fontsize=13)\n", + "axes[1].set_xlabel(\"LR Coefficient\")\n", + "\n", + "plt.suptitle(\"TF-IDF + LR: Feature Importance (Binary Task)\", fontsize=14)\n", + "plt.tight_layout()\n", + "plt.savefig(OUT_TFIDF / \"feature_importance_binary.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/classical/tfidf_lr/feature_importance_binary.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5VFBy1bJuKBd" + }, + "source": [ + "## Task B — Sarcasm Type Classification" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "GTvcYYGquKBd", + "outputId": "f3c82bbf-5d44-4c4c-d8f0-2f586415fced" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "c4Hj46aWuKB6", - "outputId": "f2ac2819-bbf4-43a3-9437-9cfdafc29c12" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "[Val] Accuracy=0.8967 Macro-F1=0.8982 Weighted-F1=0.8961\n", - " precision recall f1-score support\n", - "\n", - " irony 0.92 0.87 0.90 942\n", - " overstatement 0.88 0.97 0.92 600\n", - "rhetorical_question 0.79 1.00 0.88 157\n", - " sarcasm 0.94 0.83 0.88 1317\n", - " satire 0.88 0.91 0.90 747\n", - " understatement 0.85 0.98 0.91 487\n", - "\n", - " accuracy 0.90 4250\n", - " macro avg 0.88 0.93 0.90 4250\n", - " weighted avg 0.90 0.90 0.90 4250\n", - "\n", - "\n", - "[Test] Accuracy=0.3958 Macro-F1=0.4047 Weighted-F1=0.3947\n", - " precision recall f1-score support\n", - "\n", - " irony 0.33 0.29 0.31 902\n", - " overstatement 0.38 0.43 0.40 592\n", - "rhetorical_question 0.42 0.60 0.50 176\n", - " sarcasm 0.48 0.42 0.45 1291\n", - " satire 0.38 0.39 0.38 833\n", - " understatement 0.36 0.43 0.39 456\n", - "\n", - " accuracy 0.40 4250\n", - " macro avg 0.39 0.43 0.40 4250\n", - " weighted avg 0.40 0.40 0.39 4250\n", - "\n", - "Saved: outputs/classical/tfidf_lr/metrics_type.json\n" - ] - } - ], - "source": [ - "# ── Evaluate on val and test ──────────────────────────────────────────────────\n", - "best_type = gs_type.best_estimator_\n", - "\n", - "val_metrics_type, y_val_pred_type = evaluate(best_type, X_val_t, y_val_t, STRATEGY_LABELS, \"Val\")\n", - "test_metrics_type, y_test_pred_type = evaluate(best_type, X_test_t, y_test_t, STRATEGY_LABELS, \"Test\")\n", - "\n", - "all_metrics_type = {\"val\": val_metrics_type, \"test\": test_metrics_type}\n", - "for split_m in all_metrics_type.values():\n", - " split_m.pop(\"report\", None)\n", - "\n", - "with open(OUT_TFIDF / \"metrics_type.json\", \"w\") as f:\n", - " json.dump(all_metrics_type, f, indent=2)\n", - "print(\"Saved: outputs/classical/tfidf_lr/metrics_type.json\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n", + "Train+Val: 24,083 Train: 19,833 Val: 4,250 Test: 4,250\n" + ] + } + ], + "source": [ + "# ── Load type splits ──────────────────────────────────────────────────────────\n", + "train_type, val_type, test_type = load_splits(\"type\")\n", + "\n", + "# Encode string labels to integers\n", + "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", + "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", + "id2label = {i: lab for lab, i in label2id.items()}\n", + "\n", + "def encode_labels(df: pd.DataFrame) -> list[int]:\n", + " return [label2id[l] for l in df[\"type_label\"]]\n", + "\n", + "trainval_type = pd.concat([train_type, val_type], ignore_index=True)\n", + "\n", + "X_trainval_t = trainval_type[\"text\"].tolist()\n", + "y_trainval_t = encode_labels(trainval_type)\n", + "groups_tv_t = trainval_type[\"group_id\"].tolist()\n", + "\n", + "X_train_t = train_type[\"text\"].tolist()\n", + "y_train_t = encode_labels(train_type)\n", + "X_val_t = val_type[\"text\"].tolist()\n", + "y_val_t = encode_labels(val_type)\n", + "X_test_t = test_type[\"text\"].tolist()\n", + "y_test_t = encode_labels(test_type)\n", + "\n", + "print(f\"Strategies: {STRATEGY_LABELS}\")\n", + "print(f\"Train+Val: {len(X_trainval_t):,} Train: {len(X_train_t):,} Val: {len(X_val_t):,} Test: {len(X_test_t):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "yD1TrZZPuKBd", + "outputId": "4faad70d-dc90-4fcd-de7d-19de03226861" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "ldimkyrXuKB6", - "outputId": "e8e286df-267e-4802-8318-d0cc9f768808" - }, - "outputs": [ - { - "data": { - "image/png": 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XV1fcunVLcFkJInngHCgiolJow4YNKF++vOBkAkUZM2YMkpKSsHv37h96v/Pnz0fnzp2ZPFGR4BAeEVEpsnv3bty9exfHjx/H8uXLi8XEfx0dnQJdL0refnTCRv8tTKCIiEqR7t27Q0dHB/3798ewYcMUHQ5RqcU5UEREREQy4hwoIiIiIhkxgSIiIiKSERMoIiIiIhkxgSIiIiKSEc/CoxLHbvRxRYdQJO4vbKnoEIpEMTiLvsikZ+YoOoQioaJcOp805VL8YtRSk++xadaQ308Opd4u+ou1KgITKCIiIhIScYBKGj5CRERERDJiBYqIiIiESvFwp7wwgSIiIiIhDuFJxUeIiIiISEasQBEREZEQh/CkYgJFREREQhzCk4qPEBEREZGMWIEiIiIiIQ7hScUEioiIiIQ4hCcVHyEiIiIiGbECRUREREIcwpOKCRQREREJcQhPKj5CRERERDJiBYqIiIiEOIQnFRMoIiIiEuIQnlR8hIiIiIhkxAoUERERCXEITyomUERERCTEITyp+AgRERERyYgVKCIiIhJiBUoqJlBEREQkpMQ5UNIwxSQiIiKSEStQREREJMQhPKn4CBEaN26M0aNHKzoMIiIqLkQi+S2lFCtQhAMHDkBVVVXRYfwQSiJgtLcT2ruVgamuOqIS0rD/+husPP0MAKCiJMK4VhXQuJIprI21kJiWhctP3mPB0X8RnZAOAChjpIkRzR1Rz9FYso9DN95ideAzZGaLFXl4AjdvhGDblj/w6OEDvI+JQcCyVWjSzFOyPiUlGSuWBuDcP0GIj4+DVZmy6O7TE527dlNg1NLdvBGCbZv/wMP/P64ly4XHFRR4Gvv27Majhw8QHx+P3fsOokLFSgqMuHC2bdqANSuX4ucePTFmwmS8e/cWHVv9lG/fuQuXoNlP3j84woLZvHE9zgYFIuzlC6ira6Bq9RoYMXocbO3sJH3evA7HsoCFCL19C5kZGXD3aIAJk6fA2NhEgZFL9+k9JnktfvEemz5lEo4eOSTYpp5Hfaxet/EHR0pFgRUogpGREXR1dfNdl5GR8YOjKVpDmtnDx8MGM/Y/gOf881hw9F8MamqPPg1tAQCaasqoUlYPq04/Q5uASxiy6SbKm2ljwwA3yT7szXSgJAKm7LmH5gvOY87Bh/DxsMGEVhUVdFT5S0tNhZNTRUyaMj3f9QEL5+PK5UuYM38h9h8+jh6/9MKCebNx/uw/PzhS2aSmpsKpQkVM/spxpaamorprTYwcM/4HRyY/Dx/cw8H9e+DgWEHSZm5ugeOB5wXLwCG+0NLSgrtHAwVG+223boSgS7ce2LxjN1av/wNZWZnwHdIfqSkpAIDUlBQMHzwAIpEI6zZswR9bdyEzMxNjRgxDTk6OgqP/ttT/f4997bUIAPU8GiDw7EXJ4r8g4AdG+B1ESvJbZHDhwgW0adMGVlZWEIlEOHTokGC9WCzG9OnTYWlpCU1NTXh6euLp06eCPrGxsfDx8YGenh4MDAzQv39/JCUlCfrcvXsXDRo0gIaGBsqVK4eFCxfK/BAxgSLBEJ6trS1mz56NXr16QU9PD4MGDQIA7N+/H5UrV4a6ujpsbW0RECD8ELC1tcW8efPQr18/6OrqwtraGuvXr5esb9q0KXx9fQXbxMTEQE1NDUFBQUV7gJ9xtTNE4P0onH0YjbexqTh5JxIXH8egmrUBACAxLQs9117H8dAIvIhORuirOMzY9wBVrQ1gZaABALjwbwx+/fMuLj5+j9cfUnHmQTQ2/PMCXlUtfthxFIRHg4YYPnI0mjbLv2px904o2rRtD7dadWBVpiw6dfkZjk4VcP/e3R8cqWzqfzouz/yPq3Xbdhg8dDjqurv/4MjkIyUlGTN++xWTp/lBV09P0q6srAxjE1PBcv7sGTT7yRtaWtoKjPjbVq7bgDbtOsDewRFOFSpi5mx/REZE4NHDBwCAO6G3EfHuLWbM9oeDkxMcnJzgN8cfjx7cR8j1awqO/tvqS3mPAYCamhpMTEwli56+/g+M8DsoaAgvOTkZ1apVw+rVq/Ndv3DhQqxYsQLr1q1DcHAwtLW14eXlhbS0NEkfHx8fPHjwAIGBgTh27BguXLgg+S4DgISEBDRv3hw2Nja4efMmFi1ahJkzZwq+swqCCRTlsXjxYlSrVg23b9/GtGnTcPPmTXTt2hXdunXDvXv3MHPmTEybNg1btmwRbBcQEAA3Nzfcvn0bw4YNw9ChQ/H48WMAwIABA7Br1y6kp6dL+u/YsQNlypRB06ZNf9ix3Xr5ER5OxrAzzf3CqWSli1rljXDuUfRXt9HVVEFOjhgJqVnf7BOXUrKqdVWrVcf5c/8gOioKYrEYIdevIfxVGOrW81B0aP9pi/3nwKNBI9SuW++b/f59+ABPHv+LNu07/aDI5CMpKREAJIlERkYGRCIR1NTUJH3U1NWhpKSE0Fu3FBKjPN24cR1NG9VD+zbemDt7JuLiPio6pGKtRYsWmDNnDjp06JBnnVgsxrJlyzB16lS0a9cOVatWxbZt2/Du3TtJperRo0c4deoUNm7ciDp16qB+/fpYuXIldu/ejXfv3gEAdu7ciYyMDGzatAmVK1dGt27dMHLkSCxZskSmWJlAUR5NmzbFuHHjYG9vD3t7eyxZsgTNmjXDtGnT4OTkhD59+sDX1xeLFi0SbNeyZUsMGzYMDg4OmDhxIkxMTHD27FkAQMeOHQEAhw8flvTfsmUL+vTpA9EPnGS4Nug5jt56hzOTG+FJQAscG98Am86/xOGb7/Ltr6aihIltKuHIrXdISs8/gbIx0UKvBrb480p4UYYudxN/m4by9vbw9myEOq4u8B0yEJOmTEdNt1qKDu0/K/DUCTz+9yGGjhgjte+RQ/tha1ceVavX+AGRyUdOTg4CFvqjWg1XODg6AQBcqlaDhqYmVi5djLTUVKSmpGBZwEJkZ2fj/fsYBUf8ferVb4DZcxfg9w2bMWr0eNy8EQLfoYOQnZ2t6NCkk+MQXnp6OhISEgTL539MF9TLly8RGRkJT8//zTPT19dHnTp1cPXqVQDA1atXYWBgADe3/0278PT0hJKSEoKDgyV9GjZsKEjavby88PjxY3z8WPAElwkU5fH5Cw/Izeg9PIRVCQ8PDzx9+lTwQVC1alXJ/0UiESwsLBAdnVvZ0dDQQM+ePbFp0yYAwK1bt3D//n306dPnm7Hk98YTZ2UW+thaVbdEu5plMGr7bbRZfAnjd93BwCbl0bFWmTx9VZREWN3HFSIA0/bez3d/5vrq2DK4Nk6GRmD3tdeFjksRdu/ajnt372DpyjXYsXs/xoyfiPlzZyH46hVFh/afFBUZgSWL/DFz7kKoq6t/s29aWhpOnzxe4qpPC+bOwvNnTzHvs3lAhkZGWLB4GS6cP4cGdWuisUdtJCYmoGIlZyiV8DO4vFu0QuMmTeHoVAFNmnlixap1eHD/Hm6EXFd0aNLJcQjP398f+vr6gsXf31/mkCIjIwEA5ubmgnZzc3PJusjISJiZmQnWq6iowMjISNAnv318fh8FwbPwKA9t7cLNp/jyTD6RSCSYBDpgwABUr14db968webNm9G0aVPY2Nh8c5/+/v7w8/MTtOnX6Q7Duj6FinFy20pYF/Qcx25HAAAeRySijKEmhnk64EDIW0k/FSURVvVxRRlDTfRYfS3f6pOZnjr+HF4Xt8I+YvKee4WKR1HS0tKwavkyBCxfiQYNGwMAnCpUwJPH/2Lb1k2o4/7t4SOSv38fPcDH2A/o06OzpC07Oxuht25g31+7cCE4FMrKygCAs2dOIy0tFS1bt1NUuDJbMG82Ll04j/Wbt8PcQjhfsG49Dxw+cRpxHz9CWVkZunp68GrSAGXKllNQtEWjbLlyMDA0xOvwV6hTt2TO0SuMyZMnY+zYsYI2aX8klARMoEiqSpUq4fLly4K2y5cvw8nJSfKBXhAuLi5wc3PDhg0bsGvXLqxatUrqNvm98ar+VvizxDTVlJEjFl5qIFssFvxqwafkydZUGz1WXUNcSt6Kl7l+bvJ07008Juy6A3HxuXpBgWRlZSErKxNKX5who6SkBHExP/OptHKr7Y6dew8L2ubMmAIbOzv07DNA8F47cmg/GjRqCkMjox8dpszEYjEW+s/BuX/O4Pc/tqJM2bJf7WtgaAgACAm+htjYD2jY+MfNj/wRoiIjER8XBxNTM+mdFU2OF9JUV1eXS8Jk8f+Jd1RUFCwtLSXtUVFRqF69uqTPp5GPT7KyshAbGyvZ3sLCAlFRUYI+n25bWBT8ZCAmUCTVuHHjUKtWLcyePRs///wzrl69ilWrVmHNmjUy72vAgAHw9fWFtrZ2vpMEv5TfG0+kUvhrVgU9iMLwnxzw7mMankQmonIZPfRvbIe9wW8A5CZPa/q6onJZfQzYEAIlJRFMdHPvPz4lA5nZ4tzkydcdb2NTMe/wIxjp/C++94myj+sXlZSUZLwO/9+8rLdv3+Dxv4+gp68PS0sr1HSrhWVLFkFdQx2WlmVw88Z1HD96GGMnTFJg1NJJO674+DhERkRIPkTDXr4EABibmMDExFQhMReEtrY27B0cBW0amprQ1zcQtL8Of4XQWzewZOW6Hx1ioSyYOwunTh5HwPJV0NLWlsxr0tHRhYZG7pmtRw4dgJ1deRgaGeHunVAELJiHHj17C64VVRx967Wor6+P39euRjPP5jAxMcHr16+xfMkilLO2Rj2P+gqMuoCK4fCpnZ0dLCwsEBQUJEmYEhISEBwcjKFDhwIA3N3dERcXh5s3b6JmzZoAgH/++Qc5OTmoU6eOpM+UKVOQmZkpGTkJDAxEhQoVYPj/SXxBMIEiqVxdXbFnzx5Mnz4ds2fPhqWlJWbNmiV1/lJ+unfvjtGjR6N79+6SD88faeb+BxjbsgJmd64MY53ci2D+eSUcK/7OvY6IuYEGfnLJ/QvkxK8NBdt2W3UVwc9iUb+CKexMtWFnqo1rfp6CPnajj/+YAymAhw/uY1C/3pLbSxbNBwC0adsefnPnw3/REqxctgRTJk1AQnw8LC2tMHzE6GJ/Ic2H9+9j4GfHFbDw/4+rXXvMmjsf58/+gxlTf5OsnzQht4I5eOhwDBk+4scGWwSOHT4AM3Nz1HEvGWdL7tuzGwAw+LPnDABmzJ6HNu1y/4h6FfYSq5cvRXx8PKzKWKHvwCHw6dk7z76Km4cPvngtfvYe+23aTDx98hhHjxxCYkIiTM1M4e7ugWG+owSTl0koKSkJz549k9x++fIlQkNDYWRkBGtra4wePRpz5syBo6Mj7OzsMG3aNFhZWaF9+/YAckdMvL29MXDgQKxbtw6ZmZnw9fVFt27dYGVlBQDo0aMH/Pz80L9/f0ycOBH379/H8uXLsXTpUpliFYnFJW3wgUqysLAw2NvbIyQkBK6uroXaR3FKUuTp/sKWig6hSBTDP2TlJj2zdA53qiiXzidNuRS/GLXU5Htsmi2Xy21fqSdGFbjvuXPn0KRJkzztvXv3xpYtWyAWizFjxgysX78ecXFxqF+/PtasWQMnJydJ39jYWPj6+uLo0aNQUlJCp06dsGLFCujo6Ej63L17F8OHD0dISAhMTEwwYsQITJw4UabjYgJFP0RmZiY+fPiA8ePH4+XLl3nmVMmCCVTJUoq/s5hAlTBMoApOs9UKue0r9fhIue2rOOFlDOiHuHz5MiwtLRESEoJ160rG3A0iIqKv4Rwo+iEaN24MFjuJiEoIOZ6FV1oxgSIiIiIhJlBS8REiIiIikhErUERERCRUiifcywsTKCIiIhLiEJ5UfISIiIiIZMQKFBEREQlxCE8qJlBEREQkxCE8qfgIEREREcmIFSgiIiIS4hCeVEygiIiISEDEBEoqDuERERERyYgVKCIiIhJgBUo6JlBEREQkxPxJKg7hEREREcmIFSgiIiIS4BCedEygiIiISIAJlHQcwiMiIiKSEStQREREJMAKlHRMoIiIiEiACZR0HMIjIiIikhErUERERCTEApRUTKCIiIhIgEN40nEIj4iIiEhGrEARERGRACtQ0jGBohLn0eJWig6hSAzdd0/RIRSJtZ1dFB1CkdFUU1Z0CEVCLFZ0BEWDOUHBMYGSjkN4RERERDJiBYqIiIgEWIGSjgkUERERCTF/kopDeEREREQyYgWKiIiIBDiEJx0TKCIiIhJgAiUdh/CIiIiIZMQKFBEREQmwAiUdEygiIiISYv4kFYfwiIiIiGTEChQREREJcAhPOiZQREREJMAESjoO4RERERHJiBUoIiIiEmAFSjomUERERCTABEo6DuERERERyYgVKCIiIhJiAUoqJlBEREQkwCE86TiER0RERCQjVqCIiIhIgBUo6ZhAERERkQATKOk4hEdEREQkI1agiIiISIgFKKmYQBEREZEAh/Ck4xAeERERkYyYQBUjffr0Qfv27WXebubMmahevbrc4ylKjRs3xujRoxUdhlR/bFiPapUrYKH/XEWH8k3tqphhczcXwTKvpaNkvZ6GCgbWLYtl7SpiXefKmNncATXL6gn20drZFFM8y2Nd58pY3dH5Rx/Cd9u9ayda/NQUtWq4wKdbF9y7e1fRIclVSXktFkR2djZWr1yGll5NUadmVbT29sT6dashFosVHdp32bN7Fzp3aIN6tV1Rr7Yrevb4GZcunld0WIUiEonktpRWHML7QTIyMqCmpqboMEgG9+/dxb69u+HkVEHRoRTIm7g0LDr3UnI7J+d/X0YD65aFlqoyll98haT0LNS1McCwetbwO/0M4XFpAAAVJRFCwuPx7H0KGpY3+uHxf49TJ09g8UJ/TJ3hBxeXati5fSuGDu6Pw8dOwdjYWNHhfbeS9lqUZvMfG7D3rz8xa+4C2Ds44OGD+5gxdTJ0dHTR45deig6v0MzMLTBqzHhY29hALBbj6OFDGOU7HH/tPwgHB0fpOyhGSnPiIy//2QpUeno6Ro4cCTMzM2hoaKB+/foICQlBTk4OypYti7Vr1wr63759G0pKSnj16hUAIC4uDgMGDICpqSn09PTQtGlT3LlzR9L/U1Vo48aNsLOzg4aGBgBg3759cHFxgaamJoyNjeHp6Ynk5GTMnDkTW7duxeHDhyVZ+7lz5wAAEydOhJOTE7S0tFC+fHlMmzYNmZmZAIAtW7bAz88Pd+7ckWy3ZcsWmWLctGkTrK2toaOjg2HDhiE7OxsLFy6EhYUFzMzMMHeu8C/egu53+/btsLW1hb6+Prp164bExEQAuZW28+fPY/ny5ZKYw8LCvv9JlaOU5GRMnjgBM/zmQE9fX9HhFEiOWIyEtCzJkpSRLVnnYKyFM08/4GVsKmKSM3H0YQxSMrNha6Qp6XPofjROP/mAN/Fpigj/u2zfuhkdO3dF+w6dYO/ggKkz/KChoYFDB/YrOrTvVhJfi9LcCb2Nxk2aoWGjxihTpix+au4N93r1cf9eya4aNm7SFA0aNoKNjS1sbe0wYtQYaGlp4e6dUEWHRkXgP5tA/frrr9i/fz+2bt2KW7duwcHBAV5eXoiLi0P37t2xa9cuQf+dO3fCw8MDNjY2AIAuXbogOjoaJ0+exM2bN+Hq6opmzZohNjZWss2zZ8+wf/9+HDhwAKGhoYiIiED37t3Rr18/PHr0COfOnUPHjh0hFosxfvx4dO3aFd7e3oiIiEBERATq1asHANDV1cWWLVvw8OFDLF++HBs2bMDSpUsBAD///DPGjRuHypUrS7b7+eefCxzj8+fPcfLkSZw6dQp//vkn/vjjD7Rq1Qpv3rzB+fPnsWDBAkydOhXBwcGSbQq630OHDuHYsWM4duwYzp8/j/nz5wMAli9fDnd3dwwcOFASc7ly5eT59H63eXNmoWHDRqjrXk/RoRSYua46lrSriAWtK2BQ3XIw0lKVrHv2IQW1y+lDW00ZIgC1rfWhqqyEf6OTFRewnGRmZODRwweC50pJSQl169bD3Tu3FRiZfJTE16I01arXQHDwNbwKy62YPv73X9y+dRMeDRoqODL5yc7OxskTx5GamoJq1WooOhyZcQhPuv/kEF5ycjLWrl2LLVu2oEWLFgCADRs2IDAwEH/88Qd8fHwQEBCA8PBwWFtbIycnB7t378bUqVMBAJcuXcL169cRHR0NdXV1AMDixYtx6NAh7Nu3D4MGDQKQO2y3bds2mJqaAgBu3bqFrKwsdOzYUZKIubi4SOLS1NREeno6LCwsBPF+ul8AsLW1xfjx47F79278+uuv0NTUhI6ODlRUVATbFTTGnJwcbNq0Cbq6unB2dkaTJk3w+PFjnDhxAkpKSqhQoQIWLFiAs2fPok6dOjLtd8uWLdDV1QUA9OzZE0FBQZg7dy709fWhpqYGLS2tPMdaHJw8cRyPHj3Err/2KTqUAnvxIQUbg18jMiEDBpoqaFfFDJOblce0k0+RlpWDNZfDMayeNVZ1dEZWjhgZWTlYeekVopMyFB36d/sY9xHZ2dl5huqMjY3x8uULBUUlHyXxtVgQ/QYMQnJyEtq3aQFlZWVkZ2fDd+QYtGrdVtGhfbenTx6jZ49uyMhIh5aWFpauWA17BwdFhyW70pv3yM1/MoF6/vw5MjMz4eHhIWlTVVVF7dq18ejRI0yYMAGVKlXCrl27MGnSJJw/fx7R0dHo0qULAODOnTtISkrK84GdmpqK58+fS27b2NhIkicAqFatGpo1awYXFxd4eXmhefPm6Ny5MwwNDb8Z719//YUVK1bg+fPnSEpKQlZWFvT09L65TUFjtLW1lSQ5AGBubg5lZWUoKSkJ2qKjo79rv5aWlpJ9yCI9PR3p6emCNrGyuiR5k7fIiAgsnD8Xv2/YVGT3URTuRSRJ/v8mHnj+IQWL21RELWt9XHzxER1dzKGppoyFZ18gKT0brmX0MKyeNfyDnuNNfPo39kyKUlJfiwVx+tRJnDh2FP4LAmDv4IDH/z7CogX+MDUzQ9t2HRQd3nextbXDnv2HkJSUiMDTf2PabxPxx5YdJTOJom/6TyZQBeHj4yNJoHbt2gVvb29J0pCUlARLS0vJHKXPGRgYSP6vra0tWKesrIzAwEBcuXIFp0+fxsqVKzFlyhQEBwfDzs4u3ziuXr0KHx8f+Pn5wcvLC/r6+ti9ezcCAgK+GX9BY1RVVRWsE4lE+bbl5OR8934/7UMW/v7+8PPzE7RNmTYDU6fPlHlfBfHw4QPEfviAbl06Stqys7Nx80YIdv+5EyG370FZWblI7lueUjNzEJWYDnMdNZjqqMHTyQRTTjzBu4TcZOl1XBocTbXR1NEY2268U3C038fQwBDKysr48OGDoP3Dhw8wMTFRUFTfr7S8FvOzNGAh+g4YBO+WrQAAjk4VEBHxDps2/l7iEyhVNTVY//8Ig3PlKnhw/x527tiG6TNnKTgy2ZTmoTd5+U/OgbK3t4eamhouX74sacvMzERISAicnXNP3+7Rowfu37+PmzdvYt++ffDx8ZH0dXV1RWRkJFRUVODg4CBYpH1gi0QieHh4wM/PD7dv34aamhoOHjwIAFBTU0N2drag/5UrV2BjY4MpU6bAzc0Njo6Okonsn+S33ffE+C3y2m9+Medn8uTJiI+PFywTJk4udPzS1KlbF/sOHcVf+w9JlsqVq6Bl6zb4a/+hEvOFpa6iBFMdNcSlZkFdOfeD8MsTxMVican4kFRVU0Ml58oIvnZV0paTk4Pg4KuoWgLnnnxSWl6L+UlLS4PSF689JSVlwZmjpUVOTg4yM0reULmi5kBlZ2dj2rRpsLOzg6amJuzt7TF79mzBJS7EYjGmT58OS0tLaGpqwtPTE0+fPhXsJzY2Fj4+PtDT04OBgQH69++PpKSkL+/uu/wnK1Da2toYOnQoJkyYACMjI1hbW2PhwoVISUlB//79AeQOQdWrVw/9+/dHdnY22rb939i8p6cn3N3d0b59eyxcuBBOTk549+4djh8/jg4dOsDNzS3f+w0ODkZQUBCaN28OMzMzBAcHIyYmBpUqVZLc599//43Hjx/D2NgY+vr6cHR0RHh4OHbv3o1atWrh+PHjkoTrE1tbW7x8+RKhoaEoW7YsdHV1Cx2jNPLar62tLYKDgxEWFgYdHR0YGRkJhg0/UVfPO1yXllWo0AtEW1sHjo5OgjZNLS0Y6BvkaS9Ofq5ugdC3iXifkgFDDVW0dzGDWAwEh8chJSMbUYnp6O1WBn+FRiApI3cIz9lCB8sv/C8ZN9JShbaaMoy11CASAeUMcs8cjU7KQHqW7NXDH6ln776Y9ttEVK5cBVVcqmLH9q1ITU1F+w4dpW9cTJXU12JBNGzcBBs3rIOFpVXuEN6jR9ixbTPadeik6NC+y/KlAajfoCEsLC2RkpyME8eP4UbIdaxd/4eiQysxFixYgLVr12Lr1q2oXLkybty4gb59+0JfXx8jR44EACxcuBArVqzA1q1bYWdnh2nTpsHLywsPHz6UnPHu4+ODiIgIBAYGIjMzE3379sWgQYPynCD2Pf6TCRQAzJ8/Hzk5OejZsycSExPh5uaGv//+WzAfycfHB8OGDUOvXr2gqfm/071FIhFOnDiBKVOmoG/fvoiJiYGFhQUaNmwIc3Pzr96nnp4eLly4gGXLliEhIQE2NjYICAiQTGQfOHAgzp07Bzc3NyQlJeHs2bNo27YtxowZA19fX6Snp6NVq1aYNm0aZs6cKdlvp06dcODAATRp0gRxcXHYvHkz+vTpU6gYpSnssX9p/Pjx6N27N5ydnZGamoqXL1/C1ta20HH91xlqqmJwvXLQUVNGYno2nsYkY/aZ50hMz63yLT0fhs7VLDCqoQ00VJQRlZiOjcFvcDciUbKPDi7mqG/3v9f/LO/c69bM/+cFHhfzs/W8W7TEx9hYrFm1Au/fx6BCxUpY8/tGGJfgIbzSbNJvU7F65XL4z/FDbOwHmJqaoVOXnzF46HBFh/ZdYmM/YOrkiYiJiYaOri6cnCpg7fo/4F7PQ/rGxYyiitNXrlxBu3bt0KpV7vCura0t/vzzT1y/fh1AbvVp2bJlmDp1Ktq1awcA2LZtG8zNzXHo0CF069YNjx49wqlTpxASEiL5o37lypVo2bIlFi9eDCsrK7nEKhKX9Eu/0n9OUVagFGnovnuKDqFIrO3sIr0TFSul9VuhFIxYf5WGnMshjhNOyW1fTxd5F7jvvHnzsH79epw+fRpOTk64c+cOmjdvjiVLlsDHxwcvXryAvb09bt++LfgFjkaNGqF69epYvnw5Nm3ahHHjxuHjx4+S9VlZWdDQ0MDevXvRoYN85tn9ZytQREREVPTyO5s6v+kZADBp0iQkJCSgYsWKkktczJ07VzIPOTIyEgDyjHiYm5tL1kVGRsLMzEywXkVFBUZGRpI+8vCfnEROREREXycSyW/x9/eHvr6+YPH398/3fvfs2YOdO3di165duHXrFrZu3YrFixdj69atP/gRkI4VKCIiIhKQ5xm6kydPxtixYwVtX7u22YQJEzBp0iR069YNQO7Fpl+9egV/f3/07t1bcvHlqKgoWFpaSraLioqSDOlZWFjkue5gVlYWYmNj5XrxZlagiIiIqMioq6tDT09PsHwtgUpJSclzRraysrLkOoJ2dnawsLBAUFCQZH1CQgKCg4Ph7u4OAHB3d0dcXBxu3rwp6fPPP/8gJycHderUkdtxsQJFREREAoqacN+mTRvMnTsX1tbWqFy5Mm7fvo0lS5agX79+/x+XCKNHj8acOXPg6OgouYyBlZUV2rdvDwCoVKkSvL29MXDgQKxbtw6ZmZnw9fVFt27d5HYGHsAEioiIiL6gpKSYDGrlypWYNm0ahg0bhujoaFhZWWHw4MGYPn26pM+vv/6K5ORkDBo0CHFxcahfvz5OnToluQYUAOzcuRO+vr5o1qwZlJSU0KlTJ6xYsUKusfIyBlTi8DIGJQsvY1DylNZvBV7GoOCcfzstt309nNdcbvsqTliBIiIiIoHSnGzKCxMoIiIiEigNv5NZ1HgWHhEREZGMWIEiIiIiARagpGMCRURERAIcwpOOQ3hEREREMmIFioiIiARYgZKOCRQREREJMH+SjkN4RERERDJiBYqIiIgEOIQnHRMoIiIiEmD+JB2H8IiIiIhkxAoUERERCXAITzomUERERCTA/Ek6DuERERERyYgVKCIiIhLgEJ50TKCIiIhIgPmTdBzCIyIiIpIRK1BEREQkwCE86ViBIiIiIpIRK1BExcTazi6KDqFIvIlNVXQIRaaskaaiQygSLD4QXwPSMYEiIiIiAQ7hScchPCIiIiIZsQJFREREAixASccEioiIiAQ4hCcdh/CIiIiIZMQKFBEREQmwACUdEygiIiIS4BCedBzCIyIiIpIRK1BEREQkwAqUdEygiIiISID5k3QcwiMiIiKSEStQREREJMAhPOmYQBEREZEA8yfpOIRHREREJCNWoIiIiEiAQ3jSMYEiIiIiAeZP0nEIj4iIiEhGrEARERGRgBJLUFIxgSIiIiIB5k/ScQiPiIiISEasQBEREZEAz8KTjgkUERERCSgxf5KKQ3hEREREMmIFioiIiAQ4hCddsapAnTt3DiKRCHFxcYoORULeMYWFhUEkEiE0NFQu+1OUmTNnonr16ooOg4iIioBIJL+ltCpWCZS8iEQiHDp0SC77qlevHiIiIqCvry+X/ZVE+T2e48ePR1BQkGIC+gF279qJFj81Ra0aLvDp1gX37t5VdEhyU9KO7X7oTfhNHIme7X9CqwbVcfXCP4L1S+ZOQ6sG1QXLtHHDJOvv3g7Js/7T8uTR/R99ODIrac+XLErrsZXW4yKhYpVAZWRkKDoEgczMTKipqcHCwoLlzC/o6OjA2NhY0WEUiVMnT2DxQn8MHjYcu/ceRIUKFTF0cH98+PBB0aF9t5J4bGlpqbBzcMLQsZO/2qdmHQ9sP3RGsvw6c75kXaUq1QXrth86A6/WHWBuWQaOFSv/iEMotJL4fBVUaT220nJcIjn+K60UmkA1btwYvr6+GD16NExMTODl5QUAuHnzJtzc3KClpYV69erh8ePHgu0OHz4MV1dXaGhooHz58vDz80NWVhYAwNbWFgDQoUMHiEQiyW0AWLt2Lezt7aGmpoYKFSpg+/btgv2KRCKsXbsWbdu2hba2NubOnZvvEN7ly5fRuHFjaGlpwdDQEF5eXvj48SMA4NSpU6hfvz4MDAxgbGyM1q1b4/nz54V+jE6cOAEnJydoamqiSZMm2LJliyCe/IbSli1bJjhuANi4cSMqVaoEDQ0NVKxYEWvWrJGsy8jIgK+vLywtLaGhoQEbGxv4+/t/8/H88n5zcnIwa9YslC1bFurq6qhevTpOnTolWf9p6PLAgQNo0qQJtLS0UK1aNVy9erXQj01R2b51Mzp27or2HTrB3sEBU2f4QUNDA4cO7Fd0aN+tJB6bW9366DXQF/UaNv1qH1VVVRgZm0gWXV29r67T09fHtUvn8FPLdsX+D6OS+HwVVGk9ttJyXEoi+S2llcIrUFu3boWamhouX76MdevWAQCmTJmCgIAA3LhxAyoqKujXr5+k/8WLF9GrVy+MGjUKDx8+xO+//44tW7Zg7ty5AICQkBAAwObNmxERESG5ffDgQYwaNQrjxo3D/fv3MXjwYPTt2xdnz54VxDNz5kx06NAB9+7dE9zvJ6GhoWjWrBmcnZ1x9epVXLp0CW3atEF2djYAIDk5GWPHjsWNGzcQFBQEJSUldOjQATk5OTI/Nq9fv0bHjh3Rpk0bhIaGYsCAAZg0aZLM+9m5cyemT5+OuXPn4tGjR5g3bx6mTZuGrVu3AgBWrFiBI0eOYM+ePXj8+DF27twpSZS+9nh+afny5QgICMDixYtx9+5deHl5oW3btnj69Kmg35QpUzB+/HiEhobCyckJ3bt3lyS/xUFmRgYePXyAuu71JG1KSkqoW7ce7t65rcDIvl9pPrZ7oTfQo00TDOrRDqsXz0VCfNxX+wZfOo/EhHj81LLdjwuwEErz81Vaj620HhflT+Fn4Tk6OmLhwoUAgIiICADA3Llz0ahRIwDApEmT0KpVK6SlpUFDQwN+fn6YNGkSevfuDQAoX748Zs+ejV9//RUzZsyAqakpAMDAwAAWFhaS+1m8eDH69OmDYcNy50aMHTsW165dw+LFi9GkSRNJvx49eqBv376S2y9evBDEu3DhQri5uQkqOJUr/28YoFOnToL+mzZtgqmpKR4+fIgqVarI9Nh8qpgFBAQAACpUqIB79+5hwYIFMu1nxowZCAgIQMeOHQEAdnZ2kuSzd+/eCA8Ph6OjI+rXrw+RSAQbGxvJtl97PL+0ePFiTJw4Ed26dQMALFiwAGfPnsWyZcuwevVqSb/x48ejVatWAAA/Pz9UrlwZz549Q8WKFWU6pqLyMe4jsrOz8wxPGhsb4+XLF1/ZqmQorcdWs44H6jVqBgvLMoh4+xpb16/CjAnDsXjtNigrK+fpf/r4QbjWdoeJmbkCoi240vp8AaX32ErTcRX36mxxoPAKVM2aNfO0Va1aVfJ/S0tLAEB0dDQA4M6dO5g1axZ0dHQky8CBAxEREYGUlJSv3s+jR4/g4eEhaPPw8MCjR48EbW5ubt+M91MF6muePn2K7t27o3z58tDT05NUcsLDw7+536/FXKdOHUGbu7u7TPtITk7G8+fP0b9/f8FjNmfOHMnQYp8+fRAaGooKFSpg5MiROH36tEz3kZCQgHfv3hXo8f3Wc5uf9PR0JCQkCJb09HSZ4qPSrZGnN+rWbwxbe0e4N2yKGQtX4MmjB7h3+0aevu+jo3Dr+lU0b9VBAZESlRw8C086hVegtLW187SpqqpK/v8pC/40BJaUlAQ/Pz9JNeVzGhoaRRLP5zQ1Nb+5vk2bNrCxscGGDRtgZWWFnJwcVKlSpcgmyCspKUEsFgvaMjMzJf9PSkoCAGzYsCFPMvbpr3NXV1e8fPkSJ0+exJkzZ9C1a1d4enpi3759co/3W89tfvz9/eHn5ydomzJtBqZOnyn32ADA0MAQysrKeSZ8fvjwASYmJkVynz9KaT62z1lalYWeviEi3r5GdTfhaz7wxGHo6umjTv1GCoqu4Erz81Vaj620HhflT+EVKFm5urri8ePHcHBwyLMoKeUejqqqqmRO0ieVKlXC5cuXBW2XL1+Gs7OzTPdftWrVr56+/+HDBzx+/BhTp05Fs2bNUKlSJcnk8sKoVKkSrl+/Lmi7du2a4LapqSkiIyMFSdTn15gyNzeHlZUVXrx4kefxsrOzk/TT09PDzz//jA0bNuCvv/7C/v37ERsbCyD/x/Nzenp6sLKyksvj+6XJkycjPj5esEyY+PWzsb6XqpoaKjlXRvC1/01uz8nJQXDwVVStVqPI7vdHKM3H9rn30VFITIiDobHwC0ssFiPwxGE09W4DFRXVr2xdfJTm56u0HltpOi4lkUhuS2ml8AqUrKZPn47WrVvD2toanTt3hpKSEu7cuYP79+9jzpw5AHLPHAsKCoKHhwfU1dVhaGiICRMmoGvXrqhRowY8PT1x9OhRHDhwAGfOnJHp/idPngwXFxcMGzYMQ4YMgZqaGs6ePYsuXbrAyMgIxsbGWL9+PSwtLREeHl6oSd+fDBkyBAEBAZgwYQIGDBiAmzdvYsuWLYI+jRs3RkxMDBYuXIjOnTvj1KlTOHnyJPT0/ncWkp+fH0aOHAl9fX14e3sjPT0dN27cwMePHzF27FgsWbIElpaWqFGjBpSUlLB3715YWFjAwMDgq4/nlyZMmIAZM2bA3t4e1atXx+bNmxEaGoqdO3cW+vgBQF1dHerq6oK2tCKec96zd19M+20iKleugiouVbFj+1akpqaifYe8Vc+SpiQeW2pKCt69/d8QeGTEWzx/+i909fShq6uPXZvXwaOxJwyNjBHx9g02rV0GyzLlULN2PcF+7ty8jqiIt/BqXXKG70ri81VQpfXYSstxleK8R25KXALl5eWFY8eOYdasWViwYAFUVVVRsWJFDBgwQNInICAAY8eOxYYNG1CmTBmEhYWhffv2WL58ORYvXoxRo0bBzs4OmzdvRuPGjWW6fycnJ5w+fRq//fYbateuDU1NTdSpUwfdu3eHkpISdu/ejZEjR6JKlSqoUKECVqxYIfN9fGJtbY39+/djzJgxWLlyJWrXro158+YJzg6sVKkS1qxZg3nz5mH27Nno1KkTxo8fj/Xr10v6DBgwAFpaWli0aBEmTJgAbW1tuLi4YPTo0QAAXV1dLFy4EE+fPoWysjJq1aqFEydOSCp6+T2eXxo5ciTi4+Mxbtw4REdHw9nZGUeOHIGjo2Ohjl2RvFu0xMfYWKxZtQLv38egQsVKWPP7RhiXghJ8STy2p48fYPLIgZLbG1flnlTRzLsNho+fgrDnTxF06iiSkxJhZGKKGrXc0XPAcKiqqQn2c/r4QVSqUg3lbOxQUpTE56ugSuuxldbjorxE4i8n0FCxdu7cOTRp0gQfP36UVIj+a4q6AkXy9SY2VdEhFJmyRt+eE0n0o2jIuRzSefMtue1rX19Xue2rOClxFSgiIiIqWhzCk67ETSIvTYYMGSK4tMDny5AhQxQdHhEREX0Fh/AUKDo6GgkJCfmu09PTg5mZ2Q+OqGTgEF7JwiE8oqIn7yG8n7fK78rpf/UuWWcgFhQrUApkZmaW7+UYHBwcmDwREZHCiOS4yOrt27f45ZdfYGxsDE1NTbi4uODGjf9dGFcsFmP69OmwtLSEpqYmPD098/xsWGxsLHx8fKCnpwcDAwP0799fcl1EeWECRURERMXCx48f4eHhAVVVVZw8eRIPHz5EQECA4PI5CxcuxIoVK7Bu3ToEBwdDW1sbXl5eSEtLk/Tx8fHBgwcPEBgYiGPHjuHChQsYNGiQXGPlEB6VOBzCK1k4hEdU9OQ9hNd9W6jc9vVnr+oF7jtp0iRcvnwZFy9ezHe9WCyGlZUVxo0bh/HjxwMA4uPjYW5uji1btqBbt2549OgRnJ2dERISIvl5tlOnTqFly5Z48+YNrKysvvuYAFagiIiI6AtKIvktsvym6ZEjR+Dm5oYuXbrAzMwMNWrUwIYNGyTrX758icjISHh6ekra9PX1UadOHVy9mnsF+KtXr8LAwEDw27aenp5QUlJCcHCw/B4jue2JiIiI6Av+/v7Q19cXLP7+/vn2ffHiBdauXQtHR0f8/fffGDp0KEaOHImtW7cCACIjIwHk/kzZ58zNzSXrIiMj88wjVlFRgZGRkaSPPPA6UERERCQgkuOFoCZPnoyxY8cK2r78ia5PcnJy4Obmhnnz5gEAatSogfv372PdunXo3bu33GKSB1agiIiISEAkkt+irq4OPT09wfK1BMrS0jLPj9BXqlQJ4eG5v4dpYWEBAIiKihL0iYqKkqyzsLBAdHS0YH1WVhZiY2MlfeSBCRQREREVCx4eHnj8+LGg7cmTJ7CxsQEA2NnZwcLCAkFBQZL1CQkJCA4Ohru7OwDA3d0dcXFxuHnzpqTPP//8g5ycHNSpU0dusXIIj4iIiATkOYQnizFjxqBevXqYN28eunbtiuvXr2P9+vVYv369JK7Ro0djzpw5cHR0hJ2dHaZNmwYrKyu0b98eQG7FytvbGwMHDsS6deuQmZkJX19fdOvWTW5n4AFMoIiIiOgLSgr6LbxatWrh4MGDmDx5MmbNmgU7OzssW7YMPj4+kj6//vorkpOTMWjQIMTFxaF+/fo4deoUNDQ0JH127twJX19fNGvWDEpKSujUqRNWrFgh11h5HSgqcXgdqJKF14EiKnryvg5Unz/vym1fW7pXldu+ipNCzYG6ePEifvnlF7i7u+Pt27cAgO3bt+PSpUtyDY6IiIh+PJFIJLeltJI5gdq/fz+8vLygqamJ27dvSy6GFR8fLzntkIiIiEouRf4WXkkhcwI1Z84crFu3Dhs2bICqqqqk3cPDA7du3ZJrcERERETFkcyjpo8fP0bDhg3ztOvr6yMuLk4eMREREZECKZXioTd5kbkCZWFhgWfPnuVpv3TpEsqXLy+XoIiIiEhx5HkhzdJK5gRq4MCBGDVqFIKDgyESifDu3Tvs3LkT48ePx9ChQ4siRiIiIqJiReYhvEmTJiEnJwfNmjVDSkoKGjZsCHV1dYwfPx4jRowoihiJiIjoByrNZ8/JS6GvA5WRkYFnz54hKSkJzs7O0NHRkXdsRPnidaBKFl4Hiqjoyfs6UIP3PZDbvn7vXFlu+ypOCv2Qq6mp5fnBPyIiIqL/ApkTqCZNmnyztPfPP/98V0BERESkWDwLTzqZE6jq1asLbmdmZiI0NBT3799H79695RUXERERKQjzJ+lkTqCWLl2ab/vMmTORlJT03QERERERFXeF+i28/Pzyyy/YtGmTvHZHRERECsLfwpNObvP2r169Cg0NDXntjuirUjOyFR1CkSitnzMW+qX3c8Gwlq+iQygSby8tV3QIRaK0vscAQENFWa77k1t1pRSTOYHq2LGj4LZYLEZERARu3LiBadOmyS0wIiIiouJK5gRKX19fcFtJSQkVKlTArFmz0Lx5c7kFRkRERIpRmofe5EWmBCo7Oxt9+/aFi4sLDA0NiyomIiIiUiAl5k9SyTTMqaysjObNmyMuLq6IwiEiIiIq/mSeJ1alShW8ePGiKGIhIiKiYkBJJL+ltJI5gZozZw7Gjx+PY8eOISIiAgkJCYKFiIiISjZexkC6As+BmjVrFsaNG4eWLVsCANq2bSt4YMRiMUQiEbKzS+cp5kRERESfFDiB8vPzw5AhQ3D27NmijIeIiIgUrDQPvclLgRMosVgMAGjUqFGRBUNERESKV4pH3uRGpjlQpXksk4iIiKigZLoOlJOTk9QkKjY29rsCIiIiIsVSYsFEKpkSKD8/vzxXIiciIqLShb+FJ51MCVS3bt1gZmZWVLEQERERlQgFTqA4/4mIiOi/gV/50sl8Fh4RERGVbpwDJV2BE6icnJyijIOIiIioxJBpDhQRERGVfixASccEioiIiAR4JXLpeKYiERERkYxYgSIiIiIBTiKXjgkUERERCTB/ko5DeEREREQyYgWKiIiIBDiJXDomUERERCQgAjMoaTiER0RERCQjVqDoP23DulX44/c1gjYbWzv8dfA44uPjsGHtKly/dgVRkREwMDREw8bNMHjYSOjo6ioo4sLZumkD1qxYip979MTYXycDAIb2741bN0ME/Tp07opJU2cqIMKC27Txd5wNCkTYyxdQV9dA1eo1MHL0ONjalQcAxMfH4fc1K3HtymVERkbAwNAIjZs2w9Dho6CrwOfNw9UeY3p5wtXZGpam+ug6Zj2Onrsr6DNtaCv07VAPBrqauHrnBUbO+wvPw2Mk6x2szTBvTHu4VysPNVVl3H/6Dn5rjuHCjaeSPo1rO2HGsNao7GCF5NQM7DwajBmrjyI7WzG/JrFx3Sr8sV74HrO2tcNfB45Lbt+7E4rfVy/Hg/t3oaSsBCenili6egM0NDR+dLgy+dbnBwDMnzMDIcHX8D4mGpqaWnCpVh3DR/3vtVqccQhPOiZQ/1GZmZlQVVVVdBjFQnl7B6xc94fktrJy7tvifUwM3sfEYMSYCbArb4/IiHdYMNcP72Ni4L94mYKild3D+/dwcN8eODhVyLOuXccuGDzMV3JbXUPzR4ZWKLduhKBLtx6oXNkF2dnZWLViKYYPGYB9B49BU0sLMdHRiImOxuhxv8LO3gER797Bf84MvI+OxsIlKxQWt7amOu49eYtth6/iryWD8qwf18cTw7o3wsDp2xH29gOmD2uNo6uHo0anOUjPyAIAHFgxBM/Co9Fi8AqkpmfCt0cTHFgxBJXbzETUh0S4OJXBoZVDseCPv9F/2jZYmRlg5W/doKyshMlLD/7oQ5Yob++AFWvzvseA3ORpzIhB6NV3IMZO/A3Kyip4+uRfKCmVjAGSr31+AEDFSpXh1aINzC0tkRAfj43rVmPUsAE4cCwQysrKigi3wJhASVcyXqEEANi3bx9cXFygqakJY2NjeHp6Ijk5GSEhIfjpp59gYmICfX19NGrUCLdu3RJsKxKJsHbtWrRt2xba2tqYO3cuAODo0aOoVasWNDQ0YGJigg4dOki22b59O9zc3KCrqwsLCwv06NED0dHRkvUfP36Ej48PTE1NoampCUdHR2zevBkAEBYWBpFIhD179qBBgwbQ1NRErVq18OTJE4SEhMDNzQ06Ojpo0aIFYmJioEjKysowNjGVLAaGhgAAewdHzA9YjgaNmqBsOWu41a6LIb6jcOnCWWRlZSk05oJKSUnG9N9+xW/T/aCnq5dnvYaGhuDYdXR0FBClbFat24i27TrC3sERThUqwm+2PyIj3uHRwwcAAAdHJyxauhINGzdFuXLWqF2nLoaNGIML5xX7vJ2+/BB+a47hyNm7+a4f3qMJFmz4G8fO3cP9p+8wYNo2WJrqo22TagAAYwNtONqYIWBzIO4/fYfn4TGYtuIwtDXV4exgBQDo3NwV95++g//6U3jx+j0u3XyGKcsPYXDXBtDRUv9hx/qlr73HAGB5wHx06fYLevUdiPL2jrCxtYNn8xZQU1NTWLyy+Naxte/UFTVqusHKqgwqVnLG4OEjERUZiYh3bxUYMckLE6gSIiIiAt27d0e/fv3w6NEjnDt3Dh07doRYLEZiYiJ69+6NS5cu4dq1a3B0dETLli2RmJgo2MfMmTPRoUMH3Lt3D/369cPx48fRoUMHtGzZErdv30ZQUBBq164t6Z+ZmYnZs2fjzp07OHToEMLCwtCnTx/J+mnTpuHhw4c4efIkHj16hLVr18LExERwnzNmzMDUqVNx69YtqKiooEePHvj111+xfPlyXLx4Ec+ePcP06dOL9LGT5nV4OFr/1AgdWzfH9N8mIDLi3Vf7JiUmQVtbByoqJaN4u2jeHHg0aITadevlu/7vk8fQvHE9dO/UFqtXLEFaauoPjvD7JSXlvs719PW/3icxEdo6xfd5sy1jDEtTffwT/K+kLSEpDSH3w1Cnqi0A4ENcMh6/jESP1rWhpaEGZWUlDOhUH1EfEnD7YTgAQF1NBWnpmYJ9p6ZnQlNDDTUqWf+w4/nS6/BwtGneCJ3aNMeMKf97j8XGfsCD+3dhZGSEgX16oKVnAwwd0At3bt9UWKyyKujnR2pqCo4fOQirMmVhbmHxg6OUnUgkkttSWhXPTxPKIyIiAllZWejYsSNsbGwAAC4uLgCApk2bCvquX78eBgYGOH/+PFq3bi1p79GjB/r27Su53a1bN3Tr1g1+fn6StmrVqkn+369fP8n/y5cvjxUrVqBWrVpISkqCjo4OwsPDUaNGDbi5uQEAbG1t88Q9fvx4eHl5AQBGjRqF7t27IygoCB4eHgCA/v37Y8uWLYV5SOSicpWqmDZrLqxt7PDhfQz++H0NhvTriZ37jkBbW1vQN+7jR2zesBbtOnVRULSyOX3qBB7/+xCbd+7Jd33zFq1gaWUFE1MzPHvyGKuWL0F4WBgWKHCYS1Y5OTlYvHAeqtVwhYOjU759Pn78iI3r16Jjp64/OLqCszDJrQ5Gxwr/6In+kAhz4/9VDlsNWYW/lg5CzOXFyMkRI+ZjEtoNX4O4xNzEN/DKI/j2aIKu3jWx7/QtWBjr4bdBLQAAlqZ5K5A/QmWXqpjqNxc2NnZ4/z4Gf6xfg6H9e2LH3iN49+YNAGDj76sxYvQEOFaoiJPHjmDEkH7YufcwylnbKiTmgirI58e+PX9i9bLFSE1NhY2tHVas3QhV1eJfXeMQnnRMoEqIatWqoVmzZnBxcYGXlxeaN2+Ozp07w9DQEFFRUZg6dSrOnTuH6OhoZGdnIyUlBeHh4YJ9fEp0PgkNDcXAgQO/ep83b97EzJkzcefOHXz8+BE5ObmTUMPDw+Hs7IyhQ4eiU6dOuHXrFpo3b4727dujXj1hpaNq1aqS/5ubmwP4X+L3qe3zYcEvpaenIz09XdiWrQJ1dfkMR9Sr31Dyf0enCqjsUhXtW3oi6PQptO3QSbIuOSkJY0cOgW15ewwcPFwu912UoiIjsGShP1au2/jVx6pD5/8lFA6OTjAxNcXwQf3w5nU4ypZTXLVCFvPnzsLzZ0/xx5Zd+a5PSkrCqOGDUb68PQYN9c23T0mydHJXxMQmwrPfMqSmZ6BPh3rYv3ww6v+yCJHvExB07V/8tuwQVvzWDX/M7oX0zCzM33AK9V0dkJMjVkjM7h7/e485/P97rEMrTwQFnpJMpm7fsStat+sIAKhQ0Rk3rl/D0cMHMGzEWIXEXFAF+fzwbtEateu448P799i5bTOmTByL9Zt3yu0zjBSHQ3glhLKyMgIDA3Hy5Ek4Oztj5cqVqFChAl6+fInevXsjNDQUy5cvx5UrVxAaGgpjY2NkZGQI9vFlRUVT8+sThpOTk+Hl5QU9PT3s3LkTISEhOHgwdxLqp/22aNECr169wpgxY/Du3Ts0a9YM48ePF+zn84nqn0q5X7Z9Sszy4+/vD319fcGydPH8bz1U30VXVw/W1rZ48/qVpC05ORmjhw+ClpY2FixZCZUSMPn+34cP8DH2A3p374x6NV1Qr6YLbt0MwZ4/d6BezdzJ11+q7JKb7L55HZ5nXXG0YN4sXLpwDr9v3JbvkEhychJGDB0AbW1tLF62qlifNBH5PgEAYGYkPEvQzFgXUR9y1zWu7YSWDaqg16TNuHrnBUL/fYPR/nuQmp6JX9rUkWyzYsc/sGg4AU4tp6Nsk0mSM/1evnn/g47m2z5/j5mYmAIA7MrbC/rY2pVHVGSEIsL7Lvl9fujo6sLaxhY1arrBf/FSvHr5Euf/OaPAKAtGJJLfUloxgSpBRCIRPDw84Ofnh9u3b0NNTQ0HDx7E5cuXMXLkSLRs2RKVK1eGuro63r+X/mFZtWpVBAUF5bvu33//xYcPHzB//nw0aNAAFStWzLdSZGpqit69e2PHjh1YtmwZ1q9f/93H+bnJkycjPj5esIwZP0mu9/G5lJRkvH0TDuP//2BPTkrCqKEDoKKqisXLVpeYvxrd6rhj177D2P7XAclSybkKvFq2xva/DuR7BtCTf3Pn33w69uJKLBZjwbxZOPvPGazbuAVlypbN0ycpKQnDB/eHqqoqlqxYU+yft7C3HxARE48mdf53pqSutgZqVbFF8N0wAICWRu6wz5d/cOTkiPOdZxIRE4+09Ex09XbD64hY3P73ddEdgAxSUpLx5k04TExMYWlVBiamZnj1KkzQJzw8DBYWVooJ8Dt8+fnxJbEYEEOMjMyMfNcXJ0oikdyW0opDeCVEcHAwgoKC0Lx5c5iZmSE4OBgxMTGoVKkSHB0dJWfMJSQkYMKECd+sLn0yY8YMNGvWDPb29ujWrRuysrJw4sQJTJw4EdbW1lBTU8PKlSsxZMgQ3L9/H7NnzxZsP336dNSsWROVK1dGeno6jh07hkqVKsn1uNXV1fN8+WWn5K2eFNaKJQtRv2ETWFhZ4X10NDasWwUlJWU0926F5KQkjBw2AGlpaZg5dwGSk5OQnJwEADAwNCrWpyFra2vD3sFR0KapqQl9fQPYOzjizetw/H3yOOrVbwh9fQM8e/oYyxYvQI2abnDM53IHxcn8ubNw6uQxLFm+Glra2nj/PvcsTh0dXWhoaEiSp7S0VMz2XyR43gwV+Lxpa6rBvtz/vlhtyxijqlMZfExIwevIj1i96ywmDvDGs/AYhL39gBnDWiEiJh5Hzt4BAATffYmPCSnYOLsX5q0/idS0TPTrWA+2ZYxx6tIDyX7H9GqG01ceIScnB+2aVcf4vj/hl183KWwIb8XS3PeYpaUVYmKisXHdKigrKeMn71YQiUTw6dUPG39fBUenCnB0qogTxw7jVdhLzFu4TCHxyuJbnx9v37zGmb9Poo67BwwMDREdFYVtm3OH1D8f+qOSiwlUCaGnp4cLFy5g2bJlSEhIgI2NDQICAtCiRQtYWFhg0KBBcHV1Rbly5TBv3rw8Q2n5ady4Mfbu3YvZs2dj/vz50NPTQ8OGuW9sU1NTbNmyBb/99htWrFgBV1dXLF68GG3btpVsr6amhsmTJyMsLAyamppo0KABdu/eXWSPQVGIjorC9MnjER8fBwNDI1Sr7oqN2/6EoZERbt64jgf3coc/Orf1Fmx34HggrKzKKCJkuVBVVUVI8FXs3rkNaampMDO3QJNmP6HvwCGKDk2qfXv+BAAM6tdL0D5j9jy0bdcR/z56gPv3cpOO9q2aC/ocPXkGVmXyVqx+BFdnG5zeOEpye+H43Dky249cw6AZOxCw5Qy0NNWxamp3GOhq4kroc7QdvkZyDagPcclo57sGM4e3wcnfR0JVRQmPXkSiy5j1uPfkf6fFN/dwxq8DvKCuqoJ7T96iy5j1OH354Y892M/EREVhxhfvsQ1b/4ShoREAoJtPL2RkpGN5wAIkxMfDwakCVqzZWCLm4X3r8yMrKwuht29i967tSEyIh5GxCaq71sSGLbtgZGSs6NCl4iRy6URisVgxf5YQFdJHOVagipPSWulWKSEXRCwM07ojFB1CkXh7abmiQygSpfU9BgCGWvKtrK68/FJu+xrhYSe3fRUnpfeTjYiIiKiIcAiPiIiIBJRQist1csIEioiIiARK83CnvHAIj4iIiEhGrEARERGRAM/Ck44JFBEREQmU5gtgyguH8IiIiIhkxAoUERERCbAAJR0TKCIiIhLgEJ50HMIjIiIikhETKCIiIhIQieS3fI/58+dDJBJh9OjRkra0tDQMHz4cxsbG0NHRQadOnRAVFSXYLjw8HK1atYKWlhbMzMwwYcIEZGVlfV8wX2ACRURERAJKclwKKyQkBL///juqVq0qaB8zZgyOHj2KvXv34vz583j37h06duwoWZ+dnY1WrVohIyMDV65cwdatW7FlyxZMnz79O6LJiwkUERERFStJSUnw8fHBhg0bYGhoKGmPj4/HH3/8gSVLlqBp06aoWbMmNm/ejCtXruDatWsAgNOnT+Phw4fYsWMHqlevjhYtWmD27NlYvXo1MjIy5BYjEygiIiISEIlEclvS09ORkJAgWNLT0795/8OHD0erVq3g6ekpaL958yYyMzMF7RUrVoS1tTWuXr0KALh69SpcXFxgbm4u6ePl5YWEhAQ8ePBAbo8REygiIiISEMlx8ff3h76+vmDx9/f/6n3v3r0bt27dyrdPZGQk1NTUYGBgIGg3NzdHZGSkpM/nydOn9Z/WyQsvY0BERERFZvLkyRg7dqygTV1dPd++r1+/xqhRoxAYGAgNDY0fEV6hsQJFREREAkoikdwWdXV16OnpCZavJVA3b95EdHQ0XF1doaKiAhUVFZw/fx4rVqyAiooKzM3NkZGRgbi4OMF2UVFRsLCwAABYWFjkOSvv0+1PfeTyGMltT0RERFQqyHMITxbNmjXDvXv3EBoaKlnc3Nzg4+Mj+b+qqiqCgoIk2zx+/Bjh4eFwd3cHALi7u+PevXuIjo6W9AkMDISenh6cnZ1lfzC+gkN4REREVCzo6uqiSpUqgjZtbW0YGxtL2vv374+xY8fCyMgIenp6GDFiBNzd3VG3bl0AQPPmzeHs7IyePXti4cKFiIyMxNSpUzF8+PCvVr4KgwkUERERCRTnX3JZunQplJSU0KlTJ6Snp8PLywtr1qyRrFdWVsaxY8cwdOhQuLu7Q1tbG71798asWbPkGodILBaL5bpHoiL2MSVb0SEUieL8gfU9VJRK70wB07ojFB1CkXh7abmiQygSpfU9BgCGWspy3d+ft9/KbV/da5SR276Kk9L7yUZERERURDiER0RERAKsrkjHBIqIiIgERKV5vFNOmGQSERERyYgVKCIiIhJg/Uk6JlBEREQkwCE86ZhAUYmjrlpKR55L6QVFSvMH8fvglYoOoUi0WXdN0SEUiWND6yo6BCpFmEARERGRQCn9M1WumEARERGRQGmuHMsLk0wiIiIiGbECRURERAKsP0nHBIqIiIgEOIInHYfwiIiIiGTEChQREREJKHEQTyomUERERCTAITzpOIRHREREJCNWoIiIiEhAxCE8qZhAERERkQCH8KTjEB4RERGRjFiBIiIiIgGehScdEygiIiIS4BCedBzCIyIiIpIRK1BEREQkwAqUdEygiIiISICXMZCOQ3hEREREMmIFioiIiASUWICSigkUERERCXAITzoO4RERERHJiBUoIiIiEuBZeNIxgSIiIiIBDuFJxyE8IiIiIhkxgSK5mDlzJqpXr67oMIiISA6URPJbSisO4ZHMRCIRDh48iPbt20vaxo8fjxEjRiguqEK6eSME2zb/gYcPH+B9TAyWLF+FJs08JeuDAk9j357dePTwAeLj47F730FUqFhJgREX3M0bIdi25bNjWyY8tulTJuHokUOCbep51MfqdRt/cKTfp0Xzpoh49zZPe9duPfDb1BkKiKhwPj1fj/7/+Qr44vn68P49VixdjKtXLyMpMRE1arph4uSpsLaxVVzQX2GirYaBHtaobWMADVVlvI1Lw8Izz/AkOhkAoKGqhEH1bOBhbwg9DVVEJKThYGgkjt6PkuxjTJPyqGmtD2NtNaRmZuNBRCLWX36F1x/TFHVYeXzr8yMzMxNrVi7HpYvn8ebNG+jo6KBO3XoYOWYszMzMFRy5dBzCk44JFMmFjo4OdHR0vro+IyMDampqPzCigklNTYVThYpo16ETxo3OmwCmpqaiumtN/OTVArNnTlNAhIWXmpoKJ6evHxsA1PNoAL858yS31VSL33Mkzc7d+5CTky25/ezpUwwZ2Bc/NfdWYFSyS/vs+Rr/xfMlFosxdtRwqKioYumKNdDW1saObVswZGA/7D90DJpaWgqKOi8ddWWs6FIZoW8SMPnIv4hLzURZAw0kpWdJ+gxrYIsaZfUx7+9niExIh5u1PkY3KY8PyRm48vIjAOBJdBKCHscgKjEDehoq6F2nLBa2d4bPllvIESvq6IS+9fmRlpaGRw8fYuDgYXCqUAEJCQlYNH8eRvsOw649+xUUMckTE6j/qH379sHPzw/Pnj2DlpYWatSogcOHD+Phw4f47bffcPv2bWRmZqJ69epYunQpXF1dAQC2trYAgA4dOgAAbGxsEBYWhpkzZ+LQoUMIDQ0FAPTp0wdxcXGoVasWVq9eDXV1dbx8+RKvX7/GuHHjcPr0aSgpKaFBgwZYvny5ZL8/Wv0GDVG/QcOvrm/dth0A4N3bNz8qJLmRdmwAoKamBhMT0x8UUdEwMjIS3N60cT3KlbOGW63aCoqocDwaNITHV56v8FdhuHf3DvYePAp7B0cAwG/TZuKnJvVx6uRxdOjU5UeG+k3da5ZBdGIGFp55LmmLTEgX9KlsqYu/H0XjztsEAMDxB9Fo42KOiuY6kgTq+INoSf+oxHRsuvoaG32qwUJPHe/ihftTlG+9x3R1dbFu4yZB26TfpuGX7l0QEfEOlpZWPyLEQuNZeNJxDtR/UEREBLp3745+/frh0aNHOHfuHDp27AixWIzExET07t0bly5dwrVr1+Do6IiWLVsiMTERABASEgIA2Lx5MyIiIiS38xMUFITHjx8jMDAQx44dQ2ZmJry8vKCrq4uLFy/i8uXL0NHRgbe3NzIyMn7IsZPQjRvX0bRRPbRv4425s2ciLu6jokP6LpmZGThx7AjadegEUSn6Bvj0/lBTV5e0KSkpQU1VDaG3bioqrHy5lzfEk+gkzGjhhP0D3PB796poVdlM0OdBRCLqlTeCiXZuxbN6WT2UNdDEjfC4fPepoaIEb2dTvItPQ3Riyf2sSExKhEgkgq6unqJDkUokx6W0YgXqPygiIgJZWVno2LEjbGxsAAAuLi4AgKZNmwr6rl+/HgYGBjh//jxat24NU9PcaoWBgQEsLCy+eT/a2trYuHGjZOhux44dyMnJwcaNGyVfbps3b4aBgQHOnTuH5s2by/U46dvq1W+App7NUaZMGbx5/RorVyyF79BB2LpjN5SVlRUdXqH8E3QGiYmJaNu+g6JDkStbu/KwsLTCqmVLMGW6HzS1NLFz21ZERUUi5n2MosMTsNLTQFsXC+y9/Q47b7xBBTMd+DayQ2a2GKf/zY115fmXGNu0PPb0r4ms7BzkAAgIeo677xIF+2rrYo7BHjbQVFNGeGwqfj30EFnFZfxORunp6VixdDG8W7b65nQHKjmYQP0HVatWDc2aNYOLiwu8vLzQvHlzdO7cGYaGhoiKisLUqVNx7tw5REdHIzs7GykpKQgPD5f5flxcXATznu7cuYNnz55BV1dX0C8tLQ3Pnz//cnMAuR866enCcn22khrUP/tLnArHu0Uryf8dnSrA0akC2rT8CTdCrqNOXXcFRlZ4hw7sh0f9hiVikq4sVFVVsXjpCsyaMRWN69eBsrIyatd1h0f9hhCLi1dCIRIBT6KT8cfV1wCAZzEpsDPWQhsXc0kC1aGqBZwtdDHl6L+ISkhH1TJ6GNW4PD4kZ+LW63jJvoIev8fN8HgYa6uiq6sVprdwwoi995GZXbyOWZrMzEz8Om40xOLcodeSQKkUVXCLCofw/oOUlZURGBiIkydPwtnZGStXrkSFChXw8uVL9O7dG6GhoVi+fDmuXLmC0NBQGBsbF2qITVtbW3A7KSkJNWvWRGhoqGB58uQJevToke8+/P39oa+vL1gWL/Av1HHTt5UtVw4GhoZ4Hf5K0aEUyrt3bxF87Qo6dOqs6FCKhHPlKti97xDOXwnB6X8uYvW6jYiPj0OZsuUUHZpAbHImwmJTBG3hH1Nhrpv7R4+ashL617PGmothuPryI158SMGhu5E4+/Q9uroK5wUlZ2TjbXwa7r5LxMwTT1DOUBMN7IVz3oq7zMxMTBw3BhHv3mHthj9KTPWJQ3jSsQL1HyUSieDh4QEPDw9Mnz4dNjY2OHjwIC5fvow1a9agZcuWAIDXr1/j/fv3gm1VVVWRnZ2d326/ydXVFX/99RfMzMygp1ewOQCTJ0/G2LFjBW3ZSiXvTLGSICoyEvFxcTAxNZPeuRg6fPAAjIyM0aBhY0WHUqQ+VXDDX4Xh4YP7GOo7UsERCd2PSEQ5A01BW1kDDUQl5laSVZRFUFVWwpeFs5ycb18zSCTK/TJWVS45f/d/Sp7Cw19h/aatMDAwVHRIJEdMoP6DgoODERQUhObNm8PMzAzBwcGIiYlBpUqV4OjoiO3bt8PNzQ0JCQmYMGECNDWFH4a2trYICgqCh4cH1NXVYWhYsA8FHx8fLFq0CO3atcOsWbNQtmxZvHr1CgcOHMCvv/6KsmXL5tlGXV09z3BdSqb8yvcpKcl4/dnw5Nu3b/D430fQ09eHpaUV4uPjEBkRgejo3DOCwl6+BAAYm5gU+7PXvnVs+vr6+H3tajTzbA4TExO8fv0ay5csQjlra9TzqK/AqAsnJycHRw4dQJt27aGiUjI/1qS9FgP/PgVDI0NYWFjh2dMnWLRgLho3bQb3esXr+dp3+x1WdqmCHm5lcO7pB1Q010GrKuZY8s8LAEBKRjZC38RjcH0bpGflICoxHdXK6KF5JVOsvRgGALDUU0djJ2PceBWP+NRMmOqoobtbGaRn5SA4rPic6PCt58zExBQTxo7Cvw8fYvnqdcjJycb7/5+vpq+vD9XifsmQ0lw6kpOS+UlD30VPTw8XLlzAsmXLkJCQABsbGwQEBKBFixawsLDAoEGD4OrqinLlymHevHkYP368YPuAgACMHTsWGzZsQJkyZRAWFlag+9XS0sKFCxcwceJEdOzYEYmJiShTpgyaNWtW4IqUvD28fx8D+/WW3A5YOB8A0KZde8yaOx/nz/6DGVN/k6yfNCG3GjZ46HAMGV68Lxz68MEXx7bo/4+tbXv8Nm0mnj55jKNHDiExIRGmZqZwd/fAMN9RxfJ6XdJcu3oFERHv0L5DJ0WHUmgPH9zHoM+eryWfPV9+c+fj/ftoLFk0Hx8+fICJqSlat2mHgUOGKircr3ocnYzpxx9jQD0b9KpdFhEJaVhzIQxBj/9XyZ596ikG1rPGFC9H6GqoICohHX9cDceRe7kX0szIzkFVKz10qm4JXXUVfEzJxN23CRi59z7iUrO+dtc/3Lc+P4YM88X5s/8AALp1bi/YbsOmrXCrXeeHxVkYvJCmdCJxcZuBSCSFPCtQxUopPazSdDmBL+WU0o/PNuuuKTqEInFsaF1Fh1BktFTl+z4Lfh4vvVMB1bHXl9u+ihNWoIiIiEigFP/dIzdMoIiIiEiA+ZN0Jed0BiIiIqJighUoIiIiEmIJSiomUERERCTAs/Ck4xAeERERkYxYgSIiIiIBnoUnHRMoIiIiEmD+JB2H8IiIiIhkxAoUERERCbEEJRUTKCIiIhLgWXjScQiPiIiISEasQBEREZEAz8KTjgkUERERCTB/ko5DeEREREQyYgWKiIiIhFiCkooVKCIiIhIQyfGfLPz9/VGrVi3o6urCzMwM7du3x+PHjwV90tLSMHz4cBgbG0NHRwedOnVCVFSUoE94eDhatWoFLS0tmJmZYcKECcjKyvrux+VzTKCIiIioWDh//jyGDx+Oa9euITAwEJmZmWjevDmSk5MlfcaMGYOjR49i7969OH/+PN69e4eOHTtK1mdnZ6NVq1bIyMjAlStXsHXrVmzZsgXTp0+Xa6wisVgsluseiYpYSmYpfcmW0sMSleLTeXJK6cdnm3XXFB1CkTg2tK6iQygyWqryfZ/de5Mkt325lNUp9LYxMTEwMzPD+fPn0bBhQ8THx8PU1BS7du1C586dAQD//vsvKlWqhKtXr6Ju3bo4efIkWrdujXfv3sHc3BwAsG7dOkycOBExMTFQU1OTy3GxAkVEREQCIjku3yM+Ph4AYGRkBAC4efMmMjMz4enpKelTsWJFWFtb4+rVqwCAq1evwsXFRZI8AYCXlxcSEhLw4MGD74zofziJnIiIiIpMeno60tPTBW3q6upQV1f/5nY5OTkYPXo0PDw8UKVKFQBAZGQk1NTUYGBgIOhrbm6OyMhISZ/Pk6dP6z+tkxdWoIiIiEhIjiUof39/6OvrCxZ/f3+pIQwfPhz379/H7t275X548sAKFBEREQnI87fwJk+ejLFjxwrapFWffH19cezYMVy4cAFly5aVtFtYWCAjIwNxcXGCKlRUVBQsLCwkfa5fvy7Y36ez9D71kQdWoIiIiKjIqKurQ09PT7B8LYESi8Xw9fXFwYMH8c8//8DOzk6wvmbNmlBVVUVQUJCk7fHjxwgPD4e7uzsAwN3dHffu3UN0dLSkT2BgIPT09ODs7Cy342IFioiIiAQUdfLs8OHDsWvXLhw+fBi6urqSOUv6+vrQ1NSEvr4++vfvj7Fjx8LIyAh6enoYMWIE3N3dUbdu7lmWzZs3h7OzM3r27ImFCxciMjISU6dOxfDhw6VWvmTBBIqIiIgEFHXxkbVr1wIAGjduLGjfvHkz+vTpAwBYunQplJSU0KlTJ6Snp8PLywtr1qyR9FVWVsaxY8cwdOhQuLu7Q1tbG71798asWbPkGiuvA0UlDq8DVbLwOlAlD68DVfLI+zpQj94lS+9UQJWstOW2r+KECRSVOGnyvRo/Ef1HBJx/pugQisyUZg5y3d+jCDkmUJalM4HiEB4REREJyPMsvNKKZ+ERERERyYgVKCIiIhIoxVMX5YYJFBEREQkwf5KOQ3hEREREMmIFioiIiIRYgpKKCRQREREJ8Cw86TiER0RERCQjVqCIiIhIgGfhSccEioiIiASYP0nHITwiIiIiGbECRUREREIsQUnFBIqIiIgEeBaedBzCIyIiIpIRK1BEREQkwLPwpGMCRURERALMn6TjEB4RERGRjFiBIiIiIiGWoKRiAkVEREQCPAtPOg7hEREREcmIFSgiIiIS4Fl40jGBIiIiIgHmT9JxCI+IiIhIRqxAERERkQCH8KRjBaoAzp07B5FIhLi4OEWHQkRE9AOI5LiUTqxAFSMikQgHDx5E+/btZdrO1tYWo0ePxujRo4skLnkLCwuDnZ0dbt++jerVqys6nHzt3rUTWzf/gffvY+BUoSIm/TYNLlWrKjqs77Jn9y7s+etPvHv7FgBg7+CIwUOHoX6DRgqO7Pv8seF3BAWexsuXL6CuoYHq1Wtg9NjxsLUrr+jQvktpfb4+V5LfZ/f+3oPbh7eiUpN2qNVlEJI+ROHAtH759m04YBJsXRsAACL+DUXo0e34+O4VVNTVYV+nGWq07Q0lZeUfGT7JAROoHyQjIwNqamqKDoMK4NTJE1i80B9TZ/jBxaUadm7fiqGD++PwsVMwNjZWdHiFZmZugVFjxsPaxgZisRhHDx/CKN/h+Gv/QTg4OCo6vEK7EXIdP3f3QWUXF2RnZWPl8iUYMrA/Dhw5Di0tLUWHV2il9fn6pCS/z96HPcHTS6dgWMZO0qZlaIIu/tsF/Z5cPoUHgQdQxtkNABD75gWC1syAi/fP8Og9DilxHxD85yqIc3Lg1mnADz0GaTiEJ12pG8KztbXFsmXLBG3Vq1fHzJkzAeRWeTZu3IgOHTpAS0sLjo6OOHLkiKD/iRMn4OTkBE1NTTRp0gRhYWF57ufSpUto0KABNDU1Ua5cOYwcORLJycmCOGbPno1evXpBT08PgwYNQkZGBnx9fWFpaQkNDQ3Y2NjA399f0h8AOnToAJFIJLn9/PlztGvXDubm5tDR0UGtWrVw5swZyf00btwYr169wpgxYyASiSD67FVfkBjnzJmDXr16QUdHBzY2Njhy5AhiYmLQrl076OjooGrVqrhx44bMxz5v3jz069cPurq6sLa2xvr16yXr7exyP3Rq1KgBkUiExo0b5/NMKs72rZvRsXNXtO/QCfYODpg6ww8aGho4dGC/okP7Lo2bNEWDho1gY2MLW1s7jBg1BlpaWrh7J1TRoX2Xtev/QLsOHeHg4IgKFSti1tz5iIh4h0cPHyg6tO9SWp+vT0rq+ywzLRUXtyxCXZ8RUNPSkbQrKSlDU99IsISHXoWta32oamgCAMJuXoShlR2qtewBPTMrWDi5wLVDPzy+cByZaSmKOqR8cQBPulKXQBWEn58funbtirt376Jly5bw8fFBbGwsAOD169fo2LEj2rRpg9DQUAwYMACTJk0SbP/8+XN4e3ujU6dOuHv3Lv766y9cunQJvr6+gn6LFy9GtWrVcPv2bUybNg0rVqzAkSNHsGfPHjx+/Bg7d+6UJEohISEAgM2bNyMiIkJyOykpCS1btkRQUBBu374Nb29vtGnTBuHh4QCAAwcOoGzZspg1axYiIiIQEREhU4xLly6Fh4cHbt++jVatWqFnz57o1asXfvnlF9y6dQv29vbo1asXxGKxTPsNCAiAm5sbbt++jWHDhmHo0KF4/PgxAOD69esAgDNnziAiIgIHDhwo/JMpZ5kZGXj08AHquteTtCkpKaFu3Xq4e+e2AiOTr+zsbJw8cRypqSmoVq2GosORq6TERACAnr6+giORn9L2fJXk91nwX2tRtkotWFX89vPwIfwpPr55AYd6zSVtOVmZUFYVjkQoq6khOzMDH8KfFUm8VHT+k0N4ffr0Qffu3QEA8+bNw4oVK3D9+nV4e3tj7dq1sLe3R0BAAACgQoUKuHfvHhYsWCDZ3t/fHz4+PpI5R46OjlixYgUaNWqEtWvXQkNDAwDQtGlTjBs3TrJdeHg4HB0dUb9+fYhEItjY2EjWmZqaAgAMDAxgYWEhaa9WrRqqVasmuT179mwcPHgQR44cga+vL4yMjKCsrAxdXV3BdgWNsWXLlhg8eDAAYPr06Vi7di1q1aqFLl26AAAmTpwId3d3REVFwcLCQqb9Dhs2TLKPpUuX4uzZs6hQoYLkWI2NjQUxFwcf4z4iOzs7zxCCsbExXr58oaCo5Ofpk8fo2aMbMjLSoaWlhaUrVsPewUHRYclNTk4OFi6Yh+o1XOHo6KTocL5baX2+Sur77OWN84h9/QytJi6T2vfp5dPQtygHM3tnSZtVJVc8+ucwXoacg03NBkhL+Ii7J/4EAKTGxxZV2IXCITzp/pMJVNXPJilqa2tDT08P0dHRAIBHjx6hTp06gv7u7u6C23fu3MHdu3exc+dOSZtYLEZOTg5evnyJSpUqAQDc3NwE2/Xp0wc//fQTKlSoAG9vb7Ru3RrNmzfHtyQlJWHmzJk4fvw4IiIikJWVhdTUVEkF6msKGuPnj4W5uTkAwMXFJU9bdHQ0LCwsCrVfkUgECwsLyWMsi/T0dKSnpwvaxMrqUFdXl3lfBNja2mHP/kNISkpE4Om/Me23ifhjy45S8aUMAPPm+OH506fYsn2XokORi9L+fJUkybExCNm7Hj+NmJOnivSlrIx0vLxxHlVbdBO0Wzm7ombHfrj252pc2hoAZRVVuLTohuhnDwBR8RoQ4m/hSVfqEiglJSXJcNMnmZmZgtuqqqqC2yKRCDk5OQW+j6SkJAwePBgjR47Ms87a2lryf21tbcE6V1dXvHz5EidPnsSZM2fQtWtXeHp6Yt++fV+9r/HjxyMwMBCLFy+Gg4MDNDU10blzZ2RkZMglxs8fi0/zp/Jr+/T4FGa/n/Yjy2P8ib+/P/z8/ARtU6bNwNTpM2XeV0EYGhhCWVkZHz58ELR/+PABJiYmRXKfP5Kqmhqs/7/y6Vy5Ch7cv4edO7Zh+sxZCo7s+82bMwsXzp/Dpq07YF7MKpuFVVqfr5L4PvsQ/gxpiXE4Nv9/n33inBxEPbuPf88fhc+KQ1BSyj2T7tXty8jOSId9nWZ59uPcrAMqNW2P1PhYqGnpIOlDFG4f3gpdk9Lxmv0vKXUJlKmpqWQeEAAkJCTg5cuXBd6+UqVKeSaVX7t2TXDb1dUVDx8+hEMh/grU09PDzz//jJ9//hmdO3eGt7c3YmNjYWRkBFVVVWRnZwv6X758GX369EGHDh0A5CYwX05qV1NTy7Pd98T4LfLY76ezEb+MOT+TJ0/G2LFjBW1i5aKrPqmqqaGSc2UEX7uKps08AeQmj8HBV9Gt+y9Fdr+KkpOTg0wpyXhxJxaL4T93Nv4JCsQfW7ajbNlyig6pyJSG5wsome8zy4rV0GbqakHblW3LoG9RFpWbd5YkTwDw7MpplK1aBxq6+c/DE4lE0DLIHb4Mu3EeWoamMLK2L7rgC4MFKKmKV81QDpo2bYrt27fj4sWLuHfvHnr37g1lGa6vMWTIEDx9+hQTJkzA48ePsWvXLmzZskXQZ+LEibhy5Qp8fX0RGhqKp0+f4vDhw3kmUn9pyZIl+PPPP/Hvv//iyZMn2Lt3LywsLGBgYAAg9+y1oKAgREZG4uPHjwBy5xgdOHAAoaGhuHPnDnr06JGnkmNra4sLFy7g7du3eP/+/XfFKI089mtmZgZNTU2cOnUKUVFRiI+P/2pfdXV16OnpCZaiHr7r2bsvDuzbgyOHDuLF8+eYM2smUlNT0b5DxyK936K2fGkAbt4Iwdu3b/D0yWMsXxqAGyHX0bJ1G0WH9l3mzfbDiWNHMH9hALS1tPE+JgbvY2KQlpam6NC+S2l9vj4pae8zVQ0tGFrZChYVdQ2oa+vB0MpW0i8h+h2int2HY738p2fcD9yPj2/DEPfuFe6e+BP3T+9D7S6DBQlYccCz8KQrdRWoyZMn4+XLl2jdujX09fUxe/ZsmSpQ1tbW2L9/P8aMGYOVK1eidu3aklPyP6latSrOnz+PKVOmoEGDBhCLxbC3t8fPP//8zX3r6upi4cKFePr0KZSVlVGrVi2cOHECSkq5eWxAQADGjh2LDRs2oEyZMggLC8OSJUvQr18/1KtXDyYmJpg4cSISEhIE+501axYGDx4Me3t7pKenQywWFzpGaeSxXxUVFaxYsQKzZs3C9OnT0aBBA5w7d+674pIn7xYt8TE2FmtWrcD79zGoULES1vy+EcbFdGihoGJjP2Dq5ImIiYmGjq4unJwqYO36P+Bez0PRoX2XPX/lTsLt36enoH3WHH+0K6ZfxgVRWp+vT0rr++zZ1UBoGZjAqpJrvuvfPbiBe6f+Qk5WJgzL2KHJkGkoU9kt375UvInEX04YIirm0rIUHQERlUQB50vvpQKmNJPvdI3oxEzpnQrITFdVeqcSqNRVoIiIiOj78Cw86UrdHCgiIiKiosYKFBEREQmxACUVEygiIiISYP4kHYfwiIiIiGTEChQREREJ8LfwpGMCRURERAI8C086DuERERERyYgVKCIiIhLgEJ50rEARERERyYgJFBEREZGMOIRHREREAhzCk44JFBEREQnwLDzpOIRHREREJCNWoIiIiEiAQ3jSMYEiIiIiAeZP0nEIj4iIiEhGrEARERGREEtQUjGBIiIiIgGehScdh/CIiIiIZMQKFBEREQnwLDzpmEARERGRAPMn6TiER0RERCQjJlBEREQkJJLjUgirV6+Gra0tNDQ0UKdOHVy/fv17jqZIMIEiIiIiAZEc/8nqr7/+wtixYzFjxgzcunUL1apVg5eXF6Kjo4vgSAuPCRQREREVG0uWLMHAgQPRt29fODs7Y926ddDS0sKmTZsUHZoAJ5ETERGRgDzPwktPT0d6erqgTV1dHerq6nn6ZmRk4ObNm5g8ebKkTUlJCZ6enrh69ar8gpIHMRHlKy0tTTxjxgxxWlqaokORKx5XyVNaj43H9d8wY8YMMQDBMmPGjHz7vn37VgxAfOXKFUH7hAkTxLVr1/4B0RacSCwWixWawREVUwkJCdDX10d8fDz09PQUHY7c8LhKntJ6bDyu/wZZKlDv3r1DmTJlcOXKFbi7u0vaf/31V5w/fx7BwcFFHm9BcQiPiIiIiszXkqX8mJiYQFlZGVFRUYL2qKgoWFhYFEV4hcZJ5ERERFQsqKmpoWbNmggKCpK05eTkICgoSFCRKg5YgSIiIqJiY+zYsejduzfc3NxQu3ZtLFu2DMnJyejbt6+iQxNgAkX0Ferq6pgxY0aBS88lBY+r5Cmtx8bjovz8/PPPiImJwfTp0xEZGYnq1avj1KlTMDc3V3RoApxETkRERCQjzoEiIiIikhETKCIiIiIZMYEiIiIikhETKCIiIiIZMYEiIiIikhETKKL/gPDwcOR3wq1YLEZ4eLgCIqL/sqysLJw5cwa///47EhMTAeT+hEdSUpKCIyMqOF7GgOgzZ8+eRZMmTRQdhtwpKysjIiICZmZmgvYPHz7AzMwM2dnZCopMPnJycvDs2TNER0cjJydHsK5hw4YKiqrwPnz4gOnTp+Ps2bP5HlNsbKyCIvt+r169gre3N8LDw5Geno4nT56gfPnyGDVqFNLT07Fu3TpFh1goTZs2xYEDB2BgYCBoT0hIQPv27fHPP/8oJjAqMryQJtFnvL29UbZsWfTt2xe9e/dGuXLlFB2SXIjFYohEojztSUlJ0NDQUEBE8nPt2jX06NEDr169ylNlE4lEJTI57NmzJ549e4b+/fvD3Nw83+eupBo1ahTc3Nxw584dGBsbS9o7dOiAgQMHKjCy73Pu3DlkZGTkaU9LS8PFixcVEBEVNSZQRJ95+/Yttm/fjq1bt8LPzw9NmzZF//790b59e6ipqSk6PJmNHTsWQG4iMW3aNGhpaUnWZWdnIzg4GNWrV1dQdPIxZMgQuLm54fjx47C0tCwVycbFixdx6dIlVKtWTdGhyN3Fixdx5cqVPO8nW1tbvH37VkFRFd7du3cl/3/48CEiIyMlt7Ozs3Hq1CmUKVNGEaFREWMCRfQZExMTjBkzBmPGjMGtW7ewefNmDBs2DMOGDUOPHj3Qv3//EvWldvv2bQC5Fah79+4JvrTU1NRQrVo1jB8/XlHhycXTp0+xb98+ODg4KDoUualYsSJSU1MVHUaRyMnJybcq+ObNG+jq6iogou9TvXp1iEQiiEQiNG3aNM96TU1NrFy5UgGRUVHjHCiib3j37h3Wr1+P+fPnQ0VFBWlpaXB3d8e6detQuXJlRYdXYH379sXy5cuhp6en6FDkrmnTpvj111/h7e2t6FDkJiQkBJMmTcL06dNRpUoVqKqqCtaX5Ofx559/hr6+PtavXw9dXV3cvXsXpqamaNeuHaytrbF582ZFhyiTT0PH5cuXx/Xr12FqaipZp6amBjMzMygrKyswQioqTKCIvpCZmYnDhw9j06ZNCAwMhJubG/r374/u3bsjJiYGU6dOxa1bt/Dw4UNFh0oADh48iKlTp2LChAlwcXHJk2xUrVpVQZEV3tOnT9GjRw/cunVL0P5pLltJnNf1yevXr+Ht7Q2xWIynT5/Czc0NT58+hYmJCS5cuJDnRAei4ooJFNFnRowYgT///BNisRg9e/bEgAEDUKVKFUGfyMhIWFlZ5TkzqjhLTk7G/PnzERQUlO9ZXS9evFBQZN9PSSnv1VhEIlGJTjZq164NFRUVjBo1Kt9J5I0aNVJQZPKRlZWFv/76C3fu3EFSUhJcXV3h4+MDTU1NRYf2XZ4+ffrVMyenT5+uoKioqDCBIvpMs2bNMGDAAHTs2BHq6ur59snKysLly5dL1JdY9+7dcf78efTs2TPfidajRo1SUGTf79WrV99cb2Nj84MikR8tLS3cvn0bFSpUUHQocpWZmYmKFSvi2LFjqFSpkqLDkasNGzZg6NChMDExgYWFheA9JhKJ8lQTqeRjAkX0H2BgYIDjx4/Dw8ND0aFQATRs2BDTp0+Hp6enokORuzJlyuDMmTOlLoGysbHBsGHDMHHiREWHQj8Iz8Ij+kJpLMMbGhrCyMhI0WEUmefPn2PZsmV49OgRAMDZ2RmjRo2Cvb29giMrnBEjRmDUqFGlal7XJ8OHD8eCBQuwceNGqKiUnq+gjx8/okuXLooOg34gVqCIPlNay/A7duzA4cOHsXXrVsG1oEqDv//+G23btkX16tUlFbbLly/jzp07OHr0KH766ScFRyi70jiv65MOHTogKCgIOjo6cHFxgba2tmD9gQMHFBTZ9+nfvz9q1aqFIUOGKDoU+kGYQBF9prSW4WvUqIHnz59DLBbD1tY2T0WjpCaGQO6xeXl5Yf78+YL2SZMm4fTp0yXy2ErjvK5P+vbt+831Je0yBp/4+/tjyZIlaNWqVb5Vw5EjRyooMioqTKCIPqOnp4fQ0FCUL19e0aHIlZ+f3zfXz5gx4wdFIn8aGhq4d+8eHB0dBe1PnjxB1apVkZaWpqDI6L/Ezs7uq+tEIlGJPtOV8ld6BqCJ5KBLly44ffp0qSvDl+QESRpTU1OEhobmSaBCQ0NL7DWFtm7dChMTE7Rq1QoA8Ouvv2L9+vVwdnbGn3/+WaIrUKXVy5cvFR0C/WBMoIg+4+DggGnTpuHatWulrgwfFxeHffv24fnz55gwYQKMjIxw69YtmJubl+jf6ho4cCAGDRqEFy9eoF69egBy50AtWLBA8luAJc28efOwdu1aAMDVq1exatUqLFu2DMeOHcOYMWNK3DwhV1dXBAUFwdDQEDVq1Pjm7xWWxCHXz2VkZODly5ewt7cvVZPkKS8O4RF9prSW4e/evQtPT0/o6+sjLCwMjx8/Rvny5TF16lSEh4dj27Ztig6x0MRiMZYtW4aAgAC8e/cOAGBlZYUJEyZg5MiRJfLHhbW0tPDvv//C2toaEydOREREBLZt24YHDx6gcePGiImJUXSIMvHz88OECROgpaWFmTNnfvM5KanV0pSUFIwYMQJbt24FkDuEXL58eYwYMQJlypTBpEmTFBwhyRsTKKL/AE9PT7i6umLhwoXQ1dXFnTt3UL58eVy5cgU9evRAWFiYokOUi8TERAAokT9K+zkzMzP8/fffqFGjBmrUqIGxY8eiZ8+eeP78OapVq4akpCRFh0hfGDVqFC5fvoxly5bB29sbd+/eRfny5XH48GHMnDlT8sPeVHrkPVeWiADkVjZKy98XISEhGDx4cJ72MmXKIDIyUgERFQ1dXd0SnzwBwE8//YQBAwZgwIABePLkCVq2bAkAePDgAWxtbRUb3HcqX748Pnz4kKc9Li6uRJ+8cejQIaxatQr169cXVNgqV66M58+fKzAyKipMoIi+sG3bNri4uEBTUxOampqoWrUqtm/fruiwvou6ujoSEhLytD958kTw6/ElhaurKz5+/Agg9zIGrq6uX11KotWrV8Pd3R0xMTHYv38/jI2NAQA3b95E9+7dFRzd9wkLC8v3Olbp6el48+aNAiKSj5iYmHxPWkhOTi6Rw8gkHWe4EX1myZIlmDZtGnx9fSUXZbx06RKGDBmC9+/fY8yYMQqOsHDatm2LWbNmYc+ePQBy53OFh4dj4sSJ6NSpk4Kjk127du0kv1XYrl27UvcFZWBggFWrVuVpl3Y5iuLsyJEjkv///fff0NfXl9zOzs5GUFDQN+cgFndubm44fvw4RowYAQCS1+TGjRvh7u6uyNCoiHAOFNFn7Ozs4Ofnh169egnat27dipkzZ5bYU5Xj4+PRuXNn3LhxA4mJibCyskJkZCTc3d1x4sSJPFeDpuIhJSUF4eHhyMjIELSXxJ9y+XR19U9XVP+cqqoqbG1tERAQgNatWysivO926dIltGjRAr/88gu2bNmCwYMH4+HDh7hy5QrOnz+PmjVrKjpEkjMmUESf0dDQwP379+Hg4CBof/r0KVxcXEr8RRkvXbqEu3fvIikpCa6urqXix2rLly+PkJAQyTDXJ3FxcXB1dS2RZ07GxMSgT58+OHXqVL7rS/JPudjZ2SEkJAQmJiaKDkXunj9/jvnz5+POnTuS99jEiRPh4uKi6NCoCHAIj+gzDg4O2LNnD3777TdB+19//ZXnQo0lUf369VG/fn1FhyFXpXFOzejRoxEfH4/g4GA0btwYBw8eRFRUFObMmYOAgABFh/ddSmoVtyDs7e2xYcMGRYdBPwgTKKLP+Pn54eeff8aFCxcEP0wbFBQkmT9UUoWEhODs2bOIjo5GTk6OYN2SJUsUFFXhleY5Nf/88w8OHz4MNzc3KCkpwcbGBj/99BP09PTg7+8vuUJ5SZWcnIzz58/nOzxZki9WCwDR0dH5vsdK4rArfRuH8Ii+cOvWLSxZsgSPHj3C/7V393E13/0fwF8nndJ9Grlp6UakFCuGbK5Yzd1G5HY0dYXL3bDE6Dcxd2G7MHZtMiEhNyt6GC53WeU20R1DESmuGrFsFUqd3x9+zm9nZde6OX12zvf1fDw8Hp3P96iXHk69z+fz+b4/AODk5ITg4GC4ubkJTlZ3YWFhWLBgARwdHdGyZUuVTdcymQwnT54UmK5utHlPjampKTIzM2FrawsbGxtER0fjrbfewu3bt9GpUyeUlZWJjlhnaWlpGDRoEMrKylBaWgoLCwsUFRXB0NAQlpaWGrnkCry4Q9Lf3x/Xrl2r9v9RJpNp9LIr1YwzUET/p6KiApMnT0ZoaCh27NghOk6DWrduHbZs2YKAgADRURrMy3f42rinxtHREVlZWbC1tUWXLl2wceNG2NraIjw8HK1btxYdr16CgoIwePBghIeHw8zMDOfPn4dcLoefnx9mzZolOl6dBQYGokOHDti8eXO1NymknTgDRfQbZmZmSE9P19iln1dp3bo1kpKStGIf159RXFwMc3Nz0THqbMeOHXj+/DkCAgJw6dIlDBgwAI8ePYKenh4iIyMxevRo0RHrzNzcHMnJyXB0dIS5uTnOnTsHJycnJCcnw9/fH9evXxcdsU5MTEyQlpZW7QYU0l5spEn0G0OHDkVcXJzoGA0uKCgIX3/9tegYarFq1Srs2bNH+XjkyJGwsLCAlZUVMjIyBCarOz8/P+VsYdeuXXHnzh2kpKQgPz9fo4sn4MXy6svlV0tLS+Tl5QF48eYlPz9fZLR68fLy0tj/b1Q3nIEi+o2Xdzl5eXmha9eu1fojaeoG16qqKrz33nvIzs6Gs7Mz5HK5yvV9+/YJSlZ/dnZ22LlzJ3r16oXjx49j1KhR2LNnD/bu3Yu8vDwcO3ZMdET6jX79+iEgIABjx47FpEmTkJmZiZkzZ2L79u34+eefkZycLDpinRQVFcHf3x/du3eHi4tLtdfYkCFDBCUjdWEBRfQbf7R0J5PJNHaD60cffYSIiAj07du3xv0ZW7duFZSs/gwMDJCdnQ1ra2vMmjULT58+xcaNG5GdnY0ePXooj3zRJMOHD0f37t0xb948lfHPP/8cKSkp+O677wQlq7+XzVz79u2L+/fvY/z48Th79iw6dOiAiIgIvPHGG6Ij1sn333+PDz/8sMYjk7iJXDuxgCKSABMTE+zevVvjb3+vSZs2bRATE4NevXrB0dERy5Ytw8iRI5GVlYU333yzxl9of3UtWrTAyZMnqzVgvHz5Mry9vfHTTz8JSlZ/T548gUKhgKGhIYAXfbz2798PZ2dn9O/fX3C6urO1tcX777+P0NBQtGzZUnQcagS8C48kb/bs2Vi6dCmMjIwwe/bsVz5PJpNpbBNDCwsLtGvXTnQMtfD19cXYsWPRvn17PHz4EAMHDgQAjd7QW1JSAj09vWrjcrlcIwvC3/Lx8YGvry+mTJmC4uJi9OzZE3K5HEVFRVizZg2mTp0qOmKdPHz4EEFBQSyeJISbyEny0tLSUFFRofz4j/5oqs8++wyLFi3S6P5Br7J27Vp89NFHcHZ2xvHjx2FsbAwAKCgowLRp0wSnqxtXV1eVjfEv7d69G87OzgISNZzU1FT07t0bABATE4OWLVvizp07iIqKwvr16wWnqztfX1/88MMPomNQI+ISHpEEuLm5IScnBwqFAra2ttU2uKampgpKRjX5/vvvlTNr77zzDgAgPj4eu3btwnfffYehQ4eKDVgPhoaGuH79Otq2bYtRo0ahU6dOWLRoEfLz8+Ho6KixRf7y5cvx5Zdf4r333oOrq2u115im3oBCr8YCikgCFi9e/IfXFy1a1EhJ1GP79u3YuHEjbt26hXPnzsHGxgZffvkl7Ozs4OPjIzpenRw6dAhhYWFIT0+HgYEBOnfujEWLFsHT01N0tHrp3LkzJk6ciGHDhsHFxQVHjhyBh4cHLl26hPfeew+FhYWiI9aJtt6AQq/GAoqINNqGDRuwcOFCfPzxx1i+fDmuXLkCe3t7REZGYtu2bRq3rPL8+XOEhYUhMDAQr7/+uug4DS4mJgZjx45FZWUlvLy8lG0mVqxYgaSkJPz73/8WnJDoz2EBRSQRxcXFiImJQU5ODubOnQsLCwukpqaiZcuWsLKyEh2vzpydnREWFoahQ4fCxMQEGRkZsLe3x5UrV9CnTx8UFRWJjlhrxsbGuHLlCmxtbUVHUYvCwkIUFBSgS5cuyqaaFy5cgKmpKTp27Cg4Xf2Ul5fj9u3baNeuHXR1eZ+WNuMmciIJyMzMRIcOHbBq1Sr885//RHFxMYAXDTRDQkLEhqun27dv13jQs76+PkpLSwUkqj8vLy8kJiaKjqE2rVq1gpubm7J4AoDu3btrdPFUVlaGCRMmwNDQEJ06dVJ2WJ8xYwZWrlwpOB2pAwsoIgmYPXs2AgICcOPGDTRt2lQ5PmjQICQlJQlMVn92dnZIT0+vNn7kyBE4OTk1fqAGMHDgQMyfPx9z5szBrl27cODAAZU/9NcTEhKCjIwMJCQkqLzGvL29a7yjkjQf5xeJJCAlJQUbN26sNm5lZaWxm3Zfmj17NqZPn46nT59CoVDgwoUL2LVrF1asWIGIiAjR8erkZfuFNWvWVLvGrtZ/TXFxcdizZw969uyp0um/U6dOyMnJEZiM1IUFFJEE6Ovr19iAMTs7Gy1atBCQqOFMnDgRBgYGWLBgAcrKyjB27Fi0adMG69atw5gxY0THq5OqqirREaiWHjx4AEtLy2rjpaWl1Y5OIu3AJTwiCRgyZAiWLFmibBgqk8mQl5eHefPmYfjw4YLT1d+4ceNw48YNlJSUoLCwEHfv3sWECRNExyIJ6datGw4dOqR8/LJoioiIgIeHh6hYpEa8C49IAh4/fowRI0YoD3Jt06YNCgsL4eHhgcOHD8PIyEh0RPqd0tJSJCYmIi8vD+Xl5SrX2JTxr+f06dMYOHAg/Pz8EBkZicmTJ+Pq1as4e/YsEhMT0bVrV9ERqYGxgCKSkDNnziAjIwMlJSVwd3eHt7e36Ej1Zmdn94dLJJrYwDAtLQ2DBg1CWVkZSktLYWFhgaKiIhgaGsLS0lIj/01SkJOTg5UrV6q8xubNm1ftUGjSDiygiCQgKioKo0ePhr6+vsp4eXk5du/ejfHjxwtKVn/r1q1TeVxRUYG0tDQcOXIEc+fOxfz58wUlq7s+ffqgQ4cOCA8Ph5mZGTIyMiCXy+Hn54dZs2bB19dXdEQiyWMBRSQBTZo0QUFBQbVNrg8fPoSlpaVW3tX19ddf4+LFi9i6davoKLVmbm6O5ORkODo6wtzcHOfOnYOTkxOSk5Ph7++P69evi45IvyPF15jUcRM5kQQoFIoal7nu3r0LMzMzAYnUb+DAgYiNjRUdo07kcrmyyaSlpaWyKaOZmRny8/NFRqNXeNVcxLNnz6Cnp9fIaagxsI0BkRZzc3ODTCaDTCaDl5eXytESlZWVuH37NgYMGCAwofrExMTAwsJCdIw6cXNzQ0pKCtq3bw9PT08sXLgQRUVF2L59O1xcXETHo99Yv349gBd33UVERMDY2Fh5rbKyEklJSRrdYZ1ejQUUkRYbOnQoACA9PR39+/dX+eGup6cHW1tbjW9j8LJIfEmhUKCwsBAPHjzAN998IzBZ3YWFheHXX38FACxfvhzjx4/H1KlT0aFDB41tDqqt1q5dC+DF/7vw8HA0adJEee3layw8PFxUPFIj7oEikoBt27Zh9OjRKkdMaIvFixerPNbR0UGLFi3Qp08fjX3n/+TJEygUChgaGgIAcnNzsX//fjg7O6N///6C01FN+vbti3379qFZs2aio1AjYQFFRPQX069fP/j6+mLKlCkoLi5Gx44dIZfLUVRUhDVr1mDq1KmiIxJJHpfwiCSgsrISa9euxd69e2tszPjo0SNByeqvpiNqXsXU1FSNSRpOamqqcmkoJiYGLVu2RFpaGmJjY7Fw4UIWUH9Rd+/exYEDB2p8jdV0riFpNhZQRBKwePFiREREIDg4GAsWLMCnn36K3NxcxMXFYeHChaLj1Yu5ufl/PWvs5V2ImnIreVlZGUxMTAAAx44dg6+vL3R0dNCzZ0/cuXNHcDqqSXx8PIYMGQJ7e3tcv34dLi4uyM3NhUKhgLu7u+h4pAZsY0AkATt37sSmTZsQHBwMXV1dfPDBB4iIiMDChQtx/vx50fHqZevWrbC0tMQnn3yC/fv3Y//+/fjkk0/QsmVLbNmyBSdPnsQPP/yAkydPio76pzk4OCAuLg75+fk4evQo+vXrBwC4f/++xsyiSU1ISAjmzJmDy5cvo2nTpoiNjUV+fj48PT0xcuRI0fFIHRREpPUMDQ0Vd+7cUSgUCkWrVq0Uly5dUigUCkVOTo7C1NRUZLR6e+eddxTR0dHVxnfu3Knw9PRs/EAN4LvvvlPI5XKFjo6O4t1331WOh4WFKQYMGCAwGb2KsbGx4ubNmwqFQqEwNzdXXLlyRaFQKBTp6ekKGxsbgclIXTgDRSQBr7/+OgoKCgAA7dq1w7FjxwAAKSkp1Y530TTnzp1Dt27dqo1369YNFy5cEJCo/kaMGIG8vDxcvHgRR44cUY57eXkp90bRX4uRkZFy31Pr1q2Rk5OjvFZUVCQqFqkRCygiCRg2bBji4+MBADNmzEBoaCjat2+P8ePHIzAwUHC6+rG2tsamTZuqjUdERMDa2lpAoobRqlUruLm5KTuSA0D37t01tjWDtuvZsydOnz4NABg0aBCCg4OxfPlyBAYGomfPnoLTkTqwjQGRBJ0/fx5nz55F+/btMXjwYNFx6uXw4cMYPnw4HBwc0KNHDwDAhQsXcOPGDcTGxmLQoEGCE5IU3Lp1CyUlJejcuTNKS0sRHBysfI2tWbMGNjY2oiNSA2MBRSQBSUlJ6NWrl8pRLgDw/PlznD17Fn/7298EJWsYd+/exYYNG3Dt2jUAgJOTE6ZMmaLRM1BE9NfGAopIAnhSPDBt2jQsWbIEzZs3Fx2FtJC9vT1SUlLw2muvqYwXFxfD3d0dt27dEpSM1IV7oIgkQPF/fZB+7+HDhzAyMhKQqPHt2LGjVk03iWojNze3xjciz549w7179wQkInVjI00iLebr6wvgxUnxAQEBKnfcVVZWIjMzE7169RIVr1Fxsp3U4cCBA8qPjx49CjMzM+XjyspKxMfHw9bWVkAyUjcWUERa7OUPc4VCARMTExgYGCiv6enpoWfPnpg0aZKoeEQab+jQoQBevEnx9/dXuSaXy2Fra4vVq1cLSEbqxgKKSItt3boVAGBra4s5c+ZIZrmOqLFUVVUBAOzs7JCSksI9dhLCTeREEvDkyRMoFAoYGhoCAO7cuYP9+/fD2dlZeUyItjMxMUFGRgbs7e1FRyGJKC4uhrm5uegYpCbcRE4kAT4+PoiKigLw4od69+7dsXr1avj4+GDDhg2C0xFpvlWrVmHPnj3KxyNHjoSFhQWsrKyQkZEhMBmpCwsoIglITU1F7969AQAxMTFo1aoV7ty5g6ioKKxfv15wusbh5+fHg3hJbcLDw5V9x44fP44TJ07gyJEjGDhwIObOnSs4HakD90ARSUBZWRlMTEwAAMeOHYOvry90dHTQs2dP3LlzR3C62svMzPzTz+3cuTMAcKaN1KqwsFBZQB08eBCjRo1Cv379YGtrq+yQT9qFBRSRBDg4OCAuLg7Dhg3D0aNHERQUBAC4f/++Rs7KvPHGG5DJZK9sTfDymkwmk0STUBKvWbNmyM/Ph7W1NY4cOYJly5YBeHEHLP8PaicWUEQSsHDhQowdOxZBQUHw8vKCh4cHgBezUW5uboLT1d7t27dFRyBS4evri7Fjx6J9+/Z4+PAhBg4cCABIS0uDg4OD4HSkDrwLj0giCgsLUVBQgC5dukBH58X2xwsXLsDU1BQdO3YUnI5Is1VUVGD9+vXIy8tDQECA8o3J2rVrYWJigokTJwpOSA2NBRSRlquoqICBgQHS09Ph4uIiOo7aXL16FXl5eSgvL1cZHzJkiKBEJBUVFRWYPHkyQkNDYWdnJzoONRIu4RFpOblcjrZt22rtPoxbt25h2LBhuHz5ssq+qJdn/2nrv5v+OuRyOWJjYxEaGio6CjUitjEgkoBPP/0U//M//4NHjx6JjtLgZs2aBTs7O9y/fx+Ghob48ccfkZSUhG7duiEhIUF0PJKIoUOHIi4uTnQMakRcwiOSADc3N9y8eRMVFRWwsbGpdqRLamqqoGT117x5c5w8eRKdO3eGmZkZLly4AEdHR5w8eRLBwcFIS0sTHZEkYNmyZVi9ejW8vLzQtWvXaq+xmTNnCkpG6sIlPCIJeHngqTaqrKxU9rhq3rw5/vOf/8DR0RE2NjbIysoSnI6kYvPmzTA3N8elS5dw6dIllWsymYwFlBZiAUUkAYsWLRIdQW1cXFyQkZEBOzs79OjRA59//jn09PTw7bff8tw7ajRsrSE93ANFJBHFxcWIiIhASEiIci9Uamoq7t27JzhZ/SxYsABVVVUAgCVLluD27dvo3bs3Dh8+LJljauivo7y8HFlZWXj+/LnoKKRm3ANFJAGZmZnw9vaGmZkZcnNzkZWVBXt7eyxYsAB5eXnKg4a1xaNHj9CsWTPlnXhE6lZWVoYZM2Zg27ZtAIDs7GzY29tjxowZsLKywvz58wUnpIbGGSgiCZg9ezYCAgJw48YNNG3aVDk+aNAgJCUlCUxWf48fP652d6GFhQV+/vln/PLLL4JSkdSEhIQgIyMDCQkJKq8xb29v7NmzR2AyUhcWUEQSkJKSgsmTJ1cbt7KyQmFhoYBEDWfMmDHYvXt3tfG9e/dizJgxAhKRFMXFxeFf//oX3n77bZWZz06dOiEnJ0dgMlIXFlBEEqCvr1/jbEx2djZatGghIFHDSU5ORt++fauN9+nTB8nJyQISkRQ9ePAAlpaW1cZLS0u5lKylWEARScCQIUOwZMkSVFRUAHhxW3VeXh7mzZuH4cOHC05XP8+ePatxw25FRQWePHkiIBFJUbdu3XDo0CHl45dFU0REhPLwbtIu3EROJAGPHz/GiBEjcPHiRfz6669o06YNCgsL4eHhgcOHD1dr+qdJ+vbtCxcXF3z11Vcq49OnT0dmZiZOnTolKBlJyenTpzFw4ED4+fkhMjISkydPxtWrV3H27FkkJiaia9euoiNSA2MBRSQhp0+fRmZmJkpKSuDu7g5vb2/RkertzJkz8Pb2xptvvgkvLy8AQHx8PFJSUnDs2DH07t1bcEKSipycHKxcuRIZGRnK19i8efPg6uoqOhqpAQsoIgnIz8+HtbW16Bhqk56eji+++ALp6ekwMDBA586dERISgvbt24uORkRaigUUkQQ0adIEb7/9Nvz8/DBixAg0a9ZMdCQijVebNhmmpqZqTEIisIAikoC0tDRER0dj9+7dePDgAQYMGAA/Pz8MHjwY+vr6ouPV2i+//KL8hfTffonxFxepi46Ozp++w66yslLNaaixsYAikhCFQoGEhARER0cjNjYWVVVV8PX1xZYtW0RHq5UmTZqgoKAAlpaWr/wlplAoIJPJ+IuL1CYxMVH5cW5uLubPn4+AgADlXXfnzp3Dtm3bsGLFCvj7+4uKSWrCAopIolJTUzFhwgRkZmZqXJGRmJiIt956C7q6uiq/xGri6enZSKlIyry8vDBx4kR88MEHKuPR0dH49ttvkZCQICYYqQ0LKCIJuXv3LqKjoxEdHY0rV67Aw8MD48aNw5QpU0RHq5Pnz58jLCwMgYGBeP3110XHIQkzNDRERkZGtRsXsrOz8cYbb6CsrExQMlIXNtIkkoCNGzfC09MTNjY2iIqKwujRo5GTk4NTp05pbPEEALq6uvjiiy9qbKRJ1Jisra2xadOmauMRERFafQeslHEGikgCrK2t8cEHH2DcuHHo0qWL6DgNysfHB76+vtxjQkIdPnwYw4cPh4ODA3r06AEAuHDhAm7cuIHY2FgMGjRIcEJqaCygiCRAoVDg8ePH2Lx5M65duwYAcHZ2xoQJE2BmZiY4Xf2Eh4dj8eLFGDduHLp27Vqtq/qQIUMEJSOpuXv3Lr755htcv34dAODk5IQpU6ZwBkpLsYAikoBLly6hf//+aNq0Kbp37w4ASElJwZMnT3Ds2DG4u7sLTlh3Ojqv3onAu/CISF1YQBFJQO/eveHg4IBNmzZBV1cXwIsN2BMnTsStW7eQlJQkOCGR5isuLsaFCxdw//59VFVVqVwbP368oFSkLiygiCTAwMAAaWlp6Nixo8r41atX0a1bN94hRFRP33//PcaNG4eSkhKYmpqq9CaTyWR49OiRwHSkDrwLj0gCTE1NkZeXV208Pz8fJiYmAhI1rMTERAwePBgODg5wcHDAkCFDcOrUKdGxSEKCg4MRGBiIkpISFBcX4+eff1b+YfGknVhAEUnA6NGjMWHCBOzZswf5+fnIz8/H7t27a2z8p2l27NgBb29vGBoaYubMmZg5cyYMDAzg5eWF6Oho0fFIIu7du4eZM2fC0NBQdBRqJFzCI5KA8vJyzJ07F+Hh4cqeSXK5HFOnTsXKlSs18jy8l5ycnPCPf/wDQUFBKuNr1qzBpk2blHcdEqmTr68vxowZg1GjRomOQo2EBRSRhJSVlSEnJwcA0K5dO614t6yvr48ff/wRDg4OKuM3b96Ei4sLnj59KigZScnmzZuxZMkS/P3vf4erqyvkcrnKdbbT0D66ogMQUeMxNDSEq6ur6BgNytraGvHx8dUKqBMnTrD/DjWaSZMmAQCWLFlS7RrbaWgnFlBEpNGCg4Mxc+ZMpKeno1evXgCAM2fOIDIyEuvWrROcjqTi920LSPtxCY+INN7+/fuxevVq5X4nJycnzJ07Fz4+PoKTkVTUNPP0kkwmQ2hoaCOmocbAAoqIiKie3NzcVB5XVFTg9u3b0NXVRbt27ZCamiooGakLl/CISKPZ29sjJSUFr732msp4cXEx3N3dcevWLUHJSErS0tKqjf3yyy8ICAjAsGHDBCQideMMFBFpNB0dHRQWFsLS0lJl/KeffkLbtm3x7NkzQcmIgMuXL2Pw4MHIzc0VHYUaGGegiEgjHThwQPnx0aNHYWZmpnxcWVmJ+Ph42NraCkhG9P8eP36Mx48fi45BasAZKCLSSDo6Lw5SkMlk+P2PMblcDltbW6xevRrvv/++iHgkMevXr1d5rFAoUFBQgO3bt8PT05Nd8bUQCygi0mh2dnZISUlB8+bNRUchCbOzs1N5rKOjgxYtWuCdd95BSEiIVpw5SapYQBGR1nj69CmaNm0qOgYRSQAPEyYijVZVVYWlS5fCysoKxsbGyrvuQkNDsXnzZsHpiEhbsYAiIo22bNkyREZG4vPPP4eenp5y3MXFBREREQKTEZE2YwFFRBotKioK3377LcaNG4cmTZoox7t06YLr168LTEZE2owFFBFptHv37lU7SBh4sbRXUVEhIBERSQELKCLSaM7Ozjh16lS18ZiYmGrHaxARNRQ20iQijbZw4UL4+/vj3r17qKqqwr59+5CVlYWoqCgcPHhQdDwi0lJsY0BEGu/UqVNYsmQJMjIyUFJSAnd3dyxcuBD9+vUTHY2ItBQLKCIiIqJa4hIeEWmF8vJy3L9/H1VVVSrjbdu2FZSIiLQZCygi0mg3btxAYGAgzp49qzKuUCggk8lQWVkpKBkRaTMWUESk0QICAqCrq4uDBw+idevWkMlkoiMRkQRwDxQRaTQjIyNcunQJHTt2FB2FiCSEfaCISKM5OzujqKhIdAwikhjOQBGRxvnll1+UH1+8eBELFixAWFgYXF1dIZfLVZ5ramra2PGISAJYQBGRxtHR0VHZ6/Ryw/hvcRM5EakTN5ETkcb54YcfAADPnj3DgAEDEB4eDkdHR8GpiEhKOANFRBqtRYsWOHv2LNq3by86ChFJCDeRE5FG8/Pzw+bNm0XHICKJ4RIeEWm058+fY8uWLThx4gS6du0KIyMjletr1qwRlIyItBkLKCLSaFeuXIG7uzsAIDs7W+Uam2oSkbpwDxQRERFRLXEPFBEREVEtsYAiIiIiqiUWUERERES1xAKKiIiIqJZYQBER/RcBAQEYOnSo8nGfPn3w8ccfN3qOhIQEyGQyFBcXN/rXJiJVLKCISGMFBARAJpNBJpNBT08PDg4OWLJkCZ4/f67Wr7tv3z4sXbr0Tz2XRQ+RdmIfKCLSaAMGDMDWrVvx7NkzHD58GNOnT4dcLkdISIjK88rLy6Gnp9cgX9PCwqJBPg8RaS7OQBGRRtPX10erVq1gY2ODqVOnwtvbGwcOHFAuuy1fvhxt2rRRHjacn5+PUaNGwdzcHBYWFvDx8UFubq7y81VWVmL27NkwNzfHa6+9hk8++QS/b5f3+yW8Z8+eYd68ebC2toa+vj4cHBywefNm5Obmom/fvgCAZs2aQSaTISAgAABQVVWFFStWwM7ODgYGBujSpQtiYmJUvs7hw4fRoUMHGBgYoG/fvio5iUgsFlBEpFUMDAxQXl4OAIiPj0dWVhaOHz+OgwcPoqKiAv3794eJiQlOnTqFM2fOwNjYGAMGDFD+ndWrVyMyMhJbtmzB6dOn8ejRI+zfv/8Pv+b48eOxa9curF+/HteuXcPGjRthbGwMa2trxMbGAgCysrJQUFCAdevWAQBWrFiBqKgohIeH48cff0RQUBD8/PyQmJgI4EWh5+vri8GDByM9PR0TJ07E/Pnz1fVtI6Ja4hIeEWkFhUKB+Ph4HD16FDNmzMCDBw9gZGSEiIgI5dLdjh07UFVVhYiICOUxL1u3boW5uTkSEhLQr18/fPnllwgJCYGvry8AIDw8HEePHn3l183OzsbevXtx/PhxeHt7AwDs7e2V118u91laWsLc3BzAixmrsLAwnDhxAh4eHsq/c/r0aWzcuBGenp7YsGED2rVrh9WrVwMAHB0dcfnyZaxataoBv2tEVFcsoIhIox08eBDGxsaoqKhAVVUVxo4di88++wzTp0+Hq6uryr6njIwM3Lx5EyYmJiqf4+nTp8jJycHjx49RUFCAHj16KK/p6uqiW7du1ZbxXkpPT0eTJk3g6en5pzPfvHkTZWVlePfdd1XGy8vL4ebmBgC4du2aSg4AymKLiMRjAUVEGq1v377YsGED9PT00KZNG+jq/v+PNSMjI5XnlpSUoGvXrti5c2e1z9OiRYs6fX0DA4Na/52SkhIAwKFDh2BlZaVyTV9fv045iKhxsYAiIo1mZGQEBweHP/Vcd3d37NmzB5aWljA1Na3xOa1bt0ZycjL+9re/AQCeP3+OS5cuwd3dvcbnu7q6oqqqComJicolvN96OQNWWVmpHHN2doa+vj7y8vJeOXPl5OSEAwcOqIydP3/+v/8jiahRcBM5EUnGuHHj0Lx5c/j4+ODUqVO4ffs2EhISMHPmTNy9excAMGvWLKxcuRJxcXG4fv06pk2b9oc9nGxtbeHv74/AwEDExcUpP+fevXsBADY2NpDJZDh48CAePHiAkpISmJiYYM6cOQgKCsK2bduQk5OD1NRUfPXVV9i2bRsAYMqUKbhx4wbmzp2LrKwsREdHIzIyUt3fIiL6k1hAEZFkGBoaIikpCW3btoWvry+cnJwwYcIEPH36VDkjFRwcjA8//BD+/v7w8PCAiYkJhg0b9oefd8OGDRgxYgSmTZuGjh07YtKkSSgtLQUAWFlZYfHixZg/fz5atmyJjz76CACwdOlShIaGYsWKFXBycsKAAQNw6NAh2NnZAQDatm2L2NhYxMXFoUuXLggPD0dYWJgavztEVBsyxat2RhIRERFRjTgDRURERFRLLKCIiIiIaokFFBEREVEtsYAiIiIiqiUWUERERES1xAKKiIiIqJZYQBERERHVEgsoIiIiolpiAUVERERUSyygiIiIiGqJBRQRERFRLbGAIiIiIqql/wWgm2pyLewgzAAAAABJRU5ErkJggg==", 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f5XksU6ZMYc2aNdy5c+e9J07nh4CAACpWrMilS5ey/J02ITKTOUhCCPEvsHLlSooWLao2WV9ThgwZQmxsLJs3b9Z0KGpmzpxJ+/btJTkS70XmIAkhxEds8+bN/PXXX+zdu5eFCxcWiIn1JiYm73W/pPxW0BI2UbBJgiSEEB+xjh07YmJiQo8ePejXr5+mwxHiX0PmIAkhhBBCZCJzkIQQQgghMpEESQghhBAiE0mQhBBCCCEykQRJCCGEECITuYpNfHRG7Lml6RDyhF+FwpoOIU/YmxtoOoQ88/25IE2HkCe87Mw0HUKeKGZtoukQ8kwpB6NcPZ5hhdz7EzkJl/P+5qR5QRIkIYQQQqhTSINJXgEhhBBCiEykgiSEEEIIdQXgjuyaJgmSEEIIIdRJi01abEIIIYQQmUkFSQghhBDqpMUmCZIQQgghMpEWm7TYhBBCCCEykwqSEEIIIdRJi00SJCGEEEJkIi02abEJIYQQQmQmFSQhhBBCqJMWmyRIQgghhMhEWmzSYhNCCCGEyEwqSEIIIYRQJy02SZCEEEIIkYm02KTFJoQQQgiRmVSQhBBCCKFOWmySIAkhhBAiE2mxSYtNCCGEECIzqSAJIYQQQp1UkCRBEkIIIUQmWjIHSVJEIYQQQhQIEydORKFQqC2lSpVSbU9MTMTf3x9ra2tMTExo164dz549UztGcHAwzZs3x8jICFtbW0aMGEFqamqOY5EKkhBCCCHUabDFVrp0aQ4fPqx6rKPzd6oyZMgQ9u7dy9atWzE3N6d///60bduWU6dOAZCWlkbz5s2xt7fn9OnThISE0LVrV3R1dZk+fXqO4pAESVC3bl28vLxYsGCBpkMRQghREGjwMn8dHR3s7e2zrI+Ojmb16tVs2rSJ+vXrA7BmzRo8PDw4e/Ys1atX5+DBg9y4cYPDhw9jZ2eHl5cXU6ZMYdSoUUycOBE9Pb33jyPXzkh8tHbs2IGurq6mw8gXd45sJeTqGV6GPUFbVw8rl1J4tvDDxNZJbVxE0E1u7l9PZPBtFAotzAq74d1rEtq6+gAkx7/k6o4VPLvxJyi0cCznTZnWPdHRN9TEaXH9yiV2//wj9+8EEvniOSMnz6FazXqq7Uqlks1rl3N4707iY2MpWaY8vQaPxtHJGYCw0KdsXb+Ka5fPExXxAkvrQtRu1Ix2nXsUuPfGzq2b2bntZ0JCngDgVrQ43Xr2xdunFgCPHwWzZMEc/gq4RHJKMtW9azJk5DdYWRfSZNhZXP1tC8EBp4l+9hgdXT1sinpQsU03zO3+fi+e2bSYkJsBJERHoKNvgE1RDyq17oa5fRHVmJCbAQT8up7Ipw/R0denWLUGVGjph5a2tiZOi3vXAzi6+yce379FTOQLuo2cRtlqtdXGPHscxJ71y7l3I4D0tDTsnFz5csRULG3sAHge+oRf1i3hwc2/SE1JoZRXNdp+NRhTCytNnNIbxcfHsWn1Us6e/J3oyEjc3EvSc8BI3EuVBuCnNcv54/cDPA8PRUdHl2IlPPjiq/6U9Cyr4cjzV1JSEklJSWrr9PX10dfXz3b8nTt3cHR0xMDAAG9vb2bMmIGzszMXL14kJSWFhg0bqsaWKlUKZ2dnzpw5Q/Xq1Tlz5gxly5bFzs5ONcbX15e+ffty/fp1KlSo8N5xyxwkgZWVFaamptluS05Ozudo8tbze9dwrdGcWgO/xbv3ZNLT0zizYgKpSYmqMRFBNzm7ciI2JSpQa9Bcag+ei5tPC7WS86WNc3n5LBjv3pOp1mMcL+5f58rWJZo4JQCSEhNwLVaCngNHZbt91+Z17Nuxmd5DvmHGknUYGBgyZVR/kpMzfmg9CQ5CmZ5O7yHfMP+HLXTrN4yDv25n06rv8vM03ouNnR19Bgzhhw1bWb1+C5WqVOProf25f+8uCQnxDPHvBQoFi5b/wPLVG0hJSWHkEH/S09M1HbqaZ3evUrJOc5qNmEvDgVNJT0vl8OKxpLz2XrR2Lo5PlyG0Gr+chv2ngFLJocXjSE9PAyDi8X2OLJ2AY+lKtBi9iNrdv+bxX+e4tGuNpk6L5KREHF2L07bn0Gy3Pw99wuIx/tgWdqbfpEUMn7eWRp/6ofP/T/ZJiQl8P3koCoWCvhMXMmDaUtJSU1g14+sC9zX87tvJBFw8y5BvprLohy1UqOzN+GF9eBEeBoBjERd6DRrFoh+2MnPxGmztHZk4oh/RUREajvw9KLRybZkxYwbm5uZqy4wZM7J92mrVqrF27Vp+++03li1bxoMHD6hVqxYvX74kNDQUPT09LCws1Paxs7MjNDQUgNDQULXk6NX2V9tyQhIkQd26dRk8eDAArq6uTJkyha5du2JmZkavXr0A2L59O6VLl0ZfXx9XV1fmzp2rdgxXV1emT59O9+7dMTU1xdnZmRUrVqi2169fn/79+6vtEx4ejp6eHkeOHMnbE3yNd69JOFdtgJm9M+aOblToMIiEyHCiH99Vjbm+exVFa7bAvUF7zOydMbF1orBXTbR1MiopL589IuzmJbw+64+lS0msi3pStk0vngT8QWL0i3w7l9dVrOZDpx79qFarfpZtSqWSPds30f6LHlT1qYtrMXcGfD2JyOfh/HnyGAAVqtag/6iJeFXxxt7RiSo+dWj5aRfOnjyaz2fybjVr16NGzdoUcXbB2cWV3v6DMDQy4vrVK/wVcJnQkCeMnTiNYu4lKOZegrGTpnPzxnUunj+n6dDVNOw/heLejbBwdMHKqSg+XYcSFxFORPDf78USNZti514GE2s7rJ2LU+GTrsRHhhP3IuMXcNDFP7B0dKN8s06Y2TpiX6IsFdt059aJvaQkxmvkvDwqVqdZp56Uy1Q1emXfphV4VKzOJ1374VS0BIXsC1OmSk1MzS0BCLp5lYjwUDr2/wZHl2I4uhSj44AxPL53k7tXL+XnqbxVUlIiZ44f4cvegyldvhIOTs507NYHh8JF2L97KwB1GjbFq3J17B2dcHYrRg//YcTHxRJ0746Go38PCkWuLaNHjyY6OlptGT16dLZP27RpUz799FPKlSuHr68v+/btIyoqii1btuTzCyAJksjGnDlzKF++PJcvX2bcuHFcvHiRzz77jA4dOnD16lUmTpzIuHHjWLt2rdp+c+fOpXLlyly+fJl+/frRt29fbt26BcBXX33Fpk2b1MqsGzZsoHDhwqpesiakJMYBoGuUUUFLehlFZPBt9Ews+GPRSH6b0IVTS0bz4v4N1T6RQTfRNTTGooi7al0hdy8UCgWRwbfz9wTew7OQJ0RFvKBcpWqqdcYmprh7lOHWjb/euF98XCympmb5EeIHS0tL4/CBfSQmJFCmXHlSUpJRKBTovjbPQE9fHy0tLf4KKDi/XLOTnJDxXtQzNsl2e0pSInfPHsLE2g4jy4x2YXpqCtq66nMqtPX0SEtJ5sVriVZBkZ6eTuDFM9g4FuH7yUMZ3+0TFnzdi6vnTqjGpKakoECBzmutXV09PRQKLe7ffPP7Nb+lpaWRnp6m9l4D0NPTJ/Dq5SzjU1JSOPDrDoyNTXArViK/wiwQ9PX1MTMzU1ve1F7LzMLCghIlSnD37l3s7e1JTk4mKipKbcyzZ89Uc5bs7e2zXNX26nF285reRhIkkUX9+vUZNmwYxYoVo1ixYsybN48GDRowbtw4SpQowZdffkn//v359ttv1fZr1qwZ/fr1o3jx4owaNYpChQpx9GhGBaJt27YA7N69WzV+7dq1fPnllyg0NBlQmZ7O9V2rsHL1wMzBBYC4iIwS7K2DP+FSvTHePSdi7lSMM8vHEhv+FIDEl5HomVioHUtLWxtdI1MSX0bm6zm8j6iIjKqWhaX6/A1zSyvVtsxCnjxi/67NNGrRNs/j+xD37tymYc3K1POuwLfTJzN9ziLcihandNnyGBgYsnTRXBITEkhIiOe7Bd+SlpbGi+fhmg77jZTp6ZzftgKbYp5YOrqqbbt5fA+bhrTjpyHteHL9Io0GTlNVMx09KhJ+P5AH54+Rnp5GfNRz/tr3EwAJ0QWvjRMbHUlSYgK/79xIqQrV6D1+HmWr1mbtt2O5ez0jqXAp4YmegQG/rl9OclIiSYkJ/LJuCenpacREaqZCmx0jI2NKli7Hlh9X8uJ5GGlpaRw7uJdbN/4iIuK5atz50yf4vEkNPm1cjV+2bWDS3OWYWVhqMPL3lIsttn8iNjaWe/fu4eDgQKVKldDV1VXrOty6dYvg4GC8vb0B8Pb25urVq4SFhanGHDp0CDMzMzw9PXP03JIgiSwqV66s9jgwMBAfHx+1dT4+Pty5c4e0tDTVunLlyqn+r1AosLe3V71JDQwM6NKlCz/88AMAly5d4tq1a3z55ZdvjSUpKYmYmBi1JTUld+ZF/bVjOTGhwVTqMuLvlelKAFy9fXGu2hBzp2KUafUVxraFCf7zUK48b0H3IjyMqaP6412nYYFNkJxdXVn703ZWrPuJ1u0/Z9qEb3hw/y6WllZMmTWPUyeO07BWFXzrVCf25UtKlvJEUYDvDHzu52VEPX1I7e5Z55AVrVqPFqMX4TtkFma2jhxfNYO0/38POHpWpFLb7pz9aQkbB7Zm18ReFC79/+/fAni+SmXG91fpKjWp88nnFHZzp0HbL/CsVIMzBzI+PJmYW+I3bDI3LpxidOfGjOnSlIS4WJyKlkCrgP0B1SHfTEWJku7tfWnfqBp7dvxErfpN0HrttS9boQoLVm1m1ndrqVi1BrMnjiQqsuAlr1nkYostJ4YPH87x48cJCgri9OnTtGnTBm1tbTp27Ii5uTk9evRg6NChHD16lIsXL9KtWze8vb2pXr06AI0bN8bT05MuXbpw5coVDhw4wNixY/H393/vqtUrchWbyMLY2PiD9st8tZNCoVCbVPnVV1/h5eXF48ePWbNmDfXr18fFxeWtx5wxYwaTJk1SW+fd0R+fTgM+KMZX/tqxnGc3LuDjPx1Di7+vbtI3y/hkZ2JXRG28qW0REiIzPhUamFqSHBultj09LY2U+JcYmBa8T4YWVtYAREVGYGlto1ofHRmBa3H1Un/E83AmDOtNydLl6TN0bL7GmRO6uno4Fcl475TyKM3NG9fY+tMGRo6ZSDVvH7b+8htRkZFo62hjamrGJ41r08CpqYajzt65n5fx+Oqf+A6dhbFl1ivt9AyN0TM0xsy2MIXcSvLz8M8JDjiNW5W6AHg2aINH/dYkREegZ2RC7ItnXN69DtNCOWsn5AdjU3O0tLWxL+Kqtt7WyYUHgX+3z0p6VWXM0p+JjYlCW1sbQ2NTJvRohZWdYz5H/HYOhYswfeFqEhMSiI+PxcrahtmTRmHnWFg1xsDQEAcnZxycnClZuhx9Orfk8L6dtO/cQ4ORF1yPHz+mY8eOvHjxAhsbG2rWrMnZs2exscn42TV//ny0tLRo164dSUlJ+Pr6snTpUtX+2tra7Nmzh759++Lt7Y2xsTF+fn5Mnjw5x7FIgiTeycPDQ3UTrldOnTpFiRIl0M7BpcRly5alcuXKrFy5kk2bNvHdd+++Qmr06NEMHap+NcyEIw/f+zkzUyqVXN35PaFXz1Kj33SMrdV/iRhZ2WFgZkVc2BO19bHhT7DzqASApWspUhLiiHp0F4sixQF4fvcvlEolls4Fb26BnUNhLKysuXrpT9yKlwQy5hfdCbyGb8v2qnEvwsOYMKw3Rd098B85AS2tgleBeJP09PQsV1xaWGYkqxf/PEtkRAQ1a9fLbleNUSqV/LllOcEBZ/AdMuP9EholKJWQlpqitlqhUGBkkZEIB104jpGlDVbOxfIi7H9ER1cX5+IehD0JVlsf/vQRljZZz9/EzAKAO1cvEhsdSZkqNfMjzBwzMDTEwNCQ2JcxBPx5Gr8+g984VqlUkpKc8sbtBYaGKpCbN29+63YDAwOWLFnCkiVvvmrYxcWFffv2/eNYJEES7zRs2DCqVKnClClT+Pzzzzlz5gzfffedWtb+vr766iv69++PsbExbdq0eef47O6VoaP7/jf6yuzqjuU8vnSCqt3HoKNvSGJMxpwhXUMjtHX1USgUFKvXhlsHfsLM0Q2zwm48Pv87sWFPqOL3NQCmdkWwLVWRK1u/o1z7fqSnpXJ1x/cU9qqFgbn1B8f2TyQkxBP65JHqcVjIUx7cvYWJqRk2dg60aNeJbRtW41DYGVsHR35aswzLQjZUrVkXyEiOxg/thY2dA359BhMT/fdcKkurgnX/oGWL5+PtUws7ewfi4+I4+NteLl88z7zvMq6a3PvLTlzcimJhYcn1q1dYMGcGn3fqiourm4YjV3du81IeXDhOvd7j0NU3VM0Z0jU0RkdPn5fPQwi68AeOnhXQNzEnPvI51w5uRVtPj8JlqqiOc+3Qdgp7VkKhUBAccJprB7dRu8fXaGlp5j5ISQnxPA/9+wNGRFgITx7cwcjEDEsbO+q26sj6eRMo6lme4mUqcvPyOW5cOE2/yYtU+/z5+15snVwxMbMg6NY1dv2wiNotPsO2sLMmTumNLv15GpRKCju7EvLkEWuXzaewsxsNmrYkMSGBrRtWUbVGHSytCxETHcW+XVt4ER6GT91Gmg793QpYO1MTJEES71SxYkW2bNnC+PHjmTJlCg4ODkyePPmd84ey07FjRwYPHkzHjh0xMDDI/WDfIej0fgBOL/1Gbb3X54NwrtoAgGK1W5GeksK13atJSXiJmYMb3r0nY1zIQTW+YudhXN3xPaeXj0OhUOBQ1puybXrl34lkcu/WDSYM7a16vHbZPADq+rZgwKhJtO7gR2JiAsvnTSMu9iWlynoxbuZi9PQyks8rF88S+uQRoU8e0etz9VbU9t8v5t+JvIeoyAimjB/Ni+fhGJuYUty9BPO+W0HV6jUACA56wPLv5hMTHY2DY2H8uvfi885+Go46q9t/ZHzCPbjga7X1NboMprh3I7R19Ai7d53Ao7tJjo/FwNQCO/cyNB0+B0NTC9X4p9cvcPW3n0lPTcGysBv1+oz7ex6SBjy6d4ulEwaqHu9em1EprlK3CR0HjKFctdq07zWcIzs2sPOHhdg6OvPliCkU9fh7DmPYk0fs3biC+NgYrGzsadiuC3U++Tzfz+Vd4uNiWb9yMc/Dn2Fqao537QZ88ZU/Ojq6pKel8zg4iN8P/EpMdBSmZua4lyrNjMU/4OxW8Kp7IiuF8tWsOSHyQVBQEMWKFeP8+fNUrFjxg44xYs+tXI6qYPCrUPjdgz5C9ub5nwjnl+/PBWk6hDzhZVewb+/woYpZZ38LhX+DUg5GuXo8w2YLc+1YCfsG5dqx8pNUkES+SElJ4cWLF4wdO5bq1at/cHIkhBAiH0iLTS7zF/nj1KlTODg4cP78eZYvX67pcIQQQoi3kgqSyBd169ZFurlCCPGRKID30cpvkiAJIYQQQp0kSNJiE0IIIYTITCpIQgghhFAnk7QlQRJCCCFEJtJikxabEEIIIURmUkESQgghhDppsUmCJIQQQohMpMUmLTYhhBBCiMykgiSEEEIIddJikwRJCCGEEOoUkiBJi00IIYQQIjOpIAkhhBBCjVSQJEESQgghRGaSH0mLTQghhBAiM6kgCSGEEEKNtNgkQRJCCCFEJpIgSYtNCCGEECILqSAJIYQQQo1UkCRBEkIIIUQmkiBJi00IIYQQIgupIAkhhBBCnRSQJEESQgghhDppsUmLTQghhBAiC6kgCSGEEEKNVJAkQRIfoZ6Vi2g6hDyx9tJjTYeQJ8Y2ctd0CHnm0zKOmg4hTySnpms6hDxhYayr6RA+GpIgSYtNCCGEECILqSAJIYQQQo1UkCRBEkIIIURmkh9Ji00IIYQQIjOpIAkhhBBCjbTYJEESQgghRCaSIEmLTQghhBAiC6kgCSGEEEKNVJAkQRJCCCFEZpIfSYtNCCGEECIzqSAJIYQQQo202CRBEkIIIUQmkiBJi00IIYQQIgupIAkhhBBCjVSQJEESQgghRCaSIEmLTQghhBAiC6kgCSGEEEKdFJAkQRJCCCGEOmmxSYtNCCGEECILqSAJIYQQQo1UkCRBEkIIIUQmkiBJi00IIYQQIgupIAkhhBBCnRSQJEESQgghhDppsUmLTQghhBAiC6kgFSBffvklUVFR7Nq1K0f7TZw4kV27dhEQEJAnceWFunXr4uXlxYIFCzQaR1paGj+tXc7Rg/uIiniBVSEbGjT5hM+79lR9goqMeMHa7xcScP4MsbGxlClfkd6DRuLo5KLR2F938/BWnv51mpdhT9DW1cPKtRRlP/kSU1sn1Zjj343m+b1ravu5eTeh4mf+qsfbh3yS5dhVu4ygSMXaeRd8Dl28cJ4f16zmxo3rPA8PZ97C76jXoKFq+5FDB9m2ZTOBN64THR3N5m07KVnKQ4MRv5+0tDR+WpPpvdhU/b34Se0K2e7bre9g2nb0y89w3+j6lUvs/vlH7t8JJPLFc0ZOnkO1mvVU25VKJZvXLufw3p3Ex8ZSskx5eg0ejaOTMwBhoU/Zun4V1y6fJyriBZbWhajdqBntOvdAV1dXU6f1ThvXrmLFkgW07/AFA4Z9DcCL589ZtmgOF8+dIT4+niIurnTp3os69RtpONp3kwqSJEj5Jjk5GT09PU2HITLZvmkt+3ZvY8joyTi7FuPuressnDkRI2MTWrbvhFKpZNqYIejo6DBm2gKMjI3ZtWUDY4f2Yem6HRgYGmr6FAB4fu8aRWs2x6qIO+np6Vzf+yMnl4+n0ail6OgbqMa5VveldNPOqsfaevpZjlWp4yDsS1VSPdY1NM7b4HMoISGBEiVL0apNO4YNHpDtdq+KlWjk25QpE8dpIMIPo3ovfvPae3HG3+9FgB93HlLb5+K5UyyaNYkadRpoIuRsJSUm4FqsBA2atmT2hBFZtu/avI59OzYz4OtJ2NoXZvOaZUwZ1Z+Fa7aip6fPk+AglOnp9B7yDfaFi/DowT2WzZtKUkICfn2HaOCM3i3w+lV+2bmVYu4l1NZPnzia2JcvmT7vO8zNLTh8YB8TRw/j+x9/pkTJgp20S4L0H26xJSUlMXDgQGxtbTEwMKBmzZqcP3+e9PR0nJycWLZsmdr4y5cvo6WlxcOHDwGIioriq6++wsbGBjMzM+rXr8+VK1dU4ydOnIiXlxerVq3Czc0NA4OMX1Lbtm2jbNmyGBoaYm1tTcOGDYmLi2PixImsW7eO3bt3o1AoUCgUHDt2DIBRo0ZRokQJjIyMKFq0KOPGjSMlJQWAtWvXMmnSJK5cuaLab+3atTmK8YcffsDZ2RkTExP69etHWloas2fPxt7eHltbW6ZNm6b2WrzvcdevX4+rqyvm5uZ06NCBly9fAhmVsuPHj7Nw4UJVzEFBQf/8i/oBAq9fobpPHap418LOwRGfuo3wqlKdOzevA/D0cTC3blyl79AxlPAojZOzK/2GfkNyUhLHj+zXSMzZqdl7Eq5VG2Lm4IJFYTcqdxpMfGQ4kY/vqo3T0dPHwMxStegaGGU5lq6hsdoYbd2CldjXrFUb/4GDqd8w+0/hLVq2ondff6p7e+dzZP9M4LU3vBcDr6vGWFoXUlvOnjxG2QpVsHd0esuR81fFaj506tGParXqZ9mmVCrZs30T7b/oQVWfurgWc2fA15OIfB7OnyePAVChag36j5qIVxVv7B2dqOJTh5afduHsyaP5fCbvJz4+nqnjv2bENxMxNTVT23b9rwDaft4Jj9JlcXQqQtcevTExNeX2a19TUXD9ZxOkkSNHsn37dtatW8elS5coXrw4vr6+REVF0bFjRzZt2qQ2fuPGjfj4+ODiktFW+fTTTwkLC2P//v1cvHiRihUr0qBBAyIiIlT73L17l+3bt7Njxw4CAgIICQmhY8eOdO/encDAQI4dO0bbtm1RKpUMHz6czz77jCZNmhASEkJISAg1atQAwNTUlLVr13Ljxg0WLlzIypUrmT9/PgCff/45w4YNo3Tp0qr9Pv/88/eO8d69e+zfv5/ffvuNn376idWrV9O8eXMeP37M8ePHmTVrFmPHjuXcuXOqfd73uLt27WLPnj3s2bOH48ePM3PmTAAWLlyIt7c3PXv2VMVcpEiR3PzyvjeP0uW5culPnjzKSHwf3L1F4NUAKlXzASAlORlArfqnpaWFrq4eN64G5Hu87yslIQ4APSNTtfXBF4/x69hOHJrlz7U960hNTsyyb8D25fw6thO/zx9K0LlDKJXKfIn5v86jzNvfi5lFRrzgwpmTNGreOh+j/GeehTwhKuIF5SpVU60zNjHF3aMMt2789cb94uNisyQfBcWC2VPx9qlN5WpZE/LS5bw4eug3YqKjSU9P58jBfSQnJeNVqaoGIs2ZVx9ec2P5WP0nW2xxcXEsW7aMtWvX0rRpUwBWrlzJoUOHWL16NZ07d2bu3LkEBwfj7OxMeno6mzdvZuzYsQCcPHmSP//8k7CwMPT1M1oUc+bMYdeuXWzbto1evXoBGW21H3/8ERsbGwAuXbpEamoqbdu2VSVaZcuWVcVlaGhIUlIS9vb2avG+el4AV1dXhg8fzubNmxk5ciSGhoaYmJigo6Ojtt/7xpiens4PP/yAqakpnp6e1KtXj1u3brFv3z60tLQoWbIks2bN4ujRo1SrVi1Hx127di2mphm/oLt06cKRI0eYNm0a5ubm6OnpYWRklOVc81v7zt2Ij4+lb5c2aGlpk56eRpev/KnbqBkATi6u2NjZs27FYvoPH4u+gSG7t27gefgzIl8812jsb6JMT+fKrpVYu3lg7vD3PKkiFetgZGWLoZkV0SFBXPt1LS/DnuDd/RvVGM+mnbEpXg4dPX2e3brM5W3LSE1KoHjtlpo4lf+U9p27ER8XS98vXnsv9vSnbuNm2Y7//bdfMTQyokbtrJWagioq4gUAFpZWauvNLa1U2zILefKI/bs207X34LwOL8eOHNzH7ZuBfL9uc7bbJ86Yy6RvhvNJQx+0tXUwMDBg6rcLcCrinM+RfoCPN6/JNf/JCtK9e/dISUnBx+fvT2a6urpUrVqVwMBAvLy88PDwUFWRjh8/TlhYGJ9++ikAV65cITY2Fmtra0xMTFTLgwcPuHfvnuqYLi4uquQIoHz58jRo0ICyZcvy6aefsnLlSiIjI98Z788//4yPjw/29vaYmJgwduxYgoOD37rP+8bo6uqqSmIA7Ozs8PT0REtLS21dWFjYPzqug4OD6hg5kZSURExMjNqSnJSU4+O8ycmjBzl+aD/Dx01nwcpNDB49mZ0/r+fIb78AoKOjyzdT5vL08UM6tqhDe19vrl6+QKVqPgX2k9Hl7cuJCQmmateRauuL1miCfamKmDu64lypLpU7D+Hp1TPEPg9RjfFo3IFCRT2xcCpGyQbtKVG/LbeP7szvU/hPUr0Xx09nwapNDP5mMjs3r+fI/l+yHX9o327qNmqKnn7WeWT/Fi/Cw5g6qj/edRrSqEVbTYejJiw0hMVzZzJuykzVh8XMVi//jtiXL5m3ZBUrftzMZ527MnH0cO7dvZ3P0X68Zs6ciUKhYPDgwap1iYmJ+Pv7q34PtWvXjmfPnqntFxwcTPPmzTEyMsLW1pYRI0aQmpqao+f+T1aQ3kfnzp3ZtGkTX3/9NZs2baJJkyZYW1sDEBsbi4ODg2qO0OssLCxU/zc2Vp/cqq2tzaFDhzh9+jQHDx5k8eLFjBkzhnPnzuHm5pZtHGfOnKFz585MmjQJX19fzM3N2bx5M3Pnzn1r/O8bY+arQhQKRbbr0tPT//FxXx0jJ2bMmMGkSZPU1vUf9g0Dho/J8bGys2bZAtp37kbtBk0AcC3mTvizELZuXEODJhlVk+IlPVm0+mfiYl+SmpqCuYUVw/p0oXhJz1yJITdd3r6c0BvnqdN/BkYWhd461sq5JACxz0MwKeTwxjE3D/5MWmoK2joF9wqif4M1S7N5L4b+/73YVL2Cd/3KJZ4EBzFq4kxNhPrBLKwyfoZGRUZgaf33h8foyAhci6tPcI54Hs6EYb0pWbo8fYaOpaC5dfMGkRER9OzymWpdWloaVy5fZOfWn1i/7Vd2btnE2s27cCtWHIDiJUrx1+VL7Nr6E8NGT9BU6O+lIHwAPH/+PN9//z3lypVTWz9kyBD27t3L1q1bMTc3p3///rRt25ZTp04BGV+H5s2bY29vz+nTpwkJCaFr167o6uoyffr0937+/2SCVKxYMfT09Dh16pSq1ZWSksL58+dVWWqnTp0YO3YsFy9eZNu2bSxfvly1f8WKFQkNDUVHRwdXV9ccPbdCocDHxwcfHx/Gjx+Pi4sLO3fuZOjQoejp6ZGWlqY2/vTp07i4uDBmzN8JwauJ4q9kt98/ifFtcuu42cWcndGjRzN06FC1dcGR797vfSUlJWb5QaClpYUym2TO2CSjIvb08UPu3rpB5x79ci2Of0qpVBKw43ueXj1Dbf8ZGFu/u3UZ9eQ+AIZmlm8cE/30PrpGJpIc5YOkpEQUWpnei9rZvxcP7t1F8ZIeuBUvmV/h5Qo7h8JYWFlz9dKfqtjj42K5E3gN35btVeNehIcxYVhvirp74D9yglpFu6CoVKU6a35Sr67OnDwWZ1c3OnXtQWJixvy+7L6m6ekFf16fphOk2NhYOnfuzMqVK5k6dapqfXR0NKtXr2bTpk3Ur5/RXl6zZg0eHh6cPXuW6tWrc/DgQW7cuMHhw4exs7PDy8uLKVOmMGrUKCZOnPjeV5T/JxMkY2Nj+vbty4gRI7CyssLZ2ZnZs2cTHx9Pjx49gIwWUY0aNejRowdpaWm0bPn3J7iGDRvi7e1N69atmT17NiVKlODp06fs3buXNm3aULly5Wyf99y5cxw5coTGjRtja2vLuXPnCA8Px8PDQ/WcBw4c4NatW1hbW2Nubo67uzvBwcFs3ryZKlWqsHfvXnbuVP+mdHV15cGDBwQEBODk5ISpqekHx/guuXVcV1dXzp07R1BQECYmJlhZWWX7Q1BfXz9L+VovPv6DYs9OlRq12bJhNTZ2Dji7FuP+nZvs2rKBRs1aq8acPHoIcwtLbOzsCbp/h5WLv6VazbpUrFJwrpIK2L6MRxdP4N1jDLr6hiTGZLRudQ2M0NbTJ/Z5CI8uHcfeozJ6xqZEPw3ir12rKFSsNOaOGdXLp9f+JCk2EiuXUmjr6PLsdgA3D2+lRN02mjy1LOLj43j0Wov5yZPH3LoZiJm5OQ4OjkRHRxEaEqJq6QY9eACAdaFCFCpkk+0xC4IqNWqzZX2m9+LP6u9FyEgoTh07RA//odkfSMMSEuIJffJI9Tgs5CkP7t7CxNQMGzsHWrTrxLYNq3Eo7IytgyM/rVmGZSEbqtasC2QkR+OH9sLGzgG/PoOJif57GoKl1durovnJyNiYosXd1dYZGhpibm5B0eLupKamULiIM3NnTKbfoOGYmZtz8tjvXDh3hpnzl2go6o+Hv78/zZs3p2HDhmoJ0sWLF0lJSaFhw7/vfVaqVCmcnZ05c+YM1atX58yZM5QtWxY7OzvVGF9fX/r27cv169epUCH7+4ll9p9MkCCjr5menk6XLl14+fIllStX5sCBA1ha/v1punPnzvTr14+uXbti+Nr9bhQKBfv27WPMmDF069aN8PBw7O3tqV27ttoXJDMzMzNOnDjBggULiImJwcXFhblz56omivfs2ZNjx45RuXJlYmNjOXr0KC1btmTIkCH079+fpKQkmjdvzrhx45g4caLquO3atWPHjh3Uq1ePqKgo1qxZw5dffvlBMb7Lh557ZsOHD8fPzw9PT08SEhJ48OBBrla63lfvQaPYuHopy+ZPJzoyEqtCNjRp2Z4Ofr1UYyJehLN6yVyiIjNuWlfftwWfd+31lqPmv/unMm45cGLJN2rrK3UchGvVhmhp6xB2O4C7x38hNTkRQ4tCFC5Xg1KNP1eN1dLW5t7Jffy1azVKpRKTQg6Ua9UDt+q++Xou73Lj2jV6dv/7pohzZ2e0mT5p1ZrJ02Zy/OjvTBj79+vw9YiMRKJ3X3/6+Ge9b1JB0XvwKDauWsqyeZnei1+qv9dOHDmAUomqFVfQ3Lt1gwlDe6ser102D4C6vi0YMGoSrTv4kZiYwPJ504iLfUmpsl6Mm7kYvf/fk+vKxbOEPnlE6JNH9Pq8qdqxt/9+Mf9O5B/S0dFl9oJlfP/dfEYP9SchPoHCRYoweuI0qvsUnBuvvkluFpCSkpJIyjR3NLsPv69s3ryZS5cucf78+SzbQkND0dPTU5vSARlzZUNDQ1VjMv8+evX41Zj3oVDKNbziI3M7NPcqSAXJ2kuPNR1CnhjbyP3dgz5SjyMSNB1CnkhOzfl8wY9BIdN/74R2e7PcbYO7j/gt147V2fhslrmkEyZMUPug/8qjR4+oXLkyhw4dUs09ev0vL2zatIlu3bplSbiqVq1KvXr1mDVrFr169eLhw4ccOHBAtT0+Ph5jY2P27dunKkq8S8Fr7AohhBDiX2P06NFER0erLaNHj8527MWLFwkLC6NixYro6Oigo6PD8ePHWbRoETo6OtjZ2ZGcnExUVJTafs+ePVPdNsbe3j7LVW2vHufk1jKSIAkhhBBCjUKRe4u+vj5mZmZqy5vaaw0aNODq1asEBASolsqVK9O5c2fV/3V1dTly5Ihqn1u3bhEcHIz3/++e7+3tzdWrV9VuLXPo0CHMzMzw9Hz/q4//s3OQhBBCCJE9TV3FZmpqSpkyZdTWGRsbY21trVrfo0cPhg4dipWVFWZmZgwYMABvb2+qV68OQOPGjfH09KRLly7Mnj2b0NBQxo4di7+//xsTs+xIgiSEEEKIj8b8+fPR0tKiXbt2JCUl4evry9KlS1XbtbW12bNnD3379sXb2xtjY2P8/PyYPHlyjp5HJmmLj45M0v64yCTtj49M0v745PYk7VJfH3j3oPd0c2bBuhL2fUkFSQghhBBqtLQ0e6PIgkAmaQshhBBCZCIVJCGEEEKoKQB/ik3jJEESQgghhBpN/y22gkBabEIIIYQQmUgFSQghhBBqpIAkCZIQQgghMpEWm7TYhBBCCCGykAqSEEIIIdRIBUkSJCGEEEJkIvmRtNiEEEIIIbKQCpIQQggh1EiLTRIkIYQQQmQi+ZG02IQQQgghspAKkhBCCCHUSItNEiQhhBBCZCL5kbTYhBBCCCGykAqSEEIIIdRIi00SJCGEEEJkIvmRtNiEEEIIIbKQCpIQQggh1EiLTSpIQgghhBBZSAVJfHScrA01HUKeGNvIXdMh5Il7z+I0HUKecbL6d74XDfW0NR1CnkhXKjUdwkdDCkiSIAkhhBAiE2mxSYtNCCGEECILqSAJIYQQQo0UkCRBEkIIIUQm0mKTFpsQQgghRBZSQRJCCCGEGikgSYIkhBBCiEykxSYtNiGEEEKILKSCJIQQQgg1UkGSBEkIIYQQmUh+JC02IYQQQogspIIkhBBCCDXSYpMESQghhBCZSH4kLTYhhBBCiCykgiSEEEIINdJikwRJCCGEEJlIfiQtNiGEEEKILKSCJIQQQgg1WlJCkgRJCCGEEOokP5IWmxBCCCFEFlJBEkIIIYQauYpNEiQhhBBCZKIl+ZG02IQQQgghMpMKkhBCCCHUSIutgFWQjh07hkKhICoqStOhqOR2TEFBQSgUCgICAnLleJoyceJEvLy8NB2GEEKIPKBQ5N7ysSpQCVJuUSgU7Nq1K1eOVaNGDUJCQjA3N8+V432Msns9hw8fzpEjRzQTUC67eOE8g/z70KheLSqUKcXRI4dV21JSUlg4bw6ftvkE7yoVaFSvFmNHjyIs7JkGI34/bzsvgCOHDtK3Z3fq+lSjQplS3LoZqKFI3+7GX5eYMWYwPT/zpX2DSvx58qjadqVSyeY1y/jq08Z0alqDSSP6EvI4OMtxLp79g6/9u9KpaQ38WtVl1rih+XUK72Xd6hV06/wZ9X0q07R+TUYO6c/DoAdqYx4/CmbU0AE0qedD/ZpVGDNyCC9ePNdQxP/Ms2fPGD1qOLVrVKNqxXK0a/0J169d1XRYOfJv/dkhMhSoBCk5OVnTIahJSUlBT08Pe3t7KTdmYmJigrW1tabDyBUJCQmUKFmK0WPGZ9mWmJhI4I0b9Ozdj5+2bGfugsU8DHrA4P79NBBpzrztvF5t96pYiYFDhudzZDmTmJCAa7ESfDVwVLbbd21ex76dm+k1+Bumf7cOfQNDpnzdn+TkJNWYsyeOsHjmeOo1acmcFT8xdeEP1GrQJL9O4b1cvnSBdp93ZNWPP7Fo2SpSU1MZ1PcrEhLiAUhIiGdQv56gUPDdijWsWLORlJQURgzyJz09XcPR50xMdDRfftERHR1dlixfyY5f9jJsxCjMzD6uD6L/1p8dAIpc/Pex0miCVLduXfr378/gwYMpVKgQvr6+AFy8eJHKlStjZGREjRo1uHXrltp+u3fvpmLFihgYGFC0aFEmTZpEamoqAK6urgC0adMGhUKhegywbNkyihUrhp6eHiVLlmT9+vVqx1UoFCxbtoyWLVtibGzMtGnTsm2xnTp1irp162JkZISlpSW+vr5ERkYC8Ntvv1GzZk0sLCywtramRYsW3Lt374Nfo3379lGiRAkMDQ2pV68ea9euVYsnu1bXggUL1M4bYNWqVXh4eGBgYECpUqVYunSpaltycjL9+/fHwcEBAwMDXFxcmDFjxltfz8zPm56ezuTJk3FyckJfXx8vLy9+++031fZXrcUdO3ZQr149jIyMKF++PGfOnPng1ya31KxVG/+Bg6nfsFGWbaampixf9QONmzTF1a0o5cp78fU34wi8cZ2QkKcaiPb9ve28AFq0bEXvvv5U9/bO58hypmI1Hzp270e1mvWzbFMqlezdsYl2X/Sgqk9dXIu5M2DUJCKfh/PnyWMApKWl8sOSOXTpNQjfT9rjWMSFIq5FqVG3cT6fydstWLKCFi3bULSYO+4lSzFu0nRCQ0O4eeMGAH8FXCbk6RPGT5pOcfcSFHcvwfjJMwi8cY0Lf57VcPQ588PqldjZ2zNl2gzKliuHk1MRavjUpIizs6ZDy5F/688OyLiKLbeWj5XGK0jr1q1DT0+PU6dOsXz5cgDGjBnD3LlzuXDhAjo6OnTv3l01/o8//qBr164MGjSIGzdu8P3337N27VqmTZsGwPnz5wFYs2YNISEhqsc7d+5k0KBBDBs2jGvXrtG7d2+6devG0aPq5fqJEyfSpk0brl69qva8rwQEBNCgQQM8PT05c+YMJ0+e5JNPPiEtLQ2AuLg4hg4dyoULFzhy5AhaWlq0adPmgz7hPXr0iLZt2/LJJ58QEBDAV199xddff53j42zcuJHx48czbdo0AgMDmT59OuPGjWPdunUALFq0iF9++YUtW7Zw69YtNm7cqEqE3vR6ZrZw4ULmzp3LnDlz+Ouvv/D19aVly5bcuXNHbdyYMWMYPnw4AQEBlChRgo4dO6qS24/Fy9iXKBQKTE3NNB3Kf15YyBOiIl5QrmI11TpjE1PcPcpw+8ZfANy/c5OI52EotLQY3rsTX33amKlfDyD4wV1Nhf1eYmNfAmD2//Z+cnIyCoUCXT091Rg9fX20tLS4EnBJIzF+qONHf6d06TIMHzKQurW8+axda7Zv3aLpsPKc/Oz4uGj8KjZ3d3dmz54NQEhICADTpk2jTp06AHz99dc0b96cxMREDAwMmDRpEl9//TV+fn4AFC1alClTpjBy5EgmTJiAjY0NABYWFtjb26ueZ86cOXz55Zf065dR3hw6dChnz55lzpw51KtXTzWuU6dOdOvWTfX4/v37avHOnj2bypUrq1VgSpcurfp/u3bt1Mb/8MMP2NjYcOPGDcqUKZOj1+ZVxWvu3LkAlCxZkqtXrzJr1qwcHWfChAnMnTuXtm3bAuDm5qZKLv38/AgODsbd3Z2aNWuiUChwcXFR7fum1zOzOXPmMGrUKDp06ADArFmzOHr0KAsWLGDJkiWqccOHD6d58+YATJo0idKlS3P37l1KlSqVo3PSlKSkJBbNn0OTZs0xMTHRdDj/eZGRLwCwsLRSW29uaUXU/7c9e/oEgC3rvufLvkOxsXfk163rmTC0F4vW7cS0ALZ10tPTWTBnJuW8KlKsuDsAZcqWx8DQkCUL59K3/2CUKFmycB5paWm8eB6u4Yhz5vHjR2z5+Se6+HWjR68+XL96lVkzpqKrq0vL1m00HV6e+Nh+dsi0kgJQQapUqVKWdeXKlVP938HBAYCwsDAArly5wuTJkzExMVEtPXv2JCQkhPj4+Dc+T2BgID4+PmrrfHx8CAxUn5hauXLlt8b7qoL0Jnfu3KFjx44ULVoUMzMzVSUmODjrpNF3CQwMpFq1amrrvHPYDomLi+PevXv06NFD7TWbOnWqqvX35ZdfEhAQQMmSJRk4cCAHDx7M0XPExMTw9OnT93p93/a1zU5SUhIxMTFqS1JS0hvH56WUlBRGDhuMUgnfjJuokRhEzimVGdXbdp17UL12A4qV8MB/xEQUCgVnjh9+x96a8e2MKdy7e4epM+eo1llaWTF99nxOnjhGPZ/KNKxVjdjYl5T08ESh0PiP8hxJT1fi4VmagYOH4uHhSfvPPqdt+8/YumWzpkPLEx/jzw65iq0AVJCMjY2zrNPV1VX9/1UW+6pFFRsby6RJk1TVkNcZGBjkSTyvMzQ0fOv2Tz75BBcXF1auXImjoyPp6emUKVMmzyaga2lpoVQq1dalpKSo/h8bGwvAypUrsyRb2traAFSsWJEHDx6wf/9+Dh8+zGeffUbDhg3Ztm1brsf7tq9tdmbMmMGkSZPU1n0zdjxjxk/M9djeJiUlhVHDhhDy9Ckrflj7UXwC/C+wtMy4UCAqMgJLaxvV+ujICFyLlcgYY1UIACcXN9V2XT09bB0K8zwsNB+jfT9zZk7l1B/HWb76R2zt1Ku21bx92P7rAaIiI9HW0cbU1IxmDWtR2LephqL9MDY2NhQtVkxtXdGiRTl86ICGIso78rPj4/Vxfewg45f5rVu3KF68eJZFSyvjdHR1dVVzgl7x8PDg1KlTautOnTqFp6dnjp6/XLlyb7y8/cWLF9y6dYuxY8fSoEEDPDw8VJO3P4SHhwd//vmn2rqzZ9UnY9rY2BAaGqqWJL1+jyU7OzscHR25f/9+ltfLze3vXxhmZmZ8/vnnrFy5kp9//pnt27cTEREBZP96vs7MzAxHR8dceX0zGz16NNHR0WrL8FGj/9Exc+rVD7jg4IcsX7UGCwvLfH1+8Wa2DoWxsLLm6qW/v0/i42K5E3iNEp4Z1cqiJTzQ1dXj6aOHqjGpqSmEh4ZgY+eQ7zG/iVKpZM7MqRz//TDfff8DjoWd3jjWwtISU1MzLvx5lsiICGrVyTqBvSDzqlCRoAfqtzB4GBSEo2NhDUWUNz7mnx1aCkWuLR8rjVeQcmr8+PG0aNECZ2dn2rdvnzFB8coVrl27xtSpU4GMK6+OHDmCj48P+vr6WFpaMmLECD777DMqVKhAw4YN+fXXX9mxYweHD+esxD569GjKli1Lv3796NOnD3p6ehw9epRPP/0UKysrrK2tWbFiBQ4ODgQHB3/QpOpX+vTpw9y5cxkxYgRfffUVFy9eZO3atWpj6tatS3h4OLNnz6Z9+/b89ttv7N+/HzOzvycBTpo0iYEDB2Jubk6TJk1ISkriwoULREZGMnToUObNm4eDgwMVKlRAS0uLrVu3Ym9vj4WFxRtfz8xGjBjBhAkTKFasGF5eXqxZs4aAgAA2btz4wecPoK+vj76+vtq6+BTlG0Z/mPj4OB691gJ98uQxt24GYmZuTqFCNowYOoibN26wcMly0tPTeP7/+R7m5ubo6uq96bAa97bzcnBwJDo6itCQEFWL89UvLOtChShUyCbbY2pCQkI8oU8eqR4/C33Kg7u3MDE1w8bOgeZtO7F942ocnJyxtXdk85plWBayoWrNugAYGZvQ+JN2/Lzue6xt7bCxc+CXn38EwLtOQ02cUra+nTGFg/v3Mnv+dxgbG6vmFRmbmKqq43t278DVrRgWlpZc/SuA+d/OoEPnrri4ur3t0AXOF1398PuiI6tWLKexb1OuXf2Lbdu2MH7iZE2HliP/1p8d8HG3xnLLR5cg+fr6smfPHiZPnsysWbPQ1dWlVKlSfPXVV6oxc+fOZejQoaxcuZLChQsTFBRE69atWbhwIXPmzGHQoEG4ubmxZs0a6tatm6PnL1GiBAcPHuSbb76hatWqGBoaUq1aNTp27IiWlhabN29m4MCBlClThpIlS7Jo0aIcP8crzs7ObN++nSFDhrB48WKqVq3K9OnT1a6u8/DwYOnSpUyfPp0pU6bQrl07hg8fzooVK1RjvvrqK4yMjPj2228ZMWIExsbGlC1blsGDBwMZl6POnj2bO3fuoK2tTZUqVdi3b5+qIpfd65nZwIEDiY6OZtiwYYSFheHp6ckvv/yCu7v7B517frpx7Ro9u/upHs+dPROAT1q1pk+//hw/+jsAHdq3Vttv5Q/rqFxVvW1ZkLztvCZPm8nxo78zYew3qu1fj8i4cWLvvv708R+Qv8G+xb1bN5g4rLfq8bpl8wCo27gF/UdNonUHP5ISE/h+3jTiYl9SqqwXY2csRk/v78S6S+9BaGlrs3jGeJKTk3AvVYaJc5djUoCuJtqxNWP+Tb+efmrrx06aRouWGROXHwYFsXTxfGKio3FwLMyXPXrT8Qu/LMcq6MqULce8hd+xaME8vl+2hMJOTowc9Q3NW7TUdGg58m/92SEyKJSZJ7CIAu3YsWPUq1ePyMhIVYXnvya3K0gib917FqfpEPKMk9Xb5yR+rAz1tDUdQp5I/xf/ujPSzd2ST/s1uXfriG3dKubasfLTR1dBEkIIIUTekhbbRzhJ+9+kT58+apfev7706dNH0+EJIYQQ/1mSIGnQ5MmTCQgIyHaZPDn7yYp169ZFqVT+Z9trQggh8p6mrmJbtmwZ5cqVw8zMDDMzM7y9vdm/f79qe2JiIv7+/lhbW2NiYkK7du149kz9DwAHBwfTvHlzjIyMsLW1ZcSIER/0FxukxaZBtra22NraajoMIYQQQo2mOmxOTk7MnDkTd3d3lEol69ato1WrVly+fJnSpUszZMgQ9u7dy9atWzE3N6d///60bdtWdZuZtLQ0mjdvjr29PadPnyYkJISuXbuiq6vL9OnTcxSLTNIWHx2ZpP1xkUnaHx+ZpP3xye1J2h3WXc61Y232q/CP9reysuLbb7+lffv22NjYsGnTJtq3bw/AzZs38fDw4MyZM1SvXp39+/fTokULnj59ip2dHQDLly9n1KhRhIeHo6f3/rdXkBabEEIIIdQoFIpcWz5UWloamzdvJi4uDm9vby5evEhKSgoNG/59/7JSpUrh7OzMmTNnADhz5gxly5ZVJUeQcXugmJgYrl+/nqPnlxabEEIIIdRo5WJBKikpKcvf0MzuJsCvXL16FW9vbxITEzExMWHnzp14enoSEBCAnp5eljm4dnZ2hIZm/Nmg0NBQteTo1fZX23JCKkhCCCGEyDMzZszA3NxcbZkxY8Ybx5csWZKAgADOnTtH37598fPz48aNG/kYcQapIAkhhBBCzT9pjWU2evRohg4dqrbuTdUjAD09PYoXLw5ApUqVOH/+PAsXLuTzzz8nOTmZqKgotSrSs2fPsLfP+MPO9vb2Wf6G6aur3F6NeV9SQRJCCCGEGoUi9xZ9fX3VZfuvlrclSJmlp6eTlJREpUqV0NXVVfuD8bdu3SI4OBhvb28AvL29uXr1qupvTAIcOnQIMzOzHP/xdKkgCSGEEKJAGD16NE2bNsXZ2ZmXL1+yadMmjh07xoEDBzA3N6dHjx4MHToUKysrzMzMGDBgAN7e3lSvXh2Axo0b4+npSZcuXZg9ezahoaGMHTsWf3//HCVlIAmSEEIIITLJzRZbToSFhdG1a1dCQkIwNzenXLlyHDhwgEaNGgEwf/58tLS0aNeuHUlJSfj6+rJ06VLV/tra2uzZs4e+ffvi7e2NsbExfn5+b7z58tvIfZDER0fug/RxkfsgfXzkPkgfn9y+D9KXP/2Va8da27Fcrh0rP8kcJCGEEEKITD4oQfrjjz/44osv8Pb25smTJwCsX7+ekydP5mpwQgghhMh/BeFGkZqW4wRp+/bt+Pr6YmhoyOXLl1U3f4qOjs7x3zkRQgghRMGjyMXlY5XjBGnq1KksX76clStXoqurq1rv4+PDpUuXcjU4IYQQQghNyPFVbLdu3aJ27dpZ1pubmxMVFZUbMQkhhBBCg7Q+4tZYbslxBcne3p67d+9mWX/y5EmKFi2aK0EJIYQQQnNy80aRH6scJ0g9e/Zk0KBBnDt3DoVCwdOnT9m4cSPDhw+nb9++eRGjEEIIIUS+ynGL7euvvyY9PZ0GDRoQHx9P7dq10dfXZ/jw4QwYMCAvYhRCCCFEPvqYrz7LLR98o8jk5GTu3r1LbGwsnp6emJiY5HZsQmRLbhT5cZEbRX585EaRH5/cvlFk723Xc+1Y37cvnWvHyk8f/KdG9PT0cvyH34QQQgghPgY5TpDq1av31tLb77///o8CEkIIIYRmyVVsH5AgeXl5qT1OSUkhICCAa9eu4efnl1txCSGEEEJDJD/6gARp/vz52a6fOHEisbGx/zggIYQQQghNy7U/VvvFF1/www8/5NbhhBBCCKEh8rfY/sEk7czOnDmDgYFBbh1OiDdadS5I0yHkiRqFrTUdQp6wM9fXdAh5xtFnkKZDyBOLlo/QdAh5wsvGQtMh5JkqRc1z9Xi5Vj35iOU4QWrbtq3aY6VSSUhICBcuXGDcuHG5FpgQQgghhKbkOEEyN1fPUrW0tChZsiSTJ0+mcePGuRaYEEIIITTjY26N5ZYcJUhpaWl069aNsmXLYmlpmVcxCSGEEEKDtCQ/ylmbUVtbm8aNGxMVFZVH4QghhBBCaF6O52GVKVOG+/fv50UsQgghhCgAtBS5t3yscpwgTZ06leHDh7Nnzx5CQkKIiYlRW4QQQgjxcZPL/HMwB2ny5MkMGzaMZs2aAdCyZUu1E1cqlSgUCtLS0nI/SiGEEEKIfPTeCdKkSZPo06cPR48ezct4hBBCCKFhH3NrLLe8d4KkVCoBqFOnTp4FI4QQQgjN+4g7Y7kmR3OQPuZeohBCCCHE+8rRfZBKlCjxziQpIiLiHwUkhBBCCM3SkoJIzhKkSZMmZbmTthBCCCH+XeRvseUwQerQoQO2trZ5FYsQQgghRIHw3gmSzD8SQggh/hvkV/4HXMUmhBBCiH83mYOUgwQpPT09L+MQQgghhCgwcjQHSQghhBD/flJAkgRJCCGEEJnInbTlSj4hhBBCiCykgiSEEEIINTJJWxIkIYQQQmQi+ZG02IQQQgghspAKkhBCCCHUyCRtSZCEEEIIkYkCyZCkxSaEEEIIkYlUkMR/ysW9m7l/6RSRIY/R0dPDvpgn3p92x9K+CACJsS/5c/d6Hl2/yMuIcAxNzXGr4E211n7oGxlnOV5ibAybJ/YjLvI5Xy3ehr6RSX6fEgCBVy+xd9t6Hty5SVTEc4aM/5bKNeoCkJqaytZ1ywg4f4rwkCcYGptQpkJVOnTvj6W1jdpxLp87yc5Nqwh+cBddPT08ylZk6IQ5Gjijt3se9oyVSxfw55mTJCUm4uhUhBFjp1DSozQA61Yt5dih3wgPC0VHVxf3kp507zMAj9LlNBz538b0bsbYPs3U1t16EIpX26kALB7TgfrVSuJgY05sQhJnrzxg7MLd3A56phpfydOZKQNbUcGzCEolXLj2kDELd3H19pN8PZfXnfv1J25fOEVEyCN0dPUo7O5J7c+/wsqhiGrMlaN7CTxzlLCguyQnxtN/2Q4MjNW/dxJiY/h9/RLuXT6HQkuBe+Wa1P+iH3oGhvl9Sio3r15i77YNPLib8X02eNxs1fcZwPYNKzh7/BAR4c/Q1tXFrXgpPvXrS/FSZVRjYl9G8+PSOVw6dxItLQVVfOrRpc8wDAyNNHBGbyYtNkmQ/rNSUlLQ1dXVdBj57untq5Sp9wm2biVQpqdzdvsafpk7hk5TV6Crb0Bc1Aviol5Q47OeWDk68/JFGMfWLyY+KoIm/cZmOd7va+Zj7eRGXORzDZzN35ISE3B2K0Gdxi1ZMGWk2rbkpESC7t6kTaceOLu5Exf7kvXL5zJ34jCmLv5RNe7Pk7+zasE0PuvWj9LlK5OWlsbjh/fy+1Te6WVMDIN6++FVqQoz5i3F3NKSJ4+CMTU1U41xKuJC/2Hf4FDYieSkRLZvXs+oQX34ceseLCytNBi9uut3n9K8z2LV49S0v/+k0+XAR2zef55HIZFYmRsxpk9z9iz1p1SLCaSnKzE21GP3En/2Hr/KoBk/o6Otxbi+zflliT/uTceSmqqZPw/16OZVKjRsib1bCdLT0/hj6xq2zh5Nt5kr0dPPSG5Sk5JwK1sZt7KV+WPrD9keZ+/ymcRFRfDpqBmkpabx26o5HPxhAS36jc7P01GTlJiIc1F3ajf+hIVTR2XZ7lDYGb9+I7C1L0xyciL7d/7ErDEDmLt6B2YWlgAsnT2eqIjnfD19MWmpqayYP4XVi6bjP2pqfp/OW0mCJC22j8q2bdsoW7YshoaGWFtb07BhQ+Li4jh//jyNGjWiUKFCmJubU6dOHS5duqS2r0KhYNmyZbRs2RJjY2OmTZsGwK+//kqVKlUwMDCgUKFCtGnTRrXP+vXrqVy5Mqamptjb29OpUyfCwsJU2yMjI+ncuTM2NjYYGhri7u7OmjVrAAgKCkKhULBlyxZq1aqFoaEhVapU4fbt25w/f57KlStjYmJC06ZNCQ8Pz4dXL8MnQ6bhUbMx1oVdKVSkKA16DCM2IozwoDsAWDu50tR/HG5e1TG3dcTJw4vqbfx4cOUc6Wlpase6dnQPSQmxVPBtl2/xv4lXFR8++7IvVXzqZdlmZGzC6BlLqF67EY5FXHH3KItfvxE8uBPI87BQANLSUvlx+Vw6fTWQhs3b4eDkgpNLUarXbpTfp/JOmzf8gI2dHSPGTqFU6bI4ODpRuVoNHJ3+rlA08G1OparVcSzshGvR4vQZNIL4uFju372twcizSk1L59mLl6rlRVScatsPO05x6tI9gkMiCLj5mElLfqWIgxUujtYAlHSzx9rCmCnL9nDnYRiB90OZ9v1+7AuZ4eyguSSw/YjplKnVmEJOrtg6F6Npz+G8fBHGswd3VGMqNWlLtU864FDcI9tjvHgSTNBfF/DtPhSHYh44lSxDgy7+3Dx3jNjIF/l1KlmUr1KDT/2y/z4DqFGvCWUqVMXWoTBOLsXo3HMwCfFxBP//3J8EP+CvC2f4atAYipcqQ8kyXnTtO5yzxw8R+SL/fg6K9yMJ0kciJCSEjh070r17dwIDAzl27Bht27ZFqVTy8uVL/Pz8OHnyJGfPnsXd3Z1mzZrx8uVLtWNMnDiRNm3acPXqVbp3787evXtp06YNzZo14/Llyxw5coSqVauqxqekpDBlyhSuXLnCrl27CAoK4ssvv1RtHzduHDdu3GD//v0EBgaybNkyChUqpPacEyZMYOzYsVy6dAkdHR06derEyJEjWbhwIX/88Qd3795l/PjxefravU1SfDwA+sambxyTnBCHnoERWtraqnURTx9y/teNNOwxAsVHeMOQhLhYFAoFRv9vawTdvUXk8zAUWgq+8e+Mf8cmzBo7kEdBdzUcaVZn/jhGiVKlmfzNMNo3q0Pvrp+xd/e2N45PSUlh765tGJuYUsy9ZP4F+h6KO9tw/+A0bvw6kTXT/Chib5ntOCMDPbq2rM6Dx895HBoJwO2gZzyPjMWvdQ10dbQx0Nfly9beBN4P4eHTiPw8jbdKSshI+gxM3vw9ltnTuzfQNzLBvmgJ1TqX0hVRKBSE3AvM9RjzQmpKCkf378LI2ASX/5/H3cCrGJmYUrSEp2pcmQpVUCi0uHvzmqZCzZZCoci15WMlLbaPREhICKmpqbRt2xYXFxcAypYtC0D9+vXVxq5YsQILCwuOHz9OixYtVOs7depEt27dVI87dOhAhw4dmDRpkmpd+fLlVf/v3r276v9FixZl0aJFVKlShdjYWExMTAgODqZChQpUrlwZAFdX1yxxDx8+HF9fXwAGDRpEx44dOXLkCD4+PgD06NGDtWvXfshL8o8p09M5uXk5DsU9sXZyzXZMwstozv/6E6XrNFWtS0tJ5uD3M6nx6VeYWtsSEx6STxHnjuTkJH764Tu86zZWJUhhIRlzVrZvWMkXvYZgY+fA3u0bmTqyD3NXb8fE1FyTIasJefqYX3duoX2HLnT0+4pbgddZMm8Wujq6NG7eSjXu7MnjTB0/kqTERKysbZi18HvMLbJPQDTh/LUgeo3fwO2Hz7AvZM6Y3k05/MMQKrWfRmx8EgC9Pq3FtMGtMTHS59aDUJr3/Y6U1IxKZmx8Er49F7JlXi9G92wCwN3gMFr6LyEtTTPttcyU6ekc3bCcwu6lsXFye+/94qIjMTKzUFunpa2NgbEpcdGRuRxl7rp87g++mzmW5KRELKwKMWrad5iaWwAQFfkCM3P196C2tg4mpmZEa7Aylh1psUkF6aNRvnx5GjRoQNmyZfn0009ZuXIlkZEZPyiePXtGz549cXd3x9zcHDMzM2JjYwkODlY7xqtE5pWAgAAaNGjwxue8ePEin3zyCc7OzpiamlKnTh0A1XH79u3L5s2b8fLyYuTIkZw+fTrLMcqV+3tSrJ2dHfB3Yvdq3ettu8ySkpKIiYlRW1KTk944PieOb1xCxJMgGvfOfk5DckIcexaOx8rRmSotv1CtP7N9DZYOzpT0fvNrV1ClpqayeNpoUCrp1v9r1fp0ZcYv1NYdulG1Zn3c3D3oPXQ8CoWCcyeOaCrcbCnT03Ev4UGPvoNwL+lBi9btadaqHb/u2qo2rnylKny/bisLV/xIleo+TB07nMiIgvNL6OCpG+w4fJlrd55y+Ewgrfsvw9zEkHaNK6rGbN5/nuodZ9Kwx3zuBIezYVZ39PUyPtca6OuyfEJnzly5T52uc6jfbR437oWwY1FfDPQLxvzCwz9+x/MnQbTw/0bToeQbj/KVmbZkAxPmrqJcpep8N2M00VEFp6In3p8kSB8JbW1tDh06xP79+/H09GTx4sWULFmSBw8e4OfnR0BAAAsXLuT06dMEBARgbW1NcnKy2jGMjdWvwjI0fPPVIHFxcfj6+mJmZsbGjRs5f/48O3fuBFAdt2nTpjx8+JAhQ4bw9OlTGjRowPDhw9WO8/pE8Fel1szr0tPf/Gl3xowZmJubqy2HNix720v1Xk5sXMLDK+doPWI2JlY2WbYnJ8Tz6/yx6BkY0rT/eLR1/i62Pr55hXsX/mBpz2Ys7dmM3XMyEqzVgz7j3K71/zi2vJKamsri6aN5HhbK1zO+U1WPACysMlqjhZ2Lqtbp6ulha1+YF+Gh+R7r21gVssHFrajaOmdXN8JC1eM0NDSicBFnPMuUZ/iYSWhr67D/1535GWqORMcmcDc4jGJF/n4/xsQmci84nFOX7tFp+CpKutnRqn5GlffzppVxdrSi14QNXLwRzJ9Xg/AbvRbXwtZ8UlfzV+sd/vE77gec5bPRszHN5nvsbYzNLYmPiVJbl56WRmLcS4zNC04VMDsGBobYOxahuEdZeg4Zh5a2DscP/AKAhaU1MZkqYGlpqcS+jMHc0loT4b6RQpF7y8dKWmwfEYVCgY+PDz4+PowfPx4XFxd27tzJqVOnWLp0Kc2aZVwy/OjRI54/f/dVVeXKlePIkSNqbbdXbt68yYsXL5g5cyZFimRMfr1w4UKWcTY2Nvj5+eHn50etWrUYMWIEc+bk3mXho0ePZujQoWrrVl14+sHHUyqV/LFpKfcvnab1yNmY2dhnGZOcEMcv88agratLswET0dHVU9vetN9YUl9LPsOCbvP7mnm0HTUHM1vHD44tL71KjkKfBDNm1nJMM7Uv3IqXQldXj5DHDylZxku1T/izEArZZn2NNKl0WS8eBQeprXsc/BA7e4e37peuTCclJfmtYzTJ2FAPN6dChO79M9vtCoUCBQr0dDN+bBsZ6JGerkSpVKrGpCuVKJWa/UOjSqWSI+uXcPfiKT4fPQcLm7d/XbLjWNyTpPhYQh/cxt4tY/5O8I3LKJVKHIplP7G7oFKm//2+K+5RlvjYlzy4E4ibe8Z53Ai4gFKZrnYrgIJA/litJEgfjXPnznHkyBEaN26Mra0t586dIzw8HA8PD9zd3VVXnMXExDBixIi3VodemTBhAg0aNKBYsWJ06NCB1NRU9u3bx6hRo3B2dkZPT4/FixfTp08frl27xpQpU9T2Hz9+PJUqVaJ06dIkJSWxZ88ePDxy94eXvr4++vr6aut09D68TXJiwxJunztKswET0DUwJC46o/Stb2iMjp6+KjlKTU6kUc+RJCfGk5yYMZHb0NQcLS1tzDMlQYmx0QBYOjpr7D5IiQnxhD59pHocHvqUoHu3MDE1x8KqEAunjiLo7k2GT55PenoaUREZCbSJqTk6uroYGZvQoHlbtm1YgZWNHYVs7dm7bQMA1Wo11Mg5vUm7Dl0Y1Ksrm9aupE4DX27euMq+3dsY8vUEABIS4tm0diXetepibW1DdHQUu7dt5nl4GHXqN9Zw9H+bMaQNe09cJfhpBI625ozt05y09HS2/HYR18LWtPetxJEzgTyPjKWwnQXDujUmISmFAyevA3Dk7E2mD27NgtGfsWzzcbQUCoZ3a0xqWhrHL2juar3D6xZz8+xRWg+ehJ6BIXH/by/pGRmjq5fxvRwXFUFcdCRRzzI+7Dx//AA9AyNMrW0wNDHDurAzruUqc/CHBTT6ciDpaWkc+XEJparVxUSDlZbEhHiePX2sehz+7CkP793G2NQMEzNzdm9eQ6VqtbCwKsTLmCgO/bqNyBfhVKuV0Y4v7OxGucrerFo4ne4DviYtNZV1y76lep1GWe5JJjRPEqSPhJmZGSdOnGDBggXExMTg4uLC3Llzadq0Kfb29vTq1YuKFStSpEgRpk+fnqXVlZ26deuydetWpkyZwsyZMzEzM6N27dpARmVo7dq1fPPNNyxatIiKFSsyZ84cWrZsqdpfT0+P0aNHExQUhKGhIbVq1WLz5s159hrkhmvH9gCwa7b6vYLqdxuKR83GhD+8y7P7NwHYMLq72pgus9ZiVqhgVVNeuX87kGmj+qgeb1gxH4BaDZvT7oteXDp7AoBv+nVW22/MrOV4lq8EQMevBqGlrc2ybyeQnJxE8ZKlGTNzKcav3V+oICjlWYZJM+ezatlC1q/5HgeHwvQdPJIGvs0B0NbS5tHDIA7uG0ZMdCRm5haU8CjN/GVrcS1aXMPR/62wnQU/zuiGlbkRzyNjOR1wnzpd5/I8MhZdHW18KhSjf6e6WJoZEfbiJScv3aXel3MJj4wFMq5iazfoe8b0bsqxdcNIT1dy5eZjWvkvJfR5jMbO68rvGd9jP09X/xnUpOdwytTKSFADft/DmV0bVNs2TxuWZUzzPl9z5MclbJk1CoVCQYnKtajfpV9+nMIb3b8TyPRRfVWPN65YAGR8n3Ub8DUhj4JYeHgvL6OjMDEzp2gJT8Z+uwInl2KqffqNnMy6pd8yY7Q/CoWCKj716dp3WH6fyjvJJG1QKF+vzwrxEVh08oGmQ8gTNQoXrDkIucXOXP/dgz5SJRoUvF9suWHR8hGaDiFPeNlYaDqEPFOlaO5eabr4VO79nB3g8/5XMBYkMklbCCGEECITabEJIYQQQo0W0mOTBEkIIYQQauQiNmmxCSGEEEJkIRUkIYQQQqiRq9gkQRJCCCFEJnKjSGmxCSGEEEJkIRUkIYQQQqiRApIkSEIIIYTIRFps0mITQgghhMhCKkhCCCGEUCMFJKkgCSGEECITrVxccmLGjBlUqVIFU1NTbG1tad26Nbdu3VIbk5iYiL+/P9bW1piYmNCuXTuePXumNiY4OJjmzZtjZGSEra0tI0aMIDU1NcevgRBCCCGExh0/fhx/f3/Onj3LoUOHSElJoXHjxsTFxanGDBkyhF9//ZWtW7dy/Phxnj59Stu2bVXb09LSaN68OcnJyZw+fZp169axdu1axo8fn6NYpMUmhBBCCDUKDfXYfvvtN7XHa9euxdbWlosXL1K7dm2io6NZvXo1mzZton79+gCsWbMGDw8Pzp49S/Xq1Tl48CA3btzg8OHD2NnZ4eXlxZQpUxg1ahQTJ05ET0/vvWKRCpIQQggh1ChycUlKSiImJkZtSUpKeq84oqOjAbCysgLg4sWLpKSk0LBhQ9WYUqVK4ezszJkzZwA4c+YMZcuWxc7OTjXG19eXmJgYrl+//t6vgSRIQgghhMgzM2bMwNzcXG2ZMWPGO/dLT09n8ODB+Pj4UKZMGQBCQ0PR09PDwsJCbaydnR2hoaGqMa8nR6+2v9r2vqTFJoQQQgg1uXkfpNGjRzN06FC1dfr6+u/cz9/fn2vXrnHy5MlciyUnJEESQgghhJrcnIGkr6//XgnR6/r378+ePXs4ceIETk5OqvX29vYkJycTFRWlVkV69uwZ9vb2qjF//vmn2vFeXeX2asz7kBabEEIIIQoEpVJJ//792blzJ7///jtubm5q2ytVqoSuri5HjhxRrbt16xbBwcF4e3sD4O3tzdWrVwkLC1ONOXToEGZmZnh6er53LFJBEkIIIYQaTd0o0t/fn02bNrF7925MTU1Vc4bMzc0xNDTE3NycHj16MHToUKysrDAzM2PAgAF4e3tTvXp1ABo3boynpyddunRh9uzZhIaGMnbsWPz9/XNUyZIESQghhBBqNHWZ/7JlywCoW7eu2vo1a9bw5ZdfAjB//ny0tLRo164dSUlJ+Pr6snTpUtVYbW1t9uzZQ9++ffH29sbY2Bg/Pz8mT56co1gkQRJCCCFEgaBUKt85xsDAgCVLlrBkyZI3jnFxcWHfvn3/KBZJkIQQQgihRiYoS4IkhBBCiEw01WIrSCRJFEIIIYTIRCpIQgghhFAj9SNJkIQQQgiRibTYJEESH6Gq9paaDiFP6Gj/O38gWRjpajqEPLNr4wRNh5AnFv3xQNMh5InG7d//LspCSIIkhBBCCDUyQVkSJCGEEEJkIi02SRKFEEIIIbKQCpIQQggh1Ej9SBIkIYQQQmQiHTZpsQkhhBBCZCEVJCGEEEKo0ZImmyRIQgghhFAnLTZpsQkhhBBCZCEVJCGEEEKoUUiLTRIkIYQQQqiTFpu02IQQQgghspAKkhBCCCHUyFVskiAJIYQQIhNpsUmLTQghhBAiC6kgCSGEEEKNVJAkQRJCCCFEJnKZv7TYhBBCCCGykAqSEEIIIdRoSQFJEiQhhBBCqJMWm7TYhBBCCCGykAqSEEIIIdTIVWySIAkhhBAiE2mxSYtNCCGEECILSZBErpg4cSJeXl6aDkMIIUQu0FLk3vKxkhabyDGFQsHOnTtp3bq1at3w4cMZMGCA5oJ6TzevXWb/9g0E3b1JVMRzBo6dTSXvOqrtOzeu5NyJQ7wIf4aOji6uxUvRvmsfipUqoxoT+iSYzasXcSfwL1JTUijiVpx2X/TGo3xlTZwSADf+usSvW9fz4HYgkRHPGT5xDlV86qq2n/vjdw7v2c79OzeJfRnNrGUbcS1eMstxbt/4i81rlnL35jW0tLRxKVaCMTMWo6dvkI9n83ZrVq/g6JFDBD24j76+AeW8KjBg8DBcXd0AiI6O4vul33H2zCmehYZgYWlF3XoN6Os/EBNTUw1H/7e71wM4smsTj+7dIibyBV99PZ1y1WqrjQl9FMQv65dx93oA6Wlp2BdxpfvIqVjZ2AOwedlsbl25QEzkc/QMjHArWYZWXfti5+SiiVMCoJmnLc08bbEz1QfgYWQCP118wsVH0Zjoa/NFZScqOJlhY6JPdEIKZ4MiWX/hCfHJaWrHaViiEK3L2VPY3ID4lDRO3o9g2cmHmjilN3oe/oxVSxZw/uxJkhITcXQqwvAxUyjhURqAhPh4Vi9bwOkTvxMTHY29Y2Faf9qJFm0+03Dk7yYtNkmQRC4xMTHBxMTkjduTk5PR09PLx4iyl5SYQBE3d2o1+oTF00Zl2W5f2JkufYZjY1+Y5OQkDuz6iW/HDWT2qu2YmVsCMG/iUOwdizBq+hL09PQ5uHsz8yYN49tVO7Cwss7vUwIyzsulqDv1fFsyd9KIbLeXLONF9TqNWDF/arbHuH3jL6aPHkDrjt3o5j8CbW1tHt6/g0JRsArNly6c59PPO+FZugxpaWksWTyf/n16sHXHHgyNjAgPCyM8PIzBQ0dStFgxQp4+ZcbUiYSHhzF77kJNh6+SnJhAYdfiVG/QnNWzxmTZHh7yhAXf9MO7YQuaduiBgaExoY8eoKurrxpTpFhJKtdujKWNHfEvY9j/8w8snTSECcu3oqWtnZ+no/I8Lpm15x7xNDoRFAoalijEOF93Bm6/jgKwMtJl9dlHBEcmYGuiR/9ablgZ6zHj0F3VMVqXtadNeXt+OPuIW2GxGOhoqRKuguJlTAxDevtRvmIVps1birmFJU8eBWNiaqYas3zRt1y5+CejJszAzsGRi+fOsHjuNKwL2eBdq54GoxfvQxKk/6ht27YxadIk7t69i5GRERUqVGD37t3cuHGDb775hsuXL5OSkoKXlxfz58+nYsWKALi6ugLQpk0bAFxcXAgKCmLixIns2rWLgIAAAL788kuioqKoUqUKS5YsQV9fnwcPHvDo0SOGDRvGwYMH0dLSolatWixcuFB13LxWvnINyleu8cbt3nV91R536jmIEwd/4dGDu5T2qsLL6CiePX1Ej0FjcHZzB+DTL/05snc7Tx7e01iCVKGqDxWq+rxxe+1GzQEIC336xjHrls2jaZsOtO7wpWqdYxHX3Aox1yxetlLt8cTJM2hUz4fAwOtUrFSF4u4l+HbeItV2pyLO9BswmHHfjCQ1NRUdnYLxY8+zkjeelbzfuH3vphV4VvKmlV8/1Tobh8JqY3wat1L939rWgeadejJryJe8CAvNMja//PkwSu3xj+cf08zTllK2xhy89ZzpryVCoTFJ/Hj+EcPrF0NLAelKMNHTpkuVwkw+cIcrT2JUY4MiEvLrFN7Llg0/YGNnx/CxU1TrHByd1MbcuBpAw2YtKV+xCgDNW7dn7+6t3LxxrcAnSHIVm8xB+k8KCQmhY8eOdO/encDAQI4dO0bbtm1RKpW8fPkSPz8/Tp48ydmzZ3F3d6dZs2a8fPkSgPPnzwOwZs0aQkJCVI+zc+TIEW7dusWhQ4fYs2cPKSkp+Pr6Ympqyh9//MGpU6cwMTGhSZMmJCcn58u550RqSgpH9+/CyNhElQyZmJnj4OTCqd/3k5SYQFpaKkf378TMwhLX4qU0HPGHi46M4O7Na5hZWDJuUHd6fdqYiUN7cfNagKZDe6fY2Iz3ppmZ+VvHGJuYFJjk6F3S09O5fuE0to5FWDppKN/4tWDuyJ78de7EG/dJSkzg3O/7sLZzwLKQbT5G+2ZaCqhdzAoDXS0Cn8VmO8ZIT4f45DTSlRmPvZzM0VIosDbSZflnZVnX2YuvGxajkLHmK9CvO3PyGO6lSjNlzDA+bVaHvn6fsW/3NrUxnmW9OPvHMZ6HP0OpVBJw8U+ePHpIpapvTowLCkUuLh+rj+OnhchVISEhpKam0rZtW1xcMuYqlC1bFoD69eurjV2xYgUWFhYcP36cFi1aYGNjA4CFhQX29vZvfR5jY2NWrVqlaq1t2LCB9PR0Vq1aheL/H0/WrFmDhYUFx44do3Hjxrl6nh8q4M+TLJ01luSkRMytCjFi6mJMzS2AjPlXI6ctZuGUkfRuXw+FQgszC0uGT16I8Wul9Y/Ns5AnAGz7cSVf9BqEa/ESnDi0lykj+zJnxc84ODlrOMLspaenM3f2DMp7VaS4e4lsx0RFRrJqxTLatCv48z5eiY2OJCkxgcM7NtC8U09adu1L4KWzrJ41hv6TF+FepoJq7B/7d7D7x2UkJyZgW9iZfhMWoKOrq8HowcXKkLmtPdHT1iIhJY2pB+7wKCoxyzgzAx06VnTkt8Bw1ToHM30UCvisgiMrTgcTl5xK1ypOTG1ekv7brpH6KpPSsJCnj9mzcwvtOnShY9evuBV4naXzZ6Gjq0vjZhmVPf+ho1kwaxKdWjVCW1sHLS0Fg7+eQLkKmpuvKN6fJEj/QeXLl6dBgwaULVsWX19fGjduTPv27bG0tOTZs2eMHTuWY8eOERYWRlpaGvHx8QQHB+f4ecqWLas27+jKlSvcvXsX00wTZRMTE7l37162x0hKSiIpKUltXXJSEnr6eTcfwaNcJaYsXs/LmCiO/7abJTO/YcK8HzCzsEKpVPLj0m8xs7Dkm9nfo6enz/EDvzB/0jAmLliLhVWhPIsrLymV6QA0bN6Wek1aAuBWvBTXLp/n6IFf6NSjvybDe6NZ0ydz794dVq3dmO322NhYBvXvQ9Gixendxz+fo/twSmVGElC2ak3qtfwcACc3dx7cusapA7vUEqTKtRtTsnwVYiJf8Pvun1gzZxxDZixDV09zc3aeRCUyYNs1jPW08SlqxdB6RRn1S6BakmSoq8XEJiUIjkxg48UnqvUKBehqa/H96YdcfpzRYpt15B4bulSgnKMZlx5H5/v5ZEeZnk6JUqXp3mcQAMVLehB0/y57d25VJUi7t23i5vW/mDR7EXb2jlwNuMh3c6djXciWilWqazL8d9KSHpu02P6LtLW1OXToEPv378fT05PFixdTsmRJHjx4gJ+fHwEBASxcuJDTp08TEBCAtbX1B7XAjI2N1R7HxsZSqVIlAgIC1Jbbt2/TqVOnbI8xY8YMzM3N1ZYfv5//Qef9vvQNDLFzLELxUmXpMXgs2traHD/4CwA3rlwg4Pwp+o2aSgnP8rgWL4Wf/0j09PU5eXhvnsaVlyz/n9g5ubiprS/s7MbzsFBNhPROs6ZP4eSJ4yxfuQ47u6zVzLi4OAb264mxsRHfzl+s8apKThibmqOlrY19pjlgdk4uRD4PU1tnaGyCrWMRipf2ovuIqYQ9CX5rKy4/pKYrCYlJ4u7zeNb9+ZgHL+JpVfbvr5GhrhZTmpXMqC4dvEPaa1WhiPgUAIIj/55zFJOYSkxiKjYmBafNZmVtg7NbUbV1zq5uhD3L+H5JSkpkzfJF9B4wAu+adSlavASt2nekTgNftm1aq4GIc0ZabFJB+s9SKBT4+Pjg4+PD+PHjcXFxYefOnZw6dYqlS5fSrFkzAB49esTz58/V9tXV1SUtLS27w75VxYoV+fnnn7G1tcXM7P3aUaNHj2bo0KFq6wIe5e9kzfR0JakpGT+0k5MyPgFnvrJLodBSfer/GNnYO2JpbcPTx+qXUYc8fohXlTdP/tYEpVLJ7BlTOfb7Yb5fvY7CTk5ZxsTGxjKg71fo6ukxb+FS9POw4pgXdHR1cS7uwbMnj9TWhz99hJWN3Rv3U6JEqfz7/VpQKBQKdLUzflUa6moxpXkpUtLSmXzgDilp6t83N0Iz5io5WRjyIi7jPEz0tTEz0CEsVr2arEmly3nxODhIbd3jRw+xs3cAIDU1ldTUVBSZbgSkpaVNegFpE4q3kwrSf9C5c+eYPn06Fy5cIDg4mB07dhAeHo6Hhwfu7u6sX7+ewMBAzp07R+fOnTE0NFTb39XVlSNHjhAaGkpkZOR7P2/nzp0pVKgQrVq14o8//uDBgwccO3aMgQMH8vjx42z30dfXx8zMTG35J+21xIR4Ht67zcN7twEID33Kw3u3eREWSlJiAlvXLeXuzas8DwvhwZ1AVi2YQtSLcKrUbABA8VJlMTYxZeW8SQTfv626J1L4s6eUr/Lmq+PyWmJCPEF3bxF09xYAYaFPCLp7S1X9iY2JJujuLZ48vA/A08cPCbp7i6iIjORXoVDwyWdd2L9zM2dPHCb0ySN+XruMJ48eUq9pq+yfVENmTZ/M/n2/MnXmtxgZG/P8eTjPn4eTmJiRvMbGxtK/Tw8SEhIYP3EqsXGxqjEfktjnlaSEeB4/uMPjB3cAePEshMcP7hARnvE1a9C6I5dPHeH0wV8ID3nMiX3buXb+NDWbZFxB+jz0CQe3ryf43k0iwkO5f/Mqa74dh66ePp4VNTcJ2K+qE6UdTLE10cPFyhC/qk6UdTTl6J0XGOpqMbV5KQx0tFh4/AFGutpYGupiaairuqHg0+hEzjyIpFcNZzzsTHCxNGRovaI8jkrgr6cvNXZembX9vAuB167y07qVPHkczO8H97Jv9zY+adcBAGNjE8pVqMzK7+Zx5dJ5Qp4+5uDe3Rze/ys+deq/4+gFgJSQUCg/5o+94oMEBgYyZMgQLl26RExMDC4uLgwYMID+/ftz+fJlevXqxbVr1yhSpAjTp09n+PDhDB48mMGDBwPw66+/MnToUIKCgihcuPBbL/PftWuX2nOHhoYyatQo9u3bx8uXLylcuDANGjRgzpw5711VOns36sPP/a+LzBzdL8v6mg2a49d/FMtnj+fe7evERkdhYmaOm7sHLTt0p2gJT9XYB3cC2fbjMh7cCSQtNZXCLkVp1bHHW28f8D4M9D78vjXXr1xg8vA+WdbXadSCfiMncuzAryybMynL9vZdevJp196qx7s2r+XgL1uJfRmNS9ESdO45kFJlvD44LoBitsbvHpQDlct7ZLt+wuTpfNKqDRfO/0mfr/yyHfPLvsM4Fs69y99P33/xwfveuXaJxeMGZllftV5TvhiYcV+kM4f3cHjHBqJehGHr6EzTDj0oV60WANERz/lpyUwe3btFfNxLTM2tKFa6PE0+64Zd4X82qX7RHw8+eN9BddwoX9gMKyNd4pLTCHoRz9aAEAKexFDWwZSZLbP/+nXbGEBYbEYr31BXi141XKjhZkm6Eq6FxPD9qWCex/2zq12Xti//j/bP7Oyp4/ywbCFPHgdj71CYdh260KxVe9X2iBfP+WHZQi7+eYaXMdHY2jvQrFV72nXoorpQJbe4WOdulfTcvdyb61Wt2JuvMC3IJEESH51/kiAVZP8kQSrIcjtBKkj+SYJUkP2TBKkgy+0EqSCRBCn3yRwkIYQQQqiRi9gkQRJCCCFEJpIfySRtIYQQQogspIIkhBBCCHVSQpIESQghhBDqFJIhSYtNCCGEECIzqSAJIYQQQo1cxSYJkhBCCCEykfxIWmxCCCGEEFlIBUkIIYQQ6qSEJAmSEEIIIdTJVWzSYhNCCCGEyEIqSEIIIYRQI1exSYIkhBBCiEwkP5IWmxBCCCFEFlJBEkIIIYQ6KSFJgiSEEEIIdXIVm7TYhBBCCCGykAqSEEIIIdTIVWxSQRJCCCFEJopcXHLixIkTfPLJJzg6OqJQKNi1a5fadqVSyfjx43FwcMDQ0JCGDRty584dtTERERF07twZMzMzLCws6NGjB7GxsTmMRBIkIYQQQhQQcXFxlC9fniVLlmS7ffbs2SxatIjly5dz7tw5jI2N8fX1JTExUTWmc+fOXL9+nUOHDrFnzx5OnDhBr169chyLtNiEEEIIoU5DLbamTZvStGnTbLcplUoWLFjA2LFjadWqFQA//vgjdnZ27Nq1iw4dOhAYGMhvv/3G+fPnqVy5MgCLFy+mWbNmzJkzB0dHx/eORSpIQgghhFCjyMV/SUlJxMTEqC1JSUk5junBgweEhobSsGFD1Tpzc3OqVavGmTNnADhz5gwWFhaq5AigYcOGaGlpce7cuRw9nyRIQgghhMgzM2bMwNzcXG2ZMWNGjo8TGhoKgJ2dndp6Ozs71bbQ0FBsbW3Vtuvo6GBlZaUa876kxSaEEEIINbl5Fdvo0aMZOnSo2jp9ff3ce4I8IgmSEEIIIdTk5hQkfX39XEmI7O3tAXj27BkODg6q9c+ePcPLy0s1JiwsTG2/1NRUIiIiVPu/L2mxCSGEEKLAc3Nzw97eniNHjqjWxcTEcO7cOby9vQHw9vYmKiqKixcvqsb8/vvvpKenU61atRw9n1SQxEfH0cpQ0yHkiX/rfdm0tf+tZwal7Mw0HUKeWN2hgqZDyBNH7j7TdAh5xsXaKXcPqKFv29jYWO7evat6/ODBAwICArCyssLZ2ZnBgwczdepU3N3dcXNzY9y4cTg6OtK6dWsAPDw8aNKkCT179mT58uWkpKTQv39/OnTokKMr2EASJCGEEEJkoqm/xXbhwgXq1aunevxq7pKfnx9r165l5MiRxMXF0atXL6KioqhZsya//fYbBgYGqn02btxI//79adCgAVpaWrRr145FixblOBaFUqlU/vNTEiL/BEfk/PLQj8G/tc5ibaqn6RDyTHhMsqZDyBP6Ov/O2Rf/5gpS50q5W0G6GRKfa8cq5WCUa8fKT1JBEkIIIYQa+VtskiAJIYQQIhPJj+QqNiGEEEKILKSCJIQQQgh1UkKSBEkIIYQQ6jR1FVtBIi02IYQQQohMpIIkhBBCCDVyFZskSEIIIYTIRPIjabEJIYQQQmQhFSQhhBBCqJMSkiRIQgghhFAnV7FJi00IIYQQIgupIAkhhBBCjVzFJgmSEEIIITKR/EhabEIIIYQQWUgFSQghhBDqpIQkCZIQQggh1MlVbNJiE0IIIYTIQipIQgghhFAjV7FJgiSEEEKITCQ/khabEEIIIUQWUkESQgghhBppsUkF6b0cO3YMhUJBVFSUpkMRQggh8oEiF5ePk1SQChCFQsHOnTtp3bp1jvZzdXVl8ODBDB48OE/iym1BQUG4ublx+fJlvLy8NB0Oz8OesWrpAv48c5KkxEQcnYowfOwUSnqUVo15GHSfVUvm89fli6SnpeLsVowJ0+dha++gwcjf7nnYM1ZmOq8Rmc7rlQWzprBn11b6DhpBuw5dNBDt+7t44Tw/rlnNjRvXeR4ezryF31GvQUPV9iOHDrJty2YCb1wnOjqazdt2UrKUhwYjfn/Pw5+xaskCzp997b04Zgol/v81S4iPZ/WyBZw+8Tsx0dHYOxam9aedaNHmMw1H/mZrVixh7aplauucXdxYv/VXAJKSkli68Ft+P7iflJRkqlT3YcjIsVhZF9JEuG/1MPAvTu/5mZAHd4iNesFnQyZRqkpN1fbY6AiO/LSSe39dJDE+FpdS5Wji1x9rBycAosJDWTSoc7bHbj9wPJ7V6+TLeYj3IwlSPklOTkZPT0/TYYhMXsbEMLi3H+UrVWH6vKWYW1ry5FEwpqZmqjFPHz9iSG8/mn7SBr+v+mFkbELQg7voFuCv58uYGAb19sOrUhVmvOG8Xjl57AiB1//CupCtBiLNuYSEBEqULEWrNu0YNnhAttu9KlaikW9Tpkwcp4EIP8zLmBiG9PajfMUqTJu3FHOLjK+ZyWtfs+WLvuXKxT8ZNWEGdg6OXDx3hsVzp2FdyAbvWvU0GP3buRUtztzvVqkea+toq/7/3fxZnD11gkkz5mFsYsKCb6czbtRglqzaoIlQ3yo5KQE7l2JUqNuULfMnqG1TKpX8PHc82jo6fD5sMvqGxpzdt5UNM0bQd/YP6BkYYmZtw9ClW9X2u/j7Hs7s2UJxr6r5eSrvJC22f2GLzdXVlQULFqit8/LyYuLEiUBGlWbVqlW0adMGIyMj3N3d+eWXX9TG79u3jxIlSmBoaEi9evUICgrK8jwnT56kVq1aGBoaUqRIEQYOHEhcXJxaHFOmTKFr166YmZnRq1cvkpOT6d+/Pw4ODhgYGODi4sKMGTNU4+F/7d15WI15/wfw92lV2iQVoV1Ki5Ilw9gakwyRbaw1mMcu2WKGkBmM59FgZh4h+1gn6+CxhexEG4NKSqHsIdF6//5oOj+nE0PF7XTer+vqujrf++70vp1yPn23G+jZsyckEon0cUpKCnx8fGBiYgIdHR00b94cR44ckX6f9u3b49atWwgMDIREIoHktZ/qd8n4ww8/YMiQIdDR0YG5uTn27NmDBw8ewMfHBzo6OnB2dsbFixff+9rnzZuHoUOHQldXFw0bNsSKFSukxy0tLQEArq6ukEgkaN++fTmv5Mex9ffVqGNigikz5qJxEyfUrVcf7i1bo179BtJz1iz/BS1at8W3YyfCxs4e9eo3QOu2HVDLsLZouf/Jlne4LqCkl+nX0PmYPns+1NQU4++lNm0/x5jxE9DR84tyj3/V3QcjRo1BKw+Pj5yscrb9/ZpNnjEXjR3Kf82uXo6Dp3d3uLg1h2ldM3Tt0RtWNo1w/eoVEZP/M1VVVdQ2MpJ+GBjUAgDk5DzH/j07MGbCVLg1bwk7+yaYFjwXVxLi8NfleJFTy7Nt2hId+w6V6TUq9TjrNu7cuAbvoRNgZt0YRvUaoOvQCSjIz8eVs0cBACoqqtAxMJT5SIw+DYdW7aBRQ+tjX85bcYCtGhZI72LOnDno27cvEhIS4O3tjYEDB+Lx48cAgIyMDPj6+qJbt26Ii4vD8OHDMW3aNJmvT0lJgZeXF3r16oWEhARs3boVp06dwtixY2XO+89//gMXFxfExsZi5syZWLp0Kfbs2YNt27YhMTERGzdulBZC0dHRAIA1a9YgMzNT+jgnJwfe3t6IjIxEbGwsvLy80K1bN6SnpwMAduzYgfr16yMkJASZmZnIzMx8r4w///wzPvvsM8TGxqJr164YPHgwhgwZgkGDBiEmJgbW1tYYMmQIBEF4r+ddtGgR3N3dERsbi9GjR2PUqFFITEwEAFy4cAEAcOTIEWRmZmLHjh0VfzEr6ezJ42jUuAlCvpuEPt7tMHJIX+zfHSE9XlxcjPNnTqB+A3NMmzASfbzbYdywATgddVS0zO/i9evq7d0OI4b0xb7XrgsoubYFId+h70B/WFjZiBOUpM6eOg7bxk0w9/uSn8VRfrI/iwDg4NQU504ex8MH9yAIAuIuXcCdjFto1uLTLgZvZ6TD17sDvu7hhbkzg3Avq+T/qaRrV1FYWIhmLVpJzzW3sIKJad1PskB6m8KCAgCAmvr/9yxLVFSgpqaOjMTyC9i7N5OQdesGXNt7f5SM9H6UskDy9/dH//79YWNjg3nz5iEnJ0f6pr1s2TJYW1tj0aJFsLOzw8CBA+Hv7y/z9fPnz8fAgQMxYcIE2NraonXr1li6dCnWr1+PV69eSc/r2LEjJk2aBGtra1hbWyM9PR22trZo06YNzM3N0aZNG/Tv3x8AUKdOHQCAgYEBTE1NpY9dXFwwYsQIODo6wtbWFnPnzoW1tbW018vQ0BCqqqrQ1dWFqakpTE1N3yujt7c3RowYAVtbWwQHB+PZs2do3rw5+vTpg0aNGiEoKAjXrl3DvXv33vt5R48eDRsbGwQFBcHIyAjHjh2TudbatWvD1NQUhoaGVfPCVkDm3dv4c+c2mDVoiPk/h6Gbb1/8FvoTDu3bDQDIfvIYL3NzsXXDKjRv+RnmL16Oz9p1wpzpgYiPufgPzy6ef7ouANiyYTVUVdXQs2/5cyLo48q8ext7X3vNvurZF//9+Scc2v//r9mYidPR0NIKA3y+gPfnzfD9xFEYO+k7OLu6i5j87ewdnTEt+Af8e0kYJgbNRObd2xj3ryHIffECjx49hLq6utzQby3D2nj86KFIiSvGqF5D6BsZ4+iWcLzMeY6iwgKc3rMZzx4/wPMnj8v9mrjj/4ORWUM0aCQ/L1BsEknVfSgqxehTr2LOzs7Sz2vWrAk9PT3cv38fAHDt2jW0bNlS5nyPMl318fHxSEhIwMaNG6VtgiCguLgYqampsLcvmRDq7i77n5a/vz+++OIL2NnZwcvLC1999RU6d+781qw5OTmYPXs29u3bh8zMTBQWFuLly5fSHqQ3edeMr/9bmJiYAACcnJzk2u7fvw9TU9MKPa9EIoGpqan03/h95OXlIS8vr0wboKmp+d7PVR6huBiNGjfBsFEBAAAbO3uk3byBvbv+QOeuPiguLgYAeLTtgF79SyYv2zRqjL8ux2Hvrm1wcfs035jKXpft39f159/XlXT9KnZu24hla7fKDMuSeEpfs6EjZX8W9+38A529fQAAuyM24fpfCZizcClMTOvhctwl/LpoHmobGcOteau3Pb1oWrVuK/3c2tYO9o5O6Ne9M44dOQANzRoiJqtaqmpq6DNhDv5c+R/8+189IFFRgZVjM9i4tIAAQe78gvw8XD4Tic97DhIh7T/jvdiqYYGkoqIiHQ4qVfB312cpdXV1mccSiUT6RvgucnJyMGLECIwfP17uWMOGDaWf16xZU+aYm5sbUlNT8b///Q9HjhxB37594enpiYiIiLJPIzV58mQcPnwY//nPf2BjYwMtLS307t0b+fn5VZLx9X+L0jfK8tpK/30q8rylz/M+/8al5s+fjzlz5si0TZj6PQKDqmbyraFRHTS0tJJpa2hhiZPHSuZ56RvUgqqqGswtrcucY4Ur8bFVkuFDMDSqA/O3XNfluEvIfvIYA3p+KT1eXFSE5b8swo6tG7Fx54GPmpcAw9rl/yyeOl7ymuXlvcKasKWYNX8xWn72OQDAyqYRUpKvI2LT2k+2QCpLV1cP9Rua487tdLi3aI2CggI8f/5MphfpyeNHn+Qqtn9Sz6oRRsxfgVe5OSgqLERNPQOEzxyDelaN5M69dv4ECvLy4Nz27X8kk3iqXYFUp04d6TwcAHj27BlSU1Pf+evt7e3lJm2fO3dO5rGbmxuuXr0KG5v3n7ehp6eHfv36oV+/fujduze8vLzw+PFjGBoaQl1dHUVFRTLnnz59Gv7+/ujZsyeAkgKl7KRxDQ0Nua+rTMa3qYrnLV3NVzZzeaZPn46JEyfKtN178YaTK6CJU1PcTk+Tabudfgsmfy/fV1dXh519E2SUOefOa+d8ipo4NZXL/Pp1eXbpJveGOm3CKHh2+QpeXX0+Vkx6TRPncn4WM/7/NSssLERhYSEkKrJ/2auoqKK4WL6H4lOVm5uLu3cyYGjUDY3sHaCmpoaY6PNo17Fk0n36rVTcy8pEEycXkZNWXA1tHQDAo8zbyLyZhA59vpE7J/b4/2DXzAM19Qw+crp3xA6k6jcHqWPHjtiwYQNOnjyJy5cvw8/PD6qqqv/8hX8bOXIkkpOTMWXKFCQmJmLTpk1Yu3atzDlBQUE4c+YMxo4di7i4OCQnJ2P37t1yE5XLCg0NxebNm3H9+nUkJSXhjz/+gKmpKQwMDACUrP6KjIxEVlYWnjx5AgCwtbXFjh07EBcXh/j4eAwYMECuJ8bCwgInTpzAnTt38PDhw0pl/CdV8bzGxsbQ0tLCgQMHcO/ePTx9+vSN52pqakJPT0/mo6qG1wCg19eDce3KZWxauxJ3MtJx9OA+7N8dge69v5ae02egP6KOHMD+3RG4k5GOXX9sxtnTUejeq1+V5ahqZa8r8u/r8vn7uvT1DWBpbSvzoaamBkPD2mhgbily+rfLzX2BxOvXkHj9GgDgzp3bSLx+DZmZdwEAT59mI/H6NaSkpAAA0lJTkXj9Gh4+fCBa5nfh26/kNdu8biXu3E7H0UMlr1m3XiWvWc2aOnB2dcfKX0MRHxONzLu3cWjfbhz535/4rF1HkdO/2X+X/BtxMdHIvHsHVxJiMWPqeKioqMKzszd0dHTh3d0Xvy1eiJiLF5B47S8sCJmBJk4un2SBlP/qJbLSbiAr7QaAkn2NstJu4OnDkjmaV89FIe1qHJ7cu4vEi6fx+/ypsHP/DNbOskPxj7Pu4Nb1BLh2+HQnZ3MVWzXsQZo+fTpSU1Px1VdfQV9fH3Pnzn2vHqSGDRti+/btCAwMxC+//IIWLVpIl6yXcnZ2RlRUFL7//nu0bdsWgiDA2toa/fq9/Q1TV1cXCxcuRHJyMlRVVdG8eXPs378fKioldeqiRYswceJErFy5EmZmZkhLS0NoaCiGDh2K1q1bw8jICEFBQXj27JnM84aEhGDEiBGwtrZGXl4eBEGocMZ/UhXPq6amhqVLlyIkJATBwcFo27Ytjh8/XqlcFWXn4IjZC37GqmVL8Pua5TCta4ZRE6ai05ddpee0ad8JAVNnYvP6Vfgt9CfUN7fArHmhcHRxEyXzu2js4Ig5C35G+LIl2LBmOeqWc12K6uqVK/h2qJ/08aKFCwAA3Xx6IOTHBYg6dhSzZnwnPT5tSkkP5IhRYzByjPy+SZ8KOwdHzFrwM1a//rMYIPuafReyEKuXLcGC2dPx/NlTGJvWhf+IcZ/0RpEP7t9DyIypePY0Gwa1DOHk4oplqzfCoFbJ4oyxgUFQUVFB8LQJKMgvQPNWrRE49dPcv+ruzUSs/2GS9PGh30s2wHT5vDN8RgbhefYjHPp9GXKePoFuLUM4t+mMz33l5xjFHv8f9AzrwNrp05zDSCUkQtkJO0SfuPTHef98kgJS5L+03qa27qe7oWZlPXj29rmAikpTrdoNLgAAIm/cEzvCBzOwWf0qfb77zwv++aR3ZKyr/s8nfYKqXQ8SERERVQ5XsVXDOUhERERElcUeJCIiIpLFDiQWSERERCSL9RGH2IiIiIjksAeJiIiIZPAORCyQiIiIqAyuYuMQGxEREZEc9iARERGRDA6xsQeJiIiISA4LJCIiIqIyOMRGREREMjjExgKJiIiIyuAqNg6xEREREclhDxIRERHJ4BAbCyQiIiIqg/URh9iIiIiI5LAHiYiIiGSxC4kFEhEREcniKjYOsRERERHJYQ8SERERyeAqNhZIREREVAbrIw6xEREREclhDxIRERHJYhcSCyQiIiKSxVVsHGIjIiIiksMeJCIiIpLBVWyARBAEQewQRJ+ivLw8zJ8/H9OnT4empqbYcaoMr0vxVNdr43XRp4wFEtEbPHv2DPr6+nj69Cn09PTEjlNleF2Kp7peG6+LPmWcg0RERERUBgskIiIiojJYIBERERGVwQKJ6A00NTUxa9asajfJkteleKrrtfG66FPGSdpEREREZbAHiYiIiKgMFkhEREREZbBAIiIiIiqDBRIRERFRGSyQiIiIiMpggUSkBNLT01HeglVBEJCeni5CIlJmhYWFOHLkCJYvX47nz58DAO7evYucnByRkxH9Py7zJ3rNsWPH0KFDB7FjVDlVVVVkZmbC2NhYpv3Ro0cwNjZGUVGRSMmqRnFxMW7cuIH79++juLhY5tjnn38uUqqKe/ToEYKDg3Hs2LFyr+nx48ciJau8W7duwcvLC+np6cjLy0NSUhKsrKwQEBCAvLw8hIWFiR2xQjp27IgdO3bAwMBApv3Zs2fo0aMHjh49Kk4wqjA1sQMQfUq8vLxQv359fPPNN/Dz80ODBg3EjlQlBEGARCKRa8/JyUGNGjVESFR1zp07hwEDBuDWrVtyvWQSiUQhi7/Bgwfjxo0bGDZsGExMTMp97RRVQEAA3N3dER8fj9q1a0vbe/bsiW+//VbEZJVz/Phx5Ofny7W/evUKJ0+eFCERVRYLJKLX3LlzBxs2bMC6deswZ84cdOzYEcOGDUOPHj2goaEhdrz3NnHiRAAlhcLMmTOhra0tPVZUVITz58+jadOmIqWrGiNHjoS7uzv27duHunXrVoti4uTJkzh16hRcXFzEjlLlTp48iTNnzsj9PllYWODOnTsipaq4hIQE6edXr15FVlaW9HFRUREOHDgAMzMzMaJRJbFAInqNkZERAgMDERgYiJiYGKxZswajR4/G6NGjMWDAAAwbNkyh3rRiY2MBlPQgXb58WeZNSUNDAy4uLpg8ebJY8apEcnIyIiIiYGNjI3aUKtO4cWO8fPlS7BgfRHFxcbm9erdv34aurq4IiSqnadOmkEgkkEgk6Nixo9xxLS0t/PLLLyIko8riHCSit7h79y5WrFiBBQsWQE1NDa9evYKHhwfCwsLQpEkTseO9s2+++QZLliyBnp6e2FGqXMeOHTF16lR4eXmJHaXKREdHY9q0aQgODoajoyPU1dVljivy69ivXz/o6+tjxYoV0NXVRUJCAurUqQMfHx80bNgQa9asETvieykd2rWyssKFCxdQp04d6TENDQ0YGxtDVVVVxIRUUSyQiMooKCjA7t27sXr1ahw+fBju7u4YNmwY+vfvjwcPHmDGjBmIiYnB1atXxY5KAHbu3IkZM2ZgypQpcHJykismnJ2dRUpWccnJyRgwYABiYmJk2kvnkinivKpSGRkZ8PLygiAISE5Ohru7O5KTk2FkZIQTJ07ILSQgEgsLJKLXjBs3Dps3b4YgCBg8eDCGDx8OR0dHmXOysrJQr149uZVFn7IXL15gwYIFiIyMLHdV1M2bN0VKVnkqKvK7lUgkEoUuJlq0aAE1NTUEBASUO0m7Xbt2IiWrGoWFhdi6dSvi4+ORk5MDNzc3DBw4EFpaWmJHq5Tk5OQ3rjwMDg4WKRVVFAskotd06tQJw4cPh6+vLzQ1Ncs9p7CwEKdPn1aoN6n+/fsjKioKgwcPLncic0BAgEjJKu/WrVtvPW5ubv6RklQdbW1txMbGws7OTuwoVaqgoACNGzfG3r17YW9vL3acKrVy5UqMGjUKRkZGMDU1lfkdk0gkcr2B9OljgUSkBAwMDLBv3z589tlnYkehd/D5558jODgYnp6eYkepcmZmZjhy5Ei1K5DMzc0xevRoBAUFiR2FqghXsRGVUR27yWvVqgVDQ0OxY3wwKSkpWLx4Ma5duwYAcHBwQEBAAKytrUVOVjHjxo1DQEBAtZpXVWrMmDH46aefEB4eDjW16vMW9OTJE/Tp00fsGFSF2INE9Jrq2k3++++/Y/fu3Vi3bp3MXkjVwcGDB9G9e3c0bdpU2kN2+vRpxMfH488//8QXX3whcsL3Vx3nVZXq2bMnIiMjoaOjAycnJ9SsWVPm+I4dO0RKVjnDhg1D8+bNMXLkSLGjUBVhgUT0muraTe7q6oqUlBQIggALCwu5HglFLfyAkmv78ssvsWDBApn2adOm4dChQwp5bdVxXlWpb7755q3HFW2Zf6n58+cjNDQUXbt2LbfXb/z48SIlo4pigUT0Gj09PcTFxcHKykrsKFVqzpw5bz0+a9asj5Sk6tWoUQOXL1+Gra2tTHtSUhKcnZ3x6tUrkZKRMrG0tHzjMYlEotArRZVV9RkAJqoCffr0waFDh6pdN7kiF0D/pE6dOoiLi5MrkOLi4hR2T51169bByMgIXbt2BQBMnToVK1asgIODAzZv3qzQPUjVVWpqqtgRqIqxQCJ6jY2NDWbOnIlz585Vu27y7OxsREREICUlBVOmTIGhoSFiYmJgYmKi0PeK+vbbb/Gvf/0LN2/eROvWrQGUzEH66aefpPeiUzTz5s3DsmXLAABnz57Fr7/+isWLF2Pv3r0IDAxUuHk6bm5uiIyMRK1ateDq6vrW++Up4pDo6/Lz85Gamgpra+tqNQldGXGIjeg11bWbPCEhAZ6entDX10daWhoSExNhZWWFGTNmID09HevXrxc7YoUJgoDFixdj0aJFuHv3LgCgXr16mDJlCsaPH6+QN6/V1tbG9evX0bBhQwQFBSEzMxPr16/HX3/9hfbt2+PBgwdiR3wvc+bMwZQpU6CtrY3Zs2e/9TVR1N7O3NxcjBs3DuvWrQNQMsRrZWWFcePGwczMDNOmTRM5Ib0vFkhESsDT0xNubm5YuHAhdHV1ER8fDysrK5w5cwYDBgxAWlqa2BGrxPPnzwFAIW96+jpjY2McPHgQrq6ucHV1xcSJEzF48GCkpKTAxcUFOTk5YkekMgICAnD69GksXrwYXl5eSEhIgJWVFXbv3o3Zs2dLbxxNikN+LSkRASjpmagufz9ER0djxIgRcu1mZmbIysoSIdGHoaurq/DFEQB88cUXGD58OIYPH46kpCR4e3sDAP766y9YWFiIG66SrKys8OjRI7n27OxshV4csWvXLvz6669o06aNTA9ZkyZNkJKSImIyqigWSERlrF+/Hk5OTtDS0oKWlhacnZ2xYcMGsWNViqamJp49eybXnpSUJHP3cUXh5uaGJ0+eAChZ5u/m5vbGD0X022+/wcPDAw8ePMD27dtRu3ZtAMClS5fQv39/kdNVTlpaWrn7OOXl5eH27dsiJKoaDx48KHdRwIsXLxRymJc4SZtIRmhoKGbOnImxY8dKNx08deoURo4ciYcPHyIwMFDkhBXTvXt3hISEYNu2bQBK5lOlp6cjKCgIvXr1Ejnd+/Px8ZHeK8/Hx6favQEZGBjg119/lWv/p+0aPmV79uyRfn7w4EHo6+tLHxcVFSEyMvKtcwA/de7u7ti3bx/GjRsHANKfyfDwcHh4eIgZjSqIc5CIXmNpaYk5c+ZgyJAhMu3r1q3D7NmzFXYp79OnT9G7d29cvHgRz58/R7169ZCVlQUPDw/s379fbjdj+jTk5uYiPT0d+fn5Mu2KeKuR0t3BS3cEf526ujosLCywaNEifPXVV2LEq7RTp06hS5cuGDRoENauXYsRI0bg6tWrOHPmDKKiotCsWTOxI9J7YoFE9JoaNWrgypUrsLGxkWlPTk6Gk5OTwm86eOrUKSQkJCAnJwdubm7V4maoVlZWiI6Olg5DlcrOzoabm5tCrjx88OAB/P39ceDAgXKPK/KtRiwtLREdHQ0jIyOxo1S5lJQULFiwAPHx8dLfsaCgIDg5OYkdjSqAQ2xEr7GxscG2bdvw3XffybRv3bpVbiNCRdSmTRu0adNG7BhVqjrOaZkwYQKePn2K8+fPo3379ti5cyfu3buHH374AYsWLRI7XqUoai/su7C2tsbKlSvFjkFVhAUS0WvmzJmDfv364cSJEzI3Po2MjJTO31FU0dHROHbsGO7fv4/i4mKZY6GhoSKlqrjqPKfl6NGj2L17N9zd3aGiogJzc3N88cUX0NPTw/z586U7bCuqFy9eICoqqtzhQ0XejBUA7t+/X+7vmCIOiyo7DrERlRETE4PQ0FBcu3YNAGBvb49JkybB1dVV5GQVN2/ePMyYMQN2dnYwMTGRmdQskUhw9OhREdNVTHWe06Knp4eEhARYWFjA3NwcmzZtwmeffYbU1FQ0adIEubm5YkessNjYWHh7eyM3NxcvXryAoaEhHj58CG1tbRgbGyvkkChQssLQz88P165dk/t5lEgkCj0sqqzYg0T0t4KCAowYMQIzZ87E77//LnacKrVkyRKsXr0a/v7+YkepMqV/oVfHOS12dnZITEyEhYUFXFxcsHz5clhYWCAsLAx169YVO16lBAYGolu3bggLC4O+vj7OnTsHdXV1DBo0CAEBAWLHq7ChQ4eiUaNGWLVqldwfIaSY2INE9Bp9fX3ExcUp7NDMm9StWxcnTpyoFvOo3kV2djYMDAzEjlFhv//+OwoLC+Hv749Lly7By8sLjx8/hoaGBtauXYt+/fqJHbHCDAwMcP78edjZ2cHAwABnz56Fvb09zp8/Dz8/P1y/fl3siBWiq6uL2NhYuQUepLi4USTRa3r06IFdu3aJHaPKBQYG4rfffhM7xgfx008/YevWrdLHffr0gaGhIczMzBAfHy9isoobNGiQtLevWbNmuHXrFqKjo5GRkaHQxRFQMvxZOjxqbGyM9PR0ACV/nGRkZIgZrVI6deqksD9vVD72IBG9pnSVUKdOndCsWTO5/YEUdQJpcXExunbtiqSkJDg4OEBdXV3muKLdHf51lpaW2LhxI1q3bo3Dhw+jb9++2Lp1K7Zt24b09HQcOnRI7Ij0ms6dO8Pf3x8DBgzAt99+i4SEBIwfPx4bNmzAkydPcP78ebEjVsjDhw/h5+eHFi1awNHRUe53rHv37iIlo4pigUT0mrcNrUkkEoWdQDp27FiEh4ejQ4cO5c6PWLNmjUjJKk9LSwtJSUlo0KABAgIC8OrVKyxfvhxJSUlo2bKl9JYkiqRXr15o0aIFgoKCZNoXLlyI6Oho/PHHHyIlq7zSzUo7dOiA+/fvY8iQIThz5gwaNWqE8PBwNG3aVOyIFfLnn39i8ODB5d7Sh5O0FRMLJCIloKuriy1btij88vDy1KtXDxEREWjdujXs7Ozwww8/oE+fPkhMTETz5s3LfcP61NWpUwdHjx6V22Dw8uXL8PT0xL1790RKVnkvX76EIAjQ1tYGULKP1c6dO+Hg4IAvv/xS5HQVZ2Fhga+++gozZ86EiYmJ2HGoCnAVGym9iRMnYu7cuahZsyYmTpz4xvMkEonCbtJnaGgIa2trsWN8EL6+vhgwYABsbW3x6NEjdOnSBQAUesJsTk4ONDQ05NrV1dUVsuB7nY+PD3x9fTFy5EhkZ2ejVatWUFdXx8OHDxEaGopRo0aJHbFCHj16hMDAQBZH1QgnaZPSi42NRUFBgfTzt30oqtmzZ2PWrFkKvX/Om/z8888YO3YsHBwccPjwYejo6AAAMjMzMXr0aJHTVYyTk5PMxPNSW7ZsgYODgwiJqk5MTAzatm0LAIiIiICJiQlu3bqF9evXY+nSpSKnqzhfX18cO3ZM7BhUhTjERqQEXF1dkZKSAkEQYGFhITeBNCYmRqRkVJ4///xT2jPWsWNHAEBkZCQ2b96MP/74Az169BA3YCVoa2vj+vXraNiwIfr27YsmTZpg1qxZyMjIgJ2dncIW8T/++CMWL16Mrl27wsnJSe53TFEXeCgzFkhESmDOnDlvPT5r1qyPlOTD2LBhA5YvX46bN2/i7NmzMDc3x+LFi2FpaQkfHx+x41XIvn37MG/ePMTFxUFLSwvOzs6YNWsW2rVrJ3a0SnF2dsbw4cPRs2dPODo64sCBA/Dw8MClS5fQtWtXZGVliR2xQqrrAg9lxgKJiBTasmXLEBwcjAkTJuDHH3/ElStXYGVlhbVr12LdunUKN+xRWFiIefPmYejQoahfv77YcapcREQEBgwYgKKiInTq1Em6DcP8+fNx4sQJ/O9//xM5IVEJFkhESiI7OxsRERFISUnBlClTYGhoiJiYGJiYmMDMzEzseBXm4OCAefPmoUePHtDV1UV8fDysrKxw5coVtG/fHg8fPhQ74nvT0dHBlStXYGFhIXaUDyIrKwuZmZlwcXGRbhp54cIF6OnpoXHjxiKnq5z8/HykpqbC2toaampcB6XIOEmbSAkkJCSgUaNG+Omnn/Cf//wH2dnZAEo2iJw+fbq44SopNTW13BsJa2pq4sWLFyIkqrxOnTohKipK7BgfjKmpKVxdXaXFEQC0aNFCoYuj3NxcDBs2DNra2mjSpIl0h/Bx48ZhwYIFIqejimCBRKQEJk6cCH9/fyQnJ6NGjRrSdm9vb5w4cULEZJVnaWmJuLg4ufYDBw7A3t7+4weqAl26dMG0adMwefJkbN68GXv27JH5oE/P9OnTER8fj+PHj8v8jnl6epa7IpE+fez/I1IC0dHRWL58uVy7mZmZwk6KLTVx4kSMGTMGr169giAIuHDhAjZv3oz58+cjPDxc7HgVUro9QWhoqNwx7sr8adq1axe2bt2KVq1ayexU36RJE6SkpIiYjCqKBRKREtDU1Cx3g8GkpCTUqVNHhERVZ/jw4dDS0sKMGTOQm5uLAQMGoF69eliyZAm+/vprseNVSHFxsdgR6D09ePAAxsbGcu0vXryQu7UPKQYOsREpge7duyMkJES6IaZEIkF6ejqCgoLQq1cvkdNV3sCBA5GcnIycnBxkZWXh9u3bGDZsmNixSIm4u7tj37590selRVF4eDg8PDzEikWVwFVsRErg6dOn6N27t/RGofXq1UNWVhY8PDywf/9+1KxZU+yIVMaLFy8QFRWF9PR05OfnyxzjpoOfnlOnTqFLly4YNGgQ1q5dixEjRuDq1as4c+YMoqKi0KxZM7Ej0ntigUSkRE6fPo34+Hjk5OTAzc0Nnp6eYkeqNEtLy7cOYSjiBn2xsbHw9vZGbm4uXrx4AUNDQzx8+BDa2towNjZWyGtSBikpKViwYIHM71hQUJDcTYdJMbBAIlIC69evR79+/aCpqSnTnp+fjy1btmDIkCEiJau8JUuWyDwuKChAbGwsDhw4gClTpmDatGkiJau49u3bo1GjRggLC4O+vj7i4+Ohrq6OQYMGISAgAL6+vmJHJKr2WCARKQFVVVVkZmbKTSJ99OgRjI2Nq+WqqN9++w0XL17EmjVrxI7y3gwMDHD+/HnY2dnBwMAAZ8+ehb29Pc6fPw8/Pz9cv35d7IhUhjL+jlV3nKRNpAQEQSh3GOr27dvQ19cXIdGH16VLF2zfvl3sGBWirq4u3UTR2NhYuumgvr4+MjIyxIxGb/Cmvoa8vDxoaGh85DRUFbjMn6gac3V1hUQigUQiQadOnWRufVBUVITU1FR4eXmJmPDDiYiIgKGhodgxKsTV1RXR0dGwtbVFu3btEBwcjIcPH2LDhg1wdHQUOx69ZunSpQBKVq2Fh4dDR0dHeqyoqAgnTpxQ6B3ClRkLJKJqrEePHgCAuLg4fPnllzL/eWtoaMDCwkLhl/mXFoGlBEFAVlYWHjx4gP/+978iJqu4efPm4fnz5wCAH3/8EUOGDMGoUaPQqFEjhd38srr6+eefAZT83IWFhUFVVVV6rPR3LCwsTKx4VAmcg0SkBNatW4d+/frJ3AKhupgzZ47MYxUVFdSpUwft27dX2L/cX758CUEQoK2tDQBIS0vDzp074eDggC+//FLkdFSeDh06YMeOHahVq5bYUaiKsEAiIvrEdO7cGb6+vhg5ciSys7PRuHFjqKur4+HDhwgNDcWoUaPEjkhU7XGIjUgJFBUV4eeff8a2bdvK3Xjw8ePHIiWrvPJuofImenp6HzBJ1YmJiZEO3URERMDExASxsbHYvn07goODWSB9om7fvo09e/aU+ztW3n316NPGAolICcyZMwfh4eGYNGkSZsyYge+//x5paWnYtWsXgoODxY5XKQYGBv94r6vSVXyKstQ6NzcXurq6AIBDhw7B19cXKioqaNWqFW7duiVyOipPZGQkunfvDisrK1y/fh2Ojo5IS0uDIAhwc3MTOx5VAJf5EymBjRs3YuXKlZg0aRLU1NTQv39/hIeHIzg4GOfOnRM7XqWsWbMGxsbGmDp1Knbu3ImdO3di6tSpMDExwerVq3H06FEcO3YMR48eFTvqO7OxscGuXbuQkZGBgwcPonPnzgCA+/fvK0wvmLKZPn06Jk+ejMuXL6NGjRrYvn07MjIy0K5dO/Tp00fseFQRAhFVe9ra2sKtW7cEQRAEU1NT4dKlS4IgCEJKSoqgp6cnZrRK69ixo7Bp0ya59o0bNwrt2rX7+IGqwB9//CGoq6sLKioqwhdffCFtnzdvnuDl5SViMnoTHR0d4caNG4IgCIKBgYFw5coVQRAEIS4uTjA3NxcxGVUUe5CIlED9+vWRmZkJALC2tsahQ4cAANHR0XK3H1E0Z8+ehbu7u1y7u7s7Lly4IEKiyuvduzfS09Nx8eJFHDhwQNreqVMn6dwk+rTUrFlTOu+obt26SElJkR57+PChWLGoElggESmBnj17IjIyEgAwbtw4zJw5E7a2thgyZAiGDh0qcrrKadCgAVauXCnXHh4ejgYNGoiQqGqYmprC1dVVuqM2ALRo0UJhty6o7lq1aoVTp04BALy9vTFp0iT8+OOPGDp0KFq1aiVyOqoILvMnUkLnzp3DmTNnYGtri27duokdp1L279+PXr16wcbGBi1btgQAXLhwAcnJydi+fTu8vb1FTkjK4ObNm8jJyYGzszNevHiBSZMmSX/HQkNDYW5uLnZEek8skIiUwIkTJ9C6dWuZW40AQGFhIc6cOYPPP/9cpGRV4/bt21i2bBmuXbsGALC3t8fIkSMVugeJiMTFAolICfBO48Do0aMREhICIyMjsaNQNWRlZYXo6GjUrl1bpj07Oxtubm64efOmSMmoojgHiUgJCH/vA1TWo0ePULNmTRESfXy///77e20qSfQ+0tLSyv1DIy8vD3fu3BEhEVUWN4okqsZ8fX0BlNxp3N/fX2bFWlFRERISEtC6dWux4n1U7CynD2HPnj3Szw8ePAh9fX3p46KiIkRGRsLCwkKEZFRZLJCIqrHS/6wFQYCuri60tLSkxzQ0NNCqVSt8++23YsUjUng9evQAUPJHiJ+fn8wxdXV1WFhYYNGiRSIko8pigURUja1ZswYAYGFhgcmTJyvNcBrRx1JcXAwAsLS0RHR0NOe4VSOcpE2kBF6+fAlBEKCtrQ0AuHXrFnbu3AkHBwfpbSyqO11dXcTHx8PKykrsKKQksrOzYWBgIHYMqiBO0iZSAj4+Pli/fj2Akv+0W7RogUWLFsHHxwfLli0TOR2R4vvpp5+wdetW6eM+ffrA0NAQZmZmiI+PFzEZVRQLJCIlEBMTg7Zt2wIAIiIiYGpqilu3bmH9+vVYunSpyOk+jkGDBvFGr/TBhIWFSffdOnz4MI4cOYIDBw6gS5cumDJlisjpqCI4B4lICeTm5kJXVxcAcOjQIfj6+kJFRQWtWrXCrVu3RE73/hISEt75XGdnZwBgTxl9UFlZWdICae/evejbty86d+4MCwsL6Q7vpFhYIBEpARsbG+zatQs9e/bEwYMHERgYCAC4f/++QvaqNG3aFBKJ5I1L90uPSSQSpdgEk8RXq1YtZGRkoEGDBjhw4AB++OEHACUrSPkzqJhYIBEpgeDgYAwYMACBgYHo1KkTPDw8AJT0Jrm6uoqc7v2lpqaKHYFIhq+vLwYMGABbW1s8evQIXbp0AQDExsbCxsZG5HRUEVzFRqQksrKykJmZCRcXF+kd4i9cuAA9PT3eIZ6okgoKCrB06VKkp6fD399f+ofHzz//DF1dXQwfPlzkhPS+WCARVXMFBQXQ0tJCXFwcHB0dxY7zwVy9ehXp6enIz8+Xae/evbtIiUhZFBQUYMSIEZg5cyYsLS3FjkNVhENsRNWcuro6GjZsWG3nQdy8eRM9e/bE5cuXZeYlld57rrpeN3061NXVsX37dsycOVPsKFSFuMyfSAl8//33+O677/D48WOxo1S5gIAAWFpa4v79+9DW1sZff/2FEydOwN3dHcePHxc7HimJHj16YNeuXWLHoCrEITYiJeDq6oobN26goKAA5ubmcrcciYmJESlZ5RkZGeHo0aNwdnaGvr4+Lly4ADs7Oxw9ehSTJk1CbGys2BFJCfzwww9YtGgROnXqhGbNmsn9jo0fP16kZFRRHGIjUgKlN9SsjoqKiqR7PBkZGeHu3buws7ODubk5EhMTRU5HymLVqlUwMDDApUuXcOnSJZljEomEBZICYoFEpARmzZoldoQPxtHREfHx8bC0tETLli2xcOFCaGhoYMWKFbzvGn003Hqi+uEcJCIlkZ2djfDwcEyfPl06FykmJgZ37twROVnlzJgxQ3pH9ZCQEKSmpqJt27bYv3+/0txGhT4d+fn5SExMRGFhodhRqJI4B4lICSQkJMDT0xP6+vpIS0tDYmIirKysMGPGDKSnp0tvZFtdPH78GLVq1ZKuZCP60HJzczFu3DisW7cOAJCUlAQrKyuMGzcOZmZmmDZtmsgJ6X2xB4lICUycOBH+/v5ITk5GjRo1pO3e3t44ceKEiMkq7+nTp3Kr8wwNDfHkyRM8e/ZMpFSkbKZPn474+HgcP35c5nfM09MTW7duFTEZVRQLJCIlEB0djREjRsi1m5mZISsrS4REVefrr7/Gli1b5Nq3bduGr7/+WoREpIx27dqFX3/9FW3atJHpuWzSpAlSUlJETEYVxQKJSAloamqW25uSlJSEOnXqiJCo6pw/fx4dOnSQa2/fvj3Onz8vQiJSRg8ePICxsbFc+4sXLzjUq6BYIBEpge7duyMkJAQFBQUASpYdp6enIygoCL169RI5XeXk5eWVOyG2oKAAL1++FCERKSN3d3fs27dP+ri0KAoPD5feHJoUCydpEymBp0+fonfv3rh48SKeP3+OevXqISsrCx4eHti/f7/cpnaKpEOHDnB0dMQvv/wi0z5mzBgkJCTg5MmTIiUjZXLq1Cl06dIFgwYNwtq1azFixAhcvXoVZ86cQVRUFJo1ayZ2RHpPLJCIlMipU6eQkJCAnJwcuLm5wdPTU+xIlXb69Gl4enqiefPm6NSpEwAgMjIS0dHROHToENq2bStyQlIWKSkpWLBgAeLj46W/Y0FBQXBychI7GlUACyQiJZCRkYEGDRqIHeODiYuLw7///W/ExcVBS0sLzs7OmD59OmxtbcWORkQKigUSkRJQVVVFmzZtMGjQIPTu3Ru1atUSOxKRwnufbST09PQ+YBL6EFggESmB2NhYbNq0CVu2bMGDBw/g5eWFQYMGoVu3btDU1BQ73nt79uyZ9A3nn96k+MZEH4qKiso7r1ArKir6wGmoqrFAIlIigiDg+PHj2LRpE7Zv347i4mL4+vpi9erVYkd7L6qqqsjMzISxsfEb36QEQYBEIuEbE30wUVFR0s/T0tIwbdo0+Pv7S1etnT17FuvWrcP8+fPh5+cnVkyqIBZIREoqJiYGw4YNQ0JCgsIVEVFRUfjss8+gpqYm8yZVnnbt2n2kVKTMOnXqhOHDh6N///4y7Zs2bcKKFStw/PhxcYJRhbFAIlIit2/fxqZNm7Bp0yZcuXIFHh4eGDhwIEaOHCl2tAopLCzEvHnzMHToUNSvX1/sOKTEtLW1ER8fL7cwICkpCU2bNkVubq5IyaiiuFEkkRJYvnw52rVrB3Nzc6xfvx79+vVDSkoKTp48qbDFEQCoqanh3//+N++cTqJr0KABVq5cKdceHh5erVeQVmfsQSJSAg0aNED//v0xcOBAuLi4iB2nSvn4+MDX15dzPEhU+/fvR69evWBjY4OWLVsCAC5cuIDk5GRs374d3t7eIiek98UCiUgJCIKAp0+fYtWqVbh27RoAwMHBAcOGDYO+vr7I6SonLCwMc+bMwcCBA9GsWTO5XcG7d+8uUjJSNrdv38Z///tfXL9+HQBgb2+PkSNHsgdJQbFAIlICly5dwpdffokaNWqgRYsWAIDo6Gi8fPkShw4dgpubm8gJK05F5c0zBbiKjYgqigUSkRJo27YtbGxssHLlSqipqQEomeA8fPhw3Lx5EydOnBA5IZHiy87OxoULF3D//n0UFxfLHBsyZIhIqaiiWCARKQEtLS3ExsaicePGMu1Xr16Fu7s7V9gQVdKff/6JgQMHIicnB3p6ejJ7c0kkEjx+/FjEdFQRXMVGpAT09PSQnp4u156RkQFdXV0RElWtqKgodOvWDTY2NrCxsUH37t1x8uRJsWOREpk0aRKGDh2KnJwcZGdn48mTJ9IPFkeKiQUSkRLo168fhg0bhq1btyIjIwMZGRnYsmVLuRvbKZrff/8dnp6e0NbWxvjx4zF+/HhoaWmhU6dO2LRpk9jxSEncuXMH48ePh7a2tthRqIpwiI1ICeTn52PKlCkICwuT7hmkrq6OUaNGYcGCBQp5P7ZS9vb2+Ne//oXAwECZ9tDQUKxcuVK6ao/oQ/L19cXXX3+Nvn37ih2FqggLJCIlkpubi5SUFACAtbV1tfhrV1NTE3/99RdsbGxk2m/cuAFHR0e8evVKpGSkTFatWoWQkBB88803cHJygrq6usxxbjeheNTEDkBEH4+2tjacnJzEjlGlGjRogMjISLkC6ciRI9x/hj6ab7/9FgAQEhIid4zbTSgmFkhEpNAmTZqE8ePHIy4uDq1btwYAnD59GmvXrsWSJUtETkfKouyyflJ8HGIjIoW3c+dOLFq0SDrfyN7eHlOmTIGPj4/IyUhZlNdzVEoikWDmzJkfMQ1VBRZIREREleTq6irzuKCgAKmpqVBTU4O1tTViYmJESkYVxSE2IlJoVlZWiI6ORu3atWXas7Oz4ebmhps3b4qUjJRJbGysXNuzZ8/g7++Pnj17ipCIKos9SESk0FRUVJCVlQVjY2OZ9nv37qFhw4bIy8sTKRkRcPnyZXTr1g1paWliR6H3xB4kIlJIe/bskX5+8OBB6OvrSx8XFRUhMjISFhYWIiQj+n9Pnz7F06dPxY5BFcAeJCJSSCoqJTcCkEgkKPvfmLq6OiwsLLBo0SJ89dVXYsQjJbN06VKZx4IgIDMzExs2bEC7du24q7sCYoFERArN0tIS0dHRMDIyEjsKKTFLS0uZxyoqKqhTpw46duyI6dOnV4t7HiobFkhEVG28evUKNWrUEDsGEVUDvFktESm04uJizJ07F2ZmZtDR0ZGuWps5cyZWrVolcjoiUlQskIhIof3www9Yu3YtFi5cCA0NDWm7o6MjwsPDRUxGRIqMBRIRKbT169djxYoVGDhwIFRVVaXtLi4uuH79uojJiEiRsUAiIoV2584duRvVAiVDbwUFBSIkIqLqgAUSESk0BwcHnDx5Uq49IiJC7vYPRETvihtFEpFCCw4Ohp+fH+7cuYPi4mLs2LEDiYmJWL9+Pfbu3St2PCJSUFzmT0QK7+TJkwgJCUF8fDxycnLg5uaG4OBgdO7cWexoRKSgWCARERERlcEhNiKqFvLz83H//n0UFxfLtDds2FCkRESkyFggEZFCS05OxtChQ3HmzBmZdkEQIJFIUFRUJFIyIlJkLJCISKH5+/tDTU0Ne/fuRd26dSGRSMSORETVAOcgEZFCq1mzJi5duoTGjRuLHYWIqhHug0RECs3BwQEPHz4UOwYRVTPsQSIihfPs2TPp5xcvXsSMGTMwb948ODk5QV1dXeZcPT29jx2PiKoBFkhEpHBUVFRk5hqVTsh+HSdpE1FlcJI2ESmcY8eOAQDy8vLg5eWFsLAw2NnZiZyKiKoT9iARkUKrU6cOzpw5A1tbW7GjEFE1wknaRKTQBg0ahFWrVokdg4iqGQ6xEZFCKywsxOrVq3HkyBE0a9YMNWvWlDkeGhoqUjIiUmQskIhIoV25cgVubm4AgKSkJJlj3DSSiCqKc5CIiIiIyuAcJCIiIqIyWCARERERlcECiYiIiKgMFkhEREREZbBAIiL6B/7+/ujRo4f0cfv27TFhwoSPnuP48eOQSCTIzs7+6N+bSNmwQCIiheXv7w+JRAKJRAINDQ3Y2NggJCQEhYWFH/T77tixA3Pnzn2nc1nUECkm7oNERArNy8sLa9asQV5eHvbv348xY8ZAXV0d06dPlzkvPz8fGhoaVfI9DQ0Nq+R5iOjTxR4kIlJompqaMDU1hbm5OUaNGgVPT0/s2bNHOiz2448/ol69etKb2WZkZKBv374wMDCAoaEhfHx8kJaWJn2+oqIiTJw4EQYGBqhduzamTp2KstvFlR1iy8vLQ1BQEBo0aABNTU3Y2Nhg1apVSEtLQ4cOHQAAtWrVgkQigb+/PwCguLgY8+fPh6WlJbS0tODi4oKIiAiZ77N//340atQIWlpa6NChg0xOIvqwWCARUbWipaWF/Px8AEBkZCQSExNx+PBh7N27FwUFBfjyyy+hq6uLkydP4vTp09DR0YGXl5f0axYtWoS1a9di9erVOHXqFB4/foydO3e+9XsOGTIEmzdvxtKlS3Ht2jUsX74cOjo6aNCgAbZv3w4ASExMRGZmJpYsWQIAmD9/PtavX4+wsDD89ddfCAwMxKBBgxAVFQWgpJDz9fVFt27dEBcXh+HDh2PatGkf6p+NiMoSiIgUlJ+fn+Dj4yMIgiAUFxcLhw8fFjQ1NYXJkycLfn5+gomJiZCXlyc9f8OGDYKdnZ1QXFwsbcvLyxO0tLSEgwcPCoIgCHXr1hUWLlwoPV5QUCDUr19f+n0EQRDatWsnBAQECIIgCImJiQIA4fDhw+VmPHbsmABAePLkibTt1atXgra2tnDmzBmZc4cNGyb0799fEARBmD59uuDg4CBzPCgoSO65iOjD4BwkIlJoe/fuhY6ODgoKClBcXIwBAwZg9uzZGDNmDJycnGTmHcXHx+PGjRvQ1dWVeY5Xr14hJSUFT58+RWZmJlq2bCk9pqamBnd3d7lhtlJxcXFQVVVFu3bt3jnzjRs3kJubiy+++EKmPT8/H66urgCAa9euyeQAAA8Pj3f+HkRUOSyQiEihdejQAcuWLYOGhgbq1asHNbX//2+tZs2aMufm5OSgWbNm2Lhxo9zz1KlTp0LfX0tL672/JicnBwCwb98+mJmZyRzT1NSsUA4iqloskIhIodWsWRM2NjbvdK6bmxu2bt0KY2Nj6OnplXtO3bp1cf78eXz++ecAgMLCQly6dAlubm7lnu/k5ITi4mJERUXB09NT7nhpD1ZRUZG0zcHBAZqamkhPT39jz5O9vT327Nkj03bu3Ll/vkgiqhKcpE1ESmPgwIEwMjKCj48PTp48idTUVBw/fhzjx4/H7du3AQABAQFYsGABdu3ahevXr2P06NFv3cPIwsICfn5+GDp0KHbt2iV9zm3btgEAzM3NIZFIsHfvXjx48AA5OTnQ1dXF5MmTERgYiHXr1iElJQUxMTH45ZdfsG7dOgDAyJEjkZycjClTpiAxMRGbNm3C2rVrP/Q/ERH9jQUSESkNbW1tnDhxAg0bNoSvry/s7e0xbNgwvHr1StqjNGnSJAwePBh+fn7w8PCArq4uevbs+dbnXbZsGXr37o3Ro0ejcePG+Pbbb/HixQsAgJmZGebMmYNp06bBxMQEY8eOBQDMnTsXM2fOxPz582Fvbw8vLy/s27cPlpaWAICGDRti+/bt2LVrF1xcXBAWFoZ58+Z9wH8dInqdRHjTzEMiIiIiJcUeJCIiIqIyWCARERERlcECiYiIiKgMFkhEREREZbBAIiIiIiqDBRIRERFRGSyQiIiIiMpggURERERUBgskIiIiojJYIBERERGVwQKJiIiIqAwWSERERERl/B/ZvAv6SY/dUAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved: outputs/classical/tfidf_lr/confusion_matrix_type_test.png\n" - ] - } - ], - "source": [ - "# ── Confusion matrices ────────────────────────────────────────────────────────\n", - "save_confusion_matrix(\n", - " y_val_t, y_val_pred_type, STRATEGY_LABELS,\n", - " OUT_TFIDF / \"confusion_matrix_type_val.png\",\n", - " \"TF-IDF + LR — Type (Val)\"\n", - ")\n", - "save_confusion_matrix(\n", - " y_test_t, y_test_pred_type, STRATEGY_LABELS,\n", - " OUT_TFIDF / \"confusion_matrix_type_test.png\",\n", - " \"TF-IDF + LR — Type (Test)\"\n", - ")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Running grid search for type task...\n", + "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n", + "\n", + "Best params : {'lr__C': 3.0, 'lr__class_weight': 'balanced', 'tfidf__max_features': None, 'tfidf__min_df': 2, 'tfidf__ngram_range': (1, 2)}\n", + "Best CV F1 : 0.3840\n" + ] + } + ], + "source": [ + "# ── Define pipeline and grid (with class_weight) ──────────────────────────────\n", + "pipe_type = Pipeline([\n", + " (\"tfidf\", TfidfVectorizer(sublinear_tf=True, lowercase=True)),\n", + " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, solver=\"lbfgs\",\n", + " multi_class=\"auto\")),\n", + "])\n", + "\n", + "param_grid_type = {\n", + " \"tfidf__ngram_range\" : [(1, 1), (1, 2)],\n", + " \"tfidf__min_df\" : [2, 3, 5],\n", + " \"tfidf__max_features\": [None, 50_000],\n", + " \"lr__C\" : [0.1, 1.0, 3.0],\n", + " \"lr__class_weight\" : [None, \"balanced\"],\n", + "}\n", + "\n", + "cv_type = GroupKFold(n_splits=5)\n", + "\n", + "gs_type = GridSearchCV(\n", + " pipe_type, param_grid_type,\n", + " cv=cv_type, scoring=\"f1_macro\",\n", + " n_jobs=-1, verbose=1, refit=True,\n", + ")\n", + "\n", + "print(\"Running grid search for type task...\")\n", + "gs_type.fit(X_trainval_t, y_trainval_t, groups=groups_tv_t)\n", + "\n", + "print(f\"\\nBest params : {gs_type.best_params_}\")\n", + "print(f\"Best CV F1 : {gs_type.best_score_:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "huaF8TbjuKBd", + "outputId": "d43d2757-7486-4307-912b-462d285835ce" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "1_ZECP4MuKB6", - "outputId": "c09e503d-d620-47ec-8917-c494bc7f3f48" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved predictions_val_type.csv and predictions_test_type.csv\n" - ] - } - ], - "source": [ - "# ── Save predictions ──────────────────────────────────────────────────────────\n", - "# Decode integer predictions back to string labels\n", - "val_type_copy = val_type.copy(); val_type_copy[\"type_label\"] = y_val_t\n", - "test_type_copy = test_type.copy(); test_type_copy[\"type_label\"] = y_test_t\n", - "\n", - "val_type_copy[\"predicted_id\"] = y_val_pred_type\n", - "val_type_copy[\"predicted_label\"] = [id2label[i] for i in y_val_pred_type]\n", - "val_type_copy[\"true_label\"] = [id2label[i] for i in y_val_t]\n", - "val_type_copy[\"correct\"] = (val_type_copy[\"type_label\"] == val_type_copy[\"predicted_id\"]).astype(int)\n", - "\n", - "test_type_copy[\"predicted_id\"] = y_test_pred_type\n", - "test_type_copy[\"predicted_label\"] = [id2label[i] for i in y_test_pred_type]\n", - "test_type_copy[\"true_label\"] = [id2label[i] for i in y_test_t]\n", - "test_type_copy[\"correct\"] = (test_type_copy[\"type_label\"] == test_type_copy[\"predicted_id\"]).astype(int)\n", - "\n", - "val_type_copy.to_csv( OUT_TFIDF / \"predictions_val_type.csv\", index=False)\n", - "test_type_copy.to_csv(OUT_TFIDF / \"predictions_test_type.csv\", index=False)\n", - "print(\"Saved predictions_val_type.csv and predictions_test_type.csv\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/best_config_type.json\n" + ] + } + ], + "source": [ + "# ── Save best config ──────────────────────────────────────────────────────────\n", + "best_cfg_type = {\"task\": \"type\", \"model\": \"TF-IDF + LR\", \"seed\": SEED,\n", + " \"label_names\": STRATEGY_LABELS, \"label2id\": label2id,\n", + " \"best_params\": gs_type.best_params_, \"cv_f1_macro\": gs_type.best_score_}\n", + "with open(OUT_TFIDF / \"best_config_type.json\", \"w\") as f:\n", + " json.dump(best_cfg_type, f, indent=2)\n", + "print(\"Saved: outputs/classical/tfidf_lr/best_config_type.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "c4Hj46aWuKB6", + "outputId": "f2ac2819-bbf4-43a3-9437-9cfdafc29c12" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "xdfZfFRmuKB7" - }, - "source": [ - "## Char N-gram Variant (Optional)" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "[Val] Accuracy=0.8967 Macro-F1=0.8982 Weighted-F1=0.8961\n", + " precision recall f1-score support\n", + "\n", + " irony 0.92 0.87 0.90 942\n", + " overstatement 0.88 0.97 0.92 600\n", + "rhetorical_question 0.79 1.00 0.88 157\n", + " sarcasm 0.94 0.83 0.88 1317\n", + " satire 0.88 0.91 0.90 747\n", + " understatement 0.85 0.98 0.91 487\n", + "\n", + " accuracy 0.90 4250\n", + " macro avg 0.88 0.93 0.90 4250\n", + " weighted avg 0.90 0.90 0.90 4250\n", + "\n", + "\n", + "[Test] Accuracy=0.3958 Macro-F1=0.4047 Weighted-F1=0.3947\n", + " precision recall f1-score support\n", + "\n", + " irony 0.33 0.29 0.31 902\n", + " overstatement 0.38 0.43 0.40 592\n", + "rhetorical_question 0.42 0.60 0.50 176\n", + " sarcasm 0.48 0.42 0.45 1291\n", + " satire 0.38 0.39 0.38 833\n", + " understatement 0.36 0.43 0.39 456\n", + "\n", + " accuracy 0.40 4250\n", + " macro avg 0.39 0.43 0.40 4250\n", + " weighted avg 0.40 0.40 0.39 4250\n", + "\n", + "Saved: outputs/classical/tfidf_lr/metrics_type.json\n" + ] + } + ], + "source": [ + "# ── Evaluate on val and test ──────────────────────────────────────────────────\n", + "best_type = gs_type.best_estimator_\n", + "\n", + "val_metrics_type, y_val_pred_type = evaluate(best_type, X_val_t, y_val_t, STRATEGY_LABELS, \"Val\")\n", + "test_metrics_type, y_test_pred_type = evaluate(best_type, X_test_t, y_test_t, STRATEGY_LABELS, \"Test\")\n", + "\n", + "all_metrics_type = {\"val\": val_metrics_type, \"test\": test_metrics_type}\n", + "for split_m in all_metrics_type.values():\n", + " split_m.pop(\"report\", None)\n", + "\n", + "with open(OUT_TFIDF / \"metrics_type.json\", \"w\") as f:\n", + " json.dump(all_metrics_type, f, indent=2)\n", + "print(\"Saved: outputs/classical/tfidf_lr/metrics_type.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 }, + "id": "ldimkyrXuKB6", + "outputId": "e8e286df-267e-4802-8318-d0cc9f768808" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3Ff1Tz1iuKB7", - "outputId": "4209ddba-9cd6-46f9-e66d-f62f1ab5709f" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Char n-gram best C : {'lr__C': 3.0}\n", - "Char n-gram best CV F1 : 0.8305\n", - "Word n-gram best CV F1 : 0.8143\n", - "\n", - "\n", - "[Test (char)] Accuracy=0.8284 Macro-F1=0.8283 Weighted-F1=0.8283\n", - " precision recall f1-score support\n", - "\n", - "non-sarcastic 0.82 0.84 0.83 4250\n", - " sarcastic 0.83 0.82 0.83 4250\n", - "\n", - " accuracy 0.83 8500\n", - " macro avg 0.83 0.83 0.83 8500\n", - " weighted avg 0.83 0.83 0.83 8500\n", - "\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "# Char n-gram model for binary task (stylistic cues)\n", - "pipe_char = Pipeline([\n", - " (\"tfidf\", TfidfVectorizer(analyzer=\"char_wb\", ngram_range=(3, 5),\n", - " sublinear_tf=True, lowercase=True, max_features=50_000)),\n", - " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, C=1.0)),\n", - "])\n", - "\n", - "param_grid_char = {\"lr__C\": [0.1, 1.0, 3.0]}\n", - "gs_char = GridSearchCV(pipe_char, param_grid_char, cv=GroupKFold(5),\n", - " scoring=\"f1_macro\", n_jobs=-1)\n", - "gs_char.fit(X_trainval, y_trainval, groups=groups_tv)\n", - "\n", - "print(f\"Char n-gram best C : {gs_char.best_params_}\")\n", - "print(f\"Char n-gram best CV F1 : {gs_char.best_score_:.4f}\")\n", - "print(f\"Word n-gram best CV F1 : {gs_bin.best_score_:.4f}\")\n", - "print()\n", - "char_metrics, _ = evaluate(gs_char.best_estimator_, X_test, y_test, label_names_bin, \"Test (char)\")" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "metadata": { - "id": "t_pVeRFVuKB7" - }, - "source": [ - "## Summary" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/confusion_matrix_type_val.png\n" + ] }, { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OszOI0q1uKB7", - "outputId": "532102b9-cd0f-40e8-b1e1-48fa8520b526" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====== TF-IDF + LR RESULTS SUMMARY ======\n", - "\n", - "Task A — Binary Classification (Test Set):\n", - " Accuracy : 0.8189\n", - " Macro-F1 : 0.8189\n", - " Weighted-F1 : 0.8189\n", - "\n", - "Task B — Type Classification (Test Set):\n", - " Accuracy : 0.3958\n", - " Macro-F1 : 0.4047\n", - " Weighted-F1 : 0.3947\n", - "\n", - "Best hyperparameters:\n", - " Binary: {'lr__C': 3.0, 'tfidf__max_features': None, 'tfidf__min_df': 3, 'tfidf__ngram_range': (1, 2)}\n", - " Type: {'lr__C': 3.0, 'lr__class_weight': 'balanced', 'tfidf__max_features': None, 'tfidf__min_df': 2, 'tfidf__ngram_range': (1, 2)}\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "print(\"====== TF-IDF + LR RESULTS SUMMARY ======\")\n", - "print()\n", - "print(\"Task A — Binary Classification (Test Set):\")\n", - "print(f\" Accuracy : {test_metrics_bin['accuracy']:.4f}\")\n", - "print(f\" Macro-F1 : {test_metrics_bin['f1_macro']:.4f}\")\n", - "print(f\" Weighted-F1 : {test_metrics_bin['f1_weighted']:.4f}\")\n", - "print()\n", - "print(\"Task B — Type Classification (Test Set):\")\n", - "print(f\" Accuracy : {test_metrics_type['accuracy']:.4f}\")\n", - "print(f\" Macro-F1 : {test_metrics_type['f1_macro']:.4f}\")\n", - "print(f\" Weighted-F1 : {test_metrics_type['f1_weighted']:.4f}\")\n", - "print()\n", - "print(\"Best hyperparameters:\")\n", - "print(\" Binary:\", gs_bin.best_params_)\n", - "print(\" Type: \", gs_type.best_params_)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ftKo2U-1uKB7" - }, - "source": [ - "---\n", - "# Part 3 — Naive Bayes Baseline" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "QZo05bYauKB7" - }, - "source": [ - "# Notebook 03 — Naive Bayes Baseline\n", - "\n", - "**Purpose**: Train and evaluate Naive Bayes classifiers for:\n", - "- Task A: Binary classification (sarcastic vs non-sarcastic)\n", - "- Task B: Sarcasm type classification (6-class)\n", - "\n", - "Compares:\n", - "- `CountVectorizer + MultinomialNB`\n", - "- `TfidfVectorizer + MultinomialNB`\n", - "- `CountVectorizer + ComplementNB` (for imbalanced type task)\n", - "\n", - "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", - "\n", - "**Outputs** in `outputs/classical/naive_bayes/`" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "metadata": { - "id": "yeP7YbDYuKB8" - }, - "source": [ - "## Helper Functions" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "id": "PoPH1ijBuKB8" - }, - "outputs": [], - "source": [ - "def load_splits(task: str):\n", - " train = pd.read_csv(SPLITS / f\"train_{task}.csv\")\n", - " val = pd.read_csv(SPLITS / f\"val_{task}.csv\")\n", - " test = pd.read_csv(SPLITS / f\"test_{task}.csv\")\n", - " return train, val, test\n", - "\n", - "\n", - "def evaluate(model, X, y_true, label_names=None, split_name=\"\"):\n", - " y_pred = model.predict(X)\n", - " metrics = {\n", - " \"split\" : split_name,\n", - " \"accuracy\" : accuracy_score(y_true, y_pred),\n", - " \"precision_macro\" : precision_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", - " \"recall_macro\" : recall_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", - " \"f1_macro\" : f1_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", - " \"f1_weighted\" : f1_score(y_true, y_pred, average=\"weighted\", zero_division=0),\n", - " }\n", - " print(f\"[{split_name}] Accuracy={metrics['accuracy']:.4f} \"\n", - " f\"Macro-F1={metrics['f1_macro']:.4f} Weighted-F1={metrics['f1_weighted']:.4f}\")\n", - " print(classification_report(y_true, y_pred, target_names=label_names, zero_division=0))\n", - " return metrics, y_pred\n", - "\n", - "\n", - "def save_confusion_matrix(y_true, y_pred, label_names, out_path, title=\"\"):\n", - " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", - " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", - " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Greens\",\n", - " xticklabels=label_names, yticklabels=label_names, ax=ax)\n", - " ax.set_xlabel(\"Predicted\"); ax.set_ylabel(\"True\"); ax.set_title(title)\n", - " plt.tight_layout()\n", - " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", - " plt.show()\n", - " print(f\"Saved: {out_path.relative_to(ROOT)}\")\n", - "\n", - "\n", - "def run_nb_grid(\n", - " vectorizer_cls, nb_cls, param_grid: dict,\n", - " X_trainval, y_trainval, groups_tv,\n", - " name: str\n", - ") -> GridSearchCV:\n", - " \"\"\"Run a grid search for a given vectorizer + NB combination.\"\"\"\n", - " pipe = Pipeline([\n", - " (\"vec\", vectorizer_cls(lowercase=True)),\n", - " (\"nb\", nb_cls()),\n", - " ])\n", - " gs = GridSearchCV(\n", - " pipe, param_grid, cv=GroupKFold(5),\n", - " scoring=\"f1_macro\", n_jobs=-1, verbose=0, refit=True\n", - " )\n", - " gs.fit(X_trainval, y_trainval, groups=groups_tv)\n", - " print(f\"{name:50s} CV F1={gs.best_score_:.4f} params={gs.best_params_}\")\n", - " return gs" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/confusion_matrix_type_test.png\n" + ] + } + ], + "source": [ + "# ── Confusion matrices ────────────────────────────────────────────────────────\n", + "save_confusion_matrix(\n", + " y_val_t, y_val_pred_type, STRATEGY_LABELS,\n", + " OUT_TFIDF / \"confusion_matrix_type_val.png\",\n", + " \"TF-IDF + LR — Type (Val)\"\n", + ")\n", + "save_confusion_matrix(\n", + " y_test_t, y_test_pred_type, STRATEGY_LABELS,\n", + " OUT_TFIDF / \"confusion_matrix_type_test.png\",\n", + " \"TF-IDF + LR — Type (Test)\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "1_ZECP4MuKB6", + "outputId": "c09e503d-d620-47ec-8917-c494bc7f3f48" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "0oL34vMHuKB8" - }, - "source": [ - "## Task A — Binary Classification" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved predictions_val_type.csv and predictions_test_type.csv\n" + ] + } + ], + "source": [ + "# ── Save predictions ──────────────────────────────────────────────────────────\n", + "# Decode integer predictions back to string labels\n", + "val_type_copy = val_type.copy(); val_type_copy[\"type_label\"] = y_val_t\n", + "test_type_copy = test_type.copy(); test_type_copy[\"type_label\"] = y_test_t\n", + "\n", + "val_type_copy[\"predicted_id\"] = y_val_pred_type\n", + "val_type_copy[\"predicted_label\"] = [id2label[i] for i in y_val_pred_type]\n", + "val_type_copy[\"true_label\"] = [id2label[i] for i in y_val_t]\n", + "val_type_copy[\"correct\"] = (val_type_copy[\"type_label\"] == val_type_copy[\"predicted_id\"]).astype(int)\n", + "\n", + "test_type_copy[\"predicted_id\"] = y_test_pred_type\n", + "test_type_copy[\"predicted_label\"] = [id2label[i] for i in y_test_pred_type]\n", + "test_type_copy[\"true_label\"] = [id2label[i] for i in y_test_t]\n", + "test_type_copy[\"correct\"] = (test_type_copy[\"type_label\"] == test_type_copy[\"predicted_id\"]).astype(int)\n", + "\n", + "val_type_copy.to_csv( OUT_TFIDF / \"predictions_val_type.csv\", index=False)\n", + "test_type_copy.to_csv(OUT_TFIDF / \"predictions_test_type.csv\", index=False)\n", + "print(\"Saved predictions_val_type.csv and predictions_test_type.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xdfZfFRmuKB7" + }, + "source": [ + "## Char N-gram Variant (Optional)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "3Ff1Tz1iuKB7", + "outputId": "4209ddba-9cd6-46f9-e66d-f62f1ab5709f" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_STzCoqRuKB8", - "outputId": "c86245b0-ee21-4612-a5d5-dd2fba99214b" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train+Val: 48,166 Val: 8,500 Test: 8,500\n" - ] - } - ], - "source": [ - "train_bin, val_bin, test_bin = load_splits(\"binary\")\n", - "trainval_bin = pd.concat([train_bin, val_bin], ignore_index=True)\n", - "\n", - "X_tv = trainval_bin[\"text\"].tolist()\n", - "y_tv = trainval_bin[\"binary_label\"].tolist()\n", - "grp_tv = trainval_bin[\"group_id\"].tolist()\n", - "\n", - "X_val = val_bin[\"text\"].tolist(); y_val = val_bin[\"binary_label\"].tolist()\n", - "X_test = test_bin[\"text\"].tolist(); y_test = test_bin[\"binary_label\"].tolist()\n", - "\n", - "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", - "\n", - "print(f\"Train+Val: {len(X_tv):,} Val: {len(X_val):,} Test: {len(X_test):,}\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Char n-gram best C : {'lr__C': 3.0}\n", + "Char n-gram best CV F1 : 0.8305\n", + "Word n-gram best CV F1 : 0.8143\n", + "\n", + "\n", + "[Test (char)] Accuracy=0.8284 Macro-F1=0.8283 Weighted-F1=0.8283\n", + " precision recall f1-score support\n", + "\n", + "non-sarcastic 0.82 0.84 0.83 4250\n", + " sarcastic 0.83 0.82 0.83 4250\n", + "\n", + " accuracy 0.83 8500\n", + " macro avg 0.83 0.83 0.83 8500\n", + " weighted avg 0.83 0.83 0.83 8500\n", + "\n" + ] + } + ], + "source": [ + "# Char n-gram model for binary task (stylistic cues)\n", + "pipe_char = Pipeline([\n", + " (\"tfidf\", TfidfVectorizer(analyzer=\"char_wb\", ngram_range=(3, 5),\n", + " sublinear_tf=True, lowercase=True, max_features=50_000)),\n", + " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, C=1.0)),\n", + "])\n", + "\n", + "param_grid_char = {\"lr__C\": [0.1, 1.0, 3.0]}\n", + "gs_char = GridSearchCV(pipe_char, param_grid_char, cv=GroupKFold(5),\n", + " scoring=\"f1_macro\", n_jobs=-1)\n", + "gs_char.fit(X_trainval, y_trainval, groups=groups_tv)\n", + "\n", + "print(f\"Char n-gram best C : {gs_char.best_params_}\")\n", + "print(f\"Char n-gram best CV F1 : {gs_char.best_score_:.4f}\")\n", + "print(f\"Word n-gram best CV F1 : {gs_bin.best_score_:.4f}\")\n", + "print()\n", + "char_metrics, _ = evaluate(gs_char.best_estimator_, X_test, y_test, label_names_bin, \"Test (char)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "t_pVeRFVuKB7" + }, + "source": [ + "## Summary" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "OszOI0q1uKB7", + "outputId": "532102b9-cd0f-40e8-b1e1-48fa8520b526" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "iOJgCEMwuKB8", - "outputId": "d37b770b-3b8f-4b49-ac5e-aeb6b7a0d37a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=== Binary: Comparing Vectorizer + NB Combinations ===\n", - "CountVectorizer + MultinomialNB CV F1=0.7855 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", - "TfidfVectorizer + MultinomialNB CV F1=0.7897 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", - "CountVectorizer + ComplementNB CV F1=0.7855 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n" - ] - } - ], - "source": [ - "# ── Shared grid parameters ────────────────────────────────────────────────────\n", - "BASE_GRID = {\n", - " \"vec__ngram_range\": [(1, 1), (1, 2)],\n", - " \"vec__min_df\" : [2, 3, 5],\n", - " \"nb__alpha\" : [0.1, 0.5, 1.0],\n", - "}\n", - "\n", - "print(\"=== Binary: Comparing Vectorizer + NB Combinations ===\")\n", - "\n", - "gs_count_mnb_bin = run_nb_grid(\n", - " CountVectorizer, MultinomialNB, BASE_GRID,\n", - " X_tv, y_tv, grp_tv,\n", - " \"CountVectorizer + MultinomialNB\"\n", - ")\n", - "\n", - "gs_tfidf_mnb_bin = run_nb_grid(\n", - " TfidfVectorizer, MultinomialNB, BASE_GRID,\n", - " X_tv, y_tv, grp_tv,\n", - " \"TfidfVectorizer + MultinomialNB\"\n", - ")\n", - "\n", - "gs_count_cnb_bin = run_nb_grid(\n", - " CountVectorizer, ComplementNB, BASE_GRID,\n", - " X_tv, y_tv, grp_tv,\n", - " \"CountVectorizer + ComplementNB\"\n", - ")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "====== TF-IDF + LR RESULTS SUMMARY ======\n", + "\n", + "Task A — Binary Classification (Test Set):\n", + " Accuracy : 0.8189\n", + " Macro-F1 : 0.8189\n", + " Weighted-F1 : 0.8189\n", + "\n", + "Task B — Type Classification (Test Set):\n", + " Accuracy : 0.3958\n", + " Macro-F1 : 0.4047\n", + " Weighted-F1 : 0.3947\n", + "\n", + "Best hyperparameters:\n", + " Binary: {'lr__C': 3.0, 'tfidf__max_features': None, 'tfidf__min_df': 3, 'tfidf__ngram_range': (1, 2)}\n", + " Type: {'lr__C': 3.0, 'lr__class_weight': 'balanced', 'tfidf__max_features': None, 'tfidf__min_df': 2, 'tfidf__ngram_range': (1, 2)}\n" + ] + } + ], + "source": [ + "print(\"====== TF-IDF + LR RESULTS SUMMARY ======\")\n", + "print()\n", + "print(\"Task A — Binary Classification (Test Set):\")\n", + "print(f\" Accuracy : {test_metrics_bin['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {test_metrics_bin['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1 : {test_metrics_bin['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"Task B — Type Classification (Test Set):\")\n", + "print(f\" Accuracy : {test_metrics_type['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {test_metrics_type['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1 : {test_metrics_type['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"Best hyperparameters:\")\n", + "print(\" Binary:\", gs_bin.best_params_)\n", + "print(\" Type: \", gs_type.best_params_)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ftKo2U-1uKB7" + }, + "source": [ + "---\n", + "# Part 3 — Naive Bayes Baseline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QZo05bYauKB7" + }, + "source": [ + "# Notebook 03 — Naive Bayes Baseline\n", + "\n", + "**Purpose**: Train and evaluate Naive Bayes classifiers for:\n", + "- Task A: Binary classification (sarcastic vs non-sarcastic)\n", + "- Task B: Sarcasm type classification (6-class)\n", + "\n", + "Compares:\n", + "- `CountVectorizer + MultinomialNB`\n", + "- `TfidfVectorizer + MultinomialNB`\n", + "- `CountVectorizer + ComplementNB` (for imbalanced type task)\n", + "\n", + "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", + "\n", + "**Outputs** in `outputs/classical/naive_bayes/`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yeP7YbDYuKB8" + }, + "source": [ + "## Helper Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "id": "PoPH1ijBuKB8" + }, + "outputs": [], + "source": [ + "def load_splits(task: str):\n", + " train = pd.read_csv(SPLITS / f\"train_{task}.csv\")\n", + " val = pd.read_csv(SPLITS / f\"val_{task}.csv\")\n", + " test = pd.read_csv(SPLITS / f\"test_{task}.csv\")\n", + " return train, val, test\n", + "\n", + "\n", + "def evaluate(model, X, y_true, label_names=None, split_name=\"\"):\n", + " y_pred = model.predict(X)\n", + " metrics = {\n", + " \"split\" : split_name,\n", + " \"accuracy\" : accuracy_score(y_true, y_pred),\n", + " \"precision_macro\" : precision_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"recall_macro\" : recall_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"f1_macro\" : f1_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"f1_weighted\" : f1_score(y_true, y_pred, average=\"weighted\", zero_division=0),\n", + " }\n", + " print(f\"[{split_name}] Accuracy={metrics['accuracy']:.4f} \"\n", + " f\"Macro-F1={metrics['f1_macro']:.4f} Weighted-F1={metrics['f1_weighted']:.4f}\")\n", + " print(classification_report(y_true, y_pred, target_names=label_names, zero_division=0))\n", + " return metrics, y_pred\n", + "\n", + "\n", + "def save_confusion_matrix(y_true, y_pred, label_names, out_path, title=\"\"):\n", + " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", + " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", + " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Greens\",\n", + " xticklabels=label_names, yticklabels=label_names, ax=ax)\n", + " ax.set_xlabel(\"Predicted\"); ax.set_ylabel(\"True\"); ax.set_title(title)\n", + " plt.tight_layout()\n", + " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + " print(f\"Saved: {out_path.relative_to(ROOT)}\")\n", + "\n", + "\n", + "def run_nb_grid(\n", + " vectorizer_cls, nb_cls, param_grid: dict,\n", + " X_trainval, y_trainval, groups_tv,\n", + " name: str\n", + ") -> GridSearchCV:\n", + " \"\"\"Run a grid search for a given vectorizer + NB combination.\"\"\"\n", + " pipe = Pipeline([\n", + " (\"vec\", vectorizer_cls(lowercase=True)),\n", + " (\"nb\", nb_cls()),\n", + " ])\n", + " gs = GridSearchCV(\n", + " pipe, param_grid, cv=GroupKFold(5),\n", + " scoring=\"f1_macro\", n_jobs=-1, verbose=0, refit=True\n", + " )\n", + " gs.fit(X_trainval, y_trainval, groups=groups_tv)\n", + " print(f\"{name:50s} CV F1={gs.best_score_:.4f} params={gs.best_params_}\")\n", + " return gs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0oL34vMHuKB8" + }, + "source": [ + "## Task A — Binary Classification" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "_STzCoqRuKB8", + "outputId": "c86245b0-ee21-4612-a5d5-dd2fba99214b" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "eilEzm18uKB9", - "outputId": "360ce0ff-df02-45a3-bcb3-8213ae795e53" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Best binary NB model: TfidfVec+MultinomialNB (CV F1=0.7897)\n", - "\n", - "Comparison table:\n", - " model cv_f1_macro best_params\n", - "CountVec+MultinomialNB 0.785542 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", - "TfidfVec+MultinomialNB 0.789663 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", - " CountVec+ComplementNB 0.785542 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n" - ] - } - ], - "source": [ - "# ── Select best binary model ──────────────────────────────────────────────────\n", - "candidates_bin = [\n", - " (\"CountVec+MultinomialNB\", gs_count_mnb_bin),\n", - " (\"TfidfVec+MultinomialNB\", gs_tfidf_mnb_bin),\n", - " (\"CountVec+ComplementNB\", gs_count_cnb_bin),\n", - "]\n", - "\n", - "best_name_bin, best_gs_bin = max(candidates_bin, key=lambda x: x[1].best_score_)\n", - "print(f\"\\nBest binary NB model: {best_name_bin} (CV F1={best_gs_bin.best_score_:.4f})\")\n", - "\n", - "# Comparison table\n", - "comp_df = pd.DataFrame([\n", - " {\"model\": name, \"cv_f1_macro\": gs.best_score_, \"best_params\": str(gs.best_params_)}\n", - " for name, gs in candidates_bin\n", - "])\n", - "print(\"\\nComparison table:\")\n", - "print(comp_df.to_string(index=False))" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Train+Val: 48,166 Val: 8,500 Test: 8,500\n" + ] + } + ], + "source": [ + "train_bin, val_bin, test_bin = load_splits(\"binary\")\n", + "trainval_bin = pd.concat([train_bin, val_bin], ignore_index=True)\n", + "\n", + "X_tv = trainval_bin[\"text\"].tolist()\n", + "y_tv = trainval_bin[\"binary_label\"].tolist()\n", + "grp_tv = trainval_bin[\"group_id\"].tolist()\n", + "\n", + "X_val = val_bin[\"text\"].tolist(); y_val = val_bin[\"binary_label\"].tolist()\n", + "X_test = test_bin[\"text\"].tolist(); y_test = test_bin[\"binary_label\"].tolist()\n", + "\n", + "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", + "\n", + "print(f\"Train+Val: {len(X_tv):,} Val: {len(X_val):,} Test: {len(X_test):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "iOJgCEMwuKB8", + "outputId": "d37b770b-3b8f-4b49-ac5e-aeb6b7a0d37a" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Zh5ipG6BuKB9", - "outputId": "7f108910-a2fb-4154-eb36-f3f68b5a4dc2" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Val] Accuracy=0.8826 Macro-F1=0.8826 Weighted-F1=0.8826\n", - " precision recall f1-score support\n", - "\n", - "non-sarcastic 0.89 0.87 0.88 4250\n", - " sarcastic 0.87 0.90 0.88 4250\n", - "\n", - " accuracy 0.88 8500\n", - " macro avg 0.88 0.88 0.88 8500\n", - " weighted avg 0.88 0.88 0.88 8500\n", - "\n", - "[Test] Accuracy=0.7905 Macro-F1=0.7902 Weighted-F1=0.7902\n", - " precision recall f1-score support\n", - "\n", - "non-sarcastic 0.81 0.76 0.78 4250\n", - " sarcastic 0.77 0.83 0.80 4250\n", - "\n", - " accuracy 0.79 8500\n", - " macro avg 0.79 0.79 0.79 8500\n", - " weighted avg 0.79 0.79 0.79 8500\n", - "\n", - "\n", - "--- All models on Test ---\n", - " CountVec+MultinomialNB : acc=0.7887 macro-F1=0.7886\n", - " TfidfVec+MultinomialNB : acc=0.7905 macro-F1=0.7902\n", - " CountVec+ComplementNB : acc=0.7887 macro-F1=0.7886\n" - ] - } - ], - "source": [ - "# ── Evaluate best model ───────────────────────────────────────────────────────\n", - "best_model_bin = best_gs_bin.best_estimator_\n", - "\n", - "val_m_bin, y_val_pred_bin = evaluate(best_model_bin, X_val, y_val, label_names_bin, \"Val\")\n", - "test_m_bin, y_test_pred_bin = evaluate(best_model_bin, X_test, y_test, label_names_bin, \"Test\")\n", - "\n", - "# Also evaluate all three models on test for comparison\n", - "print(\"\\n--- All models on Test ---\")\n", - "all_test_results_bin = []\n", - "for name, gs in candidates_bin:\n", - " y_pred_t = gs.best_estimator_.predict(X_test)\n", - " f1 = f1_score(y_test, y_pred_t, average=\"macro\")\n", - " acc = accuracy_score(y_test, y_pred_t)\n", - " all_test_results_bin.append({\"model\": name, \"accuracy\": acc, \"f1_macro\": f1})\n", - " print(f\" {name:40s}: acc={acc:.4f} macro-F1={f1:.4f}\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "=== Binary: Comparing Vectorizer + NB Combinations ===\n", + "CountVectorizer + MultinomialNB CV F1=0.7855 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", + "TfidfVectorizer + MultinomialNB CV F1=0.7897 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", + "CountVectorizer + ComplementNB CV F1=0.7855 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n" + ] + } + ], + "source": [ + "# ── Shared grid parameters ────────────────────────────────────────────────────\n", + "BASE_GRID = {\n", + " \"vec__ngram_range\": [(1, 1), (1, 2)],\n", + " \"vec__min_df\" : [2, 3, 5],\n", + " \"nb__alpha\" : [0.1, 0.5, 1.0],\n", + "}\n", + "\n", + "print(\"=== Binary: Comparing Vectorizer + NB Combinations ===\")\n", + "\n", + "gs_count_mnb_bin = run_nb_grid(\n", + " CountVectorizer, MultinomialNB, BASE_GRID,\n", + " X_tv, y_tv, grp_tv,\n", + " \"CountVectorizer + MultinomialNB\"\n", + ")\n", + "\n", + "gs_tfidf_mnb_bin = run_nb_grid(\n", + " TfidfVectorizer, MultinomialNB, BASE_GRID,\n", + " X_tv, y_tv, grp_tv,\n", + " \"TfidfVectorizer + MultinomialNB\"\n", + ")\n", + "\n", + "gs_count_cnb_bin = run_nb_grid(\n", + " CountVectorizer, ComplementNB, BASE_GRID,\n", + " X_tv, y_tv, grp_tv,\n", + " \"CountVectorizer + ComplementNB\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "eilEzm18uKB9", + "outputId": "360ce0ff-df02-45a3-bcb3-8213ae795e53" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "D74tnzAduKB9", - "outputId": "1ccb358e-1daa-42ec-8aa0-09bada09116b" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved: outputs/classical/naive_bayes/confusion_matrix_binary_test.png\n", - "All binary artifacts saved.\n" - ] - } - ], - "source": [ - "# ── Save binary results ───────────────────────────────────────────────────────\n", - "cfg_bin = {\n", - " \"task\": \"binary\", \"best_model\": best_name_bin,\n", - " \"best_params\": best_gs_bin.best_params_, \"cv_f1_macro\": best_gs_bin.best_score_,\n", - " \"all_candidates\": [\n", - " {\"model\": name, \"cv_f1_macro\": gs.best_score_, \"params\": gs.best_params_}\n", - " for name, gs in candidates_bin\n", - " ]\n", - "}\n", - "with open(OUT_NB / \"best_config_binary.json\", \"w\") as f:\n", - " json.dump(cfg_bin, f, indent=2)\n", - "\n", - "all_m_bin = {\"val\": val_m_bin, \"test\": test_m_bin,\n", - " \"all_test_comparison\": all_test_results_bin}\n", - "with open(OUT_NB / \"metrics_binary.json\", \"w\") as f:\n", - " json.dump(all_m_bin, f, indent=2)\n", - "\n", - "save_confusion_matrix(\n", - " y_val, y_val_pred_bin, label_names_bin,\n", - " OUT_NB / \"confusion_matrix_binary_val.png\",\n", - " f\"NB ({best_name_bin}) — Binary (Val)\"\n", - ")\n", - "save_confusion_matrix(\n", - " y_test, y_test_pred_bin, label_names_bin,\n", - " OUT_NB / \"confusion_matrix_binary_test.png\",\n", - " f\"NB ({best_name_bin}) — Binary (Test)\"\n", - ")\n", - "\n", - "# Predictions\n", - "pred_val_bin = val_bin.copy()\n", - "pred_val_bin[\"predicted\"] = y_val_pred_bin\n", - "pred_val_bin[\"correct\"] = (pred_val_bin[\"binary_label\"] == pred_val_bin[\"predicted\"]).astype(int)\n", - "pred_val_bin.to_csv(OUT_NB / \"predictions_val_binary.csv\", index=False)\n", - "\n", - "pred_test_bin = test_bin.copy()\n", - "pred_test_bin[\"predicted\"] = y_test_pred_bin\n", - "pred_test_bin[\"correct\"] = (pred_test_bin[\"binary_label\"] == pred_test_bin[\"predicted\"]).astype(int)\n", - "pred_test_bin.to_csv(OUT_NB / \"predictions_test_binary.csv\", index=False)\n", - "\n", - "print(\"All binary artifacts saved.\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Best binary NB model: TfidfVec+MultinomialNB (CV F1=0.7897)\n", + "\n", + "Comparison table:\n", + " model cv_f1_macro best_params\n", + "CountVec+MultinomialNB 0.785542 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", + "TfidfVec+MultinomialNB 0.789663 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", + " CountVec+ComplementNB 0.785542 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n" + ] + } + ], + "source": [ + "# ── Select best binary model ──────────────────────────────────────────────────\n", + "candidates_bin = [\n", + " (\"CountVec+MultinomialNB\", gs_count_mnb_bin),\n", + " (\"TfidfVec+MultinomialNB\", gs_tfidf_mnb_bin),\n", + " (\"CountVec+ComplementNB\", gs_count_cnb_bin),\n", + "]\n", + "\n", + "best_name_bin, best_gs_bin = max(candidates_bin, key=lambda x: x[1].best_score_)\n", + "print(f\"\\nBest binary NB model: {best_name_bin} (CV F1={best_gs_bin.best_score_:.4f})\")\n", + "\n", + "# Comparison table\n", + "comp_df = pd.DataFrame([\n", + " {\"model\": name, \"cv_f1_macro\": gs.best_score_, \"best_params\": str(gs.best_params_)}\n", + " for name, gs in candidates_bin\n", + "])\n", + "print(\"\\nComparison table:\")\n", + "print(comp_df.to_string(index=False))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "Zh5ipG6BuKB9", + "outputId": "7f108910-a2fb-4154-eb36-f3f68b5a4dc2" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "UtChhm0-uKB9" - }, - "source": [ - "## Task B — Sarcasm Type Classification" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "[Val] Accuracy=0.8826 Macro-F1=0.8826 Weighted-F1=0.8826\n", + " precision recall f1-score support\n", + "\n", + "non-sarcastic 0.89 0.87 0.88 4250\n", + " sarcastic 0.87 0.90 0.88 4250\n", + "\n", + " accuracy 0.88 8500\n", + " macro avg 0.88 0.88 0.88 8500\n", + " weighted avg 0.88 0.88 0.88 8500\n", + "\n", + "[Test] Accuracy=0.7905 Macro-F1=0.7902 Weighted-F1=0.7902\n", + " precision recall f1-score support\n", + "\n", + "non-sarcastic 0.81 0.76 0.78 4250\n", + " sarcastic 0.77 0.83 0.80 4250\n", + "\n", + " accuracy 0.79 8500\n", + " macro avg 0.79 0.79 0.79 8500\n", + " weighted avg 0.79 0.79 0.79 8500\n", + "\n", + "\n", + "--- All models on Test ---\n", + " CountVec+MultinomialNB : acc=0.7887 macro-F1=0.7886\n", + " TfidfVec+MultinomialNB : acc=0.7905 macro-F1=0.7902\n", + " CountVec+ComplementNB : acc=0.7887 macro-F1=0.7886\n" + ] + } + ], + "source": [ + "# ── Evaluate best model ───────────────────────────────────────────────────────\n", + "best_model_bin = best_gs_bin.best_estimator_\n", + "\n", + "val_m_bin, y_val_pred_bin = evaluate(best_model_bin, X_val, y_val, label_names_bin, \"Val\")\n", + "test_m_bin, y_test_pred_bin = evaluate(best_model_bin, X_test, y_test, label_names_bin, \"Test\")\n", + "\n", + "# Also evaluate all three models on test for comparison\n", + "print(\"\\n--- All models on Test ---\")\n", + "all_test_results_bin = []\n", + "for name, gs in candidates_bin:\n", + " y_pred_t = gs.best_estimator_.predict(X_test)\n", + " f1 = f1_score(y_test, y_pred_t, average=\"macro\")\n", + " acc = accuracy_score(y_test, y_pred_t)\n", + " all_test_results_bin.append({\"model\": name, \"accuracy\": acc, \"f1_macro\": f1})\n", + " print(f\" {name:40s}: acc={acc:.4f} macro-F1={f1:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 }, + "id": "D74tnzAduKB9", + "outputId": "1ccb358e-1daa-42ec-8aa0-09bada09116b" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Rh8sdjlhuKB9", - "outputId": "36e9daa4-b705-4b92-8601-af8ee580ff33" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n", - "Train+Val: 24,083 Val: 4,250 Test: 4,250\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "train_type, val_type, test_type = load_splits(\"type\")\n", - "\n", - "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", - "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", - "id2label = {i: lab for lab, i in label2id.items()}\n", - "\n", - "def enc(df): return [label2id[l] for l in df[\"type_label\"]]\n", - "\n", - "trainval_type = pd.concat([train_type, val_type], ignore_index=True)\n", - "X_tv_t = trainval_type[\"text\"].tolist()\n", - "y_tv_t = enc(trainval_type)\n", - "grp_tv_t = trainval_type[\"group_id\"].tolist()\n", - "\n", - "X_val_t = val_type[\"text\"].tolist(); y_val_t = enc(val_type)\n", - "X_test_t = test_type[\"text\"].tolist(); y_test_t = enc(test_type)\n", - "\n", - "print(f\"Strategies: {STRATEGY_LABELS}\")\n", - "print(f\"Train+Val: {len(X_tv_t):,} Val: {len(X_val_t):,} Test: {len(X_test_t):,}\")" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ZupB5UlvuKB-", - "outputId": "bb5e86d3-745c-4c41-9650-d399e4668e32" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=== Type: Comparing Vectorizer + NB Combinations ===\n", - "CountVectorizer + MultinomialNB CV F1=0.3811 params={'nb__alpha': 0.5, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n", - "TfidfVectorizer + MultinomialNB CV F1=0.3422 params={'nb__alpha': 0.1, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n", - "CountVectorizer + ComplementNB (for imbalance) CV F1=0.3777 params={'nb__alpha': 1.0, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n" - ] - } - ], - "source": [ - "print(\"=== Type: Comparing Vectorizer + NB Combinations ===\")\n", - "\n", - "gs_count_mnb_type = run_nb_grid(\n", - " CountVectorizer, MultinomialNB, BASE_GRID,\n", - " X_tv_t, y_tv_t, grp_tv_t,\n", - " \"CountVectorizer + MultinomialNB\"\n", - ")\n", - "\n", - "gs_tfidf_mnb_type = run_nb_grid(\n", - " TfidfVectorizer, MultinomialNB, BASE_GRID,\n", - " X_tv_t, y_tv_t, grp_tv_t,\n", - " \"TfidfVectorizer + MultinomialNB\"\n", - ")\n", - "\n", - "gs_count_cnb_type = run_nb_grid(\n", - " CountVectorizer, ComplementNB, BASE_GRID,\n", - " X_tv_t, y_tv_t, grp_tv_t,\n", - " \"CountVectorizer + ComplementNB (for imbalance)\"\n", - ")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/naive_bayes/confusion_matrix_binary_val.png\n" + ] }, { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "J_CunIwDuKB-", - "outputId": "45a1ccc2-6af0-4542-b371-7573a94afba6" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best type NB model: CountVec+MultinomialNB (CV F1=0.3811)\n", - "\n", - "--- All models on Test (Type Task) ---\n", - " CountVec+MultinomialNB : acc=0.3953 macro-F1=0.3953\n", - " TfidfVec+MultinomialNB : acc=0.3800 macro-F1=0.3450\n", - " CountVec+ComplementNB : acc=0.3875 macro-F1=0.3880\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "candidates_type = [\n", - " (\"CountVec+MultinomialNB\", gs_count_mnb_type),\n", - " (\"TfidfVec+MultinomialNB\", gs_tfidf_mnb_type),\n", - " (\"CountVec+ComplementNB\", gs_count_cnb_type),\n", - "]\n", - "\n", - "best_name_type, best_gs_type = max(candidates_type, key=lambda x: x[1].best_score_)\n", - "print(f\"Best type NB model: {best_name_type} (CV F1={best_gs_type.best_score_:.4f})\")\n", - "\n", - "print(\"\\n--- All models on Test (Type Task) ---\")\n", - "all_test_results_type = []\n", - "for name, gs in candidates_type:\n", - " y_pred_t = gs.best_estimator_.predict(X_test_t)\n", - " f1 = f1_score(y_test_t, y_pred_t, average=\"macro\")\n", - " acc = accuracy_score(y_test_t, y_pred_t)\n", - " all_test_results_type.append({\"model\": name, \"accuracy\": acc, \"f1_macro\": f1})\n", - " print(f\" {name:40s}: acc={acc:.4f} macro-F1={f1:.4f}\")" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "o6JHLJnLuKB-", - "outputId": "5f5f236e-83ce-4a58-9f49-92e0f472d39e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Val] Accuracy=0.7694 Macro-F1=0.7744 Weighted-F1=0.7693\n", - " precision recall f1-score support\n", - "\n", - " irony 0.80 0.72 0.76 942\n", - " overstatement 0.75 0.80 0.77 600\n", - "rhetorical_question 0.78 0.88 0.83 157\n", - " sarcasm 0.79 0.78 0.79 1317\n", - " satire 0.73 0.77 0.75 747\n", - " understatement 0.75 0.75 0.75 487\n", - "\n", - " accuracy 0.77 4250\n", - " macro avg 0.77 0.78 0.77 4250\n", - " weighted avg 0.77 0.77 0.77 4250\n", - "\n", - "[Test] Accuracy=0.3953 Macro-F1=0.3953 Weighted-F1=0.3929\n", - " precision recall f1-score support\n", - "\n", - " irony 0.32 0.29 0.30 902\n", - " overstatement 0.39 0.40 0.39 592\n", - "rhetorical_question 0.52 0.41 0.46 176\n", - " sarcasm 0.45 0.51 0.47 1291\n", - " satire 0.36 0.34 0.35 833\n", - " understatement 0.40 0.38 0.39 456\n", - "\n", - " accuracy 0.40 4250\n", - " macro avg 0.41 0.39 0.40 4250\n", - " weighted avg 0.39 0.40 0.39 4250\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved: outputs/classical/naive_bayes/confusion_matrix_type_test.png\n", - "All type artifacts saved.\n" - ] - } - ], - "source": [ - "best_model_type = best_gs_type.best_estimator_\n", - "\n", - "val_m_type, y_val_pred_type = evaluate(best_model_type, X_val_t, y_val_t, STRATEGY_LABELS, \"Val\")\n", - "test_m_type, y_test_pred_type = evaluate(best_model_type, X_test_t, y_test_t, STRATEGY_LABELS, \"Test\")\n", - "\n", - "# Save\n", - "cfg_type = {\n", - " \"task\": \"type\", \"best_model\": best_name_type,\n", - " \"label_names\": STRATEGY_LABELS, \"label2id\": label2id,\n", - " \"best_params\": best_gs_type.best_params_, \"cv_f1_macro\": best_gs_type.best_score_,\n", - "}\n", - "with open(OUT_NB / \"best_config_type.json\", \"w\") as f:\n", - " json.dump(cfg_type, f, indent=2)\n", - "\n", - "all_m_type = {\"val\": val_m_type, \"test\": test_m_type,\n", - " \"all_test_comparison\": all_test_results_type}\n", - "with open(OUT_NB / \"metrics_type.json\", \"w\") as f:\n", - " json.dump(all_m_type, f, indent=2)\n", - "\n", - "save_confusion_matrix(\n", - " y_val_t, y_val_pred_type, STRATEGY_LABELS,\n", - " OUT_NB / \"confusion_matrix_type_val.png\",\n", - " f\"NB ({best_name_type}) — Type (Val)\"\n", - ")\n", - "save_confusion_matrix(\n", - " y_test_t, y_test_pred_type, STRATEGY_LABELS,\n", - " OUT_NB / \"confusion_matrix_type_test.png\",\n", - " f\"NB ({best_name_type}) — Type (Test)\"\n", - ")\n", - "\n", - "# Predictions with string labels\n", - "pred_val_type = val_type.copy()\n", - "pred_test_type = test_type.copy()\n", - "for df, y_pred, y_true in [(pred_val_type, y_val_pred_type, y_val_t),\n", - " (pred_test_type, y_test_pred_type, y_test_t)]:\n", - " df[\"predicted_label\"] = [id2label[i] for i in y_pred]\n", - " df[\"true_label\"] = [id2label[i] for i in y_true]\n", - " df[\"correct\"] = (df[\"type_label\"] == df[\"predicted_label\"]).astype(int)\n", - "\n", - "pred_val_type.to_csv( OUT_NB / \"predictions_val_type.csv\", index=False)\n", - "pred_test_type.to_csv(OUT_NB / \"predictions_test_type.csv\", index=False)\n", - "\n", - "print(\"All type artifacts saved.\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/naive_bayes/confusion_matrix_binary_test.png\n", + "All binary artifacts saved.\n" + ] + } + ], + "source": [ + "# ── Save binary results ───────────────────────────────────────────────────────\n", + "cfg_bin = {\n", + " \"task\": \"binary\", \"best_model\": best_name_bin,\n", + " \"best_params\": best_gs_bin.best_params_, \"cv_f1_macro\": best_gs_bin.best_score_,\n", + " \"all_candidates\": [\n", + " {\"model\": name, \"cv_f1_macro\": gs.best_score_, \"params\": gs.best_params_}\n", + " for name, gs in candidates_bin\n", + " ]\n", + "}\n", + "with open(OUT_NB / \"best_config_binary.json\", \"w\") as f:\n", + " json.dump(cfg_bin, f, indent=2)\n", + "\n", + "all_m_bin = {\"val\": val_m_bin, \"test\": test_m_bin,\n", + " \"all_test_comparison\": all_test_results_bin}\n", + "with open(OUT_NB / \"metrics_binary.json\", \"w\") as f:\n", + " json.dump(all_m_bin, f, indent=2)\n", + "\n", + "save_confusion_matrix(\n", + " y_val, y_val_pred_bin, label_names_bin,\n", + " OUT_NB / \"confusion_matrix_binary_val.png\",\n", + " f\"NB ({best_name_bin}) — Binary (Val)\"\n", + ")\n", + "save_confusion_matrix(\n", + " y_test, y_test_pred_bin, label_names_bin,\n", + " OUT_NB / \"confusion_matrix_binary_test.png\",\n", + " f\"NB ({best_name_bin}) — Binary (Test)\"\n", + ")\n", + "\n", + "# Predictions\n", + "pred_val_bin = val_bin.copy()\n", + "pred_val_bin[\"predicted\"] = y_val_pred_bin\n", + "pred_val_bin[\"correct\"] = (pred_val_bin[\"binary_label\"] == pred_val_bin[\"predicted\"]).astype(int)\n", + "pred_val_bin.to_csv(OUT_NB / \"predictions_val_binary.csv\", index=False)\n", + "\n", + "pred_test_bin = test_bin.copy()\n", + "pred_test_bin[\"predicted\"] = y_test_pred_bin\n", + "pred_test_bin[\"correct\"] = (pred_test_bin[\"binary_label\"] == pred_test_bin[\"predicted\"]).astype(int)\n", + "pred_test_bin.to_csv(OUT_NB / \"predictions_test_binary.csv\", index=False)\n", + "\n", + "print(\"All binary artifacts saved.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UtChhm0-uKB9" + }, + "source": [ + "## Task B — Sarcasm Type Classification" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "Rh8sdjlhuKB9", + "outputId": "36e9daa4-b705-4b92-8601-af8ee580ff33" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "TwTBPa86uKB-" - }, - "source": [ - "## Error Examples" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n", + "Train+Val: 24,083 Val: 4,250 Test: 4,250\n" + ] + } + ], + "source": [ + "train_type, val_type, test_type = load_splits(\"type\")\n", + "\n", + "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", + "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", + "id2label = {i: lab for lab, i in label2id.items()}\n", + "\n", + "def enc(df): return [label2id[l] for l in df[\"type_label\"]]\n", + "\n", + "trainval_type = pd.concat([train_type, val_type], ignore_index=True)\n", + "X_tv_t = trainval_type[\"text\"].tolist()\n", + "y_tv_t = enc(trainval_type)\n", + "grp_tv_t = trainval_type[\"group_id\"].tolist()\n", + "\n", + "X_val_t = val_type[\"text\"].tolist(); y_val_t = enc(val_type)\n", + "X_test_t = test_type[\"text\"].tolist(); y_test_t = enc(test_type)\n", + "\n", + "print(f\"Strategies: {STRATEGY_LABELS}\")\n", + "print(f\"Train+Val: {len(X_tv_t):,} Val: {len(X_val_t):,} Test: {len(X_test_t):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "ZupB5UlvuKB-", + "outputId": "bb5e86d3-745c-4c41-9650-d399e4668e32" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "b8itQIPtuKB-", - "outputId": "6805f04f-a1c4-4005-a8f7-4496ad84a58b" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Binary errors on test: 1781 total\n", - " False Positives (predicted sarcastic, actually not): 1039\n", - " False Negatives (predicted not-sarcastic, actually sarcastic): 742\n", - "\n", - "--- Sample FP ---\n", - " [True=0, Pred=1] Probability researchers note a coincidence where three coworkers wear the same shirt color.\n", - " [True=0, Pred=1] The global-warming crisis contributed to a delightful mid-February afternoon.\n", - " [True=0, Pred=1] Signs for the upcoming road section make it sound formidable or exciting.\n", - " [True=0, Pred=1] A father recounts how he saved $4.27 through quick thinking.\n", - " [True=0, Pred=1] The wedding album begins with a photo of two acorns floating in a glass of water.\n", - " [True=0, Pred=1] A governor is too embarrassed to say which state he leads.\n", - " [True=0, Pred=1] A man who plays devil's advocate may actually just want to be disagreeable.\n", - " [True=0, Pred=1] Area dad watched a show about bigfoot last night.\n", - " [True=0, Pred=1] Coroner's report cites systemic issues in Alton Sterling's death.\n", - " [True=0, Pred=1] Preschool child asks to look at a classmate's notes on shapes.\n", - "\n", - "--- Sample FN ---\n", - " [True=1, Pred=0] state department warns americans traveling abroad to avoid lame amsterdam windmill tour\n", - " [True=1, Pred=0] hollywood's biggest stars endure long lines at oscars security screening\n", - " [True=1, Pred=0] historians suggest 'goodfellas' youtube clips may be fragments of larger work\n", - " [True=1, Pred=0] members of opening band walking among crowd during intermission like gods among men\n", - " [True=1, Pred=0] woman who's been on the pill for years thinking about switching to new set of debilitating side effe\n", - " [True=1, Pred=0] congressman lets his guitar do the talking\n", - " [True=1, Pred=0] officials warn consumers of counterfeit tickets ahead of solar eclipse\n", - " [True=1, Pred=0] anderson cooper throws another box of letters from gay children into dumpster\n", - " [True=1, Pred=0] non-priest arrested on charges of child molestation\n", - " [True=1, Pred=0] huckabee sanders tells colleagues she's taking temporary post as google ceo before transitioning int\n" - ] - } - ], - "source": [ - "# ── Binary error examples ─────────────────────────────────────────────────────\n", - "err_bin = pred_test_bin[pred_test_bin[\"correct\"] == 0].copy()\n", - "fp_bin = err_bin[err_bin[\"binary_label\"] == 0].head(10) # predicted sarcastic, actually not\n", - "fn_bin = err_bin[err_bin[\"binary_label\"] == 1].head(10) # predicted not-sarcastic, actually sarcastic\n", - "\n", - "print(f\"Binary errors on test: {len(err_bin)} total\")\n", - "print(f\" False Positives (predicted sarcastic, actually not): {len(err_bin[err_bin['binary_label']==0])}\")\n", - "print(f\" False Negatives (predicted not-sarcastic, actually sarcastic): {len(err_bin[err_bin['binary_label']==1])}\")\n", - "print(\"\\n--- Sample FP ---\")\n", - "for _, row in fp_bin.iterrows():\n", - " print(f\" [True=0, Pred=1] {row['text'][:100]}\")\n", - "print(\"\\n--- Sample FN ---\")\n", - "for _, row in fn_bin.iterrows():\n", - " print(f\" [True=1, Pred=0] {row['text'][:100]}\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "=== Type: Comparing Vectorizer + NB Combinations ===\n", + "CountVectorizer + MultinomialNB CV F1=0.3811 params={'nb__alpha': 0.5, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n", + "TfidfVectorizer + MultinomialNB CV F1=0.3422 params={'nb__alpha': 0.1, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n", + "CountVectorizer + ComplementNB (for imbalance) CV F1=0.3777 params={'nb__alpha': 1.0, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n" + ] + } + ], + "source": [ + "print(\"=== Type: Comparing Vectorizer + NB Combinations ===\")\n", + "\n", + "gs_count_mnb_type = run_nb_grid(\n", + " CountVectorizer, MultinomialNB, BASE_GRID,\n", + " X_tv_t, y_tv_t, grp_tv_t,\n", + " \"CountVectorizer + MultinomialNB\"\n", + ")\n", + "\n", + "gs_tfidf_mnb_type = run_nb_grid(\n", + " TfidfVectorizer, MultinomialNB, BASE_GRID,\n", + " X_tv_t, y_tv_t, grp_tv_t,\n", + " \"TfidfVectorizer + MultinomialNB\"\n", + ")\n", + "\n", + "gs_count_cnb_type = run_nb_grid(\n", + " CountVectorizer, ComplementNB, BASE_GRID,\n", + " X_tv_t, y_tv_t, grp_tv_t,\n", + " \"CountVectorizer + ComplementNB (for imbalance)\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "J_CunIwDuKB-", + "outputId": "45a1ccc2-6af0-4542-b371-7573a94afba6" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "zmVdkTHuuKB_", - "outputId": "5bc30ec0-0f97-4f57-a165-706c7a022a82" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Type errors on test: 2570 total\n", - "\n", - "Confusion pairs (true → predicted):\n", - "type_label predicted_label\n", - "irony sarcasm 309\n", - "satire sarcasm 227\n", - "sarcasm irony 226\n", - " satire 203\n", - "satire irony 155\n", - "irony satire 155\n", - "overstatement sarcasm 117\n", - "understatement sarcasm 103\n", - "overstatement satire 102\n", - "satire overstatement 101\n" - ] - } - ], - "source": [ - "# ── Type error examples ───────────────────────────────────────────────────────\n", - "err_type = pred_test_type[pred_test_type[\"correct\"] == 0].copy()\n", - "print(f\"Type errors on test: {len(err_type)} total\")\n", - "print(\"\\nConfusion pairs (true → predicted):\")\n", - "conf_pairs = err_type.groupby([\"type_label\", \"predicted_label\"]).size().sort_values(ascending=False).head(10)\n", - "print(conf_pairs.to_string())" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Best type NB model: CountVec+MultinomialNB (CV F1=0.3811)\n", + "\n", + "--- All models on Test (Type Task) ---\n", + " CountVec+MultinomialNB : acc=0.3953 macro-F1=0.3953\n", + " TfidfVec+MultinomialNB : acc=0.3800 macro-F1=0.3450\n", + " CountVec+ComplementNB : acc=0.3875 macro-F1=0.3880\n" + ] + } + ], + "source": [ + "candidates_type = [\n", + " (\"CountVec+MultinomialNB\", gs_count_mnb_type),\n", + " (\"TfidfVec+MultinomialNB\", gs_tfidf_mnb_type),\n", + " (\"CountVec+ComplementNB\", gs_count_cnb_type),\n", + "]\n", + "\n", + "best_name_type, best_gs_type = max(candidates_type, key=lambda x: x[1].best_score_)\n", + "print(f\"Best type NB model: {best_name_type} (CV F1={best_gs_type.best_score_:.4f})\")\n", + "\n", + "print(\"\\n--- All models on Test (Type Task) ---\")\n", + "all_test_results_type = []\n", + "for name, gs in candidates_type:\n", + " y_pred_t = gs.best_estimator_.predict(X_test_t)\n", + " f1 = f1_score(y_test_t, y_pred_t, average=\"macro\")\n", + " acc = accuracy_score(y_test_t, y_pred_t)\n", + " all_test_results_type.append({\"model\": name, \"accuracy\": acc, \"f1_macro\": f1})\n", + " print(f\" {name:40s}: acc={acc:.4f} macro-F1={f1:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 }, + "id": "o6JHLJnLuKB-", + "outputId": "5f5f236e-83ce-4a58-9f49-92e0f472d39e" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "p52YKmRQuKB_" - }, - "source": [ - "## Summary" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "[Val] Accuracy=0.7694 Macro-F1=0.7744 Weighted-F1=0.7693\n", + " precision recall f1-score support\n", + "\n", + " irony 0.80 0.72 0.76 942\n", + " overstatement 0.75 0.80 0.77 600\n", + "rhetorical_question 0.78 0.88 0.83 157\n", + " sarcasm 0.79 0.78 0.79 1317\n", + " satire 0.73 0.77 0.75 747\n", + " understatement 0.75 0.75 0.75 487\n", + "\n", + " accuracy 0.77 4250\n", + " macro avg 0.77 0.78 0.77 4250\n", + " weighted avg 0.77 0.77 0.77 4250\n", + "\n", + "[Test] Accuracy=0.3953 Macro-F1=0.3953 Weighted-F1=0.3929\n", + " precision recall f1-score support\n", + "\n", + " irony 0.32 0.29 0.30 902\n", + " overstatement 0.39 0.40 0.39 592\n", + "rhetorical_question 0.52 0.41 0.46 176\n", + " sarcasm 0.45 0.51 0.47 1291\n", + " satire 0.36 0.34 0.35 833\n", + " understatement 0.40 0.38 0.39 456\n", + "\n", + " accuracy 0.40 4250\n", + " macro avg 0.41 0.39 0.40 4250\n", + " weighted avg 0.39 0.40 0.39 4250\n", + "\n" + ] }, { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "tg9D9LKxuKB_", - "outputId": "bf1f11d1-8feb-4b8c-c9fd-adac380993b2" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====== NAIVE BAYES RESULTS SUMMARY ======\n", - "\n", - "Task A — Binary (Test):\n", - " Best model : TfidfVec+MultinomialNB\n", - " Accuracy : 0.7905\n", - " Macro-F1 : 0.7902\n", - " Weighted-F1 : 0.7902\n", - "\n", - "Task B — Type (Test):\n", - " Best model : CountVec+MultinomialNB\n", - " Accuracy : 0.3953\n", - " Macro-F1 : 0.3953\n", - " Weighted-F1 : 0.3929\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "print(\"====== NAIVE BAYES RESULTS SUMMARY ======\")\n", - "print()\n", - "print(\"Task A — Binary (Test):\")\n", - "print(f\" Best model : {best_name_bin}\")\n", - "print(f\" Accuracy : {test_m_bin['accuracy']:.4f}\")\n", - "print(f\" Macro-F1 : {test_m_bin['f1_macro']:.4f}\")\n", - "print(f\" Weighted-F1 : {test_m_bin['f1_weighted']:.4f}\")\n", - "print()\n", - "print(\"Task B — Type (Test):\")\n", - "print(f\" Best model : {best_name_type}\")\n", - "print(f\" Accuracy : {test_m_type['accuracy']:.4f}\")\n", - "print(f\" Macro-F1 : {test_m_type['f1_macro']:.4f}\")\n", - "print(f\" Weighted-F1 : {test_m_type['f1_weighted']:.4f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GZHqRh2iuKB_" - }, - "source": [ - "---\n", - "# Part 4 — BERT / DistilBERT Classification" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "metadata": { - "id": "xR_41E_1uKB_" - }, - "source": [ - "# Notebook 04 — BERT / DistilBERT Classification\n", - "\n", - "**Purpose**: Fine-tune transformer models for sarcasm classification.\n", - "- Task A: Binary (sarcastic vs non-sarcastic)\n", - "- Task B: Sarcasm type (6-class, sarcastic only)\n", - "\n", - "**Models**:\n", - "- `distilbert-base-uncased` (primary, fast)\n", - "- `bert-base-uncased` (optional, if compute allows)\n", - "\n", - "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", - "\n", - "**Outputs** in `outputs/bert/distilbert_binary/` and `outputs/bert/distilbert_type/`" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/naive_bayes/confusion_matrix_type_val.png\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "id": "5Gi8VSJDuKB_" - }, - "source": [ - "## Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "K_FNBk2ouKB_", - "outputId": "70f6d378-7098-410c-d656-50135e3b0e12" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Binary config: TrainConfig(model_name='distilbert-base-uncased', max_length=128, batch_size=32, lr=2e-05, weight_decay=0.01, warmup_ratio=0.1, epochs=10, patience=3, seed=42, task='binary', use_class_weights=False)\n", - "Type config: TrainConfig(model_name='distilbert-base-uncased', max_length=128, batch_size=32, lr=2e-05, weight_decay=0.01, warmup_ratio=0.1, epochs=10, patience=3, seed=42, task='type', use_class_weights=True)\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "
" ], - "source": [ - "@dataclass\n", - "class TrainConfig:\n", - " model_name : str = \"distilbert-base-uncased\"\n", - " max_length : int = 128\n", - " batch_size : int = 32\n", - " lr : float = 2e-5\n", - " weight_decay : float = 0.01\n", - " warmup_ratio : float = 0.1\n", - " epochs : int = 10\n", - " patience : int = 3 # early stopping patience\n", - " seed : int = SEED\n", - " task : str = \"binary\" # 'binary' or 'type'\n", - " use_class_weights: bool = False\n", - "\n", - "# ── Config for binary task ────────────────────────────────────────────────────\n", - "CFG_BINARY = TrainConfig(\n", - " model_name=\"distilbert-base-uncased\",\n", - " task=\"binary\",\n", - " batch_size=32,\n", - " lr=2e-5,\n", - " use_class_weights=False,\n", - ")\n", - "\n", - "# ── Config for type task (use class weights for imbalance) ────────────────────\n", - "CFG_TYPE = TrainConfig(\n", - " model_name=\"distilbert-base-uncased\",\n", - " task=\"type\",\n", - " batch_size=32,\n", - " lr=2e-5,\n", - " use_class_weights=True,\n", - ")\n", - "\n", - "print(\"Binary config:\", CFG_BINARY)\n", - "print(\"Type config: \", CFG_TYPE)" - ] + "image/png": 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\n" + }, + "metadata": {} }, { - "cell_type": "markdown", - "metadata": { - "id": "OKgvtBDPuKCA" - }, - "source": [ - "## Dataset Class" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/naive_bayes/confusion_matrix_type_test.png\n", + "All type artifacts saved.\n" + ] + } + ], + "source": [ + "best_model_type = best_gs_type.best_estimator_\n", + "\n", + "val_m_type, y_val_pred_type = evaluate(best_model_type, X_val_t, y_val_t, STRATEGY_LABELS, \"Val\")\n", + "test_m_type, y_test_pred_type = evaluate(best_model_type, X_test_t, y_test_t, STRATEGY_LABELS, \"Test\")\n", + "\n", + "# Save\n", + "cfg_type = {\n", + " \"task\": \"type\", \"best_model\": best_name_type,\n", + " \"label_names\": STRATEGY_LABELS, \"label2id\": label2id,\n", + " \"best_params\": best_gs_type.best_params_, \"cv_f1_macro\": best_gs_type.best_score_,\n", + "}\n", + "with open(OUT_NB / \"best_config_type.json\", \"w\") as f:\n", + " json.dump(cfg_type, f, indent=2)\n", + "\n", + "all_m_type = {\"val\": val_m_type, \"test\": test_m_type,\n", + " \"all_test_comparison\": all_test_results_type}\n", + "with open(OUT_NB / \"metrics_type.json\", \"w\") as f:\n", + " json.dump(all_m_type, f, indent=2)\n", + "\n", + "save_confusion_matrix(\n", + " y_val_t, y_val_pred_type, STRATEGY_LABELS,\n", + " OUT_NB / \"confusion_matrix_type_val.png\",\n", + " f\"NB ({best_name_type}) — Type (Val)\"\n", + ")\n", + "save_confusion_matrix(\n", + " y_test_t, y_test_pred_type, STRATEGY_LABELS,\n", + " OUT_NB / \"confusion_matrix_type_test.png\",\n", + " f\"NB ({best_name_type}) — Type (Test)\"\n", + ")\n", + "\n", + "# Predictions with string labels\n", + "pred_val_type = val_type.copy()\n", + "pred_test_type = test_type.copy()\n", + "for df, y_pred, y_true in [(pred_val_type, y_val_pred_type, y_val_t),\n", + " (pred_test_type, y_test_pred_type, y_test_t)]:\n", + " df[\"predicted_label\"] = [id2label[i] for i in y_pred]\n", + " df[\"true_label\"] = [id2label[i] for i in y_true]\n", + " df[\"correct\"] = (df[\"type_label\"] == df[\"predicted_label\"]).astype(int)\n", + "\n", + "pred_val_type.to_csv( OUT_NB / \"predictions_val_type.csv\", index=False)\n", + "pred_test_type.to_csv(OUT_NB / \"predictions_test_type.csv\", index=False)\n", + "\n", + "print(\"All type artifacts saved.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TwTBPa86uKB-" + }, + "source": [ + "## Error Examples" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "b8itQIPtuKB-", + "outputId": "6805f04f-a1c4-4005-a8f7-4496ad84a58b" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "id": "tHoVxhSAuKCA" - }, - "outputs": [], - "source": [ - "import torch\n", - "from torch.utils.data import Dataset\n", - "\n", - "class HeadlineDataset(Dataset):\n", - " \"\"\"PyTorch Dataset for headline classification.\"\"\"\n", - "\n", - " def __init__(\n", - " self,\n", - " texts: list[str],\n", - " labels: list[int],\n", - " tokenizer,\n", - " max_length: int = 128,\n", - " ):\n", - " self.texts = texts\n", - " self.labels = labels\n", - " self.tokenizer = tokenizer\n", - " self.max_length = max_length\n", - "\n", - " def __len__(self) -> int:\n", - " return len(self.texts)\n", - "\n", - " def __getitem__(self, idx: int) -> dict:\n", - " encoding = self.tokenizer(\n", - " self.texts[idx],\n", - " truncation=True,\n", - " padding=\"max_length\",\n", - " max_length=self.max_length,\n", - " return_tensors=\"pt\",\n", - " )\n", - " return {\n", - " \"input_ids\" : encoding[\"input_ids\"].squeeze(0),\n", - " \"attention_mask\" : encoding[\"attention_mask\"].squeeze(0),\n", - " \"labels\" : torch.tensor(self.labels[idx], dtype=torch.long),\n", - " }" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Binary errors on test: 1781 total\n", + " False Positives (predicted sarcastic, actually not): 1039\n", + " False Negatives (predicted not-sarcastic, actually sarcastic): 742\n", + "\n", + "--- Sample FP ---\n", + " [True=0, Pred=1] Probability researchers note a coincidence where three coworkers wear the same shirt color.\n", + " [True=0, Pred=1] The global-warming crisis contributed to a delightful mid-February afternoon.\n", + " [True=0, Pred=1] Signs for the upcoming road section make it sound formidable or exciting.\n", + " [True=0, Pred=1] A father recounts how he saved $4.27 through quick thinking.\n", + " [True=0, Pred=1] The wedding album begins with a photo of two acorns floating in a glass of water.\n", + " [True=0, Pred=1] A governor is too embarrassed to say which state he leads.\n", + " [True=0, Pred=1] A man who plays devil's advocate may actually just want to be disagreeable.\n", + " [True=0, Pred=1] Area dad watched a show about bigfoot last night.\n", + " [True=0, Pred=1] Coroner's report cites systemic issues in Alton Sterling's death.\n", + " [True=0, Pred=1] Preschool child asks to look at a classmate's notes on shapes.\n", + "\n", + "--- Sample FN ---\n", + " [True=1, Pred=0] state department warns americans traveling abroad to avoid lame amsterdam windmill tour\n", + " [True=1, Pred=0] hollywood's biggest stars endure long lines at oscars security screening\n", + " [True=1, Pred=0] historians suggest 'goodfellas' youtube clips may be fragments of larger work\n", + " [True=1, Pred=0] members of opening band walking among crowd during intermission like gods among men\n", + " [True=1, Pred=0] woman who's been on the pill for years thinking about switching to new set of debilitating side effe\n", + " [True=1, Pred=0] congressman lets his guitar do the talking\n", + " [True=1, Pred=0] officials warn consumers of counterfeit tickets ahead of solar eclipse\n", + " [True=1, Pred=0] anderson cooper throws another box of letters from gay children into dumpster\n", + " [True=1, Pred=0] non-priest arrested on charges of child molestation\n", + " [True=1, Pred=0] huckabee sanders tells colleagues she's taking temporary post as google ceo before transitioning int\n" + ] + } + ], + "source": [ + "# ── Binary error examples ─────────────────────────────────────────────────────\n", + "err_bin = pred_test_bin[pred_test_bin[\"correct\"] == 0].copy()\n", + "fp_bin = err_bin[err_bin[\"binary_label\"] == 0].head(10) # predicted sarcastic, actually not\n", + "fn_bin = err_bin[err_bin[\"binary_label\"] == 1].head(10) # predicted not-sarcastic, actually sarcastic\n", + "\n", + "print(f\"Binary errors on test: {len(err_bin)} total\")\n", + "print(f\" False Positives (predicted sarcastic, actually not): {len(err_bin[err_bin['binary_label']==0])}\")\n", + "print(f\" False Negatives (predicted not-sarcastic, actually sarcastic): {len(err_bin[err_bin['binary_label']==1])}\")\n", + "print(\"\\n--- Sample FP ---\")\n", + "for _, row in fp_bin.iterrows():\n", + " print(f\" [True=0, Pred=1] {row['text'][:100]}\")\n", + "print(\"\\n--- Sample FN ---\")\n", + "for _, row in fn_bin.iterrows():\n", + " print(f\" [True=1, Pred=0] {row['text'][:100]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "zmVdkTHuuKB_", + "outputId": "5bc30ec0-0f97-4f57-a165-706c7a022a82" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "KBPvq1iRuKCA" - }, - "source": [ - "## Training and Evaluation Functions" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Type errors on test: 2570 total\n", + "\n", + "Confusion pairs (true → predicted):\n", + "type_label predicted_label\n", + "irony sarcasm 309\n", + "satire sarcasm 227\n", + "sarcasm irony 226\n", + " satire 203\n", + "satire irony 155\n", + "irony satire 155\n", + "overstatement sarcasm 117\n", + "understatement sarcasm 103\n", + "overstatement satire 102\n", + "satire overstatement 101\n" + ] + } + ], + "source": [ + "# ── Type error examples ───────────────────────────────────────────────────────\n", + "err_type = pred_test_type[pred_test_type[\"correct\"] == 0].copy()\n", + "print(f\"Type errors on test: {len(err_type)} total\")\n", + "print(\"\\nConfusion pairs (true → predicted):\")\n", + "conf_pairs = err_type.groupby([\"type_label\", \"predicted_label\"]).size().sort_values(ascending=False).head(10)\n", + "print(conf_pairs.to_string())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "p52YKmRQuKB_" + }, + "source": [ + "## Summary" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "tg9D9LKxuKB_", + "outputId": "bf1f11d1-8feb-4b8c-c9fd-adac380993b2" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "id": "Dw8sRTfruKCA" - }, - "outputs": [], - "source": [ - "def compute_class_weights(y_train: list[int], num_classes: int) -> torch.Tensor:\n", - " \"\"\"Compute inverse-frequency class weights.\"\"\"\n", - " counts = np.bincount(y_train, minlength=num_classes).astype(float)\n", - " weights = len(y_train) / (num_classes * counts)\n", - " weights = np.clip(weights, 0, 10) # cap extreme weights\n", - " return torch.tensor(weights, dtype=torch.float)\n", - "\n", - "\n", - "def train_epoch(\n", - " model, loader: DataLoader, optimizer, scheduler, criterion, device\n", - ") -> float:\n", - " model.train()\n", - " total_loss = 0.0\n", - " for batch in loader:\n", - " input_ids = batch[\"input_ids\"].to(device)\n", - " attention_mask = batch[\"attention_mask\"].to(device)\n", - " labels = batch[\"labels\"].to(device)\n", - "\n", - " optimizer.zero_grad()\n", - " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", - " logits = outputs.logits\n", - "\n", - " loss = criterion(logits, labels)\n", - " loss.backward()\n", - "\n", - " nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n", - " optimizer.step()\n", - " scheduler.step()\n", - "\n", - " total_loss += loss.item()\n", - " return total_loss / len(loader)\n", - "\n", - "\n", - "@torch.no_grad()\n", - "def eval_epoch(\n", - " model, loader: DataLoader, criterion, device, label_names: list[str]\n", - ") -> tuple[float, dict, list]:\n", - " model.eval()\n", - " total_loss = 0.0\n", - " all_preds, all_labels = [], []\n", - "\n", - " for batch in loader:\n", - " input_ids = batch[\"input_ids\"].to(device)\n", - " attention_mask = batch[\"attention_mask\"].to(device)\n", - " labels = batch[\"labels\"].to(device)\n", - "\n", - " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", - " logits = outputs.logits\n", - " loss = criterion(logits, labels)\n", - "\n", - " total_loss += loss.item()\n", - " preds = torch.argmax(logits, dim=-1)\n", - " all_preds.extend(preds.cpu().numpy())\n", - " all_labels.extend(labels.cpu().numpy())\n", - "\n", - " avg_loss = total_loss / len(loader)\n", - " metrics = {\n", - " \"loss\" : avg_loss,\n", - " \"accuracy\" : accuracy_score(all_labels, all_preds),\n", - " \"f1_macro\" : f1_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", - " \"f1_weighted\" : f1_score(all_labels, all_preds, average=\"weighted\", zero_division=0),\n", - " \"precision_macro\" : precision_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", - " \"recall_macro\" : recall_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", - " }\n", - " return avg_loss, metrics, all_preds\n", - "\n", - "\n", - "def save_confusion_matrix(y_true, y_pred, label_names, out_path, title=\"\"):\n", - " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", - " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", - " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n", - " xticklabels=label_names, yticklabels=label_names, ax=ax)\n", - " ax.set_xlabel(\"Predicted\"); ax.set_ylabel(\"True\"); ax.set_title(title)\n", - " plt.tight_layout()\n", - " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", - " plt.show()\n", - " print(f\"Saved: {out_path}\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "====== NAIVE BAYES RESULTS SUMMARY ======\n", + "\n", + "Task A — Binary (Test):\n", + " Best model : TfidfVec+MultinomialNB\n", + " Accuracy : 0.7905\n", + " Macro-F1 : 0.7902\n", + " Weighted-F1 : 0.7902\n", + "\n", + "Task B — Type (Test):\n", + " Best model : CountVec+MultinomialNB\n", + " Accuracy : 0.3953\n", + " Macro-F1 : 0.3953\n", + " Weighted-F1 : 0.3929\n" + ] + } + ], + "source": [ + "print(\"====== NAIVE BAYES RESULTS SUMMARY ======\")\n", + "print()\n", + "print(\"Task A — Binary (Test):\")\n", + "print(f\" Best model : {best_name_bin}\")\n", + "print(f\" Accuracy : {test_m_bin['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {test_m_bin['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1 : {test_m_bin['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"Task B — Type (Test):\")\n", + "print(f\" Best model : {best_name_type}\")\n", + "print(f\" Accuracy : {test_m_type['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {test_m_type['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1 : {test_m_type['f1_weighted']:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GZHqRh2iuKB_" + }, + "source": [ + "---\n", + "# Part 4 — BERT / DistilBERT Classification" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xR_41E_1uKB_" + }, + "source": [ + "# Notebook 04 — BERT / DistilBERT Classification\n", + "\n", + "**Purpose**: Fine-tune transformer models for sarcasm classification.\n", + "- Task A: Binary (sarcastic vs non-sarcastic)\n", + "- Task B: Sarcasm type (6-class, sarcastic only)\n", + "\n", + "**Models**:\n", + "- `distilbert-base-uncased` (primary, fast)\n", + "- `bert-base-uncased` (optional, if compute allows)\n", + "\n", + "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", + "\n", + "**Outputs** in `outputs/bert/distilbert_binary/` and `outputs/bert/distilbert_type/`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5Gi8VSJDuKB_" + }, + "source": [ + "## Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "K_FNBk2ouKB_", + "outputId": "70f6d378-7098-410c-d656-50135e3b0e12" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "d35JFlgzuKCB" - }, - "source": [ - "## Full Training Function" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Binary config: TrainConfig(model_name='distilbert-base-uncased', max_length=128, batch_size=32, lr=2e-05, weight_decay=0.01, warmup_ratio=0.1, epochs=10, patience=3, seed=42, task='binary', use_class_weights=False)\n", + "Type config: TrainConfig(model_name='distilbert-base-uncased', max_length=128, batch_size=32, lr=2e-05, weight_decay=0.01, warmup_ratio=0.1, epochs=10, patience=3, seed=42, task='type', use_class_weights=True)\n" + ] + } + ], + "source": [ + "@dataclass\n", + "class TrainConfig:\n", + " model_name : str = \"distilbert-base-uncased\"\n", + " max_length : int = 128\n", + " batch_size : int = 32\n", + " lr : float = 2e-5\n", + " weight_decay : float = 0.01\n", + " warmup_ratio : float = 0.1\n", + " epochs : int = 10\n", + " patience : int = 3 # early stopping patience\n", + " seed : int = SEED\n", + " task : str = \"binary\" # 'binary' or 'type'\n", + " use_class_weights: bool = False\n", + "\n", + "# ── Config for binary task ────────────────────────────────────────────────────\n", + "CFG_BINARY = TrainConfig(\n", + " model_name=\"distilbert-base-uncased\",\n", + " task=\"binary\",\n", + " batch_size=32,\n", + " lr=2e-5,\n", + " use_class_weights=False,\n", + ")\n", + "\n", + "# ── Config for type task (use class weights for imbalance) ────────────────────\n", + "CFG_TYPE = TrainConfig(\n", + " model_name=\"distilbert-base-uncased\",\n", + " task=\"type\",\n", + " batch_size=32,\n", + " lr=2e-5,\n", + " use_class_weights=True,\n", + ")\n", + "\n", + "print(\"Binary config:\", CFG_BINARY)\n", + "print(\"Type config: \", CFG_TYPE)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OKgvtBDPuKCA" + }, + "source": [ + "## Dataset Class" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "id": "tHoVxhSAuKCA" + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch.utils.data import Dataset\n", + "\n", + "class HeadlineDataset(Dataset):\n", + " \"\"\"PyTorch Dataset for headline classification.\"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " texts: list[str],\n", + " labels: list[int],\n", + " tokenizer,\n", + " max_length: int = 128,\n", + " ):\n", + " self.texts = texts\n", + " self.labels = labels\n", + " self.tokenizer = tokenizer\n", + " self.max_length = max_length\n", + "\n", + " def __len__(self) -> int:\n", + " return len(self.texts)\n", + "\n", + " def __getitem__(self, idx: int) -> dict:\n", + " encoding = self.tokenizer(\n", + " self.texts[idx],\n", + " truncation=True,\n", + " padding=\"max_length\",\n", + " max_length=self.max_length,\n", + " return_tensors=\"pt\",\n", + " )\n", + " return {\n", + " \"input_ids\" : encoding[\"input_ids\"].squeeze(0),\n", + " \"attention_mask\" : encoding[\"attention_mask\"].squeeze(0),\n", + " \"labels\" : torch.tensor(self.labels[idx], dtype=torch.long),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KBPvq1iRuKCA" + }, + "source": [ + "## Training and Evaluation Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "id": "Dw8sRTfruKCA" + }, + "outputs": [], + "source": [ + "def compute_class_weights(y_train: list[int], num_classes: int) -> torch.Tensor:\n", + " \"\"\"Compute inverse-frequency class weights.\"\"\"\n", + " counts = np.bincount(y_train, minlength=num_classes).astype(float)\n", + " weights = len(y_train) / (num_classes * counts)\n", + " weights = np.clip(weights, 0, 10) # cap extreme weights\n", + " return torch.tensor(weights, dtype=torch.float)\n", + "\n", + "\n", + "def train_epoch(\n", + " model, loader: DataLoader, optimizer, scheduler, criterion, device\n", + ") -> float:\n", + " model.train()\n", + " total_loss = 0.0\n", + " for batch in loader:\n", + " input_ids = batch[\"input_ids\"].to(device)\n", + " attention_mask = batch[\"attention_mask\"].to(device)\n", + " labels = batch[\"labels\"].to(device)\n", + "\n", + " optimizer.zero_grad()\n", + " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", + " logits = outputs.logits\n", + "\n", + " loss = criterion(logits, labels)\n", + " loss.backward()\n", + "\n", + " nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n", + " optimizer.step()\n", + " scheduler.step()\n", + "\n", + " total_loss += loss.item()\n", + " return total_loss / len(loader)\n", + "\n", + "\n", + "@torch.no_grad()\n", + "def eval_epoch(\n", + " model, loader: DataLoader, criterion, device, label_names: list[str]\n", + ") -> tuple[float, dict, list]:\n", + " model.eval()\n", + " total_loss = 0.0\n", + " all_preds, all_labels = [], []\n", + "\n", + " for batch in loader:\n", + " input_ids = batch[\"input_ids\"].to(device)\n", + " attention_mask = batch[\"attention_mask\"].to(device)\n", + " labels = batch[\"labels\"].to(device)\n", + "\n", + " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", + " logits = outputs.logits\n", + " loss = criterion(logits, labels)\n", + "\n", + " total_loss += loss.item()\n", + " preds = torch.argmax(logits, dim=-1)\n", + " all_preds.extend(preds.cpu().numpy())\n", + " all_labels.extend(labels.cpu().numpy())\n", + "\n", + " avg_loss = total_loss / len(loader)\n", + " metrics = {\n", + " \"loss\" : avg_loss,\n", + " \"accuracy\" : accuracy_score(all_labels, all_preds),\n", + " \"f1_macro\" : f1_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", + " \"f1_weighted\" : f1_score(all_labels, all_preds, average=\"weighted\", zero_division=0),\n", + " \"precision_macro\" : precision_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", + " \"recall_macro\" : recall_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", + " }\n", + " return avg_loss, metrics, all_preds\n", + "\n", + "\n", + "def save_confusion_matrix(y_true, y_pred, label_names, out_path, title=\"\"):\n", + " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", + " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", + " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n", + " xticklabels=label_names, yticklabels=label_names, ax=ax)\n", + " ax.set_xlabel(\"Predicted\"); ax.set_ylabel(\"True\"); ax.set_title(title)\n", + " plt.tight_layout()\n", + " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + " print(f\"Saved: {out_path}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d35JFlgzuKCB" + }, + "source": [ + "## Full Training Function" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "id": "wHriQLOyuKCB" + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "\n", + "from transformers import get_linear_schedule_with_warmup\n", + "\n", + "def train_bert(\n", + " cfg: TrainConfig,\n", + " X_train: list[str], y_train: list[int],\n", + " X_val: list[str], y_val: list[int],\n", + " X_test: list[str], y_test: list[int],\n", + " label_names: list[str],\n", + " out_dir: Path,\n", + " val_df: pd.DataFrame,\n", + " test_df: pd.DataFrame,\n", + ") -> dict:\n", + " \"\"\"Full training loop with early stopping. Returns test metrics dict.\"\"\"\n", + " out_dir.mkdir(parents=True, exist_ok=True)\n", + " num_labels = len(label_names)\n", + "\n", + " # FIX: define device inside function (self-contained)\n", + " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + " print(f\"Using device: {device}\")\n", + "\n", + " # Save config (default=str avoids Path serialization issues)\n", + " with open(out_dir / \"config.json\", \"w\") as f:\n", + " json.dump(cfg.__dict__, f, indent=2, default=str)\n", + " print(f\"Config saved to {out_dir / 'config.json'}\")\n", + "\n", + " # Tokenizer\n", + " print(f\"Loading tokenizer: {cfg.model_name}\")\n", + " tokenizer = AutoTokenizer.from_pretrained(cfg.model_name)\n", + "\n", + " # Datasets\n", + " train_ds = HeadlineDataset(X_train, y_train, tokenizer, cfg.max_length)\n", + " val_ds = HeadlineDataset(X_val, y_val, tokenizer, cfg.max_length)\n", + " test_ds = HeadlineDataset(X_test, y_test, tokenizer, cfg.max_length)\n", + "\n", + " g = torch.Generator()\n", + " g.manual_seed(cfg.seed)\n", + "\n", + " train_loader = DataLoader(train_ds, batch_size=cfg.batch_size, shuffle=True, generator=g)\n", + " val_loader = DataLoader(val_ds, batch_size=cfg.batch_size, shuffle=False)\n", + " test_loader = DataLoader(test_ds, batch_size=cfg.batch_size, shuffle=False)\n", + "\n", + " # Model\n", + " print(f\"Loading model: {cfg.model_name} ({num_labels} labels)\")\n", + " model = AutoModelForSequenceClassification.from_pretrained(\n", + " cfg.model_name, num_labels=num_labels\n", + " ).to(device)\n", + "\n", + " # Loss (with optional class weights)\n", + " if cfg.use_class_weights:\n", + " cw = compute_class_weights(y_train, num_labels).to(device)\n", + " criterion = nn.CrossEntropyLoss(weight=cw)\n", + " print(f\"Class weights: {cw.detach().cpu().numpy().round(3)}\")\n", + " else:\n", + " criterion = nn.CrossEntropyLoss()\n", + "\n", + " # Optimizer and scheduler\n", + " optimizer = torch.optim.AdamW(\n", + " model.parameters(), lr=cfg.lr, weight_decay=cfg.weight_decay\n", + " )\n", + " total_steps = len(train_loader) * cfg.epochs\n", + " warmup_steps = int(total_steps * cfg.warmup_ratio)\n", + "\n", + " scheduler = get_linear_schedule_with_warmup(\n", + " optimizer, num_warmup_steps=warmup_steps, num_training_steps=total_steps\n", + " )\n", + "\n", + " # Training loop\n", + " best_val_f1 = -1.0\n", + " patience_cnt = 0\n", + " best_epoch = 0\n", + " train_log = []\n", + "\n", + " for epoch in range(1, cfg.epochs + 1):\n", + " train_loss = train_epoch(model, train_loader, optimizer, scheduler, criterion, device)\n", + " _, val_metrics, _ = eval_epoch(model, val_loader, criterion, device, label_names)\n", + "\n", + " val_f1 = float(val_metrics[\"f1_macro\"])\n", + " log_row = {\n", + " \"epoch\": epoch,\n", + " \"train_loss\": float(train_loss),\n", + " **{f\"val_{k}\": float(v) if isinstance(v, (np.floating, float, int, np.integer)) else v\n", + " for k, v in val_metrics.items()}\n", + " }\n", + " train_log.append(log_row)\n", + "\n", + " print(\n", + " f\"Epoch {epoch:2d}/{cfg.epochs} | \"\n", + " f\"train_loss={train_loss:.4f} | \"\n", + " f\"val_loss={val_metrics['loss']:.4f} | \"\n", + " f\"val_acc={val_metrics['accuracy']:.4f} | \"\n", + " f\"val_macro_f1={val_f1:.4f}\"\n", + " )\n", + "\n", + " if val_f1 > best_val_f1:\n", + " best_val_f1 = val_f1\n", + " best_epoch = epoch\n", + " patience_cnt = 0\n", + "\n", + " ckpt_dir = out_dir / \"best_checkpoint\"\n", + " ckpt_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " model.save_pretrained(ckpt_dir)\n", + " tokenizer.save_pretrained(ckpt_dir)\n", + " print(f\" ★ New best val macro-F1={val_f1:.4f} — checkpoint saved\")\n", + " else:\n", + " patience_cnt += 1\n", + " if patience_cnt >= cfg.patience:\n", + " print(f\" Early stopping at epoch {epoch} (patience={cfg.patience})\")\n", + " break\n", + "\n", + " # Save training log\n", + " log_df = pd.DataFrame(train_log)\n", + " log_df.to_csv(out_dir / \"training_log.csv\", index=False)\n", + "\n", + " # Plot training curves\n", + " fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + " axes[0].plot(log_df[\"epoch\"], log_df[\"train_loss\"], label=\"Train loss\", marker=\"o\")\n", + " axes[0].plot(log_df[\"epoch\"], log_df[\"val_loss\"], label=\"Val loss\", marker=\"s\")\n", + " axes[0].set_xlabel(\"Epoch\")\n", + " axes[0].set_ylabel(\"Loss\")\n", + " axes[0].set_title(f\"{cfg.model_name} — Loss Curves ({cfg.task})\")\n", + " axes[0].legend()\n", + " axes[0].axvline(best_epoch, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", + "\n", + " axes[1].plot(log_df[\"epoch\"], log_df[\"val_f1_macro\"], label=\"Val macro-F1\", marker=\"o\")\n", + " axes[1].set_xlabel(\"Epoch\")\n", + " axes[1].set_ylabel(\"Macro-F1\")\n", + " axes[1].set_title(f\"{cfg.model_name} — Val Macro-F1 ({cfg.task})\")\n", + " axes[1].legend()\n", + " axes[1].axvline(best_epoch, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", + "\n", + " plt.tight_layout()\n", + " plt.savefig(out_dir / \"training_curves.png\", dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + "\n", + " # Reload best checkpoint and evaluate\n", + " print(f\"\\nLoading best checkpoint (epoch {best_epoch}, val macro-F1={best_val_f1:.4f})\")\n", + " best_model = AutoModelForSequenceClassification.from_pretrained(\n", + " str(out_dir / \"best_checkpoint\")\n", + " ).to(device)\n", + "\n", + " _, val_metrics_best, val_preds = eval_epoch(best_model, val_loader, criterion, device, label_names)\n", + " _, test_metrics_best, test_preds = eval_epoch(best_model, test_loader, criterion, device, label_names)\n", + "\n", + " print(\"\\n=== Val (best checkpoint) ===\")\n", + " print(classification_report(y_val, val_preds, target_names=label_names, zero_division=0))\n", + " print(\"\\n=== Test (best checkpoint) ===\")\n", + " print(classification_report(y_test, test_preds, target_names=label_names, zero_division=0))\n", + "\n", + " # Confusion matrices\n", + " save_confusion_matrix(\n", + " y_val, val_preds, label_names,\n", + " out_dir / \"confusion_matrix_val.png\",\n", + " f\"{cfg.model_name} — {cfg.task} (Val)\"\n", + " )\n", + " save_confusion_matrix(\n", + " y_test, test_preds, label_names,\n", + " out_dir / \"confusion_matrix_test.png\",\n", + " f\"{cfg.model_name} — {cfg.task} (Test)\"\n", + " )\n", + "\n", + " # Convert metrics to native Python types for JSON\n", + " def _to_py(obj):\n", + " if isinstance(obj, dict):\n", + " return {k: _to_py(v) for k, v in obj.items()}\n", + " if isinstance(obj, (list, tuple)):\n", + " return [_to_py(v) for v in obj]\n", + " if isinstance(obj, (np.integer,)):\n", + " return int(obj)\n", + " if isinstance(obj, (np.floating,)):\n", + " return float(obj)\n", + " return obj\n", + "\n", + " results = {\n", + " \"model\": cfg.model_name,\n", + " \"task\": cfg.task,\n", + " \"best_epoch\": int(best_epoch),\n", + " \"best_val_f1_macro\": float(best_val_f1),\n", + " \"val\": _to_py(val_metrics_best),\n", + " \"test\": _to_py(test_metrics_best),\n", + " }\n", + "\n", + " with open(out_dir / \"metrics.json\", \"w\") as f:\n", + " json.dump(results, f, indent=2, default=str)\n", + "\n", + " # Save predictions\n", + " for split_name, df, y_true_list, y_pred_list in [\n", + " (\"val\", val_df, y_val, val_preds),\n", + " (\"test\", test_df, y_test, test_preds),\n", + " ]:\n", + " out_pred = df.copy()\n", + " out_pred[\"predicted\"] = y_pred_list\n", + " out_pred[\"predicted_label\"] = [label_names[i] for i in y_pred_list]\n", + " out_pred[\"correct\"] = (np.array(y_true_list) == np.array(y_pred_list)).astype(int)\n", + " out_pred.to_csv(out_dir / f\"predictions_{split_name}.csv\", index=False)\n", + " print(f\"Saved predictions_{split_name}.csv\")\n", + "\n", + " print(f\"\\n=== DONE: {cfg.model_name} / {cfg.task} ===\")\n", + " print(f\" Test Accuracy : {test_metrics_best['accuracy']:.4f}\")\n", + " print(f\" Test Macro-F1 : {test_metrics_best['f1_macro']:.4f}\")\n", + " print(f\" Test Weighted-F1: {test_metrics_best['f1_weighted']:.4f}\")\n", + "\n", + " return results" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "49JUIiXSuKCB" + }, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "-bzRLKhsuKCB", + "outputId": "b3ac3196-fdb3-44d9-8417-26a434c6837a" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "id": "wHriQLOyuKCB" - }, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "\n", - "from transformers import get_linear_schedule_with_warmup\n", - "\n", - "def train_bert(\n", - " cfg: TrainConfig,\n", - " X_train: list[str], y_train: list[int],\n", - " X_val: list[str], y_val: list[int],\n", - " X_test: list[str], y_test: list[int],\n", - " label_names: list[str],\n", - " out_dir: Path,\n", - " val_df: pd.DataFrame,\n", - " test_df: pd.DataFrame,\n", - ") -> dict:\n", - " \"\"\"Full training loop with early stopping. Returns test metrics dict.\"\"\"\n", - " out_dir.mkdir(parents=True, exist_ok=True)\n", - " num_labels = len(label_names)\n", - "\n", - " # FIX: define device inside function (self-contained)\n", - " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - " print(f\"Using device: {device}\")\n", - "\n", - " # Save config (default=str avoids Path serialization issues)\n", - " with open(out_dir / \"config.json\", \"w\") as f:\n", - " json.dump(cfg.__dict__, f, indent=2, default=str)\n", - " print(f\"Config saved to {out_dir / 'config.json'}\")\n", - "\n", - " # Tokenizer\n", - " print(f\"Loading tokenizer: {cfg.model_name}\")\n", - " tokenizer = AutoTokenizer.from_pretrained(cfg.model_name)\n", - "\n", - " # Datasets\n", - " train_ds = HeadlineDataset(X_train, y_train, tokenizer, cfg.max_length)\n", - " val_ds = HeadlineDataset(X_val, y_val, tokenizer, cfg.max_length)\n", - " test_ds = HeadlineDataset(X_test, y_test, tokenizer, cfg.max_length)\n", - "\n", - " g = torch.Generator()\n", - " g.manual_seed(cfg.seed)\n", - "\n", - " train_loader = DataLoader(train_ds, batch_size=cfg.batch_size, shuffle=True, generator=g)\n", - " val_loader = DataLoader(val_ds, batch_size=cfg.batch_size, shuffle=False)\n", - " test_loader = DataLoader(test_ds, batch_size=cfg.batch_size, shuffle=False)\n", - "\n", - " # Model\n", - " print(f\"Loading model: {cfg.model_name} ({num_labels} labels)\")\n", - " model = AutoModelForSequenceClassification.from_pretrained(\n", - " cfg.model_name, num_labels=num_labels\n", - " ).to(device)\n", - "\n", - " # Loss (with optional class weights)\n", - " if cfg.use_class_weights:\n", - " cw = compute_class_weights(y_train, num_labels).to(device)\n", - " criterion = nn.CrossEntropyLoss(weight=cw)\n", - " print(f\"Class weights: {cw.detach().cpu().numpy().round(3)}\")\n", - " else:\n", - " criterion = nn.CrossEntropyLoss()\n", - "\n", - " # Optimizer and scheduler\n", - " optimizer = torch.optim.AdamW(\n", - " model.parameters(), lr=cfg.lr, weight_decay=cfg.weight_decay\n", - " )\n", - " total_steps = len(train_loader) * cfg.epochs\n", - " warmup_steps = int(total_steps * cfg.warmup_ratio)\n", - "\n", - " scheduler = get_linear_schedule_with_warmup(\n", - " optimizer, num_warmup_steps=warmup_steps, num_training_steps=total_steps\n", - " )\n", - "\n", - " # Training loop\n", - " best_val_f1 = -1.0\n", - " patience_cnt = 0\n", - " best_epoch = 0\n", - " train_log = []\n", - "\n", - " for epoch in range(1, cfg.epochs + 1):\n", - " train_loss = train_epoch(model, train_loader, optimizer, scheduler, criterion, device)\n", - " _, val_metrics, _ = eval_epoch(model, val_loader, criterion, device, label_names)\n", - "\n", - " val_f1 = float(val_metrics[\"f1_macro\"])\n", - " log_row = {\n", - " \"epoch\": epoch,\n", - " \"train_loss\": float(train_loss),\n", - " **{f\"val_{k}\": float(v) if isinstance(v, (np.floating, float, int, np.integer)) else v\n", - " for k, v in val_metrics.items()}\n", - " }\n", - " train_log.append(log_row)\n", - "\n", - " print(\n", - " f\"Epoch {epoch:2d}/{cfg.epochs} | \"\n", - " f\"train_loss={train_loss:.4f} | \"\n", - " f\"val_loss={val_metrics['loss']:.4f} | \"\n", - " f\"val_acc={val_metrics['accuracy']:.4f} | \"\n", - " f\"val_macro_f1={val_f1:.4f}\"\n", - " )\n", - "\n", - " if val_f1 > best_val_f1:\n", - " best_val_f1 = val_f1\n", - " best_epoch = epoch\n", - " patience_cnt = 0\n", - "\n", - " ckpt_dir = out_dir / \"best_checkpoint\"\n", - " ckpt_dir.mkdir(parents=True, exist_ok=True)\n", - "\n", - " model.save_pretrained(ckpt_dir)\n", - " tokenizer.save_pretrained(ckpt_dir)\n", - " print(f\" ★ New best val macro-F1={val_f1:.4f} — checkpoint saved\")\n", - " else:\n", - " patience_cnt += 1\n", - " if patience_cnt >= cfg.patience:\n", - " print(f\" Early stopping at epoch {epoch} (patience={cfg.patience})\")\n", - " break\n", - "\n", - " # Save training log\n", - " log_df = pd.DataFrame(train_log)\n", - " log_df.to_csv(out_dir / \"training_log.csv\", index=False)\n", - "\n", - " # Plot training curves\n", - " fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", - "\n", - " axes[0].plot(log_df[\"epoch\"], log_df[\"train_loss\"], label=\"Train loss\", marker=\"o\")\n", - " axes[0].plot(log_df[\"epoch\"], log_df[\"val_loss\"], label=\"Val loss\", marker=\"s\")\n", - " axes[0].set_xlabel(\"Epoch\")\n", - " axes[0].set_ylabel(\"Loss\")\n", - " axes[0].set_title(f\"{cfg.model_name} — Loss Curves ({cfg.task})\")\n", - " axes[0].legend()\n", - " axes[0].axvline(best_epoch, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", - "\n", - " axes[1].plot(log_df[\"epoch\"], log_df[\"val_f1_macro\"], label=\"Val macro-F1\", marker=\"o\")\n", - " axes[1].set_xlabel(\"Epoch\")\n", - " axes[1].set_ylabel(\"Macro-F1\")\n", - " axes[1].set_title(f\"{cfg.model_name} — Val Macro-F1 ({cfg.task})\")\n", - " axes[1].legend()\n", - " axes[1].axvline(best_epoch, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", - "\n", - " plt.tight_layout()\n", - " plt.savefig(out_dir / \"training_curves.png\", dpi=150, bbox_inches=\"tight\")\n", - " plt.show()\n", - "\n", - " # Reload best checkpoint and evaluate\n", - " print(f\"\\nLoading best checkpoint (epoch {best_epoch}, val macro-F1={best_val_f1:.4f})\")\n", - " best_model = AutoModelForSequenceClassification.from_pretrained(\n", - " str(out_dir / \"best_checkpoint\")\n", - " ).to(device)\n", - "\n", - " _, val_metrics_best, val_preds = eval_epoch(best_model, val_loader, criterion, device, label_names)\n", - " _, test_metrics_best, test_preds = eval_epoch(best_model, test_loader, criterion, device, label_names)\n", - "\n", - " print(\"\\n=== Val (best checkpoint) ===\")\n", - " print(classification_report(y_val, val_preds, target_names=label_names, zero_division=0))\n", - " print(\"\\n=== Test (best checkpoint) ===\")\n", - " print(classification_report(y_test, test_preds, target_names=label_names, zero_division=0))\n", - "\n", - " # Confusion matrices\n", - " save_confusion_matrix(\n", - " y_val, val_preds, label_names,\n", - " out_dir / \"confusion_matrix_val.png\",\n", - " f\"{cfg.model_name} — {cfg.task} (Val)\"\n", - " )\n", - " save_confusion_matrix(\n", - " y_test, test_preds, label_names,\n", - " out_dir / \"confusion_matrix_test.png\",\n", - " f\"{cfg.model_name} — {cfg.task} (Test)\"\n", - " )\n", - "\n", - " # Convert metrics to native Python types for JSON\n", - " def _to_py(obj):\n", - " if isinstance(obj, dict):\n", - " return {k: _to_py(v) for k, v in obj.items()}\n", - " if isinstance(obj, (list, tuple)):\n", - " return [_to_py(v) for v in obj]\n", - " if isinstance(obj, (np.integer,)):\n", - " return int(obj)\n", - " if isinstance(obj, (np.floating,)):\n", - " return float(obj)\n", - " return obj\n", - "\n", - " results = {\n", - " \"model\": cfg.model_name,\n", - " \"task\": cfg.task,\n", - " \"best_epoch\": int(best_epoch),\n", - " \"best_val_f1_macro\": float(best_val_f1),\n", - " \"val\": _to_py(val_metrics_best),\n", - " \"test\": _to_py(test_metrics_best),\n", - " }\n", - "\n", - " with open(out_dir / \"metrics.json\", \"w\") as f:\n", - " json.dump(results, f, indent=2, default=str)\n", - "\n", - " # Save predictions\n", - " for split_name, df, y_true_list, y_pred_list in [\n", - " (\"val\", val_df, y_val, val_preds),\n", - " (\"test\", test_df, y_test, test_preds),\n", - " ]:\n", - " out_pred = df.copy()\n", - " out_pred[\"predicted\"] = y_pred_list\n", - " out_pred[\"predicted_label\"] = [label_names[i] for i in y_pred_list]\n", - " out_pred[\"correct\"] = (np.array(y_true_list) == np.array(y_pred_list)).astype(int)\n", - " out_pred.to_csv(out_dir / f\"predictions_{split_name}.csv\", index=False)\n", - " print(f\"Saved predictions_{split_name}.csv\")\n", - "\n", - " print(f\"\\n=== DONE: {cfg.model_name} / {cfg.task} ===\")\n", - " print(f\" Test Accuracy : {test_metrics_best['accuracy']:.4f}\")\n", - " print(f\" Test Macro-F1 : {test_metrics_best['f1_macro']:.4f}\")\n", - " print(f\" Test Weighted-F1: {test_metrics_best['f1_weighted']:.4f}\")\n", - "\n", - " return results" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Binary — Train: 39,666 Val: 8,500 Test: 8,500\n" + ] + } + ], + "source": [ + "# ── Binary splits ─────────────────────────────────────────────────────────────\n", + "train_bin = pd.read_csv(SPLITS / \"train_binary.csv\")\n", + "val_bin = pd.read_csv(SPLITS / \"val_binary.csv\")\n", + "test_bin = pd.read_csv(SPLITS / \"test_binary.csv\")\n", + "\n", + "X_train_bin = train_bin[\"text\"].tolist(); y_train_bin = train_bin[\"binary_label\"].tolist()\n", + "X_val_bin = val_bin[\"text\"].tolist(); y_val_bin = val_bin[\"binary_label\"].tolist()\n", + "X_test_bin = test_bin[\"text\"].tolist(); y_test_bin = test_bin[\"binary_label\"].tolist()\n", + "\n", + "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", + "\n", + "print(f\"Binary — Train: {len(X_train_bin):,} Val: {len(X_val_bin):,} Test: {len(X_test_bin):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "OkXIlQAtuKCC", + "outputId": "676ecfa7-874b-4536-9ac3-b8b833e2cfff" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "49JUIiXSuKCB" - }, - "source": [ - "## Load Data" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Type — Train: 19,833 Val: 4,250 Test: 4,250\n", + "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n" + ] + } + ], + "source": [ + "# ── Type splits ───────────────────────────────────────────────────────────────\n", + "train_type = pd.read_csv(SPLITS / \"train_type.csv\")\n", + "val_type = pd.read_csv(SPLITS / \"val_type.csv\")\n", + "test_type = pd.read_csv(SPLITS / \"test_type.csv\")\n", + "\n", + "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", + "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", + "id2label = {i: lab for lab, i in label2id.items()}\n", + "\n", + "def enc(df): return [label2id[l] for l in df[\"type_label\"]]\n", + "\n", + "X_train_type = train_type[\"text\"].tolist(); y_train_type = enc(train_type)\n", + "X_val_type = val_type[\"text\"].tolist(); y_val_type = enc(val_type)\n", + "X_test_type = test_type[\"text\"].tolist(); y_test_type = enc(test_type)\n", + "\n", + "print(f\"Type — Train: {len(X_train_type):,} Val: {len(X_val_type):,} Test: {len(X_test_type):,}\")\n", + "print(f\"Strategies: {STRATEGY_LABELS}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "varh9ZaouKCC" + }, + "source": [ + "## Train DistilBERT — Binary Task" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 396, + "referenced_widgets": [ + "8b9080237c194037a2cd1dc711a694d2", + "d0d70eea6290419fb4a39bd74964bacb", + "30e0eec43be54d08a0b5bd855e06d3c3", + "959391d9e6cc405ead852002ce0276ff", + "143c9b378b5a4260b15e328dc744374f", + "384fe210d84b4e8d8bc1cd8b99944d5e", + "2a97e4558e6d41df919bd3ce83576627", + "bfc3291175a14250a407af3dd6376d58", + "01c169be55ca4399b34eb7cdae152075", + "2f42691301be4d01858524488e30fe78", + "8faab66da6c74399a140fa5aad07dbbb" + ] }, + "id": "Yq8zSNjluKCC", + "outputId": "44080f86-f045-426f-f630-765eb5f599c7" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "-bzRLKhsuKCB", - "outputId": "b3ac3196-fdb3-44d9-8417-26a434c6837a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Binary — Train: 39,666 Val: 8,500 Test: 8,500\n" - ] - } - ], - "source": [ - "# ── Binary splits ─────────────────────────────────────────────────────────────\n", - "train_bin = pd.read_csv(SPLITS / \"train_binary.csv\")\n", - "val_bin = pd.read_csv(SPLITS / \"val_binary.csv\")\n", - "test_bin = pd.read_csv(SPLITS / \"test_binary.csv\")\n", - "\n", - "X_train_bin = train_bin[\"text\"].tolist(); y_train_bin = train_bin[\"binary_label\"].tolist()\n", - "X_val_bin = val_bin[\"text\"].tolist(); y_val_bin = val_bin[\"binary_label\"].tolist()\n", - "X_test_bin = test_bin[\"text\"].tolist(); y_test_bin = test_bin[\"binary_label\"].tolist()\n", - "\n", - "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", - "\n", - "print(f\"Binary — Train: {len(X_train_bin):,} Val: {len(X_val_bin):,} Test: {len(X_test_bin):,}\")" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "Using device: cpu\n", + "Config saved to /content/outputs/bert/distilbert_binary/config.json\n", + "Loading tokenizer: distilbert-base-uncased\n", + "Loading model: distilbert-base-uncased (2 labels)\n" + ] }, { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OkXIlQAtuKCC", - "outputId": "676ecfa7-874b-4536-9ac3-b8b833e2cfff" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Type — Train: 19,833 Val: 4,250 Test: 4,250\n", - "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n" - ] - } + "output_type": "display_data", + "data": { + "text/plain": [ + "Loading weights: 0%| | 0/100 [00:00 dict | None:\n", - " if path.exists():\n", - " with open(path) as f:\n", - " return json.load(f)\n", - " print(f\" [WARNING] Not found: {path.relative_to(ROOT)}\")\n", - " return None\n", - "\n", - "\n", - "# All metrics\n", - "tfidf_lr_binary = load_metrics(CLASSICAL / \"tfidf_lr\" / \"metrics_binary.json\")\n", - "tfidf_lr_type = load_metrics(CLASSICAL / \"tfidf_lr\" / \"metrics_type.json\")\n", - "nb_binary = load_metrics(CLASSICAL / \"naive_bayes\" / \"metrics_binary.json\")\n", - "nb_type = load_metrics(CLASSICAL / \"naive_bayes\" / \"metrics_type.json\")\n", - "distilbert_bin = load_metrics(BERT_OUT / \"distilbert_binary\" / \"metrics.json\")\n", - "distilbert_type = load_metrics(BERT_OUT / \"distilbert_type\" / \"metrics.json\")\n", - "bert_base_bin = load_metrics(BERT_OUT / \"bert_base_binary\" / \"metrics.json\") # optional\n", - "bert_base_type = load_metrics(BERT_OUT / \"bert_base_type\" / \"metrics.json\") # optional\n", - "\n", - "print(\"Metrics loaded (None = not yet run):\")\n", - "for name, m in [(\"TF-IDF+LR binary\", tfidf_lr_binary), (\"TF-IDF+LR type\", tfidf_lr_type),\n", - " (\"NB binary\", nb_binary), (\"NB type\", nb_type),\n", - " (\"DistilBERT binary\", distilbert_bin), (\"DistilBERT type\", distilbert_type),\n", - " (\"BERT-base binary\", bert_base_bin), (\"BERT-base type\", bert_base_type)]:\n", - " print(f\" {name}: {'✓' if m else '✗'}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "H_xQm80nuKCE" - }, - "source": [ - "## 2. Model Comparison Tables" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "2RsVxgMtuKCE" - }, - "outputs": [], - "source": [ - "def extract_test_metrics(metrics_dict: dict | None, split: str = \"test\") -> dict:\n", - " \"\"\"Extract test-split metrics from a metrics dict, handling different formats.\"\"\"\n", - " if metrics_dict is None:\n", - " return {\"accuracy\": None, \"f1_macro\": None, \"f1_weighted\": None,\n", - " \"precision_macro\": None, \"recall_macro\": None}\n", - " test_m = metrics_dict.get(split, metrics_dict) # BERT format uses 'test' key directly\n", - " return {\n", - " \"accuracy\" : test_m.get(\"accuracy\"),\n", - " \"f1_macro\" : test_m.get(\"f1_macro\"),\n", - " \"f1_weighted\" : test_m.get(\"f1_weighted\"),\n", - " \"precision_macro\" : test_m.get(\"precision_macro\"),\n", - " \"recall_macro\" : test_m.get(\"recall_macro\"),\n", - " }\n", - "\n", - "\n", - "binary_rows = []\n", - "type_rows = []\n", - "\n", - "model_map_bin = [\n", - " (\"TF-IDF + LR\", tfidf_lr_binary),\n", - " (\"Naive Bayes\", nb_binary),\n", - " (\"DistilBERT\", distilbert_bin),\n", - " (\"BERT-base\", bert_base_bin),\n", - "]\n", - "\n", - "model_map_type = [\n", - " (\"TF-IDF + LR\", tfidf_lr_type),\n", - " (\"Naive Bayes\", nb_type),\n", - " (\"DistilBERT\", distilbert_type),\n", - " (\"BERT-base\", bert_base_type),\n", - "]\n", - "\n", - "for name, m in model_map_bin:\n", - " row = {\"Model\": name}\n", - " row.update(extract_test_metrics(m))\n", - " binary_rows.append(row)\n", - "\n", - "for name, m in model_map_type:\n", - " row = {\"Model\": name}\n", - " row.update(extract_test_metrics(m))\n", - " type_rows.append(row)\n", - "\n", - "binary_comp = pd.DataFrame(binary_rows).set_index(\"Model\")\n", - "type_comp = pd.DataFrame(type_rows).set_index(\"Model\")\n", - "\n", - "print(\"=== BINARY TASK — Test Metrics ===\")\n", - "print(binary_comp.to_string(float_format=lambda x: f\"{x:.4f}\" if x is not None else \"N/A\"))\n", - "print()\n", - "print(\"=== TYPE TASK — Test Metrics ===\")\n", - "print(type_comp.to_string(float_format=lambda x: f\"{x:.4f}\" if x is not None else \"N/A\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "n35eLHCCuKCF" - }, - "outputs": [], - "source": [ - "# ── Comparison bar charts ─────────────────────────────────────────────────────\n", - "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n", - "\n", - "for ax, comp, title in [\n", - " (axes[0], binary_comp, \"Binary Task\"),\n", - " (axes[1], type_comp, \"Type Task\"),\n", - "]:\n", - " valid = comp.dropna(subset=[\"f1_macro\"])\n", - " if len(valid) == 0:\n", - " ax.set_title(f\"{title} — No data\")\n", - " continue\n", - " models = valid.index.tolist()\n", - " f1_vals = valid[\"f1_macro\"].tolist()\n", - " acc_vals = valid[\"accuracy\"].tolist()\n", - " x = range(len(models))\n", - " w = 0.35\n", - " bars1 = ax.bar([i - w/2 for i in x], f1_vals, w, label=\"Macro-F1\", color=\"steelblue\")\n", - " bars2 = ax.bar([i + w/2 for i in x], acc_vals, w, label=\"Accuracy\", color=\"coral\")\n", - " ax.set_xticks(list(x)); ax.set_xticklabels(models, rotation=20, ha=\"right\")\n", - " ax.set_ylim(0, 1.05)\n", - " ax.set_ylabel(\"Score\")\n", - " ax.set_title(f\"{title} — Model Comparison (Test)\", fontsize=13)\n", - " ax.legend()\n", - " for bar in bars1:\n", - " ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n", - " f\"{bar.get_height():.3f}\", ha=\"center\", fontsize=8)\n", - " for bar in bars2:\n", - " ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n", - " f\"{bar.get_height():.3f}\", ha=\"center\", fontsize=8)\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(REPORTS_DIR / \"model_comparison_chart.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "print(\"Saved: outputs/reports/model_comparison_chart.png\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GC0rNp0auKCF" - }, - "source": [ - "## 3. Error Analysis — Binary Task" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "jokvmFh9uKCF" - }, - "outputs": [], - "source": [ - "def load_predictions(path: Path) -> pd.DataFrame | None:\n", - " if path.exists():\n", - " return pd.read_csv(path)\n", - " print(f\" [WARNING] Not found: {path}\")\n", - " return None\n", - "\n", - "\n", - "# Use best available binary model predictions for error analysis\n", - "# Priority: BERT > TF-IDF+LR > NB\n", - "pred_paths_bin = [\n", - " (\"DistilBERT\", BERT_OUT / \"distilbert_binary\" / \"predictions_test.csv\"),\n", - " (\"TF-IDF + LR\", CLASSICAL / \"tfidf_lr\" / \"predictions_test_binary.csv\"),\n", - " (\"Naive Bayes\", CLASSICAL / \"naive_bayes\" / \"predictions_test_binary.csv\"),\n", - "]\n", - "\n", - "error_source_bin = None\n", - "error_model_bin = None\n", - "for model_name, path in pred_paths_bin:\n", - " df = load_predictions(path)\n", - " if df is not None:\n", - " error_source_bin = df\n", - " error_model_bin = model_name\n", - " print(f\"Using {model_name} predictions for binary error analysis\")\n", - " break\n", - "\n", - "if error_source_bin is None:\n", - " print(\"No binary predictions found. Run at least one model first.\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "0qFoK11OuKCF" - }, - "outputs": [], - "source": [ - "if error_source_bin is not None:\n", - " pred_col = \"predicted\" if \"predicted\" in error_source_bin.columns else \"predicted_id\"\n", - " true_col = \"binary_label\"\n", - " err_bin = error_source_bin[error_source_bin[\"correct\"] == 0].copy()\n", - "\n", - " # Categorize\n", - " fp_bin = err_bin[err_bin[true_col] == 0].copy() # False Positives (predicted sarcastic)\n", - " fn_bin = err_bin[err_bin[true_col] == 1].copy() # False Negatives (predicted not-sarcastic)\n", - "\n", - " print(f\"Total binary test errors: {len(err_bin)}\")\n", - " print(f\" False Positives (non-sarcastic predicted as sarcastic) : {len(fp_bin)}\")\n", - " print(f\" False Negatives (sarcastic predicted as non-sarcastic) : {len(fn_bin)}\")\n", - "\n", - " # Select 20+ errors: mix of FP and FN\n", - " n_each = 12\n", - " sample_fp = fp_bin.head(n_each)\n", - " sample_fn = fn_bin.head(n_each)\n", - " error_sample_bin = pd.concat([sample_fp, sample_fn], ignore_index=True)\n", - " error_sample_bin[\"error_type\"] = [\"FP\"] * len(sample_fp) + [\"FN\"] * len(sample_fn)\n", - "\n", - " print(f\"\\nError sample size: {len(error_sample_bin)} examples\")\n", - " print(\"\\n--- False Positives (non-sarcastic → predicted sarcastic) ---\")\n", - " for _, row in sample_fp.iterrows():\n", - " print(f\" {row['text']}\")\n", - "\n", - " print(\"\\n--- False Negatives (sarcastic → predicted non-sarcastic) ---\")\n", - " for _, row in sample_fn.iterrows():\n", - " print(f\" {row['text']}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "gAh7r-JhuKCF" - }, - "outputs": [], - "source": [ - "if error_source_bin is not None:\n", - " # Failure mode categorization (heuristic rules)\n", - " def categorize_binary_error(text: str, error_type: str) -> str:\n", - " text_lower = text.lower()\n", - " if \"?\" in text and error_type == \"FP\":\n", - " return \"rhetorical_phrasing\"\n", - " if any(w in text_lower for w in [\"report\", \"study\", \"survey\", \"finds\", \"shows\"]):\n", - " return \"neutral_reporting_style_confused\"\n", - " if any(w in text_lower for w in [\"best\", \"great\", \"amazing\", \"wonderful\", \"perfect\"]):\n", - " return \"positive_framing_confused\"\n", - " if \"onion\" in text_lower:\n", - " return \"domain_leak\"\n", - " if len(text.split()) <= 5:\n", - " return \"very_short_text\"\n", - " return \"lexical_ambiguity\"\n", - "\n", - " error_sample_bin[\"failure_mode\"] = error_sample_bin.apply(\n", - " lambda r: categorize_binary_error(r[\"text\"], r[\"error_type\"]), axis=1\n", - " )\n", - "\n", - " print(\"Failure mode distribution:\")\n", - " print(error_sample_bin[\"failure_mode\"].value_counts())\n", - "\n", - " # Save\n", - " error_sample_bin.to_csv(REPORTS_DIR / \"error_examples_binary.csv\", index=False)\n", - " print(\"\\nSaved: outputs/reports/error_examples_binary.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3Ybue8H2uKCF" - }, - "source": [ - "## 4. Error Analysis — Type Task" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "-yBp16Z6uKCG" - }, - "outputs": [], - "source": [ - "pred_paths_type = [\n", - " (\"DistilBERT\", BERT_OUT / \"distilbert_type\" / \"predictions_test.csv\"),\n", - " (\"TF-IDF + LR\", CLASSICAL / \"tfidf_lr\" / \"predictions_test_type.csv\"),\n", - " (\"Naive Bayes\", CLASSICAL / \"naive_bayes\" / \"predictions_test_type.csv\"),\n", - "]\n", - "\n", - "error_source_type = None\n", - "error_model_type = None\n", - "for model_name, path in pred_paths_type:\n", - " df = load_predictions(path)\n", - " if df is not None:\n", - " error_source_type = df\n", - " error_model_type = model_name\n", - " print(f\"Using {model_name} predictions for type error analysis\")\n", - " break\n", - "\n", - "if error_source_type is None:\n", - " print(\"No type predictions found.\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "r21XN805uKCG" - }, - "outputs": [], - "source": [ - "if error_source_type is not None:\n", - " err_type = error_source_type[error_source_type[\"correct\"] == 0].copy()\n", - "\n", - " # Identify label columns\n", - " true_col_type = \"type_label\"\n", - " pred_col_type = \"predicted_label\" if \"predicted_label\" in err_type.columns else \"predicted\"\n", - "\n", - " print(f\"Total type errors: {len(err_type)}\")\n", - " print(\"\\nTop confusion pairs (true → predicted):\")\n", - " if pred_col_type in err_type.columns:\n", - " pairs = err_type.groupby([true_col_type, pred_col_type]).size().sort_values(ascending=False).head(15)\n", - " print(pairs.to_string())\n", - "\n", - " # Sample 20+ errors\n", - " error_sample_type = err_type.sample(min(25, len(err_type)), random_state=42)\n", - "\n", - " print(f\"\\nError sample size: {len(error_sample_type)} examples\")\n", - " print(\"\\n--- Sample type misclassifications ---\")\n", - " display_cols = [\"text\", true_col_type]\n", - " if pred_col_type in error_sample_type.columns:\n", - " display_cols.append(pred_col_type)\n", - " for _, row in error_sample_type.head(15).iterrows():\n", - " true_l = row[true_col_type]\n", - " pred_l = row.get(pred_col_type, \"?\")\n", - " print(f\" [True={true_l:20s} Pred={pred_l:20s}] {row['text'][:90]}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "IzgDVYhfuKCG" - }, - "outputs": [], - "source": [ - "if error_source_type is not None:\n", - " def categorize_type_error(text: str, true_label: str, pred_label: str) -> str:\n", - " \"\"\"Heuristic failure mode categorization for type task.\"\"\"\n", - " text_lower = text.lower()\n", - " # Strategy overlap: sarcasm ↔ irony ↔ satire are frequently confused\n", - " overlap_pairs = {\n", - " frozenset({\"sarcasm\", \"irony\"}),\n", - " frozenset({\"sarcasm\", \"satire\"}),\n", - " frozenset({\"irony\", \"satire\"}),\n", - " }\n", - " if frozenset({true_label, pred_label}) in overlap_pairs:\n", - " return \"strategy_semantic_overlap\"\n", - " if \"rhetorical_question\" in [true_label, pred_label]:\n", - " if \"?\" in text:\n", - " return \"rhetorical_vs_other_with_question\"\n", - " return \"rhetorical_without_question_mark\"\n", - " if true_label in [\"overstatement\", \"understatement\"] and pred_label in [\"sarcasm\", \"irony\"]:\n", - " return \"scale_confusion_with_sarcasm\"\n", - " if \"report\" in text_lower or \"according\" in text_lower:\n", - " return \"generation_artifact_formal_phrasing\"\n", - " return \"lexical_ambiguity\"\n", - "\n", - " if pred_col_type in error_sample_type.columns:\n", - " error_sample_type[\"failure_mode\"] = error_sample_type.apply(\n", - " lambda r: categorize_type_error(r[\"text\"], r[true_col_type], r[pred_col_type]),\n", - " axis=1\n", - " )\n", - " print(\"Failure mode distribution:\")\n", - " print(error_sample_type[\"failure_mode\"].value_counts())\n", - "\n", - " error_sample_type.to_csv(REPORTS_DIR / \"error_examples_type.csv\", index=False)\n", - " print(\"\\nSaved: outputs/reports/error_examples_type.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NEmnBPTtuKCG" - }, - "source": [ - "## 5. Generate Final Reports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "xH7naLCouKCG" - }, - "outputs": [], - "source": [ - "def fmt(v) -> str:\n", - " \"\"\"Format a metric value for markdown.\"\"\"\n", - " if v is None:\n", - " return \"N/A\"\n", - " return f\"{v:.4f}\"\n", - "\n", - "\n", - "def build_model_comparison_md() -> str:\n", - " lines = []\n", - " lines.append(\"# Model Comparison Report\")\n", - " lines.append(\"\")\n", - " lines.append(\"> Auto-generated from outputs of notebooks 02–04.\")\n", - " lines.append(\"\")\n", - "\n", - " # Binary task table\n", - " lines.append(\"## Task A — Binary Classification (Test Set)\")\n", - " lines.append(\"\")\n", - " lines.append(\"| Model | Accuracy | Precision (M) | Recall (M) | F1 (Macro) | F1 (Weighted) |\")\n", - " lines.append(\"|-------|----------|--------------|------------|------------|--------------|\")\n", - " for name, m in model_map_bin:\n", - " r = extract_test_metrics(m)\n", - " lines.append(\n", - " f\"| {name} | {fmt(r['accuracy'])} | {fmt(r['precision_macro'])} | \"\n", - " f\"{fmt(r['recall_macro'])} | {fmt(r['f1_macro'])} | {fmt(r['f1_weighted'])} |\"\n", - " )\n", - " lines.append(\"\")\n", - " lines.append(\"_Primary metric: Macro-F1. Best value bolded (manually after review)._\")\n", - " lines.append(\"\")\n", - "\n", - " # Type task table\n", - " lines.append(\"## Task B — Sarcasm Type Classification (Test Set)\")\n", - " lines.append(\"\")\n", - " lines.append(\"| Model | Accuracy | Precision (M) | Recall (M) | F1 (Macro) | F1 (Weighted) |\")\n", - " lines.append(\"|-------|----------|--------------|------------|------------|--------------|\")\n", - " for name, m in model_map_type:\n", - " r = extract_test_metrics(m)\n", - " lines.append(\n", - " f\"| {name} | {fmt(r['accuracy'])} | {fmt(r['precision_macro'])} | \"\n", - " f\"{fmt(r['recall_macro'])} | {fmt(r['f1_macro'])} | {fmt(r['f1_weighted'])} |\"\n", - " )\n", - " lines.append(\"\")\n", - "\n", - " # Recommendation\n", - " lines.append(\"## Recommendation\")\n", - " lines.append(\"\")\n", - "\n", - " # Find best binary model\n", - " best_bin_name, best_bin_f1 = \"N/A\", -1\n", - " for name, m in model_map_bin:\n", - " r = extract_test_metrics(m)\n", - " if r[\"f1_macro\"] is not None and r[\"f1_macro\"] > best_bin_f1:\n", - " best_bin_f1 = r[\"f1_macro\"]\n", - " best_bin_name = name\n", - "\n", - " best_type_name, best_type_f1 = \"N/A\", -1\n", - " for name, m in model_map_type:\n", - " r = extract_test_metrics(m)\n", - " if r[\"f1_macro\"] is not None and r[\"f1_macro\"] > best_type_f1:\n", - " best_type_f1 = r[\"f1_macro\"]\n", - " best_type_name = name\n", - "\n", - " lines.append(f\"### Best model for Binary Task: **{best_bin_name}** (Macro-F1 = {fmt(best_bin_f1)})\")\n", - " lines.append(\"\")\n", - " lines.append(f\"### Best model for Type Task: **{best_type_name}** (Macro-F1 = {fmt(best_type_f1)})\")\n", - " lines.append(\"\")\n", - " lines.append(\"### Trade-offs\")\n", - " lines.append(\"\")\n", - " lines.append(\"| Aspect | TF-IDF + LR | Naive Bayes | DistilBERT |\")\n", - " lines.append(\"|--------|------------|-------------|-----------|\")\n", - " lines.append(\"| Training speed | Fast (minutes) | Very fast (seconds) | Slow (hours) |\")\n", - " lines.append(\"| Inference speed | Very fast | Very fast | Moderate |\")\n", - " lines.append(\"| Interpretability | High (feature weights) | High (log-probs) | Low (black-box) |\")\n", - " lines.append(\"| Handles context | No | No | Yes (self-attention) |\")\n", - " lines.append(\"| Handles rare words | Via TF-IDF | Via smoothing | Via sub-word tokenization |\")\n", - " lines.append(\"| GPU required | No | No | Recommended |\")\n", - " lines.append(\"\")\n", - " lines.append(\"**Deployment recommendation**: Use TF-IDF+LR if real-time speed and interpretability are priorities.\")\n", - " lines.append(\"Use DistilBERT for maximum accuracy when GPU inference is available.\")\n", - "\n", - " return \"\\n\".join(lines)\n", - "\n", - "\n", - "comparison_md = build_model_comparison_md()\n", - "with open(REPORTS_DIR / \"model_comparison.md\", \"w\", encoding=\"utf-8\") as f:\n", - " f.write(comparison_md)\n", - "\n", - "print(\"Saved: outputs/reports/model_comparison.md\")\n", - "print(\"\\nPreview:\")\n", - "print(comparison_md[:2000])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "CmgZF4S1uKCH" - }, - "outputs": [], - "source": [ - "def build_error_analysis_md() -> str:\n", - " lines = []\n", - " lines.append(\"# Error Analysis Report\")\n", - " lines.append(\"\")\n", - " lines.append(\"> Auto-generated from model predictions. Examples drawn from test set.\")\n", - " lines.append(\"\")\n", - "\n", - " # ── Binary ────────────────────────────────────────────────────────────────\n", - " lines.append(\"## Task A — Binary Classification Errors\")\n", - " lines.append(\"\")\n", - "\n", - " if error_source_bin is not None:\n", - " err_df = error_source_bin[error_source_bin[\"correct\"] == 0]\n", - " n_fp = len(err_df[err_df[\"binary_label\"] == 0])\n", - " n_fn = len(err_df[err_df[\"binary_label\"] == 1])\n", - " lines.append(f\"**Model**: {error_model_bin} | **Total errors**: {len(err_df):,} | FP: {n_fp:,} | FN: {n_fn:,}\")\n", - " lines.append(\"\")\n", - " lines.append(\"### Failure Mode Summary\")\n", - " lines.append(\"\")\n", - " lines.append(\"| Failure Mode | Description |\")\n", - " lines.append(\"|-------------|-------------|\")\n", - " lines.append(\"| Lexical ambiguity | Sarcasm/irony requires pragmatic context beyond lexical cues |\")\n", - " lines.append(\"| Neutral reporting style confused | Formal generated text mimics neutral news, but still classified as sarcastic |\")\n", - " lines.append(\"| Positive framing confused | Genuine positive news shares superlative vocabulary with ironic praise |\")\n", - " lines.append(\"| Rhetorical phrasing | Questions that look rhetorical but are literal |\")\n", - " lines.append(\"| Very short text | Too little context for confident classification |\")\n", - " lines.append(\"\")\n", - " lines.append(\"### False Positive Examples (Non-sarcastic predicted as sarcastic)\")\n", - " lines.append(\"\")\n", - " fp_examples = error_source_bin[\n", - " (error_source_bin[\"correct\"] == 0) & (error_source_bin[\"binary_label\"] == 0)\n", - " ].head(10)\n", - " for _, row in fp_examples.iterrows():\n", - " lines.append(f\"- `{row['text']}`\")\n", - " lines.append(\"\")\n", - " lines.append(\"### False Negative Examples (Sarcastic predicted as non-sarcastic)\")\n", - " lines.append(\"\")\n", - " fn_examples = error_source_bin[\n", - " (error_source_bin[\"correct\"] == 0) & (error_source_bin[\"binary_label\"] == 1)\n", - " ].head(10)\n", - " for _, row in fn_examples.iterrows():\n", - " lines.append(f\"- `{row['text']}`\")\n", - " else:\n", - " lines.append(\"_Binary predictions not available. Run at least one model._\")\n", - "\n", - " lines.append(\"\")\n", - "\n", - " # ── Type ──────────────────────────────────────────────────────────────────\n", - " lines.append(\"## Task B — Sarcasm Type Classification Errors\")\n", - " lines.append(\"\")\n", - "\n", - " if error_source_type is not None:\n", - " err_df_t = error_source_type[error_source_type[\"correct\"] == 0]\n", - " lines.append(f\"**Model**: {error_model_type} | **Total errors**: {len(err_df_t):,}\")\n", - " lines.append(\"\")\n", - " lines.append(\"### Failure Mode Summary\")\n", - " lines.append(\"\")\n", - " lines.append(\"| Failure Mode | Description |\")\n", - " lines.append(\"|-------------|-------------|\")\n", - " lines.append(\"| Strategy semantic overlap | sarcasm/irony/satire share similar surface forms; labels conflated |\")\n", - " lines.append(\"| Scale confusion | overstatement/understatement confused with sarcasm due to exaggeration cues |\")\n", - " lines.append(\"| Rhetorical vs. other | rhetorical_question confused with irony/sarcasm when ? is absent |\")\n", - " lines.append(\"| Generation artifact | formal paraphrase style shifts text away from original strategy markers |\")\n", - " lines.append(\"| Lexical ambiguity | strategy relies on world knowledge rather than surface vocabulary |\")\n", - " lines.append(\"\")\n", - " lines.append(\"### Key Insight\")\n", - " lines.append(\"\")\n", - " lines.append(\"The primary failure mode is **strategy semantic overlap**: sarcasm, irony, and satire\")\n", - " lines.append(\"are conceptually related and frequently share similar linguistic surface patterns.\")\n", - " lines.append(\"The label `sarcasm` as a strategy within a sarcasm dataset introduces circularity.\")\n", - " lines.append(\"The `rhetorical_question` class is syntactically distinctive (ends with ?) and should\")\n", - " lines.append(\"be easier to classify; errors in this class suggest the classifier may ignore punctuation.\")\n", - " lines.append(\"\")\n", - " lines.append(\"### Sample Misclassifications\")\n", - " lines.append(\"\")\n", - " sample_t = error_source_type[error_source_type[\"correct\"] == 0].head(20)\n", - " true_c = \"type_label\"\n", - " pred_c = \"predicted_label\" if \"predicted_label\" in sample_t.columns else \"predicted\"\n", - " for _, row in sample_t.iterrows():\n", - " true_l = row[true_c]\n", - " pred_l = row.get(pred_c, \"?\")\n", - " lines.append(f\"- **True**: {true_l} | **Pred**: {pred_l} | `{row['text'][:100]}`\")\n", - " else:\n", - " lines.append(\"_Type predictions not available. Run at least one model._\")\n", - "\n", - " lines.append(\"\")\n", - " lines.append(\"## Summary of Observations\")\n", - " lines.append(\"\")\n", - " lines.append(\"1. **Binary task** is relatively tractable — TheOnion writing style has strong lexical signatures\")\n", - " lines.append(\"2. **Generated headlines** (is_generated=1) may fool classifiers trained mainly on original text\")\n", - " lines.append(\"3. **Type task** is fundamentally harder because strategies are not mutually exclusive\")\n", - " lines.append(\"4. **Class imbalance** (rhetorical_question = 3.7%) is a significant challenge\")\n", - " lines.append(\"5. **BERT** should better capture contextual incongruence; classical models rely on surface vocabulary\")\n", - "\n", - " return \"\\n\".join(lines)\n", - "\n", - "\n", - "error_md = build_error_analysis_md()\n", - "with open(REPORTS_DIR / \"error_analysis.md\", \"w\", encoding=\"utf-8\") as f:\n", - " f.write(error_md)\n", - "\n", - "print(\"Saved: outputs/reports/error_analysis.md\")" - ] + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "8b9080237c194037a2cd1dc711a694d2" + } + }, + "metadata": {} }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "fdUb27BQuKCH" - }, - "outputs": [], - "source": [ - "print(\"====== ERROR ANALYSIS COMPLETE ======\")\n", - "print()\n", - "print(\"Saved artifacts:\")\n", - "for p in sorted(REPORTS_DIR.iterdir()):\n", - " print(f\" {p.relative_to(ROOT)}\")\n", - "print()\n", - "print(\"Pipeline complete. See outputs/reports/model_comparison.md for final results.\")" - ] - } - ], - "metadata": { - "colab": { - "machine_shape": "hm", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.11.0" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "01c169be55ca4399b34eb7cdae152075": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "143c9b378b5a4260b15e328dc744374f": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - 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Consider training on your downstream task.\n" + ] } + ], + "source": [ + "import torch\n", + "from transformers import (\n", + " AutoTokenizer,\n", + " AutoModelForSequenceClassification,\n", + " TrainingArguments,\n", + " Trainer,\n", + " DataCollatorWithPadding,\n", + ")\n", + "import torch\n", + "from torch.utils.data import Dataset, DataLoader\n", + "\n", + "\n", + "results_distilbert_binary = train_bert(\n", + " cfg = CFG_BINARY,\n", + " X_train = X_train_bin, y_train = y_train_bin,\n", + " X_val = X_val_bin, y_val = y_val_bin,\n", + " X_test = X_test_bin, y_test = y_test_bin,\n", + " label_names= label_names_bin,\n", + " out_dir = BERT_OUT / \"distilbert_binary\",\n", + " val_df = val_bin,\n", + " test_df = test_bin,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rtkEQF58uKCC" + }, + "source": [ + "## Train DistilBERT — Type Task" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yBICJ8OMuKCC" + }, + "outputs": [], + "source": [ + "results_distilbert_type = train_bert(\n", + " cfg = CFG_TYPE,\n", + " X_train = X_train_type, y_train = y_train_type,\n", + " X_val = X_val_type, y_val = y_val_type,\n", + " X_test = X_test_type, y_test = y_test_type,\n", + " label_names= STRATEGY_LABELS,\n", + " out_dir = BERT_OUT / \"distilbert_type\",\n", + " val_df = val_type,\n", + " test_df = test_type,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yFgqLSPouKCC" + }, + "source": [ + "## (Optional) BERT-base — Binary Task\n", + "\n", + "Run only if compute allows. Comment out if not needed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7spIQwpluKCD" + }, + "outputs": [], + "source": [ + "# Uncomment to run BERT-base (requires more GPU memory and time)\n", + "\n", + "CFG_BERT_BINARY = TrainConfig(\n", + " model_name=\"bert-base-uncased\",\n", + " task=\"binary\",\n", + " batch_size=16, # smaller batch for bert-base\n", + " lr=2e-5,\n", + ")\n", + "\n", + "results_bert_binary = train_bert(\n", + " cfg = CFG_BERT_BINARY,\n", + " X_train = X_train_bin, y_train = y_train_bin,\n", + " X_val = X_val_bin, y_val = y_val_bin,\n", + " X_test = X_test_bin, y_test = y_test_bin,\n", + " label_names = label_names_bin,\n", + " out_dir = BERT_OUT / \"bert_base_binary\",\n", + " val_df = val_bin,\n", + " test_df = test_bin,\n", + ")\n", + "\n", + "# print(\"BERT-base is commented out. Uncomment cells above to run.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PUSxauAluKCD" + }, + "source": [ + "## (Optional) BERT-base — Type Task" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uaqm4IUjuKCD" + }, + "outputs": [], + "source": [ + "CFG_BERT_TYPE = TrainConfig(\n", + " model_name=\"bert-base-uncased\",\n", + " task=\"type\",\n", + " batch_size=16,\n", + " lr=2e-5,\n", + " use_class_weights=True,\n", + ")\n", + "\n", + "results_bert_type = train_bert(\n", + " cfg = CFG_BERT_TYPE,\n", + " X_train = X_train_type, y_train = y_train_type,\n", + " X_val = X_val_type, y_val = y_val_type,\n", + " X_test = X_test_type, y_test = y_test_type,\n", + " label_names = STRATEGY_LABELS,\n", + " out_dir = BERT_OUT / \"bert_base_type\",\n", + " val_df = val_type,\n", + " test_df = test_type,\n", + ")\n", + "\n", + "# print(\"BERT-base type task is commented out. Uncomment to run.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cFQdWUqpuKCD" + }, + "source": [ + "## Summary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "89y9LuozuKCD" + }, + "outputs": [], + "source": [ + "print(\"====== BERT RESULTS SUMMARY ======\")\n", + "print()\n", + "print(\"DistilBERT — Binary (Test):\")\n", + "print(f\" Accuracy : {results_distilbert_binary['test']['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {results_distilbert_binary['test']['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1: {results_distilbert_binary['test']['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"DistilBERT — Type (Test):\")\n", + "print(f\" Accuracy : {results_distilbert_type['test']['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {results_distilbert_type['test']['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1: {results_distilbert_type['test']['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"Artifacts saved in outputs/bert/distilbert_binary/ and outputs/bert/distilbert_type/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9dAXibWCuKCE" + }, + "source": [ + "---\n", + "# Part 5 — Error Analysis & Model Comparison" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FIcicb-xuKCE" + }, + "source": [ + "# Notebook 05 — Error Analysis & Model Comparison\n", + "\n", + "**Purpose**:\n", + "1. Compare all models on both tasks\n", + "2. Analyze errors (binary + type)\n", + "3. Generate final comparison report\n", + "\n", + "**Prerequisite**: Run notebooks 01–04 first.\n", + "\n", + "**Outputs**:\n", + "- `outputs/reports/model_comparison.md`\n", + "- `outputs/reports/error_analysis.md`\n", + "- `outputs/reports/error_examples_binary.csv`\n", + "- `outputs/reports/error_examples_type.csv`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "saP8-m-MuKCE" + }, + "source": [ + "## 1. Load All Metrics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cs2Mce2ZuKCE" + }, + "outputs": [], + "source": [ + "def load_metrics(path: Path) -> dict | None:\n", + " if path.exists():\n", + " with open(path) as f:\n", + " return json.load(f)\n", + " print(f\" [WARNING] Not found: {path.relative_to(ROOT)}\")\n", + " return None\n", + "\n", + "\n", + "# All metrics\n", + "tfidf_lr_binary = load_metrics(CLASSICAL / \"tfidf_lr\" / \"metrics_binary.json\")\n", + "tfidf_lr_type = load_metrics(CLASSICAL / \"tfidf_lr\" / \"metrics_type.json\")\n", + "nb_binary = load_metrics(CLASSICAL / \"naive_bayes\" / \"metrics_binary.json\")\n", + "nb_type = load_metrics(CLASSICAL / \"naive_bayes\" / \"metrics_type.json\")\n", + "distilbert_bin = load_metrics(BERT_OUT / \"distilbert_binary\" / \"metrics.json\")\n", + "distilbert_type = load_metrics(BERT_OUT / \"distilbert_type\" / \"metrics.json\")\n", + "bert_base_bin = load_metrics(BERT_OUT / \"bert_base_binary\" / \"metrics.json\") # optional\n", + "bert_base_type = load_metrics(BERT_OUT / \"bert_base_type\" / \"metrics.json\") # optional\n", + "\n", + "print(\"Metrics loaded (None = not yet run):\")\n", + "for name, m in [(\"TF-IDF+LR binary\", tfidf_lr_binary), (\"TF-IDF+LR type\", tfidf_lr_type),\n", + " (\"NB binary\", nb_binary), (\"NB type\", nb_type),\n", + " (\"DistilBERT binary\", distilbert_bin), (\"DistilBERT type\", distilbert_type),\n", + " (\"BERT-base binary\", bert_base_bin), (\"BERT-base type\", bert_base_type)]:\n", + " print(f\" {name}: {'✓' if m else '✗'}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H_xQm80nuKCE" + }, + "source": [ + "## 2. Model Comparison Tables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2RsVxgMtuKCE" + }, + "outputs": [], + "source": [ + "def extract_test_metrics(metrics_dict: dict | None, split: str = \"test\") -> dict:\n", + " \"\"\"Extract test-split metrics from a metrics dict, handling different formats.\"\"\"\n", + " if metrics_dict is None:\n", + " return {\"accuracy\": None, \"f1_macro\": None, \"f1_weighted\": None,\n", + " \"precision_macro\": None, \"recall_macro\": None}\n", + " test_m = metrics_dict.get(split, metrics_dict) # BERT format uses 'test' key directly\n", + " return {\n", + " \"accuracy\" : test_m.get(\"accuracy\"),\n", + " \"f1_macro\" : test_m.get(\"f1_macro\"),\n", + " \"f1_weighted\" : test_m.get(\"f1_weighted\"),\n", + " \"precision_macro\" : test_m.get(\"precision_macro\"),\n", + " \"recall_macro\" : test_m.get(\"recall_macro\"),\n", + " }\n", + "\n", + "\n", + "binary_rows = []\n", + "type_rows = []\n", + "\n", + "model_map_bin = [\n", + " (\"TF-IDF + LR\", tfidf_lr_binary),\n", + " (\"Naive Bayes\", nb_binary),\n", + " (\"DistilBERT\", distilbert_bin),\n", + " (\"BERT-base\", bert_base_bin),\n", + "]\n", + "\n", + "model_map_type = [\n", + " (\"TF-IDF + LR\", tfidf_lr_type),\n", + " (\"Naive Bayes\", nb_type),\n", + " (\"DistilBERT\", distilbert_type),\n", + " (\"BERT-base\", bert_base_type),\n", + "]\n", + "\n", + "for name, m in model_map_bin:\n", + " row = {\"Model\": name}\n", + " row.update(extract_test_metrics(m))\n", + " binary_rows.append(row)\n", + "\n", + "for name, m in model_map_type:\n", + " row = {\"Model\": name}\n", + " row.update(extract_test_metrics(m))\n", + " type_rows.append(row)\n", + "\n", + "binary_comp = pd.DataFrame(binary_rows).set_index(\"Model\")\n", + "type_comp = pd.DataFrame(type_rows).set_index(\"Model\")\n", + "\n", + "print(\"=== BINARY TASK — Test Metrics ===\")\n", + "print(binary_comp.to_string(float_format=lambda x: f\"{x:.4f}\" if x is not None else \"N/A\"))\n", + "print()\n", + "print(\"=== TYPE TASK — Test Metrics ===\")\n", + "print(type_comp.to_string(float_format=lambda x: f\"{x:.4f}\" if x is not None else \"N/A\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "n35eLHCCuKCF" + }, + "outputs": [], + "source": [ + "# ── Comparison bar charts ─────────────────────────────────────────────────────\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n", + "\n", + "for ax, comp, title in [\n", + " (axes[0], binary_comp, \"Binary Task\"),\n", + " (axes[1], type_comp, \"Type Task\"),\n", + "]:\n", + " valid = comp.dropna(subset=[\"f1_macro\"])\n", + " if len(valid) == 0:\n", + " ax.set_title(f\"{title} — No data\")\n", + " continue\n", + " models = valid.index.tolist()\n", + " f1_vals = valid[\"f1_macro\"].tolist()\n", + " acc_vals = valid[\"accuracy\"].tolist()\n", + " x = range(len(models))\n", + " w = 0.35\n", + " bars1 = ax.bar([i - w/2 for i in x], f1_vals, w, label=\"Macro-F1\", color=\"steelblue\")\n", + " bars2 = ax.bar([i + w/2 for i in x], acc_vals, w, label=\"Accuracy\", color=\"coral\")\n", + " ax.set_xticks(list(x)); ax.set_xticklabels(models, rotation=20, ha=\"right\")\n", + " ax.set_ylim(0, 1.05)\n", + " ax.set_ylabel(\"Score\")\n", + " ax.set_title(f\"{title} — Model Comparison (Test)\", fontsize=13)\n", + " ax.legend()\n", + " for bar in bars1:\n", + " ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n", + " f\"{bar.get_height():.3f}\", ha=\"center\", fontsize=8)\n", + " for bar in bars2:\n", + " ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n", + " f\"{bar.get_height():.3f}\", ha=\"center\", fontsize=8)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(REPORTS_DIR / \"model_comparison_chart.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/reports/model_comparison_chart.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GC0rNp0auKCF" + }, + "source": [ + "## 3. Error Analysis — Binary Task" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jokvmFh9uKCF" + }, + "outputs": [], + "source": [ + "def load_predictions(path: Path) -> pd.DataFrame | None:\n", + " if path.exists():\n", + " return pd.read_csv(path)\n", + " print(f\" [WARNING] Not found: {path}\")\n", + " return None\n", + "\n", + "\n", + "# Use best available binary model predictions for error analysis\n", + "# Priority: BERT > TF-IDF+LR > NB\n", + "pred_paths_bin = [\n", + " (\"DistilBERT\", BERT_OUT / \"distilbert_binary\" / \"predictions_test.csv\"),\n", + " (\"TF-IDF + LR\", CLASSICAL / \"tfidf_lr\" / \"predictions_test_binary.csv\"),\n", + " (\"Naive Bayes\", CLASSICAL / \"naive_bayes\" / \"predictions_test_binary.csv\"),\n", + "]\n", + "\n", + "error_source_bin = None\n", + "error_model_bin = None\n", + "for model_name, path in pred_paths_bin:\n", + " df = load_predictions(path)\n", + " if df is not None:\n", + " error_source_bin = df\n", + " error_model_bin = model_name\n", + " print(f\"Using {model_name} predictions for binary error analysis\")\n", + " break\n", + "\n", + "if error_source_bin is None:\n", + " print(\"No binary predictions found. Run at least one model first.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0qFoK11OuKCF" + }, + "outputs": [], + "source": [ + "if error_source_bin is not None:\n", + " pred_col = \"predicted\" if \"predicted\" in error_source_bin.columns else \"predicted_id\"\n", + " true_col = \"binary_label\"\n", + " err_bin = error_source_bin[error_source_bin[\"correct\"] == 0].copy()\n", + "\n", + " # Categorize\n", + " fp_bin = err_bin[err_bin[true_col] == 0].copy() # False Positives (predicted sarcastic)\n", + " fn_bin = err_bin[err_bin[true_col] == 1].copy() # False Negatives (predicted not-sarcastic)\n", + "\n", + " print(f\"Total binary test errors: {len(err_bin)}\")\n", + " print(f\" False Positives (non-sarcastic predicted as sarcastic) : {len(fp_bin)}\")\n", + " print(f\" False Negatives (sarcastic predicted as non-sarcastic) : {len(fn_bin)}\")\n", + "\n", + " # Select 20+ errors: mix of FP and FN\n", + " n_each = 12\n", + " sample_fp = fp_bin.head(n_each)\n", + " sample_fn = fn_bin.head(n_each)\n", + " error_sample_bin = pd.concat([sample_fp, sample_fn], ignore_index=True)\n", + " error_sample_bin[\"error_type\"] = [\"FP\"] * len(sample_fp) + [\"FN\"] * len(sample_fn)\n", + "\n", + " print(f\"\\nError sample size: {len(error_sample_bin)} examples\")\n", + " print(\"\\n--- False Positives (non-sarcastic → predicted sarcastic) ---\")\n", + " for _, row in sample_fp.iterrows():\n", + " print(f\" {row['text']}\")\n", + "\n", + " print(\"\\n--- False Negatives (sarcastic → predicted non-sarcastic) ---\")\n", + " for _, row in sample_fn.iterrows():\n", + " print(f\" {row['text']}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "gAh7r-JhuKCF" + }, + "outputs": [], + "source": [ + "if error_source_bin is not None:\n", + " # Failure mode categorization (heuristic rules)\n", + " def categorize_binary_error(text: str, error_type: str) -> str:\n", + " text_lower = text.lower()\n", + " if \"?\" in text and error_type == \"FP\":\n", + " return \"rhetorical_phrasing\"\n", + " if any(w in text_lower for w in [\"report\", \"study\", \"survey\", \"finds\", \"shows\"]):\n", + " return \"neutral_reporting_style_confused\"\n", + " if any(w in text_lower for w in [\"best\", \"great\", \"amazing\", \"wonderful\", \"perfect\"]):\n", + " return \"positive_framing_confused\"\n", + " if \"onion\" in text_lower:\n", + " return \"domain_leak\"\n", + " if len(text.split()) <= 5:\n", + " return \"very_short_text\"\n", + " return \"lexical_ambiguity\"\n", + "\n", + " error_sample_bin[\"failure_mode\"] = error_sample_bin.apply(\n", + " lambda r: categorize_binary_error(r[\"text\"], r[\"error_type\"]), axis=1\n", + " )\n", + "\n", + " print(\"Failure mode distribution:\")\n", + " print(error_sample_bin[\"failure_mode\"].value_counts())\n", + "\n", + " # Save\n", + " error_sample_bin.to_csv(REPORTS_DIR / \"error_examples_binary.csv\", index=False)\n", + " print(\"\\nSaved: outputs/reports/error_examples_binary.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3Ybue8H2uKCF" + }, + "source": [ + "## 4. Error Analysis — Type Task" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-yBp16Z6uKCG" + }, + "outputs": [], + "source": [ + "pred_paths_type = [\n", + " (\"DistilBERT\", BERT_OUT / \"distilbert_type\" / \"predictions_test.csv\"),\n", + " (\"TF-IDF + LR\", CLASSICAL / \"tfidf_lr\" / \"predictions_test_type.csv\"),\n", + " (\"Naive Bayes\", CLASSICAL / \"naive_bayes\" / \"predictions_test_type.csv\"),\n", + "]\n", + "\n", + "error_source_type = None\n", + "error_model_type = None\n", + "for model_name, path in pred_paths_type:\n", + " df = load_predictions(path)\n", + " if df is not None:\n", + " error_source_type = df\n", + " error_model_type = model_name\n", + " print(f\"Using {model_name} predictions for type error analysis\")\n", + " break\n", + "\n", + "if error_source_type is None:\n", + " print(\"No type predictions found.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "r21XN805uKCG" + }, + "outputs": [], + "source": [ + "if error_source_type is not None:\n", + " err_type = error_source_type[error_source_type[\"correct\"] == 0].copy()\n", + "\n", + " # Identify label columns\n", + " true_col_type = \"type_label\"\n", + " pred_col_type = \"predicted_label\" if \"predicted_label\" in err_type.columns else \"predicted\"\n", + "\n", + " print(f\"Total type errors: {len(err_type)}\")\n", + " print(\"\\nTop confusion pairs (true → predicted):\")\n", + " if pred_col_type in err_type.columns:\n", + " pairs = err_type.groupby([true_col_type, pred_col_type]).size().sort_values(ascending=False).head(15)\n", + " print(pairs.to_string())\n", + "\n", + " # Sample 20+ errors\n", + " error_sample_type = err_type.sample(min(25, len(err_type)), random_state=42)\n", + "\n", + " print(f\"\\nError sample size: {len(error_sample_type)} examples\")\n", + " print(\"\\n--- Sample type misclassifications ---\")\n", + " display_cols = [\"text\", true_col_type]\n", + " if pred_col_type in error_sample_type.columns:\n", + " display_cols.append(pred_col_type)\n", + " for _, row in error_sample_type.head(15).iterrows():\n", + " true_l = row[true_col_type]\n", + " pred_l = row.get(pred_col_type, \"?\")\n", + " print(f\" [True={true_l:20s} Pred={pred_l:20s}] {row['text'][:90]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IzgDVYhfuKCG" + }, + "outputs": [], + "source": [ + "if error_source_type is not None:\n", + " def categorize_type_error(text: str, true_label: str, pred_label: str) -> str:\n", + " \"\"\"Heuristic failure mode categorization for type task.\"\"\"\n", + " text_lower = text.lower()\n", + " # Strategy overlap: sarcasm ↔ irony ↔ satire are frequently confused\n", + " overlap_pairs = {\n", + " frozenset({\"sarcasm\", \"irony\"}),\n", + " frozenset({\"sarcasm\", \"satire\"}),\n", + " frozenset({\"irony\", \"satire\"}),\n", + " }\n", + " if frozenset({true_label, pred_label}) in overlap_pairs:\n", + " return \"strategy_semantic_overlap\"\n", + " if \"rhetorical_question\" in [true_label, pred_label]:\n", + " if \"?\" in text:\n", + " return \"rhetorical_vs_other_with_question\"\n", + " return \"rhetorical_without_question_mark\"\n", + " if true_label in [\"overstatement\", \"understatement\"] and pred_label in [\"sarcasm\", \"irony\"]:\n", + " return \"scale_confusion_with_sarcasm\"\n", + " if \"report\" in text_lower or \"according\" in text_lower:\n", + " return \"generation_artifact_formal_phrasing\"\n", + " return \"lexical_ambiguity\"\n", + "\n", + " if pred_col_type in error_sample_type.columns:\n", + " error_sample_type[\"failure_mode\"] = error_sample_type.apply(\n", + " lambda r: categorize_type_error(r[\"text\"], r[true_col_type], r[pred_col_type]),\n", + " axis=1\n", + " )\n", + " print(\"Failure mode distribution:\")\n", + " print(error_sample_type[\"failure_mode\"].value_counts())\n", + "\n", + " error_sample_type.to_csv(REPORTS_DIR / \"error_examples_type.csv\", index=False)\n", + " print(\"\\nSaved: outputs/reports/error_examples_type.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NEmnBPTtuKCG" + }, + "source": [ + "## 5. Generate Final Reports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xH7naLCouKCG" + }, + "outputs": [], + "source": [ + "def fmt(v) -> str:\n", + " \"\"\"Format a metric value for markdown.\"\"\"\n", + " if v is None:\n", + " return \"N/A\"\n", + " return f\"{v:.4f}\"\n", + "\n", + "\n", + "def build_model_comparison_md() -> str:\n", + " lines = []\n", + " lines.append(\"# Model Comparison Report\")\n", + " lines.append(\"\")\n", + " lines.append(\"> Auto-generated from outputs of notebooks 02–04.\")\n", + " lines.append(\"\")\n", + "\n", + " # Binary task table\n", + " lines.append(\"## Task A — Binary Classification (Test Set)\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Model | Accuracy | Precision (M) | Recall (M) | F1 (Macro) | F1 (Weighted) |\")\n", + " lines.append(\"|-------|----------|--------------|------------|------------|--------------|\")\n", + " for name, m in model_map_bin:\n", + " r = extract_test_metrics(m)\n", + " lines.append(\n", + " f\"| {name} | {fmt(r['accuracy'])} | {fmt(r['precision_macro'])} | \"\n", + " f\"{fmt(r['recall_macro'])} | {fmt(r['f1_macro'])} | {fmt(r['f1_weighted'])} |\"\n", + " )\n", + " lines.append(\"\")\n", + " lines.append(\"_Primary metric: Macro-F1. Best value bolded (manually after review)._\")\n", + " lines.append(\"\")\n", + "\n", + " # Type task table\n", + " lines.append(\"## Task B — Sarcasm Type Classification (Test Set)\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Model | Accuracy | Precision (M) | Recall (M) | F1 (Macro) | F1 (Weighted) |\")\n", + " lines.append(\"|-------|----------|--------------|------------|------------|--------------|\")\n", + " for name, m in model_map_type:\n", + " r = extract_test_metrics(m)\n", + " lines.append(\n", + " f\"| {name} | {fmt(r['accuracy'])} | {fmt(r['precision_macro'])} | \"\n", + " f\"{fmt(r['recall_macro'])} | {fmt(r['f1_macro'])} | {fmt(r['f1_weighted'])} |\"\n", + " )\n", + " lines.append(\"\")\n", + "\n", + " # Recommendation\n", + " lines.append(\"## Recommendation\")\n", + " lines.append(\"\")\n", + "\n", + " # Find best binary model\n", + " best_bin_name, best_bin_f1 = \"N/A\", -1\n", + " for name, m in model_map_bin:\n", + " r = extract_test_metrics(m)\n", + " if r[\"f1_macro\"] is not None and r[\"f1_macro\"] > best_bin_f1:\n", + " best_bin_f1 = r[\"f1_macro\"]\n", + " best_bin_name = name\n", + "\n", + " best_type_name, best_type_f1 = \"N/A\", -1\n", + " for name, m in model_map_type:\n", + " r = extract_test_metrics(m)\n", + " if r[\"f1_macro\"] is not None and r[\"f1_macro\"] > best_type_f1:\n", + " best_type_f1 = r[\"f1_macro\"]\n", + " best_type_name = name\n", + "\n", + " lines.append(f\"### Best model for Binary Task: **{best_bin_name}** (Macro-F1 = {fmt(best_bin_f1)})\")\n", + " lines.append(\"\")\n", + " lines.append(f\"### Best model for Type Task: **{best_type_name}** (Macro-F1 = {fmt(best_type_f1)})\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Trade-offs\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Aspect | TF-IDF + LR | Naive Bayes | DistilBERT |\")\n", + " lines.append(\"|--------|------------|-------------|-----------|\")\n", + " lines.append(\"| Training speed | Fast (minutes) | Very fast (seconds) | Slow (hours) |\")\n", + " lines.append(\"| Inference speed | Very fast | Very fast | Moderate |\")\n", + " lines.append(\"| Interpretability | High (feature weights) | High (log-probs) | Low (black-box) |\")\n", + " lines.append(\"| Handles context | No | No | Yes (self-attention) |\")\n", + " lines.append(\"| Handles rare words | Via TF-IDF | Via smoothing | Via sub-word tokenization |\")\n", + " lines.append(\"| GPU required | No | No | Recommended |\")\n", + " lines.append(\"\")\n", + " lines.append(\"**Deployment recommendation**: Use TF-IDF+LR if real-time speed and interpretability are priorities.\")\n", + " lines.append(\"Use DistilBERT for maximum accuracy when GPU inference is available.\")\n", + "\n", + " return \"\\n\".join(lines)\n", + "\n", + "\n", + "comparison_md = build_model_comparison_md()\n", + "with open(REPORTS_DIR / \"model_comparison.md\", \"w\", encoding=\"utf-8\") as f:\n", + " f.write(comparison_md)\n", + "\n", + "print(\"Saved: outputs/reports/model_comparison.md\")\n", + "print(\"\\nPreview:\")\n", + "print(comparison_md[:2000])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "CmgZF4S1uKCH" + }, + "outputs": [], + "source": [ + "def build_error_analysis_md() -> str:\n", + " lines = []\n", + " lines.append(\"# Error Analysis Report\")\n", + " lines.append(\"\")\n", + " lines.append(\"> Auto-generated from model predictions. Examples drawn from test set.\")\n", + " lines.append(\"\")\n", + "\n", + " # ── Binary ────────────────────────────────────────────────────────────────\n", + " lines.append(\"## Task A — Binary Classification Errors\")\n", + " lines.append(\"\")\n", + "\n", + " if error_source_bin is not None:\n", + " err_df = error_source_bin[error_source_bin[\"correct\"] == 0]\n", + " n_fp = len(err_df[err_df[\"binary_label\"] == 0])\n", + " n_fn = len(err_df[err_df[\"binary_label\"] == 1])\n", + " lines.append(f\"**Model**: {error_model_bin} | **Total errors**: {len(err_df):,} | FP: {n_fp:,} | FN: {n_fn:,}\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Failure Mode Summary\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Failure Mode | Description |\")\n", + " lines.append(\"|-------------|-------------|\")\n", + " lines.append(\"| Lexical ambiguity | Sarcasm/irony requires pragmatic context beyond lexical cues |\")\n", + " lines.append(\"| Neutral reporting style confused | Formal generated text mimics neutral news, but still classified as sarcastic |\")\n", + " lines.append(\"| Positive framing confused | Genuine positive news shares superlative vocabulary with ironic praise |\")\n", + " lines.append(\"| Rhetorical phrasing | Questions that look rhetorical but are literal |\")\n", + " lines.append(\"| Very short text | Too little context for confident classification |\")\n", + " lines.append(\"\")\n", + " lines.append(\"### False Positive Examples (Non-sarcastic predicted as sarcastic)\")\n", + " lines.append(\"\")\n", + " fp_examples = error_source_bin[\n", + " (error_source_bin[\"correct\"] == 0) & (error_source_bin[\"binary_label\"] == 0)\n", + " ].head(10)\n", + " for _, row in fp_examples.iterrows():\n", + " lines.append(f\"- `{row['text']}`\")\n", + " lines.append(\"\")\n", + " lines.append(\"### False Negative Examples (Sarcastic predicted as non-sarcastic)\")\n", + " lines.append(\"\")\n", + " fn_examples = error_source_bin[\n", + " (error_source_bin[\"correct\"] == 0) & (error_source_bin[\"binary_label\"] == 1)\n", + " ].head(10)\n", + " for _, row in fn_examples.iterrows():\n", + " lines.append(f\"- `{row['text']}`\")\n", + " else:\n", + " lines.append(\"_Binary predictions not available. Run at least one model._\")\n", + "\n", + " lines.append(\"\")\n", + "\n", + " # ── Type ──────────────────────────────────────────────────────────────────\n", + " lines.append(\"## Task B — Sarcasm Type Classification Errors\")\n", + " lines.append(\"\")\n", + "\n", + " if error_source_type is not None:\n", + " err_df_t = error_source_type[error_source_type[\"correct\"] == 0]\n", + " lines.append(f\"**Model**: {error_model_type} | **Total errors**: {len(err_df_t):,}\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Failure Mode Summary\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Failure Mode | Description |\")\n", + " lines.append(\"|-------------|-------------|\")\n", + " lines.append(\"| Strategy semantic overlap | sarcasm/irony/satire share similar surface forms; labels conflated |\")\n", + " lines.append(\"| Scale confusion | overstatement/understatement confused with sarcasm due to exaggeration cues |\")\n", + " lines.append(\"| Rhetorical vs. other | rhetorical_question confused with irony/sarcasm when ? is absent |\")\n", + " lines.append(\"| Generation artifact | formal paraphrase style shifts text away from original strategy markers |\")\n", + " lines.append(\"| Lexical ambiguity | strategy relies on world knowledge rather than surface vocabulary |\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Key Insight\")\n", + " lines.append(\"\")\n", + " lines.append(\"The primary failure mode is **strategy semantic overlap**: sarcasm, irony, and satire\")\n", + " lines.append(\"are conceptually related and frequently share similar linguistic surface patterns.\")\n", + " lines.append(\"The label `sarcasm` as a strategy within a sarcasm dataset introduces circularity.\")\n", + " lines.append(\"The `rhetorical_question` class is syntactically distinctive (ends with ?) and should\")\n", + " lines.append(\"be easier to classify; errors in this class suggest the classifier may ignore punctuation.\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Sample Misclassifications\")\n", + " lines.append(\"\")\n", + " sample_t = error_source_type[error_source_type[\"correct\"] == 0].head(20)\n", + " true_c = \"type_label\"\n", + " pred_c = \"predicted_label\" if \"predicted_label\" in sample_t.columns else \"predicted\"\n", + " for _, row in sample_t.iterrows():\n", + " true_l = row[true_c]\n", + " pred_l = row.get(pred_c, \"?\")\n", + " lines.append(f\"- **True**: {true_l} | **Pred**: {pred_l} | `{row['text'][:100]}`\")\n", + " else:\n", + " lines.append(\"_Type predictions not available. Run at least one model._\")\n", + "\n", + " lines.append(\"\")\n", + " lines.append(\"## Summary of Observations\")\n", + " lines.append(\"\")\n", + " lines.append(\"1. **Binary task** is relatively tractable — TheOnion writing style has strong lexical signatures\")\n", + " lines.append(\"2. **Generated headlines** (is_generated=1) may fool classifiers trained mainly on original text\")\n", + " lines.append(\"3. **Type task** is fundamentally harder because strategies are not mutually exclusive\")\n", + " lines.append(\"4. **Class imbalance** (rhetorical_question = 3.7%) is a significant challenge\")\n", + " lines.append(\"5. **BERT** should better capture contextual incongruence; classical models rely on surface vocabulary\")\n", + "\n", + " return \"\\n\".join(lines)\n", + "\n", + "\n", + "error_md = build_error_analysis_md()\n", + "with open(REPORTS_DIR / \"error_analysis.md\", \"w\", encoding=\"utf-8\") as f:\n", + " f.write(error_md)\n", + "\n", + "print(\"Saved: outputs/reports/error_analysis.md\")" + ] }, - "nbformat": 4, - "nbformat_minor": 0 -} + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fdUb27BQuKCH" + }, + "outputs": [], + "source": [ + "print(\"====== ERROR ANALYSIS COMPLETE ======\")\n", + "print()\n", + "print(\"Saved artifacts:\")\n", + "for p in sorted(REPORTS_DIR.iterdir()):\n", + " print(f\" {p.relative_to(ROOT)}\")\n", + "print()\n", + "print(\"Pipeline complete. See outputs/reports/model_comparison.md for final results.\")" + ] + } + ] +} \ No newline at end of file diff --git a/colab_package/sarcasm_classification_fixed.ipynb b/colab_package/sarcasm_classification_fixed.ipynb deleted file mode 100644 index 31b7923..0000000 --- a/colab_package/sarcasm_classification_fixed.ipynb +++ /dev/null @@ -1,4576 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.11.0" - }, - "colab": { - "provenance": [], - "machine_shape": "hm" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "version_major": 2, - "version_minor": 0, - "state": { - "8b9080237c194037a2cd1dc711a694d2": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - 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"1. Setup & Data Preparation\n", - "2. TF-IDF + Logistic Regression Baseline\n", - "3. Naive Bayes Baseline\n", - "4. BERT / DistilBERT Classification\n", - "5. Error Analysis & Model Comparison\n", - "\n", - "**Run all cells in order (Runtime → Run all).**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "1xG7cJfAuKBR", - "outputId": "12069dde-b810-4ab5-f89b-5fa1af05cf8c" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "cwd: /content\n", - "files in cwd: ['.config', 'sarcasm_pairs_step35_clean.jsonl', 'sample_data']\n", - "Data : /content/sarcasm_pairs_step35_clean.jsonl\n", - "Root : /content\n", - "Output : /content/outputs\n" - ] - } - ], - "source": [ - "# ============================================================\n", - "# SETUP — imports, file upload, paths\n", - "# ============================================================\n", - "from __future__ import annotations\n", - "import json, hashlib, random, os, warnings, shutil\n", - "from dataclasses import dataclass\n", - "from pathlib import Path\n", - "from collections import Counter\n", - "from urllib.parse import urlparse\n", - "from typing import Optional\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.gridspec as gridspec\n", - "import seaborn as sns\n", - "\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.naive_bayes import MultinomialNB, ComplementNB\n", - "from sklearn.model_selection import GridSearchCV, GroupKFold\n", - "from sklearn.metrics import (\n", - " accuracy_score, precision_score, recall_score,\n", - " f1_score, classification_report, confusion_matrix,\n", - ")\n", - "\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "SEED = 42\n", - "random.seed(SEED)\n", - "np.random.seed(SEED)\n", - "\n", - "# ── Locate or upload the JSONL data file ─────────────────────────────────\n", - "FILENAME = \"sarcasm_pairs_step35_clean.jsonl\"\n", - "\n", - "def _locate_file(filename):\n", - " candidates = []\n", - " for root in [Path.cwd()] + list(Path.cwd().parents):\n", - " for sub in [\n", - " Path(\"data\") / \"processed\" / filename,\n", - " Path(\"data\") / filename,\n", - " Path(filename),\n", - " ]:\n", - " candidates.append(root / sub)\n", - " for p in [\n", - " Path(\"/content\") / filename,\n", - " Path(\"/mnt/data\") / filename,\n", - " ]:\n", - " candidates.append(p)\n", - " _c = Path('/content')\n", - " for p in (_c.rglob(filename) if _c.exists() else []):\n", - " candidates.append(p)\n", - " for p in candidates:\n", - " if p.is_file():\n", - " return p\n", - " return None\n", - "\n", - "print(f'cwd: {Path.cwd()}')\n", - "print(f'files in cwd: {[p.name for p in Path.cwd().iterdir()][:10]}')\n", - "\n", - "DATA_FILE = _locate_file(FILENAME)\n", - "if DATA_FILE is None:\n", - " try:\n", - " from google.colab import files as _cf\n", - " print(f\"Upload {FILENAME!r}:\")\n", - " _up = _cf.upload()\n", - " if not _up:\n", - " raise RuntimeError(\"No file uploaded.\")\n", - " _name = list(_up.keys())[0]\n", - " DATA_FILE = Path(\"/content\") / FILENAME\n", - " if Path(_name) != DATA_FILE:\n", - " shutil.move(_name, str(DATA_FILE))\n", - " print(f\"Saved to {DATA_FILE}\")\n", - " except ImportError:\n", - " raise FileNotFoundError(\n", - " f\"Cannot find {FILENAME!r}. Place it in the same folder as this notebook.\"\n", - " )\n", - "\n", - "# ── Project root + all output directories ────────────────────────────────\n", - "def _find_root(data_file):\n", - " for parent in [data_file.parent] + list(data_file.parents):\n", - " if any((parent / m).exists() for m in [\"outputs\",\"notebooks\",\"data\"]):\n", - " return parent\n", - " return data_file.parent\n", - "\n", - "ROOT = _find_root(DATA_FILE)\n", - "\n", - "OUT_DATASETS = ROOT / 'outputs' / 'datasets'\n", - "OUT_SPLITS = ROOT / 'outputs' / 'splits'\n", - "OUT_TFIDF = ROOT / 'outputs' / 'classical' / 'tfidf_lr'\n", - "OUT_NB = ROOT / 'outputs' / 'classical' / 'naive_bayes'\n", - "BERT_OUT = ROOT / 'outputs' / 'bert'\n", - "REPORTS_DIR = ROOT / 'outputs' / 'reports'\n", - "SPLITS = OUT_SPLITS\n", - "\n", - "for d in [OUT_DATASETS, OUT_SPLITS, OUT_TFIDF, OUT_NB, BERT_OUT, REPORTS_DIR]:\n", - " d.mkdir(parents=True, exist_ok=True)\n", - "\n", - "print(f\"Data : {DATA_FILE}\")\n", - "print(f\"Root : {ROOT}\")\n", - "print(f\"Output : {ROOT / 'outputs'}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xDqObfuPuKBU" - }, - "source": [ - "---\n", - "# Part 1 — Data Preparation" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RVi4jvpmuKBV" - }, - "source": [ - "# Notebook 01 — Data Preparation\n", - "\n", - "**Purpose**: Parse JSONL dataset, derive binary and type labels, perform audit checks, generate group-safe train/val/test splits.\n", - "\n", - "**Outputs**:\n", - "- `outputs/datasets/binary_dataset.csv`\n", - "- `outputs/datasets/type_dataset.csv`\n", - "- `outputs/splits/train_binary.csv`, `val_binary.csv`, `test_binary.csv`\n", - "- `outputs/splits/train_type.csv`, `val_type.csv`, `test_type.csv`\n", - "- `outputs/splits/split_metadata.json`" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "egvV3LLJuKBV" - }, - "source": [ - "## 1. Load and Validate Raw Data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "oqhoOTiguKBV", - "outputId": "9211b95a-f157-40ce-9d18-7275e0c157a3" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Loaded rows : 28,333\n", - "Error rows : 0\n" - ] - } - ], - "source": [ - "REQUIRED_FIELDS = {\"original_headline\", \"generated_headline\", \"strategy\", \"type\", \"model_used\", \"article_link\"}\n", - "ALLOWED_TYPES = {\"sarcastic_to_non\", \"non_to_sarcastic\"}\n", - "ALLOWED_STRATS = {\"irony\", \"overstatement\", \"rhetorical_question\", \"sarcasm\", \"satire\", \"understatement\"}\n", - "\n", - "raw_rows = []\n", - "errors = []\n", - "\n", - "with open(DATA_FILE, \"r\", encoding=\"utf-8\") as f:\n", - " for line_num, line in enumerate(f):\n", - " line = line.strip()\n", - " if not line:\n", - " continue\n", - " try:\n", - " row = json.loads(line)\n", - " except json.JSONDecodeError as e:\n", - " errors.append((line_num, f\"JSON parse error: {e}\"))\n", - " continue\n", - "\n", - " # Schema check\n", - " missing = REQUIRED_FIELDS - set(row.keys())\n", - " if missing:\n", - " errors.append((line_num, f\"Missing fields: {missing}\"))\n", - " continue\n", - "\n", - " # Value checks\n", - " if row[\"type\"] not in ALLOWED_TYPES:\n", - " errors.append((line_num, f\"Unknown type: {row['type']!r}\"))\n", - " continue\n", - " if row[\"strategy\"] not in ALLOWED_STRATS:\n", - " errors.append((line_num, f\"Unknown strategy: {row['strategy']!r}\"))\n", - " continue\n", - "\n", - " row[\"_line_num\"] = line_num\n", - " raw_rows.append(row)\n", - "\n", - "print(f\"Loaded rows : {len(raw_rows):,}\")\n", - "print(f\"Error rows : {len(errors)}\")\n", - "if errors:\n", - " for ln, msg in errors[:5]:\n", - " print(f\" Line {ln}: {msg}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yvg9qFhouKBW" - }, - "source": [ - "## 2. Dataset Audit" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "KISNWAQhuKBW", - "outputId": "e8755fd1-8661-4393-f3a9-429cb93065fd" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "=== Type distribution ===\n", - " non_to_sarcastic: 14,925 (52.7%)\n", - " sarcastic_to_non: 13,408 (47.3%)\n", - "\n", - "=== Strategy distribution ===\n", - " sarcasm: 8,699 (30.7%)\n", - " irony: 6,102 (21.5%)\n", - " satire: 5,224 (18.4%)\n", - " overstatement: 3,976 (14.0%)\n", - " understatement: 3,295 (11.6%)\n", - " rhetorical_question: 1,037 (3.7%)\n", - "\n", - "=== Model distribution ===\n", - " stepfun/step-3.5-flash:free: 28,333\n" - ] - } - ], - "source": [ - "# ── Type and Strategy distributions ──────────────────────────────────────────\n", - "type_counts = Counter(r[\"type\"] for r in raw_rows)\n", - "strategy_counts = Counter(r[\"strategy\"] for r in raw_rows)\n", - "model_counts = Counter(r[\"model_used\"] for r in raw_rows)\n", - "\n", - "print(\"=== Type distribution ===\")\n", - "for k, v in type_counts.most_common():\n", - " print(f\" {k}: {v:,} ({v/len(raw_rows)*100:.1f}%)\")\n", - "\n", - "print(\"\\n=== Strategy distribution ===\")\n", - "for k, v in strategy_counts.most_common():\n", - " print(f\" {k}: {v:,} ({v/len(raw_rows)*100:.1f}%)\")\n", - "\n", - "print(\"\\n=== Model distribution ===\")\n", - "for k, v in model_counts.most_common():\n", - " print(f\" {k}: {v:,}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "28wpsTTnuKBX", - "outputId": "bdcc2561-9baa-41a3-bac8-14101d7e4596" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Duplicate original_headlines : 70\n", - "Duplicate generated_headlines: 1\n", - "Duplicate article_links : 2\n" - ] - } - ], - "source": [ - "# ── Duplicate checks ──────────────────────────────────────────────────────────\n", - "orig_headlines = [r[\"original_headline\"] for r in raw_rows]\n", - "gen_headlines = [r[\"generated_headline\"] for r in raw_rows]\n", - "article_links = [r[\"article_link\"] for r in raw_rows]\n", - "\n", - "dup_orig = len(orig_headlines) - len(set(orig_headlines))\n", - "dup_gen = len(gen_headlines) - len(set(gen_headlines))\n", - "dup_article = len(article_links) - len(set(article_links))\n", - "\n", - "print(f\"Duplicate original_headlines : {dup_orig}\")\n", - "print(f\"Duplicate generated_headlines: {dup_gen}\")\n", - "print(f\"Duplicate article_links : {dup_article}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "SLUFPDIHuKBX", - "outputId": "3d7b15de-a43f-410f-a381-6a9544171b45" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "original_headline : 0 nulls, 0 empty strings\n", - "generated_headline : 0 nulls, 0 empty strings\n", - "strategy : 0 nulls, 0 empty strings\n", - "type : 0 nulls, 0 empty strings\n", - "model_used : 0 nulls, 0 empty strings\n", - "article_link : 0 nulls, 0 empty strings\n" - ] - } - ], - "source": [ - "# ── Null / empty checks ───────────────────────────────────────────────────────\n", - "for field in [\"original_headline\", \"generated_headline\", \"strategy\", \"type\", \"model_used\", \"article_link\"]:\n", - " null_count = sum(1 for r in raw_rows if r.get(field) is None)\n", - " empty_count = sum(1 for r in raw_rows if r.get(field) == \"\")\n", - " print(f\"{field:25s}: {null_count} nulls, {empty_count} empty strings\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 524 - }, - "id": "E-B0fc5vuKBX", - "outputId": "a7845ca8-655c-48df-c59b-28b16307b91d" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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- }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/datasets/distributions.png\n" - ] - } - ], - "source": [ - "# ── Strategy distribution plot ────────────────────────────────────────────────\n", - "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", - "\n", - "# Type dist\n", - "ax = axes[0]\n", - "labels, counts = zip(*sorted(type_counts.items(), key=lambda x: -x[1]))\n", - "ax.barh(labels, counts, color=[\"steelblue\", \"coral\"])\n", - "ax.set_title(\"Row-level Type Distribution\", fontsize=14)\n", - "ax.set_xlabel(\"Count\")\n", - "for i, c in enumerate(counts):\n", - " ax.text(c + 50, i, f\"{c:,}\", va=\"center\")\n", - "\n", - "# Strategy dist\n", - "ax = axes[1]\n", - "labels2, counts2 = zip(*sorted(strategy_counts.items(), key=lambda x: -x[1]))\n", - "ax.barh(labels2, counts2, color=\"steelblue\")\n", - "ax.set_title(\"Strategy Distribution (Type Task Labels)\", fontsize=14)\n", - "ax.set_xlabel(\"Count\")\n", - "for i, c in enumerate(counts2):\n", - " ax.text(c + 30, i, f\"{c:,}\", va=\"center\")\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(OUT_DATASETS / \"distributions.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "print(\"Saved: outputs/datasets/distributions.png\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JxKcjhRSuKBY" - }, - "source": [ - "## 3. Label Derivation and Dataset Expansion" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nukGQcmKuKBY", - "outputId": "2fcfb601-8ad0-4876-b813-c21b007bb164" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Binary dataset : 56,666 samples\n", - " sarcastic (1): 28,333\n", - " non-sarc (0): 28,333\n", - "\n", - "Type dataset : 28,333 samples\n", - "type_label\n", - "sarcasm 8699\n", - "irony 6102\n", - "satire 5224\n", - "overstatement 3976\n", - "understatement 3295\n", - "rhetorical_question 1037\n" - ] - } - ], - "source": [ - "from __future__ import annotations\n", - "from urllib.parse import urlparse\n", - "\n", - "\n", - "def normalize_url(url: str) -> str:\n", - " \"\"\"Lowercase and strip scheme/trailing slash for stable group IDs.\"\"\"\n", - " url = url.strip().lower()\n", - " parsed = urlparse(url)\n", - " normalized = (parsed.netloc + parsed.path).rstrip(\"/\")\n", - " return normalized if normalized else url\n", - "\n", - "\n", - "def derive_labels(raw_rows):\n", - " \"\"\"\n", - " Expand each JSONL row into 2 samples (sarcastic + non-sarcastic).\n", - "\n", - " Rules:\n", - " sarcastic_to_non -> original=sarcastic(1), generated=non-sarcastic(0)\n", - " non_to_sarcastic -> original=non-sarcastic(0), generated=sarcastic(1)\n", - "\n", - " Returns binary_df, type_df\n", - " \"\"\"\n", - " binary_records = []\n", - " sample_id = 0\n", - "\n", - " for pair_id, row in enumerate(raw_rows):\n", - " group_id = normalize_url(row[\"article_link\"]) if row[\"article_link\"] else str(pair_id)\n", - " row_type = row[\"type\"]\n", - " strategy = row[\"strategy\"]\n", - " model_used = row[\"model_used\"]\n", - " article = row[\"article_link\"]\n", - "\n", - " if row_type == \"sarcastic_to_non\":\n", - " orig_label, orig_strat = 1, strategy\n", - " gen_label, gen_strat = 0, None\n", - " else:\n", - " orig_label, orig_strat = 0, None\n", - " gen_label, gen_strat = 1, strategy\n", - "\n", - " binary_records.append({\n", - " \"sample_id\" : sample_id,\n", - " \"pair_id\" : pair_id,\n", - " \"group_id\" : group_id,\n", - " \"text\" : row[\"original_headline\"],\n", - " \"binary_label\" : orig_label,\n", - " \"is_generated\" : 0,\n", - " \"strategy\" : orig_strat,\n", - " \"source_type\" : row_type,\n", - " \"article_link\" : article,\n", - " \"model_used\" : model_used,\n", - " })\n", - " sample_id += 1\n", - "\n", - " binary_records.append({\n", - " \"sample_id\" : sample_id,\n", - " \"pair_id\" : pair_id,\n", - " \"group_id\" : group_id,\n", - " \"text\" : row[\"generated_headline\"],\n", - " \"binary_label\" : gen_label,\n", - " \"is_generated\" : 1,\n", - " \"strategy\" : gen_strat,\n", - " \"source_type\" : row_type,\n", - " \"article_link\" : article,\n", - " \"model_used\" : model_used,\n", - " })\n", - " sample_id += 1\n", - "\n", - " binary_df = pd.DataFrame(binary_records)\n", - "\n", - " # Validate counts\n", - " counts = binary_df[\"binary_label\"].value_counts().to_dict()\n", - " n_expected = len(raw_rows)\n", - " n0 = int(counts.get(0, 0))\n", - " n1 = int(counts.get(1, 0))\n", - " if n0 != n_expected or n1 != n_expected:\n", - " raise ValueError(\n", - " f\"Label count mismatch!\\n\"\n", - " f\" Expected {n_expected} per class\\n\"\n", - " f\" Got: sarcastic(1)={n1}, non-sarcastic(0)={n0}\"\n", - " )\n", - "\n", - " # Type dataset: sarcastic only\n", - " type_df = binary_df[binary_df[\"binary_label\"] == 1].copy()\n", - " type_df = type_df.rename(columns={\"strategy\": \"type_label\"})\n", - " type_df = type_df[[\"sample_id\", \"pair_id\", \"group_id\", \"text\",\n", - " \"type_label\", \"is_generated\", \"source_type\",\n", - " \"article_link\", \"model_used\"]]\n", - "\n", - " missing = type_df[\"type_label\"].isna().sum()\n", - " if missing > 0:\n", - " raise ValueError(f\"{missing} sarcastic samples have no type_label!\")\n", - "\n", - " return binary_df, type_df\n", - "\n", - "\n", - "binary_df, type_df = derive_labels(raw_rows)\n", - "\n", - "print(f\"Binary dataset : {len(binary_df):,} samples\")\n", - "print(f\" sarcastic (1): {(binary_df['binary_label']==1).sum():,}\")\n", - "print(f\" non-sarc (0): {(binary_df['binary_label']==0).sum():,}\")\n", - "print()\n", - "print(f\"Type dataset : {len(type_df):,} samples\")\n", - "print(type_df[\"type_label\"].value_counts().to_string())\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "5beT2LM9uKBY", - "outputId": "203852f9-bcfd-4ac5-8f83-61f9e0d10219" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/datasets/binary_dataset.csv\n", - "Saved: outputs/datasets/type_dataset.csv\n" - ] - } - ], - "source": [ - "# Save full datasets\n", - "binary_df.to_csv(OUT_DATASETS / \"binary_dataset.csv\", index=False)\n", - "type_df.to_csv( OUT_DATASETS / \"type_dataset.csv\", index=False)\n", - "print(\"Saved: outputs/datasets/binary_dataset.csv\")\n", - "print(\"Saved: outputs/datasets/type_dataset.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5GtLZhBwuKBZ" - }, - "source": [ - "## 4. Group-Aware Train / Val / Test Split" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OMGqQn3PuKBZ", - "outputId": "0eb38913-1ff6-4524-e2e6-030cf7c34b90" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "=== Binary splits ===\n", - " train: 39,666 samples | sarcastic=19,833 non=19,833\n", - " val : 8,500 samples | sarcastic=4,250 non=4,250\n", - " test : 8,500 samples | sarcastic=4,250 non=4,250\n", - "\n", - "=== Type splits ===\n", - " train: 19,833 samples\n", - " sarcasm: 6,091\n", - " irony: 4,258\n", - " satire: 3,644\n", - " overstatement: 2,784\n", - " understatement: 2,352\n", - " rhetorical_question: 704\n", - " val : 4,250 samples\n", - " sarcasm: 1,317\n", - " irony: 942\n", - " satire: 747\n", - " overstatement: 600\n", - " understatement: 487\n", - " rhetorical_question: 157\n", - " test : 4,250 samples\n", - " sarcasm: 1,291\n", - " irony: 902\n", - " satire: 833\n", - " overstatement: 592\n", - " understatement: 456\n", - " rhetorical_question: 176\n" - ] - } - ], - "source": [ - "def group_aware_split(\n", - " df: pd.DataFrame,\n", - " group_col: str = \"group_id\",\n", - " train_frac: float = 0.70,\n", - " val_frac: float = 0.15,\n", - " seed: int = SEED,\n", - ") -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n", - " \"\"\"\n", - " Split df into train/val/test at the group level.\n", - " All rows sharing a group_id go to exactly one split.\n", - " \"\"\"\n", - " groups = df[group_col].unique().tolist()\n", - " rng = np.random.default_rng(seed)\n", - " rng.shuffle(groups)\n", - "\n", - " n = len(groups)\n", - " n_train = int(n * train_frac)\n", - " n_val = int(n * val_frac)\n", - "\n", - " train_groups = set(groups[:n_train])\n", - " val_groups = set(groups[n_train : n_train + n_val])\n", - " test_groups = set(groups[n_train + n_val :])\n", - "\n", - " train_df = df[df[group_col].isin(train_groups)].copy()\n", - " val_df = df[df[group_col].isin(val_groups)].copy()\n", - " test_df = df[df[group_col].isin(test_groups)].copy()\n", - "\n", - " # Sanity: no overlap\n", - " assert train_groups.isdisjoint(val_groups), \"Train/val group overlap!\"\n", - " assert train_groups.isdisjoint(test_groups), \"Train/test group overlap!\"\n", - " assert val_groups.isdisjoint(test_groups), \"Val/test group overlap!\"\n", - "\n", - " return train_df, val_df, test_df\n", - "\n", - "\n", - "# ── Binary splits ─────────────────────────────────────────────────────────────\n", - "train_bin, val_bin, test_bin = group_aware_split(binary_df)\n", - "\n", - "print(\"=== Binary splits ===\")\n", - "for name, df in [(\"train\", train_bin), (\"val\", val_bin), (\"test\", test_bin)]:\n", - " dist = df[\"binary_label\"].value_counts().to_dict()\n", - " print(f\" {name:5s}: {len(df):,} samples | sarcastic={dist.get(1,0):,} non={dist.get(0,0):,}\")\n", - "\n", - "# ── Type splits ───────────────────────────────────────────────────────────────\n", - "train_type, val_type, test_type = group_aware_split(type_df)\n", - "\n", - "print(\"\\n=== Type splits ===\")\n", - "for name, df in [(\"train\", train_type), (\"val\", val_type), (\"test\", test_type)]:\n", - " print(f\" {name:5s}: {len(df):,} samples\")\n", - " dist = df[\"type_label\"].value_counts()\n", - " for label, cnt in dist.items():\n", - " print(f\" {label}: {cnt:,}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Oo78ufPjuKBZ", - "outputId": "6b324c55-2718-4236-d356-e7c7a44e91a5" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Train/Val pair_id overlap : 0 (must be 0)\n", - "Train/Test pair_id overlap : 0 (must be 0)\n", - "Val/Test pair_id overlap : 0 (must be 0)\n", - "\n", - "Leakage check PASSED: No pair_id crosses split boundaries.\n" - ] - } - ], - "source": [ - "# ── Verify no pair crosses split boundary ─────────────────────────────────────\n", - "# A pair_id should appear in at most ONE binary split\n", - "train_pairs = set(train_bin[\"pair_id\"])\n", - "val_pairs = set(val_bin[\"pair_id\"])\n", - "test_pairs = set(test_bin[\"pair_id\"])\n", - "\n", - "tv_overlap = train_pairs & val_pairs\n", - "tt_overlap = train_pairs & test_pairs\n", - "vt_overlap = val_pairs & test_pairs\n", - "\n", - "print(f\"Train/Val pair_id overlap : {len(tv_overlap)} (must be 0)\")\n", - "print(f\"Train/Test pair_id overlap : {len(tt_overlap)} (must be 0)\")\n", - "print(f\"Val/Test pair_id overlap : {len(vt_overlap)} (must be 0)\")\n", - "\n", - "assert len(tv_overlap) == 0 and len(tt_overlap) == 0 and len(vt_overlap) == 0, \\\n", - " \"Leakage detected: pairs crossing split boundaries!\"\n", - "print(\"\\nLeakage check PASSED: No pair_id crosses split boundaries.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "HbbbOgpZuKBZ", - "outputId": "2716fcd8-e654-46c8-9b14-765336c8f3b9" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/splits/train_binary.csv\n", - "Saved: outputs/splits/val_binary.csv\n", - "Saved: outputs/splits/test_binary.csv\n", - "Saved: outputs/splits/train_type.csv\n", - "Saved: outputs/splits/val_type.csv\n", - "Saved: outputs/splits/test_type.csv\n", - "\n", - "Saved: outputs/splits/split_metadata.json\n" - ] - } - ], - "source": [ - "# ── Save splits ───────────────────────────────────────────────────────────────\n", - "split_files = {\n", - " \"train_binary\": train_bin,\n", - " \"val_binary\" : val_bin,\n", - " \"test_binary\" : test_bin,\n", - " \"train_type\" : train_type,\n", - " \"val_type\" : val_type,\n", - " \"test_type\" : test_type,\n", - "}\n", - "for fname, df in split_files.items():\n", - " path = OUT_SPLITS / f\"{fname}.csv\"\n", - " df.to_csv(path, index=False)\n", - " print(f\"Saved: {path.relative_to(ROOT)}\")\n", - "\n", - "# Metadata\n", - "import json as _json\n", - "metadata = {\n", - " \"seed\": SEED,\n", - " \"train_frac\": 0.70,\n", - " \"val_frac\": 0.15,\n", - " \"test_frac\": 0.15,\n", - " \"group_col\": \"group_id\",\n", - " \"total_pairs\": len(raw_rows),\n", - " \"binary\": {\n", - " \"train\": len(train_bin), \"val\": len(val_bin), \"test\": len(test_bin)\n", - " },\n", - " \"type\": {\n", - " \"train\": len(train_type), \"val\": len(val_type), \"test\": len(test_type)\n", - " },\n", - "}\n", - "with open(OUT_SPLITS / \"split_metadata.json\", \"w\") as f:\n", - " _json.dump(metadata, f, indent=2)\n", - "print(\"\\nSaved: outputs/splits/split_metadata.json\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "j7ZguloUuKBa" - }, - "source": [ - "## 5. Split Distribution Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 419 - }, - "id": "nLrEPxJ9uKBa", - "outputId": "508ba885-ab89-400e-8f7c-c251d636a688" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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- }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/splits/type_split_distribution.png\n" - ] - } - ], - "source": [ - "# Type label distribution per split\n", - "fig, axes = plt.subplots(1, 3, figsize=(18, 5), sharey=True)\n", - "split_dfs = [(\"Train\", train_type), (\"Val\", val_type), (\"Test\", test_type)]\n", - "\n", - "for ax, (name, df) in zip(axes, split_dfs):\n", - " dist = df[\"type_label\"].value_counts()\n", - " dist.plot(kind=\"barh\", ax=ax, color=\"steelblue\")\n", - " ax.set_title(f\"{name} — Type Labels (n={len(df):,})\", fontsize=13)\n", - " ax.set_xlabel(\"Count\")\n", - " for i, (label, cnt) in enumerate(dist.items()):\n", - " ax.text(cnt + 5, i, f\"{cnt:,}\", va=\"center\", fontsize=9)\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(OUT_SPLITS / \"type_split_distribution.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "print(\"Saved: outputs/splits/type_split_distribution.png\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 424 - }, - "id": "R48OKnIouKBa", - "outputId": "cc63cf5a-635b-4937-ff28-9702de59974e" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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- }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/splits/binary_split_distribution.png\n" - ] - } - ], - "source": [ - "# Binary label distribution per split\n", - "fig, axes = plt.subplots(1, 3, figsize=(12, 4), sharey=True)\n", - "split_dfs_bin = [(\"Train\", train_bin), (\"Val\", val_bin), (\"Test\", test_bin)]\n", - "\n", - "for ax, (name, df) in zip(axes, split_dfs_bin):\n", - " dist = df[\"binary_label\"].value_counts().sort_index()\n", - " ax.bar([\"Non-sarcastic (0)\", \"Sarcastic (1)\"], dist.values, color=[\"coral\", \"steelblue\"])\n", - " ax.set_title(f\"{name} — Binary Labels (n={len(df):,})\", fontsize=12)\n", - " ax.set_ylabel(\"Count\")\n", - " for i, v in enumerate(dist.values):\n", - " ax.text(i, v + 20, f\"{v:,}\", ha=\"center\")\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(OUT_SPLITS / \"binary_split_distribution.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "print(\"Saved: outputs/splits/binary_split_distribution.png\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "y59RyyxDuKBa", - "outputId": "9fcf80b4-639e-4dc0-da4e-9e5d7492337a" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "=== Data Preparation Complete ===\n", - "Binary dataset : 56,666 samples (balanced)\n", - "Type dataset : 28,333 samples (imbalanced, 6 classes)\n", - "Splits saved to: outputs/splits/\n", - "Datasets saved : outputs/datasets/\n", - "\n", - "Ready for classical baseline training (Notebooks 02 and 03).\n" - ] - } - ], - "source": [ - "print(\"\\n=== Data Preparation Complete ===\")\n", - "print(f\"Binary dataset : {len(binary_df):,} samples (balanced)\")\n", - "print(f\"Type dataset : {len(type_df):,} samples (imbalanced, 6 classes)\")\n", - "print(f\"Splits saved to: outputs/splits/\")\n", - "print(f\"Datasets saved : outputs/datasets/\")\n", - "print(\"\\nReady for classical baseline training (Notebooks 02 and 03).\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cENhnFfwuKBb" - }, - "source": [ - "---\n", - "# Part 2 — TF-IDF + Logistic Regression Baseline" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "j_2MU9WJuKBb" - }, - "source": [ - "# Notebook 02 — TF-IDF + Logistic Regression Baseline\n", - "\n", - "**Purpose**: Train and evaluate TF-IDF + Logistic Regression pipelines for:\n", - "- Task A: Binary classification (sarcastic vs non-sarcastic)\n", - "- Task B: Sarcasm type classification (6-class)\n", - "\n", - "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", - "\n", - "**Outputs**:\n", - "- `outputs/classical/tfidf_lr/best_config_binary.json`\n", - "- `outputs/classical/tfidf_lr/best_config_type.json`\n", - "- `outputs/classical/tfidf_lr/metrics_binary.json`\n", - "- `outputs/classical/tfidf_lr/metrics_type.json`\n", - "- Predictions CSV + confusion matrices PNG" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "r8BzGF97uKBb" - }, - "source": [ - "## Helper Functions" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "id": "iU0Dnjl_uKBb" - }, - "outputs": [], - "source": [ - "def load_splits(task: str) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n", - " \"\"\"Load train/val/test CSVs for a given task ('binary' or 'type').\"\"\"\n", - " train = pd.read_csv(SPLITS / f\"train_{task}.csv\")\n", - " val = pd.read_csv(SPLITS / f\"val_{task}.csv\")\n", - " test = pd.read_csv(SPLITS / f\"test_{task}.csv\")\n", - " return train, val, test\n", - "\n", - "\n", - "def evaluate(\n", - " model,\n", - " X: list[str],\n", - " y_true: list,\n", - " label_names: list[str] | None = None,\n", - " split_name: str = \"\",\n", - ") -> dict:\n", - " \"\"\"Compute classification metrics and return as dict.\"\"\"\n", - " y_pred = model.predict(X)\n", - " metrics = {\n", - " \"split\" : split_name,\n", - " \"accuracy\" : accuracy_score(y_true, y_pred),\n", - " \"precision_macro\" : precision_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", - " \"recall_macro\" : recall_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", - " \"f1_macro\" : f1_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", - " \"f1_weighted\" : f1_score(y_true, y_pred, average=\"weighted\", zero_division=0),\n", - " \"report\" : classification_report(y_true, y_pred, target_names=label_names,\n", - " zero_division=0, output_dict=True),\n", - " }\n", - " print(f\"\\n[{split_name}] Accuracy={metrics['accuracy']:.4f} \"\n", - " f\"Macro-F1={metrics['f1_macro']:.4f} Weighted-F1={metrics['f1_weighted']:.4f}\")\n", - " print(classification_report(y_true, y_pred, target_names=label_names, zero_division=0))\n", - " return metrics, y_pred\n", - "\n", - "\n", - "def save_confusion_matrix(\n", - " y_true, y_pred, label_names: list[str], out_path: Path, title: str = \"\"\n", - ") -> None:\n", - " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", - " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", - " sns.heatmap(\n", - " cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n", - " xticklabels=label_names, yticklabels=label_names, ax=ax\n", - " )\n", - " ax.set_xlabel(\"Predicted\")\n", - " ax.set_ylabel(\"True\")\n", - " ax.set_title(title)\n", - " plt.tight_layout()\n", - " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", - " plt.show()\n", - " print(f\"Saved: {out_path.relative_to(ROOT)}\")\n", - "\n", - "\n", - "def save_predictions(\n", - " df: pd.DataFrame, y_pred, label_col: str, out_path: Path\n", - ") -> None:\n", - " out = df[[\"sample_id\", \"pair_id\", \"group_id\", \"text\", label_col]].copy()\n", - " out[\"predicted\"] = y_pred\n", - " out[\"correct\"] = (out[label_col] == out[\"predicted\"]).astype(int)\n", - " out.to_csv(out_path, index=False)\n", - " print(f\"Saved: {out_path.relative_to(ROOT)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KZYEW3EruKBb" - }, - "source": [ - "## Task A — Binary Classification" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "sUWB4dQKuKBb", - "outputId": "c9c34b33-e3bb-4eca-e21a-158777f966cb" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Train+Val: 48,166 | Train: 39,666 Val: 8,500 Test: 8,500\n" - ] - } - ], - "source": [ - "# ── Load binary splits ────────────────────────────────────────────────────────\n", - "train_bin, val_bin, test_bin = load_splits(\"binary\")\n", - "\n", - "# Combine train+val for cross-validation, keep test for final eval\n", - "trainval_bin = pd.concat([train_bin, val_bin], ignore_index=True)\n", - "\n", - "X_trainval = trainval_bin[\"text\"].tolist()\n", - "y_trainval = trainval_bin[\"binary_label\"].tolist()\n", - "groups_tv = trainval_bin[\"group_id\"].tolist()\n", - "\n", - "X_train = train_bin[\"text\"].tolist()\n", - "y_train = train_bin[\"binary_label\"].tolist()\n", - "\n", - "X_val = val_bin[\"text\"].tolist()\n", - "y_val = val_bin[\"binary_label\"].tolist()\n", - "\n", - "X_test = test_bin[\"text\"].tolist()\n", - "y_test = test_bin[\"binary_label\"].tolist()\n", - "\n", - "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", - "\n", - "print(f\"Train+Val: {len(X_trainval):,} | Train: {len(X_train):,} Val: {len(X_val):,} Test: {len(X_test):,}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "BatmO-vAuKBc", - "outputId": "34229238-2bbd-4838-976b-464bb496054f" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Grid size: 4 param combos → 180 fits\n", - "Running grid search...\n", - "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n", - "\n", - "Best params : {'lr__C': 3.0, 'tfidf__max_features': None, 'tfidf__min_df': 3, 'tfidf__ngram_range': (1, 2)}\n", - "Best CV F1 : 0.8143\n" - ] - } - ], - "source": [ - "# ── Define pipeline and grid ──────────────────────────────────────────────────\n", - "pipe_bin = Pipeline([\n", - " (\"tfidf\", TfidfVectorizer(sublinear_tf=True, lowercase=True)),\n", - " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, solver=\"lbfgs\")),\n", - "])\n", - "\n", - "param_grid_bin = {\n", - " \"tfidf__ngram_range\" : [(1, 1), (1, 2)],\n", - " \"tfidf__min_df\" : [2, 3, 5],\n", - " \"tfidf__max_features\": [None, 50_000],\n", - " \"lr__C\" : [0.1, 1.0, 3.0],\n", - "}\n", - "\n", - "cv_bin = GroupKFold(n_splits=5)\n", - "\n", - "gs_bin = GridSearchCV(\n", - " pipe_bin, param_grid_bin,\n", - " cv=cv_bin, scoring=\"f1_macro\",\n", - " n_jobs=-1, verbose=1, refit=True,\n", - ")\n", - "\n", - "print(f\"Grid size: {len(gs_bin.param_grid)} param combos → {5 * 2*3*2*3} fits\")\n", - "print(\"Running grid search...\")\n", - "gs_bin.fit(X_trainval, y_trainval, groups=groups_tv)\n", - "\n", - "print(f\"\\nBest params : {gs_bin.best_params_}\")\n", - "print(f\"Best CV F1 : {gs_bin.best_score_:.4f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "JstBvt1vuKBc", - "outputId": "aff1b1fd-aa2f-4c9c-92ae-7d836436f66d" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/classical/tfidf_lr/best_config_binary.json\n" - ] - } - ], - "source": [ - "# ── Save best config ──────────────────────────────────────────────────────────\n", - "best_cfg_bin = {\"task\": \"binary\", \"model\": \"TF-IDF + LR\", \"seed\": SEED,\n", - " \"best_params\": gs_bin.best_params_, \"cv_f1_macro\": gs_bin.best_score_}\n", - "with open(OUT_TFIDF / \"best_config_binary.json\", \"w\") as f:\n", - " json.dump(best_cfg_bin, f, indent=2)\n", - "print(\"Saved: outputs/classical/tfidf_lr/best_config_binary.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "AjH8_5UQuKBc", - "outputId": "afe39734-c73a-410a-9447-6b8639b18389" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "[Val] Accuracy=0.9186 Macro-F1=0.9186 Weighted-F1=0.9186\n", - " precision recall f1-score support\n", - "\n", - "non-sarcastic 0.92 0.92 0.92 4250\n", - " sarcastic 0.92 0.92 0.92 4250\n", - "\n", - " accuracy 0.92 8500\n", - " macro avg 0.92 0.92 0.92 8500\n", - " weighted avg 0.92 0.92 0.92 8500\n", - "\n", - "\n", - "[Test] Accuracy=0.8189 Macro-F1=0.8189 Weighted-F1=0.8189\n", - " precision recall f1-score support\n", - "\n", - "non-sarcastic 0.82 0.82 0.82 4250\n", - " sarcastic 0.82 0.82 0.82 4250\n", - "\n", - " accuracy 0.82 8500\n", - " macro avg 0.82 0.82 0.82 8500\n", - " weighted avg 0.82 0.82 0.82 8500\n", - "\n", - "Saved: outputs/classical/tfidf_lr/metrics_binary.json\n" - ] - } - ], - "source": [ - "# ── Evaluate on val and test ──────────────────────────────────────────────────\n", - "best_bin = gs_bin.best_estimator_\n", - "\n", - "val_metrics_bin, y_val_pred_bin = evaluate(best_bin, X_val, y_val, label_names_bin, \"Val\")\n", - "test_metrics_bin, y_test_pred_bin = evaluate(best_bin, X_test, y_test, label_names_bin, \"Test\")\n", - "\n", - "all_metrics_bin = {\"val\": val_metrics_bin, \"test\": test_metrics_bin}\n", - "\n", - "# Remove classification_report dict (too nested for JSON)\n", - "for split_m in all_metrics_bin.values():\n", - " split_m.pop(\"report\", None)\n", - "\n", - "with open(OUT_TFIDF / \"metrics_binary.json\", \"w\") as f:\n", - " json.dump(all_metrics_bin, f, indent=2)\n", - "print(\"Saved: outputs/classical/tfidf_lr/metrics_binary.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "ichiimejuKBc", - "outputId": "e3d5e010-d983-4f86-893b-cd38907ecc68" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/classical/tfidf_lr/confusion_matrix_binary_test.png\n" - ] - } - ], - "source": [ - "# ── Confusion matrices ────────────────────────────────────────────────────────\n", - "save_confusion_matrix(\n", - " y_val, y_val_pred_bin, label_names_bin,\n", - " OUT_TFIDF / \"confusion_matrix_binary_val.png\",\n", - " \"TF-IDF + LR — Binary (Val)\"\n", - ")\n", - "save_confusion_matrix(\n", - " y_test, y_test_pred_bin, label_names_bin,\n", - " OUT_TFIDF / \"confusion_matrix_binary_test.png\",\n", - " \"TF-IDF + LR — Binary (Test)\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_2QLSOmTuKBc", - "outputId": "8d05bf4c-dcc5-47d0-8ae4-7ccc6754d50a" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/classical/tfidf_lr/predictions_val_binary.csv\n", - "Saved: outputs/classical/tfidf_lr/predictions_test_binary.csv\n" - ] - } - ], - "source": [ - "# ── Save predictions ──────────────────────────────────────────────────────────\n", - "save_predictions(val_bin, y_val_pred_bin, \"binary_label\", OUT_TFIDF / \"predictions_val_binary.csv\")\n", - "save_predictions(test_bin, y_test_pred_bin, \"binary_label\", OUT_TFIDF / \"predictions_test_binary.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "bigHRH2MuKBc", - "outputId": "40c0448d-3f53-4ba4-cc17-3347f11dc23b" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Top 20 sarcastic indicators (positive coef):\n", - " because +22.1982\n", - " finally +9.5906\n", - " obviously +9.4255\n", - " just +9.2842\n", - " so +8.8645\n", - " shocking +8.6605\n", - " clearly +8.4488\n", - " as if +8.3217\n", - " proving +7.5734\n", - " groundbreaking +7.5588\n", - " nation +7.4750\n", - " like +7.1686\n", - " nothing +6.5188\n", - " shocker +6.5093\n", - " never +6.4249\n", - " totally +6.3599\n", - " always +6.1954\n", - " sure +6.1921\n", - " area +6.0594\n", - " who needs +6.0482\n", - "\n", - "Top 20 non-sarcastic indicators (negative coef):\n", - " the -11.8202\n", - " an -11.5473\n", - " is -10.1138\n", - " was -7.7886\n", - " an area -6.0294\n", - " man is -6.0119\n", - " his -5.8977\n", - " are -5.7913\n", - " finds that -5.7222\n", - " the nation -5.6480\n", - " indicates -5.5894\n", - " donald trump -5.1844\n", - " donald -5.0836\n", - " says he -4.9047\n", - " did not -4.7563\n", - " because of -4.7343\n", - " may -4.7339\n", - " large -4.6423\n", - " her -4.5442\n", - " how to -4.3584\n" - ] - } - ], - "source": [ - "# ── Feature importance: top discriminative terms ──────────────────────────────\n", - "tfidf_vocab = best_bin.named_steps[\"tfidf\"].get_feature_names_out()\n", - "lr_coefs = best_bin.named_steps[\"lr\"].coef_[0] # binary: shape (n_features,)\n", - "\n", - "n_top = 20\n", - "top_pos_idx = np.argsort(lr_coefs)[-n_top:][::-1]\n", - "top_neg_idx = np.argsort(lr_coefs)[:n_top]\n", - "\n", - "top_positive = [(tfidf_vocab[i], lr_coefs[i]) for i in top_pos_idx]\n", - "top_negative = [(tfidf_vocab[i], lr_coefs[i]) for i in top_neg_idx]\n", - "\n", - "print(\"Top 20 sarcastic indicators (positive coef):\")\n", - "for term, coef in top_positive:\n", - " print(f\" {term:35s} {coef:+.4f}\")\n", - "\n", - "print(\"\\nTop 20 non-sarcastic indicators (negative coef):\")\n", - "for term, coef in top_negative:\n", - " print(f\" {term:35s} {coef:+.4f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 644 - }, - "id": "pEArvWzTuKBd", - "outputId": "c4709ebc-9b56-468b-eb9f-c47f1ba006b8" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": 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- }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/classical/tfidf_lr/feature_importance_binary.png\n" - ] - } - ], - "source": [ - "# ── Feature importance plot ───────────────────────────────────────────────────\n", - "fig, axes = plt.subplots(1, 2, figsize=(16, 7))\n", - "\n", - "terms_pos, coefs_pos = zip(*top_positive)\n", - "axes[0].barh(list(reversed(terms_pos)), list(reversed(coefs_pos)), color=\"steelblue\")\n", - "axes[0].set_title(\"Top 20 — Sarcastic (positive)\", fontsize=13)\n", - "axes[0].set_xlabel(\"LR Coefficient\")\n", - "\n", - "terms_neg, coefs_neg = zip(*top_negative)\n", - "axes[1].barh(list(reversed(terms_neg)), list(reversed(coefs_neg)), color=\"coral\")\n", - "axes[1].set_title(\"Top 20 — Non-Sarcastic (negative)\", fontsize=13)\n", - "axes[1].set_xlabel(\"LR Coefficient\")\n", - "\n", - "plt.suptitle(\"TF-IDF + LR: Feature Importance (Binary Task)\", fontsize=14)\n", - "plt.tight_layout()\n", - "plt.savefig(OUT_TFIDF / \"feature_importance_binary.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "print(\"Saved: outputs/classical/tfidf_lr/feature_importance_binary.png\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5VFBy1bJuKBd" - }, - "source": [ - "## Task B — Sarcasm Type Classification" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "GTvcYYGquKBd", - "outputId": "f3c82bbf-5d44-4c4c-d8f0-2f586415fced" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n", - "Train+Val: 24,083 Train: 19,833 Val: 4,250 Test: 4,250\n" - ] - } - ], - "source": [ - "# ── Load type splits ──────────────────────────────────────────────────────────\n", - "train_type, val_type, test_type = load_splits(\"type\")\n", - "\n", - "# Encode string labels to integers\n", - "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", - "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", - "id2label = {i: lab for lab, i in label2id.items()}\n", - "\n", - "def encode_labels(df: pd.DataFrame) -> list[int]:\n", - " return [label2id[l] for l in df[\"type_label\"]]\n", - "\n", - "trainval_type = pd.concat([train_type, val_type], ignore_index=True)\n", - "\n", - "X_trainval_t = trainval_type[\"text\"].tolist()\n", - "y_trainval_t = encode_labels(trainval_type)\n", - "groups_tv_t = trainval_type[\"group_id\"].tolist()\n", - "\n", - "X_train_t = train_type[\"text\"].tolist()\n", - "y_train_t = encode_labels(train_type)\n", - "X_val_t = val_type[\"text\"].tolist()\n", - "y_val_t = encode_labels(val_type)\n", - "X_test_t = test_type[\"text\"].tolist()\n", - "y_test_t = encode_labels(test_type)\n", - "\n", - "print(f\"Strategies: {STRATEGY_LABELS}\")\n", - "print(f\"Train+Val: {len(X_trainval_t):,} Train: {len(X_train_t):,} Val: {len(X_val_t):,} Test: {len(X_test_t):,}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "yD1TrZZPuKBd", - "outputId": "4faad70d-dc90-4fcd-de7d-19de03226861" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Running grid search for type task...\n", - "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n", - "\n", - "Best params : {'lr__C': 3.0, 'lr__class_weight': 'balanced', 'tfidf__max_features': None, 'tfidf__min_df': 2, 'tfidf__ngram_range': (1, 2)}\n", - "Best CV F1 : 0.3840\n" - ] - } - ], - "source": [ - "# ── Define pipeline and grid (with class_weight) ──────────────────────────────\n", - "pipe_type = Pipeline([\n", - " (\"tfidf\", TfidfVectorizer(sublinear_tf=True, lowercase=True)),\n", - " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, solver=\"lbfgs\",\n", - " multi_class=\"auto\")),\n", - "])\n", - "\n", - "param_grid_type = {\n", - " \"tfidf__ngram_range\" : [(1, 1), (1, 2)],\n", - " \"tfidf__min_df\" : [2, 3, 5],\n", - " \"tfidf__max_features\": [None, 50_000],\n", - " \"lr__C\" : [0.1, 1.0, 3.0],\n", - " \"lr__class_weight\" : [None, \"balanced\"],\n", - "}\n", - "\n", - "cv_type = GroupKFold(n_splits=5)\n", - "\n", - "gs_type = GridSearchCV(\n", - " pipe_type, param_grid_type,\n", - " cv=cv_type, scoring=\"f1_macro\",\n", - " n_jobs=-1, verbose=1, refit=True,\n", - ")\n", - "\n", - "print(\"Running grid search for type task...\")\n", - "gs_type.fit(X_trainval_t, y_trainval_t, groups=groups_tv_t)\n", - "\n", - "print(f\"\\nBest params : {gs_type.best_params_}\")\n", - "print(f\"Best CV F1 : {gs_type.best_score_:.4f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "huaF8TbjuKBd", - "outputId": "d43d2757-7486-4307-912b-462d285835ce" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/classical/tfidf_lr/best_config_type.json\n" - ] - } - ], - "source": [ - "# ── Save best config ──────────────────────────────────────────────────────────\n", - "best_cfg_type = {\"task\": \"type\", \"model\": \"TF-IDF + LR\", \"seed\": SEED,\n", - " \"label_names\": STRATEGY_LABELS, \"label2id\": label2id,\n", - " \"best_params\": gs_type.best_params_, \"cv_f1_macro\": gs_type.best_score_}\n", - "with open(OUT_TFIDF / \"best_config_type.json\", \"w\") as f:\n", - " json.dump(best_cfg_type, f, indent=2)\n", - "print(\"Saved: outputs/classical/tfidf_lr/best_config_type.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "c4Hj46aWuKB6", - "outputId": "f2ac2819-bbf4-43a3-9437-9cfdafc29c12" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "[Val] Accuracy=0.8967 Macro-F1=0.8982 Weighted-F1=0.8961\n", - " precision recall f1-score support\n", - "\n", - " irony 0.92 0.87 0.90 942\n", - " overstatement 0.88 0.97 0.92 600\n", - "rhetorical_question 0.79 1.00 0.88 157\n", - " sarcasm 0.94 0.83 0.88 1317\n", - " satire 0.88 0.91 0.90 747\n", - " understatement 0.85 0.98 0.91 487\n", - "\n", - " accuracy 0.90 4250\n", - " macro avg 0.88 0.93 0.90 4250\n", - " weighted avg 0.90 0.90 0.90 4250\n", - "\n", - "\n", - "[Test] Accuracy=0.3958 Macro-F1=0.4047 Weighted-F1=0.3947\n", - " precision recall f1-score support\n", - "\n", - " irony 0.33 0.29 0.31 902\n", - " overstatement 0.38 0.43 0.40 592\n", - "rhetorical_question 0.42 0.60 0.50 176\n", - " sarcasm 0.48 0.42 0.45 1291\n", - " satire 0.38 0.39 0.38 833\n", - " understatement 0.36 0.43 0.39 456\n", - "\n", - " accuracy 0.40 4250\n", - " macro avg 0.39 0.43 0.40 4250\n", - " weighted avg 0.40 0.40 0.39 4250\n", - "\n", - "Saved: outputs/classical/tfidf_lr/metrics_type.json\n" - ] - } - ], - "source": [ - "# ── Evaluate on val and test ──────────────────────────────────────────────────\n", - "best_type = gs_type.best_estimator_\n", - "\n", - "val_metrics_type, y_val_pred_type = evaluate(best_type, X_val_t, y_val_t, STRATEGY_LABELS, \"Val\")\n", - "test_metrics_type, y_test_pred_type = evaluate(best_type, X_test_t, y_test_t, STRATEGY_LABELS, \"Test\")\n", - "\n", - "all_metrics_type = {\"val\": val_metrics_type, \"test\": test_metrics_type}\n", - "for split_m in all_metrics_type.values():\n", - " split_m.pop(\"report\", None)\n", - "\n", - "with open(OUT_TFIDF / \"metrics_type.json\", \"w\") as f:\n", - " json.dump(all_metrics_type, f, indent=2)\n", - "print(\"Saved: outputs/classical/tfidf_lr/metrics_type.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "ldimkyrXuKB6", - "outputId": "e8e286df-267e-4802-8318-d0cc9f768808" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/classical/tfidf_lr/confusion_matrix_type_test.png\n" - ] - } - ], - "source": [ - "# ── Confusion matrices ────────────────────────────────────────────────────────\n", - "save_confusion_matrix(\n", - " y_val_t, y_val_pred_type, STRATEGY_LABELS,\n", - " OUT_TFIDF / \"confusion_matrix_type_val.png\",\n", - " \"TF-IDF + LR — Type (Val)\"\n", - ")\n", - "save_confusion_matrix(\n", - " y_test_t, y_test_pred_type, STRATEGY_LABELS,\n", - " OUT_TFIDF / \"confusion_matrix_type_test.png\",\n", - " \"TF-IDF + LR — Type (Test)\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "1_ZECP4MuKB6", - "outputId": "c09e503d-d620-47ec-8917-c494bc7f3f48" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved predictions_val_type.csv and predictions_test_type.csv\n" - ] - } - ], - "source": [ - "# ── Save predictions ──────────────────────────────────────────────────────────\n", - "# Decode integer predictions back to string labels\n", - "val_type_copy = val_type.copy(); val_type_copy[\"type_label\"] = y_val_t\n", - "test_type_copy = test_type.copy(); test_type_copy[\"type_label\"] = y_test_t\n", - "\n", - "val_type_copy[\"predicted_id\"] = y_val_pred_type\n", - "val_type_copy[\"predicted_label\"] = [id2label[i] for i in y_val_pred_type]\n", - "val_type_copy[\"true_label\"] = [id2label[i] for i in y_val_t]\n", - "val_type_copy[\"correct\"] = (val_type_copy[\"type_label\"] == val_type_copy[\"predicted_id\"]).astype(int)\n", - "\n", - "test_type_copy[\"predicted_id\"] = y_test_pred_type\n", - "test_type_copy[\"predicted_label\"] = [id2label[i] for i in y_test_pred_type]\n", - "test_type_copy[\"true_label\"] = [id2label[i] for i in y_test_t]\n", - "test_type_copy[\"correct\"] = (test_type_copy[\"type_label\"] == test_type_copy[\"predicted_id\"]).astype(int)\n", - "\n", - "val_type_copy.to_csv( OUT_TFIDF / \"predictions_val_type.csv\", index=False)\n", - "test_type_copy.to_csv(OUT_TFIDF / \"predictions_test_type.csv\", index=False)\n", - "print(\"Saved predictions_val_type.csv and predictions_test_type.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xdfZfFRmuKB7" - }, - "source": [ - "## Char N-gram Variant (Optional)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3Ff1Tz1iuKB7", - "outputId": "4209ddba-9cd6-46f9-e66d-f62f1ab5709f" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Char n-gram best C : {'lr__C': 3.0}\n", - "Char n-gram best CV F1 : 0.8305\n", - "Word n-gram best CV F1 : 0.8143\n", - "\n", - "\n", - "[Test (char)] Accuracy=0.8284 Macro-F1=0.8283 Weighted-F1=0.8283\n", - " precision recall f1-score support\n", - "\n", - "non-sarcastic 0.82 0.84 0.83 4250\n", - " sarcastic 0.83 0.82 0.83 4250\n", - "\n", - " accuracy 0.83 8500\n", - " macro avg 0.83 0.83 0.83 8500\n", - " weighted avg 0.83 0.83 0.83 8500\n", - "\n" - ] - } - ], - "source": [ - "# Char n-gram model for binary task (stylistic cues)\n", - "pipe_char = Pipeline([\n", - " (\"tfidf\", TfidfVectorizer(analyzer=\"char_wb\", ngram_range=(3, 5),\n", - " sublinear_tf=True, lowercase=True, max_features=50_000)),\n", - " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, C=1.0)),\n", - "])\n", - "\n", - "param_grid_char = {\"lr__C\": [0.1, 1.0, 3.0]}\n", - "gs_char = GridSearchCV(pipe_char, param_grid_char, cv=GroupKFold(5),\n", - " scoring=\"f1_macro\", n_jobs=-1)\n", - "gs_char.fit(X_trainval, y_trainval, groups=groups_tv)\n", - "\n", - "print(f\"Char n-gram best C : {gs_char.best_params_}\")\n", - "print(f\"Char n-gram best CV F1 : {gs_char.best_score_:.4f}\")\n", - "print(f\"Word n-gram best CV F1 : {gs_bin.best_score_:.4f}\")\n", - "print()\n", - "char_metrics, _ = evaluate(gs_char.best_estimator_, X_test, y_test, label_names_bin, \"Test (char)\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "t_pVeRFVuKB7" - }, - "source": [ - "## Summary" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OszOI0q1uKB7", - "outputId": "532102b9-cd0f-40e8-b1e1-48fa8520b526" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "====== TF-IDF + LR RESULTS SUMMARY ======\n", - "\n", - "Task A — Binary Classification (Test Set):\n", - " Accuracy : 0.8189\n", - " Macro-F1 : 0.8189\n", - " Weighted-F1 : 0.8189\n", - "\n", - "Task B — Type Classification (Test Set):\n", - " Accuracy : 0.3958\n", - " Macro-F1 : 0.4047\n", - " Weighted-F1 : 0.3947\n", - "\n", - "Best hyperparameters:\n", - " Binary: {'lr__C': 3.0, 'tfidf__max_features': None, 'tfidf__min_df': 3, 'tfidf__ngram_range': (1, 2)}\n", - " Type: {'lr__C': 3.0, 'lr__class_weight': 'balanced', 'tfidf__max_features': None, 'tfidf__min_df': 2, 'tfidf__ngram_range': (1, 2)}\n" - ] - } - ], - "source": [ - "print(\"====== TF-IDF + LR RESULTS SUMMARY ======\")\n", - "print()\n", - "print(\"Task A — Binary Classification (Test Set):\")\n", - "print(f\" Accuracy : {test_metrics_bin['accuracy']:.4f}\")\n", - "print(f\" Macro-F1 : {test_metrics_bin['f1_macro']:.4f}\")\n", - "print(f\" Weighted-F1 : {test_metrics_bin['f1_weighted']:.4f}\")\n", - "print()\n", - "print(\"Task B — Type Classification (Test Set):\")\n", - "print(f\" Accuracy : {test_metrics_type['accuracy']:.4f}\")\n", - "print(f\" Macro-F1 : {test_metrics_type['f1_macro']:.4f}\")\n", - "print(f\" Weighted-F1 : {test_metrics_type['f1_weighted']:.4f}\")\n", - "print()\n", - "print(\"Best hyperparameters:\")\n", - "print(\" Binary:\", gs_bin.best_params_)\n", - "print(\" Type: \", gs_type.best_params_)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ftKo2U-1uKB7" - }, - "source": [ - "---\n", - "# Part 3 — Naive Bayes Baseline" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "QZo05bYauKB7" - }, - "source": [ - "# Notebook 03 — Naive Bayes Baseline\n", - "\n", - "**Purpose**: Train and evaluate Naive Bayes classifiers for:\n", - "- Task A: Binary classification (sarcastic vs non-sarcastic)\n", - "- Task B: Sarcasm type classification (6-class)\n", - "\n", - "Compares:\n", - "- `CountVectorizer + MultinomialNB`\n", - "- `TfidfVectorizer + MultinomialNB`\n", - "- `CountVectorizer + ComplementNB` (for imbalanced type task)\n", - "\n", - "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", - "\n", - "**Outputs** in `outputs/classical/naive_bayes/`" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yeP7YbDYuKB8" - }, - "source": [ - "## Helper Functions" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "id": "PoPH1ijBuKB8" - }, - "outputs": [], - "source": [ - "def load_splits(task: str):\n", - " train = pd.read_csv(SPLITS / f\"train_{task}.csv\")\n", - " val = pd.read_csv(SPLITS / f\"val_{task}.csv\")\n", - " test = pd.read_csv(SPLITS / f\"test_{task}.csv\")\n", - " return train, val, test\n", - "\n", - "\n", - "def evaluate(model, X, y_true, label_names=None, split_name=\"\"):\n", - " y_pred = model.predict(X)\n", - " metrics = {\n", - " \"split\" : split_name,\n", - " \"accuracy\" : accuracy_score(y_true, y_pred),\n", - " \"precision_macro\" : precision_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", - " \"recall_macro\" : recall_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", - " \"f1_macro\" : f1_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", - " \"f1_weighted\" : f1_score(y_true, y_pred, average=\"weighted\", zero_division=0),\n", - " }\n", - " print(f\"[{split_name}] Accuracy={metrics['accuracy']:.4f} \"\n", - " f\"Macro-F1={metrics['f1_macro']:.4f} Weighted-F1={metrics['f1_weighted']:.4f}\")\n", - " print(classification_report(y_true, y_pred, target_names=label_names, zero_division=0))\n", - " return metrics, y_pred\n", - "\n", - "\n", - "def save_confusion_matrix(y_true, y_pred, label_names, out_path, title=\"\"):\n", - " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", - " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", - " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Greens\",\n", - " xticklabels=label_names, yticklabels=label_names, ax=ax)\n", - " ax.set_xlabel(\"Predicted\"); ax.set_ylabel(\"True\"); ax.set_title(title)\n", - " plt.tight_layout()\n", - " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", - " plt.show()\n", - " print(f\"Saved: {out_path.relative_to(ROOT)}\")\n", - "\n", - "\n", - "def run_nb_grid(\n", - " vectorizer_cls, nb_cls, param_grid: dict,\n", - " X_trainval, y_trainval, groups_tv,\n", - " name: str\n", - ") -> GridSearchCV:\n", - " \"\"\"Run a grid search for a given vectorizer + NB combination.\"\"\"\n", - " pipe = Pipeline([\n", - " (\"vec\", vectorizer_cls(lowercase=True)),\n", - " (\"nb\", nb_cls()),\n", - " ])\n", - " gs = GridSearchCV(\n", - " pipe, param_grid, cv=GroupKFold(5),\n", - " scoring=\"f1_macro\", n_jobs=-1, verbose=0, refit=True\n", - " )\n", - " gs.fit(X_trainval, y_trainval, groups=groups_tv)\n", - " print(f\"{name:50s} CV F1={gs.best_score_:.4f} params={gs.best_params_}\")\n", - " return gs" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "0oL34vMHuKB8" - }, - "source": [ - "## Task A — Binary Classification" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_STzCoqRuKB8", - "outputId": "c86245b0-ee21-4612-a5d5-dd2fba99214b" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Train+Val: 48,166 Val: 8,500 Test: 8,500\n" - ] - } - ], - "source": [ - "train_bin, val_bin, test_bin = load_splits(\"binary\")\n", - "trainval_bin = pd.concat([train_bin, val_bin], ignore_index=True)\n", - "\n", - "X_tv = trainval_bin[\"text\"].tolist()\n", - "y_tv = trainval_bin[\"binary_label\"].tolist()\n", - "grp_tv = trainval_bin[\"group_id\"].tolist()\n", - "\n", - "X_val = val_bin[\"text\"].tolist(); y_val = val_bin[\"binary_label\"].tolist()\n", - "X_test = test_bin[\"text\"].tolist(); y_test = test_bin[\"binary_label\"].tolist()\n", - "\n", - "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", - "\n", - "print(f\"Train+Val: {len(X_tv):,} Val: {len(X_val):,} Test: {len(X_test):,}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "iOJgCEMwuKB8", - "outputId": "d37b770b-3b8f-4b49-ac5e-aeb6b7a0d37a" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "=== Binary: Comparing Vectorizer + NB Combinations ===\n", - "CountVectorizer + MultinomialNB CV F1=0.7855 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", - "TfidfVectorizer + MultinomialNB CV F1=0.7897 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", - "CountVectorizer + ComplementNB CV F1=0.7855 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n" - ] - } - ], - "source": [ - "# ── Shared grid parameters ────────────────────────────────────────────────────\n", - "BASE_GRID = {\n", - " \"vec__ngram_range\": [(1, 1), (1, 2)],\n", - " \"vec__min_df\" : [2, 3, 5],\n", - " \"nb__alpha\" : [0.1, 0.5, 1.0],\n", - "}\n", - "\n", - "print(\"=== Binary: Comparing Vectorizer + NB Combinations ===\")\n", - "\n", - "gs_count_mnb_bin = run_nb_grid(\n", - " CountVectorizer, MultinomialNB, BASE_GRID,\n", - " X_tv, y_tv, grp_tv,\n", - " \"CountVectorizer + MultinomialNB\"\n", - ")\n", - "\n", - "gs_tfidf_mnb_bin = run_nb_grid(\n", - " TfidfVectorizer, MultinomialNB, BASE_GRID,\n", - " X_tv, y_tv, grp_tv,\n", - " \"TfidfVectorizer + MultinomialNB\"\n", - ")\n", - "\n", - "gs_count_cnb_bin = run_nb_grid(\n", - " CountVectorizer, ComplementNB, BASE_GRID,\n", - " X_tv, y_tv, grp_tv,\n", - " \"CountVectorizer + ComplementNB\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "eilEzm18uKB9", - "outputId": "360ce0ff-df02-45a3-bcb3-8213ae795e53" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Best binary NB model: TfidfVec+MultinomialNB (CV F1=0.7897)\n", - "\n", - "Comparison table:\n", - " model cv_f1_macro best_params\n", - "CountVec+MultinomialNB 0.785542 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", - "TfidfVec+MultinomialNB 0.789663 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", - " CountVec+ComplementNB 0.785542 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n" - ] - } - ], - "source": [ - "# ── Select best binary model ──────────────────────────────────────────────────\n", - "candidates_bin = [\n", - " (\"CountVec+MultinomialNB\", gs_count_mnb_bin),\n", - " (\"TfidfVec+MultinomialNB\", gs_tfidf_mnb_bin),\n", - " (\"CountVec+ComplementNB\", gs_count_cnb_bin),\n", - "]\n", - "\n", - "best_name_bin, best_gs_bin = max(candidates_bin, key=lambda x: x[1].best_score_)\n", - "print(f\"\\nBest binary NB model: {best_name_bin} (CV F1={best_gs_bin.best_score_:.4f})\")\n", - "\n", - "# Comparison table\n", - "comp_df = pd.DataFrame([\n", - " {\"model\": name, \"cv_f1_macro\": gs.best_score_, \"best_params\": str(gs.best_params_)}\n", - " for name, gs in candidates_bin\n", - "])\n", - "print(\"\\nComparison table:\")\n", - "print(comp_df.to_string(index=False))" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Zh5ipG6BuKB9", - "outputId": "7f108910-a2fb-4154-eb36-f3f68b5a4dc2" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "[Val] Accuracy=0.8826 Macro-F1=0.8826 Weighted-F1=0.8826\n", - " precision recall f1-score support\n", - "\n", - "non-sarcastic 0.89 0.87 0.88 4250\n", - " sarcastic 0.87 0.90 0.88 4250\n", - "\n", - " accuracy 0.88 8500\n", - " macro avg 0.88 0.88 0.88 8500\n", - " weighted avg 0.88 0.88 0.88 8500\n", - "\n", - "[Test] Accuracy=0.7905 Macro-F1=0.7902 Weighted-F1=0.7902\n", - " precision recall f1-score support\n", - "\n", - "non-sarcastic 0.81 0.76 0.78 4250\n", - " sarcastic 0.77 0.83 0.80 4250\n", - "\n", - " accuracy 0.79 8500\n", - " macro avg 0.79 0.79 0.79 8500\n", - " weighted avg 0.79 0.79 0.79 8500\n", - "\n", - "\n", - "--- All models on Test ---\n", - " CountVec+MultinomialNB : acc=0.7887 macro-F1=0.7886\n", - " TfidfVec+MultinomialNB : acc=0.7905 macro-F1=0.7902\n", - " CountVec+ComplementNB : acc=0.7887 macro-F1=0.7886\n" - ] - } - ], - "source": [ - "# ── Evaluate best model ───────────────────────────────────────────────────────\n", - "best_model_bin = best_gs_bin.best_estimator_\n", - "\n", - "val_m_bin, y_val_pred_bin = evaluate(best_model_bin, X_val, y_val, label_names_bin, \"Val\")\n", - "test_m_bin, y_test_pred_bin = evaluate(best_model_bin, X_test, y_test, label_names_bin, \"Test\")\n", - "\n", - "# Also evaluate all three models on test for comparison\n", - "print(\"\\n--- All models on Test ---\")\n", - "all_test_results_bin = []\n", - "for name, gs in candidates_bin:\n", - " y_pred_t = gs.best_estimator_.predict(X_test)\n", - " f1 = f1_score(y_test, y_pred_t, average=\"macro\")\n", - " acc = accuracy_score(y_test, y_pred_t)\n", - " all_test_results_bin.append({\"model\": name, \"accuracy\": acc, \"f1_macro\": f1})\n", - " print(f\" {name:40s}: acc={acc:.4f} macro-F1={f1:.4f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "D74tnzAduKB9", - "outputId": "1ccb358e-1daa-42ec-8aa0-09bada09116b" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/classical/naive_bayes/confusion_matrix_binary_test.png\n", - "All binary artifacts saved.\n" - ] - } - ], - "source": [ - "# ── Save binary results ───────────────────────────────────────────────────────\n", - "cfg_bin = {\n", - " \"task\": \"binary\", \"best_model\": best_name_bin,\n", - " \"best_params\": best_gs_bin.best_params_, \"cv_f1_macro\": best_gs_bin.best_score_,\n", - " \"all_candidates\": [\n", - " {\"model\": name, \"cv_f1_macro\": gs.best_score_, \"params\": gs.best_params_}\n", - " for name, gs in candidates_bin\n", - " ]\n", - "}\n", - "with open(OUT_NB / \"best_config_binary.json\", \"w\") as f:\n", - " json.dump(cfg_bin, f, indent=2)\n", - "\n", - "all_m_bin = {\"val\": val_m_bin, \"test\": test_m_bin,\n", - " \"all_test_comparison\": all_test_results_bin}\n", - "with open(OUT_NB / \"metrics_binary.json\", \"w\") as f:\n", - " json.dump(all_m_bin, f, indent=2)\n", - "\n", - "save_confusion_matrix(\n", - " y_val, y_val_pred_bin, label_names_bin,\n", - " OUT_NB / \"confusion_matrix_binary_val.png\",\n", - " f\"NB ({best_name_bin}) — Binary (Val)\"\n", - ")\n", - "save_confusion_matrix(\n", - " y_test, y_test_pred_bin, label_names_bin,\n", - " OUT_NB / \"confusion_matrix_binary_test.png\",\n", - " f\"NB ({best_name_bin}) — Binary (Test)\"\n", - ")\n", - "\n", - "# Predictions\n", - "pred_val_bin = val_bin.copy()\n", - "pred_val_bin[\"predicted\"] = y_val_pred_bin\n", - "pred_val_bin[\"correct\"] = (pred_val_bin[\"binary_label\"] == pred_val_bin[\"predicted\"]).astype(int)\n", - "pred_val_bin.to_csv(OUT_NB / \"predictions_val_binary.csv\", index=False)\n", - "\n", - "pred_test_bin = test_bin.copy()\n", - "pred_test_bin[\"predicted\"] = y_test_pred_bin\n", - "pred_test_bin[\"correct\"] = (pred_test_bin[\"binary_label\"] == pred_test_bin[\"predicted\"]).astype(int)\n", - "pred_test_bin.to_csv(OUT_NB / \"predictions_test_binary.csv\", index=False)\n", - "\n", - "print(\"All binary artifacts saved.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UtChhm0-uKB9" - }, - "source": [ - "## Task B — Sarcasm Type Classification" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Rh8sdjlhuKB9", - "outputId": "36e9daa4-b705-4b92-8601-af8ee580ff33" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n", - "Train+Val: 24,083 Val: 4,250 Test: 4,250\n" - ] - } - ], - "source": [ - "train_type, val_type, test_type = load_splits(\"type\")\n", - "\n", - "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", - "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", - "id2label = {i: lab for lab, i in label2id.items()}\n", - "\n", - "def enc(df): return [label2id[l] for l in df[\"type_label\"]]\n", - "\n", - "trainval_type = pd.concat([train_type, val_type], ignore_index=True)\n", - "X_tv_t = trainval_type[\"text\"].tolist()\n", - "y_tv_t = enc(trainval_type)\n", - "grp_tv_t = trainval_type[\"group_id\"].tolist()\n", - "\n", - "X_val_t = val_type[\"text\"].tolist(); y_val_t = enc(val_type)\n", - "X_test_t = test_type[\"text\"].tolist(); y_test_t = enc(test_type)\n", - "\n", - "print(f\"Strategies: {STRATEGY_LABELS}\")\n", - "print(f\"Train+Val: {len(X_tv_t):,} Val: {len(X_val_t):,} Test: {len(X_test_t):,}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ZupB5UlvuKB-", - "outputId": "bb5e86d3-745c-4c41-9650-d399e4668e32" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "=== Type: Comparing Vectorizer + NB Combinations ===\n", - "CountVectorizer + MultinomialNB CV F1=0.3811 params={'nb__alpha': 0.5, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n", - "TfidfVectorizer + MultinomialNB CV F1=0.3422 params={'nb__alpha': 0.1, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n", - "CountVectorizer + ComplementNB (for imbalance) CV F1=0.3777 params={'nb__alpha': 1.0, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n" - ] - } - ], - "source": [ - "print(\"=== Type: Comparing Vectorizer + NB Combinations ===\")\n", - "\n", - "gs_count_mnb_type = run_nb_grid(\n", - " CountVectorizer, MultinomialNB, BASE_GRID,\n", - " X_tv_t, y_tv_t, grp_tv_t,\n", - " \"CountVectorizer + MultinomialNB\"\n", - ")\n", - "\n", - "gs_tfidf_mnb_type = run_nb_grid(\n", - " TfidfVectorizer, MultinomialNB, BASE_GRID,\n", - " X_tv_t, y_tv_t, grp_tv_t,\n", - " \"TfidfVectorizer + MultinomialNB\"\n", - ")\n", - "\n", - "gs_count_cnb_type = run_nb_grid(\n", - " CountVectorizer, ComplementNB, BASE_GRID,\n", - " X_tv_t, y_tv_t, grp_tv_t,\n", - " \"CountVectorizer + ComplementNB (for imbalance)\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "J_CunIwDuKB-", - "outputId": "45a1ccc2-6af0-4542-b371-7573a94afba6" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Best type NB model: CountVec+MultinomialNB (CV F1=0.3811)\n", - "\n", - "--- All models on Test (Type Task) ---\n", - " CountVec+MultinomialNB : acc=0.3953 macro-F1=0.3953\n", - " TfidfVec+MultinomialNB : acc=0.3800 macro-F1=0.3450\n", - " CountVec+ComplementNB : acc=0.3875 macro-F1=0.3880\n" - ] - } - ], - "source": [ - "candidates_type = [\n", - " (\"CountVec+MultinomialNB\", gs_count_mnb_type),\n", - " (\"TfidfVec+MultinomialNB\", gs_tfidf_mnb_type),\n", - " (\"CountVec+ComplementNB\", gs_count_cnb_type),\n", - "]\n", - "\n", - "best_name_type, best_gs_type = max(candidates_type, key=lambda x: x[1].best_score_)\n", - "print(f\"Best type NB model: {best_name_type} (CV F1={best_gs_type.best_score_:.4f})\")\n", - "\n", - "print(\"\\n--- All models on Test (Type Task) ---\")\n", - "all_test_results_type = []\n", - "for name, gs in candidates_type:\n", - " y_pred_t = gs.best_estimator_.predict(X_test_t)\n", - " f1 = f1_score(y_test_t, y_pred_t, average=\"macro\")\n", - " acc = accuracy_score(y_test_t, y_pred_t)\n", - " all_test_results_type.append({\"model\": name, \"accuracy\": acc, \"f1_macro\": f1})\n", - " print(f\" {name:40s}: acc={acc:.4f} macro-F1={f1:.4f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "o6JHLJnLuKB-", - "outputId": "5f5f236e-83ce-4a58-9f49-92e0f472d39e" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "[Val] Accuracy=0.7694 Macro-F1=0.7744 Weighted-F1=0.7693\n", - " precision recall f1-score support\n", - "\n", - " irony 0.80 0.72 0.76 942\n", - " overstatement 0.75 0.80 0.77 600\n", - "rhetorical_question 0.78 0.88 0.83 157\n", - " sarcasm 0.79 0.78 0.79 1317\n", - " satire 0.73 0.77 0.75 747\n", - " understatement 0.75 0.75 0.75 487\n", - "\n", - " accuracy 0.77 4250\n", - " macro avg 0.77 0.78 0.77 4250\n", - " weighted avg 0.77 0.77 0.77 4250\n", - "\n", - "[Test] Accuracy=0.3953 Macro-F1=0.3953 Weighted-F1=0.3929\n", - " precision recall f1-score support\n", - "\n", - " irony 0.32 0.29 0.30 902\n", - " overstatement 0.39 0.40 0.39 592\n", - "rhetorical_question 0.52 0.41 0.46 176\n", - " sarcasm 0.45 0.51 0.47 1291\n", - " satire 0.36 0.34 0.35 833\n", - " understatement 0.40 0.38 0.39 456\n", - "\n", - " accuracy 0.40 4250\n", - " macro avg 0.41 0.39 0.40 4250\n", - " weighted avg 0.39 0.40 0.39 4250\n", - "\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/classical/naive_bayes/confusion_matrix_type_test.png\n", - "All type artifacts saved.\n" - ] - } - ], - "source": [ - "best_model_type = best_gs_type.best_estimator_\n", - "\n", - "val_m_type, y_val_pred_type = evaluate(best_model_type, X_val_t, y_val_t, STRATEGY_LABELS, \"Val\")\n", - "test_m_type, y_test_pred_type = evaluate(best_model_type, X_test_t, y_test_t, STRATEGY_LABELS, \"Test\")\n", - "\n", - "# Save\n", - "cfg_type = {\n", - " \"task\": \"type\", \"best_model\": best_name_type,\n", - " \"label_names\": STRATEGY_LABELS, \"label2id\": label2id,\n", - " \"best_params\": best_gs_type.best_params_, \"cv_f1_macro\": best_gs_type.best_score_,\n", - "}\n", - "with open(OUT_NB / \"best_config_type.json\", \"w\") as f:\n", - " json.dump(cfg_type, f, indent=2)\n", - "\n", - "all_m_type = {\"val\": val_m_type, \"test\": test_m_type,\n", - " \"all_test_comparison\": all_test_results_type}\n", - "with open(OUT_NB / \"metrics_type.json\", \"w\") as f:\n", - " json.dump(all_m_type, f, indent=2)\n", - "\n", - "save_confusion_matrix(\n", - " y_val_t, y_val_pred_type, STRATEGY_LABELS,\n", - " OUT_NB / \"confusion_matrix_type_val.png\",\n", - " f\"NB ({best_name_type}) — Type (Val)\"\n", - ")\n", - "save_confusion_matrix(\n", - " y_test_t, y_test_pred_type, STRATEGY_LABELS,\n", - " OUT_NB / \"confusion_matrix_type_test.png\",\n", - " f\"NB ({best_name_type}) — Type (Test)\"\n", - ")\n", - "\n", - "# Predictions with string labels\n", - "pred_val_type = val_type.copy()\n", - "pred_test_type = test_type.copy()\n", - "for df, y_pred, y_true in [(pred_val_type, y_val_pred_type, y_val_t),\n", - " (pred_test_type, y_test_pred_type, y_test_t)]:\n", - " df[\"predicted_label\"] = [id2label[i] for i in y_pred]\n", - " df[\"true_label\"] = [id2label[i] for i in y_true]\n", - " df[\"correct\"] = (df[\"type_label\"] == df[\"predicted_label\"]).astype(int)\n", - "\n", - "pred_val_type.to_csv( OUT_NB / \"predictions_val_type.csv\", index=False)\n", - "pred_test_type.to_csv(OUT_NB / \"predictions_test_type.csv\", index=False)\n", - "\n", - "print(\"All type artifacts saved.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TwTBPa86uKB-" - }, - "source": [ - "## Error Examples" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "b8itQIPtuKB-", - "outputId": "6805f04f-a1c4-4005-a8f7-4496ad84a58b" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Binary errors on test: 1781 total\n", - " False Positives (predicted sarcastic, actually not): 1039\n", - " False Negatives (predicted not-sarcastic, actually sarcastic): 742\n", - "\n", - "--- Sample FP ---\n", - " [True=0, Pred=1] Probability researchers note a coincidence where three coworkers wear the same shirt color.\n", - " [True=0, Pred=1] The global-warming crisis contributed to a delightful mid-February afternoon.\n", - " [True=0, Pred=1] Signs for the upcoming road section make it sound formidable or exciting.\n", - " [True=0, Pred=1] A father recounts how he saved $4.27 through quick thinking.\n", - " [True=0, Pred=1] The wedding album begins with a photo of two acorns floating in a glass of water.\n", - " [True=0, Pred=1] A governor is too embarrassed to say which state he leads.\n", - " [True=0, Pred=1] A man who plays devil's advocate may actually just want to be disagreeable.\n", - " [True=0, Pred=1] Area dad watched a show about bigfoot last night.\n", - " [True=0, Pred=1] Coroner's report cites systemic issues in Alton Sterling's death.\n", - " [True=0, Pred=1] Preschool child asks to look at a classmate's notes on shapes.\n", - "\n", - "--- Sample FN ---\n", - " [True=1, Pred=0] state department warns americans traveling abroad to avoid lame amsterdam windmill tour\n", - " [True=1, Pred=0] hollywood's biggest stars endure long lines at oscars security screening\n", - " [True=1, Pred=0] historians suggest 'goodfellas' youtube clips may be fragments of larger work\n", - " [True=1, Pred=0] members of opening band walking among crowd during intermission like gods among men\n", - " [True=1, Pred=0] woman who's been on the pill for years thinking about switching to new set of debilitating side effe\n", - " [True=1, Pred=0] congressman lets his guitar do the talking\n", - " [True=1, Pred=0] officials warn consumers of counterfeit tickets ahead of solar eclipse\n", - " [True=1, Pred=0] anderson cooper throws another box of letters from gay children into dumpster\n", - " [True=1, Pred=0] non-priest arrested on charges of child molestation\n", - " [True=1, Pred=0] huckabee sanders tells colleagues she's taking temporary post as google ceo before transitioning int\n" - ] - } - ], - "source": [ - "# ── Binary error examples ─────────────────────────────────────────────────────\n", - "err_bin = pred_test_bin[pred_test_bin[\"correct\"] == 0].copy()\n", - "fp_bin = err_bin[err_bin[\"binary_label\"] == 0].head(10) # predicted sarcastic, actually not\n", - "fn_bin = err_bin[err_bin[\"binary_label\"] == 1].head(10) # predicted not-sarcastic, actually sarcastic\n", - "\n", - "print(f\"Binary errors on test: {len(err_bin)} total\")\n", - "print(f\" False Positives (predicted sarcastic, actually not): {len(err_bin[err_bin['binary_label']==0])}\")\n", - "print(f\" False Negatives (predicted not-sarcastic, actually sarcastic): {len(err_bin[err_bin['binary_label']==1])}\")\n", - "print(\"\\n--- Sample FP ---\")\n", - "for _, row in fp_bin.iterrows():\n", - " print(f\" [True=0, Pred=1] {row['text'][:100]}\")\n", - "print(\"\\n--- Sample FN ---\")\n", - "for _, row in fn_bin.iterrows():\n", - " print(f\" [True=1, Pred=0] {row['text'][:100]}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "zmVdkTHuuKB_", - "outputId": "5bc30ec0-0f97-4f57-a165-706c7a022a82" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Type errors on test: 2570 total\n", - "\n", - "Confusion pairs (true → predicted):\n", - "type_label predicted_label\n", - "irony sarcasm 309\n", - "satire sarcasm 227\n", - "sarcasm irony 226\n", - " satire 203\n", - "satire irony 155\n", - "irony satire 155\n", - "overstatement sarcasm 117\n", - "understatement sarcasm 103\n", - "overstatement satire 102\n", - "satire overstatement 101\n" - ] - } - ], - "source": [ - "# ── Type error examples ───────────────────────────────────────────────────────\n", - "err_type = pred_test_type[pred_test_type[\"correct\"] == 0].copy()\n", - "print(f\"Type errors on test: {len(err_type)} total\")\n", - "print(\"\\nConfusion pairs (true → predicted):\")\n", - "conf_pairs = err_type.groupby([\"type_label\", \"predicted_label\"]).size().sort_values(ascending=False).head(10)\n", - "print(conf_pairs.to_string())" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "p52YKmRQuKB_" - }, - "source": [ - "## Summary" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "tg9D9LKxuKB_", - "outputId": "bf1f11d1-8feb-4b8c-c9fd-adac380993b2" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "====== NAIVE BAYES RESULTS SUMMARY ======\n", - "\n", - "Task A — Binary (Test):\n", - " Best model : TfidfVec+MultinomialNB\n", - " Accuracy : 0.7905\n", - " Macro-F1 : 0.7902\n", - " Weighted-F1 : 0.7902\n", - "\n", - "Task B — Type (Test):\n", - " Best model : CountVec+MultinomialNB\n", - " Accuracy : 0.3953\n", - " Macro-F1 : 0.3953\n", - " Weighted-F1 : 0.3929\n" - ] - } - ], - "source": [ - "print(\"====== NAIVE BAYES RESULTS SUMMARY ======\")\n", - "print()\n", - "print(\"Task A — Binary (Test):\")\n", - "print(f\" Best model : {best_name_bin}\")\n", - "print(f\" Accuracy : {test_m_bin['accuracy']:.4f}\")\n", - "print(f\" Macro-F1 : {test_m_bin['f1_macro']:.4f}\")\n", - "print(f\" Weighted-F1 : {test_m_bin['f1_weighted']:.4f}\")\n", - "print()\n", - "print(\"Task B — Type (Test):\")\n", - "print(f\" Best model : {best_name_type}\")\n", - "print(f\" Accuracy : {test_m_type['accuracy']:.4f}\")\n", - "print(f\" Macro-F1 : {test_m_type['f1_macro']:.4f}\")\n", - "print(f\" Weighted-F1 : {test_m_type['f1_weighted']:.4f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GZHqRh2iuKB_" - }, - "source": [ - "---\n", - "# Part 4 — BERT / DistilBERT Classification" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xR_41E_1uKB_" - }, - "source": [ - "# Notebook 04 — BERT / DistilBERT Classification\n", - "\n", - "**Purpose**: Fine-tune transformer models for sarcasm classification.\n", - "- Task A: Binary (sarcastic vs non-sarcastic)\n", - "- Task B: Sarcasm type (6-class, sarcastic only)\n", - "\n", - "**Models**:\n", - "- `distilbert-base-uncased` (primary, fast)\n", - "- `bert-base-uncased` (optional, if compute allows)\n", - "\n", - "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", - "\n", - "**Outputs** in `outputs/bert/distilbert_binary/` and `outputs/bert/distilbert_type/`" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5Gi8VSJDuKB_" - }, - "source": [ - "## Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "K_FNBk2ouKB_", - "outputId": "70f6d378-7098-410c-d656-50135e3b0e12" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Binary config: TrainConfig(model_name='distilbert-base-uncased', max_length=128, batch_size=32, lr=2e-05, weight_decay=0.01, warmup_ratio=0.1, epochs=10, patience=3, seed=42, task='binary', use_class_weights=False)\n", - "Type config: TrainConfig(model_name='distilbert-base-uncased', max_length=128, batch_size=32, lr=2e-05, weight_decay=0.01, warmup_ratio=0.1, epochs=10, patience=3, seed=42, task='type', use_class_weights=True)\n" - ] - } - ], - "source": [ - "@dataclass\n", - "class TrainConfig:\n", - " model_name : str = \"distilbert-base-uncased\"\n", - " max_length : int = 128\n", - " batch_size : int = 32\n", - " lr : float = 2e-5\n", - " weight_decay : float = 0.01\n", - " warmup_ratio : float = 0.1\n", - " epochs : int = 10\n", - " patience : int = 3 # early stopping patience\n", - " seed : int = SEED\n", - " task : str = \"binary\" # 'binary' or 'type'\n", - " use_class_weights: bool = False\n", - "\n", - "# ── Config for binary task ────────────────────────────────────────────────────\n", - "CFG_BINARY = TrainConfig(\n", - " model_name=\"distilbert-base-uncased\",\n", - " task=\"binary\",\n", - " batch_size=32,\n", - " lr=2e-5,\n", - " use_class_weights=False,\n", - ")\n", - "\n", - "# ── Config for type task (use class weights for imbalance) ────────────────────\n", - "CFG_TYPE = TrainConfig(\n", - " model_name=\"distilbert-base-uncased\",\n", - " task=\"type\",\n", - " batch_size=32,\n", - " lr=2e-5,\n", - " use_class_weights=True,\n", - ")\n", - "\n", - "print(\"Binary config:\", CFG_BINARY)\n", - "print(\"Type config: \", CFG_TYPE)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OKgvtBDPuKCA" - }, - "source": [ - "## Dataset Class" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "id": "tHoVxhSAuKCA" - }, - "outputs": [], - "source": [ - "import torch\n", - "from torch.utils.data import Dataset\n", - "\n", - "class HeadlineDataset(Dataset):\n", - " \"\"\"PyTorch Dataset for headline classification.\"\"\"\n", - "\n", - " def __init__(\n", - " self,\n", - " texts: list[str],\n", - " labels: list[int],\n", - " tokenizer,\n", - " max_length: int = 128,\n", - " ):\n", - " self.texts = texts\n", - " self.labels = labels\n", - " self.tokenizer = tokenizer\n", - " self.max_length = max_length\n", - "\n", - " def __len__(self) -> int:\n", - " return len(self.texts)\n", - "\n", - " def __getitem__(self, idx: int) -> dict:\n", - " encoding = self.tokenizer(\n", - " self.texts[idx],\n", - " truncation=True,\n", - " padding=\"max_length\",\n", - " max_length=self.max_length,\n", - " return_tensors=\"pt\",\n", - " )\n", - " return {\n", - " \"input_ids\" : encoding[\"input_ids\"].squeeze(0),\n", - " \"attention_mask\" : encoding[\"attention_mask\"].squeeze(0),\n", - " \"labels\" : torch.tensor(self.labels[idx], dtype=torch.long),\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KBPvq1iRuKCA" - }, - "source": [ - "## Training and Evaluation Functions" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "id": "Dw8sRTfruKCA" - }, - "outputs": [], - "source": [ - "def compute_class_weights(y_train: list[int], num_classes: int) -> torch.Tensor:\n", - " \"\"\"Compute inverse-frequency class weights.\"\"\"\n", - " counts = np.bincount(y_train, minlength=num_classes).astype(float)\n", - " weights = len(y_train) / (num_classes * counts)\n", - " weights = np.clip(weights, 0, 10) # cap extreme weights\n", - " return torch.tensor(weights, dtype=torch.float)\n", - "\n", - "\n", - "def train_epoch(\n", - " model, loader: DataLoader, optimizer, scheduler, criterion, device\n", - ") -> float:\n", - " model.train()\n", - " total_loss = 0.0\n", - " for batch in loader:\n", - " input_ids = batch[\"input_ids\"].to(device)\n", - " attention_mask = batch[\"attention_mask\"].to(device)\n", - " labels = batch[\"labels\"].to(device)\n", - "\n", - " optimizer.zero_grad()\n", - " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", - " logits = outputs.logits\n", - "\n", - " loss = criterion(logits, labels)\n", - " loss.backward()\n", - "\n", - " nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n", - " optimizer.step()\n", - " scheduler.step()\n", - "\n", - " total_loss += loss.item()\n", - " return total_loss / len(loader)\n", - "\n", - "\n", - "@torch.no_grad()\n", - "def eval_epoch(\n", - " model, loader: DataLoader, criterion, device, label_names: list[str]\n", - ") -> tuple[float, dict, list]:\n", - " model.eval()\n", - " total_loss = 0.0\n", - " all_preds, all_labels = [], []\n", - "\n", - " for batch in loader:\n", - " input_ids = batch[\"input_ids\"].to(device)\n", - " attention_mask = batch[\"attention_mask\"].to(device)\n", - " labels = batch[\"labels\"].to(device)\n", - "\n", - " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", - " logits = outputs.logits\n", - " loss = criterion(logits, labels)\n", - "\n", - " total_loss += loss.item()\n", - " preds = torch.argmax(logits, dim=-1)\n", - " all_preds.extend(preds.cpu().numpy())\n", - " all_labels.extend(labels.cpu().numpy())\n", - "\n", - " avg_loss = total_loss / len(loader)\n", - " metrics = {\n", - " \"loss\" : avg_loss,\n", - " \"accuracy\" : accuracy_score(all_labels, all_preds),\n", - " \"f1_macro\" : f1_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", - " \"f1_weighted\" : f1_score(all_labels, all_preds, average=\"weighted\", zero_division=0),\n", - " \"precision_macro\" : precision_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", - " \"recall_macro\" : recall_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", - " }\n", - " return avg_loss, metrics, all_preds\n", - "\n", - "\n", - "def save_confusion_matrix(y_true, y_pred, label_names, out_path, title=\"\"):\n", - " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", - " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", - " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n", - " xticklabels=label_names, yticklabels=label_names, ax=ax)\n", - " ax.set_xlabel(\"Predicted\"); ax.set_ylabel(\"True\"); ax.set_title(title)\n", - " plt.tight_layout()\n", - " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", - " plt.show()\n", - " print(f\"Saved: {out_path}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "d35JFlgzuKCB" - }, - "source": [ - "## Full Training Function" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "id": "wHriQLOyuKCB" - }, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "\n", - "from transformers import get_linear_schedule_with_warmup\n", - "\n", - "def train_bert(\n", - " cfg: TrainConfig,\n", - " X_train: list[str], y_train: list[int],\n", - " X_val: list[str], y_val: list[int],\n", - " X_test: list[str], y_test: list[int],\n", - " label_names: list[str],\n", - " out_dir: Path,\n", - " val_df: pd.DataFrame,\n", - " test_df: pd.DataFrame,\n", - ") -> dict:\n", - " \"\"\"Full training loop with early stopping. Returns test metrics dict.\"\"\"\n", - " out_dir.mkdir(parents=True, exist_ok=True)\n", - " num_labels = len(label_names)\n", - "\n", - " # FIX: define device inside function (self-contained)\n", - " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - " print(f\"Using device: {device}\")\n", - "\n", - " # Save config (default=str avoids Path serialization issues)\n", - " with open(out_dir / \"config.json\", \"w\") as f:\n", - " json.dump(cfg.__dict__, f, indent=2, default=str)\n", - " print(f\"Config saved to {out_dir / 'config.json'}\")\n", - "\n", - " # Tokenizer\n", - " print(f\"Loading tokenizer: {cfg.model_name}\")\n", - " tokenizer = AutoTokenizer.from_pretrained(cfg.model_name)\n", - "\n", - " # Datasets\n", - " train_ds = HeadlineDataset(X_train, y_train, tokenizer, cfg.max_length)\n", - " val_ds = HeadlineDataset(X_val, y_val, tokenizer, cfg.max_length)\n", - " test_ds = HeadlineDataset(X_test, y_test, tokenizer, cfg.max_length)\n", - "\n", - " g = torch.Generator()\n", - " g.manual_seed(cfg.seed)\n", - "\n", - " train_loader = DataLoader(train_ds, batch_size=cfg.batch_size, shuffle=True, generator=g)\n", - " val_loader = DataLoader(val_ds, batch_size=cfg.batch_size, shuffle=False)\n", - " test_loader = DataLoader(test_ds, batch_size=cfg.batch_size, shuffle=False)\n", - "\n", - " # Model\n", - " print(f\"Loading model: {cfg.model_name} ({num_labels} labels)\")\n", - " model = AutoModelForSequenceClassification.from_pretrained(\n", - " cfg.model_name, num_labels=num_labels\n", - " ).to(device)\n", - "\n", - " # Loss (with optional class weights)\n", - " if cfg.use_class_weights:\n", - " cw = compute_class_weights(y_train, num_labels).to(device)\n", - " criterion = nn.CrossEntropyLoss(weight=cw)\n", - " print(f\"Class weights: {cw.detach().cpu().numpy().round(3)}\")\n", - " else:\n", - " criterion = nn.CrossEntropyLoss()\n", - "\n", - " # Optimizer and scheduler\n", - " optimizer = torch.optim.AdamW(\n", - " model.parameters(), lr=cfg.lr, weight_decay=cfg.weight_decay\n", - " )\n", - " total_steps = len(train_loader) * cfg.epochs\n", - " warmup_steps = int(total_steps * cfg.warmup_ratio)\n", - "\n", - " scheduler = get_linear_schedule_with_warmup(\n", - " optimizer, num_warmup_steps=warmup_steps, num_training_steps=total_steps\n", - " )\n", - "\n", - " # Training loop\n", - " best_val_f1 = -1.0\n", - " patience_cnt = 0\n", - " best_epoch = 0\n", - " train_log = []\n", - "\n", - " for epoch in range(1, cfg.epochs + 1):\n", - " train_loss = train_epoch(model, train_loader, optimizer, scheduler, criterion, device)\n", - " _, val_metrics, _ = eval_epoch(model, val_loader, criterion, device, label_names)\n", - "\n", - " val_f1 = float(val_metrics[\"f1_macro\"])\n", - " log_row = {\n", - " \"epoch\": epoch,\n", - " \"train_loss\": float(train_loss),\n", - " **{f\"val_{k}\": float(v) if isinstance(v, (np.floating, float, int, np.integer)) else v\n", - " for k, v in val_metrics.items()}\n", - " }\n", - " train_log.append(log_row)\n", - "\n", - " print(\n", - " f\"Epoch {epoch:2d}/{cfg.epochs} | \"\n", - " f\"train_loss={train_loss:.4f} | \"\n", - " f\"val_loss={val_metrics['loss']:.4f} | \"\n", - " f\"val_acc={val_metrics['accuracy']:.4f} | \"\n", - " f\"val_macro_f1={val_f1:.4f}\"\n", - " )\n", - "\n", - " if val_f1 > best_val_f1:\n", - " best_val_f1 = val_f1\n", - " best_epoch = epoch\n", - " patience_cnt = 0\n", - "\n", - " ckpt_dir = out_dir / \"best_checkpoint\"\n", - " ckpt_dir.mkdir(parents=True, exist_ok=True)\n", - "\n", - " model.save_pretrained(ckpt_dir)\n", - " tokenizer.save_pretrained(ckpt_dir)\n", - " print(f\" ★ New best val macro-F1={val_f1:.4f} — checkpoint saved\")\n", - " else:\n", - " patience_cnt += 1\n", - " if patience_cnt >= cfg.patience:\n", - " print(f\" Early stopping at epoch {epoch} (patience={cfg.patience})\")\n", - " break\n", - "\n", - " # Save training log\n", - " log_df = pd.DataFrame(train_log)\n", - " log_df.to_csv(out_dir / \"training_log.csv\", index=False)\n", - "\n", - " # Plot training curves\n", - " fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", - "\n", - " axes[0].plot(log_df[\"epoch\"], log_df[\"train_loss\"], label=\"Train loss\", marker=\"o\")\n", - " axes[0].plot(log_df[\"epoch\"], log_df[\"val_loss\"], label=\"Val loss\", marker=\"s\")\n", - " axes[0].set_xlabel(\"Epoch\")\n", - " axes[0].set_ylabel(\"Loss\")\n", - " axes[0].set_title(f\"{cfg.model_name} — Loss Curves ({cfg.task})\")\n", - " axes[0].legend()\n", - " axes[0].axvline(best_epoch, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", - "\n", - " axes[1].plot(log_df[\"epoch\"], log_df[\"val_f1_macro\"], label=\"Val macro-F1\", marker=\"o\")\n", - " axes[1].set_xlabel(\"Epoch\")\n", - " axes[1].set_ylabel(\"Macro-F1\")\n", - " axes[1].set_title(f\"{cfg.model_name} — Val Macro-F1 ({cfg.task})\")\n", - " axes[1].legend()\n", - " axes[1].axvline(best_epoch, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", - "\n", - " plt.tight_layout()\n", - " plt.savefig(out_dir / \"training_curves.png\", dpi=150, bbox_inches=\"tight\")\n", - " plt.show()\n", - "\n", - " # Reload best checkpoint and evaluate\n", - " print(f\"\\nLoading best checkpoint (epoch {best_epoch}, val macro-F1={best_val_f1:.4f})\")\n", - " best_model = AutoModelForSequenceClassification.from_pretrained(\n", - " str(out_dir / \"best_checkpoint\")\n", - " ).to(device)\n", - "\n", - " _, val_metrics_best, val_preds = eval_epoch(best_model, val_loader, criterion, device, label_names)\n", - " _, test_metrics_best, test_preds = eval_epoch(best_model, test_loader, criterion, device, label_names)\n", - "\n", - " print(\"\\n=== Val (best checkpoint) ===\")\n", - " print(classification_report(y_val, val_preds, target_names=label_names, zero_division=0))\n", - " print(\"\\n=== Test (best checkpoint) ===\")\n", - " print(classification_report(y_test, test_preds, target_names=label_names, zero_division=0))\n", - "\n", - " # Confusion matrices\n", - " save_confusion_matrix(\n", - " y_val, val_preds, label_names,\n", - " out_dir / \"confusion_matrix_val.png\",\n", - " f\"{cfg.model_name} — {cfg.task} (Val)\"\n", - " )\n", - " save_confusion_matrix(\n", - " y_test, test_preds, label_names,\n", - " out_dir / \"confusion_matrix_test.png\",\n", - " f\"{cfg.model_name} — {cfg.task} (Test)\"\n", - " )\n", - "\n", - " # Convert metrics to native Python types for JSON\n", - " def _to_py(obj):\n", - " if isinstance(obj, dict):\n", - " return {k: _to_py(v) for k, v in obj.items()}\n", - " if isinstance(obj, (list, tuple)):\n", - " return [_to_py(v) for v in obj]\n", - " if isinstance(obj, (np.integer,)):\n", - " return int(obj)\n", - " if isinstance(obj, (np.floating,)):\n", - " return float(obj)\n", - " return obj\n", - "\n", - " results = {\n", - " \"model\": cfg.model_name,\n", - " \"task\": cfg.task,\n", - " \"best_epoch\": int(best_epoch),\n", - " \"best_val_f1_macro\": float(best_val_f1),\n", - " \"val\": _to_py(val_metrics_best),\n", - " \"test\": _to_py(test_metrics_best),\n", - " }\n", - "\n", - " with open(out_dir / \"metrics.json\", \"w\") as f:\n", - " json.dump(results, f, indent=2, default=str)\n", - "\n", - " # Save predictions\n", - " for split_name, df, y_true_list, y_pred_list in [\n", - " (\"val\", val_df, y_val, val_preds),\n", - " (\"test\", test_df, y_test, test_preds),\n", - " ]:\n", - " out_pred = df.copy()\n", - " out_pred[\"predicted\"] = y_pred_list\n", - " out_pred[\"predicted_label\"] = [label_names[i] for i in y_pred_list]\n", - " out_pred[\"correct\"] = (np.array(y_true_list) == np.array(y_pred_list)).astype(int)\n", - " out_pred.to_csv(out_dir / f\"predictions_{split_name}.csv\", index=False)\n", - " print(f\"Saved predictions_{split_name}.csv\")\n", - "\n", - " print(f\"\\n=== DONE: {cfg.model_name} / {cfg.task} ===\")\n", - " print(f\" Test Accuracy : {test_metrics_best['accuracy']:.4f}\")\n", - " print(f\" Test Macro-F1 : {test_metrics_best['f1_macro']:.4f}\")\n", - " print(f\" Test Weighted-F1: {test_metrics_best['f1_weighted']:.4f}\")\n", - "\n", - " return results" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "49JUIiXSuKCB" - }, - "source": [ - "## Load Data" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "-bzRLKhsuKCB", - "outputId": "b3ac3196-fdb3-44d9-8417-26a434c6837a" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Binary — Train: 39,666 Val: 8,500 Test: 8,500\n" - ] - } - ], - "source": [ - "# ── Binary splits ─────────────────────────────────────────────────────────────\n", - "train_bin = pd.read_csv(SPLITS / \"train_binary.csv\")\n", - "val_bin = pd.read_csv(SPLITS / \"val_binary.csv\")\n", - "test_bin = pd.read_csv(SPLITS / \"test_binary.csv\")\n", - "\n", - "X_train_bin = train_bin[\"text\"].tolist(); y_train_bin = train_bin[\"binary_label\"].tolist()\n", - "X_val_bin = val_bin[\"text\"].tolist(); y_val_bin = val_bin[\"binary_label\"].tolist()\n", - "X_test_bin = test_bin[\"text\"].tolist(); y_test_bin = test_bin[\"binary_label\"].tolist()\n", - "\n", - "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", - "\n", - "print(f\"Binary — Train: {len(X_train_bin):,} Val: {len(X_val_bin):,} Test: {len(X_test_bin):,}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OkXIlQAtuKCC", - "outputId": "676ecfa7-874b-4536-9ac3-b8b833e2cfff" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Type — Train: 19,833 Val: 4,250 Test: 4,250\n", - "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n" - ] - } - ], - "source": [ - "# ── Type splits ───────────────────────────────────────────────────────────────\n", - "train_type = pd.read_csv(SPLITS / \"train_type.csv\")\n", - "val_type = pd.read_csv(SPLITS / \"val_type.csv\")\n", - "test_type = pd.read_csv(SPLITS / \"test_type.csv\")\n", - "\n", - "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", - "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", - "id2label = {i: lab for lab, i in label2id.items()}\n", - "\n", - "def enc(df): return [label2id[l] for l in df[\"type_label\"]]\n", - "\n", - "X_train_type = train_type[\"text\"].tolist(); y_train_type = enc(train_type)\n", - "X_val_type = val_type[\"text\"].tolist(); y_val_type = enc(val_type)\n", - "X_test_type = test_type[\"text\"].tolist(); y_test_type = enc(test_type)\n", - "\n", - "print(f\"Type — Train: {len(X_train_type):,} Val: {len(X_val_type):,} Test: {len(X_test_type):,}\")\n", - "print(f\"Strategies: {STRATEGY_LABELS}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "varh9ZaouKCC" - }, - "source": [ - "## Train DistilBERT — Binary Task" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 396, - "referenced_widgets": [ - "8b9080237c194037a2cd1dc711a694d2", - "d0d70eea6290419fb4a39bd74964bacb", - "30e0eec43be54d08a0b5bd855e06d3c3", - "959391d9e6cc405ead852002ce0276ff", - "143c9b378b5a4260b15e328dc744374f", - "384fe210d84b4e8d8bc1cd8b99944d5e", - "2a97e4558e6d41df919bd3ce83576627", - "bfc3291175a14250a407af3dd6376d58", - "01c169be55ca4399b34eb7cdae152075", - "2f42691301be4d01858524488e30fe78", - "8faab66da6c74399a140fa5aad07dbbb" - ] - }, - "id": "Yq8zSNjluKCC", - "outputId": "44080f86-f045-426f-f630-765eb5f599c7" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Using device: cpu\n", - "Config saved to /content/outputs/bert/distilbert_binary/config.json\n", - "Loading tokenizer: distilbert-base-uncased\n", - "Loading model: distilbert-base-uncased (2 labels)\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Loading weights: 0%| | 0/100 [00:00 dict | None:\n", - " if path.exists():\n", - " with open(path) as f:\n", - " return json.load(f)\n", - " print(f\" [WARNING] Not found: {path.relative_to(ROOT)}\")\n", - " return None\n", - "\n", - "\n", - "# All metrics\n", - "tfidf_lr_binary = load_metrics(CLASSICAL / \"tfidf_lr\" / \"metrics_binary.json\")\n", - "tfidf_lr_type = load_metrics(CLASSICAL / \"tfidf_lr\" / \"metrics_type.json\")\n", - "nb_binary = load_metrics(CLASSICAL / \"naive_bayes\" / \"metrics_binary.json\")\n", - "nb_type = load_metrics(CLASSICAL / \"naive_bayes\" / \"metrics_type.json\")\n", - "distilbert_bin = load_metrics(BERT_OUT / \"distilbert_binary\" / \"metrics.json\")\n", - "distilbert_type = load_metrics(BERT_OUT / \"distilbert_type\" / \"metrics.json\")\n", - "bert_base_bin = load_metrics(BERT_OUT / \"bert_base_binary\" / \"metrics.json\") # optional\n", - "bert_base_type = load_metrics(BERT_OUT / \"bert_base_type\" / \"metrics.json\") # optional\n", - "\n", - "print(\"Metrics loaded (None = not yet run):\")\n", - "for name, m in [(\"TF-IDF+LR binary\", tfidf_lr_binary), (\"TF-IDF+LR type\", tfidf_lr_type),\n", - " (\"NB binary\", nb_binary), (\"NB type\", nb_type),\n", - " (\"DistilBERT binary\", distilbert_bin), (\"DistilBERT type\", distilbert_type),\n", - " (\"BERT-base binary\", bert_base_bin), (\"BERT-base type\", bert_base_type)]:\n", - " print(f\" {name}: {'✓' if m else '✗'}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "H_xQm80nuKCE" - }, - "source": [ - "## 2. Model Comparison Tables" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "2RsVxgMtuKCE" - }, - "outputs": [], - "source": [ - "def extract_test_metrics(metrics_dict: dict | None, split: str = \"test\") -> dict:\n", - " \"\"\"Extract test-split metrics from a metrics dict, handling different formats.\"\"\"\n", - " if metrics_dict is None:\n", - " return {\"accuracy\": None, \"f1_macro\": None, \"f1_weighted\": None,\n", - " \"precision_macro\": None, \"recall_macro\": None}\n", - " test_m = metrics_dict.get(split, metrics_dict) # BERT format uses 'test' key directly\n", - " return {\n", - " \"accuracy\" : test_m.get(\"accuracy\"),\n", - " \"f1_macro\" : test_m.get(\"f1_macro\"),\n", - " \"f1_weighted\" : test_m.get(\"f1_weighted\"),\n", - " \"precision_macro\" : test_m.get(\"precision_macro\"),\n", - " \"recall_macro\" : test_m.get(\"recall_macro\"),\n", - " }\n", - "\n", - "\n", - "binary_rows = []\n", - "type_rows = []\n", - "\n", - "model_map_bin = [\n", - " (\"TF-IDF + LR\", tfidf_lr_binary),\n", - " (\"Naive Bayes\", nb_binary),\n", - " (\"DistilBERT\", distilbert_bin),\n", - " (\"BERT-base\", bert_base_bin),\n", - "]\n", - "\n", - "model_map_type = [\n", - " (\"TF-IDF + LR\", tfidf_lr_type),\n", - " (\"Naive Bayes\", nb_type),\n", - " (\"DistilBERT\", distilbert_type),\n", - " (\"BERT-base\", bert_base_type),\n", - "]\n", - "\n", - "for name, m in model_map_bin:\n", - " row = {\"Model\": name}\n", - " row.update(extract_test_metrics(m))\n", - " binary_rows.append(row)\n", - "\n", - "for name, m in model_map_type:\n", - " row = {\"Model\": name}\n", - " row.update(extract_test_metrics(m))\n", - " type_rows.append(row)\n", - "\n", - "binary_comp = pd.DataFrame(binary_rows).set_index(\"Model\")\n", - "type_comp = pd.DataFrame(type_rows).set_index(\"Model\")\n", - "\n", - "print(\"=== BINARY TASK — Test Metrics ===\")\n", - "print(binary_comp.to_string(float_format=lambda x: f\"{x:.4f}\" if x is not None else \"N/A\"))\n", - "print()\n", - "print(\"=== TYPE TASK — Test Metrics ===\")\n", - "print(type_comp.to_string(float_format=lambda x: f\"{x:.4f}\" if x is not None else \"N/A\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "n35eLHCCuKCF" - }, - "outputs": [], - "source": [ - "# ── Comparison bar charts ─────────────────────────────────────────────────────\n", - "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n", - "\n", - "for ax, comp, title in [\n", - " (axes[0], binary_comp, \"Binary Task\"),\n", - " (axes[1], type_comp, \"Type Task\"),\n", - "]:\n", - " valid = comp.dropna(subset=[\"f1_macro\"])\n", - " if len(valid) == 0:\n", - " ax.set_title(f\"{title} — No data\")\n", - " continue\n", - " models = valid.index.tolist()\n", - " f1_vals = valid[\"f1_macro\"].tolist()\n", - " acc_vals = valid[\"accuracy\"].tolist()\n", - " x = range(len(models))\n", - " w = 0.35\n", - " bars1 = ax.bar([i - w/2 for i in x], f1_vals, w, label=\"Macro-F1\", color=\"steelblue\")\n", - " bars2 = ax.bar([i + w/2 for i in x], acc_vals, w, label=\"Accuracy\", color=\"coral\")\n", - " ax.set_xticks(list(x)); ax.set_xticklabels(models, rotation=20, ha=\"right\")\n", - " ax.set_ylim(0, 1.05)\n", - " ax.set_ylabel(\"Score\")\n", - " ax.set_title(f\"{title} — Model Comparison (Test)\", fontsize=13)\n", - " ax.legend()\n", - " for bar in bars1:\n", - " ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n", - " f\"{bar.get_height():.3f}\", ha=\"center\", fontsize=8)\n", - " for bar in bars2:\n", - " ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n", - " f\"{bar.get_height():.3f}\", ha=\"center\", fontsize=8)\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(REPORTS_DIR / \"model_comparison_chart.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "print(\"Saved: outputs/reports/model_comparison_chart.png\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GC0rNp0auKCF" - }, - "source": [ - "## 3. Error Analysis — Binary Task" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "jokvmFh9uKCF" - }, - "outputs": [], - "source": [ - "def load_predictions(path: Path) -> pd.DataFrame | None:\n", - " if path.exists():\n", - " return pd.read_csv(path)\n", - " print(f\" [WARNING] Not found: {path}\")\n", - " return None\n", - "\n", - "\n", - "# Use best available binary model predictions for error analysis\n", - "# Priority: BERT > TF-IDF+LR > NB\n", - "pred_paths_bin = [\n", - " (\"DistilBERT\", BERT_OUT / \"distilbert_binary\" / \"predictions_test.csv\"),\n", - " (\"TF-IDF + LR\", CLASSICAL / \"tfidf_lr\" / \"predictions_test_binary.csv\"),\n", - " (\"Naive Bayes\", CLASSICAL / \"naive_bayes\" / \"predictions_test_binary.csv\"),\n", - "]\n", - "\n", - "error_source_bin = None\n", - "error_model_bin = None\n", - "for model_name, path in pred_paths_bin:\n", - " df = load_predictions(path)\n", - " if df is not None:\n", - " error_source_bin = df\n", - " error_model_bin = model_name\n", - " print(f\"Using {model_name} predictions for binary error analysis\")\n", - " break\n", - "\n", - "if error_source_bin is None:\n", - " print(\"No binary predictions found. Run at least one model first.\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "0qFoK11OuKCF" - }, - "outputs": [], - "source": [ - "if error_source_bin is not None:\n", - " pred_col = \"predicted\" if \"predicted\" in error_source_bin.columns else \"predicted_id\"\n", - " true_col = \"binary_label\"\n", - " err_bin = error_source_bin[error_source_bin[\"correct\"] == 0].copy()\n", - "\n", - " # Categorize\n", - " fp_bin = err_bin[err_bin[true_col] == 0].copy() # False Positives (predicted sarcastic)\n", - " fn_bin = err_bin[err_bin[true_col] == 1].copy() # False Negatives (predicted not-sarcastic)\n", - "\n", - " print(f\"Total binary test errors: {len(err_bin)}\")\n", - " print(f\" False Positives (non-sarcastic predicted as sarcastic) : {len(fp_bin)}\")\n", - " print(f\" False Negatives (sarcastic predicted as non-sarcastic) : {len(fn_bin)}\")\n", - "\n", - " # Select 20+ errors: mix of FP and FN\n", - " n_each = 12\n", - " sample_fp = fp_bin.head(n_each)\n", - " sample_fn = fn_bin.head(n_each)\n", - " error_sample_bin = pd.concat([sample_fp, sample_fn], ignore_index=True)\n", - " error_sample_bin[\"error_type\"] = [\"FP\"] * len(sample_fp) + [\"FN\"] * len(sample_fn)\n", - "\n", - " print(f\"\\nError sample size: {len(error_sample_bin)} examples\")\n", - " print(\"\\n--- False Positives (non-sarcastic → predicted sarcastic) ---\")\n", - " for _, row in sample_fp.iterrows():\n", - " print(f\" {row['text']}\")\n", - "\n", - " print(\"\\n--- False Negatives (sarcastic → predicted non-sarcastic) ---\")\n", - " for _, row in sample_fn.iterrows():\n", - " print(f\" {row['text']}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "gAh7r-JhuKCF" - }, - "outputs": [], - "source": [ - "if error_source_bin is not None:\n", - " # Failure mode categorization (heuristic rules)\n", - " def categorize_binary_error(text: str, error_type: str) -> str:\n", - " text_lower = text.lower()\n", - " if \"?\" in text and error_type == \"FP\":\n", - " return \"rhetorical_phrasing\"\n", - " if any(w in text_lower for w in [\"report\", \"study\", \"survey\", \"finds\", \"shows\"]):\n", - " return \"neutral_reporting_style_confused\"\n", - " if any(w in text_lower for w in [\"best\", \"great\", \"amazing\", \"wonderful\", \"perfect\"]):\n", - " return \"positive_framing_confused\"\n", - " if \"onion\" in text_lower:\n", - " return \"domain_leak\"\n", - " if len(text.split()) <= 5:\n", - " return \"very_short_text\"\n", - " return \"lexical_ambiguity\"\n", - "\n", - " error_sample_bin[\"failure_mode\"] = error_sample_bin.apply(\n", - " lambda r: categorize_binary_error(r[\"text\"], r[\"error_type\"]), axis=1\n", - " )\n", - "\n", - " print(\"Failure mode distribution:\")\n", - " print(error_sample_bin[\"failure_mode\"].value_counts())\n", - "\n", - " # Save\n", - " error_sample_bin.to_csv(REPORTS_DIR / \"error_examples_binary.csv\", index=False)\n", - " print(\"\\nSaved: outputs/reports/error_examples_binary.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3Ybue8H2uKCF" - }, - "source": [ - "## 4. Error Analysis — Type Task" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "-yBp16Z6uKCG" - }, - "outputs": [], - "source": [ - "pred_paths_type = [\n", - " (\"DistilBERT\", BERT_OUT / \"distilbert_type\" / \"predictions_test.csv\"),\n", - " (\"TF-IDF + LR\", CLASSICAL / \"tfidf_lr\" / \"predictions_test_type.csv\"),\n", - " (\"Naive Bayes\", CLASSICAL / \"naive_bayes\" / \"predictions_test_type.csv\"),\n", - "]\n", - "\n", - "error_source_type = None\n", - "error_model_type = None\n", - "for model_name, path in pred_paths_type:\n", - " df = load_predictions(path)\n", - " if df is not None:\n", - " error_source_type = df\n", - " error_model_type = model_name\n", - " print(f\"Using {model_name} predictions for type error analysis\")\n", - " break\n", - "\n", - "if error_source_type is None:\n", - " print(\"No type predictions found.\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "r21XN805uKCG" - }, - "outputs": [], - "source": [ - "if error_source_type is not None:\n", - " err_type = error_source_type[error_source_type[\"correct\"] == 0].copy()\n", - "\n", - " # Identify label columns\n", - " true_col_type = \"type_label\"\n", - " pred_col_type = \"predicted_label\" if \"predicted_label\" in err_type.columns else \"predicted\"\n", - "\n", - " print(f\"Total type errors: {len(err_type)}\")\n", - " print(\"\\nTop confusion pairs (true → predicted):\")\n", - " if pred_col_type in err_type.columns:\n", - " pairs = err_type.groupby([true_col_type, pred_col_type]).size().sort_values(ascending=False).head(15)\n", - " print(pairs.to_string())\n", - "\n", - " # Sample 20+ errors\n", - " error_sample_type = err_type.sample(min(25, len(err_type)), random_state=42)\n", - "\n", - " print(f\"\\nError sample size: {len(error_sample_type)} examples\")\n", - " print(\"\\n--- Sample type misclassifications ---\")\n", - " display_cols = [\"text\", true_col_type]\n", - " if pred_col_type in error_sample_type.columns:\n", - " display_cols.append(pred_col_type)\n", - " for _, row in error_sample_type.head(15).iterrows():\n", - " true_l = row[true_col_type]\n", - " pred_l = row.get(pred_col_type, \"?\")\n", - " print(f\" [True={true_l:20s} Pred={pred_l:20s}] {row['text'][:90]}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "IzgDVYhfuKCG" - }, - "outputs": [], - "source": [ - "if error_source_type is not None:\n", - " def categorize_type_error(text: str, true_label: str, pred_label: str) -> str:\n", - " \"\"\"Heuristic failure mode categorization for type task.\"\"\"\n", - " text_lower = text.lower()\n", - " # Strategy overlap: sarcasm ↔ irony ↔ satire are frequently confused\n", - " overlap_pairs = {\n", - " frozenset({\"sarcasm\", \"irony\"}),\n", - " frozenset({\"sarcasm\", \"satire\"}),\n", - " frozenset({\"irony\", \"satire\"}),\n", - " }\n", - " if frozenset({true_label, pred_label}) in overlap_pairs:\n", - " return \"strategy_semantic_overlap\"\n", - " if \"rhetorical_question\" in [true_label, pred_label]:\n", - " if \"?\" in text:\n", - " return \"rhetorical_vs_other_with_question\"\n", - " return \"rhetorical_without_question_mark\"\n", - " if true_label in [\"overstatement\", \"understatement\"] and pred_label in [\"sarcasm\", \"irony\"]:\n", - " return \"scale_confusion_with_sarcasm\"\n", - " if \"report\" in text_lower or \"according\" in text_lower:\n", - " return \"generation_artifact_formal_phrasing\"\n", - " return \"lexical_ambiguity\"\n", - "\n", - " if pred_col_type in error_sample_type.columns:\n", - " error_sample_type[\"failure_mode\"] = error_sample_type.apply(\n", - " lambda r: categorize_type_error(r[\"text\"], r[true_col_type], r[pred_col_type]),\n", - " axis=1\n", - " )\n", - " print(\"Failure mode distribution:\")\n", - " print(error_sample_type[\"failure_mode\"].value_counts())\n", - "\n", - " error_sample_type.to_csv(REPORTS_DIR / \"error_examples_type.csv\", index=False)\n", - " print(\"\\nSaved: outputs/reports/error_examples_type.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NEmnBPTtuKCG" - }, - "source": [ - "## 5. Generate Final Reports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "xH7naLCouKCG" - }, - "outputs": [], - "source": [ - "def fmt(v) -> str:\n", - " \"\"\"Format a metric value for markdown.\"\"\"\n", - " if v is None:\n", - " return \"N/A\"\n", - " return f\"{v:.4f}\"\n", - "\n", - "\n", - "def build_model_comparison_md() -> str:\n", - " lines = []\n", - " lines.append(\"# Model Comparison Report\")\n", - " lines.append(\"\")\n", - " lines.append(\"> Auto-generated from outputs of notebooks 02–04.\")\n", - " lines.append(\"\")\n", - "\n", - " # Binary task table\n", - " lines.append(\"## Task A — Binary Classification (Test Set)\")\n", - " lines.append(\"\")\n", - " lines.append(\"| Model | Accuracy | Precision (M) | Recall (M) | F1 (Macro) | F1 (Weighted) |\")\n", - " lines.append(\"|-------|----------|--------------|------------|------------|--------------|\")\n", - " for name, m in model_map_bin:\n", - " r = extract_test_metrics(m)\n", - " lines.append(\n", - " f\"| {name} | {fmt(r['accuracy'])} | {fmt(r['precision_macro'])} | \"\n", - " f\"{fmt(r['recall_macro'])} | {fmt(r['f1_macro'])} | {fmt(r['f1_weighted'])} |\"\n", - " )\n", - " lines.append(\"\")\n", - " lines.append(\"_Primary metric: Macro-F1. Best value bolded (manually after review)._\")\n", - " lines.append(\"\")\n", - "\n", - " # Type task table\n", - " lines.append(\"## Task B — Sarcasm Type Classification (Test Set)\")\n", - " lines.append(\"\")\n", - " lines.append(\"| Model | Accuracy | Precision (M) | Recall (M) | F1 (Macro) | F1 (Weighted) |\")\n", - " lines.append(\"|-------|----------|--------------|------------|------------|--------------|\")\n", - " for name, m in model_map_type:\n", - " r = extract_test_metrics(m)\n", - " lines.append(\n", - " f\"| {name} | {fmt(r['accuracy'])} | {fmt(r['precision_macro'])} | \"\n", - " f\"{fmt(r['recall_macro'])} | {fmt(r['f1_macro'])} | {fmt(r['f1_weighted'])} |\"\n", - " )\n", - " lines.append(\"\")\n", - "\n", - " # Recommendation\n", - " lines.append(\"## Recommendation\")\n", - " lines.append(\"\")\n", - "\n", - " # Find best binary model\n", - " best_bin_name, best_bin_f1 = \"N/A\", -1\n", - " for name, m in model_map_bin:\n", - " r = extract_test_metrics(m)\n", - " if r[\"f1_macro\"] is not None and r[\"f1_macro\"] > best_bin_f1:\n", - " best_bin_f1 = r[\"f1_macro\"]\n", - " best_bin_name = name\n", - "\n", - " best_type_name, best_type_f1 = \"N/A\", -1\n", - " for name, m in model_map_type:\n", - " r = extract_test_metrics(m)\n", - " if r[\"f1_macro\"] is not None and r[\"f1_macro\"] > best_type_f1:\n", - " best_type_f1 = r[\"f1_macro\"]\n", - " best_type_name = name\n", - "\n", - " lines.append(f\"### Best model for Binary Task: **{best_bin_name}** (Macro-F1 = {fmt(best_bin_f1)})\")\n", - " lines.append(\"\")\n", - " lines.append(f\"### Best model for Type Task: **{best_type_name}** (Macro-F1 = {fmt(best_type_f1)})\")\n", - " lines.append(\"\")\n", - " lines.append(\"### Trade-offs\")\n", - " lines.append(\"\")\n", - " lines.append(\"| Aspect | TF-IDF + LR | Naive Bayes | DistilBERT |\")\n", - " lines.append(\"|--------|------------|-------------|-----------|\")\n", - " lines.append(\"| Training speed | Fast (minutes) | Very fast (seconds) | Slow (hours) |\")\n", - " lines.append(\"| Inference speed | Very fast | Very fast | Moderate |\")\n", - " lines.append(\"| Interpretability | High (feature weights) | High (log-probs) | Low (black-box) |\")\n", - " lines.append(\"| Handles context | No | No | Yes (self-attention) |\")\n", - " lines.append(\"| Handles rare words | Via TF-IDF | Via smoothing | Via sub-word tokenization |\")\n", - " lines.append(\"| GPU required | No | No | Recommended |\")\n", - " lines.append(\"\")\n", - " lines.append(\"**Deployment recommendation**: Use TF-IDF+LR if real-time speed and interpretability are priorities.\")\n", - " lines.append(\"Use DistilBERT for maximum accuracy when GPU inference is available.\")\n", - "\n", - " return \"\\n\".join(lines)\n", - "\n", - "\n", - "comparison_md = build_model_comparison_md()\n", - "with open(REPORTS_DIR / \"model_comparison.md\", \"w\", encoding=\"utf-8\") as f:\n", - " f.write(comparison_md)\n", - "\n", - "print(\"Saved: outputs/reports/model_comparison.md\")\n", - "print(\"\\nPreview:\")\n", - "print(comparison_md[:2000])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "CmgZF4S1uKCH" - }, - "outputs": [], - "source": [ - "def build_error_analysis_md() -> str:\n", - " lines = []\n", - " lines.append(\"# Error Analysis Report\")\n", - " lines.append(\"\")\n", - " lines.append(\"> Auto-generated from model predictions. Examples drawn from test set.\")\n", - " lines.append(\"\")\n", - "\n", - " # ── Binary ────────────────────────────────────────────────────────────────\n", - " lines.append(\"## Task A — Binary Classification Errors\")\n", - " lines.append(\"\")\n", - "\n", - " if error_source_bin is not None:\n", - " err_df = error_source_bin[error_source_bin[\"correct\"] == 0]\n", - " n_fp = len(err_df[err_df[\"binary_label\"] == 0])\n", - " n_fn = len(err_df[err_df[\"binary_label\"] == 1])\n", - " lines.append(f\"**Model**: {error_model_bin} | **Total errors**: {len(err_df):,} | FP: {n_fp:,} | FN: {n_fn:,}\")\n", - " lines.append(\"\")\n", - " lines.append(\"### Failure Mode Summary\")\n", - " lines.append(\"\")\n", - " lines.append(\"| Failure Mode | Description |\")\n", - " lines.append(\"|-------------|-------------|\")\n", - " lines.append(\"| Lexical ambiguity | Sarcasm/irony requires pragmatic context beyond lexical cues |\")\n", - " lines.append(\"| Neutral reporting style confused | Formal generated text mimics neutral news, but still classified as sarcastic |\")\n", - " lines.append(\"| Positive framing confused | Genuine positive news shares superlative vocabulary with ironic praise |\")\n", - " lines.append(\"| Rhetorical phrasing | Questions that look rhetorical but are literal |\")\n", - " lines.append(\"| Very short text | Too little context for confident classification |\")\n", - " lines.append(\"\")\n", - " lines.append(\"### False Positive Examples (Non-sarcastic predicted as sarcastic)\")\n", - " lines.append(\"\")\n", - " fp_examples = error_source_bin[\n", - " (error_source_bin[\"correct\"] == 0) & (error_source_bin[\"binary_label\"] == 0)\n", - " ].head(10)\n", - " for _, row in fp_examples.iterrows():\n", - " lines.append(f\"- `{row['text']}`\")\n", - " lines.append(\"\")\n", - " lines.append(\"### False Negative Examples (Sarcastic predicted as non-sarcastic)\")\n", - " lines.append(\"\")\n", - " fn_examples = error_source_bin[\n", - " (error_source_bin[\"correct\"] == 0) & (error_source_bin[\"binary_label\"] == 1)\n", - " ].head(10)\n", - " for _, row in fn_examples.iterrows():\n", - " lines.append(f\"- `{row['text']}`\")\n", - " else:\n", - " lines.append(\"_Binary predictions not available. Run at least one model._\")\n", - "\n", - " lines.append(\"\")\n", - "\n", - " # ── Type ──────────────────────────────────────────────────────────────────\n", - " lines.append(\"## Task B — Sarcasm Type Classification Errors\")\n", - " lines.append(\"\")\n", - "\n", - " if error_source_type is not None:\n", - " err_df_t = error_source_type[error_source_type[\"correct\"] == 0]\n", - " lines.append(f\"**Model**: {error_model_type} | **Total errors**: {len(err_df_t):,}\")\n", - " lines.append(\"\")\n", - " lines.append(\"### Failure Mode Summary\")\n", - " lines.append(\"\")\n", - " lines.append(\"| Failure Mode | Description |\")\n", - " lines.append(\"|-------------|-------------|\")\n", - " lines.append(\"| Strategy semantic overlap | sarcasm/irony/satire share similar surface forms; labels conflated |\")\n", - " lines.append(\"| Scale confusion | overstatement/understatement confused with sarcasm due to exaggeration cues |\")\n", - " lines.append(\"| Rhetorical vs. other | rhetorical_question confused with irony/sarcasm when ? is absent |\")\n", - " lines.append(\"| Generation artifact | formal paraphrase style shifts text away from original strategy markers |\")\n", - " lines.append(\"| Lexical ambiguity | strategy relies on world knowledge rather than surface vocabulary |\")\n", - " lines.append(\"\")\n", - " lines.append(\"### Key Insight\")\n", - " lines.append(\"\")\n", - " lines.append(\"The primary failure mode is **strategy semantic overlap**: sarcasm, irony, and satire\")\n", - " lines.append(\"are conceptually related and frequently share similar linguistic surface patterns.\")\n", - " lines.append(\"The label `sarcasm` as a strategy within a sarcasm dataset introduces circularity.\")\n", - " lines.append(\"The `rhetorical_question` class is syntactically distinctive (ends with ?) and should\")\n", - " lines.append(\"be easier to classify; errors in this class suggest the classifier may ignore punctuation.\")\n", - " lines.append(\"\")\n", - " lines.append(\"### Sample Misclassifications\")\n", - " lines.append(\"\")\n", - " sample_t = error_source_type[error_source_type[\"correct\"] == 0].head(20)\n", - " true_c = \"type_label\"\n", - " pred_c = \"predicted_label\" if \"predicted_label\" in sample_t.columns else \"predicted\"\n", - " for _, row in sample_t.iterrows():\n", - " true_l = row[true_c]\n", - " pred_l = row.get(pred_c, \"?\")\n", - " lines.append(f\"- **True**: {true_l} | **Pred**: {pred_l} | `{row['text'][:100]}`\")\n", - " else:\n", - " lines.append(\"_Type predictions not available. Run at least one model._\")\n", - "\n", - " lines.append(\"\")\n", - " lines.append(\"## Summary of Observations\")\n", - " lines.append(\"\")\n", - " lines.append(\"1. **Binary task** is relatively tractable — TheOnion writing style has strong lexical signatures\")\n", - " lines.append(\"2. **Generated headlines** (is_generated=1) may fool classifiers trained mainly on original text\")\n", - " lines.append(\"3. **Type task** is fundamentally harder because strategies are not mutually exclusive\")\n", - " lines.append(\"4. **Class imbalance** (rhetorical_question = 3.7%) is a significant challenge\")\n", - " lines.append(\"5. **BERT** should better capture contextual incongruence; classical models rely on surface vocabulary\")\n", - "\n", - " return \"\\n\".join(lines)\n", - "\n", - "\n", - "error_md = build_error_analysis_md()\n", - "with open(REPORTS_DIR / \"error_analysis.md\", \"w\", encoding=\"utf-8\") as f:\n", - " f.write(error_md)\n", - "\n", - "print(\"Saved: outputs/reports/error_analysis.md\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "fdUb27BQuKCH" - }, - "outputs": [], - "source": [ - "print(\"====== ERROR ANALYSIS COMPLETE ======\")\n", - "print()\n", - "print(\"Saved artifacts:\")\n", - "for p in sorted(REPORTS_DIR.iterdir()):\n", - " print(f\" {p.relative_to(ROOT)}\")\n", - "print()\n", - "print(\"Pipeline complete. See outputs/reports/model_comparison.md for final results.\")" - ] - } - ] -} \ No newline at end of file From 089982f2db3da5591d4036ffb270eac272f16290 Mon Sep 17 00:00:00 2001 From: reallyeasy Date: Tue, 3 Mar 2026 23:56:26 +0800 Subject: [PATCH 6/6] Feat: Finish evaluation of transformers and fix character issue in main ipynb --- colab_package/sarcasm_classification.ipynb | 22478 +++++++++++++++---- 1 file changed, 18050 insertions(+), 4428 deletions(-) diff --git a/colab_package/sarcasm_classification.ipynb b/colab_package/sarcasm_classification.ipynb index 31b7923..a701cf4 100644 --- a/colab_package/sarcasm_classification.ipynb +++ b/colab_package/sarcasm_classification.ipynb @@ -1,4576 +1,18198 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.11.0" - }, - "colab": { - "provenance": [], - "machine_shape": "hm" - }, - "widgets": { - 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"# Sarcasm Classification — Complete Pipeline\n", - "\n", - "**Sections**:\n", - "1. Setup & Data Preparation\n", - "2. TF-IDF + Logistic Regression Baseline\n", - "3. Naive Bayes Baseline\n", - "4. BERT / DistilBERT Classification\n", - "5. Error Analysis & Model Comparison\n", - "\n", - "**Run all cells in order (Runtime → Run all).**" - ] + }, + "accelerator": "GPU" }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "w41uXUSeuKBO" + }, + "source": [ + "# Sarcasm Classification — Complete Pipeline\n", + "\n", + "**Sections**:\n", + "1. Setup & Data Preparation\n", + "2. TF-IDF + Logistic Regression Baseline\n", + "3. Naive Bayes Baseline\n", + "4. BERT / DistilBERT Classification\n", + "5. Error Analysis & Model Comparison\n", + "\n", + "**Run all cells in order (Runtime → Run all).**" + ] }, - "id": "1xG7cJfAuKBR", - "outputId": "12069dde-b810-4ab5-f89b-5fa1af05cf8c" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "cwd: /content\n", - "files in cwd: ['.config', 'sarcasm_pairs_step35_clean.jsonl', 'sample_data']\n", - "Data : /content/sarcasm_pairs_step35_clean.jsonl\n", - "Root : /content\n", - "Output : /content/outputs\n" - ] - } - ], - "source": [ - "# ============================================================\n", - "# SETUP — imports, file upload, paths\n", - "# ============================================================\n", - "from __future__ import annotations\n", - "import json, hashlib, random, os, warnings, shutil\n", - "from dataclasses import dataclass\n", - "from pathlib import Path\n", - "from collections import Counter\n", - "from urllib.parse import urlparse\n", - "from typing import Optional\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.gridspec as gridspec\n", - "import seaborn as sns\n", - "\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.naive_bayes import MultinomialNB, ComplementNB\n", - "from sklearn.model_selection import GridSearchCV, GroupKFold\n", - "from sklearn.metrics import (\n", - " accuracy_score, precision_score, recall_score,\n", - " f1_score, classification_report, confusion_matrix,\n", - ")\n", - "\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "SEED = 42\n", - "random.seed(SEED)\n", - "np.random.seed(SEED)\n", - "\n", - "# ── Locate or upload the JSONL data file ─────────────────────────────────\n", - "FILENAME = \"sarcasm_pairs_step35_clean.jsonl\"\n", - "\n", - "def _locate_file(filename):\n", - " candidates = []\n", - " for root in [Path.cwd()] + list(Path.cwd().parents):\n", - " for sub in [\n", - " Path(\"data\") / \"processed\" / filename,\n", - " Path(\"data\") / filename,\n", - " Path(filename),\n", - " ]:\n", - " candidates.append(root / sub)\n", - " for p in [\n", - " Path(\"/content\") / filename,\n", - " Path(\"/mnt/data\") / filename,\n", - " ]:\n", - " candidates.append(p)\n", - " _c = Path('/content')\n", - " for p in (_c.rglob(filename) if _c.exists() else []):\n", - " candidates.append(p)\n", - " for p in candidates:\n", - " if p.is_file():\n", - " return p\n", - " return None\n", - "\n", - "print(f'cwd: {Path.cwd()}')\n", - "print(f'files in cwd: {[p.name for p in Path.cwd().iterdir()][:10]}')\n", - "\n", - "DATA_FILE = _locate_file(FILENAME)\n", - "if DATA_FILE is None:\n", - " try:\n", - " from google.colab import files as _cf\n", - " print(f\"Upload {FILENAME!r}:\")\n", - " _up = _cf.upload()\n", - " if not _up:\n", - " raise RuntimeError(\"No file uploaded.\")\n", - " _name = list(_up.keys())[0]\n", - " DATA_FILE = Path(\"/content\") / FILENAME\n", - " if Path(_name) != DATA_FILE:\n", - " shutil.move(_name, str(DATA_FILE))\n", - " print(f\"Saved to {DATA_FILE}\")\n", - " except ImportError:\n", - " raise FileNotFoundError(\n", - " f\"Cannot find {FILENAME!r}. Place it in the same folder as this notebook.\"\n", - " )\n", - "\n", - "# ── Project root + all output directories ────────────────────────────────\n", - "def _find_root(data_file):\n", - " for parent in [data_file.parent] + list(data_file.parents):\n", - " if any((parent / m).exists() for m in [\"outputs\",\"notebooks\",\"data\"]):\n", - " return parent\n", - " return data_file.parent\n", - "\n", - "ROOT = _find_root(DATA_FILE)\n", - "\n", - "OUT_DATASETS = ROOT / 'outputs' / 'datasets'\n", - "OUT_SPLITS = ROOT / 'outputs' / 'splits'\n", - "OUT_TFIDF = ROOT / 'outputs' / 'classical' / 'tfidf_lr'\n", - "OUT_NB = ROOT / 'outputs' / 'classical' / 'naive_bayes'\n", - "BERT_OUT = ROOT / 'outputs' / 'bert'\n", - "REPORTS_DIR = ROOT / 'outputs' / 'reports'\n", - "SPLITS = OUT_SPLITS\n", - "\n", - "for d in [OUT_DATASETS, OUT_SPLITS, OUT_TFIDF, OUT_NB, BERT_OUT, REPORTS_DIR]:\n", - " d.mkdir(parents=True, exist_ok=True)\n", - "\n", - "print(f\"Data : {DATA_FILE}\")\n", - "print(f\"Root : {ROOT}\")\n", - "print(f\"Output : {ROOT / 'outputs'}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xDqObfuPuKBU" - }, - "source": [ - "---\n", - "# Part 1 — Data Preparation" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RVi4jvpmuKBV" - }, - "source": [ - "# Notebook 01 — Data Preparation\n", - "\n", - "**Purpose**: Parse JSONL dataset, derive binary and type labels, perform audit checks, generate group-safe train/val/test splits.\n", - "\n", - "**Outputs**:\n", - "- `outputs/datasets/binary_dataset.csv`\n", - "- `outputs/datasets/type_dataset.csv`\n", - "- `outputs/splits/train_binary.csv`, `val_binary.csv`, `test_binary.csv`\n", - "- `outputs/splits/train_type.csv`, `val_type.csv`, `test_type.csv`\n", - "- `outputs/splits/split_metadata.json`" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "egvV3LLJuKBV" - }, - "source": [ - "## 1. Load and Validate Raw Data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ktuzJGUPWUPg", + "outputId": "f86ec43d-61d6-4898-f292-16f5bb1b0843" + }, + "execution_count": 57, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ] }, - "id": "oqhoOTiguKBV", - "outputId": "9211b95a-f157-40ce-9d18-7275e0c157a3" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Loaded rows : 28,333\n", - "Error rows : 0\n" - ] - } - ], - "source": [ - "REQUIRED_FIELDS = {\"original_headline\", \"generated_headline\", \"strategy\", \"type\", \"model_used\", \"article_link\"}\n", - "ALLOWED_TYPES = {\"sarcastic_to_non\", \"non_to_sarcastic\"}\n", - "ALLOWED_STRATS = {\"irony\", \"overstatement\", \"rhetorical_question\", \"sarcasm\", \"satire\", \"understatement\"}\n", - "\n", - "raw_rows = []\n", - "errors = []\n", - "\n", - "with open(DATA_FILE, \"r\", encoding=\"utf-8\") as f:\n", - " for line_num, line in enumerate(f):\n", - " line = line.strip()\n", - " if not line:\n", - " continue\n", - " try:\n", - " row = json.loads(line)\n", - " except json.JSONDecodeError as e:\n", - " errors.append((line_num, f\"JSON parse error: {e}\"))\n", - " continue\n", - "\n", - " # Schema check\n", - " missing = REQUIRED_FIELDS - set(row.keys())\n", - " if missing:\n", - " errors.append((line_num, f\"Missing fields: {missing}\"))\n", - " continue\n", - "\n", - " # Value checks\n", - " if row[\"type\"] not in ALLOWED_TYPES:\n", - " errors.append((line_num, f\"Unknown type: {row['type']!r}\"))\n", - " continue\n", - " if row[\"strategy\"] not in ALLOWED_STRATS:\n", - " errors.append((line_num, f\"Unknown strategy: {row['strategy']!r}\"))\n", - " continue\n", - "\n", - " row[\"_line_num\"] = line_num\n", - " raw_rows.append(row)\n", - "\n", - "print(f\"Loaded rows : {len(raw_rows):,}\")\n", - "print(f\"Error rows : {len(errors)}\")\n", - "if errors:\n", - " for ln, msg in errors[:5]:\n", - " print(f\" Line {ln}: {msg}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yvg9qFhouKBW" - }, - "source": [ - "## 2. Dataset Audit" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1xG7cJfAuKBR", + "outputId": "e1766c0e-2d26-417f-b495-894c0868a710" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "cwd: /content\n", + "files in cwd: ['.config', 'sarcasm_pairs_step35_clean.jsonl', 'sample_data']\n", + "Data : /content/sarcasm_pairs_step35_clean.jsonl\n", + "Root : /content\n", + "Output : /content/outputs\n" + ] + } + ], + "source": [ + "# ============================================================\n", + "# SETUP — imports, file upload, paths\n", + "# ============================================================\n", + "from __future__ import annotations\n", + "import json, hashlib, random, os, warnings, shutil\n", + "from dataclasses import dataclass\n", + "from pathlib import Path\n", + "from collections import Counter\n", + "from urllib.parse import urlparse\n", + "from typing import Optional\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "import seaborn as sns\n", + "\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.naive_bayes import MultinomialNB, ComplementNB\n", + "from sklearn.model_selection import GridSearchCV, GroupKFold\n", + "from sklearn.metrics import (\n", + " accuracy_score, precision_score, recall_score,\n", + " f1_score, classification_report, confusion_matrix,\n", + ")\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "SEED = 42\n", + "random.seed(SEED)\n", + "np.random.seed(SEED)\n", + "\n", + "# ── Locate or upload the JSONL data file ─────────────────────────────────\n", + "FILENAME = \"sarcasm_pairs_step35_clean.jsonl\"\n", + "\n", + "def _locate_file(filename):\n", + " candidates = []\n", + " for root in [Path.cwd()] + list(Path.cwd().parents):\n", + " for sub in [\n", + " Path(\"data\") / \"processed\" / filename,\n", + " Path(\"data\") / filename,\n", + " Path(filename),\n", + " ]:\n", + " candidates.append(root / sub)\n", + " for p in [\n", + " Path(\"/content\") / filename,\n", + " Path(\"/mnt/data\") / filename,\n", + " ]:\n", + " candidates.append(p)\n", + " _c = Path('/content')\n", + " for p in (_c.rglob(filename) if _c.exists() else []):\n", + " candidates.append(p)\n", + " for p in candidates:\n", + " if p.is_file():\n", + " return p\n", + " return None\n", + "\n", + "print(f'cwd: {Path.cwd()}')\n", + "print(f'files in cwd: {[p.name for p in Path.cwd().iterdir()][:10]}')\n", + "\n", + "DATA_FILE = _locate_file(FILENAME)\n", + "if DATA_FILE is None:\n", + " try:\n", + " from google.colab import files as _cf\n", + " print(f\"Upload {FILENAME!r}:\")\n", + " _up = _cf.upload()\n", + " if not _up:\n", + " raise RuntimeError(\"No file uploaded.\")\n", + " _name = list(_up.keys())[0]\n", + " DATA_FILE = Path(\"/content\") / FILENAME\n", + " if Path(_name) != DATA_FILE:\n", + " shutil.move(_name, str(DATA_FILE))\n", + " print(f\"Saved to {DATA_FILE}\")\n", + " except ImportError:\n", + " raise FileNotFoundError(\n", + " f\"Cannot find {FILENAME!r}. Place it in the same folder as this notebook.\"\n", + " )\n", + "\n", + "# ── Project root + all output directories ────────────────────────────────\n", + "def _find_root(data_file):\n", + " for parent in [data_file.parent] + list(data_file.parents):\n", + " if any((parent / m).exists() for m in [\"outputs\",\"notebooks\",\"data\"]):\n", + " return parent\n", + " return data_file.parent\n", + "\n", + "ROOT = _find_root(DATA_FILE)\n", + "\n", + "OUT_DATASETS = ROOT / 'outputs' / 'datasets'\n", + "OUT_SPLITS = ROOT / 'outputs' / 'splits'\n", + "OUT_TFIDF = ROOT / 'outputs' / 'classical' / 'tfidf_lr'\n", + "OUT_NB = ROOT / 'outputs' / 'classical' / 'naive_bayes'\n", + "BERT_OUT = ROOT / 'outputs' / 'bert'\n", + "REPORTS_DIR = ROOT / 'outputs' / 'reports'\n", + "SPLITS = OUT_SPLITS\n", + "\n", + "for d in [OUT_DATASETS, OUT_SPLITS, OUT_TFIDF, OUT_NB, BERT_OUT, REPORTS_DIR]:\n", + " d.mkdir(parents=True, exist_ok=True)\n", + "\n", + "print(f\"Data : {DATA_FILE}\")\n", + "print(f\"Root : {ROOT}\")\n", + "print(f\"Output : {ROOT / 'outputs'}\")\n" + ] }, - "id": "KISNWAQhuKBW", - "outputId": "e8755fd1-8661-4393-f3a9-429cb93065fd" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "=== Type distribution ===\n", - " non_to_sarcastic: 14,925 (52.7%)\n", - " sarcastic_to_non: 13,408 (47.3%)\n", - "\n", - "=== Strategy distribution ===\n", - " sarcasm: 8,699 (30.7%)\n", - " irony: 6,102 (21.5%)\n", - " satire: 5,224 (18.4%)\n", - " overstatement: 3,976 (14.0%)\n", - " understatement: 3,295 (11.6%)\n", - " rhetorical_question: 1,037 (3.7%)\n", - "\n", - "=== Model distribution ===\n", - " stepfun/step-3.5-flash:free: 28,333\n" - ] - } - ], - "source": [ - "# ── Type and Strategy distributions ──────────────────────────────────────────\n", - "type_counts = Counter(r[\"type\"] for r in raw_rows)\n", - "strategy_counts = Counter(r[\"strategy\"] for r in raw_rows)\n", - "model_counts = Counter(r[\"model_used\"] for r in raw_rows)\n", - "\n", - "print(\"=== Type distribution ===\")\n", - "for k, v in type_counts.most_common():\n", - " print(f\" {k}: {v:,} ({v/len(raw_rows)*100:.1f}%)\")\n", - "\n", - "print(\"\\n=== Strategy distribution ===\")\n", - "for k, v in strategy_counts.most_common():\n", - " print(f\" {k}: {v:,} ({v/len(raw_rows)*100:.1f}%)\")\n", - "\n", - "print(\"\\n=== Model distribution ===\")\n", - "for k, v in model_counts.most_common():\n", - " print(f\" {k}: {v:,}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "xDqObfuPuKBU" + }, + "source": [ + "---\n", + "# Part 1 — Data Preparation" + ] }, - "id": "28wpsTTnuKBX", - "outputId": "bdcc2561-9baa-41a3-bac8-14101d7e4596" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Duplicate original_headlines : 70\n", - "Duplicate generated_headlines: 1\n", - "Duplicate article_links : 2\n" - ] - } - ], - "source": [ - "# ── Duplicate checks ──────────────────────────────────────────────────────────\n", - "orig_headlines = [r[\"original_headline\"] for r in raw_rows]\n", - "gen_headlines = [r[\"generated_headline\"] for r in raw_rows]\n", - "article_links = [r[\"article_link\"] for r in raw_rows]\n", - "\n", - "dup_orig = len(orig_headlines) - len(set(orig_headlines))\n", - "dup_gen = len(gen_headlines) - len(set(gen_headlines))\n", - "dup_article = len(article_links) - len(set(article_links))\n", - "\n", - "print(f\"Duplicate original_headlines : {dup_orig}\")\n", - "print(f\"Duplicate generated_headlines: {dup_gen}\")\n", - "print(f\"Duplicate article_links : {dup_article}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "RVi4jvpmuKBV" + }, + "source": [ + "# Notebook 01 — Data Preparation\n", + "\n", + "**Purpose**: Parse JSONL dataset, derive binary and type labels, perform audit checks, generate group-safe train/val/test splits.\n", + "\n", + "**Outputs**:\n", + "- `outputs/datasets/binary_dataset.csv`\n", + "- `outputs/datasets/type_dataset.csv`\n", + "- `outputs/splits/train_binary.csv`, `val_binary.csv`, `test_binary.csv`\n", + "- `outputs/splits/train_type.csv`, `val_type.csv`, `test_type.csv`\n", + "- `outputs/splits/split_metadata.json`" + ] }, - "id": "SLUFPDIHuKBX", - "outputId": "3d7b15de-a43f-410f-a381-6a9544171b45" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "original_headline : 0 nulls, 0 empty strings\n", - "generated_headline : 0 nulls, 0 empty strings\n", - "strategy : 0 nulls, 0 empty strings\n", - "type : 0 nulls, 0 empty strings\n", - "model_used : 0 nulls, 0 empty strings\n", - "article_link : 0 nulls, 0 empty strings\n" - ] - } - ], - "source": [ - "# ── Null / empty checks ───────────────────────────────────────────────────────\n", - "for field in [\"original_headline\", \"generated_headline\", \"strategy\", \"type\", \"model_used\", \"article_link\"]:\n", - " null_count = sum(1 for r in raw_rows if r.get(field) is None)\n", - " empty_count = sum(1 for r in raw_rows if r.get(field) == \"\")\n", - " print(f\"{field:25s}: {null_count} nulls, {empty_count} empty strings\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 524 + "cell_type": "markdown", + "metadata": { + "id": "egvV3LLJuKBV" + }, + "source": [ + "## 1. Load and Validate Raw Data" + ] }, - "id": "E-B0fc5vuKBX", - "outputId": "a7845ca8-655c-48df-c59b-28b16307b91d" - }, - "outputs": [ { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oqhoOTiguKBV", + "outputId": "8b20c077-79dd-4430-f545-22735bcaab54" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loaded rows : 28,333\n", + "Error rows : 0\n" + ] + } ], - "image/png": 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- }, - "metadata": {} + "source": [ + "REQUIRED_FIELDS = {\"original_headline\", \"generated_headline\", \"strategy\", \"type\", \"model_used\", \"article_link\"}\n", + "ALLOWED_TYPES = {\"sarcastic_to_non\", \"non_to_sarcastic\"}\n", + "ALLOWED_STRATS = {\"irony\", \"overstatement\", \"rhetorical_question\", \"sarcasm\", \"satire\", \"understatement\"}\n", + "\n", + "raw_rows = []\n", + "errors = []\n", + "\n", + "with open(DATA_FILE, \"r\", encoding=\"utf-8\") as f:\n", + " for line_num, line in enumerate(f):\n", + " line = line.strip()\n", + " if not line:\n", + " continue\n", + " try:\n", + " row = json.loads(line)\n", + " except json.JSONDecodeError as e:\n", + " errors.append((line_num, f\"JSON parse error: {e}\"))\n", + " continue\n", + "\n", + " # Schema check\n", + " missing = REQUIRED_FIELDS - set(row.keys())\n", + " if missing:\n", + " errors.append((line_num, f\"Missing fields: {missing}\"))\n", + " continue\n", + "\n", + " # Value checks\n", + " if row[\"type\"] not in ALLOWED_TYPES:\n", + " errors.append((line_num, f\"Unknown type: {row['type']!r}\"))\n", + " continue\n", + " if row[\"strategy\"] not in ALLOWED_STRATS:\n", + " errors.append((line_num, f\"Unknown strategy: {row['strategy']!r}\"))\n", + " continue\n", + "\n", + " row[\"_line_num\"] = line_num\n", + " raw_rows.append(row)\n", + "\n", + "print(f\"Loaded rows : {len(raw_rows):,}\")\n", + "print(f\"Error rows : {len(errors)}\")\n", + "if errors:\n", + " for ln, msg in errors[:5]:\n", + " print(f\" Line {ln}: {msg}\")" + ] }, { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/datasets/distributions.png\n" - ] - } - ], - "source": [ - "# ── Strategy distribution plot ────────────────────────────────────────────────\n", - "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", - "\n", - "# Type dist\n", - "ax = axes[0]\n", - "labels, counts = zip(*sorted(type_counts.items(), key=lambda x: -x[1]))\n", - "ax.barh(labels, counts, color=[\"steelblue\", \"coral\"])\n", - "ax.set_title(\"Row-level Type Distribution\", fontsize=14)\n", - "ax.set_xlabel(\"Count\")\n", - "for i, c in enumerate(counts):\n", - " ax.text(c + 50, i, f\"{c:,}\", va=\"center\")\n", - "\n", - "# Strategy dist\n", - "ax = axes[1]\n", - "labels2, counts2 = zip(*sorted(strategy_counts.items(), key=lambda x: -x[1]))\n", - "ax.barh(labels2, counts2, color=\"steelblue\")\n", - "ax.set_title(\"Strategy Distribution (Type Task Labels)\", fontsize=14)\n", - "ax.set_xlabel(\"Count\")\n", - "for i, c in enumerate(counts2):\n", - " ax.text(c + 30, i, f\"{c:,}\", va=\"center\")\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(OUT_DATASETS / \"distributions.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "print(\"Saved: outputs/datasets/distributions.png\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JxKcjhRSuKBY" - }, - "source": [ - "## 3. Label Derivation and Dataset Expansion" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "yvg9qFhouKBW" + }, + "source": [ + "## 2. Dataset Audit" + ] }, - "id": "nukGQcmKuKBY", - "outputId": "2fcfb601-8ad0-4876-b813-c21b007bb164" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Binary dataset : 56,666 samples\n", - " sarcastic (1): 28,333\n", - " non-sarc (0): 28,333\n", - "\n", - "Type dataset : 28,333 samples\n", - "type_label\n", - "sarcasm 8699\n", - "irony 6102\n", - "satire 5224\n", - "overstatement 3976\n", - "understatement 3295\n", - "rhetorical_question 1037\n" - ] - } - ], - "source": [ - "from __future__ import annotations\n", - "from urllib.parse import urlparse\n", - "\n", - "\n", - "def normalize_url(url: str) -> str:\n", - " \"\"\"Lowercase and strip scheme/trailing slash for stable group IDs.\"\"\"\n", - " url = url.strip().lower()\n", - " parsed = urlparse(url)\n", - " normalized = (parsed.netloc + parsed.path).rstrip(\"/\")\n", - " return normalized if normalized else url\n", - "\n", - "\n", - "def derive_labels(raw_rows):\n", - " \"\"\"\n", - " Expand each JSONL row into 2 samples (sarcastic + non-sarcastic).\n", - "\n", - " Rules:\n", - " sarcastic_to_non -> original=sarcastic(1), generated=non-sarcastic(0)\n", - " non_to_sarcastic -> original=non-sarcastic(0), generated=sarcastic(1)\n", - "\n", - " Returns binary_df, type_df\n", - " \"\"\"\n", - " binary_records = []\n", - " sample_id = 0\n", - "\n", - " for pair_id, row in enumerate(raw_rows):\n", - " group_id = normalize_url(row[\"article_link\"]) if row[\"article_link\"] else str(pair_id)\n", - " row_type = row[\"type\"]\n", - " strategy = row[\"strategy\"]\n", - " model_used = row[\"model_used\"]\n", - " article = row[\"article_link\"]\n", - "\n", - " if row_type == \"sarcastic_to_non\":\n", - " orig_label, orig_strat = 1, strategy\n", - " gen_label, gen_strat = 0, None\n", - " else:\n", - " orig_label, orig_strat = 0, None\n", - " gen_label, gen_strat = 1, strategy\n", - "\n", - " binary_records.append({\n", - " \"sample_id\" : sample_id,\n", - " \"pair_id\" : pair_id,\n", - " \"group_id\" : group_id,\n", - " \"text\" : row[\"original_headline\"],\n", - " \"binary_label\" : orig_label,\n", - " \"is_generated\" : 0,\n", - " \"strategy\" : orig_strat,\n", - " \"source_type\" : row_type,\n", - " \"article_link\" : article,\n", - " \"model_used\" : model_used,\n", - " })\n", - " sample_id += 1\n", - "\n", - " binary_records.append({\n", - " \"sample_id\" : sample_id,\n", - " \"pair_id\" : pair_id,\n", - " \"group_id\" : group_id,\n", - " \"text\" : row[\"generated_headline\"],\n", - " \"binary_label\" : gen_label,\n", - " \"is_generated\" : 1,\n", - " \"strategy\" : gen_strat,\n", - " \"source_type\" : row_type,\n", - " \"article_link\" : article,\n", - " \"model_used\" : model_used,\n", - " })\n", - " sample_id += 1\n", - "\n", - " binary_df = pd.DataFrame(binary_records)\n", - "\n", - " # Validate counts\n", - " counts = binary_df[\"binary_label\"].value_counts().to_dict()\n", - " n_expected = len(raw_rows)\n", - " n0 = int(counts.get(0, 0))\n", - " n1 = int(counts.get(1, 0))\n", - " if n0 != n_expected or n1 != n_expected:\n", - " raise ValueError(\n", - " f\"Label count mismatch!\\n\"\n", - " f\" Expected {n_expected} per class\\n\"\n", - " f\" Got: sarcastic(1)={n1}, non-sarcastic(0)={n0}\"\n", - " )\n", - "\n", - " # Type dataset: sarcastic only\n", - " type_df = binary_df[binary_df[\"binary_label\"] == 1].copy()\n", - " type_df = type_df.rename(columns={\"strategy\": \"type_label\"})\n", - " type_df = type_df[[\"sample_id\", \"pair_id\", \"group_id\", \"text\",\n", - " \"type_label\", \"is_generated\", \"source_type\",\n", - " \"article_link\", \"model_used\"]]\n", - "\n", - " missing = type_df[\"type_label\"].isna().sum()\n", - " if missing > 0:\n", - " raise ValueError(f\"{missing} sarcastic samples have no type_label!\")\n", - "\n", - " return binary_df, type_df\n", - "\n", - "\n", - "binary_df, type_df = derive_labels(raw_rows)\n", - "\n", - "print(f\"Binary dataset : {len(binary_df):,} samples\")\n", - "print(f\" sarcastic (1): {(binary_df['binary_label']==1).sum():,}\")\n", - "print(f\" non-sarc (0): {(binary_df['binary_label']==0).sum():,}\")\n", - "print()\n", - "print(f\"Type dataset : {len(type_df):,} samples\")\n", - "print(type_df[\"type_label\"].value_counts().to_string())\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KISNWAQhuKBW", + "outputId": "73491a3f-7fed-4a53-e126-2051a7cfd699" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "=== Type distribution ===\n", + " non_to_sarcastic: 14,925 (52.7%)\n", + " sarcastic_to_non: 13,408 (47.3%)\n", + "\n", + "=== Strategy distribution ===\n", + " sarcasm: 8,699 (30.7%)\n", + " irony: 6,102 (21.5%)\n", + " satire: 5,224 (18.4%)\n", + " overstatement: 3,976 (14.0%)\n", + " understatement: 3,295 (11.6%)\n", + " rhetorical_question: 1,037 (3.7%)\n", + "\n", + "=== Model distribution ===\n", + " stepfun/step-3.5-flash:free: 28,333\n" + ] + } + ], + "source": [ + "# ── Type and Strategy distributions ──────────────────────────────────────────\n", + "type_counts = Counter(r[\"type\"] for r in raw_rows)\n", + "strategy_counts = Counter(r[\"strategy\"] for r in raw_rows)\n", + "model_counts = Counter(r[\"model_used\"] for r in raw_rows)\n", + "\n", + "print(\"=== Type distribution ===\")\n", + "for k, v in type_counts.most_common():\n", + " print(f\" {k}: {v:,} ({v/len(raw_rows)*100:.1f}%)\")\n", + "\n", + "print(\"\\n=== Strategy distribution ===\")\n", + "for k, v in strategy_counts.most_common():\n", + " print(f\" {k}: {v:,} ({v/len(raw_rows)*100:.1f}%)\")\n", + "\n", + "print(\"\\n=== Model distribution ===\")\n", + "for k, v in model_counts.most_common():\n", + " print(f\" {k}: {v:,}\")" + ] }, - "id": "5beT2LM9uKBY", - "outputId": "203852f9-bcfd-4ac5-8f83-61f9e0d10219" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/datasets/binary_dataset.csv\n", - "Saved: outputs/datasets/type_dataset.csv\n" - ] - } - ], - "source": [ - "# Save full datasets\n", - "binary_df.to_csv(OUT_DATASETS / \"binary_dataset.csv\", index=False)\n", - "type_df.to_csv( OUT_DATASETS / \"type_dataset.csv\", index=False)\n", - "print(\"Saved: outputs/datasets/binary_dataset.csv\")\n", - "print(\"Saved: outputs/datasets/type_dataset.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5GtLZhBwuKBZ" - }, - "source": [ - "## 4. Group-Aware Train / Val / Test Split" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "28wpsTTnuKBX", + "outputId": "fb04bfcc-87f0-4e11-8066-a01c750e932d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Duplicate original_headlines : 70\n", + "Duplicate generated_headlines: 1\n", + "Duplicate article_links : 2\n" + ] + } + ], + "source": [ + "# ── Duplicate checks ──────────────────────────────────────────────────────────\n", + "orig_headlines = [r[\"original_headline\"] for r in raw_rows]\n", + "gen_headlines = [r[\"generated_headline\"] for r in raw_rows]\n", + "article_links = [r[\"article_link\"] for r in raw_rows]\n", + "\n", + "dup_orig = len(orig_headlines) - len(set(orig_headlines))\n", + "dup_gen = len(gen_headlines) - len(set(gen_headlines))\n", + "dup_article = len(article_links) - len(set(article_links))\n", + "\n", + "print(f\"Duplicate original_headlines : {dup_orig}\")\n", + "print(f\"Duplicate generated_headlines: {dup_gen}\")\n", + "print(f\"Duplicate article_links : {dup_article}\")" + ] }, - "id": "OMGqQn3PuKBZ", - "outputId": "0eb38913-1ff6-4524-e2e6-030cf7c34b90" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "=== Binary splits ===\n", - " train: 39,666 samples | sarcastic=19,833 non=19,833\n", - " val : 8,500 samples | sarcastic=4,250 non=4,250\n", - " test : 8,500 samples | sarcastic=4,250 non=4,250\n", - "\n", - "=== Type splits ===\n", - " train: 19,833 samples\n", - " sarcasm: 6,091\n", - " irony: 4,258\n", - " satire: 3,644\n", - " overstatement: 2,784\n", - " understatement: 2,352\n", - " rhetorical_question: 704\n", - " val : 4,250 samples\n", - " sarcasm: 1,317\n", - " irony: 942\n", - " satire: 747\n", - " overstatement: 600\n", - " understatement: 487\n", - " rhetorical_question: 157\n", - " test : 4,250 samples\n", - " sarcasm: 1,291\n", - " irony: 902\n", - " satire: 833\n", - " overstatement: 592\n", - " understatement: 456\n", - " rhetorical_question: 176\n" - ] - } - ], - "source": [ - "def group_aware_split(\n", - " df: pd.DataFrame,\n", - " group_col: str = \"group_id\",\n", - " train_frac: float = 0.70,\n", - " val_frac: float = 0.15,\n", - " seed: int = SEED,\n", - ") -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n", - " \"\"\"\n", - " Split df into train/val/test at the group level.\n", - " All rows sharing a group_id go to exactly one split.\n", - " \"\"\"\n", - " groups = df[group_col].unique().tolist()\n", - " rng = np.random.default_rng(seed)\n", - " rng.shuffle(groups)\n", - "\n", - " n = len(groups)\n", - " n_train = int(n * train_frac)\n", - " n_val = int(n * val_frac)\n", - "\n", - " train_groups = set(groups[:n_train])\n", - " val_groups = set(groups[n_train : n_train + n_val])\n", - " test_groups = set(groups[n_train + n_val :])\n", - "\n", - " train_df = df[df[group_col].isin(train_groups)].copy()\n", - " val_df = df[df[group_col].isin(val_groups)].copy()\n", - " test_df = df[df[group_col].isin(test_groups)].copy()\n", - "\n", - " # Sanity: no overlap\n", - " assert train_groups.isdisjoint(val_groups), \"Train/val group overlap!\"\n", - " assert train_groups.isdisjoint(test_groups), \"Train/test group overlap!\"\n", - " assert val_groups.isdisjoint(test_groups), \"Val/test group overlap!\"\n", - "\n", - " return train_df, val_df, test_df\n", - "\n", - "\n", - "# ── Binary splits ─────────────────────────────────────────────────────────────\n", - "train_bin, val_bin, test_bin = group_aware_split(binary_df)\n", - "\n", - "print(\"=== Binary splits ===\")\n", - "for name, df in [(\"train\", train_bin), (\"val\", val_bin), (\"test\", test_bin)]:\n", - " dist = df[\"binary_label\"].value_counts().to_dict()\n", - " print(f\" {name:5s}: {len(df):,} samples | sarcastic={dist.get(1,0):,} non={dist.get(0,0):,}\")\n", - "\n", - "# ── Type splits ───────────────────────────────────────────────────────────────\n", - "train_type, val_type, test_type = group_aware_split(type_df)\n", - "\n", - "print(\"\\n=== Type splits ===\")\n", - "for name, df in [(\"train\", train_type), (\"val\", val_type), (\"test\", test_type)]:\n", - " print(f\" {name:5s}: {len(df):,} samples\")\n", - " dist = df[\"type_label\"].value_counts()\n", - " for label, cnt in dist.items():\n", - " print(f\" {label}: {cnt:,}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SLUFPDIHuKBX", + "outputId": "df8eb49d-e528-4a2a-af32-fa9a5c7f2d63" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "original_headline : 0 nulls, 0 empty strings\n", + "generated_headline : 0 nulls, 0 empty strings\n", + "strategy : 0 nulls, 0 empty strings\n", + "type : 0 nulls, 0 empty strings\n", + "model_used : 0 nulls, 0 empty strings\n", + "article_link : 0 nulls, 0 empty strings\n" + ] + } + ], + "source": [ + "# ── Null / empty checks ───────────────────────────────────────────────────────\n", + "for field in [\"original_headline\", \"generated_headline\", \"strategy\", \"type\", \"model_used\", \"article_link\"]:\n", + " null_count = sum(1 for r in raw_rows if r.get(field) is None)\n", + " empty_count = sum(1 for r in raw_rows if r.get(field) == \"\")\n", + " print(f\"{field:25s}: {null_count} nulls, {empty_count} empty strings\")" + ] }, - "id": "Oo78ufPjuKBZ", - "outputId": "6b324c55-2718-4236-d356-e7c7a44e91a5" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Train/Val pair_id overlap : 0 (must be 0)\n", - "Train/Test pair_id overlap : 0 (must be 0)\n", - "Val/Test pair_id overlap : 0 (must be 0)\n", - "\n", - "Leakage check PASSED: No pair_id crosses split boundaries.\n" - ] - } - ], - "source": [ - "# ── Verify no pair crosses split boundary ─────────────────────────────────────\n", - "# A pair_id should appear in at most ONE binary split\n", - "train_pairs = set(train_bin[\"pair_id\"])\n", - "val_pairs = set(val_bin[\"pair_id\"])\n", - "test_pairs = set(test_bin[\"pair_id\"])\n", - "\n", - "tv_overlap = train_pairs & val_pairs\n", - "tt_overlap = train_pairs & test_pairs\n", - "vt_overlap = val_pairs & test_pairs\n", - "\n", - "print(f\"Train/Val pair_id overlap : {len(tv_overlap)} (must be 0)\")\n", - "print(f\"Train/Test pair_id overlap : {len(tt_overlap)} (must be 0)\")\n", - "print(f\"Val/Test pair_id overlap : {len(vt_overlap)} (must be 0)\")\n", - "\n", - "assert len(tv_overlap) == 0 and len(tt_overlap) == 0 and len(vt_overlap) == 0, \\\n", - " \"Leakage detected: pairs crossing split boundaries!\"\n", - "print(\"\\nLeakage check PASSED: No pair_id crosses split boundaries.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 524 + }, + "id": "E-B0fc5vuKBX", + "outputId": "d697bd94-9e1c-45e5-faec-4723160947f8" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/datasets/distributions.png\n" + ] + } + ], + "source": [ + "# ── Strategy distribution plot ────────────────────────────────────────────────\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Type dist\n", + "ax = axes[0]\n", + "labels, counts = zip(*sorted(type_counts.items(), key=lambda x: -x[1]))\n", + "ax.barh(labels, counts, color=[\"steelblue\", \"coral\"])\n", + "ax.set_title(\"Row-level Type Distribution\", fontsize=14)\n", + "ax.set_xlabel(\"Count\")\n", + "for i, c in enumerate(counts):\n", + " ax.text(c + 50, i, f\"{c:,}\", va=\"center\")\n", + "\n", + "# Strategy dist\n", + "ax = axes[1]\n", + "labels2, counts2 = zip(*sorted(strategy_counts.items(), key=lambda x: -x[1]))\n", + "ax.barh(labels2, counts2, color=\"steelblue\")\n", + "ax.set_title(\"Strategy Distribution (Type Task Labels)\", fontsize=14)\n", + "ax.set_xlabel(\"Count\")\n", + "for i, c in enumerate(counts2):\n", + " ax.text(c + 30, i, f\"{c:,}\", va=\"center\")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(OUT_DATASETS / \"distributions.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/datasets/distributions.png\")" + ] }, - "id": "HbbbOgpZuKBZ", - "outputId": "2716fcd8-e654-46c8-9b14-765336c8f3b9" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/splits/train_binary.csv\n", - "Saved: outputs/splits/val_binary.csv\n", - "Saved: outputs/splits/test_binary.csv\n", - "Saved: outputs/splits/train_type.csv\n", - "Saved: outputs/splits/val_type.csv\n", - "Saved: outputs/splits/test_type.csv\n", - "\n", - "Saved: outputs/splits/split_metadata.json\n" - ] - } - ], - "source": [ - "# ── Save splits ───────────────────────────────────────────────────────────────\n", - "split_files = {\n", - " \"train_binary\": train_bin,\n", - " \"val_binary\" : val_bin,\n", - " \"test_binary\" : test_bin,\n", - " \"train_type\" : train_type,\n", - " \"val_type\" : val_type,\n", - " \"test_type\" : test_type,\n", - "}\n", - "for fname, df in split_files.items():\n", - " path = OUT_SPLITS / f\"{fname}.csv\"\n", - " df.to_csv(path, index=False)\n", - " print(f\"Saved: {path.relative_to(ROOT)}\")\n", - "\n", - "# Metadata\n", - "import json as _json\n", - "metadata = {\n", - " \"seed\": SEED,\n", - " \"train_frac\": 0.70,\n", - " \"val_frac\": 0.15,\n", - " \"test_frac\": 0.15,\n", - " \"group_col\": \"group_id\",\n", - " \"total_pairs\": len(raw_rows),\n", - " \"binary\": {\n", - " \"train\": len(train_bin), \"val\": len(val_bin), \"test\": len(test_bin)\n", - " },\n", - " \"type\": {\n", - " \"train\": len(train_type), \"val\": len(val_type), \"test\": len(test_type)\n", - " },\n", - "}\n", - "with open(OUT_SPLITS / \"split_metadata.json\", \"w\") as f:\n", - " _json.dump(metadata, f, indent=2)\n", - "print(\"\\nSaved: outputs/splits/split_metadata.json\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "j7ZguloUuKBa" - }, - "source": [ - "## 5. Split Distribution Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 419 + "cell_type": "markdown", + "metadata": { + "id": "JxKcjhRSuKBY" + }, + "source": [ + "## 3. Label Derivation and Dataset Expansion" + ] }, - "id": "nLrEPxJ9uKBa", - "outputId": "508ba885-ab89-400e-8f7c-c251d636a688" - }, - "outputs": [ { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nukGQcmKuKBY", + "outputId": "d447eb42-9d77-47b8-e4ad-5c1101fa0aeb" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Binary dataset : 56,666 samples\n", + " sarcastic (1): 28,333\n", + " non-sarc (0): 28,333\n", + "\n", + "Type dataset : 28,333 samples\n", + "type_label\n", + "sarcasm 8699\n", + "irony 6102\n", + "satire 5224\n", + "overstatement 3976\n", + "understatement 3295\n", + "rhetorical_question 1037\n" + ] + } ], - "image/png": 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- }, - "metadata": {} + "source": [ + "from __future__ import annotations\n", + "from urllib.parse import urlparse\n", + "\n", + "\n", + "def normalize_url(url: str) -> str:\n", + " \"\"\"Lowercase and strip scheme/trailing slash for stable group IDs.\"\"\"\n", + " url = url.strip().lower()\n", + " parsed = urlparse(url)\n", + " normalized = (parsed.netloc + parsed.path).rstrip(\"/\")\n", + " return normalized if normalized else url\n", + "\n", + "\n", + "def derive_labels(raw_rows):\n", + " \"\"\"\n", + " Expand each JSONL row into 2 samples (sarcastic + non-sarcastic).\n", + "\n", + " Rules:\n", + " sarcastic_to_non -> original=sarcastic(1), generated=non-sarcastic(0)\n", + " non_to_sarcastic -> original=non-sarcastic(0), generated=sarcastic(1)\n", + "\n", + " Returns binary_df, type_df\n", + " \"\"\"\n", + " binary_records = []\n", + " sample_id = 0\n", + "\n", + " for pair_id, row in enumerate(raw_rows):\n", + " group_id = normalize_url(row[\"article_link\"]) if row[\"article_link\"] else str(pair_id)\n", + " row_type = row[\"type\"]\n", + " strategy = row[\"strategy\"]\n", + " model_used = row[\"model_used\"]\n", + " article = row[\"article_link\"]\n", + "\n", + " if row_type == \"sarcastic_to_non\":\n", + " orig_label, orig_strat = 1, strategy\n", + " gen_label, gen_strat = 0, None\n", + " else:\n", + " orig_label, orig_strat = 0, None\n", + " gen_label, gen_strat = 1, strategy\n", + "\n", + " binary_records.append({\n", + " \"sample_id\" : sample_id,\n", + " \"pair_id\" : pair_id,\n", + " \"group_id\" : group_id,\n", + " \"text\" : row[\"original_headline\"],\n", + " \"binary_label\" : orig_label,\n", + " \"is_generated\" : 0,\n", + " \"strategy\" : orig_strat,\n", + " \"source_type\" : row_type,\n", + " \"article_link\" : article,\n", + " \"model_used\" : model_used,\n", + " })\n", + " sample_id += 1\n", + "\n", + " binary_records.append({\n", + " \"sample_id\" : sample_id,\n", + " \"pair_id\" : pair_id,\n", + " \"group_id\" : group_id,\n", + " \"text\" : row[\"generated_headline\"],\n", + " \"binary_label\" : gen_label,\n", + " \"is_generated\" : 1,\n", + " \"strategy\" : gen_strat,\n", + " \"source_type\" : row_type,\n", + " \"article_link\" : article,\n", + " \"model_used\" : model_used,\n", + " })\n", + " sample_id += 1\n", + "\n", + " binary_df = pd.DataFrame(binary_records)\n", + "\n", + " # Validate counts\n", + " counts = binary_df[\"binary_label\"].value_counts().to_dict()\n", + " n_expected = len(raw_rows)\n", + " n0 = int(counts.get(0, 0))\n", + " n1 = int(counts.get(1, 0))\n", + " if n0 != n_expected or n1 != n_expected:\n", + " raise ValueError(\n", + " f\"Label count mismatch!\\n\"\n", + " f\" Expected {n_expected} per class\\n\"\n", + " f\" Got: sarcastic(1)={n1}, non-sarcastic(0)={n0}\"\n", + " )\n", + "\n", + " # Type dataset: sarcastic only\n", + " type_df = binary_df[binary_df[\"binary_label\"] == 1].copy()\n", + " type_df = type_df.rename(columns={\"strategy\": \"type_label\"})\n", + " type_df = type_df[[\"sample_id\", \"pair_id\", \"group_id\", \"text\",\n", + " \"type_label\", \"is_generated\", \"source_type\",\n", + " \"article_link\", \"model_used\"]]\n", + "\n", + " missing = type_df[\"type_label\"].isna().sum()\n", + " if missing > 0:\n", + " raise ValueError(f\"{missing} sarcastic samples have no type_label!\")\n", + "\n", + " return binary_df, type_df\n", + "\n", + "\n", + "binary_df, type_df = derive_labels(raw_rows)\n", + "\n", + "print(f\"Binary dataset : {len(binary_df):,} samples\")\n", + "print(f\" sarcastic (1): {(binary_df['binary_label']==1).sum():,}\")\n", + "print(f\" non-sarc (0): {(binary_df['binary_label']==0).sum():,}\")\n", + "print()\n", + "print(f\"Type dataset : {len(type_df):,} samples\")\n", + "print(type_df[\"type_label\"].value_counts().to_string())\n" + ] }, { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/splits/type_split_distribution.png\n" - ] - } - ], - "source": [ - "# Type label distribution per split\n", - "fig, axes = plt.subplots(1, 3, figsize=(18, 5), sharey=True)\n", - "split_dfs = [(\"Train\", train_type), (\"Val\", val_type), (\"Test\", test_type)]\n", - "\n", - "for ax, (name, df) in zip(axes, split_dfs):\n", - " dist = df[\"type_label\"].value_counts()\n", - " dist.plot(kind=\"barh\", ax=ax, color=\"steelblue\")\n", - " ax.set_title(f\"{name} — Type Labels (n={len(df):,})\", fontsize=13)\n", - " ax.set_xlabel(\"Count\")\n", - " for i, (label, cnt) in enumerate(dist.items()):\n", - " ax.text(cnt + 5, i, f\"{cnt:,}\", va=\"center\", fontsize=9)\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(OUT_SPLITS / \"type_split_distribution.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "print(\"Saved: outputs/splits/type_split_distribution.png\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 424 + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5beT2LM9uKBY", + "outputId": "0fbca3fe-9024-423f-e6ba-a352abf1b391" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/datasets/binary_dataset.csv\n", + "Saved: outputs/datasets/type_dataset.csv\n" + ] + } + ], + "source": [ + "# Save full datasets\n", + "binary_df.to_csv(OUT_DATASETS / \"binary_dataset.csv\", index=False)\n", + "type_df.to_csv( OUT_DATASETS / \"type_dataset.csv\", index=False)\n", + "print(\"Saved: outputs/datasets/binary_dataset.csv\")\n", + "print(\"Saved: outputs/datasets/type_dataset.csv\")" + ] }, - "id": "R48OKnIouKBa", - "outputId": "cc63cf5a-635b-4937-ff28-9702de59974e" - }, - "outputs": [ { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": { + "id": "5GtLZhBwuKBZ" + }, + "source": [ + "## 4. Group-Aware Train / Val / Test Split" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OMGqQn3PuKBZ", + "outputId": "ccee79cb-f590-4706-abe2-51a51ece30c2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "=== Binary splits ===\n", + " train: 39,666 samples | sarcastic=19,833 non=19,833\n", + " val : 8,500 samples | sarcastic=4,250 non=4,250\n", + " test : 8,500 samples | sarcastic=4,250 non=4,250\n", + "\n", + "=== Type splits ===\n", + " train: 19,833 samples\n", + " sarcasm: 6,091\n", + " irony: 4,258\n", + " satire: 3,644\n", + " overstatement: 2,784\n", + " understatement: 2,352\n", + " rhetorical_question: 704\n", + " val : 4,250 samples\n", + " sarcasm: 1,317\n", + " irony: 942\n", + " satire: 747\n", + " overstatement: 600\n", + " understatement: 487\n", + " rhetorical_question: 157\n", + " test : 4,250 samples\n", + " sarcasm: 1,291\n", + " irony: 902\n", + " satire: 833\n", + " overstatement: 592\n", + " understatement: 456\n", + " rhetorical_question: 176\n" + ] + } ], - "image/png": 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- }, - "metadata": {} + "source": [ + "def group_aware_split(\n", + " df: pd.DataFrame,\n", + " group_col: str = \"group_id\",\n", + " train_frac: float = 0.70,\n", + " val_frac: float = 0.15,\n", + " seed: int = SEED,\n", + ") -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n", + " \"\"\"\n", + " Split df into train/val/test at the group level.\n", + " All rows sharing a group_id go to exactly one split.\n", + " \"\"\"\n", + " groups = df[group_col].unique().tolist()\n", + " rng = np.random.default_rng(seed)\n", + " rng.shuffle(groups)\n", + "\n", + " n = len(groups)\n", + " n_train = int(n * train_frac)\n", + " n_val = int(n * val_frac)\n", + "\n", + " train_groups = set(groups[:n_train])\n", + " val_groups = set(groups[n_train : n_train + n_val])\n", + " test_groups = set(groups[n_train + n_val :])\n", + "\n", + " train_df = df[df[group_col].isin(train_groups)].copy()\n", + " val_df = df[df[group_col].isin(val_groups)].copy()\n", + " test_df = df[df[group_col].isin(test_groups)].copy()\n", + "\n", + " # Sanity: no overlap\n", + " assert train_groups.isdisjoint(val_groups), \"Train/val group overlap!\"\n", + " assert train_groups.isdisjoint(test_groups), \"Train/test group overlap!\"\n", + " assert val_groups.isdisjoint(test_groups), \"Val/test group overlap!\"\n", + "\n", + " return train_df, val_df, test_df\n", + "\n", + "\n", + "# ── Binary splits ─────────────────────────────────────────────────────────────\n", + "train_bin, val_bin, test_bin = group_aware_split(binary_df)\n", + "\n", + "print(\"=== Binary splits ===\")\n", + "for name, df in [(\"train\", train_bin), (\"val\", val_bin), (\"test\", test_bin)]:\n", + " dist = df[\"binary_label\"].value_counts().to_dict()\n", + " print(f\" {name:5s}: {len(df):,} samples | sarcastic={dist.get(1,0):,} non={dist.get(0,0):,}\")\n", + "\n", + "# ── Type splits ───────────────────────────────────────────────────────────────\n", + "train_type, val_type, test_type = group_aware_split(type_df)\n", + "\n", + "print(\"\\n=== Type splits ===\")\n", + "for name, df in [(\"train\", train_type), (\"val\", val_type), (\"test\", test_type)]:\n", + " print(f\" {name:5s}: {len(df):,} samples\")\n", + " dist = df[\"type_label\"].value_counts()\n", + " for label, cnt in dist.items():\n", + " print(f\" {label}: {cnt:,}\")" + ] }, { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/splits/binary_split_distribution.png\n" - ] - } - ], - "source": [ - "# Binary label distribution per split\n", - "fig, axes = plt.subplots(1, 3, figsize=(12, 4), sharey=True)\n", - "split_dfs_bin = [(\"Train\", train_bin), (\"Val\", val_bin), (\"Test\", test_bin)]\n", - "\n", - "for ax, (name, df) in zip(axes, split_dfs_bin):\n", - " dist = df[\"binary_label\"].value_counts().sort_index()\n", - " ax.bar([\"Non-sarcastic (0)\", \"Sarcastic (1)\"], dist.values, color=[\"coral\", \"steelblue\"])\n", - " ax.set_title(f\"{name} — Binary Labels (n={len(df):,})\", fontsize=12)\n", - " ax.set_ylabel(\"Count\")\n", - " for i, v in enumerate(dist.values):\n", - " ax.text(i, v + 20, f\"{v:,}\", ha=\"center\")\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(OUT_SPLITS / \"binary_split_distribution.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "print(\"Saved: outputs/splits/binary_split_distribution.png\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Oo78ufPjuKBZ", + "outputId": "890a680b-4a74-4642-8a64-30178949caf6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train/Val pair_id overlap : 0 (must be 0)\n", + "Train/Test pair_id overlap : 0 (must be 0)\n", + "Val/Test pair_id overlap : 0 (must be 0)\n", + "\n", + "Leakage check PASSED: No pair_id crosses split boundaries.\n" + ] + } + ], + "source": [ + "# ── Verify no pair crosses split boundary ─────────────────────────────────────\n", + "# A pair_id should appear in at most ONE binary split\n", + "train_pairs = set(train_bin[\"pair_id\"])\n", + "val_pairs = set(val_bin[\"pair_id\"])\n", + "test_pairs = set(test_bin[\"pair_id\"])\n", + "\n", + "tv_overlap = train_pairs & val_pairs\n", + "tt_overlap = train_pairs & test_pairs\n", + "vt_overlap = val_pairs & test_pairs\n", + "\n", + "print(f\"Train/Val pair_id overlap : {len(tv_overlap)} (must be 0)\")\n", + "print(f\"Train/Test pair_id overlap : {len(tt_overlap)} (must be 0)\")\n", + "print(f\"Val/Test pair_id overlap : {len(vt_overlap)} (must be 0)\")\n", + "\n", + "assert len(tv_overlap) == 0 and len(tt_overlap) == 0 and len(vt_overlap) == 0, \\\n", + " \"Leakage detected: pairs crossing split boundaries!\"\n", + "print(\"\\nLeakage check PASSED: No pair_id crosses split boundaries.\")" + ] }, - "id": "y59RyyxDuKBa", - "outputId": "9fcf80b4-639e-4dc0-da4e-9e5d7492337a" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "=== Data Preparation Complete ===\n", - "Binary dataset : 56,666 samples (balanced)\n", - "Type dataset : 28,333 samples (imbalanced, 6 classes)\n", - "Splits saved to: outputs/splits/\n", - "Datasets saved : outputs/datasets/\n", - "\n", - "Ready for classical baseline training (Notebooks 02 and 03).\n" - ] - } - ], - "source": [ - "print(\"\\n=== Data Preparation Complete ===\")\n", - "print(f\"Binary dataset : {len(binary_df):,} samples (balanced)\")\n", - "print(f\"Type dataset : {len(type_df):,} samples (imbalanced, 6 classes)\")\n", - "print(f\"Splits saved to: outputs/splits/\")\n", - "print(f\"Datasets saved : outputs/datasets/\")\n", - "print(\"\\nReady for classical baseline training (Notebooks 02 and 03).\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cENhnFfwuKBb" - }, - "source": [ - "---\n", - "# Part 2 — TF-IDF + Logistic Regression Baseline" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "j_2MU9WJuKBb" - }, - "source": [ - "# Notebook 02 — TF-IDF + Logistic Regression Baseline\n", - "\n", - "**Purpose**: Train and evaluate TF-IDF + Logistic Regression pipelines for:\n", - "- Task A: Binary classification (sarcastic vs non-sarcastic)\n", - "- Task B: Sarcasm type classification (6-class)\n", - "\n", - "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", - "\n", - "**Outputs**:\n", - "- `outputs/classical/tfidf_lr/best_config_binary.json`\n", - "- `outputs/classical/tfidf_lr/best_config_type.json`\n", - "- `outputs/classical/tfidf_lr/metrics_binary.json`\n", - "- `outputs/classical/tfidf_lr/metrics_type.json`\n", - "- Predictions CSV + confusion matrices PNG" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "r8BzGF97uKBb" - }, - "source": [ - "## Helper Functions" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "id": "iU0Dnjl_uKBb" - }, - "outputs": [], - "source": [ - "def load_splits(task: str) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n", - " \"\"\"Load train/val/test CSVs for a given task ('binary' or 'type').\"\"\"\n", - " train = pd.read_csv(SPLITS / f\"train_{task}.csv\")\n", - " val = pd.read_csv(SPLITS / f\"val_{task}.csv\")\n", - " test = pd.read_csv(SPLITS / f\"test_{task}.csv\")\n", - " return train, val, test\n", - "\n", - "\n", - "def evaluate(\n", - " model,\n", - " X: list[str],\n", - " y_true: list,\n", - " label_names: list[str] | None = None,\n", - " split_name: str = \"\",\n", - ") -> dict:\n", - " \"\"\"Compute classification metrics and return as dict.\"\"\"\n", - " y_pred = model.predict(X)\n", - " metrics = {\n", - " \"split\" : split_name,\n", - " \"accuracy\" : accuracy_score(y_true, y_pred),\n", - " \"precision_macro\" : precision_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", - " \"recall_macro\" : recall_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", - " \"f1_macro\" : f1_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", - " \"f1_weighted\" : f1_score(y_true, y_pred, average=\"weighted\", zero_division=0),\n", - " \"report\" : classification_report(y_true, y_pred, target_names=label_names,\n", - " zero_division=0, output_dict=True),\n", - " }\n", - " print(f\"\\n[{split_name}] Accuracy={metrics['accuracy']:.4f} \"\n", - " f\"Macro-F1={metrics['f1_macro']:.4f} Weighted-F1={metrics['f1_weighted']:.4f}\")\n", - " print(classification_report(y_true, y_pred, target_names=label_names, zero_division=0))\n", - " return metrics, y_pred\n", - "\n", - "\n", - "def save_confusion_matrix(\n", - " y_true, y_pred, label_names: list[str], out_path: Path, title: str = \"\"\n", - ") -> None:\n", - " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", - " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", - " sns.heatmap(\n", - " cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n", - " xticklabels=label_names, yticklabels=label_names, ax=ax\n", - " )\n", - " ax.set_xlabel(\"Predicted\")\n", - " ax.set_ylabel(\"True\")\n", - " ax.set_title(title)\n", - " plt.tight_layout()\n", - " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", - " plt.show()\n", - " print(f\"Saved: {out_path.relative_to(ROOT)}\")\n", - "\n", - "\n", - "def save_predictions(\n", - " df: pd.DataFrame, y_pred, label_col: str, out_path: Path\n", - ") -> None:\n", - " out = df[[\"sample_id\", \"pair_id\", \"group_id\", \"text\", label_col]].copy()\n", - " out[\"predicted\"] = y_pred\n", - " out[\"correct\"] = (out[label_col] == out[\"predicted\"]).astype(int)\n", - " out.to_csv(out_path, index=False)\n", - " print(f\"Saved: {out_path.relative_to(ROOT)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KZYEW3EruKBb" - }, - "source": [ - "## Task A — Binary Classification" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HbbbOgpZuKBZ", + "outputId": "46096a80-ab51-43e1-b5cd-2c9d15f82805" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/splits/train_binary.csv\n", + "Saved: outputs/splits/val_binary.csv\n", + "Saved: outputs/splits/test_binary.csv\n", + "Saved: outputs/splits/train_type.csv\n", + "Saved: outputs/splits/val_type.csv\n", + "Saved: outputs/splits/test_type.csv\n", + "\n", + "Saved: outputs/splits/split_metadata.json\n" + ] + } + ], + "source": [ + "# ── Save splits ───────────────────────────────────────────────────────────────\n", + "split_files = {\n", + " \"train_binary\": train_bin,\n", + " \"val_binary\" : val_bin,\n", + " \"test_binary\" : test_bin,\n", + " \"train_type\" : train_type,\n", + " \"val_type\" : val_type,\n", + " \"test_type\" : test_type,\n", + "}\n", + "for fname, df in split_files.items():\n", + " path = OUT_SPLITS / f\"{fname}.csv\"\n", + " df.to_csv(path, index=False)\n", + " print(f\"Saved: {path.relative_to(ROOT)}\")\n", + "\n", + "# Metadata\n", + "import json as _json\n", + "metadata = {\n", + " \"seed\": SEED,\n", + " \"train_frac\": 0.70,\n", + " \"val_frac\": 0.15,\n", + " \"test_frac\": 0.15,\n", + " \"group_col\": \"group_id\",\n", + " \"total_pairs\": len(raw_rows),\n", + " \"binary\": {\n", + " \"train\": len(train_bin), \"val\": len(val_bin), \"test\": len(test_bin)\n", + " },\n", + " \"type\": {\n", + " \"train\": len(train_type), \"val\": len(val_type), \"test\": len(test_type)\n", + " },\n", + "}\n", + "with open(OUT_SPLITS / \"split_metadata.json\", \"w\") as f:\n", + " _json.dump(metadata, f, indent=2)\n", + "print(\"\\nSaved: outputs/splits/split_metadata.json\")" + ] }, - "id": "sUWB4dQKuKBb", - "outputId": "c9c34b33-e3bb-4eca-e21a-158777f966cb" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Train+Val: 48,166 | Train: 39,666 Val: 8,500 Test: 8,500\n" - ] - } - ], - "source": [ - "# ── Load binary splits ────────────────────────────────────────────────────────\n", - "train_bin, val_bin, test_bin = load_splits(\"binary\")\n", - "\n", - "# Combine train+val for cross-validation, keep test for final eval\n", - "trainval_bin = pd.concat([train_bin, val_bin], ignore_index=True)\n", - "\n", - "X_trainval = trainval_bin[\"text\"].tolist()\n", - "y_trainval = trainval_bin[\"binary_label\"].tolist()\n", - "groups_tv = trainval_bin[\"group_id\"].tolist()\n", - "\n", - "X_train = train_bin[\"text\"].tolist()\n", - "y_train = train_bin[\"binary_label\"].tolist()\n", - "\n", - "X_val = val_bin[\"text\"].tolist()\n", - "y_val = val_bin[\"binary_label\"].tolist()\n", - "\n", - "X_test = test_bin[\"text\"].tolist()\n", - "y_test = test_bin[\"binary_label\"].tolist()\n", - "\n", - "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", - "\n", - "print(f\"Train+Val: {len(X_trainval):,} | Train: {len(X_train):,} Val: {len(X_val):,} Test: {len(X_test):,}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "j7ZguloUuKBa" + }, + "source": [ + "## 5. Split Distribution Visualization" + ] }, - "id": "BatmO-vAuKBc", - "outputId": "34229238-2bbd-4838-976b-464bb496054f" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Grid size: 4 param combos → 180 fits\n", - "Running grid search...\n", - "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n", - "\n", - "Best params : {'lr__C': 3.0, 'tfidf__max_features': None, 'tfidf__min_df': 3, 'tfidf__ngram_range': (1, 2)}\n", - "Best CV F1 : 0.8143\n" - ] - } - ], - "source": [ - "# ── Define pipeline and grid ──────────────────────────────────────────────────\n", - "pipe_bin = Pipeline([\n", - " (\"tfidf\", TfidfVectorizer(sublinear_tf=True, lowercase=True)),\n", - " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, solver=\"lbfgs\")),\n", - "])\n", - "\n", - "param_grid_bin = {\n", - " \"tfidf__ngram_range\" : [(1, 1), (1, 2)],\n", - " \"tfidf__min_df\" : [2, 3, 5],\n", - " \"tfidf__max_features\": [None, 50_000],\n", - " \"lr__C\" : [0.1, 1.0, 3.0],\n", - "}\n", - "\n", - "cv_bin = GroupKFold(n_splits=5)\n", - "\n", - "gs_bin = GridSearchCV(\n", - " pipe_bin, param_grid_bin,\n", - " cv=cv_bin, scoring=\"f1_macro\",\n", - " n_jobs=-1, verbose=1, refit=True,\n", - ")\n", - "\n", - "print(f\"Grid size: {len(gs_bin.param_grid)} param combos → {5 * 2*3*2*3} fits\")\n", - "print(\"Running grid search...\")\n", - "gs_bin.fit(X_trainval, y_trainval, groups=groups_tv)\n", - "\n", - "print(f\"\\nBest params : {gs_bin.best_params_}\")\n", - "print(f\"Best CV F1 : {gs_bin.best_score_:.4f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "id": "nLrEPxJ9uKBa", + "outputId": "376a5d78-75f5-4f7b-cb5c-4c9ee15e5dee" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/splits/type_split_distribution.png\n" + ] + } + ], + "source": [ + "# Type label distribution per split\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 5), sharey=True)\n", + "split_dfs = [(\"Train\", train_type), (\"Val\", val_type), (\"Test\", test_type)]\n", + "\n", + "for ax, (name, df) in zip(axes, split_dfs):\n", + " dist = df[\"type_label\"].value_counts()\n", + " dist.plot(kind=\"barh\", ax=ax, color=\"steelblue\")\n", + " ax.set_title(f\"{name} — Type Labels (n={len(df):,})\", fontsize=13)\n", + " ax.set_xlabel(\"Count\")\n", + " for i, (label, cnt) in enumerate(dist.items()):\n", + " ax.text(cnt + 5, i, f\"{cnt:,}\", va=\"center\", fontsize=9)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(OUT_SPLITS / \"type_split_distribution.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/splits/type_split_distribution.png\")" + ] }, - "id": "JstBvt1vuKBc", - "outputId": "aff1b1fd-aa2f-4c9c-92ae-7d836436f66d" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/classical/tfidf_lr/best_config_binary.json\n" - ] - } - ], - "source": [ - "# ── Save best config ──────────────────────────────────────────────────────────\n", - "best_cfg_bin = {\"task\": \"binary\", \"model\": \"TF-IDF + LR\", \"seed\": SEED,\n", - " \"best_params\": gs_bin.best_params_, \"cv_f1_macro\": gs_bin.best_score_}\n", - "with open(OUT_TFIDF / \"best_config_binary.json\", \"w\") as f:\n", - " json.dump(best_cfg_bin, f, indent=2)\n", - "print(\"Saved: outputs/classical/tfidf_lr/best_config_binary.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + }, + "id": "R48OKnIouKBa", + "outputId": "00f1f550-5816-484f-ba2f-495c1ed98444" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/splits/binary_split_distribution.png\n" + ] + } + ], + "source": [ + "# Binary label distribution per split\n", + "fig, axes = plt.subplots(1, 3, figsize=(12, 4), sharey=True)\n", + "split_dfs_bin = [(\"Train\", train_bin), (\"Val\", val_bin), (\"Test\", test_bin)]\n", + "\n", + "for ax, (name, df) in zip(axes, split_dfs_bin):\n", + " dist = df[\"binary_label\"].value_counts().sort_index()\n", + " ax.bar([\"Non-sarcastic (0)\", \"Sarcastic (1)\"], dist.values, color=[\"coral\", \"steelblue\"])\n", + " ax.set_title(f\"{name} — Binary Labels (n={len(df):,})\", fontsize=12)\n", + " ax.set_ylabel(\"Count\")\n", + " for i, v in enumerate(dist.values):\n", + " ax.text(i, v + 20, f\"{v:,}\", ha=\"center\")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(OUT_SPLITS / \"binary_split_distribution.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/splits/binary_split_distribution.png\")" + ] }, - "id": "AjH8_5UQuKBc", - "outputId": "afe39734-c73a-410a-9447-6b8639b18389" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "[Val] Accuracy=0.9186 Macro-F1=0.9186 Weighted-F1=0.9186\n", - " precision recall f1-score support\n", - "\n", - "non-sarcastic 0.92 0.92 0.92 4250\n", - " sarcastic 0.92 0.92 0.92 4250\n", - "\n", - " accuracy 0.92 8500\n", - " macro avg 0.92 0.92 0.92 8500\n", - " weighted avg 0.92 0.92 0.92 8500\n", - "\n", - "\n", - "[Test] Accuracy=0.8189 Macro-F1=0.8189 Weighted-F1=0.8189\n", - " precision recall f1-score support\n", - "\n", - "non-sarcastic 0.82 0.82 0.82 4250\n", - " sarcastic 0.82 0.82 0.82 4250\n", - "\n", - " accuracy 0.82 8500\n", - " macro avg 0.82 0.82 0.82 8500\n", - " weighted avg 0.82 0.82 0.82 8500\n", - "\n", - "Saved: outputs/classical/tfidf_lr/metrics_binary.json\n" - ] - } - ], - "source": [ - "# ── Evaluate on val and test ──────────────────────────────────────────────────\n", - "best_bin = gs_bin.best_estimator_\n", - "\n", - "val_metrics_bin, y_val_pred_bin = evaluate(best_bin, X_val, y_val, label_names_bin, \"Val\")\n", - "test_metrics_bin, y_test_pred_bin = evaluate(best_bin, X_test, y_test, label_names_bin, \"Test\")\n", - "\n", - "all_metrics_bin = {\"val\": val_metrics_bin, \"test\": test_metrics_bin}\n", - "\n", - "# Remove classification_report dict (too nested for JSON)\n", - "for split_m in all_metrics_bin.values():\n", - " split_m.pop(\"report\", None)\n", - "\n", - "with open(OUT_TFIDF / \"metrics_binary.json\", \"w\") as f:\n", - " json.dump(all_metrics_bin, f, indent=2)\n", - "print(\"Saved: outputs/classical/tfidf_lr/metrics_binary.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "y59RyyxDuKBa", + "outputId": "ba37546e-1c0f-4db1-8a6d-4f8dfd9f0afd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "=== Data Preparation Complete ===\n", + "Binary dataset : 56,666 samples (balanced)\n", + "Type dataset : 28,333 samples (imbalanced, 6 classes)\n", + "Splits saved to: outputs/splits/\n", + "Datasets saved : outputs/datasets/\n", + "\n", + "Ready for classical baseline training (Notebooks 02 and 03).\n" + ] + } + ], + "source": [ + "print(\"\\n=== Data Preparation Complete ===\")\n", + "print(f\"Binary dataset : {len(binary_df):,} samples (balanced)\")\n", + "print(f\"Type dataset : {len(type_df):,} samples (imbalanced, 6 classes)\")\n", + "print(f\"Splits saved to: outputs/splits/\")\n", + "print(f\"Datasets saved : outputs/datasets/\")\n", + "print(\"\\nReady for classical baseline training (Notebooks 02 and 03).\")" + ] }, - "id": "ichiimejuKBc", - "outputId": "e3d5e010-d983-4f86-893b-cd38907ecc68" - }, - "outputs": [ { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": { + "id": "cENhnFfwuKBb" + }, + "source": [ + "---\n", + "# Part 2 — TF-IDF + Logistic Regression Baseline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j_2MU9WJuKBb" + }, + "source": [ + "# Notebook 02 — TF-IDF + Logistic Regression Baseline\n", + "\n", + "**Purpose**: Train and evaluate TF-IDF + Logistic Regression pipelines for:\n", + "- Task A: Binary classification (sarcastic vs non-sarcastic)\n", + "- Task B: Sarcasm type classification (6-class)\n", + "\n", + "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", + "\n", + "**Outputs**:\n", + "- `outputs/classical/tfidf_lr/best_config_binary.json`\n", + "- `outputs/classical/tfidf_lr/best_config_type.json`\n", + "- `outputs/classical/tfidf_lr/metrics_binary.json`\n", + "- `outputs/classical/tfidf_lr/metrics_type.json`\n", + "- Predictions CSV + confusion matrices PNG" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r8BzGF97uKBb" + }, + "source": [ + "## Helper Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "iU0Dnjl_uKBb" + }, + "outputs": [], + "source": [ + "def load_splits(task: str) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n", + " \"\"\"Load train/val/test CSVs for a given task ('binary' or 'type').\"\"\"\n", + " train = pd.read_csv(SPLITS / f\"train_{task}.csv\")\n", + " val = pd.read_csv(SPLITS / f\"val_{task}.csv\")\n", + " test = pd.read_csv(SPLITS / f\"test_{task}.csv\")\n", + " return train, val, test\n", + "\n", + "\n", + "def evaluate(\n", + " model,\n", + " X: list[str],\n", + " y_true: list,\n", + " label_names: list[str] | None = None,\n", + " split_name: str = \"\",\n", + ") -> dict:\n", + " \"\"\"Compute classification metrics and return as dict.\"\"\"\n", + " y_pred = model.predict(X)\n", + " metrics = {\n", + " \"split\" : split_name,\n", + " \"accuracy\" : accuracy_score(y_true, y_pred),\n", + " \"precision_macro\" : precision_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"recall_macro\" : recall_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"f1_macro\" : f1_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"f1_weighted\" : f1_score(y_true, y_pred, average=\"weighted\", zero_division=0),\n", + " \"report\" : classification_report(y_true, y_pred, target_names=label_names,\n", + " zero_division=0, output_dict=True),\n", + " }\n", + " print(f\"\\n[{split_name}] Accuracy={metrics['accuracy']:.4f} \"\n", + " f\"Macro-F1={metrics['f1_macro']:.4f} Weighted-F1={metrics['f1_weighted']:.4f}\")\n", + " print(classification_report(y_true, y_pred, target_names=label_names, zero_division=0))\n", + " return metrics, y_pred\n", + "\n", + "\n", + "def save_confusion_matrix(\n", + " y_true, y_pred, label_names: list[str], out_path: Path, title: str = \"\"\n", + ") -> None:\n", + " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", + " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", + " sns.heatmap(\n", + " cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n", + " xticklabels=label_names, yticklabels=label_names, ax=ax\n", + " )\n", + " ax.set_xlabel(\"Predicted\")\n", + " ax.set_ylabel(\"True\")\n", + " ax.set_title(title)\n", + " plt.tight_layout()\n", + " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + " print(f\"Saved: {out_path.relative_to(ROOT)}\")\n", + "\n", + "\n", + "def save_predictions(\n", + " df: pd.DataFrame, y_pred, label_col: str, out_path: Path\n", + ") -> None:\n", + " out = df[[\"sample_id\", \"pair_id\", \"group_id\", \"text\", label_col]].copy()\n", + " out[\"predicted\"] = y_pred\n", + " out[\"correct\"] = (out[label_col] == out[\"predicted\"]).astype(int)\n", + " out.to_csv(out_path, index=False)\n", + " print(f\"Saved: {out_path.relative_to(ROOT)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KZYEW3EruKBb" + }, + "source": [ + "## Task A — Binary Classification" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sUWB4dQKuKBb", + "outputId": "6e71869a-dea8-4988-9b06-369f294b6624" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train+Val: 48,166 | Train: 39,666 Val: 8,500 Test: 8,500\n" + ] + } ], - "image/png": 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\n" - }, - "metadata": {} + "source": [ + "# ── Load binary splits ────────────────────────────────────────────────────────\n", + "train_bin, val_bin, test_bin = load_splits(\"binary\")\n", + "\n", + "# Combine train+val for cross-validation, keep test for final eval\n", + "trainval_bin = pd.concat([train_bin, val_bin], ignore_index=True)\n", + "\n", + "X_trainval = trainval_bin[\"text\"].tolist()\n", + "y_trainval = trainval_bin[\"binary_label\"].tolist()\n", + "groups_tv = trainval_bin[\"group_id\"].tolist()\n", + "\n", + "X_train = train_bin[\"text\"].tolist()\n", + "y_train = train_bin[\"binary_label\"].tolist()\n", + "\n", + "X_val = val_bin[\"text\"].tolist()\n", + "y_val = val_bin[\"binary_label\"].tolist()\n", + "\n", + "X_test = test_bin[\"text\"].tolist()\n", + "y_test = test_bin[\"binary_label\"].tolist()\n", + "\n", + "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", + "\n", + "print(f\"Train+Val: {len(X_trainval):,} | Train: {len(X_train):,} Val: {len(X_val):,} Test: {len(X_test):,}\")" + ] }, { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/classical/tfidf_lr/confusion_matrix_binary_val.png\n" - ] + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BatmO-vAuKBc", + "outputId": "a2cff6ab-d795-4027-afda-641d16dd2e61" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Grid size: 4 param combos → 180 fits\n", + "Running grid search...\n", + "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n", + "\n", + "Best params : {'lr__C': 3.0, 'tfidf__max_features': None, 'tfidf__min_df': 3, 'tfidf__ngram_range': (1, 2)}\n", + "Best CV F1 : 0.8143\n" + ] + } + ], + "source": [ + "# ── Define pipeline and grid ──────────────────────────────────────────────────\n", + "pipe_bin = Pipeline([\n", + " (\"tfidf\", TfidfVectorizer(sublinear_tf=True, lowercase=True)),\n", + " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, solver=\"lbfgs\")),\n", + "])\n", + "\n", + "param_grid_bin = {\n", + " \"tfidf__ngram_range\" : [(1, 1), (1, 2)],\n", + " \"tfidf__min_df\" : [2, 3, 5],\n", + " \"tfidf__max_features\": [None, 50_000],\n", + " \"lr__C\" : [0.1, 1.0, 3.0],\n", + "}\n", + "\n", + "cv_bin = GroupKFold(n_splits=5)\n", + "\n", + "gs_bin = GridSearchCV(\n", + " pipe_bin, param_grid_bin,\n", + " cv=cv_bin, scoring=\"f1_macro\",\n", + " n_jobs=-1, verbose=1, refit=True,\n", + ")\n", + "\n", + "print(f\"Grid size: {len(gs_bin.param_grid)} param combos → {5 * 2*3*2*3} fits\")\n", + "print(\"Running grid search...\")\n", + "gs_bin.fit(X_trainval, y_trainval, groups=groups_tv)\n", + "\n", + "print(f\"\\nBest params : {gs_bin.best_params_}\")\n", + "print(f\"Best CV F1 : {gs_bin.best_score_:.4f}\")" + ] }, { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JstBvt1vuKBc", + "outputId": "efd32cd4-5faf-4a11-a341-b3aaae8b3ca2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/best_config_binary.json\n" + ] + } ], - "image/png": 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\n" - }, - "metadata": {} + "source": [ + "# ── Save best config ──────────────────────────────────────────────────────────\n", + "best_cfg_bin = {\"task\": \"binary\", \"model\": \"TF-IDF + LR\", \"seed\": SEED,\n", + " \"best_params\": gs_bin.best_params_, \"cv_f1_macro\": gs_bin.best_score_}\n", + "with open(OUT_TFIDF / \"best_config_binary.json\", \"w\") as f:\n", + " json.dump(best_cfg_bin, f, indent=2)\n", + "print(\"Saved: outputs/classical/tfidf_lr/best_config_binary.json\")" + ] }, { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/classical/tfidf_lr/confusion_matrix_binary_test.png\n" - ] - } - ], - "source": [ - "# ── Confusion matrices ────────────────────────────────────────────────────────\n", - "save_confusion_matrix(\n", - " y_val, y_val_pred_bin, label_names_bin,\n", - " OUT_TFIDF / \"confusion_matrix_binary_val.png\",\n", - " \"TF-IDF + LR — Binary (Val)\"\n", - ")\n", - "save_confusion_matrix(\n", - " y_test, y_test_pred_bin, label_names_bin,\n", - " OUT_TFIDF / \"confusion_matrix_binary_test.png\",\n", - " \"TF-IDF + LR — Binary (Test)\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AjH8_5UQuKBc", + "outputId": "25ceb060-ced0-4238-e80b-d9f8231cb376" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "[Val] Accuracy=0.9186 Macro-F1=0.9186 Weighted-F1=0.9186\n", + " precision recall f1-score support\n", + "\n", + "non-sarcastic 0.92 0.92 0.92 4250\n", + " sarcastic 0.92 0.92 0.92 4250\n", + "\n", + " accuracy 0.92 8500\n", + " macro avg 0.92 0.92 0.92 8500\n", + " weighted avg 0.92 0.92 0.92 8500\n", + "\n", + "\n", + "[Test] Accuracy=0.8189 Macro-F1=0.8189 Weighted-F1=0.8189\n", + " precision recall f1-score support\n", + "\n", + "non-sarcastic 0.82 0.82 0.82 4250\n", + " sarcastic 0.82 0.82 0.82 4250\n", + "\n", + " accuracy 0.82 8500\n", + " macro avg 0.82 0.82 0.82 8500\n", + " weighted avg 0.82 0.82 0.82 8500\n", + "\n", + "Saved: outputs/classical/tfidf_lr/metrics_binary.json\n" + ] + } + ], + "source": [ + "# ── Evaluate on val and test ──────────────────────────────────────────────────\n", + "best_bin = gs_bin.best_estimator_\n", + "\n", + "val_metrics_bin, y_val_pred_bin = evaluate(best_bin, X_val, y_val, label_names_bin, \"Val\")\n", + "test_metrics_bin, y_test_pred_bin = evaluate(best_bin, X_test, y_test, label_names_bin, \"Test\")\n", + "\n", + "all_metrics_bin = {\"val\": val_metrics_bin, \"test\": test_metrics_bin}\n", + "\n", + "# Remove classification_report dict (too nested for JSON)\n", + "for split_m in all_metrics_bin.values():\n", + " split_m.pop(\"report\", None)\n", + "\n", + "with open(OUT_TFIDF / \"metrics_binary.json\", \"w\") as f:\n", + " json.dump(all_metrics_bin, f, indent=2)\n", + "print(\"Saved: outputs/classical/tfidf_lr/metrics_binary.json\")" + ] }, - "id": "_2QLSOmTuKBc", - "outputId": "8d05bf4c-dcc5-47d0-8ae4-7ccc6754d50a" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/classical/tfidf_lr/predictions_val_binary.csv\n", - "Saved: outputs/classical/tfidf_lr/predictions_test_binary.csv\n" - ] - } - ], - "source": [ - "# ── Save predictions ──────────────────────────────────────────────────────────\n", - "save_predictions(val_bin, y_val_pred_bin, \"binary_label\", OUT_TFIDF / \"predictions_val_binary.csv\")\n", - "save_predictions(test_bin, y_test_pred_bin, \"binary_label\", OUT_TFIDF / \"predictions_test_binary.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "ichiimejuKBc", + "outputId": "ed82d1a4-c109-41df-fbb5-35204969bb6c" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/confusion_matrix_binary_val.png\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/confusion_matrix_binary_test.png\n" + ] + } + ], + "source": [ + "# ── Confusion matrices ────────────────────────────────────────────────────────\n", + "save_confusion_matrix(\n", + " y_val, y_val_pred_bin, label_names_bin,\n", + " OUT_TFIDF / \"confusion_matrix_binary_val.png\",\n", + " \"TF-IDF + LR — Binary (Val)\"\n", + ")\n", + "save_confusion_matrix(\n", + " y_test, y_test_pred_bin, label_names_bin,\n", + " OUT_TFIDF / \"confusion_matrix_binary_test.png\",\n", + " \"TF-IDF + LR — Binary (Test)\"\n", + ")" + ] }, - "id": "bigHRH2MuKBc", - "outputId": "40c0448d-3f53-4ba4-cc17-3347f11dc23b" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Top 20 sarcastic indicators (positive coef):\n", - " because +22.1982\n", - " finally +9.5906\n", - " obviously +9.4255\n", - " just +9.2842\n", - " so +8.8645\n", - " shocking +8.6605\n", - " clearly +8.4488\n", - " as if +8.3217\n", - " proving +7.5734\n", - " groundbreaking +7.5588\n", - " nation +7.4750\n", - " like +7.1686\n", - " nothing +6.5188\n", - " shocker +6.5093\n", - " never +6.4249\n", - " totally +6.3599\n", - " always +6.1954\n", - " sure +6.1921\n", - " area +6.0594\n", - " who needs +6.0482\n", - "\n", - "Top 20 non-sarcastic indicators (negative coef):\n", - " the -11.8202\n", - " an -11.5473\n", - " is -10.1138\n", - " was -7.7886\n", - " an area -6.0294\n", - " man is -6.0119\n", - " his -5.8977\n", - " are -5.7913\n", - " finds that -5.7222\n", - " the nation -5.6480\n", - " indicates -5.5894\n", - " donald trump -5.1844\n", - " donald -5.0836\n", - " says he -4.9047\n", - " did not -4.7563\n", - " because of -4.7343\n", - " may -4.7339\n", - " large -4.6423\n", - " her -4.5442\n", - " how to -4.3584\n" - ] - } - ], - "source": [ - "# ── Feature importance: top discriminative terms ──────────────────────────────\n", - "tfidf_vocab = best_bin.named_steps[\"tfidf\"].get_feature_names_out()\n", - "lr_coefs = best_bin.named_steps[\"lr\"].coef_[0] # binary: shape (n_features,)\n", - "\n", - "n_top = 20\n", - "top_pos_idx = np.argsort(lr_coefs)[-n_top:][::-1]\n", - "top_neg_idx = np.argsort(lr_coefs)[:n_top]\n", - "\n", - "top_positive = [(tfidf_vocab[i], lr_coefs[i]) for i in top_pos_idx]\n", - "top_negative = [(tfidf_vocab[i], lr_coefs[i]) for i in top_neg_idx]\n", - "\n", - "print(\"Top 20 sarcastic indicators (positive coef):\")\n", - "for term, coef in top_positive:\n", - " print(f\" {term:35s} {coef:+.4f}\")\n", - "\n", - "print(\"\\nTop 20 non-sarcastic indicators (negative coef):\")\n", - "for term, coef in top_negative:\n", - " print(f\" {term:35s} {coef:+.4f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 644 + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_2QLSOmTuKBc", + "outputId": "f93f94b4-bbb7-4c4d-da70-202ddead1c1d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/predictions_val_binary.csv\n", + "Saved: outputs/classical/tfidf_lr/predictions_test_binary.csv\n" + ] + } + ], + "source": [ + "# ── Save predictions ──────────────────────────────────────────────────────────\n", + "save_predictions(val_bin, y_val_pred_bin, \"binary_label\", OUT_TFIDF / \"predictions_val_binary.csv\")\n", + "save_predictions(test_bin, y_test_pred_bin, \"binary_label\", OUT_TFIDF / \"predictions_test_binary.csv\")" + ] }, - "id": "pEArvWzTuKBd", - "outputId": "c4709ebc-9b56-468b-eb9f-c47f1ba006b8" - }, - "outputs": [ { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bigHRH2MuKBc", + "outputId": "1b396986-a495-4f0b-9537-18a751ff1eb6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Top 20 sarcastic indicators (positive coef):\n", + " because +22.1982\n", + " finally +9.5906\n", + " obviously +9.4255\n", + " just +9.2842\n", + " so +8.8645\n", + " shocking +8.6605\n", + " clearly +8.4488\n", + " as if +8.3217\n", + " proving +7.5734\n", + " groundbreaking +7.5588\n", + " nation +7.4750\n", + " like +7.1686\n", + " nothing +6.5188\n", + " shocker +6.5093\n", + " never +6.4249\n", + " totally +6.3599\n", + " always +6.1954\n", + " sure +6.1921\n", + " area +6.0594\n", + " who needs +6.0482\n", + "\n", + "Top 20 non-sarcastic indicators (negative coef):\n", + " the -11.8202\n", + " an -11.5473\n", + " is -10.1138\n", + " was -7.7886\n", + " an area -6.0294\n", + " man is -6.0119\n", + " his -5.8977\n", + " are -5.7913\n", + " finds that -5.7222\n", + " the nation -5.6480\n", + " indicates -5.5894\n", + " donald trump -5.1844\n", + " donald -5.0836\n", + " says he -4.9047\n", + " did not -4.7563\n", + " because of -4.7343\n", + " may -4.7339\n", + " large -4.6423\n", + " her -4.5442\n", + " how to -4.3584\n" + ] + } ], - "image/png": 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- }, - "metadata": {} + "source": [ + "# ── Feature importance: top discriminative terms ──────────────────────────────\n", + "tfidf_vocab = best_bin.named_steps[\"tfidf\"].get_feature_names_out()\n", + "lr_coefs = best_bin.named_steps[\"lr\"].coef_[0] # binary: shape (n_features,)\n", + "\n", + "n_top = 20\n", + "top_pos_idx = np.argsort(lr_coefs)[-n_top:][::-1]\n", + "top_neg_idx = np.argsort(lr_coefs)[:n_top]\n", + "\n", + "top_positive = [(tfidf_vocab[i], lr_coefs[i]) for i in top_pos_idx]\n", + "top_negative = [(tfidf_vocab[i], lr_coefs[i]) for i in top_neg_idx]\n", + "\n", + "print(\"Top 20 sarcastic indicators (positive coef):\")\n", + "for term, coef in top_positive:\n", + " print(f\" {term:35s} {coef:+.4f}\")\n", + "\n", + "print(\"\\nTop 20 non-sarcastic indicators (negative coef):\")\n", + "for term, coef in top_negative:\n", + " print(f\" {term:35s} {coef:+.4f}\")" + ] }, { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/classical/tfidf_lr/feature_importance_binary.png\n" - ] - } - ], - "source": [ - "# ── Feature importance plot ───────────────────────────────────────────────────\n", - "fig, axes = plt.subplots(1, 2, figsize=(16, 7))\n", - "\n", - "terms_pos, coefs_pos = zip(*top_positive)\n", - "axes[0].barh(list(reversed(terms_pos)), list(reversed(coefs_pos)), color=\"steelblue\")\n", - "axes[0].set_title(\"Top 20 — Sarcastic (positive)\", fontsize=13)\n", - "axes[0].set_xlabel(\"LR Coefficient\")\n", - "\n", - "terms_neg, coefs_neg = zip(*top_negative)\n", - "axes[1].barh(list(reversed(terms_neg)), list(reversed(coefs_neg)), color=\"coral\")\n", - "axes[1].set_title(\"Top 20 — Non-Sarcastic (negative)\", fontsize=13)\n", - "axes[1].set_xlabel(\"LR Coefficient\")\n", - "\n", - "plt.suptitle(\"TF-IDF + LR: Feature Importance (Binary Task)\", fontsize=14)\n", - "plt.tight_layout()\n", - "plt.savefig(OUT_TFIDF / \"feature_importance_binary.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "print(\"Saved: outputs/classical/tfidf_lr/feature_importance_binary.png\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5VFBy1bJuKBd" - }, - "source": [ - "## Task B — Sarcasm Type Classification" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 644 + }, + "id": "pEArvWzTuKBd", + "outputId": "b663e9c6-e319-460c-e5f7-9f97e56e52e9" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/feature_importance_binary.png\n" + ] + } + ], + "source": [ + "# ── Feature importance plot ───────────────────────────────────────────────────\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 7))\n", + "\n", + "terms_pos, coefs_pos = zip(*top_positive)\n", + "axes[0].barh(list(reversed(terms_pos)), list(reversed(coefs_pos)), color=\"steelblue\")\n", + "axes[0].set_title(\"Top 20 — Sarcastic (positive)\", fontsize=13)\n", + "axes[0].set_xlabel(\"LR Coefficient\")\n", + "\n", + "terms_neg, coefs_neg = zip(*top_negative)\n", + "axes[1].barh(list(reversed(terms_neg)), list(reversed(coefs_neg)), color=\"coral\")\n", + "axes[1].set_title(\"Top 20 — Non-Sarcastic (negative)\", fontsize=13)\n", + "axes[1].set_xlabel(\"LR Coefficient\")\n", + "\n", + "plt.suptitle(\"TF-IDF + LR: Feature Importance (Binary Task)\", fontsize=14)\n", + "plt.tight_layout()\n", + "plt.savefig(OUT_TFIDF / \"feature_importance_binary.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/classical/tfidf_lr/feature_importance_binary.png\")" + ] }, - "id": "GTvcYYGquKBd", - "outputId": "f3c82bbf-5d44-4c4c-d8f0-2f586415fced" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n", - "Train+Val: 24,083 Train: 19,833 Val: 4,250 Test: 4,250\n" - ] - } - ], - "source": [ - "# ── Load type splits ──────────────────────────────────────────────────────────\n", - "train_type, val_type, test_type = load_splits(\"type\")\n", - "\n", - "# Encode string labels to integers\n", - "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", - "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", - "id2label = {i: lab for lab, i in label2id.items()}\n", - "\n", - "def encode_labels(df: pd.DataFrame) -> list[int]:\n", - " return [label2id[l] for l in df[\"type_label\"]]\n", - "\n", - "trainval_type = pd.concat([train_type, val_type], ignore_index=True)\n", - "\n", - "X_trainval_t = trainval_type[\"text\"].tolist()\n", - "y_trainval_t = encode_labels(trainval_type)\n", - "groups_tv_t = trainval_type[\"group_id\"].tolist()\n", - "\n", - "X_train_t = train_type[\"text\"].tolist()\n", - "y_train_t = encode_labels(train_type)\n", - "X_val_t = val_type[\"text\"].tolist()\n", - "y_val_t = encode_labels(val_type)\n", - "X_test_t = test_type[\"text\"].tolist()\n", - "y_test_t = encode_labels(test_type)\n", - "\n", - "print(f\"Strategies: {STRATEGY_LABELS}\")\n", - "print(f\"Train+Val: {len(X_trainval_t):,} Train: {len(X_train_t):,} Val: {len(X_val_t):,} Test: {len(X_test_t):,}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "5VFBy1bJuKBd" + }, + "source": [ + "## Task B — Sarcasm Type Classification" + ] }, - "id": "yD1TrZZPuKBd", - "outputId": "4faad70d-dc90-4fcd-de7d-19de03226861" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Running grid search for type task...\n", - "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n", - "\n", - "Best params : {'lr__C': 3.0, 'lr__class_weight': 'balanced', 'tfidf__max_features': None, 'tfidf__min_df': 2, 'tfidf__ngram_range': (1, 2)}\n", - "Best CV F1 : 0.3840\n" - ] - } - ], - "source": [ - "# ── Define pipeline and grid (with class_weight) ──────────────────────────────\n", - "pipe_type = Pipeline([\n", - " (\"tfidf\", TfidfVectorizer(sublinear_tf=True, lowercase=True)),\n", - " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, solver=\"lbfgs\",\n", - " multi_class=\"auto\")),\n", - "])\n", - "\n", - "param_grid_type = {\n", - " \"tfidf__ngram_range\" : [(1, 1), (1, 2)],\n", - " \"tfidf__min_df\" : [2, 3, 5],\n", - " \"tfidf__max_features\": [None, 50_000],\n", - " \"lr__C\" : [0.1, 1.0, 3.0],\n", - " \"lr__class_weight\" : [None, \"balanced\"],\n", - "}\n", - "\n", - "cv_type = GroupKFold(n_splits=5)\n", - "\n", - "gs_type = GridSearchCV(\n", - " pipe_type, param_grid_type,\n", - " cv=cv_type, scoring=\"f1_macro\",\n", - " n_jobs=-1, verbose=1, refit=True,\n", - ")\n", - "\n", - "print(\"Running grid search for type task...\")\n", - "gs_type.fit(X_trainval_t, y_trainval_t, groups=groups_tv_t)\n", - "\n", - "print(f\"\\nBest params : {gs_type.best_params_}\")\n", - "print(f\"Best CV F1 : {gs_type.best_score_:.4f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GTvcYYGquKBd", + "outputId": "7a799948-40b7-4124-ccda-3437bb09c9b7" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n", + "Train+Val: 24,083 Train: 19,833 Val: 4,250 Test: 4,250\n" + ] + } + ], + "source": [ + "# ── Load type splits ──────────────────────────────────────────────────────────\n", + "train_type, val_type, test_type = load_splits(\"type\")\n", + "\n", + "# Encode string labels to integers\n", + "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", + "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", + "id2label = {i: lab for lab, i in label2id.items()}\n", + "\n", + "def encode_labels(df: pd.DataFrame) -> list[int]:\n", + " return [label2id[l] for l in df[\"type_label\"]]\n", + "\n", + "trainval_type = pd.concat([train_type, val_type], ignore_index=True)\n", + "\n", + "X_trainval_t = trainval_type[\"text\"].tolist()\n", + "y_trainval_t = encode_labels(trainval_type)\n", + "groups_tv_t = trainval_type[\"group_id\"].tolist()\n", + "\n", + "X_train_t = train_type[\"text\"].tolist()\n", + "y_train_t = encode_labels(train_type)\n", + "X_val_t = val_type[\"text\"].tolist()\n", + "y_val_t = encode_labels(val_type)\n", + "X_test_t = test_type[\"text\"].tolist()\n", + "y_test_t = encode_labels(test_type)\n", + "\n", + "print(f\"Strategies: {STRATEGY_LABELS}\")\n", + "print(f\"Train+Val: {len(X_trainval_t):,} Train: {len(X_train_t):,} Val: {len(X_val_t):,} Test: {len(X_test_t):,}\")" + ] }, - "id": "huaF8TbjuKBd", - "outputId": "d43d2757-7486-4307-912b-462d285835ce" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/classical/tfidf_lr/best_config_type.json\n" - ] - } - ], - "source": [ - "# ── Save best config ──────────────────────────────────────────────────────────\n", - "best_cfg_type = {\"task\": \"type\", \"model\": \"TF-IDF + LR\", \"seed\": SEED,\n", - " \"label_names\": STRATEGY_LABELS, \"label2id\": label2id,\n", - " \"best_params\": gs_type.best_params_, \"cv_f1_macro\": gs_type.best_score_}\n", - "with open(OUT_TFIDF / \"best_config_type.json\", \"w\") as f:\n", - " json.dump(best_cfg_type, f, indent=2)\n", - "print(\"Saved: outputs/classical/tfidf_lr/best_config_type.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yD1TrZZPuKBd", + "outputId": "3fe1a168-c9bc-4ee5-9dd3-d56607b04517" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Running grid search for type task...\n", + "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n", + "\n", + "Best params : {'lr__C': 3.0, 'lr__class_weight': 'balanced', 'tfidf__max_features': None, 'tfidf__min_df': 2, 'tfidf__ngram_range': (1, 2)}\n", + "Best CV F1 : 0.3848\n" + ] + } + ], + "source": [ + "# ── Define pipeline and grid (with class_weight) ──────────────────────────────\n", + "pipe_type = Pipeline([\n", + " (\"tfidf\", TfidfVectorizer(sublinear_tf=True, lowercase=True)),\n", + " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, solver=\"lbfgs\",\n", + " multi_class=\"auto\")),\n", + "])\n", + "\n", + "param_grid_type = {\n", + " \"tfidf__ngram_range\" : [(1, 1), (1, 2)],\n", + " \"tfidf__min_df\" : [2, 3, 5],\n", + " \"tfidf__max_features\": [None, 50_000],\n", + " \"lr__C\" : [0.1, 1.0, 3.0],\n", + " \"lr__class_weight\" : [None, \"balanced\"],\n", + "}\n", + "\n", + "cv_type = GroupKFold(n_splits=5)\n", + "\n", + "gs_type = GridSearchCV(\n", + " pipe_type, param_grid_type,\n", + " cv=cv_type, scoring=\"f1_macro\",\n", + " n_jobs=-1, verbose=1, refit=True,\n", + ")\n", + "\n", + "print(\"Running grid search for type task...\")\n", + "gs_type.fit(X_trainval_t, y_trainval_t, groups=groups_tv_t)\n", + "\n", + "print(f\"\\nBest params : {gs_type.best_params_}\")\n", + "print(f\"Best CV F1 : {gs_type.best_score_:.4f}\")" + ] }, - "id": "c4Hj46aWuKB6", - "outputId": "f2ac2819-bbf4-43a3-9437-9cfdafc29c12" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "[Val] Accuracy=0.8967 Macro-F1=0.8982 Weighted-F1=0.8961\n", - " precision recall f1-score support\n", - "\n", - " irony 0.92 0.87 0.90 942\n", - " overstatement 0.88 0.97 0.92 600\n", - "rhetorical_question 0.79 1.00 0.88 157\n", - " sarcasm 0.94 0.83 0.88 1317\n", - " satire 0.88 0.91 0.90 747\n", - " understatement 0.85 0.98 0.91 487\n", - "\n", - " accuracy 0.90 4250\n", - " macro avg 0.88 0.93 0.90 4250\n", - " weighted avg 0.90 0.90 0.90 4250\n", - "\n", - "\n", - "[Test] Accuracy=0.3958 Macro-F1=0.4047 Weighted-F1=0.3947\n", - " precision recall f1-score support\n", - "\n", - " irony 0.33 0.29 0.31 902\n", - " overstatement 0.38 0.43 0.40 592\n", - "rhetorical_question 0.42 0.60 0.50 176\n", - " sarcasm 0.48 0.42 0.45 1291\n", - " satire 0.38 0.39 0.38 833\n", - " understatement 0.36 0.43 0.39 456\n", - "\n", - " accuracy 0.40 4250\n", - " macro avg 0.39 0.43 0.40 4250\n", - " weighted avg 0.40 0.40 0.39 4250\n", - "\n", - "Saved: outputs/classical/tfidf_lr/metrics_type.json\n" - ] - } - ], - "source": [ - "# ── Evaluate on val and test ──────────────────────────────────────────────────\n", - "best_type = gs_type.best_estimator_\n", - "\n", - "val_metrics_type, y_val_pred_type = evaluate(best_type, X_val_t, y_val_t, STRATEGY_LABELS, \"Val\")\n", - "test_metrics_type, y_test_pred_type = evaluate(best_type, X_test_t, y_test_t, STRATEGY_LABELS, \"Test\")\n", - "\n", - "all_metrics_type = {\"val\": val_metrics_type, \"test\": test_metrics_type}\n", - "for split_m in all_metrics_type.values():\n", - " split_m.pop(\"report\", None)\n", - "\n", - "with open(OUT_TFIDF / \"metrics_type.json\", \"w\") as f:\n", - " json.dump(all_metrics_type, f, indent=2)\n", - "print(\"Saved: outputs/classical/tfidf_lr/metrics_type.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "huaF8TbjuKBd", + "outputId": "5be99663-2b1a-471c-f1be-2db177eca440" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/best_config_type.json\n" + ] + } + ], + "source": [ + "# ── Save best config ──────────────────────────────────────────────────────────\n", + "best_cfg_type = {\"task\": \"type\", \"model\": \"TF-IDF + LR\", \"seed\": SEED,\n", + " \"label_names\": STRATEGY_LABELS, \"label2id\": label2id,\n", + " \"best_params\": gs_type.best_params_, \"cv_f1_macro\": gs_type.best_score_}\n", + "with open(OUT_TFIDF / \"best_config_type.json\", \"w\") as f:\n", + " json.dump(best_cfg_type, f, indent=2)\n", + "print(\"Saved: outputs/classical/tfidf_lr/best_config_type.json\")" + ] }, - "id": "ldimkyrXuKB6", - "outputId": "e8e286df-267e-4802-8318-d0cc9f768808" - }, - "outputs": [ { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "c4Hj46aWuKB6", + "outputId": "f1d8acad-2144-49c6-e79e-20cea219f732" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "[Val] Accuracy=0.8979 Macro-F1=0.8991 Weighted-F1=0.8973\n", + " precision recall f1-score support\n", + "\n", + " irony 0.92 0.87 0.90 942\n", + " overstatement 0.88 0.97 0.92 600\n", + "rhetorical_question 0.79 1.00 0.88 157\n", + " sarcasm 0.94 0.83 0.88 1317\n", + " satire 0.88 0.92 0.90 747\n", + " understatement 0.85 0.98 0.91 487\n", + "\n", + " accuracy 0.90 4250\n", + " macro avg 0.88 0.93 0.90 4250\n", + " weighted avg 0.90 0.90 0.90 4250\n", + "\n", + "\n", + "[Test] Accuracy=0.3953 Macro-F1=0.4047 Weighted-F1=0.3942\n", + " precision recall f1-score support\n", + "\n", + " irony 0.33 0.29 0.31 902\n", + " overstatement 0.38 0.43 0.40 592\n", + "rhetorical_question 0.42 0.61 0.50 176\n", + " sarcasm 0.48 0.41 0.44 1291\n", + " satire 0.38 0.39 0.38 833\n", + " understatement 0.36 0.43 0.39 456\n", + "\n", + " accuracy 0.40 4250\n", + " macro avg 0.39 0.43 0.40 4250\n", + " weighted avg 0.40 0.40 0.39 4250\n", + "\n", + "Saved: outputs/classical/tfidf_lr/metrics_type.json\n" + ] + } ], - "image/png": 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\n" - }, - "metadata": {} + "source": [ + "# ── Evaluate on val and test ──────────────────────────────────────────────────\n", + "best_type = gs_type.best_estimator_\n", + "\n", + "val_metrics_type, y_val_pred_type = evaluate(best_type, X_val_t, y_val_t, STRATEGY_LABELS, \"Val\")\n", + "test_metrics_type, y_test_pred_type = evaluate(best_type, X_test_t, y_test_t, STRATEGY_LABELS, \"Test\")\n", + "\n", + "all_metrics_type = {\"val\": val_metrics_type, \"test\": test_metrics_type}\n", + "for split_m in all_metrics_type.values():\n", + " split_m.pop(\"report\", None)\n", + "\n", + "with open(OUT_TFIDF / \"metrics_type.json\", \"w\") as f:\n", + " json.dump(all_metrics_type, f, indent=2)\n", + "print(\"Saved: outputs/classical/tfidf_lr/metrics_type.json\")" + ] }, { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/classical/tfidf_lr/confusion_matrix_type_val.png\n" - ] + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "ldimkyrXuKB6", + "outputId": "135d3379-d50a-4db9-8121-9686341ad692" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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Msu+seQvg3rDJL44we9avXY1z/qfxIvQ5NDW1UL5iJQwbORo2traSPu/eRWPxgr9w/epVxMfHw9rGBr37DYR7w0YKjFw6ae+xb82cPhV7d+/EmHET4dHd8xdHmgO8DpRUrEARjI2Noa+vn+W65OTkXxxN3hroXhIebtaYuvcBGsy5gLmHH6J//ZLoWdsGAKCtoYpyxQyw7NRTtPS7hIHrbqKEmS7W9HXJcn8TWpVB5KfMiVV+kJCQAHv7Mpg4aUqW60+f+1ewTJs+CyKRCO4N8vcfrYSEBNiXzvq4EhMTERIcjH4DBmP7rr3wW7QUL1+EYuTQwQqINOeCH9zD/r27UMqutKTN3NwCR09fECz9Bg6Fjo4OXN1qKTDan7t1IxAdu3TD+i07sHz1P0hNTcHQgX2Q8OWLpM/USRPw8sUL+C1Zjh37DqJeg4aYOHYUHoYEKzBy6aS9x746638a9+7egamZ2S+KTA5EKvJbZHDx4kW0bNkSRYoUgUgkwoEDBwTrxWIxpkyZAktLS2hra6NBgwZ48uSJoM+HDx/g4eEBAwMDGBkZoU+fPoiLixP0uXv3LmrVqgUtLS0UL14c8+bNk/khYgJFgiE8GxsbzJgxAz169ICBgQH69+8PANi7dy/Kli0LTU1N2NjYwM/PT7APGxsbzJ49G71794a+vj6srKywevVqyfr69etj6NChgm2io6OhoaEBf3//vD3AbzjbFsLp+5E4FxyFNx8ScPxOBP59FI0KVkYAgM+Jqei+8jqOBoXjeVQ8gl7GYOqeByhvZYQiRlqCfdVxMEWtMqaYfTDkl8Uvi5q1amPI8JGo75511aJwYVPBcv7cWVSpWg3Fihf/xZHKRnJcDTIfl76+PlatXYdGTZrCxrYEyleoiAl/eiMk+AHCw98qIFrZffkSj6l/jsNEbx/oGxhI2lVVVWFS2FSwXDh3Bu4Nm0BHR1eBEf/c0lVr0LJ1W5QsZQf70mUwbYYvIsLDERL8QNLnblAQOnf1QDmn8ihWrDj69h8EfX19PPymT34k7T0GAFGRkZg7eyZmz/kLamoc9JEmPj4eFSpUwPLly7NcP2/ePCxZsgSrVq1CQEAAdHV10bhxYyQmJkr6eHh44MGDBzh9+jSOHDmCixcvSv6WAUBsbCwaNWoEa2tr3Lx5E3/99RemTZsm+JuVHUygKJP58+ejQoUKuH37Nry9vXHz5k106tQJXbp0wb179zBt2jR4e3tjw4YNgu38/Pzg4uKC27dvY/DgwRg0aBAePXoEAOjbty+2bduGpKT/qjVbtmxB0aJFUb9+/V92bLdCP8LN3gS2phl/cByK6KNKCWOcD4n64Tb62mpITxcjNiFV0lZYTwO+nZ3gtSUICSlpeR53Xnv/7h0u/XsBbdq2V3Qocvc57jNEIhH09Q2kd84H5vvOhFutOqhavcZP+z0MfoDHjx6iZZuC9ZzFxX0GABgYGkraylesiNMnj+PTpxikp6fj5PGjSEpKRuUqVRUVplykp6dj8p/j4NmrD0qWslN0OLIRieS3yKBp06aYOXMm2rZtm2mdWCzGokWLMHnyZLRu3Rrly5fHpk2b8PbtW0mlKiQkBCdOnMDatWtRrVo11KxZE0uXLsWOHTvw9m3Gl6itW7ciOTkZ69atQ9myZdGlSxcMHz4cCxYskClWJlCUSf369TF69GiULFkSJUuWxIIFC+Du7g5vb2/Y29ujZ8+eGDp0KP766y/Bds2aNcPgwYNRqlQpjB8/HoULF8a5c+cAAO3atQMAHDx4UNJ/w4YN6NmzJ0S/cKx9pf8zHL71Fmcm1sFjv6Y4MqYW1l0IxcGbWVcnNNRUML6lAw7deou4pP8SqL88KmDb5TDce/XpV4Wepw4fOgAdHV3Uz+fDd7JKSkrCkoXz0aRZc+jp6Sk6HKlOnziGRw+DMWjYKKl9Dx3Ym1Flq1jpF0QmH+np6fCb54sKlZxRys5e0j7nr4VITU2Fey1XuLpUwOwZ0zB/0VIUt7JWYLS5t37dGqiqqqKrR3dFhyI7BQ3h/UxoaCgiIiLQoMF/88wMDQ1RrVo1XL16FQBw9epVGBkZwcXlv2kXDRo0gIqKCgICAiR9ateuDQ0NDUmfxo0b49GjR/j48WO242E9kTL59oUHZGT0rVu3FrS5ublh0aJFSEtLg6qqKgCgfPnykvUikQgWFhaIisqo7GhpaaF79+5Yt24dOnXqhFu3buH+/fs4dOjQT2NJSkoSVK0AQJyaApFazia9N69oidaVi2LE5tt4EhEHx6IG8G7riMhPidgX+EbQV01FhOU9nSEC4L37vqS9Z20b6GqqYcWZpzmKIT86uH8vmjZvAU1NTUWHIjcpKSkYN3okxGLgT+9pig5HqsiIcCz4yxdLVq6V+jwkJibi1PGj6NVv4C+KTj7mzpqOZ0+fYO2GrYL2lcuX4HPsZ6xYvQ5GhQrh/Fl/TBg7CmvXb0Epe/sf7C1/C35wH9u3bMa2XXt/6ZfE/Cirz3FNTU2ZP28iIiIAAObmwhNCzM3NJesiIiJg9t1cMzU1NRgbGwv62H5zEsO3+4yIiEChQoWyFQ8rUJSJrm7O5lN8fyafSCRCenq65Hbfvn1x+vRpvH79GuvXr0f9+vVhbf3zb5i+vr4wNDQULDE3duUoPgCY2MoBq/yf4cjtcDwK/4z9N95g3flQDG5QStBPTUWEZT2dUbSQNrqvDBBUn1ztTOBsUwiP5jfFE7+mOD+pLgDgkJcb5nerkOPYFOXWzRt48SIUbdt3VHQocpOSkoLxo0ch/O1brFzzT4GoPj0MeYCPH96jZ7cOcHNxgpuLE27fDMSu7Vvg5uKEtLT/horPnTmFxMQENGvR+id7zF/mzp6BSxcvYNXajTC3sJC0v34Vhl3bt2LK9JmoWt0V9qXLoP+gIXB0LItdO7cpMOLcuX3rJj58eI9mjerDpWJZuFQsi/C3b7Fg/lw0a/zrpi3kmByH8LL6HPf19VX0EeYaK1AklYODAy5fvixou3z5Muzt7SXVp+xwcnKCi4sL1qxZg23btmHZsmVSt5k4cSK8vLwEbeX/PJvt+/yetoYq0sViQVuaWCy46O7X5MnGVBfdll1DzJcUQX+fvQ/gd/SR5La5oRY2DaqGYRtvI+hlTI5jU5QD+/bAwbEsSpfO+rIGBc3X5Cks7CVWr9sII6PsfZtUNJeqrti6+6CgbebUSbC2tUX3nn0F77VDB/aiVp36KGRs/KvDlJlYLMY835k4f/YM/v5nI4oWKyZYn5iQMflXRUX4fV5FVRXib76AFTTNW7ZCtequgrbBA/uieYvWaN0m8/yefEeOQ29ZfY7npNpt8f/EOzIyEpaWlpL2yMhIVKxYUdLn68jHV6mpqfjw4YNkewsLC0RGRgr6fL1t8U1yLw0TKJJq9OjRqFKlCmbMmIHOnTvj6tWrWLZsGVasWCHzvvr27YuhQ4dCV1c3y0mC38uqzJvT4TsA8H8QiSENS+Htx0Q8jviMskUN0KeuLXYHvAaQkTyt6OWMssUM0XdNIFRURCisn3H/n74kIyVNjLcxiYJ9xidnVAZevv+CiE/CdYr05Us8XoWFSW6/efMajx6GwMDQEJaWRQAAcXFxOH36JLzGjFdUmDL72XEVLmyKsV4j8DA4GIuXr0J6ehrevYsGkDFXQl1d40e7VThdXd1ME421tLVhaGgkaH8V9hJBt25gwdJVvzrEHJk7azpOHD8Kv8XLoKOrK3k+9PT0oaWlBRtbWxS3ssLs6VMxYvQ4GBkZ4fxZfwRcvYKFy1YqOPqfk/Ye+z55V1NTQ+HChWFjW+JXh6pQORmuy4qtrS0sLCzg7+8vSZhiY2MREBCAQYMGAQBcXV0RExODmzdvonLlygCAs2fPIj09HdWqVZP0mTRpElJSUiQjJ6dPn0bp0qWzPXwHMIGibHB2dsauXbswZcoUzJgxA5aWlpg+fTp69uwp8766du2KkSNHomvXrtDS0pK+gZxN2/sAXs1KY0aHsjDRy7iQ5vYrYVhyMuM6IuZGWmjolPEN5Ni42oJtuyy7ioCnH355zDkV/OA++vX+74J9fn/NAQC0bNUG02dl/P/k8aOAWIwmTZsrJMacCL7/3XHN+/9xtW6DgYOH4sK5jApllw5tBNutWbcRLlWr/bI488qRg/tgZm6Oaq5uig4lW/bs2gEAGNBbePHIqTNmo2XrtlBTV8fi5X9j6aIF8Bo2GF++fEFxKytMm+mLmrXqKCLkbMvOe6zAUtC8rbi4ODx9+t/80tDQUAQFBcHY2BhWVlYYOXIkZs6cCTs7O9ja2sLb2xtFihRBmzZtAGSMmDRp0gT9+vXDqlWrkJKSgqFDh6JLly4oUiTji2O3bt3g4+ODPn36YPz48bh//z4WL16MhQsXyhSrSCz+bjyDKA+9ePECJUuWRGBgIJydnXO0D9uRR+UcVf7wYF4zRYeQN5R4/mxSSsEdYvoZNVXlfNJUlXgyt46GfI9Nu9liue0r4diIbPc9f/486tWrl6nd09MTGzZsgFgsxtSpU7F69WrExMSgZs2aWLFiBey/Odngw4cPGDp0KA4fPgwVFRW0b98eS5YsEcyFvHv3LoYMGYLAwEAULlwYw4YNw/jxslXimUDRL5GSkoL3799jzJgxCA0NzTSnShZMoAoY5f2bxQSqgGEClX2KSqAKEg7h0S9x+fJl1KtXD/b29tizZ4+iwyEiop9R4mRTXphA0S9Rt25dsNhJRFRAyPEsPGXFR4iIiIhIRqxAERERkRArUFIxgSIiIiIhzoGSiikmERERkYxYgSIiIiIhDuFJxQSKiIiIhDiEJxVTTCIiIiIZsQJFREREQhzCk4oJFBEREQlxCE8qpphEREREMmIFioiIiARErEBJxQSKiIiIBJhAScchPCIiIiIZsQJFREREQixAScUEioiIiAQ4hCcdh/CIiIiIZMQKFBEREQmwAiUdEygiIiISYAIlHYfwiIiIiGTEChQREREJsAIlHRMoIiIiEmL+JBWH8IiIiIhkxAoUERERCXAITzomUERERCTABEo6DuERERERyYgVKCpwQuY3V3QIeWLQnnuKDiFPrOzgpOgQ8oy2hqqiQ8gTYrGiI8gbLKpkHytQ0rECRURERCQjVqCIiIhIgBUo6ZhAERERkRDzJ6k4hEdEREQkI1agiIiISIBDeNIxgSIiIiIBJlDScQiPiIiISEasQBEREZEAK1DSMYEiIiIiIeZPUnEIj4iIiEhGrEARERGRAIfwpGMCRURERAJMoKTjEB4RERGRjFiBIiIiIgFWoKRjAkVEREQCTKCk4xAeERERkYxYgSIiIiIhFqCkYgJFREREAhzCk45DeEREREQyYgWKiIiIBFiBko4JFBEREQkwgZKOQ3hEREREMmIFioiIiIRYgJKKCRQREREJcAhPOg7hEREREcmICVQ+0rNnT7Rp00bm7aZNm4aKFSvKPZ68VLduXYwcOVLRYUj1z5rVqFC2NOb5zlJ0KD/VupwZ1ndxEiyzm9lJ1htoqaFf9WJY1LoMVnUoi2mNSqFyMQPBPlo4mmJSgxJY1aEslrdz/NWHkGs7tm1F04b1UaWSEzy6dMS9u3cVHZJcFZTXYnakpaVh+dJFaNa4PqpVLo8WTRpg9arlEIvFig4tV3bt2IYObVuiRlVn1KjqjO7dOuPSvxcUHVaOiEQiuS3KikN4v0hycjI0NDQUHQbJ4P69u9izewfs7UsrOpRseR2TiL/Oh0pup6f/98eoX/Vi0FFXxeJ/XyIuKRXVrY0wuIYVfE49RVhMIgBATUWEwLBPePruC2qXMP7l8efGiePHMH+eLyZP9YGTUwVs3bwRgwb0wcEjJ2BiYqLo8HKtoL0WpVn/zxrs3rkd02fNRclSpRD84D6mTp4IPT19dPujh6LDyzEzcwuMGDUGVtbWEIvFOHzwAEYMHYKde/ejVCk76TvIR5Q58ZGX37YClZSUhOHDh8PMzAxaWlqoWbMmAgMDkZ6ejmLFimHlypWC/rdv34aKigpevnwJAIiJiUHfvn1hamoKAwMD1K9fH3fu3JH0/1oVWrt2LWxtbaGlpQUA2LNnD5ycnKCtrQ0TExM0aNAA8fHxmDZtGjZu3IiDBw9Ksvbz588DAMaPHw97e3vo6OigRIkS8Pb2RkpKCgBgw4YN8PHxwZ07dyTbbdiwQaYY161bBysrK+jp6WHw4MFIS0vDvHnzYGFhATMzM8yaJfzGm939bt68GTY2NjA0NESXLl3w+fNnABmVtgsXLmDx4sWSmF+8eJH7J1WOvsTHY+L4sZjqMxMGhoaKDidb0sVixCamSpa45DTJulImOjjz5D1CPyQgOj4Fh4Oj8SUlDTbG2pI+B+5H4dTj93j9KVER4efK5o3r0a5DJ7Rp2x4lS5XC5Kk+0NLSwoF9exUdWq4VxNeiNHeCbqNuPXfUrlMXRYsWQ8NGTeBaoybu3yvYVcO69eqjVu06sLa2gY2NLYaNGAUdHR3cvROk6NAoD/y2CdS4ceOwd+9ebNy4Ebdu3UKpUqXQuHFjxMTEoGvXrti2bZug/9atW+Hm5gZra2sAQMeOHREVFYXjx4/j5s2bcHZ2hru7Oz58+CDZ5unTp9i7dy/27duHoKAghIeHo2vXrujduzdCQkJw/vx5tGvXDmKxGGPGjEGnTp3QpEkThIeHIzw8HDVq1AAA6OvrY8OGDQgODsbixYuxZs0aLFy4EADQuXNnjB49GmXLlpVs17lz52zH+OzZMxw/fhwnTpzA9u3b8c8//6B58+Z4/fo1Lly4gLlz52Ly5MkICAiQbJPd/R44cABHjhzBkSNHcOHCBcyZMwcAsHjxYri6uqJfv36SmIsXLy7PpzfXZs+cjtq166C6aw1Fh5Jt5vqaWNC6DOa2KI3+1YvDWEddsu7p+y+oWtwQuhqqEAGoamUIdVUVPIyKV1zAcpKSnIyQ4AeC50pFRQXVq9fA3Tu3FRiZfBTE16I0FSpWQkDANbx8kVExffTwIW7fugm3WrUVHJn8pKWl4fixo0hI+IIKFSopOhyZcQhPut9yCC8+Ph4rV67Ehg0b0LRpUwDAmjVrcPr0afzzzz/w8PCAn58fwsLCYGVlhfT0dOzYsQOTJ08GAFy6dAnXr19HVFQUNDU1AQDz58/HgQMHsGfPHvTv3x9AxrDdpk2bYGpqCgC4desWUlNT0a5dO0ki5uTkJIlLW1sbSUlJsLCwEMT79X4BwMbGBmPGjMGOHTswbtw4aGtrQ09PD2pqaoLtshtjeno61q1bB319fTg6OqJevXp49OgRjh07BhUVFZQuXRpz587FuXPnUK1aNZn2u2HDBujr6wMAunfvDn9/f8yaNQuGhobQ0NCAjo5OpmPND44fO4qQkGBs27lH0aFk2/P3X7A24BUiYpNhpK2G1uXMMNG9BLyPP0FiajpWXA7D4BpWWNbOEanpYiSnpmPppZeIiktWdOi59jHmI9LS0jIN1ZmYmCA09LmCopKPgvhazI7effsjPj4ObVo2haqqKtLS0jB0+Cg0b9FK0aHl2pPHj9C9WxckJydBR0cHC5csR8lSpRQdluyUN++Rm98ygXr27BlSUlLg5uYmaVNXV0fVqlUREhKCsWPHwsHBAdu2bcOECRNw4cIFREVFoWPHjgCAO3fuIC4uLtMHdkJCAp49eya5bW1tLUmeAKBChQpwd3eHk5MTGjdujEaNGqFDhw4oVKjQT+PduXMnlixZgmfPniEuLg6pqakwMDD46TbZjdHGxkaS5ACAubk5VFVVoaKiImiLiorK1X4tLS0l+5BFUlISkpKSBG1iVU1J8iZvEeHhmDdnFv5esy7P7iMv3AuPk/z/9Sfg2fsvmN+yDKpYGeLf5x/Rzskc2hqqmHfuOeKS0uBc1ACDa1jB1/8ZXn9K+smeSVEK6msxO06dOI5jRw7Dd64fSpYqhUcPQ/DXXF+YmpmhVeu2ig4vV2xsbLFr7wHExX3G6VMn4f3nePyzYUvBTKLop37LBCo7PDw8JAnUtm3b0KRJE0nSEBcXB0tLS8kcpW8ZGRlJ/q+rqytYp6qqitOnT+PKlSs4deoUli5dikmTJiEgIAC2trZZxnH16lV4eHjAx8cHjRs3hqGhIXbs2AE/P7+fxp/dGNXV1QXrRCJRlm3p6em53u/XfcjC19cXPj4+grZJ3lMxeco0mfeVHcHBD/Dh/Xt06dhO0paWloabNwKxY/tWBN6+B1VV1Ty5b3lKSElH5OckmOtpwFRPAw3sC2PSscd4G5uRLL2KSYSdqS7q25lg0423Co42dwoZFYKqqirev38vaH///j0KFy6soKhyT1lei1lZ6DcPvfr2R5NmzQEAdvalER7+FuvW/l3gEyh1DQ1Y/X+EwbFsOTy4fw9bt2zClGnTFRyZbJR56E1efss5UCVLloSGhgYuX74saUtJSUFgYCAcHTNO3+7WrRvu37+PmzdvYs+ePfDw8JD0dXZ2RkREBNTU1FCqVCnBIu0DWyQSwc3NDT4+Prh9+zY0NDSwf/9+AICGhgbS0tIE/a9cuQJra2tMmjQJLi4usLOzk0xk/yqr7XIT48/Ia79ZxZyViRMn4tOnT4Jl7PiJOY5fmmrVq2PPgcPYufeAZClbthyatWiJnXsPFJg/WJpqKjDV00BMQio0VTM+CL8/QVwsFivFh6S6hgYcHMsi4NpVSVt6ejoCAq6ifAGce/KVsrwWs5KYmAiV7157KiqqgjNHlUV6ejpSkgveULmi5kClpaXB29sbtra20NbWRsmSJTFjxgzBJS7EYjGmTJkCS0tLaGtro0GDBnjy5IlgPx8+fICHhwcMDAxgZGSEPn36IC4u7vu7y5XfsgKlq6uLQYMGYezYsTA2NoaVlRXmzZuHL1++oE+fPgAyhqBq1KiBPn36IC0tDa1a/Tc236BBA7i6uqJNmzaYN28e7O3t8fbtWxw9ehRt27aFi4tLlvcbEBAAf39/NGrUCGZmZggICEB0dDQcHBwk93ny5Ek8evQIJiYmMDQ0hJ2dHcLCwrBjxw5UqVIFR48elSRcX9nY2CA0NBRBQUEoVqwY9PX1cxyjNPLar42NDQICAvDixQvo6enB2NhYMGz4laZm5uG6xNQchZ4turp6sLOzF7Rp6+jAyNAoU3t+0rmiBYLefMa7L8kopKWONk5mEIuBgLAYfElOQ+TnJHi6FMXOoHDEJWcM4Tla6GHxxf+ScWMddehqqMJERwMiEVDcKOPM0ai4ZCSlyl49/JW6e/aC95/jUbZsOZRzKo8tmzciISEBbdq2k75xPlVQX4vZUbtuPaxdswoWlkUyhvBCQrBl03q0btte0aHlyuKFfqhZqzYsLC3xJT4ex44ewY3A61i5+h9Fh1ZgzJ07FytXrsTGjRtRtmxZ3LhxA7169YKhoSGGDx8OAJg3bx6WLFmCjRs3wtbWFt7e3mjcuDGCg4MlZ7x7eHggPDwcp0+fRkpKCnr16oX+/ftnOkEsN37LBAoA5syZg/T0dHTv3h2fP3+Gi4sLTp48KZiP5OHhgcGDB6NHjx7Q1v7vdG+RSIRjx45h0qRJ6NWrF6Kjo2FhYYHatWvD3Nz8h/dpYGCAixcvYtGiRYiNjYW1tTX8/PwkE9n79euH8+fPw8XFBXFxcTh37hxatWqFUaNGYejQoUhKSkLz5s3h7e2NadOmSfbbvn177Nu3D/Xq1UNMTAzWr1+Pnj175ihGaXJ67N8bM2YMPD094ejoiISEBISGhsLGxibHcf3uCmmrY0CN4tDTUMXnpDQ8iY7HjDPP8Dkpo8q38MILdKhggRG1raGlporIz0lYG/Aad8M/S/bR1skcNW3/e/1Pb5Jx3Zo5Z5/jUT4/W69J02b4+OEDVixbgnfvolG6jANW/L0WJgV4CE+ZTfhzMpYvXQzfmT748OE9TE3N0L5jZwwYNETRoeXKhw/vMXnieERHR0FPXx/29qWxcvU/cK3hJn3jfEZRxekrV66gdevWaN48Y3jXxsYG27dvx/Xr1wFkVJ8WLVqEyZMno3Xr1gCATZs2wdzcHAcOHECXLl0QEhKCEydOIDAwUPKlfunSpWjWrBnmz5+PIkWKyCVWkbigX/qVfjt5WYFSpEF77ik6hDyxsoOT9E6UryjrXwUlGLH+IS05l0Psxp6Q276e/NUk231nz56N1atX49SpU7C3t8edO3fQqFEjLFiwAB4eHnj+/DlKliyJ27dvC36Bo06dOqhYsSIWL16MdevWYfTo0fj48aNkfWpqKrS0tLB79260bSufeXa/bQWKiIiI8l5WZ1NnNT0DACZMmIDY2FiUKVNGcomLWbNmSeYhR0REAECmEQ9zc3PJuoiICJiZmQnWq6mpwdjYWNJHHn7LSeRERET0YyKR/BZfX18YGhoKFl9f3yzvd9euXdi6dSu2bduGW7duYePGjZg/fz42btz4ix8B6ViBIiIiIgF5nqE7ceJEeHl5Cdp+dG2zsWPHYsKECejSpQuAjItNv3z5Er6+vvD09JRcfDkyMhKWlpaS7SIjIyVDehYWFpmuO5iamooPHz7I9eLNrEARERFRntHU1ISBgYFg+VEC9eXLl0xnZKuqqkquI2hrawsLCwv4+/tL1sfGxiIgIACurq4AAFdXV8TExODmzZuSPmfPnkV6ejqqVasmt+NiBYqIiIgEFDXhvmXLlpg1axasrKxQtmxZ3L59GwsWLEDv3r3/H5cII0eOxMyZM2FnZye5jEGRIkXQpk0bAICDgwOaNGmCfv36YdWqVUhJScHQoUPRpUsXuZ2BBzCBIiIiou+oqCgmg1q6dCm8vb0xePBgREVFoUiRIhgwYACmTJki6TNu3DjEx8ejf//+iImJQc2aNXHixAnJNaAAYOvWrRg6dCjc3d2hoqKC9u3bY8mSJXKNlZcxoAKHlzEoWHgZg4JHWf8q8DIG2ef45ym57St4diO57Ss/YQWKiIiIBJQ52ZQXJlBEREQkoAy/k5nXeBYeERERkYxYgSIiIiIBFqCkYwJFREREAhzCk45DeEREREQyYgWKiIiIBFiBko4JFBEREQkwf5KOQ3hEREREMmIFioiIiAQ4hCcdEygiIiISYP4kHYfwiIiIiGTEChQREREJcAhPOiZQREREJMD8SToO4RERERHJiBUoIiIiEuAQnnRMoIiIiEiA+ZN0HMIjIiIikhErUERERCTAITzpmEARERGRAPMn6ZhAEeUTKzs4KTqEPPH6Q4KiQ8gzxYy1FR1CnuAfTyLpmEARERGRAIfwpGMCRURERALMn6TjWXhEREREMmIFioiIiAQ4hCcdEygiIiISYP4kHYfwiIiIiGTEChQREREJcAhPOiZQREREJMAESjoO4RERERHJiBUoIiIiEmABSjomUERERCTAITzpOIRHREREJCNWoIiIiEiABSjpmEARERGRAIfwpOMQHhEREZGMWIEiIiIiARagpGMCRURERAIqzKCk4hAeERERkYxYgSIiIiIBFqCkYwJFREREAjwLTzoO4RERERHJiBUoIiIiElBhAUoqJlBEREQkwCE86TiER0RERCSjfJVAnT9/HiKRCDExMYoORULeMb148QIikQhBQUFy2Z+iTJs2DRUrVlR0GERElAdEIvktyipfJVDyIhKJcODAAbnsq0aNGggPD4ehoaFc9lcQZfV4jhkzBv7+/ooJ6BfYsW0rmjasjyqVnODRpSPu3b2r6JDkpqAd2/2gm/AZPxzd2zRE81oVcfXiWcH6BbO80bxWRcHiPXqwZP3d24GZ1n9dHofc/9WHI7OC9nzJQlmPTRmOSyTHf8oqXyVQycnJig5BICUlBRoaGrCwsOB48Hf09PRgYmKi6DDyxInjxzB/ni8GDB6CHbv3o3TpMhg0oA/ev3+v6NByrSAeW2JiAmxL2WOQ18Qf9qlczQ2bD5yRLOOmzZGscyhXUbBu84EzaNyiLcwti8KuTNlfcQg5VhCfr+xS1mNT1uOizBSaQNWtWxdDhw7FyJEjUbhwYTRu3BgAcPPmTbi4uEBHRwc1atTAo0ePBNsdPHgQzs7O0NLSQokSJeDj44PU1FQAgI2NDQCgbdu2EIlEktsAsHLlSpQsWRIaGhooXbo0Nm/eLNivSCTCypUr0apVK+jq6mLWrFlZDuFdvnwZdevWhY6ODgoVKoTGjRvj48ePAIATJ06gZs2aMDIygomJCVq0aIFnz57l+DE6duwY7O3toa2tjXr16mHDhg2CeLIaSlu0aJHguAFg7dq1cHBwgJaWFsqUKYMVK1ZI1iUnJ2Po0KGwtLSElpYWrK2t4evr+9PH8/v7TU9Px/Tp01GsWDFoamqiYsWKOHHihGT916HLffv2oV69etDR0UGFChVw9erVHD82eWXzxvVo16ET2rRtj5KlSmHyVB9oaWnhwL69ig4t1wrisblUr4ke/YaiRu36P+yjrq4OY5PCkkVf3+CH6wwMDXHt0nk0bNY6338xKojPV3Yp67Epy3GpiOS3KCuFV6A2btwIDQ0NXL58GatWrQIATJo0CX5+frhx4wbU1NTQu3dvSf9///0XPXr0wIgRIxAcHIy///4bGzZswKxZswAAgYGBAID169cjPDxccnv//v0YMWIERo8ejfv372PAgAHo1asXzp07J4hn2rRpaNu2Le7duye436+CgoLg7u4OR0dHXL16FZcuXULLli2RlpYGAIiPj4eXlxdu3LgBf39/qKiooG3btkhPT5f5sXn16hXatWuHli1bIigoCH379sWECRNk3s/WrVsxZcoUzJo1CyEhIZg9eza8vb2xceNGAMCSJUtw6NAh7Nq1C48ePcLWrVslidKPHs/vLV68GH5+fpg/fz7u3r2Lxo0bo1WrVnjy5Img36RJkzBmzBgEBQXB3t4eXbt2lSS/+UFKcjJCgh+gumsNSZuKigqqV6+Bu3duKzCy3FPmY7sXdAPdWtZD/26tsXz+LMR+ivlh34BLF/A59hMaNmv96wLMAWV+vpT12JTpuEQikdwWZaXwyxjY2dlh3rx5AIDw8HAAwKxZs1CnTh0AwIQJE9C8eXMkJiZCS0sLPj4+mDBhAjw9PQEAJUqUwIwZMzBu3DhMnToVpqamAAAjIyNYWFhI7mf+/Pno2bMnBg/OmBvh5eWFa9euYf78+ahXr56kX7du3dCrVy/J7efPnwvinTdvHlxcXAQVnLJl/xsGaN++vaD/unXrYGpqiuDgYJQrV06mx+ZrxczPzw8AULp0ady7dw9z586VaT9Tp06Fn58f2rVrBwCwtbWVJJ+enp4ICwuDnZ0datasCZFIBGtra8m2P3o8vzd//nyMHz8eXbp0AQDMnTsX586dw6JFi7B8+XJJvzFjxqB58+YAAB8fH5QtWxZPnz5FmTJlZDqmvPIx5iPS0tIyDU+amJggNPT5D7YqGJT12CpXc0ONOu6wsCyK8DevsHH1MkwdOwTzV26Cqqpqpv6nju6Hc1VXFDYzV0C02aeszxegvMemrMdFWVN4Bapy5cqZ2sqXLy/5v6WlJQAgKioKAHDnzh1Mnz4denp6kqVfv34IDw/Hly9ffng/ISEhcHNzE7S5ubkhJCRE0Obi4vLTeL9WoH7kyZMn6Nq1K0qUKAEDAwNJJScsLOyn+/1RzNWqVRO0ubq6yrSP+Ph4PHv2DH369BE8ZjNnzpQMLfbs2RNBQUEoXbo0hg8fjlOnTsl0H7GxsXj79m22Ht+fPbdZSUpKQmxsrGBJSkqSKT5SbnUaNEH1mnVhU9IOrrXrY+q8JXgc8gD3bt/I1PddVCRuXb+KRs3bKiBSooKDZ+FJp/AKlK6ubqY2dXV1yf+/lv++DoHFxcXBx8dHUk35lpaWVp7E8y1tbe2frm/ZsiWsra2xZs0aFClSBOnp6ShXrlyeTZBXUVGBWCwWtKWkpEj+HxcXBwBYs2ZNpmTs67dzZ2dnhIaG4vjx4zhz5gw6deqEBg0aYM+ePXKP92fPbVZ8fX3h4+MjaJvkPRWTp0yTe2wAUMioEFRVVTNN+Hz//j0KFy6cJ/f5qyjzsX3LskgxGBgWQvibV6joInzNnz52EPoGhqhWs46Coss+ZX6+lPXYlOm4VJQ585EThVegZOXs7IxHjx6hVKlSmRYVlYzDUVdXl8xJ+srBwQGXL18WtF2+fBmOjo4y3X/58uV/ePr++/fv8ejRI0yePBnu7u5wcHCQTC7PCQcHB1y/fl3Qdu3aNcFtU1NTRERECJKob68xZW5ujiJFiuD58+eZHi9bW1tJPwMDA3Tu3Blr1qzBzp07sXfvXnz48AFA1o/ntwwMDFCkSBG5PL7fmzhxIj59+iRYxo7/8dlYuaWuoQEHx7IIuPbf5Pb09HQEBFxF+QqV8ux+fwVlPrZvvYuKxOfYGBQyEf7BEovFOH3sIOo3aQk1NfUfbJ1/KPPzpazHpqzHRVlTeAVKVlOmTEGLFi1gZWWFDh06QEVFBXfu3MH9+/cxc+ZMABlnjvn7+8PNzQ2ampooVKgQxo4di06dOqFSpUpo0KABDh8+jH379uHMmTMy3f/EiRPh5OSEwYMHY+DAgdDQ0MC5c+fQsWNHGBsbw8TEBKtXr4alpSXCwsJyNOn7q4EDB8LPzw9jx45F3759cfPmTWzYsEHQp27duoiOjsa8efPQoUMHnDhxAsePH4eBwX9nIfn4+GD48OEwNDREkyZNkJSUhBs3buDjx4/w8vLCggULYGlpiUqVKkFFRQW7d++GhYUFjIyMfvh4fm/s2LGYOnUqSpYsiYoVK2L9+vUICgrC1q1bc3z8AKCpqQlNTU1BW2Iezznv7tkL3n+OR9my5VDOqTy2bN6IhIQEtGmbuepZ0BTEY0v48gVv3/w3BB4R/gbPnjyEvoEh9PUNsW39KrjVbYBCxiYIf/Ma61YugmXR4qhctYZgP3duXkdk+Bs0blFwhu8K4vOVXcp6bMpyXCxASVfgEqjGjRvjyJEjmD59OubOnQt1dXWUKVMGffv2lfTx8/ODl5cX1qxZg6JFi+LFixdo06YNFi9ejPnz52PEiBGwtbXF+vXrUbduXZnu397eHqdOncKff/6JqlWrQltbG9WqVUPXrl2hoqKCHTt2YPjw4ShXrhxKly6NJUuWyHwfX1lZWWHv3r0YNWoUli5diqpVq2L27NmCswMdHBywYsUKzJ49GzNmzED79u0xZswYrF69WtKnb9++0NHRwV9//YWxY8dCV1cXTk5OGDlyJABAX18f8+bNw5MnT6CqqooqVarg2LFjkopeVo/n94YPH45Pnz5h9OjRiIqKgqOjIw4dOgQ7O7scHbsiNWnaDB8/fMCKZUvw7l00SpdxwIq/18KkgJXgs1IQj+3JoweYOLyf5PbaZRknVbg3aYkhYybhxbMn8D9xGPFxn2Fc2BSVqriie98hUNfQEOzn1NH9cChXAcWtbVFQFMTnK7uU9diU5biU+ew5eRGJv59AQ/na+fPnUa9ePXz8+FFSIfrd5HUFiuTr9YcERYeQZ4oZ/3xOJNGvoiXnckiH9bfktq89vZzltq/8pMBVoIiIiChvsQAlXYGbRK5MBg4cKLi0wLfLwIEDFR0eERH9plREIrktyopDeAoUFRWF2NjYLNcZGBjAzMzsF0dUMHAIr2DhEB5R3pP3EF7njfK7cvpOT+U8A5EVKAUyMzPL8nIMpUqVYvJEREQKI5LjIqs3b97gjz/+gImJCbS1teHk5IQbN/67MK5YLMaUKVNgaWkJbW1tNGjQINPPhn348AEeHh4wMDCAkZER+vTpI7kuorwwgSIiIiIBRf0W3sePH+Hm5gZ1dXUcP34cwcHB8PPzE1w+Z968eViyZAlWrVqFgIAA6OrqonHjxkhMTJT08fDwwIMHD3D69GkcOXIEFy9eRP/+/eX2+AAcwqMCiEN4BQuH8IjynryH8LpuCpLbvrb3qJjtvhMmTMDly5fx77//ZrleLBajSJEiGD16NMaMGQMA+PTpE8zNzbFhwwZ06dIFISEhcHR0RGBgoOTn2U6cOIFmzZrh9evXKFKkSK6PCWAFioiIiL6jIpLfIstvmh46dAguLi7o2LEjzMzMUKlSJaxZs0ayPjQ0FBEREWjQoIGkzdDQENWqVcPVqxlXgL969SqMjIwEv23boEEDqKioICAgQH6Pkdz2REREREpBnkN4vr6+MDQ0FCy+vr5Z3u/z58+xcuVK2NnZ4eTJkxg0aBCGDx+OjRs3AgAiIiIAZPxM2bfMzc0l6yIiIjLNI1ZTU4OxsbGkjzzwOlBERESUZyZOnAgvLy9B2/c/0fVVeno6XFxcMHv2bABApUqVcP/+faxatQqenp55HqssWIEiIiIiAZFIfoumpiYMDAwEy48SKEtLy0w/Qu/g4ICwsIzfw7SwsAAAREZGCvpERkZK1llYWCAqKkqwPjU1FR8+fJD0kQcmUERERCSgqLPw3Nzc8OjRI0Hb48ePYW1tDQCwtbWFhYUF/P39JetjY2MREBAAV1dXAICrqytiYmJw8+ZNSZ+zZ88iPT0d1apVy+lDkgmH8IiIiChfGDVqFGrUqIHZs2ejU6dOuH79OlavXo3Vq1cDyEjsRo4ciZkzZ8LOzg62trbw9vZGkSJF0KZNGwAZFasmTZqgX79+WLVqFVJSUjB06FB06dJFbmfgAUygiIiI6DsqCvoFlipVqmD//v2YOHEipk+fDltbWyxatAgeHh6SPuPGjUN8fDz69++PmJgY1KxZEydOnICWlpakz9atWzF06FC4u7tDRUUF7du3x5IlS+QaK68DRQUOrwNVsPA6UER5T97Xgeq1457c9rW+i5Pc9pWf5GgO1L///os//vgDrq6uePPmDQBg8+bNuHTpklyDIyIiIsqPZE6g9u7di8aNG0NbWxu3b9+WXAzr06dPktMOiYiIqOBS5G/hFRQyJ1AzZ87EqlWrsGbNGqirq0va3dzccOvWLbkGR0RERL+eikgkt0VZyZxAPXr0CLVr187UbmhoiJiYGHnERERERJSvyZxAWVhY4OnTp5naL126hBIlSsglKCIiIlIceV5IU1nJnED169cPI0aMQEBAAEQiEd6+fYutW7dizJgxGDRoUF7ESERERL+Qoi6kWZDIfOLjhAkTkJ6eDnd3d3z58gW1a9eGpqYmxowZg2HDhuVFjERERET5So6vA5WcnIynT58iLi4Ojo6O0NPTk3dsRFnidaAKFl4Hiijvyfs6UAP2PJDbvv7uUFZu+8pPcvyQa2hoZPrBPyIiIir4lPnsOXmROYGqV6/eT8c0z549m6uAiIiIiPI7mROoihUrCm6npKQgKCgI9+/fh6enp7ziIiIiIgVhAUo6mROohQsXZtk+bdo0xMXF5TogIiIiUixlPntOXnL0W3hZ+eOPP7Bu3Tp57Y6IiIgo35LbvP2rV69CS0tLXrsj+qGE5DRFh5AnlPULn4Wh8n4uFKoyVNEh5Ik3lxYrOoQ8oazvMQDQUlOV6/7kVl1RYjInUO3atRPcFovFCA8Px40bN+Dt7S23wIiIiEgxOIQnncwJlKGhoeC2iooKSpcujenTp6NRo0ZyC4yIiIgov5IpgUpLS0OvXr3g5OSEQoUK5VVMREREpEAqLEBJJdMwp6qqKho1aoSYmJg8CoeIiIgUTUUkv0VZyTxPrFy5cnj+/HlexEJERERUIMicQM2cORNjxozBkSNHEB4ejtjYWMFCREREBZtIJJLboqyyPQdq+vTpGD16NJo1awYAaNWqleCBEYvFEIlESEtTzlPMiYiIfhfKPPQmL9lOoHx8fDBw4ECcO3cuL+MhIiIiyveynUCJxWIAQJ06dfIsGCIiIlI8JR55kxuZLmOgzGOZRERElEGFf++lkimBsre3l5pEffjwIVcBEREREeV3MiVQPj4+ma5ETkRERMqFv4UnnUwJVJcuXWBmZpZXsRAREVE+wBE86bKdZHL+ExEREVEGmc/CIyIiIuXGSeTSZTuBSk9Pz8s4iIiIKJ9g/iQd54kRERERyUimSeRERESk/PhTLtIxgSIiIiIBzoGSjkN4RERERDJiBYqIiIgEWICSjgkUERERCXAOlHQcwiMiIiKSEStQREREJCACS1DSMIEiIiIiAQ7hSccEin5ra1Ytwz9/rxC0WdvYYuf+owCAOTOnIjDgGt5FR0FbWwdOFSpiyIjRsLEtoYhwc2zjujVYsWQhOnfrDq9xEwXrxGIxRg0dgKuXL2HegiWoU7+BgqLMnnVr/8Y5/9N4EfocmppaKF+xEoaP/O85+fQpBn+vWIprVy4jIiIcRoWMUbe+OwYNGQF9fX2Fxe3mXBKjejSAs6MVLE0N0WnUahw+f1fQx3tQc/RqWwNG+tq4euc5hs/eiWdh0ZL1pazMMHtUG7hWKAENdVXcf/IWPiuO4OKNJ5I+davaY+rgFihbqgjiE5Kx9XAApi4/jLQ0xfyaxNpVy/DPauF7zMrGFjv3HZXcvncnCH8vX4wH9+9CRVUF9vZlsHD5Gmhpaf3qcGXyu3x+UNaYQP2mUlJSoK6urugw8oUSJUth6ap/JLdVVf97W5RxKIvGTVvC3NISsZ8+Ye2q5RgxuC/2HTkNVVVVRYQrs+D797B/zy6Usi+d5fodWzYBBahcf+tGIDp26YayZZ2QlpaGZUsWYsjAvtiz/wi0dXQQHRWF6KgojBw9DrYlSyH87Vv4zpyKd1FRmLdgicLi1tXWxL3Hb7Dp4FXsXNA/0/rRPRtgcNc66DdlM168eY8pg1vg8PIhqNR+JpKSUwEA+5YMxNOwKDQdsAQJSSkY2q0e9i0ZiLItpyHy/Wc42RfFgaWDMPefk+jjvQlFzIyw9M8uUFVVwcSF+3/1IUuUKFkKS1Zm/R67dycIo4b1R49e/eA1/k+oqqrhyeOHUFEpGFN0lfXzgxUo6QrGK5QAAHv27IGTkxO0tbVhYmKCBg0aID4+HoGBgWjYsCEKFy4MQ0ND1KlTB7du3RJsKxKJsHLlSrRq1Qq6urqYNWsWAODw4cOoUqUKtLS0ULhwYbRt21ayzebNm+Hi4gJ9fX1YWFigW7duiIqKkqz/+PEjPDw8YGpqCm1tbdjZ2WH9+vUAgBcvXkAkEmHXrl2oVasWtLW1UaVKFTx+/BiBgYFwcXGBnp4emjZtiujoaCiSqqoqTAqbShajQoUk69q074RKlV1QpEhRlHFwxIAhwxEZEYHwt28UGHH2ffkSjyl/jsOfU3xgoG+Qaf3jhyHYunkDvH1mKiC6nFm2ai1atW6HkqXsYF+6DHxm+CIi/C1Cgh8AAErZ2eOvhUtRu259FC9uharVqmPwsFG4eOEcUlNTFRb3qcvB8FlxBIfO3c1y/ZBu9TB3zUkcOX8P95+8RV/vTbA0NUSrehUAACZGurCzNoPf+tO4/+QtnoVFw3vJQehqa8KxVBEAQIdGzrj/5C18V5/A81fvcOnmU0xafAADOtWCno7mLzvW7/3sPbbYbw46dvkDPXr1Q4mSdrC2sUWDRk2hoaGhsHhloayfHyKRSG6LsmICVUCEh4eja9eu6N27N0JCQnD+/Hm0a9cOYrEYnz9/hqenJy5duoRr167Bzs4OzZo1w+fPnwX7mDZtGtq2bYt79+6hd+/eOHr0KNq2bYtmzZrh9u3b8Pf3R9WqVSX9U1JSMGPGDNy5cwcHDhzAixcv0LNnT8l6b29vBAcH4/jx4wgJCcHKlStRuHBhwX1OnToVkydPxq1bt6CmpoZu3bph3LhxWLx4Mf799188ffoUU6ZMydPHTppXYWFo0bAO2rVohCl/jkVE+Nss+yUkfMHRQ/tRpGgxmFtY/OIoc+av2TPhVqsOqlavkWldYkICvP8ci7ETJ8OksKkCopOPuLiM17mBoeGP+3z+DF09Paip5c+iu01RE1iaGuJswENJW2xcIgLvv0C18jYAgPcx8XgUGoFuLapCR0sDqqoq6Nu+JiLfx+J2cBgAQFNDDYlJKYJ9JySlQFtLA5UcrH7Z8XzvVVgYWjaqg/YtG2HqpP/eYx8+vMeD+3dhbGyMfj27oVmDWhjUtwfu3L6psFhlpcyfH/Rz+fPThDIJDw9Hamoq2rVrB2trawCAk5MTAKB+/fqCvqtXr4aRkREuXLiAFi1aSNq7deuGXr16SW536dIFXbp0gY+Pj6StQoUKkv/37t1b8v8SJUpgyZIlqFKlCuLi4qCnp4ewsDBUqlQJLi4uAAAbG5tMcY8ZMwaNGzcGAIwYMQJdu3aFv78/3NzcAAB9+vTBhg0bcvKQyEXZcuXhPX0WrKxt8f5dNP75ewUG9u6OrXsOQVdXFwCwZ9d2LF80HwkJCbC2scWSlWuhrp7/vx2fOnEMjx4GY/3WXVmuXzh/DspXqIQ69dx/cWTyk56ejvnzZqNCJWeUsrPPss/Hjx+xdvVKtGvf6RdHl30WhTOqg1EfhF96ot5/hrnJf5XD5gOXYefC/oi+PB/p6WJEf4xD6yErEPM5AQBw+koIhnarh05NKmPPqVuwMDHAn/2bAgAsTTNXIH+Fsk7lMdlnFqytbfHuXTT+Wb0Cg/p0x5bdh/D29WsAwNq/l2PYyLGwK10Gx48cwrCBvbF190EUt7JRSMzZpcyfHxzCk44VqAKiQoUKcHd3h5OTEzp27Ig1a9bg48ePAIDIyEj069cPdnZ2MDQ0hIGBAeLi4hAWFibYx9dE56ugoCC4u//4j+fNmzfRsmVLWFlZQV9fH3Xq1AEAyX4HDRqEHTt2oGLFihg3bhyuXLmSaR/ly5eX/N/c3BzAf4nf17ZvhwW/l5SUhNjYWMGSlJT0w/6yqlGzNtwbNoGdfWlUr1ETC5atwue4z/A/dULSp0nTFti4fS9Wrt2E4lY2mDTeS64x5IXIiHAsmOcLn9nzoKmZeejm4vmzuHE9AKPGTlBAdPIzZ9Z0PHv6BL5zF2S5Pi4uDiOGDECJEiXRf9DQXxyd/C2c2AnRHz6jQe9FqNX9Lxw6dwd7Fw+QJGD+1x7iz0UHsOTPLvgUsAh3D07ByUsZQ5vp6WKFxOzqlvEeK/X1Pbb0/++x0yeQLs6Y2N6mXSe0aN0Opcs4YuSYCbCytsXhg/sUEq8slPXzA8i4Erm8FmXFBKqAUFVVxenTp3H8+HE4Ojpi6dKlKF26NEJDQ+Hp6YmgoCAsXrwYV65cQVBQEExMTJCcnCzYx9dvRF9pa2v/8P7i4+PRuHFjGBgYYOvWrQgMDMT+/RmTUL/ut2nTpnj58iVGjRqFt2/fwt3dHWPGjBHs59uJ6l/Hwr9vS0//8dlBvr6+MDQ0FCwL58/52UOVK/r6BrCyssHrVy8lbXr6+rCytkGlyi7wnb8QL0NDceHsmTyLQR4eBj/Axw/v4dm1A2pUdkKNyk64dTMQu7ZvQY3KTrh+7QrevH6FBrWqS9YDwIQxIzGoj6eCo8+eubOn49LF8/h77aYsh0Ti4+MwbFBf6OrqYv6iZfn6pImId7EAADNj4VmCZib6iHyfsa5uVXs0q1UOPSasx9U7zxH08DVG+u5CQlIK/mhZTbLNki1nYVF7LOybTUGxehMkZ/qFvn73i47m5759jxX+/9CxbYmSgj42tiUQGRGuiPByRVk+Pyh7OIRXgIhEIri5ucHNzQ1TpkyBtbU19u/fj8uXL2PFihVo1qwZAODVq1d49076h2X58uXh7+8vGNb76uHDh3j//j3mzJmD4sWLAwBu3LiRqZ+pqSk8PT3h6emJWrVqYezYsZg/f34uj/Q/EydOhJeXl6DtS1revWy/fInHm9dhaNK8ZZbrxWJADDGSU5KzXJ9fuFRzxbY9BwVtM6ZMgrWtLXr06gsjIyO07dBZsL5bh9YYOWY8atWp9ytDlZlYLMY83xk4d/YMVv+zCUWLFcvUJy4uDkMH9oGGhgYWLFmRZRUuP3nx5j3Coz+hXrXSuPs4Y4Kxvq4WqpSzwZrdlwAAOloZwz7ff+FITxdnOVE3PPoTAKBTExe8Cv+A2w9f5eUhZNuXL/F4/f/3mGWRoihsaoaXL18I+oSFvYBrjVqKCTAXlOXzAwBUlLl0JCdMoAqIgIAA+Pv7o1GjRjAzM0NAQACio6Ph4OAAOzs7yRlzsbGxGDt27E+rS19NnToV7u7uKFmyJLp06YLU1FQcO3YM48ePh5WVFTQ0NLB06VIMHDgQ9+/fx4wZMwTbT5kyBZUrV0bZsmWRlJSEI0eOwMHBQa7HrampmemPX9qXNLntf8mCeahZux4sihTBu6gorFm1DCoqqmjUpDnevH6FMyePo5qrG4wKFUJUZCQ2rV8LTU1N1KhZW24x5AVdXV2ULGUnaNPW1oahoZGkPauJ4xYWlihSNHNCkp/MmTUdJ44fwYLFy6Gjq4t37zLO4tTT04eWlhbi4uIwZEAfJCYmYIbvX4iPj0N8fBwAoFAhY4WdPq6rrYGSxf97zG2KmqC8fVF8jP2CVxEfsXzbOYzv2wRPw6Lx4s17TB3cHOHRn3Do3B0AQMDdUHyM/YK1M3pg9urjSEhMQe92NWBT1AQn/j9MBwCjerjj1JUQpKeno7V7RYzp1RB/jFunsCG8JQsz3mOWlkUQHR2FtauWQVVFFQ2bNIdIJIJHj95Y+/cy2NmXhp19GRw7chAvX4Ri9rxFColXFsr6+QFwDlR2MIEqIAwMDHDx4kUsWrQIsbGxsLa2hp+fH5o2bQoLCwv0798fzs7OKF68OGbPnp1pKC0rdevWxe7duzFjxgzMmTMHBgYGqF07441tamqKDRs24M8//8SSJUvg7OyM+fPno1WrVpLtNTQ0MHHiRLx48QLa2tqoVasWduzYkWePQV6IiozElIlj8OlTDIwKGaNCRWes3bQdhYyNkZqaiqDbN7Fj22Z8jv0EY5PCqOhcGWs2bIOxsYmiQ/9t7dm1HQDQv3cPQfvUGbPRqnU7PAx5gPv3MpKONs0bCfocPn5GYQmis6M1Tq0dIbk9b0x7AMDmQ9fQf+oW+G04Ax1tTSyb3BVG+tq4EvQMrYaskFwD6n1MPFoPXYFpQ1ri+N/Doa6mgpDnEeg4ajXuPf7vtPhGbo4Y17cxNNXVcO/xG3QctRqnLgf/2oP9RnRkJKZ+9x5bs3E7ChUyBgB08eiB5OQkLPabi9hPn1DKvjSWrFiLYsUVd9ZgdvHz4/cmEovFivlaQpRDH+VYgcpPlLVirlZALoiYE6bVhyk6hDzx5tJiRYeQJ5T1PQYAhXTkW1ldejlUbvsa5mYrt33lJ6xAERERkYBKAfp1AkVR3q+GRERERHmEFSgiIiISUObhTnlhAkVEREQCPAtPOg7hEREREcmIFSgiIiIS4IU0pWMCRURERALMn6TjEB4RERGRjFiBIiIiIgEO4UnHChQREREJiETyW3Jjzpw5EIlEGDlypKQtMTERQ4YMgYmJCfT09NC+fXtERkYKtgsLC0Pz5s2ho6MDMzMzjB07FqmpqbkL5jtMoIiIiCjfCQwMxN9//43y5csL2keNGoXDhw9j9+7duHDhAt6+fYt27dpJ1qelpaF58+ZITk7GlStXsHHjRmzYsAFTpkyRa3xMoIiIiEhARY5LTsTFxcHDwwNr1qxBoUKFJO2fPn3CP//8gwULFqB+/fqoXLky1q9fjytXruDatWsAgFOnTiE4OBhbtmxBxYoV0bRpU8yYMQPLly9HcnJyDiPKjAkUERERCYhEIrktOTFkyBA0b94cDRo0ELTfvHkTKSkpgvYyZcrAysoKV69eBQBcvXoVTk5OMDc3l/Rp3LgxYmNj8eDBgxzFkxVOIiciIqI8k5SUhKSkJEGbpqYmNDU1s+y/Y8cO3Lp1C4GBgZnWRUREQENDA0ZGRoJ2c3NzRERESPp8mzx9Xf91nbywAkVEREQCIjkuvr6+MDQ0FCy+vr5Z3u+rV68wYsQIbN26FVpaWnl5iLnGChQREREJyPMyBhMnToSXl5eg7UfVp5s3byIqKgrOzs6StrS0NFy8eBHLli3DyZMnkZycjJiYGEEVKjIyEhYWFgAACwsLXL9+XbDfr2fpfe0jD6xAERERUZ7R1NSEgYGBYPlRAuXu7o579+4hKChIsri4uMDDw0Pyf3V1dfj7+0u2efToEcLCwuDq6goAcHV1xb179xAVFSXpc/r0aRgYGMDR0VFux8UKFBEREQko6jKa+vr6KFeunKBNV1cXJiYmkvY+ffrAy8sLxsbGMDAwwLBhw+Dq6orq1asDABo1agRHR0d0794d8+bNQ0REBCZPnowhQ4b8MHHLCSZQREREJJCfL0S+cOFCqKiooH379khKSkLjxo2xYsUKyXpVVVUcOXIEgwYNgqurK3R1deHp6Ynp06fLNQ6RWCwWy3WPRHns45c0RYeQJ/LzB1ZuqKko70wB0+rDFB1CnnhzabGiQ8gTyvoeA4BCOqpy3d+2W6/ltq9uzsXktq/8hBUoIiIiEsjp9Zt+J0ygiIiISEB568byw8eIiIiISEasQBEREZEAh/CkYwWKiIiISEasQBEREZEA60/SMYEiIiIiAQ7hSccEigocTXUlHXlW0iuyKfMH8buApYoOIU+0WHlV0SHkiaODXRUdAikRJlBEREQkoKRfU+WKCRQREREJKHPlWF6YZBIRERHJiBUoIiIiEmD9STomUERERCTAETzpOIRHREREJCNWoIiIiEhAhYN4UjGBIiIiIgEO4UnHITwiIiIiGbECRURERAIiDuFJxQSKiIiIBDiEJx2H8IiIiIhkxAoUERERCfAsPOmYQBEREZEAh/Ck4xAeERERkYxYgSIiIiIBVqCkYwJFREREAryMgXQcwiMiIiKSEStQREREJKDCApRUTKCIiIhIgEN40nEIj4iIiEhGrEARERGRAM/Ck44JFBEREQlwCE86DuERERERyYgJFMnFtGnTULFiRUWHQUREcqAikt+irDiERzITiUTYv38/2rRpI2kbM2YMhg0bprigcujmjUBsWv8PgoMf4F10NBYsXoZ67g0k6/1Pn8KeXTsQEvwAnz59wo49+1G6jIMCI86+mzcCsWnDN8e2SHhsq1YsxcnjxxARGQF1NXU4OJbF0OEj4VS+ggKjll3TRvUR/vZNpvZOXbrhz8lTFRBRznx9vkL+/3z5ffd8ffkSjyUL/XD+rD8+fYpBkaLF0NWjOzp06qLAqLNWWFcD/Wtao6q1EbTUVfAmJhFzTz/F46h4AICWugr6u1mjZgljGGirIfxTEvbdCcfhe5GC/Tha6KFPDWs4WOghPV2Mp+/iMW5/CJLT0hVxWJn87PMjJSUFK5YuxqV/L+D169fQ09NDteo1MHyUF8zMzBUcuXQcwpOOCRTJhZ6eHvT09H64Pjk5GRoaGr8wouxJSEiAfekyaN22PUaPzJwAJiQkoKJzZTRs3BQzpnkrIMKcS0hIgL39j4/N2toG4//0RrFixZGUlIgtmzdi8IA+OHj0FIyNjRUQcc5s3bEH6elpkttPnzzBwH690LBREwVGJbvEb56vMVk8X37z5iDwegBmzpmHIkWK4uqVy5gzazpMTc1Qp159BUScNT1NVSztVA63X8diwsEQxCSkoJiRFuKSUiV9htSyQaXihph18gkiYpNQxdoII+uVwPu4ZFwJ/QggI3ma28YR2268wdLzz5GWLkZJU12IIVbUoWXys8+PxMREhAQHo9+AwbAvXRqxsbH4a85sjBw6GNt27VVQxCRPTKB+U3v27IGPjw+ePn0KHR0dVKpUCQcPHkRwcDD+/PNP3L59GykpKahYsSIWLlwIZ2dnAICNjQ0AoG3btgAAa2trvHjxAtOmTcOBAwcQFBQEAOjZsydiYmJQpUoVLF++HJqamggNDcWrV68wevRonDp1CioqKqhVqxYWL14s2e+vVrNWbdSsVfuH61u0ag0AePvm9a8KSW6kHVvT5i0Ft0ePnYAD+/bgyeNHqFbdNa/Dk5vvk711a1ejeHEruFSpqqCIcsatVm24/eT5unsnCC1btYFLlWoAgPYdO2Pv7p24f+9uvkqguroURdTnZMw7/VTSFhGbJOhT1tIAJ0OicedNLADgyP1ItCxnjjIWepIEakhtW+wLCsf2G/9VF1/FJP6CI8i+n73H9PX1sWrtOkHbhD+98UfXjggPfwtLyyK/IsQc41l40nEO1G8oPDwcXbt2Re/evRESEoLz58+jXbt2EIvF+Pz5Mzw9PXHp0iVcu3YNdnZ2aNasGT5//gwACAwMBACsX78e4eHhkttZ8ff3x6NHj3D69GkcOXIEKSkpaNy4MfT19fHvv//i8uXL0NPTQ5MmTZCcnPxLjp2ylpKSjH17dkJPXx/2pcsoOpwcS0lJxrEjh9C6bXuIlOwvQPkKFXHh/FlERUZCLBYj8Po1hL18geo13BQdmkANW2M8iorD1Gb22NevClZ3LY/mZc0EfR6Ex6JGCWMU1s2oSlcsZoBihbRx4+UnAICRtjocLfURk5CCpR3LYW8/FyxqXxbliuj/8uORp89xnyESiaCvb6DoUKQSyXFRVqxA/YbCw8ORmpqKdu3awdraGgDg5OQEAKhfX/hNdvXq1TAyMsKFCxfQokULmJqaAgCMjIxgYWHx0/vR1dXF2rVrJUN3W7ZsQXp6OtauXSv547Z+/XoYGRnh/PnzaNSokVyPk6S7eOEcJowdjcTEBBQ2NcWq1etQqFAhRYeVY2f9z+Dz589o1aatokORu/F/emOmjzeaNKgDNTU1iEQieE+bgcouVRQdmkARQy20drLA7ttvsTXwDcqY62FYXVukpotxMiQaALDkQihG1y+J3X1dkJqWjnQx4Of/DHffZlSkLA01AQCe1Ypj1aWXeBodj0YOpvBrWxa9twbhTT6rRGVHUlISliycjybNmv90ugMVHEygfkMVKlSAu7s7nJyc0LhxYzRq1AgdOnRAoUKFEBkZicmTJ+P8+fOIiopCWloavnz5grCwMJnvx8nJSTDv6c6dO3j69Cn09YXfIhMTE/Hs2bMs95GUlISkJGH5P01FA5qamjLHQ5lVqVINO/bsR8zHj9i3dzfGjRmJzVt3wdjERNGh5ciBfXvhVrN2gZikK6sd2zbj3t07WLh0BSwti+LWzUDJHKhqrjUUHZ6ESAQ8iozD2isZnxlPo+Nha6KDlk4WkgSqbQVLOFjq489DIYj8nITyRQwwol4JvItPxq1Xn6Dy/y9YR+5H4kRwlGQ/zsUN0dTRTLLvgiIlJQXjRo+EWAz86T1N0eFki4qSVXDzAofwfkOqqqo4ffo0jh8/DkdHRyxduhSlS5dGaGgoPD09ERQUhMWLF+PKlSsICgqCiYlJjobYdHV1Bbfj4uJQuXJlBAUFCZbHjx+jW7duWe7D19cXhoaGgmX+XN8cHTdlpq2jAysra5SvUBHTps+Cqqoa9u/fo+iwcuTt2zcIuHYFbdt3UHQocpeYmIhlixfBa+wE1KlbH/alS6NLtz/QqEkzbNq4TvoOfqH38Sl4+SFB0PbywxeY6Wd8mdJQVUHfGlZYefEFroZ+xPN3X3DgbgTOPX6Hzs5F/r+PjM+bF++/CPYT9iEB5voF68tTSkoKxo8ehfC3b7FyzT8FpvrEITzpWIH6TYlEIri5ucHNzQ1TpkyBtbU19u/fj8uXL2PFihVo1qwZAODVq1d49+6dYFt1dXWkpaVltdufcnZ2xs6dO2FmZgYDg+zNAZg4cSK8vLwEbWkq+e9sPmUhTk9HSgGdj3Zw/z4YG5ugVu26ig5F7lJTU5GamgIVkfA7r4qKCsTp+eOU/q8ehMeieCFtQVuxQtqI/P9EcjVVEdRVVZAuFp5Nly4WS4b2I2KTEB2XlHk/Rlq4/jIm74KXs6/JU1jYS6xetxFGRgV3eJwyYwL1GwoICIC/vz8aNWoEMzMzBAQEIDo6Gg4ODrCzs8PmzZvh4uKC2NhYjB07Ftrawg8xGxsb+Pv7w83NDZqamtmeM+Ph4YG//voLrVu3xvTp01GsWDG8fPkS+/btw7hx41CsWLFM22hqamYarvuSIr/TmL98icerb4Yn37x5jUcPQ2BgaAhLyyL49CkGEeHhiIrKGEZ4ERoKADApXBiFC5vKLY688LNjMzI0wto1q1Cnbn0UNjVFzMeP2LVjG6KiIgvc6f8AkJ6ejkMH9qFl6zZQUyuYH2vSXouVXapg0YK/oKmlCUvLorh54zqOHj4Ir7ETFBh1Zrtvh2NZx3LwqFIU5x6/h4OFHlqUM8cC/4xh+i/JaQh6/QkDa9ogKfU5Ij8noUJRAzRyMMWKiy8k+9l58y16Vi+OZ+++4Gl0PBo7mMLKWBvTjj1S0JFl9rPnrHBhU4z1GoGHwcFYvHwV0tPT8O5dxhCmoaEh1NXz+RdBZS4dyUnB/KShXDEwMMDFixexaNEixMbGwtraGn5+fmjatCksLCzQv39/ODs7o3jx4pg9ezbGjBkj2N7Pzw9eXl5Ys2YNihYtihcvXmTrfnV0dHDx4kWMHz8e7dq1w+fPn1G0aFG4u7tnuyIlb8H376Nfb0/Jbb95cwAALVu3wfRZc3Dh3FlMnfynZP2EsRnVsAGDhmDgkPx94dDgB98d21//P7ZWbTBpig9ehIbi8KHhiPn4EYZGRihb1gnrNm5FyVJ2igo5x65dvYLw8Ldo07a9okPJseAH99H/m+drwTfPl8+sOfD9awGWLlqASRPGIvbTJ1haFsGQYSPz3YU0H0XGwfvoI/SrYYUeVYsjPDYRyy+E4syj/yrZ048/Rj83a0xqYgcDLTVExibhnythOPTNhTT3BoVDQ00FQ2rbQF9LDc+i4zFmfzDefkrK6m4V4mefHwMHD8WFc2cBAF06tBFst2bdRrhUrfbL4swJXkhTOpFYLM4/VyUjygZ5VqDyFSU9LGW7nMC3vh+GUhYtVl5VdAh54ujggnN9M1npqMv3fRbw7JPc9lWtpKHc9pWfsAJFREREAkr8vUdumEARERGRAPMn6XgZAyIiIiIZsQJFREREQixBScUEioiIiAR4Fp50HMIjIiIikhErUERERCTAs/CkYwJFREREAsyfpOMQHhEREZGMWIEiIiIiIZagpGICRURERAI8C086DuERERERyYgVKCIiIhLgWXjSMYEiIiIiAeZP0nEIj4iIiEhGrEARERGREEtQUrECRURERAIiOf6Tha+vL6pUqQJ9fX2YmZmhTZs2ePTokaBPYmIihgwZAhMTE+jp6aF9+/aIjIwU9AkLC0Pz5s2ho6MDMzMzjB07Fqmpqbl+XL7FBIqIiIjyhQsXLmDIkCG4du0aTp8+jZSUFDRq1Ajx8fGSPqNGjcLhw4exe/duXLhwAW/fvkW7du0k69PS0tC8eXMkJyfjypUr2LhxIzZs2IApU6bINVaRWCwWy3WPRHnsS4qSvmSV9LBESnw6T7qSfny2WHlV0SHkiaODXRUdQp7RUZfv++ze6zi57cupmF6Ot42OjoaZmRkuXLiA2rVr49OnTzA1NcW2bdvQoUMHAMDDhw/h4OCAq1evonr16jh+/DhatGiBt2/fwtzcHACwatUqjB8/HtHR0dDQ0JDLcbECRURERAIiOS658enTJwCAsbExAODmzZtISUlBgwYNJH3KlCkDKysrXL2akfhfvXoVTk5OkuQJABo3bozY2Fg8ePAglxH9h5PIiYiIKM8kJSUhKSlJ0KapqQlNTc2fbpeeno6RI0fCzc0N5cqVAwBERERAQ0MDRkZGgr7m5uaIiIiQ9Pk2efq6/us6eWEFioiIiITkWILy9fWFoaGhYPH19ZUawpAhQ3D//n3s2LFD7ocnD6xAERERkYA8fwtv4sSJ8PLyErRJqz4NHToUR44cwcWLF1GsWDFJu4WFBZKTkxETEyOoQkVGRsLCwkLS5/r164L9fT1L72sfeWAFioiIiPKMpqYmDAwMBMuPEiixWIyhQ4di//79OHv2LGxtbQXrK1euDHV1dfj7+0vaHj16hLCwMLi6Zpwk4Orqinv37iEqKkrS5/Tp0zAwMICjo6PcjosVKCIiIhJQ1MmzQ4YMwbZt23Dw4EHo6+tL5iwZGhpCW1sbhoaG6NOnD7y8vGBsbAwDAwMMGzYMrq6uqF69OgCgUaNGcHR0RPfu3TFv3jxERERg8uTJGDJkiNTKlyyYQBEREZGAoi4+snLlSgBA3bp1Be3r169Hz549AQALFy6EiooK2rdvj6SkJDRu3BgrVqyQ9FVVVcWRI0cwaNAguLq6QldXF56enpg+fbpcY+V1oKjA4XWgChZeB6rg4XWgCh55Xwcq5G289E7Z5FBEV277yk+YQFGBkyjfq/ET0W/C78JTRYeQZya5l5Lr/kLC5ZhAWSpnAsUhPCIiIhKQ51l4yopn4RERERHJiBUoIiIiElDiqYtywwSKiIiIBJg/ScchPCIiIiIZsQJFREREQixBScUEioiIiAR4Fp50HMIjIiIikhErUERERCTAs/CkYwJFREREAsyfpOMQHhEREZGMWIEiIiIiIZagpGICRURERAI8C086DuERERERyYgVKCIiIhLgWXjSMYEiIiIiAeZP0nEIj4iIiEhGrEARERGREEtQUjGBIiIiIgGehScdh/CIiIiIZMQKFBEREQnwLDzpmEARERGRAPMn6TiER0RERCQjVqCIiIhIgEN40rEClQ3nz5+HSCRCTEyMokMhIiL6BURyXJQTK1D5iEgkwv79+9GmTRuZtrOxscHIkSMxcuTIPIlL3l68eAFbW1vcvn0bFStWVHQ4WdqxbSs2rv8H795Fw750GUz40xtO5csrOqxc2bVjG3bt3I63b94AAEqWssOAQYNRs1YdBUeWO/+s+Rv+p08hNPQ5NLW0ULFiJYz0GgMb2xKKDi1XlPX5+lZBfp/dO7kLtw9uhEO91qjSsT/i3kdin3fvLPvW7jsBNs61AADhD4MQdHgzPr59CTVNTZSs5o5KrTyhoqr6K8MnOWAC9YskJydDQ0ND0WFQNpw4fgzz5/li8lQfODlVwNbNGzFoQB8cPHICJiYmig4vx8zMLTBi1BhYWVtDLBbj8MEDGDF0CHbu3Y9SpewUHV6O3Qi8js5dPVDWyQlpqWlYungBBvbrg32HjkJHR0fR4eWYsj5fXxXk99m7F4/x5NIJFCpqK2nTKVQYHX03C/o9vnwCD07vQ1FHFwDAh9fP4b9iKpyadIab52h8iXmPgO3LIE5Ph0v7vr/0GKThEJ50SjeEZ2Njg0WLFgnaKlasiGnTpgHIqPKsXbsWbdu2hY6ODuzs7HDo0CFB/2PHjsHe3h7a2tqoV68eXrx4kel+Ll26hFq1akFbWxvFixfH8OHDER8fL4hjxowZ6NGjBwwMDNC/f38kJydj6NChsLS0hJaWFqytreHr6yvpDwBt27aFSCSS3H727Blat24Nc3Nz6OnpoUqVKjhz5ozkfurWrYuXL19i1KhREIlEEH3zqs9OjDNnzkSPHj2gp6cHa2trHDp0CNHR0WjdujX09PRQvnx53LhxQ+Zjnz17Nnr37g19fX1YWVlh9erVkvW2thkfOpUqVYJIJELdunWzeCYVZ/PG9WjXoRPatG2PkqVKYfJUH2hpaeHAvr2KDi1X6tarj1q168Da2gY2NrYYNmIUdHR0cPdOkKJDy5WVq/9B67btUKqUHUqXKYPps+YgPPwtQoIfKDq0XFHW5+urgvo+S0lMwL8b/kJ1j2HQ0NGTtKuoqELb0FiwhAVdhY1zTahraQMAXtz8F4WK2KJCs24wMCsCC3snOLftjUcXjyIl8YuiDilLHMCTTukSqOzw8fFBp06dcPfuXTRr1gweHh748OEDAODVq1do164dWrZsiaCgIPTt2xcTJkwQbP/s2TM0adIE7du3x927d7Fz505cunQJQ4cOFfSbP38+KlSogNu3b8Pb2xtLlizBoUOHsGvXLjx69Ahbt26VJEqBgYEAgPXr1yM8PFxyOy4uDs2aNYO/vz9u376NJk2aoGXLlggLCwMA7Nu3D8WKFcP06dMRHh6O8PBwmWJcuHAh3NzccPv2bTRv3hzdu3dHjx498Mcff+DWrVsoWbIkevToAbFYLNN+/fz84OLigtu3b2Pw4MEYNGgQHj16BAC4fv06AODMmTMIDw/Hvn37cv5kyllKcjJCgh+gumsNSZuKigqqV6+Bu3duKzAy+UpLS8PxY0eRkPAFFSpUUnQ4chX3+TMAwMDQUMGRyI+yPV8F+X0WsHMlipWrgiJlfv48vA97go+vn6NUjUaStvTUFKiqC0ciVDU0kJaSjPdhT/MkXso7v+UQXs+ePdG1a1cAwOzZs7FkyRJcv34dTZo0wcqVK1GyZEn4+fkBAEqXLo179+5h7ty5ku19fX3h4eEhmXNkZ2eHJUuWoE6dOli5ciW0tLQAAPXr18fo0aMl24WFhcHOzg41a9aESCSCtbW1ZJ2pqSkAwMjICBYWFpL2ChUqoEKFCpLbM2bMwP79+3Ho0CEMHToUxsbGUFVVhb6+vmC77MbYrFkzDBgwAAAwZcoUrFy5ElWqVEHHjh0BAOPHj4erqysiIyNhYWEh034HDx4s2cfChQtx7tw5lC5dWnKsJiYmgpjzg48xH5GWlpZpCMHExAShoc8VFJX8PHn8CN27dUFychJ0dHSwcMlylCxVStFhyU16ejrmzZ2NipWcYWdnr+hwck1Zn6+C+j4LvXEBH149RfPxi6T2fXL5FAwtisOspKOkrYiDM0LOHkRo4HlYV66FxNiPuHtsOwAg4dOHvAo7RziEJ91vmUCV/2aSoq6uLgwMDBAVFQUACAkJQbVq1QT9XV1dBbfv3LmDu3fvYuvWrZI2sViM9PR0hIaGwsHBAQDg4uIi2K5nz55o2LAhSpcujSZNmqBFixZo1KgRfiYuLg7Tpk3D0aNHER4ejtTUVCQkJEgqUD+S3Ri/fSzMzc0BAE5OTpnaoqKiYGFhkaP9ikQiWFhYSB5jWSQlJSEpKUnQJlbVhKampsz7IsDGxha79h5AXNxnnD51Et5/jsc/G7YoxR9lAJg90wfPnjzBhs3bFB2KXCj781WQxH+IRuDu1Wg4bGamKtL3UpOTEHrjAso37SJoL+LojMrteuPa9uW4tNEPqmrqcGraBVFPHwCi/DUgxN/Ck07pEigVFRXJcNNXKSkpgtvq6uqC2yKRCOnp6dm+j7i4OAwYMADDhw/PtM7Kykryf11dXcE6Z2dnhIaG4vjx4zhz5gw6deqEBg0aYM+ePT+8rzFjxuD06dOYP38+SpUqBW1tbXTo0AHJyclyifHbx+Lr/Kms2r4+PjnZ79f9yPIYf+Xr6wsfHx9B2yTvqZg8ZZrM+8qOQkaFoKqqivfv3wva379/j8KFC+fJff5K6hoasPp/5dOxbDk8uH8PW7dswpRp0xUcWe7NnjkdFy+cx7qNW2CezyqbOaWsz1dBfJ+9D3uKxM8xODLnv88+cXo6Ip/ex8MLh+Gx5ABUVDLOpHt5+zLSkpNQspp7pv04ureFQ/02SPj0ARo6eoh7H4nbBzdCv7ByvGZ/J0qXQJmamkrmAQFAbGwsQkNDs729g4NDpknl165dE9x2dnZGcHAwSuXgW6CBgQE6d+6Mzp07o0OHDmjSpAk+fPgAY2NjqKurIy0tTdD/8uXL6NmzJ9q2bQsgI4H5flK7hoZGpu1yE+PPyGO/X89G/D7mrEycOBFeXl6CNrFq3lWf1DU04OBYFgHXrqK+ewMAGcljQMBVdOn6R57dr6Kkp6cjRUoynt+JxWL4zpqBs/6n8c+GzShWrLiiQ8ozyvB8AQXzfWZZpgJaTl4uaLuyaREMLYqhbKMOkuQJAJ5eOYVi5atBSz/reXgikQg6RhnDly9uXIBOIVMYW5XMu+BzggUoqfJXzVAO6tevj82bN+Pff//FvXv34OnpCVUZrq8xcOBAPHnyBGPHjsWjR4+wbds2bNiwQdBn/PjxuHLlCoYOHYqgoCA8efIEBw8ezDSR+nsLFizA9u3b8fDhQzx+/Bi7d++GhYUFjIyMAGScvebv74+IiAh8/PgRQMYco3379iEoKAh37txBt27dMlVybGxscPHiRbx58wbv3r3LVYzSyGO/ZmZm0NbWxokTJxAZGYlPnz79sK+mpiYMDAwES14P33X37IV9e3bh0IH9eP7sGWZOn4aEhAS0adsuT+83ry1e6IebNwLx5s1rPHn8CIsX+uFG4HU0a9FS0aHlyuwZPjh25BDmzPODro4u3kVH4110NBITExUdWq4o6/P1VUF7n6lr6aBQERvBoqapBU1dAxQqYiPpFxv1FpFP78OuRtbTM+6f3ouPb14g5u1L3D22HfdP7UHVjgMECVh+wLPwpFO6CtTEiRMRGhqKFi1awNDQEDNmzJCpAmVlZYW9e/di1KhRWLp0KapWrSo5Jf+r8uXL48KFC5g0aRJq1aoFsViMkiVLonPnzj/dt76+PubNm4cnT55AVVUVVapUwbFjx6CikpHH+vn5wcvLC2vWrEHRokXx4sULLFiwAL1790aNGjVQuHBhjB8/HrGxsYL9Tp8+HQMGDEDJkiWRlJQEsVic4xilkcd+1dTUsGTJEkyfPh1TpkxBrVq1cP78+VzFJU9NmjbDxw8fsGLZErx7F43SZRyw4u+1MMmnQwvZ9eHDe0yeOB7R0VHQ09eHvX1prFz9D1xruCk6tFzZtTNjEm6fnt0F7dNn+qJ1Pv1jnB3K+nx9pazvs6dXT0PHqDCKODhnuf7tgxu4d2In0lNTUKioLeoN9EbRsi5Z9qX8TST+fsIQUT6XmKroCIioIPK7oLyXCpjkLt/pGlGfU6R3yiYzfXXpnQogpatAERERUe7wLDzplG4OFBEREVFeYwWKiIiIhFiAkooJFBEREQkwf5KOQ3hEREREMmIFioiIiAT4W3jSMYEiIiIiAZ6FJx2H8IiIiIhkxAoUERERCXAITzpWoIiIiIhkxASKiIiISEYcwiMiIiIBDuFJxwSKiIiIBHgWnnQcwiMiIiKSEStQREREJMAhPOmYQBEREZEA8yfpOIRHREREJCNWoIiIiEiIJSipmEARERGRAM/Ck45DeEREREQyYgWKiIiIBHgWnnRMoIiIiEiA+ZN0HMIjIiIikhETKCIiIhISyXHJgeXLl8PGxgZaWlqoVq0arl+/npujyRNMoIiIiEhAJMd/stq5cye8vLwwdepU3Lp1CxUqVEDjxo0RFRWVB0eac0ygiIiIKN9YsGAB+vXrh169esHR0RGrVq2Cjo4O1q1bp+jQBDiJnIiIiATkeRZeUlISkpKSBG2amprQ1NTM1Dc5ORk3b97ExIkTJW0qKipo0KABrl69Kr+g5EFMRFlKTEwUT506VZyYmKjoUOSKx1XwKOux8bh+D1OnThUDECxTp07Nsu+bN2/EAMRXrlwRtI8dO1ZctWrVXxBt9onEYrFYoRkcUT4VGxsLQ0NDfPr0CQYGBooOR254XAWPsh4bj+v3IEsF6u3btyhatCiuXLkCV1dXSfu4ceNw4cIFBAQE5Hm82cUhPCIiIsozP0qWslK4cGGoqqoiMjJS0B4ZGQkLC4u8CC/HOImciIiI8gUNDQ1UrlwZ/v7+krb09HT4+/sLKlL5AStQRERElG94eXnB09MTLi4uqFq1KhYtWoT4+Hj06tVL0aEJMIEi+gFNTU1MnTo126XngoLHVfAo67HxuCgrnTt3RnR0NKZMmYKIiAhUrFgRJ06cgLm5uaJDE+AkciIiIiIZcQ4UERERkYyYQBERERHJiAkUERERkYyYQBERERHJiAkUERERkYyYQBH9BsLCwpDVCbdisRhhYWEKiIh+Z6mpqThz5gz+/vtvfP78GUDGT3jExcUpODKi7ONlDIi+ce7cOdSrV0/RYcidqqoqwsPDYWZmJmh///49zMzMkJaWpqDI5CM9PR1Pnz5FVFQU0tPTBetq166toKhy7v3795gyZQrOnTuX5TF9+PBBQZHl3suXL9GkSROEhYUhKSkJjx8/RokSJTBixAgkJSVh1apVig4xR+rXr499+/bByMhI0B4bG4s2bdrg7NmzigmM8gwvpEn0jSZNmqBYsWLo1asXPD09Ubx4cUWHJBdisRgikShTe1xcHLS0tBQQkfxcu3YN3bp1w8uXLzNV2UQiUYFMDrt3746nT5+iT58+MDc3z/K5K6hGjBgBFxcX3LlzByYmJpL2tm3bol+/fgqMLHfOnz+P5OTkTO2JiYn4999/FRAR5TUmUETfePPmDTZv3oyNGzfCx8cH9evXR58+fdCmTRtoaGgoOjyZeXl5AchIJLy9vaGjoyNZl5aWhoCAAFSsWFFB0cnHwIED4eLigqNHj8LS0lIpko1///0Xly5dQoUKFRQditz9+++/uHLlSqb3k42NDd68eaOgqHLu7t27kv8HBwcjIiJCcjstLQ0nTpxA0aJFFREa5TEmUETfKFy4MEaNGoVRo0bh1q1bWL9+PQYPHozBgwejW7du6NOnT4H6o3b79m0AGRWoe/fuCf5oaWhooEKFChgzZoyiwpOLJ0+eYM+ePShVqpSiQ5GbMmXKICEhQdFh5In09PQsq4KvX7+Gvr6+AiLKnYoVK0IkEkEkEqF+/fqZ1mtra2Pp0qUKiIzyGudAEf3E27dvsXr1asyZMwdqampITEyEq6srVq1ahbJlyyo6vGzr1asXFi9eDAMDA0WHInf169fHuHHj0KRJE0WHIjeBgYGYMGECpkyZgnLlykFdXV2wviA/j507d4ahoSFWr14NfX193L17F6ampmjdujWsrKywfv16RYcok69DxyVKlMD169dhamoqWaehoQEzMzOoqqoqMELKK0ygiL6TkpKCgwcPYt26dTh9+jRcXFzQp08fdO3aFdHR0Zg8eTJu3bqF4OBgRYdKAPbv34/Jkydj7NixcHJyypRslC9fXkGR5dyTJ0/QrVs33Lp1S9D+dS5bQZzX9dWrV6/QpEkTiMViPHnyBC4uLnjy5AkKFy6MixcvZjrRgSi/YgJF9I1hw4Zh+/btEIvF6N69O/r27Yty5coJ+kRERKBIkSKZzozKz+Lj4zFnzhz4+/tneVbX8+fPFRRZ7qmoZL4ai0gkKtDJRtWqVaGmpoYRI0ZkOYm8Tp06CopMPlJTU7Fz507cuXMHcXFxcHZ2hoeHB7S1tRUdWq48efLkh2dOTpkyRUFRUV5hAkX0DXd3d/Tt2xft2rWDpqZmln1SU1Nx+fLlAvVHrGvXrrhw4QK6d++e5UTrESNGKCiy3Hv58uVP11tbW/+iSORHR0cHt2/fRunSpRUdilylpKSgTJkyOHLkCBwcHBQdjlytWbMGgwYNQuHChWFhYSF4j4lEokzVRCr4mEAR/QaMjIxw9OhRuLm5KToUyobatWtjypQpaNCggaJDkbuiRYvizJkzSpdAWVtbY/DgwRg/fryiQ6FfhGfhEX1HGcvwhQoVgrGxsaLDyDPPnj3DokWLEBISAgBwdHTEiBEjULJkSQVHljPDhg3DiBEjlGpe11dDhgzB3LlzsXbtWqipKc+foI8fP6Jjx46KDoN+IVagiL6hrGX4LVu24ODBg9i4caPgWlDK4OTJk2jVqhUqVqwoqbBdvnwZd+7cweHDh9GwYUMFRyg7ZZzX9VXbtm3h7+8PPT09ODk5QVdXV7B+3759Coosd/r06YMqVapg4MCBig6FfhEmUETfUNYyfKVKlfDs2TOIxWLY2NhkqmgU1MQQyDi2xo0bY86cOYL2CRMm4NSpUwXy2JRxXtdXvXr1+un6gnYZg698fX2xYMECNG/ePMuq4fDhwxUUGeUVJlBE3zAwMEBQUBBKlCih6FDkysfH56frp06d+osikT8tLS3cu3cPdnZ2gvbHjx+jfPnySExMVFBk9DuxtbX94TqRSFSgz3SlrCnPADSRHHTs2BGnTp1SujJ8QU6QpDE1NUVQUFCmBCooKKjAXlNo48aNKFy4MJo3bw4AGDduHFavXg1HR0ds3769QFeglFVoaKiiQ6BfjAkU0TdKlSoFb29vXLt2TenK8DExMdizZw+ePXuGsWPHwtjYGLdu3YK5uXmB/q2ufv36oX///nj+/Dlq1KgBIGMO1Ny5cyW/BVjQzJ49GytXrgQAXL16FcuWLcOiRYtw5MgRjBo1qsDNE3J2doa/vz8KFSqESpUq/fT3CgvikOu3kpOTERoaipIlSyrVJHnKjEN4RN9Q1jL83bt30aBBAxgaGuLFixd49OgRSpQogcmTJyMsLAybNm1SdIg5JhaLsWjRIvj5+eHt27cAgCJFimDs2LEYPnx4gfxxYR0dHTx8+BBWVlYYP348wsPDsWnTJjx48AB169ZFdHS0okOUiY+PD8aOHQsdHR1Mmzbtp89JQa2WfvnyBcOGDcPGjRsBZAwhlyhRAsOGDUPRokUxYcIEBUdI8sYEiug30KBBAzg7O2PevHnQ19fHnTt3UKJECVy5cgXdunXDixcvFB2iXHz+/BkACuSP0n7LzMwMJ0+eRKVKlVCpUiV4eXmhe/fuePbsGSpUqIC4uDhFh0jfGTFiBC5fvoxFixahSZMmuHv3LkqUKIGDBw9i2rRpkh/2JuWR+VxZIgKQUdlQlu8XgYGBGDBgQKb2okWLIiIiQgER5Q19ff0CnzwBQMOGDdG3b1/07dsXjx8/RrNmzQAADx48gI2NjWKDy6USJUrg/fv3mdpjYmIK9MkbBw4cwLJly1CzZk1Bha1s2bJ49uyZAiOjvMIEiug7mzZtgpOTE7S1taGtrY3y5ctj8+bNig4rVzQ1NREbG5up/fHjx4Jfjy8onJ2d8fHjRwAZlzFwdnb+4VIQLV++HK6uroiOjsbevXthYmICALh58ya6du2q4Ohy58WLF1lexyopKQmvX79WQETyER0dneVJC/Hx8QVyGJmk4ww3om8sWLAA3t7eGDp0qOSijJcuXcLAgQPx7t07jBo1SsER5kyrVq0wffp07Nq1C0DGfK6wsDCMHz8e7du3V3B0smvdurXktwpbt26tdH+gjIyMsGzZskzt0i5HkZ8dOnRI8v+TJ0/C0NBQcjstLQ3+/v4/nYOY37m4uODo0aMYNmwYAEhek2vXroWrq6siQ6M8wjlQRN+wtbWFj48PevToIWjfuHEjpk2bVmBPVf706RM6dOiAGzdu4PPnzyhSpAgiIiLg6uqKY8eOZboaNOUPX758QVhYGJKTkwXtBfGnXL5eXf3rFdW/pa6uDhsbG/j5+aFFixaKCC/XLl26hKZNm+KPP/7Ahg0bMGDAAAQHB+PKlSu4cOECKleurOgQSc6YQBF9Q0tLC/fv30epUqUE7U+ePIGTk1OBvyjjpUuXcPfuXcTFxcHZ2Vkpfqy2RIkSCAwMlAxzfRUTEwNnZ+cCeeZkdHQ0evbsiRMnTmS5viD/lIutrS0CAwNRuHBhRYcid8+ePcOcOXNw584dyXts/PjxcHJyUnRolAc4hEf0jVKlSmHXrl34888/Be07d+7MdKHGgqhmzZqoWbOmosOQK2WcUzNy5Eh8+vQJAQEBqFu3Lvbv34/IyEjMnDkTfn5+ig4vVwpqFTc7SpYsiTVr1ig6DPpFmEARfcPHxwedO3fGxYsXBT9M6+/vL5k/VFAFBgbi3LlziIqKQnp6umDdggULFBRVzinznJqzZ8/i4MGDcHFxgYqKCqytrdGwYUMYGBjA19dXcoXygio+Ph4XLlzIcniyIF+sFgCioqKyfI8VxGFX+jkO4RF959atW1iwYAFCQkIAAA4ODhg9ejQqVaqk4Mhybvbs2Zg8eTJKly4Nc3NzwaRrkUiEs2fPKjC6nFHmOTUGBga4e/cubGxsYG1tjW3btsHNzQ2hoaEoW7Ysvnz5ougQc+z27dto1qwZvnz5gvj4eBgbG+Pdu3fQ0dGBmZlZgRxyBTLOkPT09ERISEim16NIJCrQw66UNVagiP4vJSUFAwYMgLe3N7Zs2aLocORq8eLFWLduHXr27KnoUOTm6zd8ZZxTU7p0aTx69Ag2NjaoUKEC/v77b9jY2GDVqlWwtLRUdHi5MmrUKLRs2RKrVq2CoaEhrl27BnV1dfzxxx8YMWKEosPLsd69e8Pe3h7//PNPpi8ppJxYgSL6hqGhIYKCggrs0M+PWFpa4uLFi0oxjys7YmJiYGRkpOgwcmzLli1ITU1Fz549cfPmTTRp0gQfPnyAhoYGNmzYgM6dOys6xBwzMjJCQEAASpcuDSMjI1y9ehUODg4ICAiAp6cnHj58qOgQc0RfXx+3b9/OdAIKKS9eSJPoG23atMGBAwcUHYbcjRo1CsuXL1d0GHli7ty52Llzp+R2x44dYWxsjKJFi+LOnTsKjCzn/vjjD0m1sHLlynj58iUCAwPx6tWrAp08ARnDq1+HX83MzBAWFgYg48vLq1evFBlarri7uxfY1xvlDCtQRN/4epaTu7s7KleunOn6SAV1gmt6ejqaN2+Ox48fw9HREerq6oL1+/btU1BkuWdra4utW7eiRo0aOH36NDp16oSdO3di165dCAsLw6lTpxQdIn2jUaNG6NmzJ7p164Z+/frh7t27GD58ODZv3oyPHz8iICBA0SHmyLt37+Dp6YmqVauiXLlymd5jrVq1UlBklFeYQBF942dDdyKRqMBOcB06dCjWrl2LevXqZTk/Y/369QqKLPe0tbXx+PFjFC9eHCNGjEBiYiL+/vtvPH78GNWqVZP85EtB0r59e1StWhXjx48XtM+bNw+BgYHYvXu3giLLva8Xc61Xrx6ioqLQo0cPXLlyBfb29li7di0qVqyo6BBz5PDhw+jevXuWP5nESeTKiQkU0W9AX18fO3bsKPCnv2elSJEi2LNnD2rUqIHSpUtj5syZ6NixIx49eoQqVapk+QctvzM1NcXZs2czXYDx3r17aNCgASIjIxUUWe4lJCRALBZDR0cHQMZ1vPbv3w9HR0c0btxYwdHlnI2NDVq0aAFvb2+Ym5srOhz6BXgWHv32vLy8MGPGDOjq6sLLy+uH/UQiUYG9iKGxsTFKliyp6DDyRLt27dCtWzfY2dnh/fv3aNq0KQAU6Am9cXFx0NDQyNSurq5eIBPCb7Vu3Rrt2rXDwIEDERMTg+rVq0NdXR3v3r3DggULMGjQIEWHmCPv37/HqFGjmDz9RjiJnH57t2/fRkpKiuT/P1sKqmnTpmHq1KkF+vpBP7Jw4UIMHToUjo6OOH36NPT09AAA4eHhGDx48P/au/eoKqv8f+Dvg1zkjqRoEnIRLyBoIKPSZGiQoo2iaGrCKIM2ao6WoimT4kiJVoOlzSQmKlLhJUiWKaMmxsVLglw1FRFBwIFRMzBAuT6/P/x5vnMCmxAOu3Oe92st1+Ls58h5y/LAh73389mC0z0ZV1dXlY3xj+zbtw/Ozs4CEnWe7OxsjB49GgAQHx+P3r1748aNG4iNjcXWrVsFp3ty/v7++Pbbb0XHoC7EJTwiGXBzc0NRUREkSYKdnV2rDa7Z2dmCklFbvv76a+XM2osvvggASE5Oxt69e/Hll19iypQpYgN2gJGREa5cuYJ+/fphxowZGDJkCNatW4eysjIMGjRIY4v8DRs24KOPPsLLL78MV1fXVu8xTb0BhR6PBRSRDKxfv/4Xr69bt66LkqjHZ599hu3bt+P69es4e/YsbG1t8dFHH8He3h5+fn6i4z2RI0eOICIiArm5uTA0NMTQoUOxbt06eHl5iY7WIUOHDsX8+fMxdepUuLi44OjRo/D09ERWVhZefvllVFZWio74RLT1BhR6PBZQRKTRtm3bhrCwMLz55pvYsGEDLl68CAcHB8TExGDPnj0at6zS1NSEiIgIBAcH45lnnhEdp9PFx8dj9uzZaG5uhre3t7LNxMaNG5GWloZ//etfghMS/TosoIhkoqqqCvHx8SgqKsLKlSthaWmJ7Oxs9O7dG9bW1qLjPTFnZ2dERERgypQpMDU1RV5eHhwcHHDx4kWMGTMGd+7cER2x3UxMTHDx4kXY2dmJjqIWlZWVqKiowLBhw5RNNTMyMmBmZobBgwcLTtcxDQ0NKC4uRv/+/aGry/u0tBk3kRPJQH5+PgYOHIj33nsPf//731FVVQXgYQPN0NBQseE6qLi4uM2Dng0MDFBbWysgUcd5e3sjNTVVdAy16dOnD9zc3JTFEwCMGDFCo4unuro6zJs3D0ZGRhgyZIiyw/qSJUuwadMmwelIHVhAEcnA8uXLERQUhMLCQnTv3l05PnHiRKSlpQlM1nH29vbIzc1tNX706FE4OTl1faBOMGHCBKxevRorVqzA3r17cejQIZU/9NsTGhqKvLw8pKSkqLzHfHx82ryjkjQf5xeJZCAzMxPbt29vNW5tba2xm3YfWb58ORYvXowHDx5AkiRkZGRg79692LhxI6Kjo0XHeyKP2i9s3ry51TV2tf5tSkxMxP79+zFq1CiVTv9DhgxBUVGRwGSkLiygiGTAwMCgzQaMV69eRa9evQQk6jzz58+HoaEh1qxZg7q6OsyePRt9+/bFli1bMGvWLNHxnkhLS4voCNROt2/fhpWVVavx2traVkcnkXbgEh6RDEyePBnh4eHKhqEKhQKlpaVYtWoVpk2bJjhdxwUEBKCwsBA1NTWorKxEeXk55s2bJzoWyYiHhweOHDmifPyoaIqOjoanp6eoWKRGvAuPSAaqq6sxffp05UGuffv2RWVlJTw9PZGUlARjY2PREelnamtrkZqaitLSUjQ0NKhcY1PG355Tp05hwoQJCAwMRExMDBYsWIBLly7hzJkzSE1NxfDhw0VHpE7GAopIRk6fPo28vDzU1NTA3d0dPj4+oiN1mL29/S8ukWhiA8OcnBxMnDgRdXV1qK2thaWlJe7cuQMjIyNYWVlp5L9JDoqKirBp0yaV99iqVataHQpN2oEFFJEMxMbGYubMmTAwMFAZb2howL59+zBnzhxByTpuy5YtKo8bGxuRk5ODo0ePYuXKlVi9erWgZE9uzJgxGDhwIKKiomBubo68vDzo6ekhMDAQb7zxBvz9/UVHJJI9FlBEMtCtWzdUVFS02uT6ww8/wMrKSivv6vrnP/+J8+fPY/fu3aKjtJuFhQXOnTuHQYMGwcLCAmfPnoWTkxPOnTuHuXPn4sqVK6Ij0s/I8T0md9xETiQDkiS1ucxVXl4Oc3NzAYnUb8KECUhISBAd44no6ekpm0xaWVkpmzKam5ujrKxMZDR6jMfNRdTX10NfX7+L01BXYBsDIi3m5uYGhUIBhUIBb29vlaMlmpubUVxcDF9fX4EJ1Sc+Ph6WlpaiYzwRNzc3ZGZmYsCAAfDy8kJYWBju3LmDzz77DC4uLqLj0X/ZunUrgId33UVHR8PExER5rbm5GWlpaRrdYZ0ejwUUkRabMmUKACA3Nxfjx49X+eaur68POzs7jW9j8KhIfESSJFRWVuL27dv45JNPBCZ7chEREfjpp58AABs2bMCcOXOwaNEiDBw4UGObg2qrDz/8EMDD/3dRUVHo1q2b8tqj91hUVJSoeKRG3ANFJAN79uzBzJkzVY6Y0Bbr169Xeayjo4NevXphzJgxGvub//379yFJEoyMjAAAJSUlOHjwIJydnTF+/HjB6agtY8eOxVdffYUePXqIjkJdhAUUEdFvzLhx4+Dv74+FCxeiqqoKgwcPhp6eHu7cuYPNmzdj0aJFoiMSyR6X8IhkoLm5GR9++CEOHDjQZmPGu3fvCkrWcW0dUfM4ZmZmakzSebKzs5VLQ/Hx8ejduzdycnKQkJCAsLAwFlC/UeXl5Th06FCb77G2zjUkzcYCikgG1q9fj+joaISEhGDNmjV4++23UVJSgsTERISFhYmO1yEWFhb/86yxR3chasqt5HV1dTA1NQUAHD9+HP7+/tDR0cGoUaNw48YNwemoLcnJyZg8eTIcHBxw5coVuLi4oKSkBJIkwd3dXXQ8UgO2MSCSgS+++AI7duxASEgIdHV18eqrryI6OhphYWH47rvvRMfrkN27d8PKygpvvfUWDh48iIMHD+Ktt95C7969sWvXLpw8eRLffvstTp48KTrqr+bo6IjExESUlZXh2LFjGDduHADg1q1bGjOLJjehoaFYsWIFLly4gO7duyMhIQFlZWXw8vLCK6+8IjoeqYNERFrPyMhIunHjhiRJktSnTx8pKytLkiRJKioqkszMzERG67AXX3xRiouLazX+xRdfSF5eXl0fqBN8+eWXkp6enqSjoyO99NJLyvGIiAjJ19dXYDJ6HBMTE+natWuSJEmShYWFdPHiRUmSJCk3N1eytbUVmIzUhTNQRDLwzDPPoKKiAgDQv39/HD9+HACQmZnZ6ngXTXP27Fl4eHi0Gvfw8EBGRoaARB03ffp0lJaW4vz58zh69Khy3NvbW7k3in5bjI2Nlfuenn76aRQVFSmv3blzR1QsUiMWUEQyMHXqVCQnJwMAlixZgrVr12LAgAGYM2cOgoODBafrGBsbG+zYsaPVeHR0NGxsbAQk6hx9+vSBm5ubsiM5AIwYMUJjWzNou1GjRuHUqVMAgIkTJyIkJAQbNmxAcHAwRo0aJTgdqQPbGBDJ0HfffYczZ85gwIABmDRpkug4HZKUlIRp06bB0dERI0eOBABkZGSgsLAQCQkJmDhxouCEJAfXr19HTU0Nhg4ditraWoSEhCjfY5s3b4atra3oiNTJWEARyUBaWhqee+45laNcAKCpqQlnzpzBCy+8IChZ5ygvL8e2bdtw+fJlAICTkxMWLlyo0TNQRPTbxgKKSAZ4Ujzw+uuvIzw8HD179hQdhbSQg4MDMjMz8dRTT6mMV1VVwd3dHdevXxeUjNSFe6CIZED6/32Qfu6HH36AsbGxgERd7/PPP29X002i9igpKWnzF5H6+nrcvHlTQCJSNzbSJNJi/v7+AB6eFB8UFKRyx11zczPy8/Px3HPPiYrXpTjZTupw6NAh5cfHjh2Dubm58nFzczOSk5NhZ2cnIBmpGwsoIi326Ju5JEkwNTWFoaGh8pq+vj5GjRqF1157TVQ8Io03ZcoUAA9/SZk7d67KNT09PdjZ2SEyMlJAMlI3FlBEWmz37t0AADs7O6xYsUI2y3VEXaWlpQUAYG9vj8zMTO6xkxFuIieSgfv370OSJBgZGQEAbty4gYMHD8LZ2Vl5TIi2MzU1RV5eHhwcHERHIZmoqqqChYWF6BikJtxETiQDfn5+iI2NBfDwm/qIESMQGRkJPz8/bNu2TXA6Is333nvvYf/+/crHr7zyCiwtLWFtbY28vDyByUhdWEARyUB2djZGjx4NAIiPj0efPn1w48YNxMbGYuvWrYLTdY3AwEAexEtqExUVpew79s033+DEiRM4evQoJkyYgJUrVwpOR+rAPVBEMlBXVwdTU1MAwPHjx+Hv7w8dHR2MGjUKN27cEJyu/fLz83/1c4cOHQoAnGkjtaqsrFQWUIcPH8aMGTMwbtw42NnZKTvkk3ZhAUUkA46OjkhMTMTUqVNx7NgxLFu2DABw69YtjZyVefbZZ6FQKB7bmuDRNYVCIYsmoSRejx49UFZWBhsbGxw9ehTvvvsugId3wPL/oHZiAUUkA2FhYZg9ezaWLVsGb29veHp6Ang4G+Xm5iY4XfsVFxeLjkCkwt/fH7Nnz8aAAQPwww8/YMKECQCAnJwcODo6Ck5H6sC78IhkorKyEhUVFRg2bBh0dB5uf8zIyICZmRkGDx4sOB2RZmtsbMTWrVtRWlqKoKAg5S8mH374IUxNTTF//nzBCamzsYAi0nKNjY0wNDREbm4uXFxcRMdRm0uXLqG0tBQNDQ0q45MnTxaUiOSisbERCxYswNq1a2Fvby86DnURLuERaTk9PT3069dPa/dhXL9+HVOnTsWFCxdU9kU9OvtPW//d9Nuhp6eHhIQErF27VnQU6kJsY0AkA2+//Tb++te/4u7du6KjdLo33ngD9vb2uHXrFoyMjPD9998jLS0NHh4eSElJER2PZGLKlClITEwUHYO6EJfwiGTAzc0N165dQ2NjI2xtbVsd6ZKdnS0oWcf17NkTJ0+exNChQ2Fubo6MjAwMGjQIJ0+eREhICHJyckRHJBl49913ERkZCW9vbwwfPrzVe2zp0qWCkpG6cAmPSAYeHXiqjZqbm5U9rnr27Il///vfGDRoEGxtbVFQUCA4HcnFzp07YWFhgaysLGRlZalcUygULKC0EAsoIhlYt26d6Ahq4+Ligry8PNjb22PkyJF4//33oa+vj08//ZTn3lGXYWsN+eEeKCKZqKqqQnR0NEJDQ5V7obKzs3Hz5k3ByTpmzZo1aGlpAQCEh4ejuLgYo0ePRlJSkmyOqaHfjoaGBhQUFKCpqUl0FFIz7oEikoH8/Hz4+PjA3NwcJSUlKCgogIODA9asWYPS0lLlQcPa4u7du+jRo4fyTjwidaurq8OSJUuwZ88eAMDVq1fh4OCAJUuWwNraGqtXrxackDobZ6CIZGD58uUICgpCYWEhunfvrhyfOHEi0tLSBCbruOrq6lZ3F1paWuLHH3/EvXv3BKUiuQkNDUVeXh5SUlJU3mM+Pj7Yv3+/wGSkLiygiGQgMzMTCxYsaDVubW2NyspKAYk6z6xZs7Bv375W4wcOHMCsWbMEJCI5SkxMxD/+8Q88//zzKjOfQ4YMQVFRkcBkpC4soIhkwMDAoM3ZmKtXr6JXr14CEnWec+fOYezYsa3Gx4wZg3PnzglIRHJ0+/ZtWFlZtRqvra3lUrKWYgFFJAOTJ09GeHg4GhsbATy8rbq0tBSrVq3CtGnTBKfrmPr6+jY37DY2NuL+/fsCEpEceXh44MiRI8rHj4qm6Oho5eHdpF24iZxIBqqrqzF9+nScP38eP/30E/r27YvKykp4enoiKSmpVdM/TTJ27Fi4uLjg448/VhlfvHgx8vPzkZ6eLigZycmpU6cwYcIEBAYGIiYmBgsWLMClS5dw5swZpKamYvjw4aIjUidjAUUkI6dOnUJ+fj5qamrg7u4OHx8f0ZE67PTp0/Dx8cHvfvc7eHt7AwCSk5ORmZmJ48ePY/To0YITklwUFRVh06ZNyMvLU77HVq1aBVdXV9HRSA1YQBHJQFlZGWxsbETHUJvc3Fx88MEHyM3NhaGhIYYOHYrQ0FAMGDBAdDQi0lIsoIhkoFu3bnj++ecRGBiI6dOno0ePHqIjEWm89rTJMDMzU2MSEoEFFJEM5OTkIC4uDvv27cPt27fh6+uLwMBATJo0CQYGBqLjtdu9e/eUP5D+1w8x/uAiddHR0fnVd9g1NzerOQ11NRZQRDIiSRJSUlIQFxeHhIQEtLS0wN/fH7t27RIdrV26deuGiooKWFlZPfaHmCRJUCgU/MFFapOamqr8uKSkBKtXr0ZQUJDyrruzZ89iz5492LhxI+bOnSsqJqkJCygimcrOzsa8efOQn5+vcUVGamoqfv/730NXV1flh1hbvLy8uigVyZm3tzfmz5+PV199VWU8Li4On376KVJSUsQEI7VhAUUkI+Xl5YiLi0NcXBwuXrwIT09PBAQEYOHChaKjPZGmpiZEREQgODgYzzzzjOg4JGNGRkbIy8trdePC1atX8eyzz6Kurk5QMlIXNtIkkoHt27fDy8sLtra2iI2NxcyZM1FUVIT09HSNLZ4AQFdXFx988EGbjTSJupKNjQ127NjRajw6Olqr74CVM85AEcmAjY0NXn31VQQEBGDYsGGi43QqPz8/+Pv7c48JCZWUlIRp06bB0dERI0eOBABkZGSgsLAQCQkJmDhxouCE1NlYQBHJgCRJqK6uxs6dO3H58mUAgLOzM+bNmwdzc3PB6TomKioK69evR0BAAIYPH96qq/rkyZMFJSO5KS8vxyeffIIrV64AAJycnLBw4ULOQGkpFlBEMpCVlYXx48eje/fuGDFiBAAgMzMT9+/fx/Hjx+Hu7i444ZPT0Xn8TgTehUdE6sICikgGRo8eDUdHR+zYsQO6uroAHm7Anj9/Pq5fv460tDTBCYk0X1VVFTIyMnDr1i20tLSoXJszZ46gVKQuLKCIZMDQ0BA5OTkYPHiwyvilS5fg4eHBO4SIOujrr79GQEAAampqYGZmptKbTKFQ4O7duwLTkTrwLjwiGTAzM0NpaWmr8bKyMpiamgpI1LlSU1MxadIkODo6wtHREZMnT0Z6erroWCQjISEhCA4ORk1NDaqqqvDjjz8q/7B40k4soIhkYObMmZg3bx7279+PsrIylJWVYd++fW02/tM0n3/+OXx8fGBkZISlS5di6dKlMDQ0hLe3N+Li4kTHI5m4efMmli5dCiMjI9FRqItwCY9IBhoaGrBy5UpERUUpeybp6elh0aJF2LRpk0aeh/eIk5MT/vznP2PZsmUq45s3b8aOHTuUdx0SqZO/vz9mzZqFGTNmiI5CXYQFFJGM1NXVoaioCADQv39/rfht2cDAAN9//z0cHR1Vxq9duwYXFxc8ePBAUDKSk507dyI8PBx/+tOf4OrqCj09PZXrbKehfXRFByCirmNkZARXV1fRMTqVjY0NkpOTWxVQJ06cYP8d6jKvvfYaACA8PLzVNbbT0E4soIhIo4WEhGDp0qXIzc3Fc889BwA4ffo0YmJisGXLFsHpSC5+3raAtB+X8IhI4x08eBCRkZHK/U5OTk5YuXIl/Pz8BCcjuWhr5ukRhUKBtWvXdmEa6gosoIiIiDrIzc1N5XFjYyOKi4uhq6uL/v37Izs7W1AyUhcu4RGRRnNwcEBmZiaeeuoplfGqqiq4u7vj+vXrgpKRnOTk5LQau3fvHoKCgjB16lQBiUjdOANFRBpNR0cHlZWVsLKyUhn/z3/+g379+qG+vl5QMiLgwoULmDRpEkpKSkRHoU7GGSgi0kiHDh1Sfnzs2DGYm5srHzc3NyM5ORl2dnYCkhH9n+rqalRXV4uOQWrAGSgi0kg6Og8PUlAoFPj5tzE9PT3Y2dkhMjISf/jDH0TEI5nZunWrymNJklBRUYHPPvsMXl5e7IqvhVhAEZFGs7e3R2ZmJnr27Ck6CsmYvb29ymMdHR306tULL774IkJDQ7XizElSxQKKiLTGgwcP0L17d9ExiEgGeJgwEWm0lpYWvPPOO7C2toaJiYnyrru1a9di586dgtMRkbZiAUVEGu3dd99FTEwM3n//fejr6yvHXVxcEB0dLTAZEWkzFlBEpNFiY2Px6aefIiAgAN26dVOODxs2DFeuXBGYjIi0GQsoItJoN2/ebHWQMPBwaa+xsVFAIiKSAxZQRKTRnJ2dkZ6e3mo8Pj6+1fEaRESdhY00iUijhYWFYe7cubh58yZaWlrw1VdfoaCgALGxsTh8+LDoeESkpdjGgIg0Xnp6OsLDw5GXl4eamhq4u7sjLCwM48aNEx2NiLQUCygiIiKiduISHhFphYaGBty6dQstLS0q4/369ROUiIi0GQsoItJohYWFCA4OxpkzZ1TGJUmCQqFAc3OzoGREpM1YQBGRRgsKCoKuri4OHz6Mp59+GgqFQnQkIpIB7oEiIo1mbGyMrKwsDB48WHQUIpIR9oEiIo3m7OyMO3fuiI5BRDLDGSgi0jj37t1Tfnz+/HmsWbMGERERcHV1hZ6enspzzczMujoeEckACygi0jg6Ojoqe50ebRj/b9xETkTqxE3kRKRxvv32WwBAfX09fH19ERUVhUGDBglORURywhkoItJovXr1wpkzZzBgwADRUYhIRriJnIg0WmBgIHbu3Ck6BhHJDJfwiEijNTU1YdeuXThx4gSGDx8OY2NjleubN28WlIyItBkLKCLSaBcvXoS7uzsA4OrVqyrX2FSTiNSFe6CIiIiI2ol7oIiIiIjaiQUUERERUTuxgCIiIiJqJxZQRERERO3EAoqI6H8ICgrClClTlI/HjBmDN998s8tzpKSkQKFQoKqqqstfm4hUsYAiIo0VFBQEhUIBhUIBfX19ODo6Ijw8HE1NTWp93a+++grvvPPOr3ouix4i7cQ+UESk0Xx9fbF7927U19cjKSkJixcvhp6eHkJDQ1We19DQAH19/U55TUtLy075PESkuTgDRUQazcDAAH369IGtrS0WLVoEHx8fHDp0SLnstmHDBvTt21d52HBZWRlmzJgBCwsLWFpaws/PDyUlJcrP19zcjOXLl8PCwgJPPfUU3nrrLfy8Xd7Pl/Dq6+uxatUq2NjYwMDAAI6Ojti5cydKSkowduxYAECPHj2gUCgQFBQEAGhpacHGjRthb28PQ0NDDBs2DPHx8Sqvk5SUhIEDB8LQ0BBjx45VyUlEYrGAIiKtYmhoiIaGBgBAcnIyCgoK8M033+Dw4cNobGzE+PHjYWpqivT0dJw+fRomJibw9fVV/p3IyEjExMRg165dOHXqFO7evYuDBw/+4mvOmTMHe/fuxdatW3H58mVs374dJiYmsLGxQUJCAgCgoKAAFRUV2LJlCwBg48aNiI2NRVRUFL7//nssW7YMgYGBSE1NBfCw0PP398ekSZOQm5uL+fPnY/Xq1er6shFRO3EJj4i0giRJSE5OxrFjx7BkyRLcvn0bxsbGiI6OVi7dff7552hpaUF0dLTymJfdu3fDwsICKSkpGDduHD766COEhobC398fABAVFYVjx4499nWvXr2KAwcO4JtvvoGPjw8AwMHBQXn90XKflZUVLCwsADycsYqIiMCJEyfg6emp/DunTp3C9u3b4eXlhW3btqF///6IjIwEAAwaNAgXLlzAe++914lfNSJ6UiygiEijHT58GCYmJmhsbERLSwtmz56Nv/3tb1i8eDFcXV1V9j3l5eXh2rVrMDU1VfkcDx48QFFREaqrq1FRUYGRI0cqr+nq6sLDw6PVMt4jubm56NatG7y8vH515mvXrqGurg4vvfSSynhDQwPc3NwAAJcvX1bJAUBZbBGReCygiEijjR07Ftu2bYO+vj769u0LXd3/+7ZmbGys8tyamhoMHz4cX3zxRavP06tXryd6fUNDw3b/nZqaGgDAkSNHYG1trXLNwMDgiXIQUddiAUVEGs3Y2BiOjo6/6rnu7u7Yv38/rKysYGZm1uZznn76aZw7dw4vvPACAKCpqQlZWVlwd3dv8/murq5oaWlBamqqcgnvvz2aAWtublaOOTs7w8DAAKWlpY+duXJycsKhQ4dUxr777rv//Y8koi7BTeREJBsBAQHo2bMn/Pz8kJ6ejuLiYqSkpGDp0qUoLy8HALzxxhvYtGkTEhMTceXKFbz++uu/2MPJzs4Oc+fORXBwMBITE5Wf88CBAwAAW1tbKBQKHD58GLdv30ZNTQ1MTU2xYsUKLFu2DHv27EFRURGys7Px8ccfY8+ePQCAhQsXorCwECtXrkRBQQHi4uIQExOj7i8REf1KLKCISDaMjIyQlpaGfv36wd/fH05OTpg3bx4ePHignJEKCQnBH//4R8ydOxeenp4wNTXF1KlTf/Hzbtu2DdOnT8frr7+OwYMH47XXXkNtbS0AwNraGuvXr8fq1avRu3dv/OUvfwEAvPPOO1i7di02btwIJycn+Pr64siRI7C3twcA9OvXDwkJCUhMTMSwYcMQFRWFiIgINX51iKg9FNLjdkYSERERUZs4A0VERETUTiygiIiIiNqJBRQRERFRO7GAIiIiImonFlBERERE7cQCioiIiKidWEARERERtRMLKCIiIqJ2YgFFRERE1E4soIiIiIjaiQUUERERUTuxgCIiIiJqp/8Hg/oqcoiJfB4AAAAASUVORK5CYII=\n" 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/tfidf_lr/confusion_matrix_type_test.png\n" + ] + } + ], + "source": [ + "# ── Confusion matrices ────────────────────────────────────────────────────────\n", + "save_confusion_matrix(\n", + " y_val_t, y_val_pred_type, STRATEGY_LABELS,\n", + " OUT_TFIDF / \"confusion_matrix_type_val.png\",\n", + " \"TF-IDF + LR — Type (Val)\"\n", + ")\n", + "save_confusion_matrix(\n", + " y_test_t, y_test_pred_type, STRATEGY_LABELS,\n", + " OUT_TFIDF / \"confusion_matrix_type_test.png\",\n", + " \"TF-IDF + LR — Type (Test)\"\n", + ")" + ] }, { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1_ZECP4MuKB6", + "outputId": "7942d47e-af5d-4a35-b1eb-952c610ce493" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved predictions_val_type.csv and predictions_test_type.csv\n" + ] + } ], - "image/png": 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\n" - }, - "metadata": {} + "source": [ + "# ── Save predictions ──────────────────────────────────────────────────────────\n", + "# Decode integer predictions back to string labels\n", + "val_type_copy = val_type.copy(); val_type_copy[\"type_label\"] = y_val_t\n", + "test_type_copy = test_type.copy(); test_type_copy[\"type_label\"] = y_test_t\n", + "\n", + "val_type_copy[\"predicted_id\"] = y_val_pred_type\n", + "val_type_copy[\"predicted_label\"] = [id2label[i] for i in y_val_pred_type]\n", + "val_type_copy[\"true_label\"] = [id2label[i] for i in y_val_t]\n", + "val_type_copy[\"correct\"] = (val_type_copy[\"type_label\"] == val_type_copy[\"predicted_id\"]).astype(int)\n", + "\n", + "test_type_copy[\"predicted_id\"] = y_test_pred_type\n", + "test_type_copy[\"predicted_label\"] = [id2label[i] for i in y_test_pred_type]\n", + "test_type_copy[\"true_label\"] = [id2label[i] for i in y_test_t]\n", + "test_type_copy[\"correct\"] = (test_type_copy[\"type_label\"] == test_type_copy[\"predicted_id\"]).astype(int)\n", + "\n", + "val_type_copy.to_csv( OUT_TFIDF / \"predictions_val_type.csv\", index=False)\n", + "test_type_copy.to_csv(OUT_TFIDF / \"predictions_test_type.csv\", index=False)\n", + "print(\"Saved predictions_val_type.csv and predictions_test_type.csv\")" + ] }, { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/classical/tfidf_lr/confusion_matrix_type_test.png\n" - ] - } - ], - "source": [ - "# ── Confusion matrices ────────────────────────────────────────────────────────\n", - "save_confusion_matrix(\n", - " y_val_t, y_val_pred_type, STRATEGY_LABELS,\n", - " OUT_TFIDF / \"confusion_matrix_type_val.png\",\n", - " \"TF-IDF + LR — Type (Val)\"\n", - ")\n", - "save_confusion_matrix(\n", - " y_test_t, y_test_pred_type, STRATEGY_LABELS,\n", - " OUT_TFIDF / \"confusion_matrix_type_test.png\",\n", - " \"TF-IDF + LR — Type (Test)\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "xdfZfFRmuKB7" + }, + "source": [ + "## Char N-gram Variant (Optional)" + ] }, - "id": "1_ZECP4MuKB6", - "outputId": "c09e503d-d620-47ec-8917-c494bc7f3f48" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved predictions_val_type.csv and predictions_test_type.csv\n" - ] - } - ], - "source": [ - "# ── Save predictions ──────────────────────────────────────────────────────────\n", - "# Decode integer predictions back to string labels\n", - "val_type_copy = val_type.copy(); val_type_copy[\"type_label\"] = y_val_t\n", - "test_type_copy = test_type.copy(); test_type_copy[\"type_label\"] = y_test_t\n", - "\n", - "val_type_copy[\"predicted_id\"] = y_val_pred_type\n", - "val_type_copy[\"predicted_label\"] = [id2label[i] for i in y_val_pred_type]\n", - "val_type_copy[\"true_label\"] = [id2label[i] for i in y_val_t]\n", - "val_type_copy[\"correct\"] = (val_type_copy[\"type_label\"] == val_type_copy[\"predicted_id\"]).astype(int)\n", - "\n", - "test_type_copy[\"predicted_id\"] = y_test_pred_type\n", - "test_type_copy[\"predicted_label\"] = [id2label[i] for i in y_test_pred_type]\n", - "test_type_copy[\"true_label\"] = [id2label[i] for i in y_test_t]\n", - "test_type_copy[\"correct\"] = (test_type_copy[\"type_label\"] == test_type_copy[\"predicted_id\"]).astype(int)\n", - "\n", - "val_type_copy.to_csv( OUT_TFIDF / \"predictions_val_type.csv\", index=False)\n", - "test_type_copy.to_csv(OUT_TFIDF / \"predictions_test_type.csv\", index=False)\n", - "print(\"Saved predictions_val_type.csv and predictions_test_type.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xdfZfFRmuKB7" - }, - "source": [ - "## Char N-gram Variant (Optional)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3Ff1Tz1iuKB7", + "outputId": "6e57b3f3-af6b-4053-b42b-26e45fd7d144" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Char n-gram best C : {'lr__C': 3.0}\n", + "Char n-gram best CV F1 : 0.8308\n", + "Word n-gram best CV F1 : 0.8143\n", + "\n", + "\n", + "[Test (char)] Accuracy=0.8289 Macro-F1=0.8289 Weighted-F1=0.8289\n", + " precision recall f1-score support\n", + "\n", + "non-sarcastic 0.82 0.84 0.83 4250\n", + " sarcastic 0.84 0.82 0.83 4250\n", + "\n", + " accuracy 0.83 8500\n", + " macro avg 0.83 0.83 0.83 8500\n", + " weighted avg 0.83 0.83 0.83 8500\n", + "\n" + ] + } + ], + "source": [ + "# Char n-gram model for binary task (stylistic cues)\n", + "pipe_char = Pipeline([\n", + " (\"tfidf\", TfidfVectorizer(analyzer=\"char_wb\", ngram_range=(3, 5),\n", + " sublinear_tf=True, lowercase=True, max_features=50_000)),\n", + " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, C=1.0)),\n", + "])\n", + "\n", + "param_grid_char = {\"lr__C\": [0.1, 1.0, 3.0]}\n", + "gs_char = GridSearchCV(pipe_char, param_grid_char, cv=GroupKFold(5),\n", + " scoring=\"f1_macro\", n_jobs=-1)\n", + "gs_char.fit(X_trainval, y_trainval, groups=groups_tv)\n", + "\n", + "print(f\"Char n-gram best C : {gs_char.best_params_}\")\n", + "print(f\"Char n-gram best CV F1 : {gs_char.best_score_:.4f}\")\n", + "print(f\"Word n-gram best CV F1 : {gs_bin.best_score_:.4f}\")\n", + "print()\n", + "char_metrics, _ = evaluate(gs_char.best_estimator_, X_test, y_test, label_names_bin, \"Test (char)\")" + ] }, - "id": "3Ff1Tz1iuKB7", - "outputId": "4209ddba-9cd6-46f9-e66d-f62f1ab5709f" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Char n-gram best C : {'lr__C': 3.0}\n", - "Char n-gram best CV F1 : 0.8305\n", - "Word n-gram best CV F1 : 0.8143\n", - "\n", - "\n", - "[Test (char)] Accuracy=0.8284 Macro-F1=0.8283 Weighted-F1=0.8283\n", - " precision recall f1-score support\n", - "\n", - "non-sarcastic 0.82 0.84 0.83 4250\n", - " sarcastic 0.83 0.82 0.83 4250\n", - "\n", - " accuracy 0.83 8500\n", - " macro avg 0.83 0.83 0.83 8500\n", - " weighted avg 0.83 0.83 0.83 8500\n", - "\n" - ] - } - ], - "source": [ - "# Char n-gram model for binary task (stylistic cues)\n", - "pipe_char = Pipeline([\n", - " (\"tfidf\", TfidfVectorizer(analyzer=\"char_wb\", ngram_range=(3, 5),\n", - " sublinear_tf=True, lowercase=True, max_features=50_000)),\n", - " (\"lr\", LogisticRegression(max_iter=1000, random_state=SEED, C=1.0)),\n", - "])\n", - "\n", - "param_grid_char = {\"lr__C\": [0.1, 1.0, 3.0]}\n", - "gs_char = GridSearchCV(pipe_char, param_grid_char, cv=GroupKFold(5),\n", - " scoring=\"f1_macro\", n_jobs=-1)\n", - "gs_char.fit(X_trainval, y_trainval, groups=groups_tv)\n", - "\n", - "print(f\"Char n-gram best C : {gs_char.best_params_}\")\n", - "print(f\"Char n-gram best CV F1 : {gs_char.best_score_:.4f}\")\n", - "print(f\"Word n-gram best CV F1 : {gs_bin.best_score_:.4f}\")\n", - "print()\n", - "char_metrics, _ = evaluate(gs_char.best_estimator_, X_test, y_test, label_names_bin, \"Test (char)\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "t_pVeRFVuKB7" - }, - "source": [ - "## Summary" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "t_pVeRFVuKB7" + }, + "source": [ + "## Summary" + ] }, - "id": "OszOI0q1uKB7", - "outputId": "532102b9-cd0f-40e8-b1e1-48fa8520b526" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "====== TF-IDF + LR RESULTS SUMMARY ======\n", - "\n", - "Task A — Binary Classification (Test Set):\n", - " Accuracy : 0.8189\n", - " Macro-F1 : 0.8189\n", - " Weighted-F1 : 0.8189\n", - "\n", - "Task B — Type Classification (Test Set):\n", - " Accuracy : 0.3958\n", - " Macro-F1 : 0.4047\n", - " Weighted-F1 : 0.3947\n", - "\n", - "Best hyperparameters:\n", - " Binary: {'lr__C': 3.0, 'tfidf__max_features': None, 'tfidf__min_df': 3, 'tfidf__ngram_range': (1, 2)}\n", - " Type: {'lr__C': 3.0, 'lr__class_weight': 'balanced', 'tfidf__max_features': None, 'tfidf__min_df': 2, 'tfidf__ngram_range': (1, 2)}\n" - ] - } - ], - "source": [ - "print(\"====== TF-IDF + LR RESULTS SUMMARY ======\")\n", - "print()\n", - "print(\"Task A — Binary Classification (Test Set):\")\n", - "print(f\" Accuracy : {test_metrics_bin['accuracy']:.4f}\")\n", - "print(f\" Macro-F1 : {test_metrics_bin['f1_macro']:.4f}\")\n", - "print(f\" Weighted-F1 : {test_metrics_bin['f1_weighted']:.4f}\")\n", - "print()\n", - "print(\"Task B — Type Classification (Test Set):\")\n", - "print(f\" Accuracy : {test_metrics_type['accuracy']:.4f}\")\n", - "print(f\" Macro-F1 : {test_metrics_type['f1_macro']:.4f}\")\n", - "print(f\" Weighted-F1 : {test_metrics_type['f1_weighted']:.4f}\")\n", - "print()\n", - "print(\"Best hyperparameters:\")\n", - "print(\" Binary:\", gs_bin.best_params_)\n", - "print(\" Type: \", gs_type.best_params_)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ftKo2U-1uKB7" - }, - "source": [ - "---\n", - "# Part 3 — Naive Bayes Baseline" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "QZo05bYauKB7" - }, - "source": [ - "# Notebook 03 — Naive Bayes Baseline\n", - "\n", - "**Purpose**: Train and evaluate Naive Bayes classifiers for:\n", - "- Task A: Binary classification (sarcastic vs non-sarcastic)\n", - "- Task B: Sarcasm type classification (6-class)\n", - "\n", - "Compares:\n", - "- `CountVectorizer + MultinomialNB`\n", - "- `TfidfVectorizer + MultinomialNB`\n", - "- `CountVectorizer + ComplementNB` (for imbalanced type task)\n", - "\n", - "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", - "\n", - "**Outputs** in `outputs/classical/naive_bayes/`" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yeP7YbDYuKB8" - }, - "source": [ - "## Helper Functions" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "id": "PoPH1ijBuKB8" - }, - "outputs": [], - "source": [ - "def load_splits(task: str):\n", - " train = pd.read_csv(SPLITS / f\"train_{task}.csv\")\n", - " val = pd.read_csv(SPLITS / f\"val_{task}.csv\")\n", - " test = pd.read_csv(SPLITS / f\"test_{task}.csv\")\n", - " return train, val, test\n", - "\n", - "\n", - "def evaluate(model, X, y_true, label_names=None, split_name=\"\"):\n", - " y_pred = model.predict(X)\n", - " metrics = {\n", - " \"split\" : split_name,\n", - " \"accuracy\" : accuracy_score(y_true, y_pred),\n", - " \"precision_macro\" : precision_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", - " \"recall_macro\" : recall_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", - " \"f1_macro\" : f1_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", - " \"f1_weighted\" : f1_score(y_true, y_pred, average=\"weighted\", zero_division=0),\n", - " }\n", - " print(f\"[{split_name}] Accuracy={metrics['accuracy']:.4f} \"\n", - " f\"Macro-F1={metrics['f1_macro']:.4f} Weighted-F1={metrics['f1_weighted']:.4f}\")\n", - " print(classification_report(y_true, y_pred, target_names=label_names, zero_division=0))\n", - " return metrics, y_pred\n", - "\n", - "\n", - "def save_confusion_matrix(y_true, y_pred, label_names, out_path, title=\"\"):\n", - " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", - " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", - " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Greens\",\n", - " xticklabels=label_names, yticklabels=label_names, ax=ax)\n", - " ax.set_xlabel(\"Predicted\"); ax.set_ylabel(\"True\"); ax.set_title(title)\n", - " plt.tight_layout()\n", - " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", - " plt.show()\n", - " print(f\"Saved: {out_path.relative_to(ROOT)}\")\n", - "\n", - "\n", - "def run_nb_grid(\n", - " vectorizer_cls, nb_cls, param_grid: dict,\n", - " X_trainval, y_trainval, groups_tv,\n", - " name: str\n", - ") -> GridSearchCV:\n", - " \"\"\"Run a grid search for a given vectorizer + NB combination.\"\"\"\n", - " pipe = Pipeline([\n", - " (\"vec\", vectorizer_cls(lowercase=True)),\n", - " (\"nb\", nb_cls()),\n", - " ])\n", - " gs = GridSearchCV(\n", - " pipe, param_grid, cv=GroupKFold(5),\n", - " scoring=\"f1_macro\", n_jobs=-1, verbose=0, refit=True\n", - " )\n", - " gs.fit(X_trainval, y_trainval, groups=groups_tv)\n", - " print(f\"{name:50s} CV F1={gs.best_score_:.4f} params={gs.best_params_}\")\n", - " return gs" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "0oL34vMHuKB8" - }, - "source": [ - "## Task A — Binary Classification" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OszOI0q1uKB7", + "outputId": "b52f12c6-322c-4e87-88b4-dc711cf97c0c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "====== TF-IDF + LR RESULTS SUMMARY ======\n", + "\n", + "Task A — Binary Classification (Test Set):\n", + " Accuracy : 0.8189\n", + " Macro-F1 : 0.8189\n", + " Weighted-F1 : 0.8189\n", + "\n", + "Task B — Type Classification (Test Set):\n", + " Accuracy : 0.3953\n", + " Macro-F1 : 0.4047\n", + " Weighted-F1 : 0.3942\n", + "\n", + "Best hyperparameters:\n", + " Binary: {'lr__C': 3.0, 'tfidf__max_features': None, 'tfidf__min_df': 3, 'tfidf__ngram_range': (1, 2)}\n", + " Type: {'lr__C': 3.0, 'lr__class_weight': 'balanced', 'tfidf__max_features': None, 'tfidf__min_df': 2, 'tfidf__ngram_range': (1, 2)}\n" + ] + } + ], + "source": [ + "print(\"====== TF-IDF + LR RESULTS SUMMARY ======\")\n", + "print()\n", + "print(\"Task A — Binary Classification (Test Set):\")\n", + "print(f\" Accuracy : {test_metrics_bin['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {test_metrics_bin['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1 : {test_metrics_bin['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"Task B — Type Classification (Test Set):\")\n", + "print(f\" Accuracy : {test_metrics_type['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {test_metrics_type['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1 : {test_metrics_type['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"Best hyperparameters:\")\n", + "print(\" Binary:\", gs_bin.best_params_)\n", + "print(\" Type: \", gs_type.best_params_)" + ] }, - "id": "_STzCoqRuKB8", - "outputId": "c86245b0-ee21-4612-a5d5-dd2fba99214b" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Train+Val: 48,166 Val: 8,500 Test: 8,500\n" - ] - } - ], - "source": [ - "train_bin, val_bin, test_bin = load_splits(\"binary\")\n", - "trainval_bin = pd.concat([train_bin, val_bin], ignore_index=True)\n", - "\n", - "X_tv = trainval_bin[\"text\"].tolist()\n", - "y_tv = trainval_bin[\"binary_label\"].tolist()\n", - "grp_tv = trainval_bin[\"group_id\"].tolist()\n", - "\n", - "X_val = val_bin[\"text\"].tolist(); y_val = val_bin[\"binary_label\"].tolist()\n", - "X_test = test_bin[\"text\"].tolist(); y_test = test_bin[\"binary_label\"].tolist()\n", - "\n", - "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", - "\n", - "print(f\"Train+Val: {len(X_tv):,} Val: {len(X_val):,} Test: {len(X_test):,}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "ftKo2U-1uKB7" + }, + "source": [ + "---\n", + "# Part 3 — Naive Bayes Baseline" + ] }, - "id": "iOJgCEMwuKB8", - "outputId": "d37b770b-3b8f-4b49-ac5e-aeb6b7a0d37a" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "=== Binary: Comparing Vectorizer + NB Combinations ===\n", - "CountVectorizer + MultinomialNB CV F1=0.7855 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", - "TfidfVectorizer + MultinomialNB CV F1=0.7897 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", - "CountVectorizer + ComplementNB CV F1=0.7855 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n" - ] - } - ], - "source": [ - "# ── Shared grid parameters ────────────────────────────────────────────────────\n", - "BASE_GRID = {\n", - " \"vec__ngram_range\": [(1, 1), (1, 2)],\n", - " \"vec__min_df\" : [2, 3, 5],\n", - " \"nb__alpha\" : [0.1, 0.5, 1.0],\n", - "}\n", - "\n", - "print(\"=== Binary: Comparing Vectorizer + NB Combinations ===\")\n", - "\n", - "gs_count_mnb_bin = run_nb_grid(\n", - " CountVectorizer, MultinomialNB, BASE_GRID,\n", - " X_tv, y_tv, grp_tv,\n", - " \"CountVectorizer + MultinomialNB\"\n", - ")\n", - "\n", - "gs_tfidf_mnb_bin = run_nb_grid(\n", - " TfidfVectorizer, MultinomialNB, BASE_GRID,\n", - " X_tv, y_tv, grp_tv,\n", - " \"TfidfVectorizer + MultinomialNB\"\n", - ")\n", - "\n", - "gs_count_cnb_bin = run_nb_grid(\n", - " CountVectorizer, ComplementNB, BASE_GRID,\n", - " X_tv, y_tv, grp_tv,\n", - " \"CountVectorizer + ComplementNB\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "QZo05bYauKB7" + }, + "source": [ + "# Notebook 03 — Naive Bayes Baseline\n", + "\n", + "**Purpose**: Train and evaluate Naive Bayes classifiers for:\n", + "- Task A: Binary classification (sarcastic vs non-sarcastic)\n", + "- Task B: Sarcasm type classification (6-class)\n", + "\n", + "Compares:\n", + "- `CountVectorizer + MultinomialNB`\n", + "- `TfidfVectorizer + MultinomialNB`\n", + "- `CountVectorizer + ComplementNB` (for imbalanced type task)\n", + "\n", + "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", + "\n", + "**Outputs** in `outputs/classical/naive_bayes/`" + ] }, - "id": "eilEzm18uKB9", - "outputId": "360ce0ff-df02-45a3-bcb3-8213ae795e53" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Best binary NB model: TfidfVec+MultinomialNB (CV F1=0.7897)\n", - "\n", - "Comparison table:\n", - " model cv_f1_macro best_params\n", - "CountVec+MultinomialNB 0.785542 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", - "TfidfVec+MultinomialNB 0.789663 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", - " CountVec+ComplementNB 0.785542 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n" - ] - } - ], - "source": [ - "# ── Select best binary model ──────────────────────────────────────────────────\n", - "candidates_bin = [\n", - " (\"CountVec+MultinomialNB\", gs_count_mnb_bin),\n", - " (\"TfidfVec+MultinomialNB\", gs_tfidf_mnb_bin),\n", - " (\"CountVec+ComplementNB\", gs_count_cnb_bin),\n", - "]\n", - "\n", - "best_name_bin, best_gs_bin = max(candidates_bin, key=lambda x: x[1].best_score_)\n", - "print(f\"\\nBest binary NB model: {best_name_bin} (CV F1={best_gs_bin.best_score_:.4f})\")\n", - "\n", - "# Comparison table\n", - "comp_df = pd.DataFrame([\n", - " {\"model\": name, \"cv_f1_macro\": gs.best_score_, \"best_params\": str(gs.best_params_)}\n", - " for name, gs in candidates_bin\n", - "])\n", - "print(\"\\nComparison table:\")\n", - "print(comp_df.to_string(index=False))" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "yeP7YbDYuKB8" + }, + "source": [ + "## Helper Functions" + ] }, - "id": "Zh5ipG6BuKB9", - "outputId": "7f108910-a2fb-4154-eb36-f3f68b5a4dc2" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "[Val] Accuracy=0.8826 Macro-F1=0.8826 Weighted-F1=0.8826\n", - " precision recall f1-score support\n", - "\n", - "non-sarcastic 0.89 0.87 0.88 4250\n", - " sarcastic 0.87 0.90 0.88 4250\n", - "\n", - " accuracy 0.88 8500\n", - " macro avg 0.88 0.88 0.88 8500\n", - " weighted avg 0.88 0.88 0.88 8500\n", - "\n", - "[Test] Accuracy=0.7905 Macro-F1=0.7902 Weighted-F1=0.7902\n", - " precision recall f1-score support\n", - "\n", - "non-sarcastic 0.81 0.76 0.78 4250\n", - " sarcastic 0.77 0.83 0.80 4250\n", - "\n", - " accuracy 0.79 8500\n", - " macro avg 0.79 0.79 0.79 8500\n", - " weighted avg 0.79 0.79 0.79 8500\n", - "\n", - "\n", - "--- All models on Test ---\n", - " CountVec+MultinomialNB : acc=0.7887 macro-F1=0.7886\n", - " TfidfVec+MultinomialNB : acc=0.7905 macro-F1=0.7902\n", - " CountVec+ComplementNB : acc=0.7887 macro-F1=0.7886\n" - ] - } - ], - "source": [ - "# ── Evaluate best model ───────────────────────────────────────────────────────\n", - "best_model_bin = best_gs_bin.best_estimator_\n", - "\n", - "val_m_bin, y_val_pred_bin = evaluate(best_model_bin, X_val, y_val, label_names_bin, \"Val\")\n", - "test_m_bin, y_test_pred_bin = evaluate(best_model_bin, X_test, y_test, label_names_bin, \"Test\")\n", - "\n", - "# Also evaluate all three models on test for comparison\n", - "print(\"\\n--- All models on Test ---\")\n", - "all_test_results_bin = []\n", - "for name, gs in candidates_bin:\n", - " y_pred_t = gs.best_estimator_.predict(X_test)\n", - " f1 = f1_score(y_test, y_pred_t, average=\"macro\")\n", - " acc = accuracy_score(y_test, y_pred_t)\n", - " all_test_results_bin.append({\"model\": name, \"accuracy\": acc, \"f1_macro\": f1})\n", - " print(f\" {name:40s}: acc={acc:.4f} macro-F1={f1:.4f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 + "cell_type": "code", + "execution_count": 32, + "metadata": { + "id": "PoPH1ijBuKB8" + }, + "outputs": [], + "source": [ + "def load_splits(task: str):\n", + " train = pd.read_csv(SPLITS / f\"train_{task}.csv\")\n", + " val = pd.read_csv(SPLITS / f\"val_{task}.csv\")\n", + " test = pd.read_csv(SPLITS / f\"test_{task}.csv\")\n", + " return train, val, test\n", + "\n", + "\n", + "def evaluate(model, X, y_true, label_names=None, split_name=\"\"):\n", + " y_pred = model.predict(X)\n", + " metrics = {\n", + " \"split\" : split_name,\n", + " \"accuracy\" : accuracy_score(y_true, y_pred),\n", + " \"precision_macro\" : precision_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"recall_macro\" : recall_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"f1_macro\" : f1_score(y_true, y_pred, average=\"macro\", zero_division=0),\n", + " \"f1_weighted\" : f1_score(y_true, y_pred, average=\"weighted\", zero_division=0),\n", + " }\n", + " print(f\"[{split_name}] Accuracy={metrics['accuracy']:.4f} \"\n", + " f\"Macro-F1={metrics['f1_macro']:.4f} Weighted-F1={metrics['f1_weighted']:.4f}\")\n", + " print(classification_report(y_true, y_pred, target_names=label_names, zero_division=0))\n", + " return metrics, y_pred\n", + "\n", + "\n", + "def save_confusion_matrix(y_true, y_pred, label_names, out_path, title=\"\"):\n", + " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", + " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", + " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Greens\",\n", + " xticklabels=label_names, yticklabels=label_names, ax=ax)\n", + " ax.set_xlabel(\"Predicted\"); ax.set_ylabel(\"True\"); ax.set_title(title)\n", + " plt.tight_layout()\n", + " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + " print(f\"Saved: {out_path.relative_to(ROOT)}\")\n", + "\n", + "\n", + "def run_nb_grid(\n", + " vectorizer_cls, nb_cls, param_grid: dict,\n", + " X_trainval, y_trainval, groups_tv,\n", + " name: str\n", + ") -> GridSearchCV:\n", + " \"\"\"Run a grid search for a given vectorizer + NB combination.\"\"\"\n", + " pipe = Pipeline([\n", + " (\"vec\", vectorizer_cls(lowercase=True)),\n", + " (\"nb\", nb_cls()),\n", + " ])\n", + " gs = GridSearchCV(\n", + " pipe, param_grid, cv=GroupKFold(5),\n", + " scoring=\"f1_macro\", n_jobs=-1, verbose=0, refit=True\n", + " )\n", + " gs.fit(X_trainval, y_trainval, groups=groups_tv)\n", + " print(f\"{name:50s} CV F1={gs.best_score_:.4f} params={gs.best_params_}\")\n", + " return gs" + ] }, - "id": "D74tnzAduKB9", - "outputId": "1ccb358e-1daa-42ec-8aa0-09bada09116b" - }, - "outputs": [ { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": { + "id": "0oL34vMHuKB8" + }, + "source": [ + "## Task A — Binary Classification" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_STzCoqRuKB8", + "outputId": "2d775936-6525-4cf5-92db-a0d38169a6a6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train+Val: 48,166 Val: 8,500 Test: 8,500\n" + ] + } ], - "image/png": 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\n" - }, - "metadata": {} + "source": [ + "train_bin, val_bin, test_bin = load_splits(\"binary\")\n", + "trainval_bin = pd.concat([train_bin, val_bin], ignore_index=True)\n", + "\n", + "X_tv = trainval_bin[\"text\"].tolist()\n", + "y_tv = trainval_bin[\"binary_label\"].tolist()\n", + "grp_tv = trainval_bin[\"group_id\"].tolist()\n", + "\n", + "X_val = val_bin[\"text\"].tolist(); y_val = val_bin[\"binary_label\"].tolist()\n", + "X_test = test_bin[\"text\"].tolist(); y_test = test_bin[\"binary_label\"].tolist()\n", + "\n", + "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", + "\n", + "print(f\"Train+Val: {len(X_tv):,} Val: {len(X_val):,} Test: {len(X_test):,}\")" + ] }, { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/classical/naive_bayes/confusion_matrix_binary_val.png\n" - ] + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iOJgCEMwuKB8", + "outputId": "1503b29b-d3ef-46d1-e56b-0ab8f97dc05d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "=== Binary: Comparing Vectorizer + NB Combinations ===\n", + "CountVectorizer + MultinomialNB CV F1=0.7857 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", + "TfidfVectorizer + MultinomialNB CV F1=0.7896 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", + "CountVectorizer + ComplementNB CV F1=0.7857 params={'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n" + ] + } + ], + "source": [ + "# ── Shared grid parameters ────────────────────────────────────────────────────\n", + "BASE_GRID = {\n", + " \"vec__ngram_range\": [(1, 1), (1, 2)],\n", + " \"vec__min_df\" : [2, 3, 5],\n", + " \"nb__alpha\" : [0.1, 0.5, 1.0],\n", + "}\n", + "\n", + "print(\"=== Binary: Comparing Vectorizer + NB Combinations ===\")\n", + "\n", + "gs_count_mnb_bin = run_nb_grid(\n", + " CountVectorizer, MultinomialNB, BASE_GRID,\n", + " X_tv, y_tv, grp_tv,\n", + " \"CountVectorizer + MultinomialNB\"\n", + ")\n", + "\n", + "gs_tfidf_mnb_bin = run_nb_grid(\n", + " TfidfVectorizer, MultinomialNB, BASE_GRID,\n", + " X_tv, y_tv, grp_tv,\n", + " \"TfidfVectorizer + MultinomialNB\"\n", + ")\n", + "\n", + "gs_count_cnb_bin = run_nb_grid(\n", + " CountVectorizer, ComplementNB, BASE_GRID,\n", + " X_tv, y_tv, grp_tv,\n", + " \"CountVectorizer + ComplementNB\"\n", + ")" + ] }, { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eilEzm18uKB9", + "outputId": "1e09f56a-f7f2-4ace-cac1-9d9d4508c617" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Best binary NB model: TfidfVec+MultinomialNB (CV F1=0.7896)\n", + "\n", + "Comparison table:\n", + " model cv_f1_macro best_params\n", + "CountVec+MultinomialNB 0.785667 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", + "TfidfVec+MultinomialNB 0.789619 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n", + " CountVec+ComplementNB 0.785667 {'nb__alpha': 1.0, 'vec__min_df': 2, 'vec__ngram_range': (1, 2)}\n" + ] + } ], - "image/png": 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\n" - }, - "metadata": {} + "source": [ + "# ── Select best binary model ──────────────────────────────────────────────────\n", + "candidates_bin = [\n", + " (\"CountVec+MultinomialNB\", gs_count_mnb_bin),\n", + " (\"TfidfVec+MultinomialNB\", gs_tfidf_mnb_bin),\n", + " (\"CountVec+ComplementNB\", gs_count_cnb_bin),\n", + "]\n", + "\n", + "best_name_bin, best_gs_bin = max(candidates_bin, key=lambda x: x[1].best_score_)\n", + "print(f\"\\nBest binary NB model: {best_name_bin} (CV F1={best_gs_bin.best_score_:.4f})\")\n", + "\n", + "# Comparison table\n", + "comp_df = pd.DataFrame([\n", + " {\"model\": name, \"cv_f1_macro\": gs.best_score_, \"best_params\": str(gs.best_params_)}\n", + " for name, gs in candidates_bin\n", + "])\n", + "print(\"\\nComparison table:\")\n", + "print(comp_df.to_string(index=False))" + ] }, { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/classical/naive_bayes/confusion_matrix_binary_test.png\n", - "All binary artifacts saved.\n" - ] - } - ], - "source": [ - "# ── Save binary results ───────────────────────────────────────────────────────\n", - "cfg_bin = {\n", - " \"task\": \"binary\", \"best_model\": best_name_bin,\n", - " \"best_params\": best_gs_bin.best_params_, \"cv_f1_macro\": best_gs_bin.best_score_,\n", - " \"all_candidates\": [\n", - " {\"model\": name, \"cv_f1_macro\": gs.best_score_, \"params\": gs.best_params_}\n", - " for name, gs in candidates_bin\n", - " ]\n", - "}\n", - "with open(OUT_NB / \"best_config_binary.json\", \"w\") as f:\n", - " json.dump(cfg_bin, f, indent=2)\n", - "\n", - "all_m_bin = {\"val\": val_m_bin, \"test\": test_m_bin,\n", - " \"all_test_comparison\": all_test_results_bin}\n", - "with open(OUT_NB / \"metrics_binary.json\", \"w\") as f:\n", - " json.dump(all_m_bin, f, indent=2)\n", - "\n", - "save_confusion_matrix(\n", - " y_val, y_val_pred_bin, label_names_bin,\n", - " OUT_NB / \"confusion_matrix_binary_val.png\",\n", - " f\"NB ({best_name_bin}) — Binary (Val)\"\n", - ")\n", - "save_confusion_matrix(\n", - " y_test, y_test_pred_bin, label_names_bin,\n", - " OUT_NB / \"confusion_matrix_binary_test.png\",\n", - " f\"NB ({best_name_bin}) — Binary (Test)\"\n", - ")\n", - "\n", - "# Predictions\n", - "pred_val_bin = val_bin.copy()\n", - "pred_val_bin[\"predicted\"] = y_val_pred_bin\n", - "pred_val_bin[\"correct\"] = (pred_val_bin[\"binary_label\"] == pred_val_bin[\"predicted\"]).astype(int)\n", - "pred_val_bin.to_csv(OUT_NB / \"predictions_val_binary.csv\", index=False)\n", - "\n", - "pred_test_bin = test_bin.copy()\n", - "pred_test_bin[\"predicted\"] = y_test_pred_bin\n", - "pred_test_bin[\"correct\"] = (pred_test_bin[\"binary_label\"] == pred_test_bin[\"predicted\"]).astype(int)\n", - "pred_test_bin.to_csv(OUT_NB / \"predictions_test_binary.csv\", index=False)\n", - "\n", - "print(\"All binary artifacts saved.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UtChhm0-uKB9" - }, - "source": [ - "## Task B — Sarcasm Type Classification" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 36, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Zh5ipG6BuKB9", + "outputId": "a7e25be8-bb1b-4dc4-9d50-6eff0325b46c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[Val] Accuracy=0.8826 Macro-F1=0.8826 Weighted-F1=0.8826\n", + " precision recall f1-score support\n", + "\n", + "non-sarcastic 0.89 0.87 0.88 4250\n", + " sarcastic 0.87 0.90 0.88 4250\n", + "\n", + " accuracy 0.88 8500\n", + " macro avg 0.88 0.88 0.88 8500\n", + " weighted avg 0.88 0.88 0.88 8500\n", + "\n", + "[Test] Accuracy=0.7905 Macro-F1=0.7902 Weighted-F1=0.7902\n", + " precision recall f1-score support\n", + "\n", + "non-sarcastic 0.81 0.76 0.78 4250\n", + " sarcastic 0.77 0.83 0.80 4250\n", + "\n", + " accuracy 0.79 8500\n", + " macro avg 0.79 0.79 0.79 8500\n", + " weighted avg 0.79 0.79 0.79 8500\n", + "\n", + "\n", + "--- All models on Test ---\n", + " CountVec+MultinomialNB : acc=0.7887 macro-F1=0.7886\n", + " TfidfVec+MultinomialNB : acc=0.7905 macro-F1=0.7902\n", + " CountVec+ComplementNB : acc=0.7887 macro-F1=0.7886\n" + ] + } + ], + "source": [ + "# ── Evaluate best model ───────────────────────────────────────────────────────\n", + "best_model_bin = best_gs_bin.best_estimator_\n", + "\n", + "val_m_bin, y_val_pred_bin = evaluate(best_model_bin, X_val, y_val, label_names_bin, \"Val\")\n", + "test_m_bin, y_test_pred_bin = evaluate(best_model_bin, X_test, y_test, label_names_bin, \"Test\")\n", + "\n", + "# Also evaluate all three models on test for comparison\n", + "print(\"\\n--- All models on Test ---\")\n", + "all_test_results_bin = []\n", + "for name, gs in candidates_bin:\n", + " y_pred_t = gs.best_estimator_.predict(X_test)\n", + " f1 = f1_score(y_test, y_pred_t, average=\"macro\")\n", + " acc = accuracy_score(y_test, y_pred_t)\n", + " all_test_results_bin.append({\"model\": name, \"accuracy\": acc, \"f1_macro\": f1})\n", + " print(f\" {name:40s}: acc={acc:.4f} macro-F1={f1:.4f}\")" + ] }, - "id": "Rh8sdjlhuKB9", - "outputId": "36e9daa4-b705-4b92-8601-af8ee580ff33" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n", - "Train+Val: 24,083 Val: 4,250 Test: 4,250\n" - ] - } - ], - "source": [ - "train_type, val_type, test_type = load_splits(\"type\")\n", - "\n", - "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", - "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", - "id2label = {i: lab for lab, i in label2id.items()}\n", - "\n", - "def enc(df): return [label2id[l] for l in df[\"type_label\"]]\n", - "\n", - "trainval_type = pd.concat([train_type, val_type], ignore_index=True)\n", - "X_tv_t = trainval_type[\"text\"].tolist()\n", - "y_tv_t = enc(trainval_type)\n", - "grp_tv_t = trainval_type[\"group_id\"].tolist()\n", - "\n", - "X_val_t = val_type[\"text\"].tolist(); y_val_t = enc(val_type)\n", - "X_test_t = test_type[\"text\"].tolist(); y_test_t = enc(test_type)\n", - "\n", - "print(f\"Strategies: {STRATEGY_LABELS}\")\n", - "print(f\"Train+Val: {len(X_tv_t):,} Val: {len(X_val_t):,} Test: {len(X_test_t):,}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 37, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "D74tnzAduKB9", + "outputId": "01c42b37-aa4d-41d6-e054-da413bca6578" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/naive_bayes/confusion_matrix_binary_val.png\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/naive_bayes/confusion_matrix_binary_test.png\n", + "All binary artifacts saved.\n" + ] + } + ], + "source": [ + "# ── Save binary results ───────────────────────────────────────────────────────\n", + "cfg_bin = {\n", + " \"task\": \"binary\", \"best_model\": best_name_bin,\n", + " \"best_params\": best_gs_bin.best_params_, \"cv_f1_macro\": best_gs_bin.best_score_,\n", + " \"all_candidates\": [\n", + " {\"model\": name, \"cv_f1_macro\": gs.best_score_, \"params\": gs.best_params_}\n", + " for name, gs in candidates_bin\n", + " ]\n", + "}\n", + "with open(OUT_NB / \"best_config_binary.json\", \"w\") as f:\n", + " json.dump(cfg_bin, f, indent=2)\n", + "\n", + "all_m_bin = {\"val\": val_m_bin, \"test\": test_m_bin,\n", + " \"all_test_comparison\": all_test_results_bin}\n", + "with open(OUT_NB / \"metrics_binary.json\", \"w\") as f:\n", + " json.dump(all_m_bin, f, indent=2)\n", + "\n", + "save_confusion_matrix(\n", + " y_val, y_val_pred_bin, label_names_bin,\n", + " OUT_NB / \"confusion_matrix_binary_val.png\",\n", + " f\"NB ({best_name_bin}) — Binary (Val)\"\n", + ")\n", + "save_confusion_matrix(\n", + " y_test, y_test_pred_bin, label_names_bin,\n", + " OUT_NB / \"confusion_matrix_binary_test.png\",\n", + " f\"NB ({best_name_bin}) — Binary (Test)\"\n", + ")\n", + "\n", + "# Predictions\n", + "pred_val_bin = val_bin.copy()\n", + "pred_val_bin[\"predicted\"] = y_val_pred_bin\n", + "pred_val_bin[\"correct\"] = (pred_val_bin[\"binary_label\"] == pred_val_bin[\"predicted\"]).astype(int)\n", + "pred_val_bin.to_csv(OUT_NB / \"predictions_val_binary.csv\", index=False)\n", + "\n", + "pred_test_bin = test_bin.copy()\n", + "pred_test_bin[\"predicted\"] = y_test_pred_bin\n", + "pred_test_bin[\"correct\"] = (pred_test_bin[\"binary_label\"] == pred_test_bin[\"predicted\"]).astype(int)\n", + "pred_test_bin.to_csv(OUT_NB / \"predictions_test_binary.csv\", index=False)\n", + "\n", + "print(\"All binary artifacts saved.\")" + ] }, - "id": "ZupB5UlvuKB-", - "outputId": "bb5e86d3-745c-4c41-9650-d399e4668e32" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "=== Type: Comparing Vectorizer + NB Combinations ===\n", - "CountVectorizer + MultinomialNB CV F1=0.3811 params={'nb__alpha': 0.5, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n", - "TfidfVectorizer + MultinomialNB CV F1=0.3422 params={'nb__alpha': 0.1, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n", - "CountVectorizer + ComplementNB (for imbalance) CV F1=0.3777 params={'nb__alpha': 1.0, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n" - ] - } - ], - "source": [ - "print(\"=== Type: Comparing Vectorizer + NB Combinations ===\")\n", - "\n", - "gs_count_mnb_type = run_nb_grid(\n", - " CountVectorizer, MultinomialNB, BASE_GRID,\n", - " X_tv_t, y_tv_t, grp_tv_t,\n", - " \"CountVectorizer + MultinomialNB\"\n", - ")\n", - "\n", - "gs_tfidf_mnb_type = run_nb_grid(\n", - " TfidfVectorizer, MultinomialNB, BASE_GRID,\n", - " X_tv_t, y_tv_t, grp_tv_t,\n", - " \"TfidfVectorizer + MultinomialNB\"\n", - ")\n", - "\n", - "gs_count_cnb_type = run_nb_grid(\n", - " CountVectorizer, ComplementNB, BASE_GRID,\n", - " X_tv_t, y_tv_t, grp_tv_t,\n", - " \"CountVectorizer + ComplementNB (for imbalance)\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "UtChhm0-uKB9" + }, + "source": [ + "## Task B — Sarcasm Type Classification" + ] }, - "id": "J_CunIwDuKB-", - "outputId": "45a1ccc2-6af0-4542-b371-7573a94afba6" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Best type NB model: CountVec+MultinomialNB (CV F1=0.3811)\n", - "\n", - "--- All models on Test (Type Task) ---\n", - " CountVec+MultinomialNB : acc=0.3953 macro-F1=0.3953\n", - " TfidfVec+MultinomialNB : acc=0.3800 macro-F1=0.3450\n", - " CountVec+ComplementNB : acc=0.3875 macro-F1=0.3880\n" - ] - } - ], - "source": [ - "candidates_type = [\n", - " (\"CountVec+MultinomialNB\", gs_count_mnb_type),\n", - " (\"TfidfVec+MultinomialNB\", gs_tfidf_mnb_type),\n", - " (\"CountVec+ComplementNB\", gs_count_cnb_type),\n", - "]\n", - "\n", - "best_name_type, best_gs_type = max(candidates_type, key=lambda x: x[1].best_score_)\n", - "print(f\"Best type NB model: {best_name_type} (CV F1={best_gs_type.best_score_:.4f})\")\n", - "\n", - "print(\"\\n--- All models on Test (Type Task) ---\")\n", - "all_test_results_type = []\n", - "for name, gs in candidates_type:\n", - " y_pred_t = gs.best_estimator_.predict(X_test_t)\n", - " f1 = f1_score(y_test_t, y_pred_t, average=\"macro\")\n", - " acc = accuracy_score(y_test_t, y_pred_t)\n", - " all_test_results_type.append({\"model\": name, \"accuracy\": acc, \"f1_macro\": f1})\n", - " print(f\" {name:40s}: acc={acc:.4f} macro-F1={f1:.4f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 + "cell_type": "code", + "execution_count": 38, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Rh8sdjlhuKB9", + "outputId": "0ae14ac8-bb80-40e0-aed3-ed5f20b72925" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n", + "Train+Val: 24,083 Val: 4,250 Test: 4,250\n" + ] + } + ], + "source": [ + "train_type, val_type, test_type = load_splits(\"type\")\n", + "\n", + "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", + "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", + "id2label = {i: lab for lab, i in label2id.items()}\n", + "\n", + "def enc(df): return [label2id[l] for l in df[\"type_label\"]]\n", + "\n", + "trainval_type = pd.concat([train_type, val_type], ignore_index=True)\n", + "X_tv_t = trainval_type[\"text\"].tolist()\n", + "y_tv_t = enc(trainval_type)\n", + "grp_tv_t = trainval_type[\"group_id\"].tolist()\n", + "\n", + "X_val_t = val_type[\"text\"].tolist(); y_val_t = enc(val_type)\n", + "X_test_t = test_type[\"text\"].tolist(); y_test_t = enc(test_type)\n", + "\n", + "print(f\"Strategies: {STRATEGY_LABELS}\")\n", + "print(f\"Train+Val: {len(X_tv_t):,} Val: {len(X_val_t):,} Test: {len(X_test_t):,}\")" + ] }, - "id": "o6JHLJnLuKB-", - "outputId": "5f5f236e-83ce-4a58-9f49-92e0f472d39e" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "[Val] Accuracy=0.7694 Macro-F1=0.7744 Weighted-F1=0.7693\n", - " precision recall f1-score support\n", - "\n", - " irony 0.80 0.72 0.76 942\n", - " overstatement 0.75 0.80 0.77 600\n", - "rhetorical_question 0.78 0.88 0.83 157\n", - " sarcasm 0.79 0.78 0.79 1317\n", - " satire 0.73 0.77 0.75 747\n", - " understatement 0.75 0.75 0.75 487\n", - "\n", - " accuracy 0.77 4250\n", - " macro avg 0.77 0.78 0.77 4250\n", - " weighted avg 0.77 0.77 0.77 4250\n", - "\n", - "[Test] Accuracy=0.3953 Macro-F1=0.3953 Weighted-F1=0.3929\n", - " precision recall f1-score support\n", - "\n", - " irony 0.32 0.29 0.30 902\n", - " overstatement 0.39 0.40 0.39 592\n", - "rhetorical_question 0.52 0.41 0.46 176\n", - " sarcasm 0.45 0.51 0.47 1291\n", - " satire 0.36 0.34 0.35 833\n", - " understatement 0.40 0.38 0.39 456\n", - "\n", - " accuracy 0.40 4250\n", - " macro avg 0.41 0.39 0.40 4250\n", - " weighted avg 0.39 0.40 0.39 4250\n", - "\n" - ] + "cell_type": "code", + "execution_count": 39, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZupB5UlvuKB-", + "outputId": "d45927cd-2c0e-407d-de2d-76c8461eaae6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "=== Type: Comparing Vectorizer + NB Combinations ===\n", + "CountVectorizer + MultinomialNB CV F1=0.3813 params={'nb__alpha': 0.5, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n", + "TfidfVectorizer + MultinomialNB CV F1=0.3432 params={'nb__alpha': 0.1, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n", + "CountVectorizer + ComplementNB (for imbalance) CV F1=0.3786 params={'nb__alpha': 1.0, 'vec__min_df': 3, 'vec__ngram_range': (1, 2)}\n" + ] + } + ], + "source": [ + "print(\"=== Type: Comparing Vectorizer + NB Combinations ===\")\n", + "\n", + "gs_count_mnb_type = run_nb_grid(\n", + " CountVectorizer, MultinomialNB, BASE_GRID,\n", + " X_tv_t, y_tv_t, grp_tv_t,\n", + " \"CountVectorizer + MultinomialNB\"\n", + ")\n", + "\n", + "gs_tfidf_mnb_type = run_nb_grid(\n", + " TfidfVectorizer, MultinomialNB, BASE_GRID,\n", + " X_tv_t, y_tv_t, grp_tv_t,\n", + " \"TfidfVectorizer + MultinomialNB\"\n", + ")\n", + "\n", + "gs_count_cnb_type = run_nb_grid(\n", + " CountVectorizer, ComplementNB, BASE_GRID,\n", + " X_tv_t, y_tv_t, grp_tv_t,\n", + " \"CountVectorizer + ComplementNB (for imbalance)\"\n", + ")" + ] }, { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 40, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "J_CunIwDuKB-", + "outputId": "19cb8ab8-eff2-4883-8129-9213bf3f4b2e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Best type NB model: CountVec+MultinomialNB (CV F1=0.3813)\n", + "\n", + "--- All models on Test (Type Task) ---\n", + " CountVec+MultinomialNB : acc=0.3953 macro-F1=0.3953\n", + " TfidfVec+MultinomialNB : acc=0.3800 macro-F1=0.3450\n", + " CountVec+ComplementNB : acc=0.3875 macro-F1=0.3880\n" + ] + } ], - "image/png": 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\n" - }, - "metadata": {} + "source": [ + "candidates_type = [\n", + " (\"CountVec+MultinomialNB\", gs_count_mnb_type),\n", + " (\"TfidfVec+MultinomialNB\", gs_tfidf_mnb_type),\n", + " (\"CountVec+ComplementNB\", gs_count_cnb_type),\n", + "]\n", + "\n", + "best_name_type, best_gs_type = max(candidates_type, key=lambda x: x[1].best_score_)\n", + "print(f\"Best type NB model: {best_name_type} (CV F1={best_gs_type.best_score_:.4f})\")\n", + "\n", + "print(\"\\n--- All models on Test (Type Task) ---\")\n", + "all_test_results_type = []\n", + "for name, gs in candidates_type:\n", + " y_pred_t = gs.best_estimator_.predict(X_test_t)\n", + " f1 = f1_score(y_test_t, y_pred_t, average=\"macro\")\n", + " acc = accuracy_score(y_test_t, y_pred_t)\n", + " all_test_results_type.append({\"model\": name, \"accuracy\": acc, \"f1_macro\": f1})\n", + " print(f\" {name:40s}: acc={acc:.4f} macro-F1={f1:.4f}\")" + ] }, { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/classical/naive_bayes/confusion_matrix_type_val.png\n" - ] + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "o6JHLJnLuKB-", + "outputId": "e9a91472-0623-4eae-fd65-2b37784bc73e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[Val] Accuracy=0.7694 Macro-F1=0.7744 Weighted-F1=0.7693\n", + " precision recall f1-score support\n", + "\n", + " irony 0.80 0.72 0.76 942\n", + " overstatement 0.75 0.80 0.77 600\n", + "rhetorical_question 0.78 0.88 0.83 157\n", + " sarcasm 0.79 0.78 0.79 1317\n", + " satire 0.73 0.77 0.75 747\n", + " understatement 0.75 0.75 0.75 487\n", + "\n", + " accuracy 0.77 4250\n", + " macro avg 0.77 0.78 0.77 4250\n", + " weighted avg 0.77 0.77 0.77 4250\n", + "\n", + "[Test] Accuracy=0.3953 Macro-F1=0.3953 Weighted-F1=0.3929\n", + " precision recall f1-score support\n", + "\n", + " irony 0.32 0.29 0.30 902\n", + " overstatement 0.39 0.40 0.39 592\n", + "rhetorical_question 0.52 0.41 0.46 176\n", + " sarcasm 0.45 0.51 0.47 1291\n", + " satire 0.36 0.34 0.35 833\n", + " understatement 0.40 0.38 0.39 456\n", + "\n", + " accuracy 0.40 4250\n", + " macro avg 0.41 0.39 0.40 4250\n", + " weighted avg 0.39 0.40 0.39 4250\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/naive_bayes/confusion_matrix_type_val.png\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/classical/naive_bayes/confusion_matrix_type_test.png\n", + "All type artifacts saved.\n" + ] + } + ], + "source": [ + "best_model_type = best_gs_type.best_estimator_\n", + "\n", + "val_m_type, y_val_pred_type = evaluate(best_model_type, X_val_t, y_val_t, STRATEGY_LABELS, \"Val\")\n", + "test_m_type, y_test_pred_type = evaluate(best_model_type, X_test_t, y_test_t, STRATEGY_LABELS, \"Test\")\n", + "\n", + "# Save\n", + "cfg_type = {\n", + " \"task\": \"type\", \"best_model\": best_name_type,\n", + " \"label_names\": STRATEGY_LABELS, \"label2id\": label2id,\n", + " \"best_params\": best_gs_type.best_params_, \"cv_f1_macro\": best_gs_type.best_score_,\n", + "}\n", + "with open(OUT_NB / \"best_config_type.json\", \"w\") as f:\n", + " json.dump(cfg_type, f, indent=2)\n", + "\n", + "all_m_type = {\"val\": val_m_type, \"test\": test_m_type,\n", + " \"all_test_comparison\": all_test_results_type}\n", + "with open(OUT_NB / \"metrics_type.json\", \"w\") as f:\n", + " json.dump(all_m_type, f, indent=2)\n", + "\n", + "save_confusion_matrix(\n", + " y_val_t, y_val_pred_type, STRATEGY_LABELS,\n", + " OUT_NB / \"confusion_matrix_type_val.png\",\n", + " f\"NB ({best_name_type}) — Type (Val)\"\n", + ")\n", + "save_confusion_matrix(\n", + " y_test_t, y_test_pred_type, STRATEGY_LABELS,\n", + " OUT_NB / \"confusion_matrix_type_test.png\",\n", + " f\"NB ({best_name_type}) — Type (Test)\"\n", + ")\n", + "\n", + "# Predictions with string labels\n", + "pred_val_type = val_type.copy()\n", + "pred_test_type = test_type.copy()\n", + "for df, y_pred, y_true in [(pred_val_type, y_val_pred_type, y_val_t),\n", + " (pred_test_type, y_test_pred_type, y_test_t)]:\n", + " df[\"predicted_label\"] = [id2label[i] for i in y_pred]\n", + " df[\"true_label\"] = [id2label[i] for i in y_true]\n", + " df[\"correct\"] = (df[\"type_label\"] == df[\"predicted_label\"]).astype(int)\n", + "\n", + "pred_val_type.to_csv( OUT_NB / \"predictions_val_type.csv\", index=False)\n", + "pred_test_type.to_csv(OUT_NB / \"predictions_test_type.csv\", index=False)\n", + "\n", + "print(\"All type artifacts saved.\")" + ] }, { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" + "cell_type": "markdown", + "metadata": { + "id": "TwTBPa86uKB-" + }, + "source": [ + "## Error Examples" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "b8itQIPtuKB-", + "outputId": "35323e29-00c2-4781-ff9b-7aca0ae800ad" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Binary errors on test: 1781 total\n", + " False Positives (predicted sarcastic, actually not): 1039\n", + " False Negatives (predicted not-sarcastic, actually sarcastic): 742\n", + "\n", + "--- Sample FP ---\n", + " [True=0, Pred=1] Probability researchers note a coincidence where three coworkers wear the same shirt color.\n", + " [True=0, Pred=1] The global-warming crisis contributed to a delightful mid-February afternoon.\n", + " [True=0, Pred=1] Signs for the upcoming road section make it sound formidable or exciting.\n", + " [True=0, Pred=1] A father recounts how he saved $4.27 through quick thinking.\n", + " [True=0, Pred=1] The wedding album begins with a photo of two acorns floating in a glass of water.\n", + " [True=0, Pred=1] A governor is too embarrassed to say which state he leads.\n", + " [True=0, Pred=1] A man who plays devil's advocate may actually just want to be disagreeable.\n", + " [True=0, Pred=1] Area dad watched a show about bigfoot last night.\n", + " [True=0, Pred=1] Coroner's report cites systemic issues in Alton Sterling's death.\n", + " [True=0, Pred=1] Preschool child asks to look at a classmate's notes on shapes.\n", + "\n", + "--- Sample FN ---\n", + " [True=1, Pred=0] state department warns americans traveling abroad to avoid lame amsterdam windmill tour\n", + " [True=1, Pred=0] hollywood's biggest stars endure long lines at oscars security screening\n", + " [True=1, Pred=0] historians suggest 'goodfellas' youtube clips may be fragments of larger work\n", + " [True=1, Pred=0] members of opening band walking among crowd during intermission like gods among men\n", + " [True=1, Pred=0] woman who's been on the pill for years thinking about switching to new set of debilitating side effe\n", + " [True=1, Pred=0] congressman lets his guitar do the talking\n", + " [True=1, Pred=0] officials warn consumers of counterfeit tickets ahead of solar eclipse\n", + " [True=1, Pred=0] anderson cooper throws another box of letters from gay children into dumpster\n", + " [True=1, Pred=0] non-priest arrested on charges of child molestation\n", + " [True=1, Pred=0] huckabee sanders tells colleagues she's taking temporary post as google ceo before transitioning int\n" + ] + } ], - "image/png": 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\n" - }, - "metadata": {} + "source": [ + "# ── Binary error examples ─────────────────────────────────────────────────────\n", + "err_bin = pred_test_bin[pred_test_bin[\"correct\"] == 0].copy()\n", + "fp_bin = err_bin[err_bin[\"binary_label\"] == 0].head(10) # predicted sarcastic, actually not\n", + "fn_bin = err_bin[err_bin[\"binary_label\"] == 1].head(10) # predicted not-sarcastic, actually sarcastic\n", + "\n", + "print(f\"Binary errors on test: {len(err_bin)} total\")\n", + "print(f\" False Positives (predicted sarcastic, actually not): {len(err_bin[err_bin['binary_label']==0])}\")\n", + "print(f\" False Negatives (predicted not-sarcastic, actually sarcastic): {len(err_bin[err_bin['binary_label']==1])}\")\n", + "print(\"\\n--- Sample FP ---\")\n", + "for _, row in fp_bin.iterrows():\n", + " print(f\" [True=0, Pred=1] {row['text'][:100]}\")\n", + "print(\"\\n--- Sample FN ---\")\n", + "for _, row in fn_bin.iterrows():\n", + " print(f\" [True=1, Pred=0] {row['text'][:100]}\")" + ] }, { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saved: outputs/classical/naive_bayes/confusion_matrix_type_test.png\n", - "All type artifacts saved.\n" - ] - } - ], - "source": [ - "best_model_type = best_gs_type.best_estimator_\n", - "\n", - "val_m_type, y_val_pred_type = evaluate(best_model_type, X_val_t, y_val_t, STRATEGY_LABELS, \"Val\")\n", - "test_m_type, y_test_pred_type = evaluate(best_model_type, X_test_t, y_test_t, STRATEGY_LABELS, \"Test\")\n", - "\n", - "# Save\n", - "cfg_type = {\n", - " \"task\": \"type\", \"best_model\": best_name_type,\n", - " \"label_names\": STRATEGY_LABELS, \"label2id\": label2id,\n", - " \"best_params\": best_gs_type.best_params_, \"cv_f1_macro\": best_gs_type.best_score_,\n", - "}\n", - "with open(OUT_NB / \"best_config_type.json\", \"w\") as f:\n", - " json.dump(cfg_type, f, indent=2)\n", - "\n", - "all_m_type = {\"val\": val_m_type, \"test\": test_m_type,\n", - " \"all_test_comparison\": all_test_results_type}\n", - "with open(OUT_NB / \"metrics_type.json\", \"w\") as f:\n", - " json.dump(all_m_type, f, indent=2)\n", - "\n", - "save_confusion_matrix(\n", - " y_val_t, y_val_pred_type, STRATEGY_LABELS,\n", - " OUT_NB / \"confusion_matrix_type_val.png\",\n", - " f\"NB ({best_name_type}) — Type (Val)\"\n", - ")\n", - "save_confusion_matrix(\n", - " y_test_t, y_test_pred_type, STRATEGY_LABELS,\n", - " OUT_NB / \"confusion_matrix_type_test.png\",\n", - " f\"NB ({best_name_type}) — Type (Test)\"\n", - ")\n", - "\n", - "# Predictions with string labels\n", - "pred_val_type = val_type.copy()\n", - "pred_test_type = test_type.copy()\n", - "for df, y_pred, y_true in [(pred_val_type, y_val_pred_type, y_val_t),\n", - " (pred_test_type, y_test_pred_type, y_test_t)]:\n", - " df[\"predicted_label\"] = [id2label[i] for i in y_pred]\n", - " df[\"true_label\"] = [id2label[i] for i in y_true]\n", - " df[\"correct\"] = (df[\"type_label\"] == df[\"predicted_label\"]).astype(int)\n", - "\n", - "pred_val_type.to_csv( OUT_NB / \"predictions_val_type.csv\", index=False)\n", - "pred_test_type.to_csv(OUT_NB / \"predictions_test_type.csv\", index=False)\n", - "\n", - "print(\"All type artifacts saved.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TwTBPa86uKB-" - }, - "source": [ - "## Error Examples" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 43, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zmVdkTHuuKB_", + "outputId": "b4f6763f-52ae-4d0f-f62a-66baa1e87bcf" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Type errors on test: 2570 total\n", + "\n", + "Confusion pairs (true → predicted):\n", + "type_label predicted_label\n", + "irony sarcasm 309\n", + "satire sarcasm 227\n", + "sarcasm irony 226\n", + " satire 203\n", + "satire irony 155\n", + "irony satire 155\n", + "overstatement sarcasm 117\n", + "understatement sarcasm 103\n", + "overstatement satire 102\n", + "satire overstatement 101\n" + ] + } + ], + "source": [ + "# ── Type error examples ───────────────────────────────────────────────────────\n", + "err_type = pred_test_type[pred_test_type[\"correct\"] == 0].copy()\n", + "print(f\"Type errors on test: {len(err_type)} total\")\n", + "print(\"\\nConfusion pairs (true → predicted):\")\n", + "conf_pairs = err_type.groupby([\"type_label\", \"predicted_label\"]).size().sort_values(ascending=False).head(10)\n", + "print(conf_pairs.to_string())" + ] }, - "id": "b8itQIPtuKB-", - "outputId": "6805f04f-a1c4-4005-a8f7-4496ad84a58b" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Binary errors on test: 1781 total\n", - " False Positives (predicted sarcastic, actually not): 1039\n", - " False Negatives (predicted not-sarcastic, actually sarcastic): 742\n", - "\n", - "--- Sample FP ---\n", - " [True=0, Pred=1] Probability researchers note a coincidence where three coworkers wear the same shirt color.\n", - " [True=0, Pred=1] The global-warming crisis contributed to a delightful mid-February afternoon.\n", - " [True=0, Pred=1] Signs for the upcoming road section make it sound formidable or exciting.\n", - " [True=0, Pred=1] A father recounts how he saved $4.27 through quick thinking.\n", - " [True=0, Pred=1] The wedding album begins with a photo of two acorns floating in a glass of water.\n", - " [True=0, Pred=1] A governor is too embarrassed to say which state he leads.\n", - " [True=0, Pred=1] A man who plays devil's advocate may actually just want to be disagreeable.\n", - " [True=0, Pred=1] Area dad watched a show about bigfoot last night.\n", - " [True=0, Pred=1] Coroner's report cites systemic issues in Alton Sterling's death.\n", - " [True=0, Pred=1] Preschool child asks to look at a classmate's notes on shapes.\n", - "\n", - "--- Sample FN ---\n", - " [True=1, Pred=0] state department warns americans traveling abroad to avoid lame amsterdam windmill tour\n", - " [True=1, Pred=0] hollywood's biggest stars endure long lines at oscars security screening\n", - " [True=1, Pred=0] historians suggest 'goodfellas' youtube clips may be fragments of larger work\n", - " [True=1, Pred=0] members of opening band walking among crowd during intermission like gods among men\n", - " [True=1, Pred=0] woman who's been on the pill for years thinking about switching to new set of debilitating side effe\n", - " [True=1, Pred=0] congressman lets his guitar do the talking\n", - " [True=1, Pred=0] officials warn consumers of counterfeit tickets ahead of solar eclipse\n", - " [True=1, Pred=0] anderson cooper throws another box of letters from gay children into dumpster\n", - " [True=1, Pred=0] non-priest arrested on charges of child molestation\n", - " [True=1, Pred=0] huckabee sanders tells colleagues she's taking temporary post as google ceo before transitioning int\n" - ] - } - ], - "source": [ - "# ── Binary error examples ─────────────────────────────────────────────────────\n", - "err_bin = pred_test_bin[pred_test_bin[\"correct\"] == 0].copy()\n", - "fp_bin = err_bin[err_bin[\"binary_label\"] == 0].head(10) # predicted sarcastic, actually not\n", - "fn_bin = err_bin[err_bin[\"binary_label\"] == 1].head(10) # predicted not-sarcastic, actually sarcastic\n", - "\n", - "print(f\"Binary errors on test: {len(err_bin)} total\")\n", - "print(f\" False Positives (predicted sarcastic, actually not): {len(err_bin[err_bin['binary_label']==0])}\")\n", - "print(f\" False Negatives (predicted not-sarcastic, actually sarcastic): {len(err_bin[err_bin['binary_label']==1])}\")\n", - "print(\"\\n--- Sample FP ---\")\n", - "for _, row in fp_bin.iterrows():\n", - " print(f\" [True=0, Pred=1] {row['text'][:100]}\")\n", - "print(\"\\n--- Sample FN ---\")\n", - "for _, row in fn_bin.iterrows():\n", - " print(f\" [True=1, Pred=0] {row['text'][:100]}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "p52YKmRQuKB_" + }, + "source": [ + "## Summary" + ] }, - "id": "zmVdkTHuuKB_", - "outputId": "5bc30ec0-0f97-4f57-a165-706c7a022a82" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Type errors on test: 2570 total\n", - "\n", - "Confusion pairs (true → predicted):\n", - "type_label predicted_label\n", - "irony sarcasm 309\n", - "satire sarcasm 227\n", - "sarcasm irony 226\n", - " satire 203\n", - "satire irony 155\n", - "irony satire 155\n", - "overstatement sarcasm 117\n", - "understatement sarcasm 103\n", - "overstatement satire 102\n", - "satire overstatement 101\n" - ] - } - ], - "source": [ - "# ── Type error examples ───────────────────────────────────────────────────────\n", - "err_type = pred_test_type[pred_test_type[\"correct\"] == 0].copy()\n", - "print(f\"Type errors on test: {len(err_type)} total\")\n", - "print(\"\\nConfusion pairs (true → predicted):\")\n", - "conf_pairs = err_type.groupby([\"type_label\", \"predicted_label\"]).size().sort_values(ascending=False).head(10)\n", - "print(conf_pairs.to_string())" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "p52YKmRQuKB_" - }, - "source": [ - "## Summary" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "code", + "execution_count": 44, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tg9D9LKxuKB_", + "outputId": "bc54cc8f-2bc6-4bcd-b969-086a612d2f48" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "====== NAIVE BAYES RESULTS SUMMARY ======\n", + "\n", + "Task A — Binary (Test):\n", + " Best model : TfidfVec+MultinomialNB\n", + " Accuracy : 0.7905\n", + " Macro-F1 : 0.7902\n", + " Weighted-F1 : 0.7902\n", + "\n", + "Task B — Type (Test):\n", + " Best model : CountVec+MultinomialNB\n", + " Accuracy : 0.3953\n", + " Macro-F1 : 0.3953\n", + " Weighted-F1 : 0.3929\n" + ] + } + ], + "source": [ + "print(\"====== NAIVE BAYES RESULTS SUMMARY ======\")\n", + "print()\n", + "print(\"Task A — Binary (Test):\")\n", + "print(f\" Best model : {best_name_bin}\")\n", + "print(f\" Accuracy : {test_m_bin['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {test_m_bin['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1 : {test_m_bin['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"Task B — Type (Test):\")\n", + "print(f\" Best model : {best_name_type}\")\n", + "print(f\" Accuracy : {test_m_type['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {test_m_type['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1 : {test_m_type['f1_weighted']:.4f}\")" + ] }, - "id": "tg9D9LKxuKB_", - "outputId": "bf1f11d1-8feb-4b8c-c9fd-adac380993b2" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "====== NAIVE BAYES RESULTS SUMMARY ======\n", - "\n", - "Task A — Binary (Test):\n", - " Best model : TfidfVec+MultinomialNB\n", - " Accuracy : 0.7905\n", - " Macro-F1 : 0.7902\n", - " Weighted-F1 : 0.7902\n", - "\n", - "Task B — Type (Test):\n", - " Best model : CountVec+MultinomialNB\n", - " Accuracy : 0.3953\n", - " Macro-F1 : 0.3953\n", - " Weighted-F1 : 0.3929\n" - ] - } - ], - "source": [ - "print(\"====== NAIVE BAYES RESULTS SUMMARY ======\")\n", - "print()\n", - "print(\"Task A — Binary (Test):\")\n", - "print(f\" Best model : {best_name_bin}\")\n", - "print(f\" Accuracy : {test_m_bin['accuracy']:.4f}\")\n", - "print(f\" Macro-F1 : {test_m_bin['f1_macro']:.4f}\")\n", - "print(f\" Weighted-F1 : {test_m_bin['f1_weighted']:.4f}\")\n", - "print()\n", - "print(\"Task B — Type (Test):\")\n", - "print(f\" Best model : {best_name_type}\")\n", - "print(f\" Accuracy : {test_m_type['accuracy']:.4f}\")\n", - "print(f\" Macro-F1 : {test_m_type['f1_macro']:.4f}\")\n", - "print(f\" Weighted-F1 : {test_m_type['f1_weighted']:.4f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GZHqRh2iuKB_" - }, - "source": [ - "---\n", - "# Part 4 — BERT / DistilBERT Classification" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xR_41E_1uKB_" - }, - "source": [ - "# Notebook 04 — BERT / DistilBERT Classification\n", - "\n", - "**Purpose**: Fine-tune transformer models for sarcasm classification.\n", - "- Task A: Binary (sarcastic vs non-sarcastic)\n", - "- Task B: Sarcasm type (6-class, sarcastic only)\n", - "\n", - "**Models**:\n", - "- `distilbert-base-uncased` (primary, fast)\n", - "- `bert-base-uncased` (optional, if compute allows)\n", - "\n", - "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", - "\n", - "**Outputs** in `outputs/bert/distilbert_binary/` and `outputs/bert/distilbert_type/`" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5Gi8VSJDuKB_" - }, - "source": [ - "## Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "GZHqRh2iuKB_" + }, + "source": [ + "---\n", + "# Part 4 — BERT / DistilBERT Classification" + ] }, - "id": "K_FNBk2ouKB_", - "outputId": "70f6d378-7098-410c-d656-50135e3b0e12" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Binary config: TrainConfig(model_name='distilbert-base-uncased', max_length=128, batch_size=32, lr=2e-05, weight_decay=0.01, warmup_ratio=0.1, epochs=10, patience=3, seed=42, task='binary', use_class_weights=False)\n", - "Type config: TrainConfig(model_name='distilbert-base-uncased', max_length=128, batch_size=32, lr=2e-05, weight_decay=0.01, warmup_ratio=0.1, epochs=10, patience=3, seed=42, task='type', use_class_weights=True)\n" - ] - } - ], - "source": [ - "@dataclass\n", - "class TrainConfig:\n", - " model_name : str = \"distilbert-base-uncased\"\n", - " max_length : int = 128\n", - " batch_size : int = 32\n", - " lr : float = 2e-5\n", - " weight_decay : float = 0.01\n", - " warmup_ratio : float = 0.1\n", - " epochs : int = 10\n", - " patience : int = 3 # early stopping patience\n", - " seed : int = SEED\n", - " task : str = \"binary\" # 'binary' or 'type'\n", - " use_class_weights: bool = False\n", - "\n", - "# ── Config for binary task ────────────────────────────────────────────────────\n", - "CFG_BINARY = TrainConfig(\n", - " model_name=\"distilbert-base-uncased\",\n", - " task=\"binary\",\n", - " batch_size=32,\n", - " lr=2e-5,\n", - " use_class_weights=False,\n", - ")\n", - "\n", - "# ── Config for type task (use class weights for imbalance) ────────────────────\n", - "CFG_TYPE = TrainConfig(\n", - " model_name=\"distilbert-base-uncased\",\n", - " task=\"type\",\n", - " batch_size=32,\n", - " lr=2e-5,\n", - " use_class_weights=True,\n", - ")\n", - "\n", - "print(\"Binary config:\", CFG_BINARY)\n", - "print(\"Type config: \", CFG_TYPE)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OKgvtBDPuKCA" - }, - "source": [ - "## Dataset Class" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "id": "tHoVxhSAuKCA" - }, - "outputs": [], - "source": [ - "import torch\n", - "from torch.utils.data import Dataset\n", - "\n", - "class HeadlineDataset(Dataset):\n", - " \"\"\"PyTorch Dataset for headline classification.\"\"\"\n", - "\n", - " def __init__(\n", - " self,\n", - " texts: list[str],\n", - " labels: list[int],\n", - " tokenizer,\n", - " max_length: int = 128,\n", - " ):\n", - " self.texts = texts\n", - " self.labels = labels\n", - " self.tokenizer = tokenizer\n", - " self.max_length = max_length\n", - "\n", - " def __len__(self) -> int:\n", - " return len(self.texts)\n", - "\n", - " def __getitem__(self, idx: int) -> dict:\n", - " encoding = self.tokenizer(\n", - " self.texts[idx],\n", - " truncation=True,\n", - " padding=\"max_length\",\n", - " max_length=self.max_length,\n", - " return_tensors=\"pt\",\n", - " )\n", - " return {\n", - " \"input_ids\" : encoding[\"input_ids\"].squeeze(0),\n", - " \"attention_mask\" : encoding[\"attention_mask\"].squeeze(0),\n", - " \"labels\" : torch.tensor(self.labels[idx], dtype=torch.long),\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KBPvq1iRuKCA" - }, - "source": [ - "## Training and Evaluation Functions" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "id": "Dw8sRTfruKCA" - }, - "outputs": [], - "source": [ - "def compute_class_weights(y_train: list[int], num_classes: int) -> torch.Tensor:\n", - " \"\"\"Compute inverse-frequency class weights.\"\"\"\n", - " counts = np.bincount(y_train, minlength=num_classes).astype(float)\n", - " weights = len(y_train) / (num_classes * counts)\n", - " weights = np.clip(weights, 0, 10) # cap extreme weights\n", - " return torch.tensor(weights, dtype=torch.float)\n", - "\n", - "\n", - "def train_epoch(\n", - " model, loader: DataLoader, optimizer, scheduler, criterion, device\n", - ") -> float:\n", - " model.train()\n", - " total_loss = 0.0\n", - " for batch in loader:\n", - " input_ids = batch[\"input_ids\"].to(device)\n", - " attention_mask = batch[\"attention_mask\"].to(device)\n", - " labels = batch[\"labels\"].to(device)\n", - "\n", - " optimizer.zero_grad()\n", - " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", - " logits = outputs.logits\n", - "\n", - " loss = criterion(logits, labels)\n", - " loss.backward()\n", - "\n", - " nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n", - " optimizer.step()\n", - " scheduler.step()\n", - "\n", - " total_loss += loss.item()\n", - " return total_loss / len(loader)\n", - "\n", - "\n", - "@torch.no_grad()\n", - "def eval_epoch(\n", - " model, loader: DataLoader, criterion, device, label_names: list[str]\n", - ") -> tuple[float, dict, list]:\n", - " model.eval()\n", - " total_loss = 0.0\n", - " all_preds, all_labels = [], []\n", - "\n", - " for batch in loader:\n", - " input_ids = batch[\"input_ids\"].to(device)\n", - " attention_mask = batch[\"attention_mask\"].to(device)\n", - " labels = batch[\"labels\"].to(device)\n", - "\n", - " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", - " logits = outputs.logits\n", - " loss = criterion(logits, labels)\n", - "\n", - " total_loss += loss.item()\n", - " preds = torch.argmax(logits, dim=-1)\n", - " all_preds.extend(preds.cpu().numpy())\n", - " all_labels.extend(labels.cpu().numpy())\n", - "\n", - " avg_loss = total_loss / len(loader)\n", - " metrics = {\n", - " \"loss\" : avg_loss,\n", - " \"accuracy\" : accuracy_score(all_labels, all_preds),\n", - " \"f1_macro\" : f1_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", - " \"f1_weighted\" : f1_score(all_labels, all_preds, average=\"weighted\", zero_division=0),\n", - " \"precision_macro\" : precision_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", - " \"recall_macro\" : recall_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", - " }\n", - " return avg_loss, metrics, all_preds\n", - "\n", - "\n", - "def save_confusion_matrix(y_true, y_pred, label_names, out_path, title=\"\"):\n", - " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", - " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", - " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n", - " xticklabels=label_names, yticklabels=label_names, ax=ax)\n", - " ax.set_xlabel(\"Predicted\"); ax.set_ylabel(\"True\"); ax.set_title(title)\n", - " plt.tight_layout()\n", - " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", - " plt.show()\n", - " print(f\"Saved: {out_path}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "d35JFlgzuKCB" - }, - "source": [ - "## Full Training Function" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "id": "wHriQLOyuKCB" - }, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "\n", - "from transformers import get_linear_schedule_with_warmup\n", - "\n", - "def train_bert(\n", - " cfg: TrainConfig,\n", - " X_train: list[str], y_train: list[int],\n", - " X_val: list[str], y_val: list[int],\n", - " X_test: list[str], y_test: list[int],\n", - " label_names: list[str],\n", - " out_dir: Path,\n", - " val_df: pd.DataFrame,\n", - " test_df: pd.DataFrame,\n", - ") -> dict:\n", - " \"\"\"Full training loop with early stopping. Returns test metrics dict.\"\"\"\n", - " out_dir.mkdir(parents=True, exist_ok=True)\n", - " num_labels = len(label_names)\n", - "\n", - " # FIX: define device inside function (self-contained)\n", - " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - " print(f\"Using device: {device}\")\n", - "\n", - " # Save config (default=str avoids Path serialization issues)\n", - " with open(out_dir / \"config.json\", \"w\") as f:\n", - " json.dump(cfg.__dict__, f, indent=2, default=str)\n", - " print(f\"Config saved to {out_dir / 'config.json'}\")\n", - "\n", - " # Tokenizer\n", - " print(f\"Loading tokenizer: {cfg.model_name}\")\n", - " tokenizer = AutoTokenizer.from_pretrained(cfg.model_name)\n", - "\n", - " # Datasets\n", - " train_ds = HeadlineDataset(X_train, y_train, tokenizer, cfg.max_length)\n", - " val_ds = HeadlineDataset(X_val, y_val, tokenizer, cfg.max_length)\n", - " test_ds = HeadlineDataset(X_test, y_test, tokenizer, cfg.max_length)\n", - "\n", - " g = torch.Generator()\n", - " g.manual_seed(cfg.seed)\n", - "\n", - " train_loader = DataLoader(train_ds, batch_size=cfg.batch_size, shuffle=True, generator=g)\n", - " val_loader = DataLoader(val_ds, batch_size=cfg.batch_size, shuffle=False)\n", - " test_loader = DataLoader(test_ds, batch_size=cfg.batch_size, shuffle=False)\n", - "\n", - " # Model\n", - " print(f\"Loading model: {cfg.model_name} ({num_labels} labels)\")\n", - " model = AutoModelForSequenceClassification.from_pretrained(\n", - " cfg.model_name, num_labels=num_labels\n", - " ).to(device)\n", - "\n", - " # Loss (with optional class weights)\n", - " if cfg.use_class_weights:\n", - " cw = compute_class_weights(y_train, num_labels).to(device)\n", - " criterion = nn.CrossEntropyLoss(weight=cw)\n", - " print(f\"Class weights: {cw.detach().cpu().numpy().round(3)}\")\n", - " else:\n", - " criterion = nn.CrossEntropyLoss()\n", - "\n", - " # Optimizer and scheduler\n", - " optimizer = torch.optim.AdamW(\n", - " model.parameters(), lr=cfg.lr, weight_decay=cfg.weight_decay\n", - " )\n", - " total_steps = len(train_loader) * cfg.epochs\n", - " warmup_steps = int(total_steps * cfg.warmup_ratio)\n", - "\n", - " scheduler = get_linear_schedule_with_warmup(\n", - " optimizer, num_warmup_steps=warmup_steps, num_training_steps=total_steps\n", - " )\n", - "\n", - " # Training loop\n", - " best_val_f1 = -1.0\n", - " patience_cnt = 0\n", - " best_epoch = 0\n", - " train_log = []\n", - "\n", - " for epoch in range(1, cfg.epochs + 1):\n", - " train_loss = train_epoch(model, train_loader, optimizer, scheduler, criterion, device)\n", - " _, val_metrics, _ = eval_epoch(model, val_loader, criterion, device, label_names)\n", - "\n", - " val_f1 = float(val_metrics[\"f1_macro\"])\n", - " log_row = {\n", - " \"epoch\": epoch,\n", - " \"train_loss\": float(train_loss),\n", - " **{f\"val_{k}\": float(v) if isinstance(v, (np.floating, float, int, np.integer)) else v\n", - " for k, v in val_metrics.items()}\n", - " }\n", - " train_log.append(log_row)\n", - "\n", - " print(\n", - " f\"Epoch {epoch:2d}/{cfg.epochs} | \"\n", - " f\"train_loss={train_loss:.4f} | \"\n", - " f\"val_loss={val_metrics['loss']:.4f} | \"\n", - " f\"val_acc={val_metrics['accuracy']:.4f} | \"\n", - " f\"val_macro_f1={val_f1:.4f}\"\n", - " )\n", - "\n", - " if val_f1 > best_val_f1:\n", - " best_val_f1 = val_f1\n", - " best_epoch = epoch\n", - " patience_cnt = 0\n", - "\n", - " ckpt_dir = out_dir / \"best_checkpoint\"\n", - " ckpt_dir.mkdir(parents=True, exist_ok=True)\n", - "\n", - " model.save_pretrained(ckpt_dir)\n", - " tokenizer.save_pretrained(ckpt_dir)\n", - " print(f\" ★ New best val macro-F1={val_f1:.4f} — checkpoint saved\")\n", - " else:\n", - " patience_cnt += 1\n", - " if patience_cnt >= cfg.patience:\n", - " print(f\" Early stopping at epoch {epoch} (patience={cfg.patience})\")\n", - " break\n", - "\n", - " # Save training log\n", - " log_df = pd.DataFrame(train_log)\n", - " log_df.to_csv(out_dir / \"training_log.csv\", index=False)\n", - "\n", - " # Plot training curves\n", - " fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", - "\n", - " axes[0].plot(log_df[\"epoch\"], log_df[\"train_loss\"], label=\"Train loss\", marker=\"o\")\n", - " axes[0].plot(log_df[\"epoch\"], log_df[\"val_loss\"], label=\"Val loss\", marker=\"s\")\n", - " axes[0].set_xlabel(\"Epoch\")\n", - " axes[0].set_ylabel(\"Loss\")\n", - " axes[0].set_title(f\"{cfg.model_name} — Loss Curves ({cfg.task})\")\n", - " axes[0].legend()\n", - " axes[0].axvline(best_epoch, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", - "\n", - " axes[1].plot(log_df[\"epoch\"], log_df[\"val_f1_macro\"], label=\"Val macro-F1\", marker=\"o\")\n", - " axes[1].set_xlabel(\"Epoch\")\n", - " axes[1].set_ylabel(\"Macro-F1\")\n", - " axes[1].set_title(f\"{cfg.model_name} — Val Macro-F1 ({cfg.task})\")\n", - " axes[1].legend()\n", - " axes[1].axvline(best_epoch, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", - "\n", - " plt.tight_layout()\n", - " plt.savefig(out_dir / \"training_curves.png\", dpi=150, bbox_inches=\"tight\")\n", - " plt.show()\n", - "\n", - " # Reload best checkpoint and evaluate\n", - " print(f\"\\nLoading best checkpoint (epoch {best_epoch}, val macro-F1={best_val_f1:.4f})\")\n", - " best_model = AutoModelForSequenceClassification.from_pretrained(\n", - " str(out_dir / \"best_checkpoint\")\n", - " ).to(device)\n", - "\n", - " _, val_metrics_best, val_preds = eval_epoch(best_model, val_loader, criterion, device, label_names)\n", - " _, test_metrics_best, test_preds = eval_epoch(best_model, test_loader, criterion, device, label_names)\n", - "\n", - " print(\"\\n=== Val (best checkpoint) ===\")\n", - " print(classification_report(y_val, val_preds, target_names=label_names, zero_division=0))\n", - " print(\"\\n=== Test (best checkpoint) ===\")\n", - " print(classification_report(y_test, test_preds, target_names=label_names, zero_division=0))\n", - "\n", - " # Confusion matrices\n", - " save_confusion_matrix(\n", - " y_val, val_preds, label_names,\n", - " out_dir / \"confusion_matrix_val.png\",\n", - " f\"{cfg.model_name} — {cfg.task} (Val)\"\n", - " )\n", - " save_confusion_matrix(\n", - " y_test, test_preds, label_names,\n", - " out_dir / \"confusion_matrix_test.png\",\n", - " f\"{cfg.model_name} — {cfg.task} (Test)\"\n", - " )\n", - "\n", - " # Convert metrics to native Python types for JSON\n", - " def _to_py(obj):\n", - " if isinstance(obj, dict):\n", - " return {k: _to_py(v) for k, v in obj.items()}\n", - " if isinstance(obj, (list, tuple)):\n", - " return [_to_py(v) for v in obj]\n", - " if isinstance(obj, (np.integer,)):\n", - " return int(obj)\n", - " if isinstance(obj, (np.floating,)):\n", - " return float(obj)\n", - " return obj\n", - "\n", - " results = {\n", - " \"model\": cfg.model_name,\n", - " \"task\": cfg.task,\n", - " \"best_epoch\": int(best_epoch),\n", - " \"best_val_f1_macro\": float(best_val_f1),\n", - " \"val\": _to_py(val_metrics_best),\n", - " \"test\": _to_py(test_metrics_best),\n", - " }\n", - "\n", - " with open(out_dir / \"metrics.json\", \"w\") as f:\n", - " json.dump(results, f, indent=2, default=str)\n", - "\n", - " # Save predictions\n", - " for split_name, df, y_true_list, y_pred_list in [\n", - " (\"val\", val_df, y_val, val_preds),\n", - " (\"test\", test_df, y_test, test_preds),\n", - " ]:\n", - " out_pred = df.copy()\n", - " out_pred[\"predicted\"] = y_pred_list\n", - " out_pred[\"predicted_label\"] = [label_names[i] for i in y_pred_list]\n", - " out_pred[\"correct\"] = (np.array(y_true_list) == np.array(y_pred_list)).astype(int)\n", - " out_pred.to_csv(out_dir / f\"predictions_{split_name}.csv\", index=False)\n", - " print(f\"Saved predictions_{split_name}.csv\")\n", - "\n", - " print(f\"\\n=== DONE: {cfg.model_name} / {cfg.task} ===\")\n", - " print(f\" Test Accuracy : {test_metrics_best['accuracy']:.4f}\")\n", - " print(f\" Test Macro-F1 : {test_metrics_best['f1_macro']:.4f}\")\n", - " print(f\" Test Weighted-F1: {test_metrics_best['f1_weighted']:.4f}\")\n", - "\n", - " return results" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "49JUIiXSuKCB" - }, - "source": [ - "## Load Data" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "xR_41E_1uKB_" + }, + "source": [ + "# Notebook 04 — BERT / DistilBERT Classification\n", + "\n", + "**Purpose**: Fine-tune transformer models for sarcasm classification.\n", + "- Task A: Binary (sarcastic vs non-sarcastic)\n", + "- Task B: Sarcasm type (6-class, sarcastic only)\n", + "\n", + "**Models**:\n", + "- `distilbert-base-uncased` (primary, fast)\n", + "- `bert-base-uncased` (optional, if compute allows)\n", + "\n", + "**Prerequisite**: Run `01_data_preparation.ipynb` first.\n", + "\n", + "**Outputs** in `outputs/bert/distilbert_binary/` and `outputs/bert/distilbert_type/`" + ] }, - "id": "-bzRLKhsuKCB", - "outputId": "b3ac3196-fdb3-44d9-8417-26a434c6837a" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Binary — Train: 39,666 Val: 8,500 Test: 8,500\n" - ] - } - ], - "source": [ - "# ── Binary splits ─────────────────────────────────────────────────────────────\n", - "train_bin = pd.read_csv(SPLITS / \"train_binary.csv\")\n", - "val_bin = pd.read_csv(SPLITS / \"val_binary.csv\")\n", - "test_bin = pd.read_csv(SPLITS / \"test_binary.csv\")\n", - "\n", - "X_train_bin = train_bin[\"text\"].tolist(); y_train_bin = train_bin[\"binary_label\"].tolist()\n", - "X_val_bin = val_bin[\"text\"].tolist(); y_val_bin = val_bin[\"binary_label\"].tolist()\n", - "X_test_bin = test_bin[\"text\"].tolist(); y_test_bin = test_bin[\"binary_label\"].tolist()\n", - "\n", - "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", - "\n", - "print(f\"Binary — Train: {len(X_train_bin):,} Val: {len(X_val_bin):,} Test: {len(X_test_bin):,}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "5Gi8VSJDuKB_" + }, + "source": [ + "## Configuration" + ] }, - "id": "OkXIlQAtuKCC", - "outputId": "676ecfa7-874b-4536-9ac3-b8b833e2cfff" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Type — Train: 19,833 Val: 4,250 Test: 4,250\n", - "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n" - ] - } - ], - "source": [ - "# ── Type splits ───────────────────────────────────────────────────────────────\n", - "train_type = pd.read_csv(SPLITS / \"train_type.csv\")\n", - "val_type = pd.read_csv(SPLITS / \"val_type.csv\")\n", - "test_type = pd.read_csv(SPLITS / \"test_type.csv\")\n", - "\n", - "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", - "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", - "id2label = {i: lab for lab, i in label2id.items()}\n", - "\n", - "def enc(df): return [label2id[l] for l in df[\"type_label\"]]\n", - "\n", - "X_train_type = train_type[\"text\"].tolist(); y_train_type = enc(train_type)\n", - "X_val_type = val_type[\"text\"].tolist(); y_val_type = enc(val_type)\n", - "X_test_type = test_type[\"text\"].tolist(); y_test_type = enc(test_type)\n", - "\n", - "print(f\"Type — Train: {len(X_train_type):,} Val: {len(X_val_type):,} Test: {len(X_test_type):,}\")\n", - "print(f\"Strategies: {STRATEGY_LABELS}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "varh9ZaouKCC" - }, - "source": [ - "## Train DistilBERT — Binary Task" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 396, - "referenced_widgets": [ - "8b9080237c194037a2cd1dc711a694d2", - "d0d70eea6290419fb4a39bd74964bacb", - "30e0eec43be54d08a0b5bd855e06d3c3", - "959391d9e6cc405ead852002ce0276ff", - "143c9b378b5a4260b15e328dc744374f", - "384fe210d84b4e8d8bc1cd8b99944d5e", - "2a97e4558e6d41df919bd3ce83576627", - "bfc3291175a14250a407af3dd6376d58", - "01c169be55ca4399b34eb7cdae152075", - "2f42691301be4d01858524488e30fe78", - "8faab66da6c74399a140fa5aad07dbbb" - ] + "cell_type": "code", + "execution_count": 45, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "K_FNBk2ouKB_", + "outputId": "3f6c86bc-d711-4d3b-9a37-8971c7b576c3" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Binary config: TrainConfig(model_name='distilbert-base-uncased', max_length=128, batch_size=32, lr=2e-05, weight_decay=0.01, warmup_ratio=0.1, epochs=10, patience=3, seed=42, task='binary', use_class_weights=False)\n", + "Type config: TrainConfig(model_name='distilbert-base-uncased', max_length=128, batch_size=32, lr=2e-05, weight_decay=0.01, warmup_ratio=0.1, epochs=10, patience=3, seed=42, task='type', use_class_weights=True)\n" + ] + } + ], + "source": [ + "@dataclass\n", + "class TrainConfig:\n", + " model_name : str = \"distilbert-base-uncased\"\n", + " max_length : int = 128\n", + " batch_size : int = 32\n", + " lr : float = 2e-5\n", + " weight_decay : float = 0.01\n", + " warmup_ratio : float = 0.1\n", + " epochs : int = 10\n", + " patience : int = 3 # early stopping patience\n", + " seed : int = SEED\n", + " task : str = \"binary\" # 'binary' or 'type'\n", + " use_class_weights: bool = False\n", + "\n", + "# ── Config for binary task ────────────────────────────────────────────────────\n", + "CFG_BINARY = TrainConfig(\n", + " model_name=\"distilbert-base-uncased\",\n", + " task=\"binary\",\n", + " batch_size=32,\n", + " lr=2e-5,\n", + " use_class_weights=False,\n", + ")\n", + "\n", + "# ── Config for type task (use class weights for imbalance) ────────────────────\n", + "CFG_TYPE = TrainConfig(\n", + " model_name=\"distilbert-base-uncased\",\n", + " task=\"type\",\n", + " batch_size=32,\n", + " lr=2e-5,\n", + " use_class_weights=True,\n", + ")\n", + "\n", + "print(\"Binary config:\", CFG_BINARY)\n", + "print(\"Type config: \", CFG_TYPE)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OKgvtBDPuKCA" + }, + "source": [ + "## Dataset Class" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "id": "tHoVxhSAuKCA" + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch.utils.data import Dataset\n", + "\n", + "class HeadlineDataset(Dataset):\n", + " \"\"\"PyTorch Dataset for headline classification.\"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " texts: list[str],\n", + " labels: list[int],\n", + " tokenizer,\n", + " max_length: int = 128,\n", + " ):\n", + " self.texts = texts\n", + " self.labels = labels\n", + " self.tokenizer = tokenizer\n", + " self.max_length = max_length\n", + "\n", + " def __len__(self) -> int:\n", + " return len(self.texts)\n", + "\n", + " def __getitem__(self, idx: int) -> dict:\n", + " encoding = self.tokenizer(\n", + " self.texts[idx],\n", + " truncation=True,\n", + " padding=\"max_length\",\n", + " max_length=self.max_length,\n", + " return_tensors=\"pt\",\n", + " )\n", + " return {\n", + " \"input_ids\" : encoding[\"input_ids\"].squeeze(0),\n", + " \"attention_mask\" : encoding[\"attention_mask\"].squeeze(0),\n", + " \"labels\" : torch.tensor(self.labels[idx], dtype=torch.long),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KBPvq1iRuKCA" + }, + "source": [ + "## Training and Evaluation Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "id": "Dw8sRTfruKCA" + }, + "outputs": [], + "source": [ + "def compute_class_weights(y_train: list[int], num_classes: int) -> torch.Tensor:\n", + " \"\"\"Compute inverse-frequency class weights.\"\"\"\n", + " counts = np.bincount(y_train, minlength=num_classes).astype(float)\n", + " weights = len(y_train) / (num_classes * counts)\n", + " weights = np.clip(weights, 0, 10) # cap extreme weights\n", + " return torch.tensor(weights, dtype=torch.float)\n", + "\n", + "\n", + "def train_epoch(\n", + " model, loader: DataLoader, optimizer, scheduler, criterion, device\n", + ") -> float:\n", + " model.train()\n", + " total_loss = 0.0\n", + " for batch in loader:\n", + " input_ids = batch[\"input_ids\"].to(device)\n", + " attention_mask = batch[\"attention_mask\"].to(device)\n", + " labels = batch[\"labels\"].to(device)\n", + "\n", + " optimizer.zero_grad()\n", + " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", + " logits = outputs.logits\n", + "\n", + " loss = criterion(logits, labels)\n", + " loss.backward()\n", + "\n", + " nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n", + " optimizer.step()\n", + " scheduler.step()\n", + "\n", + " total_loss += loss.item()\n", + " return total_loss / len(loader)\n", + "\n", + "\n", + "@torch.no_grad()\n", + "def eval_epoch(\n", + " model, loader: DataLoader, criterion, device, label_names: list[str]\n", + ") -> tuple[float, dict, list]:\n", + " model.eval()\n", + " total_loss = 0.0\n", + " all_preds, all_labels = [], []\n", + "\n", + " for batch in loader:\n", + " input_ids = batch[\"input_ids\"].to(device)\n", + " attention_mask = batch[\"attention_mask\"].to(device)\n", + " labels = batch[\"labels\"].to(device)\n", + "\n", + " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", + " logits = outputs.logits\n", + " loss = criterion(logits, labels)\n", + "\n", + " total_loss += loss.item()\n", + " preds = torch.argmax(logits, dim=-1)\n", + " all_preds.extend(preds.cpu().numpy())\n", + " all_labels.extend(labels.cpu().numpy())\n", + "\n", + " avg_loss = total_loss / len(loader)\n", + " metrics = {\n", + " \"loss\" : avg_loss,\n", + " \"accuracy\" : accuracy_score(all_labels, all_preds),\n", + " \"f1_macro\" : f1_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", + " \"f1_weighted\" : f1_score(all_labels, all_preds, average=\"weighted\", zero_division=0),\n", + " \"precision_macro\" : precision_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", + " \"recall_macro\" : recall_score(all_labels, all_preds, average=\"macro\", zero_division=0),\n", + " }\n", + " return avg_loss, metrics, all_preds\n", + "\n", + "\n", + "def save_confusion_matrix(y_true, y_pred, label_names, out_path, title=\"\"):\n", + " cm = confusion_matrix(y_true, y_pred, labels=range(len(label_names)))\n", + " fig, ax = plt.subplots(figsize=(max(6, len(label_names)), max(5, len(label_names))))\n", + " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n", + " xticklabels=label_names, yticklabels=label_names, ax=ax)\n", + " ax.set_xlabel(\"Predicted\"); ax.set_ylabel(\"True\"); ax.set_title(title)\n", + " plt.tight_layout()\n", + " plt.savefig(out_path, dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + " print(f\"Saved: {out_path}\")" + ] }, - "id": "Yq8zSNjluKCC", - "outputId": "44080f86-f045-426f-f630-765eb5f599c7" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "Using device: cpu\n", - "Config saved to /content/outputs/bert/distilbert_binary/config.json\n", - "Loading tokenizer: distilbert-base-uncased\n", - "Loading model: distilbert-base-uncased (2 labels)\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "d35JFlgzuKCB" + }, + "source": [ + "## Full Training Function" + ] }, { - "output_type": "display_data", - "data": { - "text/plain": [ - "Loading weights: 0%| | 0/100 [00:00 dict:\n", + " \"\"\"Full training loop with early stopping. Returns test metrics dict.\"\"\"\n", + " out_dir.mkdir(parents=True, exist_ok=True)\n", + " num_labels = len(label_names)\n", + "\n", + " # FIX: define device inside function (self-contained)\n", + " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + " print(f\"Using device: {device}\")\n", + "\n", + " # Save config (default=str avoids Path serialization issues)\n", + " with open(out_dir / \"config.json\", \"w\") as f:\n", + " json.dump(cfg.__dict__, f, indent=2, default=str)\n", + " print(f\"Config saved to {out_dir / 'config.json'}\")\n", + "\n", + " # Tokenizer\n", + " print(f\"Loading tokenizer: {cfg.model_name}\")\n", + " tokenizer = AutoTokenizer.from_pretrained(cfg.model_name)\n", + "\n", + " # Datasets\n", + " train_ds = HeadlineDataset(X_train, y_train, tokenizer, cfg.max_length)\n", + " val_ds = HeadlineDataset(X_val, y_val, tokenizer, cfg.max_length)\n", + " test_ds = HeadlineDataset(X_test, y_test, tokenizer, cfg.max_length)\n", + "\n", + " g = torch.Generator()\n", + " g.manual_seed(cfg.seed)\n", + "\n", + " train_loader = DataLoader(train_ds, batch_size=cfg.batch_size, shuffle=True, generator=g)\n", + " val_loader = DataLoader(val_ds, batch_size=cfg.batch_size, shuffle=False)\n", + " test_loader = DataLoader(test_ds, batch_size=cfg.batch_size, shuffle=False)\n", + "\n", + " # Model\n", + " print(f\"Loading model: {cfg.model_name} ({num_labels} labels)\")\n", + " model = AutoModelForSequenceClassification.from_pretrained(\n", + " cfg.model_name, num_labels=num_labels\n", + " ).to(device)\n", + "\n", + " # Loss (with optional class weights)\n", + " if cfg.use_class_weights:\n", + " cw = compute_class_weights(y_train, num_labels).to(device)\n", + " criterion = nn.CrossEntropyLoss(weight=cw)\n", + " print(f\"Class weights: {cw.detach().cpu().numpy().round(3)}\")\n", + " else:\n", + " criterion = nn.CrossEntropyLoss()\n", + "\n", + " # Optimizer and scheduler\n", + " optimizer = torch.optim.AdamW(\n", + " model.parameters(), lr=cfg.lr, weight_decay=cfg.weight_decay\n", + " )\n", + " total_steps = len(train_loader) * cfg.epochs\n", + " warmup_steps = int(total_steps * cfg.warmup_ratio)\n", + "\n", + " scheduler = get_linear_schedule_with_warmup(\n", + " optimizer, num_warmup_steps=warmup_steps, num_training_steps=total_steps\n", + " )\n", + "\n", + " # Training loop\n", + " best_val_f1 = -1.0\n", + " patience_cnt = 0\n", + " best_epoch = 0\n", + " train_log = []\n", + "\n", + " for epoch in range(1, cfg.epochs + 1):\n", + " train_loss = train_epoch(model, train_loader, optimizer, scheduler, criterion, device)\n", + " _, val_metrics, _ = eval_epoch(model, val_loader, criterion, device, label_names)\n", + "\n", + " val_f1 = float(val_metrics[\"f1_macro\"])\n", + " log_row = {\n", + " \"epoch\": epoch,\n", + " \"train_loss\": float(train_loss),\n", + " **{f\"val_{k}\": float(v) if isinstance(v, (np.floating, float, int, np.integer)) else v\n", + " for k, v in val_metrics.items()}\n", + " }\n", + " train_log.append(log_row)\n", + "\n", + " print(\n", + " f\"Epoch {epoch:2d}/{cfg.epochs} | \"\n", + " f\"train_loss={train_loss:.4f} | \"\n", + " f\"val_loss={val_metrics['loss']:.4f} | \"\n", + " f\"val_acc={val_metrics['accuracy']:.4f} | \"\n", + " f\"val_macro_f1={val_f1:.4f}\"\n", + " )\n", + "\n", + " if val_f1 > best_val_f1:\n", + " best_val_f1 = val_f1\n", + " best_epoch = epoch\n", + " patience_cnt = 0\n", + "\n", + " ckpt_dir = out_dir / \"best_checkpoint\"\n", + " ckpt_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " model.save_pretrained(ckpt_dir)\n", + " tokenizer.save_pretrained(ckpt_dir)\n", + " print(f\" ★ New best val macro-F1={val_f1:.4f} — checkpoint saved\")\n", + " else:\n", + " patience_cnt += 1\n", + " if patience_cnt >= cfg.patience:\n", + " print(f\" Early stopping at epoch {epoch} (patience={cfg.patience})\")\n", + " break\n", + "\n", + " # Save training log\n", + " log_df = pd.DataFrame(train_log)\n", + " log_df.to_csv(out_dir / \"training_log.csv\", index=False)\n", + "\n", + " # Plot training curves\n", + " fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + " axes[0].plot(log_df[\"epoch\"], log_df[\"train_loss\"], label=\"Train loss\", marker=\"o\")\n", + " axes[0].plot(log_df[\"epoch\"], log_df[\"val_loss\"], label=\"Val loss\", marker=\"s\")\n", + " axes[0].set_xlabel(\"Epoch\")\n", + " axes[0].set_ylabel(\"Loss\")\n", + " axes[0].set_title(f\"{cfg.model_name} — Loss Curves ({cfg.task})\")\n", + " axes[0].legend()\n", + " axes[0].axvline(best_epoch, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", + "\n", + " axes[1].plot(log_df[\"epoch\"], log_df[\"val_f1_macro\"], label=\"Val macro-F1\", marker=\"o\")\n", + " axes[1].set_xlabel(\"Epoch\")\n", + " axes[1].set_ylabel(\"Macro-F1\")\n", + " axes[1].set_title(f\"{cfg.model_name} — Val Macro-F1 ({cfg.task})\")\n", + " axes[1].legend()\n", + " axes[1].axvline(best_epoch, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", + "\n", + " plt.tight_layout()\n", + " plt.savefig(out_dir / \"training_curves.png\", dpi=150, bbox_inches=\"tight\")\n", + " plt.show()\n", + "\n", + " # Reload best checkpoint and evaluate\n", + " print(f\"\\nLoading best checkpoint (epoch {best_epoch}, val macro-F1={best_val_f1:.4f})\")\n", + " best_model = AutoModelForSequenceClassification.from_pretrained(\n", + " str(out_dir / \"best_checkpoint\")\n", + " ).to(device)\n", + "\n", + " _, val_metrics_best, val_preds = eval_epoch(best_model, val_loader, criterion, device, label_names)\n", + " _, test_metrics_best, test_preds = eval_epoch(best_model, test_loader, criterion, device, label_names)\n", + "\n", + " print(\"\\n=== Val (best checkpoint) ===\")\n", + " print(classification_report(y_val, val_preds, target_names=label_names, zero_division=0))\n", + " print(\"\\n=== Test (best checkpoint) ===\")\n", + " print(classification_report(y_test, test_preds, target_names=label_names, zero_division=0))\n", + "\n", + " # Confusion matrices\n", + " save_confusion_matrix(\n", + " y_val, val_preds, label_names,\n", + " out_dir / \"confusion_matrix_val.png\",\n", + " f\"{cfg.model_name} — {cfg.task} (Val)\"\n", + " )\n", + " save_confusion_matrix(\n", + " y_test, test_preds, label_names,\n", + " out_dir / \"confusion_matrix_test.png\",\n", + " f\"{cfg.model_name} — {cfg.task} (Test)\"\n", + " )\n", + "\n", + " # Convert metrics to native Python types for JSON\n", + " def _to_py(obj):\n", + " if isinstance(obj, dict):\n", + " return {k: _to_py(v) for k, v in obj.items()}\n", + " if isinstance(obj, (list, tuple)):\n", + " return [_to_py(v) for v in obj]\n", + " if isinstance(obj, (np.integer,)):\n", + " return int(obj)\n", + " if isinstance(obj, (np.floating,)):\n", + " return float(obj)\n", + " return obj\n", + "\n", + " results = {\n", + " \"model\": cfg.model_name,\n", + " \"task\": cfg.task,\n", + " \"best_epoch\": int(best_epoch),\n", + " \"best_val_f1_macro\": float(best_val_f1),\n", + " \"val\": _to_py(val_metrics_best),\n", + " \"test\": _to_py(test_metrics_best),\n", + " }\n", + "\n", + " with open(out_dir / \"metrics.json\", \"w\") as f:\n", + " json.dump(results, f, indent=2, default=str)\n", + "\n", + " # Save predictions\n", + " for split_name, df, y_true_list, y_pred_list in [\n", + " (\"val\", val_df, y_val, val_preds),\n", + " (\"test\", test_df, y_test, test_preds),\n", + " ]:\n", + " out_pred = df.copy()\n", + " out_pred[\"predicted\"] = y_pred_list\n", + " out_pred[\"predicted_label\"] = [label_names[i] for i in y_pred_list]\n", + " out_pred[\"correct\"] = (np.array(y_true_list) == np.array(y_pred_list)).astype(int)\n", + " out_pred.to_csv(out_dir / f\"predictions_{split_name}.csv\", index=False)\n", + " print(f\"Saved predictions_{split_name}.csv\")\n", + "\n", + " print(f\"\\n=== DONE: {cfg.model_name} / {cfg.task} ===\")\n", + " print(f\" Test Accuracy : {test_metrics_best['accuracy']:.4f}\")\n", + " print(f\" Test Macro-F1 : {test_metrics_best['f1_macro']:.4f}\")\n", + " print(f\" Test Weighted-F1: {test_metrics_best['f1_weighted']:.4f}\")\n", + "\n", + " return results" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "49JUIiXSuKCB" + }, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-bzRLKhsuKCB", + "outputId": "c0cc70d2-c586-4a76-cd9c-41916c103660" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Binary — Train: 39,666 Val: 8,500 Test: 8,500\n" + ] + } ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "8b9080237c194037a2cd1dc711a694d2" - } - }, - "metadata": {} + "source": [ + "# ── Binary splits ─────────────────────────────────────────────────────────────\n", + "train_bin = pd.read_csv(SPLITS / \"train_binary.csv\")\n", + "val_bin = pd.read_csv(SPLITS / \"val_binary.csv\")\n", + "test_bin = pd.read_csv(SPLITS / \"test_binary.csv\")\n", + "\n", + "X_train_bin = train_bin[\"text\"].tolist(); y_train_bin = train_bin[\"binary_label\"].tolist()\n", + "X_val_bin = val_bin[\"text\"].tolist(); y_val_bin = val_bin[\"binary_label\"].tolist()\n", + "X_test_bin = test_bin[\"text\"].tolist(); y_test_bin = test_bin[\"binary_label\"].tolist()\n", + "\n", + "label_names_bin = [\"non-sarcastic\", \"sarcastic\"]\n", + "\n", + "print(f\"Binary — Train: {len(X_train_bin):,} Val: {len(X_val_bin):,} Test: {len(X_test_bin):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OkXIlQAtuKCC", + "outputId": "0141243f-4188-4171-edf1-5ff1b73cc40c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Type — Train: 19,833 Val: 4,250 Test: 4,250\n", + "Strategies: ['irony', 'overstatement', 'rhetorical_question', 'sarcasm', 'satire', 'understatement']\n" + ] + } + ], + "source": [ + "# ── Type splits ───────────────────────────────────────────────────────────────\n", + "train_type = pd.read_csv(SPLITS / \"train_type.csv\")\n", + "val_type = pd.read_csv(SPLITS / \"val_type.csv\")\n", + "test_type = pd.read_csv(SPLITS / \"test_type.csv\")\n", + "\n", + "STRATEGY_LABELS = sorted(train_type[\"type_label\"].unique())\n", + "label2id = {lab: i for i, lab in enumerate(STRATEGY_LABELS)}\n", + "id2label = {i: lab for lab, i in label2id.items()}\n", + "\n", + "def enc(df): return [label2id[l] for l in df[\"type_label\"]]\n", + "\n", + "X_train_type = train_type[\"text\"].tolist(); y_train_type = enc(train_type)\n", + "X_val_type = val_type[\"text\"].tolist(); y_val_type = enc(val_type)\n", + "X_test_type = test_type[\"text\"].tolist(); y_test_type = enc(test_type)\n", + "\n", + "print(f\"Type — Train: {len(X_train_type):,} Val: {len(X_val_type):,} Test: {len(X_test_type):,}\")\n", + "print(f\"Strategies: {STRATEGY_LABELS}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "varh9ZaouKCC" + }, + "source": [ + "## Train DistilBERT — Binary Task" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "5416ddae7df649048c88a22930f94a58", + "2b8549e973164aedafc6d6217a6655ff", + "b47acf78a77542e5ad4ffd422f9bcd78", + "0ffac2eb9d7443108c2d3a219f3cfbc5", + "9e584990a33544fe83eb654b9bdaa0fb", + "f2e1e5f57ef744fca2e75e92d9e84e28", + "e09dfe343b5e4bb4bbe3cd4f17d39099", + "9aaf82df161c422e9c8eb5c11c243e3f", + "7c473ee6bceb47889a6dd79939ac6299", + "ad46143efc5c4196bcae145d61c40cae", + "671a44a0fdfc48429de88baf4d1a2832", + "cf5f3d09f00b4f68b05d88ca0c486772", + "bb15a7873e8940cc91e3d65b68c07c75", + "13c5851f77f540bc8cd69a3f943bade6", + "cb157407ecd34698a5530a9bae3e83ec", + "2bb4589100bb479caf5a581877dc2b9e", + "370654e1911347faa1a7cf6cd4642d21", + "bf6e2e0dcd714e29a8aeb73a0f9e1e2a", + "6d1854e1096442668d885e712e32cc35", + "2aca0c61a9e3401eb89e94953b5bc2fa", + "c76a52393c6a47b28f6a42959b5455ce", + "6ce17d7bad2247fdba86a2febe353f78", + "2709007f1ed1474ea07c9a1d10303521", + "67bdf38172964b05a083d2ed8c26a418", + "3b8257829bd64b9893eeca79a8fca156", + "6427b0b911ec482597723b04319ae932", + "ad5803f0adaf4250a9aba571fc6fb5a8", + "254189d9e58546bfb9cce67378a80329", + "14d6a059a816412b8cb0b93cc50e6e44", + "40c4f3cce8314e0a924b2aa5e8616879", + "10ca109e76eb461bb6a244bf3238214e", + "d5ca84cea4204375b6701f0fd02e53b7", + "ef025b743fce46d7badd259d7de2d820", + "092e7b213db3439a94b4d0da175a3466", + "6585042ab55a4dffb0dc35469a9b1c10", + "d60daa4b01e645b9ba28de74ed335e1e", + "887741cefca5446ab5503a3da9c8554c", + "bf8dec0375aa4505ab07975f4b63b8e7", + "61f433fb02e841c7b5b501675ad37789", + "56b34dd69f8d45e1ad22b436d84eda8e", + "31bcbe0778e849718131f63a8ead07af", + "a85fdbea6ec542ac87fd8ec94be3806a", + "6704cd1e32644c348374ed03bcce291e", + "9c76642288cb4e81b8202a64bd226f3b", + "6304489b73544972b0f15ce14ceff964", + "fb0e170c3f074247b310cbddd7140ec4", + "3c296be64eca442a855c530a8f121216", + "9c12ac9858be49e9821bf361b3bd598c", + "7790ba90787c43259e20f058b22b3c1b", + "469ced5bc873463d8a22058da84b53af", + "8d7aef90b97c47cca523dda57c75224e", + "34a5ff89b66d4ba995921ef3aa676ad6", + "21aae9f227574cc59a3c2ac39a236178", + "f6073f7807714d4686f5f47ac4e7b30e", + "d2aac7066bdd4956b6b30098f33de66d", + "c0924ca94d874e1687b2b790f5c12521", + "3f6a5e3d35a3413eb5aa1aeb16bef60d", + "7fcee6c02528407ab2e3e7aa415d453a", + "6f449125b0c348c880306acb7da7b897", + "60f6a928a8ff4e8195e04e76526fc7bf", + "047046ec44374854abe93cb177e3b557", + "a85798738d394543922014fe76f8fba3", + "c4f133096d9547a2b6c372b87f6ef869", + "2155c1d8d90149d38dabfcc40df7c2a7", + "09f0affd9a074eb6a7ff4ba946abb870", + "b08e549a6fa34b949620ec9a8287cf26", + "d290da32b6cc4a90a91c94521fcb3da2", + "d72e1fcad00e40e4882f168d65cbf17c", + "57270405bf534f1e9a43e787249ec20a", + "c9cc12ac084c4fb6b94c0a53c0577e1b", + "1b3a2e864f6046c98f37114e019d73c3", + "bb37c906f2d44571a51849c7f0d5791d", + "be4bee925bd94b419048459be45a6573", + "000cabf7c5704d48b487dbb7f2a89af3", + "877b4c83efcb4e9fbcd085247710c752", + "b3fbd98e650a45b2950ef8b7abc65d80", + "a0b5ce0bf8f843f9922653066df5e341", + "d70be7296aa84265b3d1e7b1a845c70b", + "65de5d3fb7774d0d89caf8189bcb0b2d", + "545fb7fde31c4cffb6ecd0bbd8fe770b", + "13da741123d74a7aa39c4c2df2ebd2d8", + "b9258b1f45604172bce694046d8c6bb0", + "2542af732fe84d9caae61f0fadf067d0", + "eeb9ac6deea34e548d33ab81c74732ef", + "f05ec2fb4f6545d4865454efb8593e5f", + "23079c39daab42b789c1f51ccad6cb98", + "94c945afea6c4512a5db1cc501d64f0a", + "1caf0d9104c54f3eaba55b7d0575b7d8", + "98ae33f02edf4f17b54b963719dfc15d", + "f01567296ec042008e8209e57ee5e79b", + "4742493dca094a92955d02fa6ccfbd63", + "e990ec5354b94e6691385566bd9528b7", + "ac0585fb882e4ba187fcc019719c5170", + "fd96f1bced354d31b5e2a61c90de3f5c", + "159c983eede049b5b4eacb212a81dbe3", + "b73efb5de2464edeababc255b9959028", + "498391d8aa634b5da07dd7b866eb6369", + "a97a2fdad4724e52864c87e8c8ec9a2f", + "c9b853c39572494a8aaafad36cf6cd85", + "e95efbfbc11349b2976a17f68327cc6a", + "fdce30b63b5446a39a882d54efc37745", + "1f5ffc50f7754d6e82aeb2d731e0a911", + "2638e8b72b55418fa5e06fd261fb9c79", + "7c1f7cbf640b48559ed780592dfa5761", + "1e55666bef13465b9e91ac7b19ac9bf7", + "a4b14f7fc2b946ab8174591d88861ba3", + "c6b4964524d4409487a59ce83f5099e6", + "7feeb705281b4bd8a1699d47ea124dbb", + "38fd37f9de06464ebe1ad1694c130ad3", + "6cfe4387866c4ab5b07227b393310b1a", + "48abcf56667445f48d9d77a4d5a66681", + "06db752036334117b76bbabd80948c10", + "cf2f53f7401946a0a740f352fd9f23ef", + "b9366f7af53040aa89b1e282d78122ab", + "377432a6363e4b32956f4a11b429ccd0", + "2ee087d4733e40939d080ac30d0fc306", + "0686f82825204be6979e163cd118efd0", + "de99f52856bc4aef960b61411251b275", + "fefe813f5fe84d3db42e8ae563190cc4", + "a08b55c5f3c842ebabc6ec7d66942438", + "2d0fb0cf2326402485a3eb134050563d", + "16befd712ced4f069b80a264091521db", + "03b3c085d35f485594705c2882dfe8e7", + "dee27e71295241db987ed620181e77b1", + "8b54bef1628540b69c6d9fecded8ced9", + "80e3a625f7a642c18ca2ee3a10c8bfcd", + "7398c4474eec4bcdabb432cba1e6c077", + "b3ab335cb010413587e6d23e1598fbcb", + "714fed7a5aab45d1858dc57b649b2376", + "e54726e5470d4366b16f095dd437dc3b", + "59944c26c3cd4066a6e1f1d09f855262", + "76820c02ea124b28acbb744a63504402", + "b68fb8ad46eb4310b236bc711dd053e2", + "8518c2f86234470faea2ba96858889a1", + "bd2a0f4d2d2042a8b92a2cea1552cefb", + "ec16f0538b9441d1a26b5ae8fbbc20d4", + "e0b9261785a146a59f78023c51403230", + "41d553963e724a07b1ac0a95dd9d7ee3", + "abc0b7855c1145f9871f58b782be80aa", + "d16a4a4ede1e4491a8b2bae73d797855", + "a22b526dbad1438ebbeb1182adaa4acf", + "31b61160748443468038752b2c337889", + "e608526f0703479191cdf521cb5669e8" + ] + }, + "id": "Yq8zSNjluKCC", + "outputId": "30c8eec7-4b42-48a8-bde8-af62a6343257" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Using device: cuda\n", + "Config saved to /content/outputs/bert/distilbert_binary/config.json\n", + "Loading tokenizer: distilbert-base-uncased\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/483 [00:00" + ], + "image/png": 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5csV06d27N3q9nn/++cdsm4MGDcLT07PC4rO1tTVNbqHX60lKSsLJyYnGjRtz4MAB023d3Ny4dOlSodNECiiKwqpVqwgLC0NRFLNjDQ0NJTU11Wx7ZSE4ONg0IgPA09OTxo0bc+7cOdOyVatW0bJlyyJ/nSz4NVSn05kK5BsMBpKTk8nPz6dNmzaF7oOSTlEp7eO9bt06rKysTL+yF8QxadKkO7w3KoZer+fPP//kwQcfpH79+qblPj4+DB8+nB07dpCWlgaU/Dq0t7fHxsaGrVu33lb9roLtF1UWoazc+LiA8bkyePBg1q1bR0ZGhmn5zz//jJ+fH126dAGMv8CnpKQwbNgws8dfp9PRvn1706mId3rsYBwhUaNGjWLXjxw50uy+eeSRR/Dx8WHdunUlbnvo0KFm2y54fd34mrpxpEJ2djZXrlyhQ4cOAEW+xouqOzZy5EhiY2PNTs388ccfsbe3Z9CgQYXaF7yehBCiKpB+rvRzS0P6uWVPp9Px6KOPsnv3brNyFAU1ZXv16gWU/nlzJ+677z7c3d1ZtmwZiqKwbNkyhg0bVmz7G/tdqampXLlyhe7du3Pu3DlSU1MBY/8zPT2d119/vVDN6ptHf9ra2jJmzBizZevWrcPb29ssDmtra5577jkyMjLYtm1bqY6tffv2bNq0yexSVP3gOyV9YFEVSXkEUSldvnyZa9eu0bBhw0LrGjduXOIb76xZsxg4cCCNGjWiWbNm9O3bl8cff5wWLVqUaZwNGjQo9EHYqFEjwFgfx9vbm9OnT3P48OFiO6g311SqV69eqfefkZFhlkDS6XRm+ylNfAaDgfnz5/P5559z/vx5s7qcN86c+tprr7F582batWtHgwYNuO+++xg+fDidO3cGjI9ZSkoKX3zxBV988cUtj7WgI13A1dW1xFNWilLUqfE1atQwS4KdPXu2yA/Am3377bd89NFHnDhxgry8PNPyGx+PCRMmsHz5cvr164efnx/33XcfQ4YMoW/fvqY2pX28L168iI+PT6Gi8o0bNy4xVr1ez+XLl0tsV5SbnyO36/Lly2RlZRUZZ5MmTTAYDERHR9O0adMSX4e2trbMmTOHl156CS8vLzp06MCAAQMYOXLkLetdubi4AJCenn7Hx3ErVlZW1K5du9DyoUOHMm/ePNasWcPw4cPJyMhg3bp1ptOoANMX6J49e94y9js99gKKohS77ub3TY1GQ4MGDUqslQuFX1MFndcbX1PJycnMnDmTZcuWFXr/KvjycKOi3tP69OmDj48PP/74I7169cJgMPDTTz8xcODAIpPxiqIUWQdQCCEqI+nnlo70c6Wfe7tK088dMWIEH3/8MUuXLmXq1KlcunSJ7du389xzz5lKXZX2eXMnrK2tGTx4MEuXLqVdu3ZER0eblS+42c6dO5k+fTq7d+8uNLdCamoqrq6unD17FoBmzZqVuH8/Pz9TEr/AxYsXadiwoSlRXaBJkyam9QX7u3btmmm9jY0N7u7upv9r1qxZbL3ZsiJ9YFHVSNJWVEvdunXj7Nmz/Pbbb/z555989dVXfPzxxyxcuJAnn3yyQmMxGAz06dOHV199tcj1BZ3LArfTqfvwww+ZOXOm6f+6deuW6kPpRu+++y5vvfUWTzzxBP/73/9wd3dHq9XywgsvmE0o0KRJE06ePMnvv//Ohg0bWLVqFZ9//jnTpk1j5syZpraPPfZYsfWqCr5M+Pj4mC1fsmQJo0ePLvYD6caO0o2KKtYPt/4wL8oPP/zA6NGjefDBB3nllVeoVasWOp2O2bNnmzpBALVq1SIiIoKNGzeyfv161q9fz5IlSxg5cqSpUP/tPt53Ijo6+ra+9NzoTp4jd6o0r8MXXniBsLAwfv31VzZu3Mhbb73F7Nmz+euvv4qtV9WgQQOsrKw4cuRIqeK43efVjaMrbtShQwcCAgJYvnw5w4cPZ+3atVy7do2hQ4ea2hS8Dr7//vsik69WVv99LN/JsYPxy0J5zSxcmtfUkCFD2LVrF6+88gqtWrXCyckJg8FA3759i5wFvKj3NJ1Ox/Dhw/nyyy/5/PPP2blzJ7GxscXOOHz16tUikxtCCFEdST+39KSfK/3cm4WEhBAUFMRPP/3E1KlT+emnn1AUhREjRpjalPZ5c6eGDx/OwoULmTFjBi1btiQ4OLjIdmfPnqVXr14EBQUxd+5c/P39sbGxYd26dXz88cd3FMud/IBQ4PnnnzebnKx79+6FJvotT9IHFlWRJG1FpeTp6Ym9vX2Rp32dPHmyVNtwd3dnzJgxjBkzhoyMDLp168aMGTNMndmy+MXqzJkzhX79OnXqFIBpIqHAwEAyMjLu6lfH4mIdOXKk6bRsKPzBUJr4Vq5cyb333svXX39tdtuUlBRq1qxptszR0ZGhQ4cydOhQcnNzefjhh3nnnXeYMmUKnp6eODs7o9frSzzWm0+7KphMoeAXzZSUFNzc3EzrC37dvROBgYEcPXr0lm1WrlxJ/fr1Wb16tdl9NX369EJtbWxsCAsLIywsDIPBwIQJE1i0aBFvvfUWDRo0KPXjXbduXbZs2UJGRobZKITSPL+9vb3veBbRu+mogfG16eDgUGScJ06cQKvV4u/vb1pW0usQjI/RSy+9xEsvvcTp06dp1aoVH330ET/88EORMTg4ONCzZ0/++usvoqOjzfZXlBufVze6k+fVkCFDmD9/Pmlpafz8888EBASYTosqOBYwfvEpzWv+do8dICgoiB9//NE0uuJmN79vKorCmTNnymQE1tWrV9myZQszZ85k2rRpxe6zNEaOHMlHH33E2rVrWb9+PZ6enkWeFpyfn090dDQPPPDAXcUuhBCWQvq55qSfK/3cG1VEP3fEiBG89dZbHD58mKVLl9KwYUPatm1rWn87z5s70aVLF+rUqcPWrVtNk74VZe3ateTk5LBmzRqzkaA3nloP//U/jx49SoMGDW47nrp163L48GEMBoPZwIUTJ06Y1gO8+uqrZsnFW5UqKA/SBxZVkdS0FZWSTqcjNDSUX3/9laioKNPy48ePs3HjxhJvn5SUZPa/k5MTDRo0ICcnx7TM0dERKJzIuR2xsbH88ssvpv/T0tL47rvvaNWqlWmU3ZAhQ9i9e3eRcaekpJCfn1/ifoqLtX79+vTu3dt0KTiF63bi0+l0hX6xX7FiBTExMWbLbr5PbWxsCA4ORlEU8vLy0Ol0DBo0iFWrVhXZebzxNKcbY+7du7dpREJBh+PG+meZmZlmv+jerkGDBnHo0CGz+6FAwXEX/LJ64/3w77//snv3brP2N98HWq3W1AkoeG6V9vHu378/+fn5LFiwwLRer9fzySeflHhMdnZ2he7D0l5ufo7cLp1Ox3333cdvv/1mNpIhISGBpUuX0qVLF1MJgJJeh1lZWWRnZ5u1CQwMxNnZ2ey1WpTp06ejKAqPP/642amTBcLDw03Pm7p166LT6QrV1fv8889Ld9A3GDp0KDk5OXz77bds2LCBIUOGmK0PDQ3FxcWFd9991+z0wwIFr4O7OfaOHTuiKArh4eFFrv/uu+/MSkesXLmSuLg4+vXrV6pjvJWiXitAodnBS6NFixa0aNGCr776ilWrVvHoo4+ajUQuEBkZSXZ2dqlmEhdCiMpA+rnmpJ8r/dwbVUQ/t2BU7bRp04iIiDAbZQulf97cKY1Gw//93/8xffp0Hn/88WLbFfXYpaamsmTJErN29913H87OzsyePbtQ/7I0I7P79+9PfHw8P//8s2lZfn4+n3zyCU5OTnTv3h0w1lm+8f4OCQkp+WDLkPSBRVUkI21FpTVz5kw2bNhA165dmTBhgumDo2nTphw+fPiWtw0ODqZHjx6EhITg7u7O/v37WblyJRMnTjS1KfiQee655wgNDTUVpr8djRo1YuzYsezbtw8vLy8WL15MQkKC2QfpK6+8wpo1axgwYACjR48mJCSEzMxMjhw5wsqVK7lw4UKJv9gGBgbi5ubGwoULcXZ2xtHRkfbt25d46lBp4hswYACzZs1izJgxdOrUiSNHjvDjjz+aTTIFxs6At7c3nTt3xsvLi+PHj/Ppp59y//33m+rvvPfee/z999+0b9+ep556iuDgYJKTkzlw4ACbN28mOTn5lvHed9991KlTh7Fjx/LKK6+g0+lYvHgxnp6eZl9qbscrr7zCypUrGTx4ME888QQhISEkJyezZs0aFi5cSMuWLRkwYACrV6/moYce4v777+f8+fMsXLiQ4OBgs4Tgk08+SXJyMj179qR27dpcvHiRTz75hFatWplqPpX28Q4LC6Nz5868/vrrXLhwgeDgYFavXl1kPSQ1LF68mA0bNhRa/vzzz/P222+zadMmunTpwoQJE7CysmLRokXk5OTw/vvvm9qW9Do8deoUvXr1YsiQIQQHB2NlZcUvv/xCQkJCia/FTp068dlnnzFhwgSCgoJ4/PHHadiwIenp6WzdupU1a9bw9ttvA8ZacoMHD+aTTz5Bo9EQGBjI77//XqgWVWm0bt2aBg0a8MYbb5CTk2NWGgGMNWsXLFjA448/TuvWrXn00UdNz98//viDzp078+mnn97VsXfp0gUPDw82b95cZO1cd3d3unTpwpgxY0hISGDevHk0aNCAp5566raP92YuLi5069aN999/n7y8PPz8/Pjzzz85f/78HW1v5MiRvPzyywDFnha2adMmHBwc6NOnzx3HLYQQlkb6uf+Rfq70cytavXr16NSpE7/99htAoaRtaZ83d2PgwIEMHDjwlm3uu+8+0+jnZ555hoyMDL788ktq1apFXFycqZ2Liwsff/wxTz75JG3btmX48OHUqFGDQ4cOkZWVVeIPA08//TSLFi1i9OjRhIeHExAQwMqVK9m5cyfz5s0r08l/P/30U1JSUoiNjQWMo4kvXboEwKRJk4ocQVtA+sCiSlKEqMS2bdumhISEKDY2Nkr9+vWVhQsXKtOnT1dufmrXrVtXGTVqlOn/t99+W2nXrp3i5uam2NvbK0FBQco777yj5Obmmtrk5+crkyZNUjw9PRWNRmO2TUCZPn266f8lS5YogHL+/Hmzfd5///3Kxo0blRYtWii2trZKUFCQsmLFikLHkZ6erkyZMkVp0KCBYmNjo9SsWVPp1KmT8uGHH5piOn/+vAIoH3zwQZH3xW+//aYEBwcrVlZWCqAsWbLklvddaePLzs5WXnrpJcXHx0ext7dXOnfurOzevVvp3r270r17d1O7RYsWKd26dVM8PDwUW1tbJTAwUHnllVeU1NRUs+0lJCQozz77rOLv769YW1sr3t7eSq9evZQvvvjilvEWCA8PV9q3b6/Y2NgoderUUebOnXvL+/9mN8etKIqSlJSkTJw4UfHz81NsbGyU2rVrK6NGjVKuXLmiKIqiGAwG5d1331Xq1q2r2NraKvfcc4/y+++/K6NGjVLq1q1r2s7KlSuV++67T6lVq5YpvmeeeUaJi4sz219pHu+CuB5//HHFxcVFcXV1VR5//HHl4MGDpXp8y0vBfV3cJTo6WlEURTlw4IASGhqqODk5KQ4ODsq9996r7Nq1y2xbJb0Or1y5ojz77LNKUFCQ4ujoqLi6uirt27dXli9fXup4w8PDleHDhyu+vr6KtbW1UqNGDaVXr17Kt99+q+j1elO7y5cvK4MGDVIcHByUGjVqKM8884xy9OjRQvf1qFGjFEdHx1vu84033lAApUGDBsW2+fvvv5XQ0FDF1dVVsbOzUwIDA5XRo0cr+/fvL5Njf+655wrt/++//1YA5aefflKmTJmi1KpVS7G3t1fuv/9+5eLFi2Ztb35u3+r95+b3w0uXLikPPfSQ4ubmpri6uiqDBw9WYmNjC7UreK++fPlysccRFxen6HQ6pVGjRsW2ad++vfLYY48Vu14IISor6ef+R/q50s+taJ999pkCKO3atSu0rrTPm4LndUnHU9BHK+r1c6Pu3bsrTZs2NVu2Zs0apUWLFoqdnZ0SEBCgzJkzR1m8eHGh50xB206dOin29vaKi4uL0q5dO+Wnn3665fYLJCQkKGPGjFFq1qyp2NjYKM2bN7+tx6m452xR7Yr7nnHz8RRF+sCiqtEoym1WKhdCCCGERTt37hxBQUGsX7+eXr16qR3OHbty5Qo+Pj5MmzaNt956q9D6iIgIWrduzYEDB2jVqlXFByiEEEIIISyG9IFFVSNJWyGEEKIKGj9+PGfOnLnjyToswYcffsirr77KuXPnTJPG3OjRRx/FYDCwfPnyig9OCCGEEEJYHOkDi6pEkrZCCCGEsCh//fUXkZGRvPXWW9x7772sXr1a7ZCEEEIIIYQoV9IHFjeTpK0QQgghLEqPHj3YtWsXnTt35ocffsDPz0/tkIQQQgghhChX0gcWN5OkrRBCCCGEEEIIIYQQQlgQrdoBCCGEEEIIIYQQQgghhPiPJG2FEEIIIYQQQgghhBDCglipHUBFMxgMxMbG4uzsjEajUTscIYQQQghxnaIopKen4+vri1YrYwvulvR7hRBCCCEsT2n7vNUuaRsbG4u/v7/aYQghhBBCiGJER0dTu3ZttcOo9KTfK4QQQghhuUrq81a7pK2zszNgvGNcXFxUjkYIIYSoHPR6Pbt27QKgU6dO6HQ6lSMSVVFaWhr+/v6m/pq4O9LvFUIIIW6P9HlFRShtn7faJW0LTg1zcXGRzqsQQghRSnq9HkdHR8D4GSodWFGe5FT+siH9XiGEEOL2SJ9XVKSS+rxSLEwIIYQQQgghhBBCCCEsiCRthRBCCCGEEEIIIYQQwoJI0lYIIYQQQgghhBBCCCEsSLWraVtaer2evLw8tcMQd8Da2lrqzgghhBBClILBYCA3N1ftMISFk/61EEIIUfEkaXsTRVGIj48nJSVF7VDEXXBzc8Pb21smMhFCiDKi0WgICAgwXRdCVH65ubmcP38eg8GgdiiiEpD+tRCiOpA+r7AkkrS9SUHCtlatWjg4OMiLtJJRFIWsrCwSExMB8PHxUTkiIYSoGrRarakDK4So/BRFIS4uDp1Oh7+/P1qtVE0TRZP+tRCiOpE+r7AkkrS9gV6vNyVsPTw81A5H3CF7e3sAEhMTqVWrlpzKJYQQQghxk/z8fLKysvD19cXBwUHtcISFk/61EEIIUfHkJ/UbFNSwlY5r5VfwGEpdYiGEKBuKopCZmUlmZiaKoqgdjhDiLun1egBsbGxUjkRUFtK/FkJUB9LnFZZEkrZFkJIIlZ88hkIIUbYMBgP79u1j3759Uv9SiCpE+kyitOS5IoSoDqTPKyyJJG2FEEIIIYQQQgghhBDCgkjSVhQrICCAefPmqb4NIYQQQgghykqPHj144YUX1A5DCCGEEOKWJGlbTvQGhd1nk/gtIobdZ5PQG8qvFopGo7nlZcaMGXe03X379vH000+XbbBCCCGEsGwp0RAbUfwlJVrF4ISlqcg+b1hYGH379i1y3fbt29FoNBw+fLjc9l+ZzJgxo8jvBZs3bwbg2LFjDBo0iICAADQajQyyEEKI6/QGhejkLE7Ep7GnnD/XhCiJldoBVEUbjsYxc20kcanZpmU+rnZMDwumbzOfMt9fXFyc6frPP//MtGnTOHnypGmZk5OT6bqiKOj1eqysSn7oPT09yzZQIYQQQli2lGj4NATyc4pvY2ULE8PBzb/i4hIWqaL7vGPHjmXQoEFcunSJ2rVrm61bsmQJbdq0oUWLFmW+X7Xk5ube1URxTZs2NSVpC7i7uwOQlZVF/fr1GTx4MC+++OJdxSmEEFXFhqNxzFpzlFpZlwB4P+JfvFwdyu1zTYiSyEjbMrbhaBzjfzhg1nkFiE/NZvwPB9hwNK6YW945b29v08XV1RWNRmP6/8SJEzg7O7N+/XpCQkKwtbVlx44dnD17loEDB+Ll5YWTkxNt27Yt1Km7ubSBRqPhq6++4qGHHsLBwYGGDRuyZs2a24o1KiqKgQMH4uTkhIuLC0OGDCEhIcG0/tChQ9x77704Ozvj4uJCSEgI+/fvB+DixYuEhYVRo0YNHB0dadq0KevWrbvzO04IIYQQ5rKSbp2wBeP6rKSKiUdYLDX6vAMGDMDT05NvvvnGbHlGRgYrVqxg7NixJCUlMWzYMPz8/HBwcKB58+b89NNPt7WfGTNm0KpVKxYvXkydOnVwcnJiwoQJ6PV63n//fby9valVqxbvvPOO2e3mzp1L8+bNcXR0xN/fnwkTJpCRkWHWZufOnfTo0QMHBwdq1KhBaGgoV69eBYxlGyZOnMgLL7xAzZo1CQ0NBWDbtm20a9cOW1tbfHx8eP3118nPzy/xOKysrMy+J3h7e5uSwG3btuWDDz7g0UcfxdbW9rbuHyGEqIoKPtfi0yruc02IkkjStgSKopCVm1+qS3p2HtPXHKOowfMFy2asiSQ9O69U21OUshuG//rrr/Pee+9x/PhxWrRoQUZGBv3792fLli0cPHiQvn37EhYWRlRU1C23M3PmTIYMGcLhw4fp378/I0aMIDk5uVQxGAwGBg4cSHJyMtu2bWPTpk2cO3eOoUOHmtqMGDGC2rVrs2/fPsLDw3n99dextrYG4NlnnyUnJ4d//vmHI0eOMGfOHLNRxEIIIYQQ4s5Uhj6vlZUVI0eO5JtvvjG7zYoVK9Dr9QwbNozs7GxCQkL4448/OHr0KE8//TSPP/44e/fuva374+zZs6xfv54NGzbw008/8fXXX3P//fdz6dIltm3bxpw5c3jzzTf5999/TbfRarX83//9H8eOHePbb7/lr7/+4tVXXzWtj4iIoFevXgQHB7N792527NhBWFgYer3e1Obbb7/FxsaGnTt3snDhQmJiYujfvz9t27bl0KFDLFiwgK+//pq33377to5HCCFE8fQGhZlrI2/5uTZzbaSUShAVTsojlOBanp7gaRvLZFsKEJ+WTfMZf5aqfeSsUBxsyuYhmjVrFn369DH97+7uTsuWLU3//+9//+OXX35hzZo1TJw4sdjtjB49mmHDhgHw7rvv8n//93/s3bu32PpiN9qyZQtHjhzh/Pnz+PsbT6n87rvvaNq0Kfv27aNt27ZERUXxyiuvEBQUBEDDhg1Nt4+KimLQoEE0b94cgPr169/GPSCEEOJuaDQa03u3RqNRORpRpq5dhYRISDgG57epHY1QSWXp8z7xxBN88MEHbNu2jR49egDG0giDBg3C1dUVV1dXXn75ZVP7SZMmsXHjRpYvX067du1KfQwGg4HFixfj7OxMcHAw9957LydPnmTdunVotVoaN27MnDlz+Pvvv2nfvj2A2eRmAQEBvP3224wbN47PP/8cgPfff582bdqY/gdjCYMbNWzYkPfff9/0/xtvvIG/vz+ffvopGo2GoKAgYmNjee2115g2bRpabfFjcI4cOWI2wCE4OPi2k9dCCFHVZebks/TfKNOZIwoaEg1OpuvGvxCXms3e88l0DPRQK1RRDUnStppo06aN2f8ZGRnMmDGDP/74g7i4OPLz87l27VqJI21vrBPm6OiIi4sLiYmJpYrh+PHj+Pv7m770g7Hz6ObmxvHjx2nbti2TJ0/mySef5Pvvv6d3794MHjyYwMBAAJ577jnGjx/Pn3/+Se/evRk0aFCVqlsmhBCWTKvVmt6PRSWlz4fks5Bw1JigLbikysRiovIICgqiU6dOLF68mB49enDmzBm2b9/OrFmzANDr9bz77rssX76cmJgYcnNzycnJwcHB4bb2ExAQgLOzs+l/Ly8vdDqdWZLUy8vLrB+8efNmZs+ezYkTJ0hLSyM/P5/s7GyysrJwcHAgIiKCwYMH33K/ISEhZv8fP36cjh07mv1Y1rlzZzIyMrh0yVhzMTg42LRu6tSpTJ06FYDGjRublTKTMghCCAGJadnsu3CV/ReT2X/hKpFxaWYjaBU0xBpci7ztZ3+fQauBtgHuaLUyiEGUP0nalsDeWkfkrNBStd17PpnRS/aV2O6bMW1pV8+9VPsuK46Ojmb/v/zyy2zatIkPP/yQBg0aYG9vzyOPPEJubu4tt1NQqqCARqPBYDCUWZwzZsxg+PDh/PHHH6xfv57p06ezbNkyHnroIZ588klCQ0P5448/+PPPP5k9ezYfffQRkyZNKrP9CyGEEFVCVrIxORtfkKA9CpdPQH520e1d/cGrGTjWhIPfV2yswiJUpj7v2LFjmTRpEp999hlLliwhMDCQ7t27A/DBBx8wf/585s2bZ6ov+8ILL5TYx71ZUX3eW/WDL1y4wIABAxg/fjzvvPMO7u7u7Nixg7Fjx5Kbm4uDgwP29vYl7vfmPntJfH19iYiIMP1fMNEYgI2NDQ0aNLit7QkhRFViMCicvZxhTNJeSGb/xatEJWcValfT0YYrmSV/Tuw4c4UdZ67g5WLL/c19GdDSh3v83eQsNFFuJGlbAo1GU+rTtbo29MTH1Y741Owia6FoAG9XO7o29ESn8q8yO3fuZPTo0Tz00EOAceTthQsXynWfTZo0ITo6mujoaNNo28jISFJSUsxGCDRq1IhGjRrx4osvMmzYMJYsWWKK09/fn3HjxjFu3DimTJnCl19+KUlbIYSoAIqikJNjnKDK1tZWOqeWQp8HV07/l5gtGEWbXsxkGdYOUCsYvJsZk7ReTY3/27sZ18dGSNK2mqpMfd4hQ4bw/PPPs3TpUr777jvGjx9vek/auXMnAwcO5LHHHgOMZQ5OnTpl1tcsD+Hh4RgMBj766CPTaNzly5ebtWnRogVbtmxh5syZpd5ukyZNWLVqFYqimB2js7MztWvXRqvVSmJWCCGuy87TczQm1ZSkDY+6SkpWnlkbjQaCvF1oG1CDNgHutKlbAy8XO7rM+ev655qCDcZa47noAA0aoIajDfc29uTPyAQS0nJYvPM8i3eex8/NngEtfAhr6UtTXxfpI4syJUnbMqTTapgeFsz4Hw6gAbNObMHLdnpYsOoJWzDWy1q9ejVhYWFoNBreeuutMh0xW5TevXvTvHlzRowYwbx588jPz2fChAl0796dNm3acO3aNV555RUeeeQR6tWrx6VLl9i3bx+DBg0CjHXC+vXrR6NGjbh69Sp///03TZo0KdeYhRBCGBkMBvbs2QNA165d0enK7mwQUUoZiealDeKPwpWToC9mZEiNgP8SswV/a9SDW9TAFKI01O7zOjk5MXToUKZMmUJaWhqjR482rWvYsCErV65k165d1KhRg7lz55KQkFDuSdsGDRqQl5fHJ598QlhYmGkisRtNmTKF5s2bM2HCBMaNG4eNjQ1///03gwcPpmbNmkVud8KECcybN49JkyYxceJETp48yfTp05k8efIt69mWJDc3l8jISNP1mJgYIiIicHJykiSwEKLSuJqZS/jFq+y/aEzSHr6USq7ePK9hZ63lHv8atLmepL2njhsudtaFtlXwuaZDIdgqAYDD+T6murbvPtSMvs18yMnXs/3UFdYejmVzZAIxKddY9M85Fv1zjgAPBwa08CWspS+NvZ0L7UOI2yVJ2zLWt5kPCx5rzcy1kaZC1mAcbTA9LJi+zXxUjO4/c+fO5YknnqBTp07UrFmT1157jbS0tHLdp0aj4bfffmPSpEl069YNrVZL3759+eSTTwDQ6XQkJSUxcuRIEhISqFmzJg8//LBpNIJer+fZZ5/l0qVLuLi40LdvXz7++ONyjVkIIYSocPk5cPnkDaNnrydpM4upIW/jfD0x2/SGBG0w2N7BlwUHD7CyNcZQHCtbYztRrand5x07dixff/01/fv3x9fX17T8zTff5Ny5c4SGhuLg4MDTTz/Ngw8+SGpqarnG07JlS+bOncucOXOYMmUK3bp1Y/bs2YwcOdLUplGjRvz5559MnTqVdu3aYW9vT/v27U2T/BbFz8+PdevW8corr9CyZUvc3d0ZO3Ysb7755l3FGxsbyz333GP6/8MPP+TDDz+ke/fubN269a62LYQQ5UFRFKKTr7HvQjL7Lyaz78JVziRmFGpX08mGNnXdTUnapr4uWOtK/pGr4HNt1pqjcEMFhZs/12ytdPQO9qJ3sBfZeXr+PpHI74fj2HIigQtJWXz69xk+/fsMDWs5MaCFsYRCoKdTMXsV4tY0iqIUdVZTlZWWloarqyupqam4uLiYrcvOzub8+fPUq1cPOzu7u9qP3qCw93wyienZ1HK2o109d4sYYVtdlOVjKYQQwvjD2fbt2wEZaVtmFAXS468nZY/8l5y9cgoM+UXcQAPu9c1LG3g1Bbe6xnP9ykpKNGQlFb/ewQPc/Itffxdu1U8Tt68i+r3S560+pH9dvclrXVS0fL2ByLg09l+fNGzfhatcTi/8o3Kgp6MpSds2wJ26Hg53VaIgNy+fn9f+SWZuPs1bd6BDg9KV+snMyWfz8QR+PxzHtpOXzUb8Bvu4MKClD2EtfPF3v72JMUXVVNo+r4y0LSc6rYaOgTIKRQghhKiUyjpxmXfNOBFYQVmDghG015KLbm/nelNpg2ZQKwhsbm+Sojvi5l9uSVlR9UifV4iqb8PRuEKj6n0s7ExSUfll5ORzMOoq+y5cJfxiMgejUsjK1Zu1sdZpaO7nStsAd0Lq1iCkbg08nGzLNA6dVmNKrHYI9Cj1jxOOtlYMbOXHwFZ+pGXn8eexBH4/HMuO01eIjEsjMi6N9zecpKW/G2EtfLi/hQ8+riVPUCmqN0naCiGEEELcKCUaPg0puUTAxPDCyU1FgdRLhScGSzoDShG14zVa8GhoXtrAuxm4+JXt6FkhhBDiDmw4Gsf4Hw4UmnQwPjWb8T8cYMFjrSVxK+5IfGo2+y8ms//CVfZdSOZ4XBqGm55oLnZWhNQ1ljloG+BOi9qu2Flb/tleLnbWPBJSm0dCanM1M5eNx+JZeziW3WeTOBSdwqHoFN7+4zht6tYgrKUv/Zp7U8tZzmAQhUnSVgghhBDiRllJt07YgnF92iXISDCvO5twFLKLqZ1p7164tIFnEFjLKAshhBCWR29QmLk2slDCFowTEGqAmWsj6RPsLaUSxC0ZDAqnEzOM9WgvJLP/4lUuXb1WqF3tGvamUbRtA9xpWMsJbSV/btVwtOHRdnV4tF0dLqfnsOFoHGsPx12vzWucRG3m2mO0r+dBWEtf+jbzxt3RRu2whYWQpK0QQgghxJ1Y3Lfo5VorqNnopvIGTcHZW0bPCiGEqDT2nk82K4lwMwWIS81m7/lkKZNSBd1NHePsPD2HolOMSckLyYRfvEpatnm9fq0Gmvi40Dbg+qRhdd3xdq3ao009nW15vGMAj3cMID41mz+OxPH74VgORqWw+1wSu88l8dZvR+ncoCYDWvgQ2tQbV3trtcMWKpKkrRBCCCFKpNFoTDO0383kDlWOY63/Rs16Nzf+rdnIWD5BCCGEqIRiU66x+2wSP++PLlX7xPTiE7uicrrdOsbJmbmm5Oy+C8kciUklT28+RtvBRsc9ddxMk4bdU6cGTraWl5KqqD6vt6sdY7vUY2yXekQnZ5kSuEdj0vjn1GX+OXWZN385SrdGNRnQwpfewV4WeX+J8iWPuBBCCCFKpNVqadSokdphlK/0BDi9EQ79XLr2j/8KgfeWa0hClDdFKerEZyEKMxiKqMstqoTEtGzjKL+zxpF+F5Oybuv2UouzaimpjvHnI1oT5ONiLHNw4Sr7LyZz9nJmoe14OtvS9voI2rYB7jTxccZKp62Yg7gLavR5/d0dGNc9kHHdAzl/JZPfD8Xy++E4Tiaks/l4IpuPJ2JrpaVnUC0GtPClZ1At7G0sv7avuHuStBVCCCFE9aQoEH8ETm2Ak+sh9sDt3d6+RvnEJUQFsLa2RqPRcPnyZTw9PWUEvSiWoijk5uZy+fJltFotNjZSa7Gyu5yew57rp2LvOZfEuZsSbloNNPdzpV19d1buv0RKVl6RdW01GEcLtqvnXiFxi/JXUh1jgGeXHig0YRhAw1pOtAlwp831erT+7vby2XIH6tV0ZFKvhkzq1ZBTCemmBO65K5msPxrP+qPxONjo6NXEi7AWPnRv7ImtlSRwqypJ2gohhBCiRIqikJeXB/yX7KmU8rLhwnZjkvbURuNkYjfyvQd87oHwxerEJ0QF0el01K5dm0uXLnHhwgW1wxGVgIODA3Xq1EGrtfyRcsJccmYu/15P0u4+m8TpxAyz9RoNBPu40LG+Bx0DPWhbzx0XO2MdzZA6NRj/wwE0UCiRpwDTw4JlErIqpKQ6xgAGBay0Glr5u9EmwJ22ATVoXacGNarI5FmW1Odt5OXM5Psa82KfRkTGpbH2kLGEwqWr11h7KJa1h2JxtrWiT1Mvwlr60qVBTawrwWhmUXqStBVCCCFEiQwGA7t27QKga9eu6HSV6Bf9grIHJzfAub8h74bTPq3soX4PaNwXGoaCiw/ERkjSVlQLTk5ONGzY0PTlVIji6HQ6rKysKu8PdtVMalYee84bR9HuPpvEifj0Qm2CvJ3pGOhBx/oetK/ngatD0ZMd9W3mw4LHWheqbwrQsb5HkfVNReVV2vrE7w1qziMh/uUcjTossc+r0Who6utKU19XXuvbmEOXUk0jcOPTsll9IIbVB2Jwc7Cmb1NvBrTwpUN990pRjkLcmiRthUmPHj1o1aoV8+bNK3L9jBkz+PXXX4mIiKjQuIQQQojboiiQcNSYpD21HmLCzdc7+0KjUGjcD+p1A2t78/UOHsaJxPJzit+Hla2xnRCVnE6ns4gvpEKIO5eWnce+88mmmrSRcWncXK66kZcTHet70KG+B+3re+B+G6Mi+zbzoU+wN3vPJ5OYnk3atTze+u0Ye84nERmbRrCvSxkfkVBLaesT+7k5lHMkojgajXGUcyt/N6b2b0J41FV+PxTLH0fiuZKRw7J90SzbF01NJxv6NfNhQAsf2ga4o5UR8ZWSJG3LWko0ZCUVv97BA9zK9hepsLAw8vLy2LBhQ6F127dvp1u3bhw6dIgWLVqU6X6FEEIIi1FQ9uDUBmOy9uayBz6tjEnaRn3Bp6XxXNDiuPnDxPAK/zwXQgghSiMjJ599F5LZcy6JPWeTOBKTWqjGaH1PR1O5gw71PajpZHtX+9RpNXQM/O/Hyn/PJ/P74Thmrz/O92Pb39W2heVoV88dH1e7YkskSB1jy6LVamgbYJzobVpYU/49l8Taw3FsOBrHlYxcvt9zke/3XMTLxZb7m/syoKUP9/i7FXvWhN6gmH6cqeVsfJyl/Im6JGlbllKi4dOQkkfmTAwv0y96Y8eOZdCgQVy6dInatWubrVuyZAlt2rSRhK0Qomyo8MOUEMXKSDTWpT21Ac7+DXk3TKRSVNmD2+HmL89lIYQQFuFarp79F/8bSXv4Uir6m7K0AR4OdLghSevlUroRk3fq1dAgNh6LZ/vpK/xz6jLdGnmW6/5ExdBpNTzWoQ4fbDxVaF1B6k7qGFsmnVZDpwY16dSgJrMGNmXX2STWHopl47F4EtJyWLzzPIt3nsfPzZ4BLXwIa+lLU18XUwJ3w9G4QmVQfFztmB4WLGVQVCRJ27KUlXTrhC0Y12cllekXwQEDBuDp6ck333zDm2++aVqekZHBihUr+OCDD0hKSmLixIn8888/XL16lcDAQKZOncqwYcPueL8Gg4G3336bL774gsuXL9OkSRPee+89+vbtC0Bubi6TJ09m1apVXL16FS8vL8aNG8eUKVNQFIWZM2eyePFiEhIS8PDw4JFHHuH//u//7vr+EEKUE5V+mBLCpKDsQcFo2phwzKZFcfYxlj1odL3sgY2cuieEEKLyyc7Tc+DiVWNN2nNJRESnkKc3T9LWrmFvNpLW182+mK2VjzoeDozsGMDXO87z7rrjdG5QUxJ5VYDBoPBnZCIA9tY6ruXpTeu8JYFXaVjrtHRv5En3Rp6881Aztp+6wtrDsWyOTCAm5RqL/jnHon/OEeDhwIAWvtRwtOHt3yMLTTYYn5rN+B8OsOCx1vK4q0SStiVRFPMJS24l/1rp2+VmltzO2uHWp29eZ2VlxciRI/nmm2944403TL+UrFixAr1ez7Bhw8jIyCAkJITXXnsNFxcX/vjjDx5//HECAwNp165d6eK+yfz58/noo49YtGgR99xzD4sXL+aBBx7g2LFjNGzYkP/7v/9jzZo1LF++nDp16hAdHU10dDQAq1at4uOPP2bZsmU0bdqU+Ph4Dh06dEdxCCEqiEo/TIlqLi8bLuww1qY9tRFSo83X307ZAyGEEMIC5eTriYhKYff1icMORqeQm28wa+PrakeH6wnajvU98HdX/4fJST0bsGJ/NCfi01l94BKD20j/r7L77VAMh6JTcLTRsfml7ly4kiWnyldytlY6egd70TvYi+w8PX+fSOT3w3FsOZHAhaQsPv37TLG3VTCOsJ65NpI+wd7y+KtAkrYlycuCd33LdpuL+5au3dRYsHEsVdMnnniCDz74gG3bttGjRw/AWBph0KBBuLq64urqyssvv2xqP2nSJDZu3Mjy5cvvOGn74Ycf8tprr/Hoo48CMGfOHP7++2/mzZvHZ599RlRUFA0bNqRLly5oNBrq1q1rum1UVBTe3t707t0ba2tr6tSpc8dxCCGEqGIyLsPpjXByfRFlD+yMZQ8a9TWOqnUp489oIYrw2Wef8cEHHxAfH0/Lli355JNPiu235OXlMXv2bL799ltiYmJo3Lgxc+bMMZ2JdLP33nuPKVOm8Pzzz5tNBpudnc1LL73EsmXLyMnJITQ0lM8//xwvL6/yOEQhRAXKzTdwJCbFVO4g/OJVsvPMk7S1nG3pGOhhGk1bx92h2DqUanFzsGFizwa8u+4EH/55kgEtfLG3kYkNK6us3HzmrD8JwLM9G+Djao+Pa8WO4Bbly85aR7/mPvRr7kNmTj6bjyfw3e6LhF+8WuxtFCAuNZsJP4bT3M8VDydbPBxt8HCywcPRFg8nG5xsrSzu/amqkKRtFREUFESnTp1YvHgxPXr04MyZM2zfvp1Zs2YBoNfreffdd1m+fDkxMTHk5uaSk5ODg8Od/UKblpZGbGwsnTt3NlveuXNn04jZ0aNH06dPHxo3bkzfvn0ZMGAA9913HwCDBw9m3rx51K9fn759+9K/f3/CwsKwspKnpBBCWCKNRoO3t7fpeplSFEg4ZhxNK2UPhIX5+eefmTx5MgsXLqR9+/bMmzeP0NBQTp48Sa1atQq1f/PNN/nhhx/48ssvCQoKYuPGjTz00EPs2rWLe+65x6ztvn37WLRoUZFzD7z44ov88ccfrFixAldXVyZOnMjDDz/Mzp07y+1YhRAlu5OJevL1Bo7EpJpG0u6/cNXstHOAmk42ZjVp69d0rBRJkJEdA/h210ViUq7x9Y5zTOzZUO2QxB1auO0c8WnZ+Lvb80TnemqHo5py7fNaEEdbKwa28gO4ZdK2wMZjCWw8llDkOhsrLTUdbfBwssX9ekK3pim5a3s9wWtjSvjaWVvWjzuWPAGbZMhKYu1gHPFaGvGHSzeK9okN4F2KicGsb+9L6dixY5k0aRKfffYZS5YsITAwkO7duwPwwQcfMH/+fObNm0fz5s1xdHTkhRdeIDc397b2cTtat27N+fPnWb9+PZs3b2bIkCH07t2blStX4u/vz8mTJ9m8eTObNm1iwoQJppHC1tbW5RaTEEKIO6PVagkKCiq7DebnwIXtxiTtqQ1FlD1oaUzSNu5rLIFQhTvNwrLNnTuXp556ijFjxgCwcOFC/vjjDxYvXszrr79eqP3333/PG2+8Qf/+/QEYP348mzdv5qOPPuKHH34wtcvIyGDEiBF8+eWXvP3222bbSE1N5euvv2bp0qX07NkTMJ5B1aRJE/bs2UOHDh3K63CFELdQ2ol69AaFyNg0dp+7wu6zSey7cJWMnHyzbdVwsDYlaTvW96BBLadKmSCys9bxat/GPL8sgoXbzvFouzrUdLJVOyxxm2JSrrFo21kApvZrYnFJtYpU5n1eC1fLuXSTFg5s6YudtY6kzBySMnNJysglKSOHzFw9ufkGYlOzib3hvfFWnGyt8HCyMSZ4HW2p6WQ+ctf018kGdwcbrHTauznEW7L0CdgkaVsSjabUJQqwKuWpA1b2pd/mbRgyZAjPP/88S5cu5bvvvmP8+PGmD/6dO3cycOBAHnvsMcA4idipU6cIDg6+o325uLjg6+vLzp07TYnhgv3ceLqgi4sLQ4cOZejQoTzyyCP07duX5ORk3N3dsbe3JywsjLCwMJ599lmCgoI4cuQIrVu3vot7QQihut2fwX3/A2dvtSMRlqag7MGpDcayB7kZ/60zlT0INZY+kLIHwgLk5uYSHh7OlClTTMu0Wi29e/dm9+7dRd4mJycHOzvzL0D29vbs2LHDbNmzzz7L/fffT+/evQslbcPDw8nLy6N3796mZUFBQdSpU4fdu3dL0lYIFWw4Gsf4Hw4UO1HPlP5BaDUa9pxL4t/zyaRnmydpXe2taV/P3TSStrGXM1oLGcl1t8Ja+PL1jvMcvpTK/M2n+d+DzdQOSdym9zecICffQLt67vRtJn346qRdPXd8XO2IT80u9P4Gxpq23q52zB3aqsjRp9dy9cZEbkYuyZm5XMkoSOoal13JzCX5+vqkjFxy9QYycvLJyMnnYlLp5o+q4WBtGsVb0yypa0tNx+vJXydj8tfFzrrU760lva9bwgRskrStQpycnBg6dChTpkwhLS2N0aNHm9Y1bNiQlStXsmvXLmrUqMHcuXNJSEi446QtwCuvvML06dMJDAykVatWLFmyhIiICH788UfAODLFx8eHe+65B61Wy4oVK/D29sbNzY1vvvkGvV5P+/btcXBw4IcffsDe3t6s7q0QopI6shwif4N7HoPOz0MNeV1XBYqiYDAY6+1ptdrSjQZSFEiMNNamPbUBLu3HrOyBk7cxSdu4H9TrLmUPhMW5cuUKer2+UB1ZLy8vTpw4UeRtQkNDmTt3Lt26dSMwMJAtW7awevVq9Pr/ToVetmwZBw4cYN++fUVuIz4+HhsbG9zc3ArtNz4+vth4c3JyyMn5b8LItLS0kg5RCFEKeoPCzLWFZ1aH/z7V3l1n/p7gbGtFuxuStE18XCzmdNuyptVqmNq/CY9+sYele6MY3TmAQE8ntcMSpRR+8Sq/RcSi0cC0AcGVcsR3WbqjPm8lptNqmB4WzPgfDqDBrKdOwZFPDwsu9v3L3kZHbRsHatcouR+vKArpOfmmUbo3jthNup7wTS5Ylmm8blDgalYeV7PySnU8VloN7tcTuTWdzEfu1nSywf369Rr2Nkxfc6zY93VLmYBNkrZlycEDrGxvPbu6la2xXTkZO3YsX3/9Nf3798fX979RSm+++Sbnzp0jNDQUBwcHnn76aR588EFSU1PveF/PPfccqampvPTSSyQmJhIcHMyaNWto2NBYx8jZ2Zn333+f06dPo9PpaNu2LevWrUOr1eLm5sZ7773H5MmT0ev1NG/enLVr1+LhUX73jRCigng1g4SjsP9rOPAttBgKXSZDzQZqRybuREo0ZCVh0BvYHnEKgK6tGqErOE3JwQPcbpgt2qzswUZIjTLfnk/L65OIXS97oC2/052EUMP8+fN56qmnCAoKQqPREBgYyJgxY1i8eDEA0dHRPP/882zatKnQiNy7NXv2bGbOnFmm2xRCwN7zSWanzhanpb8r/Zv50KG+B019Xcr1lF5L06G+B72b1GLz8UTmrD/BFyPbqB2SKAWDQWHW75EADA6pTTM/V5UjUp/BYGD79u0AdO3aFZ2u6peK6NvMhwWPtS5UJsC7jMsEaDQaXOyscbGzpl7Nks8+1xsUUrJy/0vumkbs5nDl+mjegiTvlYwc0rLzyTcoJKbnkJieA6TfcawFE7DtPZ9Mx0D18lQaRVGKSixXWWlpabi6upKamoqLi4vZuuzsbM6fP0+9evXuvBN9/cttsW7+civKRZk8lkIIcwd+gDXP3rqNlS1M3A9XL8L2D+Hc1usrNND0Iej6EnjLKXOVRko0fBoC+Tno0bKd9gB05V90XJ/l2soWnthkTNSfWl902YN63Y21aaXsgSjBrfppasjNzcXBwYGVK1fy4IMPmpaPGjWKlJQUfvvtt2Jvm52dTVJSEr6+vrz++uv8/vvvHDt2jF9//ZWHHnrI7EugXq9Ho9Gg1WrJyclh27Zt9OrVi6tXr5qNtq1bty4vvPACL774YpH7LGqkrb+/v8Xcn0JUFoqiEJWcxe6zSew+l8TfJxJJu6ncQVHmP9rKNLFPdXQmMZ3QedvRGxSWP9ORdvXc1Q5JlOCXg5d48edDONro+PuVHqWub1qV6fX6ape0LWDJE3KVRm6+wVSiITnzvyTvlRtH9l5P9iakZZOnLzkdWl7v66Xt88pI27Lm5i9JWSFE1ZOeAJunG683HwIdi0neFvww5VYH6nU1ng7/z4fGZN6x1cZL4/7Q9WWoHVJx8Ys7k5V067NHwLj+i27mywrKHjTqa6xTK2UPRCVlY2NDSEgIW7ZsMSVtDQYDW7ZsYeLEibe8rZ2dHX5+fuTl5bFq1SqGDBkCQK9evThy5IhZ2zFjxhAUFMRrr72GTqcjJCQEa2trtmzZwqBBgwA4efIkUVFRdOzYsdh92traYmsrEwAJcSdiUq6x+2wSu85eYc/ZpFJPqHOj6p7walDLmaFt/Vn6bxTvrDvOrxM6VflTyyuzrNx85qw/CcCzPRtU++evMJZKUHNU6d2ysdLi7WqHt2vJz+XdZ5MY9uWeEtup/bqQpK0QQohbUxT4bQJkXTGWPnjgE7Au5YdX7TYwfBnEH4HtH8GxX+HkOuOlfg/o9grU7Wyc9FFUbt4tjLVppeyBqGImT57MqFGjaNOmDe3atWPevHlkZmYyZswYAEaOHImfnx+zZ88G4N9//yUmJoZWrVoRExPDjBkzMBgMvPrqq4CxfFSzZuZnHDg6OuLh4WFa7urqytixY5k8eTLu7u64uLgwadIkOnbsKJOQCVFGEtKyjSNpr4+mjUo2nxDHWqehlb8bHet70L6eBy+tiCAhLeeWE/XIyFJ4oXdDfj0Yw6HoFH4/HEdYSznDxlIt3HaO+LRsatew54nO9dQOR4gKVdoJ2NR+X5ekrRBCiFvb+wWc2Ww8zX3QV6VP2N7IuzkM/gbuPQ07PoZDy4ylE85tBf8O0O1laNBbkrcWp5QVlIavhEZ9yjcUIVQydOhQLl++zLRp04iPj6dVq1Zs2LDBNDlZVFQU2ht+pMjOzjbNJeDk5ET//v35/vvvC00qVpKPP/4YrVbLoEGDyMnJITQ0lM8//7wsD02IauVKRg57zv2XpD13OdNsvU6robmfK50CPegY6EFI3Ro42Pz3dXnGA03veKKe6qSWsx3PdAvk482neH/jCe5r6oWtVfU5vbyyiEm5xqJtZwGY2r8JdtbyGInq5W4nYKsoUtP2BlIHteqQx1KIMpIQCV/0AH0O9PsA2j9dNtu9ehF2zoeDPxi3DcYJqrq+DEEDZJSm2lKi4MgKCP8OUi4AFF/TFuDpbeDbquLjFFWOpdW0rezk/hTVWUpWLnvOJbPnnLHkwamEDLP1Gg0083WlY6AHHet70CagBs521rfc5oajcYUm6vEp44l6qoKs3Hx6fLCVxPQc3ry/CU92ra92SOImzy87yG8RsbSr587PT3eQMhY3qM41basjtd7XpaatEEKIu5OXDaueNCZVG94H7Z4qu23XqAsD5hrLI+z+FPYvhrhDsPxx8AwyTljW9GHQycdUhbl2FSJ/g8PL4eJOtaMRQgghbktadh77ziez63rJg+Pxadw8PCnI29mUpG1fzwNXh1snaW/Wt5kPfYK9K/VEPRXBwcaKl+5rxGurjvDJX2cYHOJ/2/e1KD/hF6/yW0QsGg1MGxAsCVtRrVn6+7p8Gy6CwWAouZGwaPIYClEGtsyExGPg6AkDPyuf0gUuPhD6DnSZDP8ugH8XweUTsPop+Ptd6PICtBwGVjKxTrnIz4HTf8Lhn+HURtDnXl+hgYAuUKcT/DPn+hIFT5JM14UQQgg1Zebks//iVdPEYUdiUjHc9PHUoJYTHesbyx20r+eOh9Pd9ycq+0Q9FeWREH8W77jAyYR0Ptt6hqn9m6gdkgAMBoX//R4JwOCQ2jTzc1U5Isuj0Wjw9PQ0XRdVnyW/r0vS9gY2NjZotVpiY2Px9PTExsZGXqSVjKIo5ObmcvnyZbRaLTY2NmqHJETldGYz7LleO3Hg5+BUq3z35+gBPd+ETpNg75ew+zO4eh7WPg/b3odOz0HrkWDjUL5xVAcGA0TthiPL4dgvkJ3637pawdBiKDR/BFxrQ2yEKWmrRaEpp9SJWQghRLWXnacn/OJVU03aQ9Ep5N+UpQ3wcKBjYE06BnrQob676rN+V2c6rYbX+wcxZsk+vtl5gcc71MXfXfpxavvtUAwR0Sk42uh4ObSx2uFYJK1WS9OmTdUOQwhAkrZmtFot9erVIy4ujtjYWLXDEXfBwcGBOnXqmE0MIoQopcwr8OsE4/V2T0Oj+ypu33auxknJOoyH8G9g1yeQFgMbXoPtH0LHZ6HNWLCT2oy3LfGEcUTtkRWQGv3fcmdfY5K2xVDwNp/RHgcP4yjn/Jzit2tla2wnhBBClKGcfD0RUSnsvj552MGoFHL15mfT+bnZmyYO6xjogY+rvUrRiqL0aORJ5wYe7DyTxId/nmT+o/eoHVK1lpWbz5z1JwF4tmcD+VFDiEpAJiIrgqIo5Ofno9frKzg6URZ0Oh1WVlYySlqIO6Eo8NMwOLXeWFv26a1greIXoPwciPgRdnxsnBwLwM4N2o+D9s+Ag7t6sVUG6fFwZKUxWRt/+L/lti7Q5AFoMcRYBkF7iwkWUqIhK6n49Q4e4OZfdjGLak0mzipbcn+KyiRPb+DwpVT2XE/S7r+YTHaeeZLW28XOVJO2Y6CHjNysBI7GpBL26Q4UBdZM7EyL2m5qh1RtfbzpFPO3nKZ2DXs2T+6OnbVMsCWEWmQisrug0WiwtrbG2lqKpQshqpn9i40JW50NDPpK3YQtGEdxtnkC7nncmHzc/hEknYZt7xknMGvzBHScCM5e6sZpSXLS4fjvxkTt+W2gXP/Cq7UyTijXYgg06lv6x9bNH9z8ZSZdIYQQZUpvUDgWm8rus0nsOpvE/gvJZOaaD5qp6WRDh+sJ2o71PahX01EGZlQyzfxceaiVH6sPxvDOH8dZ9nQHeQxVEJtyjUX/nAVgav8mkrC9BenzCksiSVshhBBGl0/CxjeM13vPAO/mqoZjRmcNrYYZE47H18A/H0HCEdj1f7D3C2O9207PVd8Rn/o8OPsXHF4OJ/6A/Gv/rfNvb7zfgh8y1g4WQggh7pLeoNz2TNsGg8KJ+PTr5Q6u8O/5ZNKz883auDlY06GeMUnbKdCDBrWcJMFXBbwU2pjfj8Tx7/lkthxPpHew/Nhe0eZsOEF2noF2Ae70a+atdjhCiFKSpK0QQghjGYJVY43JvsCe0H682hEVTauDpg9B8INwaiP88wHE7DcmbvcvgZaPQpcXwSNQ7UjLn6JATLhxRO3RVeYlDDwa/DehmHt99WIUQogq7k6Sl5XdhqNxzFwbSVxqtmmZj6sd08OC6dvMx7RMURTOJGaw66yx3MG/55O4mpVnti1nOyva13M3Th5W34Mgb2e0Vfz+q4783Ox5onM9Fm47y+z1x+nR2BMrncw9UlHCL17lt4hYNBqYFhYsP4QIUYlI0lYIIQT89TbEHwF7d3hwAVj6JH4aDTTuC41CjSUA/vkQLmyHg98ba+A2GwRdX4JaTdSOtOwlnTVOJnb4Z0g+999yh5rXJxQbAr6tjfeREEKIclPa5GVVsuFoHON/OMDNk6LEp2Yz/ocDTH8gGGudlt1nk9hzLpkrGeYTWTra6Ghbz91Uk7apr2uVT3ILown3BvLzvijOXs7k5/3RjGhfV+2QqgWDQeF/v0cCMDikNs38XFWOSAhxOyRpK4QQ1d25rcYyAwADPwXnSnTKlEYD9XsYL1H/wvYP4fSfxqTmkRUQNMCYvPVrrXakdyfzChz7xZiovbTvv+XWDsZjbDHUeB/o5GNdCCEqQknJywWPta5yiVu9QWHm2shCxwyYls1YE2m23M5aS5u67nQM9KBDfQ9a1HbFWkZYVksudtY836shM9ZG8vGm0wxs5YeTrfRbytuaQ7FERKfgaKPj5fsaqx2OEOI2ybukEEJUZ1nJ8Ms44/WQMRB0v7rx3I067WHECog7ZJywLHINnPjdeAnsBd1ehrqd1I6y9HKzjJPCHV4OZzaD4XrdP40W6t9rTNQG3Q+2TurGKYQQ1UxJyUsNMHNtJH2CvU2jSBVFId+gkK9XyDMYyNcr5OsN5Bmu/9Ur5OkNt1yff315nt5wfVv/LS/p9sblJW+r4P+CbRVsO19vICffuK4kTbydCW3mTcf6HrSq44atlUziI4yGt6/LN7sucCEpiy/+OcfkPo3UDqlKy8rN5731JwCYcG8DarnYqRyREOJ2SdJWCCGqK0WBNZMgPQ48GkLoO2pHVDZ8WsKQ74wTq22faxxxe3aL8VK3s3HkbWBPyywfYNDD+X+MidrjayA34791Pq2Midpmg8BZJvAQQgi17D2fbFYS4WYKEJeaTbPpG1DgevKz5GRnVTGuRyADW/mpHYawQDZWWl7rG8T4Hw/w5T/nGNG+Dl6SSCw3i7adIz4tm9o17BnbpZ7a4Qgh7oAkbYUQoro6+L1xFKrWGgZ9BTaOakdUtjwbw8OLoMfrsHMeRCyFizuNF9/WxpG3jfqpX79XUYz1hA//DEdWQkb8f+vc6kDzIcY6tZ7qntKm0Whwd3c3XRdCiOoqMb34hO2NruUZbrleqwErnRZrrcb4V6fBSqvFSqfBRmf8a6W9vlynxUqrwbqI5cbbG6/bXG938/asS9iWjdWNbYve1+FLqUz66WCJx13LWZJwonh9m3kTUrcG4Rev8vGmU7w3qIXaIVVJsSnXWPTPWQCm9m+CnbWMeC8t6fMKSyJJWyGEqI6unIH1rxmv93wTfFupGk65cq8HYfOh+2uw6xPYvwRiD8Cy4VAr2DjytulDoK3gzmxK9PUJxZbD5eP/Lbdzg2YPG5O1/u3VTypfp9VqadFCvlgJIURpk5Jzh7SkbYC7KflpfT0pWnBdW8km4Kpdw4F31x0nPjW7yNIQGsDb1Y529dwrOjRRiWg0Gqb2D2LQgt0s3x/NmM71aOztrHZYVc77G06QnWegXYA7/ZpVovkqLID0eYUlsYxvgkIIISqOPg9WPwl5WVCvG3R6Tu2IKoaLL/SdDS8cgS6TwcYZEiNh1Vj4tC0c+B7yc8s3hmspEP4tLLkf5jWDLTONCVudLQQPhEeXwsunYMDHULejxSRshRBC/KddPXd8XO0oLuWqAXxc7RjYyg9/dwe8Xe2o6WSLq4M1jrZW2FrpKl3CFkCn1TA9LBig0LEX/D89LNhUx1eI4oTUNSYSDQrMXn+85BuI23Ig6iq/RsSi0cBbA4JltKgQlZh8GxRCiOpm62yIPWgc0fngwuqXGHTyhN7T4cUjcO8bYF8Dks/CmonwSWvY+yXkXSu7/eXnwPG18PNj8GFDWPscXNxhXBfQFR74xJioHfKdcWIxK9uy27cQQogyd2Py8mZVPXnZt5kPCx5rjber+Whjb1c7FjzWmr7NfFSKTFQ2r/YNwkqrYevJy+w8c0XtcKoMg0Fh1tpIAB5pXZvmtV1VjkgIcTc0iqJUn6r4QFpaGq6urqSmpuLi4qJ2OEIIUbEu7IRv7gcUY5IweKDaEakvJwP2L4bdn0JGgnGZkxd0nAhtngBbJ+OylGjISip+Ow4e4OZvvG4wQPQeY53aY79Adup/7TybQMuh0OyR/9pXAnq9np07dwLQuXNndDqpjSbKnvTTypbcn+Vrw9E4nv3xAPobvk35uNoxPSy4yicv9QaFveeTSUzPppazsSRCVUxSi/I1Y80xvtl1gaa+Lqyd2KVSjkC3NL8ejOGFnyNwtNHx98s9qCUTvd026fOKilDaPprUtBVCiOri2lVY/TSgwD2PScK2gK0TdH4O2j1tnJxt53xIjYZNb8GOudB+PDS5H77saRw1WxwrWxi+As5vg8MrIDXqv3XOPtB8sHFCMa9mUElPUzMYbj2pjhBCVCcd69c0JWzfe7g5dT0cq03yUqfV0DHQQ+0wRCX3XK+GrAq/xLHYNH6NiOHh1rXVDqlSy8rN5731JwCYcG8DSdjeBenzCkshSVshhKgOFAV+fxHSLoF7feg7R+2ILI+1HbR7ClqPgiPLYftcY9mEre/Cjnm3TtiCcf13D/z3v42zMTHeYggEdKn4ic6EEEKUq2NxxrMoatew59F2dVSORojKx93Rhgn3NmDOhhN8uPEk/Zv7YGct/aU7tWjbOeLTsvFzs2dsl3pqhyOEKAPVrJChEEJUU4eWGU/T1+jg4a/+O+VfFGZlYxyJPHEfDPoaagVDflbpbqvRQaN+8MgSeOU0PPgZ1O8uCVshhKiCImPTAGjmKzUjhbhTYzoH4OtqR2xqNkt2XlA7nEorNuUai/45C8DU/k0k+S1EFSFJWyGEqOqSz8G6l43X750CtUPUjaey0Oqg+SMwbifc907pbvP4LzB8GTR7GKztyzc+IYQQqjoaYxxp29RX6gULcafsrHW8HNoYgM//PkNyZq7KEVVO7284QXaegXYB7vRv7q12OEKIMiJJWyGEqMr0+cY6trkZUKcTdJmsdkSVj1ZrLG9QGnYy2koIIaqLY9dH2jb1k6StEHfjwVZ+NPV1IT0nn//bclrtcCqdA1FX+TUiFo0G3hoQjKaSzp0ghCjMIpK2n332GQEBAdjZ2dG+fXv27t1bqtstW7YMjUbDgw8+WL4BCiFEZfXPB3BpH9i6wsOL5DR9IYQQogxcy9Vz9nIGAE2lPIIQd0Wr1TC1fxMAfthzkfNXMlWOqPIwGBRmrY0E4JHWtWleW96PhKhKVE/a/vzzz0yePJnp06dz4MABWrZsSWhoKImJibe83YULF3j55Zfp2rVrBUUqhBCVTNS/8M/7xusD5oKbTJIi7o6bmxtubm5qhyGEEKo7EZ+GQYGaTrbUcrZVOxwhKr3ODWrSo7En+QaF9zecUDucSmPNoVgiolNwsNHxyvUyE+LuSZ9XWArVk7Zz587lqaeeYsyYMQQHB7Nw4UIcHBxYvHhxsbfR6/WMGDGCmTNnUr9+/QqMVgghKonsVFj9JCgGaPGosTarEHdBp9PRqlUrWrVqhU4nI7aFENXb0YLSCL4uciqyEGVkSr8maDWw/mg84ReT1Q7H4mXl5vPeemOC+9l7G1DLxU7liKoG6fMKS6Jq0jY3N5fw8HB69+5tWqbVaunduze7d+8u9nazZs2iVq1ajB07tiLCFEKIymfdK5ASBW51of8HakdT+Tl4gFUJI6msbI3thBBCVHmRsTIJmRBlrbG3M0Pa+APwzh/HURRF5Ygs2xf/nCM+LRs/N3vGdqmndjhCiHJgpebOr1y5gl6vx8vLy2y5l5cXJ04UfUrEjh07+Prrr4mIiCjVPnJycsjJyTH9n5aWdsfxCiFEpXB4BRz+GTQ6ePhLsJMvlHfNzR8mhkNWUvFtHDyM7YQQQlR5pknIpJ6tEGVqcp9G/BYRy4GoFDYcjadfcx+1Q7JIsSnXWLjtLABT+zfBzlpGhApRFamatL1d6enpPP7443z55ZfUrFmzVLeZPXs2M2fOLOfIhBDCQly9CH9MNl7v/irUaa9uPFWJm3+1Tsrq9Xr27NkDQIcOHeR0MSFEtZWnN3AiPh2AZn7yw6gQZamWix1PdavP/205zZwNJ+jVxAsbK9WrOlqc9zecIDvPQNuAGvRv7q12OFWK9HmFJVH13a9mzZrodDoSEhLMlickJODtXfiN5+zZs1y4cIGwsDCsrKywsrLiu+++Y82aNVhZWXH27NlCt5kyZQqpqammS3R0dLkdjxBCqMqgh1+egZw0qN0Our6sdkSiisnLyyMvL0/tMIQQQlVnEjPIzTfgbGuFfw0HtcMRosp5plt9ajrZciEpi6X/XlQ7HItzIOoqv0bEotHAtAFNpa52OZA+r7AUqiZtbWxsCAkJYcuWLaZlBoOBLVu20LFjx0Ltg4KCOHLkCBEREabLAw88wL333ktERAT+/oVHQNna2uLi4mJ2EUKIKmnHXIjaDTbO8PAXoKtUJ1MIIYQQlUJBaYQmvi5otZIsEaKsOdpa8WKfhgDM33Ka1GuSPCugKAqz1kYC8Ejr2jSvLSVahKjKVP9GP3nyZEaNGkWbNm1o164d8+bNIzMzkzFjxgAwcuRI/Pz8mD17NnZ2djRr1szs9m5ubgCFlgshRLVyKRz+nm28fv+H4C6TEQghhBDl4ZhMQiZEuRvaxp8lOy9wJjGDBVvP8nq/ILVDsghrDsUSEZ2Cg42OV0Ibqx2OEKKcqV4cZujQoXz44YdMmzaNVq1aERERwYYNG0yTk0VFRREXF6dylEIIYcFy0mHVWFD00GwQtBiqdkRCCCFElVUw0raZTEImRLmx0ml5va8xUbt453liUq6pHJH6snLzeW+9ccL2Z+9tQC0XO5UjEkKUN9VH2gJMnDiRiRMnFrlu69att7ztN998U/YBCSFEZbL+dbh6Hlz94f65IHWthBBCiHJhMChEXk/aNpVJyIQoV72a1KJDfXf2nEvmo40nmTu0ldohqeqLf84Rl5qNn5s9Y7vIWXVCVAeqj7QVQghxF479AhE/gEZrrGNr76Z2REIIIUSVFZWcRUZOPjZWWgI9ndQOR4gqTaPR8Eb/YAB+iYjhaEyqyhGpJy71Ggu3GSden9q/CXbWOpUjEkJUBEnaCiFEZZV6CdY+b7zeZTLU7aRuPKLKc3Z2xtnZWe0whBBCNQWlEYK8nbHWyVcpIcpb89quDGzli6LA7PXHURRF7ZBU8f6Gk2TnGWgbUIP+zb3VDqfKkz6vsBQWUR5BCCHEbTLo4ZdxkJ0Kvq2hx+tqRySqOJ1OR0hIiNphCCGEqv6bhEzq2QpRUV6+rzHrj8Sz80wSW09d5t7GtdQOqUIdiLrKLwdj0Ghg2oCmaKQUWrmSPq+wJPLzsBBCVEa7PoEL28HaEQZ9BTprtSMSQgghqryjBfVsfaWerRAVxd/dgdGdAwCYve44+XqDugFVIEVRmLU2EoBBrWvTvLb8YCREdSJJWyGEqGxiD8Jfbxuv95sDHoHqxiOEEEJUA4qiEGkaaStJWyEq0rM9GuBqb82phAxWhl9SO5wKs+ZQLBHRKTjY6Hg1tLHa4QghKpgkbYUQojLJzYRVT4IhD5o8APc8pnZEoprQ6/Xs2bOHPXv2oNfr1Q5HCCEqXGJ6DlcyctFqIMhbkrZCVCRXB2sm9WwAwNxNp8jKzVc5ovJ3LVfPe+tPAPDsvQ2o5WKnckTVg/R5hSWRpK0QQlQmG6dC0hlw9oWw+SA1rUQFys7OJjs7W+0whBBCFQX1bBvUcsLeRmZuF6KiPd6xLv7u9iSm5/DlP+fVDqfcLfrnLHGp2fi52TO2Sz21w6lWpM8rLIUkbYUQorI4/juEfwNo4OFF4OCudkRCCCFEtXE0pqCerdSUFEINtlY6XusbBBgTmonpVTepFpd6jYXbzgIwpX8QdtbyQ5EQ1ZEkbYUQojJIi4M1k4zXOz8H9bqpG48QQghRzRyTerZCqO7+5j608ncjK1fPvM2n1Q6n3Ly/4STZeQbaBtTg/uY+aocjhFCJJG2FEMLSGQzw63i4lgw+LeHeN9WOSAghhKh2jsUaR9oGS9JWCNVoNBreuL8JAD/vi+ZMYrrKEZW9g1FX+eVgDBoNTBvQFI2UQxOi2pKkrRBCWLp/F8C5v8HKHh7+Cqxs1I5ICCGEqFZSsnK5dPUaIOURhFBb2wB37gv2Qm9QTBN1VRWKojDr90gABrWuTfPa8n4jRHUmSVshhLBk8Udg8wzj9b7vgmcjVcMRQgghqqPI66Ns/d3tcbW3VjkaIcRr/YLQaTVsPp7I7rNJaodTZtYciuVgVAoONjpeCW2sdjhCCJVJ0lYIISxV3jVY9SToc6Hx/RAyRu2IRDXn4OCAg4OD2mEIUeE+++wzAgICsLOzo3379uzdu7fYtnl5ecyaNYvAwEDs7Oxo2bIlGzZsMGuzYMECWrRogYuLCy4uLnTs2JH169ebtenRowcajcbsMm7cuHI5PlGygtIITX1k1JsQliDQ04nh7eoA8O664xgMisoR3b1ruXrTyOEJPQLxcrFTOaLqS/q8wlJI0lYIISzVn2/B5RPg5AUPfAJSz0qoSKfT0a5dO9q1a4dOJzMYi+rj559/ZvLkyUyfPp0DBw7QsmVLQkNDSUxMLLL9m2++yaJFi/jkk0+IjIxk3LhxPPTQQxw8eNDUpnbt2rz33nuEh4ezf/9+evbsycCBAzl27JjZtp566ini4uJMl/fff79cj1UUTyYhE8LyPN+7IU62VhyJSWXt4Vi1w7lrX/xzjrjUbPzc7Hmya321w6m2pM8rLIkkbYUQwhKd3AD7vjRef3ABOHqoG48QQlRTc+fO5amnnmLMmDEEBwezcOFCHBwcWLx4cZHtv//+e6ZOnUr//v2pX78+48ePp3///nz00UemNmFhYfTv35+GDRvSqFEj3nnnHZycnNizZ4/ZthwcHPD29jZdXFwkYaiWo9dH2jbzk5G2QliKmk62jOtuTG6+v+Ek2Xl6lSO6c3Gp11i47SwAU/oHYWctyUIhhCRthRDC8mQkwm/PGq93eBYa9FI3HiGEqKZyc3MJDw+nd+/epmVarZbevXuze/fuIm+Tk5ODnZ35Ka329vbs2LGjyPZ6vZ5ly5aRmZlJx44dzdb9+OOP1KxZk2bNmjFlyhSysrLu8ojEnbiWq+fc5QxARtoKYWnGdqmPt4sdMSnX+G73BbXDuWPvbzjJtTw9berW4P7mPmqHI4SwEFZqByCEEOIGigK/ToCsK+DVDHpNUzsiIQBjYik8PByAkJAQOV1MVAtXrlxBr9fj5eVlttzLy4sTJ4qesTw0NJS5c+fSrVs3AgMD2bJlC6tXr0avNx8BduTIETp27Eh2djZOTk788ssvBAcHm9YPHz6cunXr4uvry+HDh3nttdc4efIkq1evLjbenJwccnJyTP+npaXdyWGLmxyPT8OgGEf11ZIak0JYFHsbHS/d14hXVh7m07/OMKSNP24ONmqHdVsORl3ll4MxAEwLC0YjJdFUJX1eYUlkpK0QQliSvV/CmU1gZQeDvgJr+XIoLEdWVpaM9BOiBPPnz6dhw4YEBQVhY2PDxIkTGTNmDFqtebe7cePGRERE8O+//zJ+/HhGjRpFZGSkaf3TTz9NaGgozZs3Z8SIEXz33Xf88ssvnD17tth9z549G1dXV9PF39+/3I6zOjlmKo0go2yFsEQPt65NkLczadn5fPLXGbXDuS2KojDrd+N7/yMhtWlR203dgAQgfV5hOSRpK4QQliLxOPz5pvF6n/9BrSbqxiOEENVczZo10el0JCQkmC1PSEjA29u7yNt4enry66+/kpmZycWLFzlx4gROTk7Ur28+qYyNjQ0NGjQgJCSE2bNn07JlS+bPn19sLO3btwfgzJniExJTpkwhNTXVdImOji7toYpbOBYjk5AJYcl0Wg1T+xv7zd/tvkBUUuVJtq05FMvBqBQcbHS8EtpY7XCEEBZGkrZCCGEJ8rJh1ZOgz4GG90G7p9SOSAghqj0bGxtCQkLYsmWLaZnBYGDLli2F6s/ezM7ODj8/P/Lz81m1ahUDBw68ZXuDwWBW2uBmERERAPj4FF/r0NbWFhcXF7OLuHsFI22b+sokZEJYqm6NPOnasCZ5eoX3NxZdvsbSXMvV8956Y6wTegTiJeVXhBA3kaStEEJYgi0zIeEoOHrCwM9AalkJIYRFmDx5Ml9++SXffvstx48fZ/z48WRmZjJmzBgARo4cyZQpU0zt//33X1avXs25c+fYvn07ffv2xWAw8Oqrr5raTJkyhX/++YcLFy5w5MgRpkyZwtatWxkxYgQAZ8+e5X//+x/h4eFcuHCBNWvWMHLkSLp160aLFi0q9g6o5vL0Bk7GpwMy0lYISze1fxM0Gvj9cBwHo66qHU6JvvjnHHGp2fi52fNk1/ol30AIUe3IRGRCCKG2M5thz+fG6wM/B6da6sYjhBDCZOjQoVy+fJlp06YRHx9Pq1at2LBhg2lysqioKLN6tdnZ2bz55pucO3cOJycn+vfvz/fff4+bm5upTWJiIiNHjiQuLg5XV1datGjBxo0b6dOnD2Ac4bt582bmzZtHZmYm/v7+DBo0iDfffLNCj13AmcQMcvUGnO2sqOPuoHY4QohbaOLjwqDWtVkZfol31x1n+TMdLXZSr7jUayzcZqxR/nq/IOysZbIrIURhkrQVQgg1ZV6BXycYr7d7Ghrdp248QgghCpk4cSITJ04sct3WrVvN/u/evbvZhGJF+frrr2+53t/fn23btt1WjKJ8HL1ezzbYx8Vikz9CiP+8dF8jfj8cy74LV/kzMoHQpkXXH1fbBxtOci1PT5u6NRjQoviyN0KI6k3KIwghhFoUBdZMgowE8AyCPrPUjkiIW7Kzs8POTuqtCSGqD6lnK0Tl4uNqz5NdjKUG5qw/QZ7eoHJEhR2MusrqgzEATAsLlh+ELJD0eYWlkJG2QgihlvAlcHId6Gxg0Ndgba92REIUS6fT0aFDB7XDEEKIChVpStpKPVshKotnutfnp71RnLuSybK9UTzeMUDtkEwURWHW78azMQa1rk2L2m7qBiQKkT6vsCQy0lYIIdRw+RRsmGq83nsmeDdTNx4hhBBCmDEYFCLjjEnbZn4y0laIysLZzpoXejcEYN7m06Rn56kc0X/WHIrlYFQKDjY6Xu3bWO1whBAWTpK2QghR0fJzYdVYyL8GgT2h/Ti1IxJCCCHETS4mZ5GRk4+tlZZAT0e1wxFC3IZH29Whfk1HkjJzWbTtnNrhAHAtV8+c9ScAmNAjEC8XOf1eCHFrkrQVQoiK9tf/IP4w2LvDgwtAK2/FwvLp9XrCw8MJDw9Hr9erHY4QQpS7Y7HGSciCvJ2x0slntRCVibVOy2v9ggD4asc54lKvqRwRfPHPOWJTs/Fzs+fJrvXVDkcUQ/q8wpJI70MIISrSua2w6/+M1wd+Cs6WOaOtEEVJT08nPT1d7TCEEKJCFExCFiyTkAlRKd0X7EXbgBpk5xn46M9TqsYSl3qNhdvOAvB6vyDsrHWqxiNuTfq8wlJI0lYIISpKVjL8Mt54PWQMBN2vbjxCCCGEKFZB0raZn0xCJkRlpNFomNq/CQCrDlwyTSyohg82nORanp42dWswoIWPanEIISoXSdoKIURFUBRY+zykx4JHQwh9R+2IhBBCCFEMRVE4FmMsj9BURtoKUWndU6cG97fwQVFg9vrjqsQQEZ3C6oMxAEwLC0aj0agShxCi8pGkbTnSGxR2n03it4gYdp9NQm9Q1A5JCKGWgz/A8TWgtYZBX4GNTGgihBBCWKqEtBySMnPRaTUEeTurHY4Q4i68FhqEtU7D9tNX+OfU5Qrdt6IozFp7DIBBrWvTorZbhe5fCFG5WakdQFW14WgcM9dGEpeabVrm42rH9LBg+jaT0yGEqFaSzsL614zXe70Fvq1UDUcIIYQQt1YwCVmgp6PUnhSikqvj4cDIjgF8veM87647TucGNdFpK2a065pDsRyISsHeWserfRtXyD6FEFWHjLQtBxuOxjH+hwNmCVuA+NRsxv9wgA1H41SKTAhR4fR5sOpJyMuEet2g4yS1IxJCCCFECUz1bKU0ghBVwqSeDXCxs+JEfDqrD1yqkH1ey9UzZ/0JACb0CMTLxa5C9iuEqDokaVvG9AaFmWsjKaoQQsGymWsjpVSCENXF1tkQewDs3ODBhaCVt11ReVlbW2Ntba12GEIIUe6OXq9nG+wrk5AJURW4OdgwsWcDAD768xTXcvXlvs8vt58jNjUbPzd7nupWv9z3J8qO9HmFpZDsQRnbez650AjbGylAXGo2e88nV1xQQgh1XNgJ2+carz/wf+Dqp248QtwFnU5H586d6dy5MzqdnCoshKjaCkbayiRkQlQdIzsG4OdmT3xaNot3ni/XfcWnZrNg61kAXu8XJGVWKhHp8wpLIknbMpaYXnzC9k7aCSEqqWspsPppQIF7HoPggWpHJIQQQohSSMnKJSblGiAjbYWoSuxuqCu7YOtZrmTklNu+3t9wgmt5ekLq1mBAC5nTRghxZyRpW8ZqOZeuTk1p2wkhKiFFgd9fhLRL4F4f+s5ROyIhhBBClFLk9VG2ddwdcLWX02OFqErCWvjS3M+VjJx85m8+XS77iIhOYfXBGACmDQhGo6mYSc+EEFWPJG3LWLt67vi42lHc27IG8HG1o10994oMSwhRkQ7/DMdWg9YKBn0Ftk5qRyTEXdPr9URERBAREYFeX/514IQQQi1HY431bJvKKFshqhytVsPU/k0AWLo3irOXM8p0+4qiMGvtMQAebu1HS3+3Mt2+KH/S5xWWRJK2ZUyn1TA9LBig2MTt9LBgdFr5tU2IKin5PPzxsvF6jyngF6JuPEKUoZSUFFJSUtQOQwghytV/9WwlaStEVdQx0INeQbXQGxTmrD9RpttecyiWA1Ep2FvreK1vUJluW1Qc6fMKSyFJ23LQt5kPCx5rjbdr4RIID7f2o28zqWkjRJWkzzfWsc1NhzqdoMuLakckhBBCiNskk5AJUfVN6R+ETqvhz8iEMpsk/Fqu3pQEntAjEC8XKYkohLg7VmoHUFX1beZDn2Bv9p5PJjE9m6MxqXy5/Tx7LySjNygy0laIyi4lGrKSzJftXwKX9oK1E/SeAVqZbVQIIYSoTLJy802nSzf1k5G2QlRVDWo5M7StP0v/jeLddcf5ZUKnu649++X2c8SmZuPrasdT3eqXUaRCiOpMkrblSKfV0DHQA4D7gr1Zvv8S0cnX+PtEIr2DvVSOTghxx1Ki4dMQyC9mxtm8DPguDCaGg5t/xcYmhBBCiDt2PC4dRQFPZ1uZOFiIKu6F3g359WAMEdEp/HEkjgEtfO94W/Gp2SzYehaA1/s3wc5aBm8IIe6elEeoIPY2Oh5ta0zefLv7grrBCCHuTlZS8QnbAvk5hUfiCiGEEMKiRcokZEJUG7Wc7XimWyAA7284SU7+nU869f6GE1zL0xNStwZhLaQcohCibEjStgI91qEuWg1sP32FM4llO0ulEEIIIYQQ4u7IJGRCVC9PdatHLWdbopKz+H73xTvaRkR0CqsPxgAwbUDwXZdZEEKIApK0rUD+7g70amIsi/CdjLYVQghRyWi1WrRa6ToIIaquo9dH2jaTSciEqBYcbKyY3KcRAJ/8dYbUrLzbur2iKMxaewwwTjre0t+trEMUKpA+r7AU8iysYKM7BQCwKvwS6dm394EghBBCqEWn09GtWze6deuGTid12oQQVU+e3sCp+OuTkEnSVohqY3Abfxp5OZF6LY/Ptp65rduuPRzHgagU7K11vBoaVE4RiookfV5hSSRpW8E6BXrQsJYTmbl6VoZfUjscIcSdyLisdgRCCCGEKGOnEzLI1RtwtrPC391e7XCEEBVEp9UwpV8TAL7ZeYHo5KxS3S47T897644DMKFHIN6uMnmhEKJsSdK2gmk0GkZeH2373e6LGAyKugEJIW5P3CH4dZzaUQghhBCijB27Xhoh2MdFalIKUc30aOxJ5wYe5OoNfPjnyVLd5ot/zhGbmo2vqx1PdatfzhEKIaojSdqq4OF7/HC2s+L8lUz+OS0j9oSoNE6uh8X9IOuK2pEIUeEMBgOHDx/m8OHDGAwGtcMRQogyVzAJWTM/KY0gRHWj0RhH22o08FtELIcvpdyyfXxqNgu2ngXg9f5NsLOW0+irCunzCksiSVsVONpaMTjEH4Bvd11QNxghROnsWQjLhkNeJtTpBDrbW7e3sgUHj4qJTYgKoCgKycnJJCcnoyhylogQouopGGnb1NdF5UiEEGpo5ufKQ638AHh33fFb9nfe33iCa3l6QurWIKyFT0WFKCqA9HmFJbFSO4DqamTHuizZdZ6tpy5z4UomATUd1Q5JCFEUfT5snAJ7vzD+HzIa+n8I6fGQlVT87Rw8wM2/QkIUQgghxN0xGBQir4+0lUnIhKi+XgptzO9H4thzLpm/TiTSq4lXoTaHolNYfSAGgGkDgqWcihCi3MhIW5UE1HSkRyNPFMVY21YIYYFy0o2ja/d+AWigz/9gwDzQWRsTsr6tir9IwlYIIYSoNC4mZ5GZq8fWSkugpwymEKK68nOz54nO9QDjaNt8vfnp8YqiMOv3SAAebu1HS3+3ig5RCFGNSNJWRaOuT0i2Yn80mTn56gYjhDCXGmOsX3t6I1jZw5DvoPNzIL+kCyGEEFXO0RhjaYQgHxesdPIVSYjqbMK9gdRwsObs5Ux+3h9ttm7t4TjCL17F3lrHq6FBKkUohKgupEeiom4NPalX05H0nHxWH4xROxwhRIHYCPiqFyQcAcdaMOYPCH5A7aiEEEIIUU6OmUojSD1bIao7FztrnuvVEIC5f57irxOJ/BYRw7ZTicz+wzjKdnyPQLxd7dQMUwhRDUjSVkVarYaRHesC8N2uC1LkWghLcHI9LOkP6XHg2QSe2gJ+IWpHJYQQQohyJJOQCSFuNKJ9XTydbEjKzOWJb/bx/LIIRi3eR1xaDjUcrHmqa321QxRCVAOStFXZIyG1cbTRcToxg11nbzGpkRCi/O1ZaKxhm5cJgT1h7EZwq6N2VEIIIYQoR4oik5AJIcz9dSKByxm5Ra67mpXHtlOJFRyREKI6slI7gOrO2c6aQSG1+W73Rb7ZdYHODWqqHZIQ1Y8+HzZOuT7hGBAyGvp/aJxwTAgBgE6no0ePHmqHIYQQZS4+LZukzFx0Wg1B3s5qhyOEUJneoDBzbWSx6zXAzLWR9An2RqeV+S6qGunzCksiI20twMiOAQBsOZ5AdHKWusEIUd3kpMOyYdcTthro8z8YME8StkIIIUQ1cSzGOMq2gacTdtY6laMRQqht7/lk4lKzi12vAHGp2ew9n1xxQQkhqiVJ2lqABrWc6NqwJgYFfthzUe1whKg+UmNgcT84/SdY2cOQ76Dzc6CRX8yFEEKI6kImIRNC3CgxvfiE7Z20E0KIOyVJWwsx6vpo22X7ormWq1c3GCGqg9gI+KoXJBwBx1ow5g8IfkDtqISwWAaDgWPHjnHs2DEMBoPa4QghRJkxTULmJ/VshRBQy9muTNuJykX6vMKSSNLWQtwbVAt/d3tSr+XxW0SM2uEIUbWdXA9L+kF6HHg2gae2gF+I2lEJYdEUReHy5ctcvnwZRVHUDkcIIcqMjLQVQtyoXT13fFztKO7cOw3g42pHu3ruFRmWqCDS5xWWRJK2FkKn1TCyQwAA3+y6IG8OQpSXPQvhp2GQlwWBPWHsRnCro3ZUQgghhFDB1cxcYlKuARAsSVshBMbv5tPDggEKJW4L/p8eFiyTkAkhyp0kbS3IkDb+2FvrOBGfLkXNhShr+nxY9wpseA1QIGQ0DF8OdnIqpBBCCFFdRcYZR9nWcXfAxU4mIRVCGPVt5sOCx1rj7WpeAsHb1Y4Fj7WmbzMflSITQlQnVmoHIP7j6mDNg/f48dPeKL7dfYH29T3UDkmIqiEnHVY+YZxwDA30mQWdJsmEY0IIIUQ1V1DPtpmfjLIVQpjr28yHPsHe7D2fTGJ6NrWcjSURZIStEKKiSNLWwozqVJef9kax8VgCsSnX8HWzVzskISq31BhYOtQ44ZiVPTz8hUw4JoQQQggAjsYU1LOVM2+EEIXptBo6BspgKiGEOqQ8goUJ8nahQ3139AaFH/+9qHY4QlRusRHwVS9jwtaxFoz5QxK2QgghhDApGGkr9WyFEEIIYWksImn72WefERAQgJ2dHe3bt2fv3r3Ftl29ejVt2rTBzc0NR0dHWrVqxffff1+B0Za/0Z0CAPhpbzTZeXp1gxGisjq5Hpb0g/Q48GwCT20BvxC1oxJCCCGEhcjKzefclUwAmkrSVgghhBAWRvWk7c8//8zkyZOZPn06Bw4coGXLloSGhpKYmFhke3d3d9544w12797N4cOHGTNmDGPGjGHjxo0VHHn56d3EC19XO5Izc/n9cJza4QhRuSgK7FkAPw2DvCwI7AljN4JbHbUjE6JS02q1dO3ala5du6LVqt59EKJC3c4Ag7y8PGbNmkVgYCB2dna0bNmSDRs2mLVZsGABLVq0wMXFBRcXFzp27Mj69evN2mRnZ/Pss8/i4eGBk5MTgwYNIiEhoVyOr7o6HpeOokAtZ1tqOduVfAMhhBBVnvR5hSVR/Rk4d+5cnnrqKcaMGUNwcDALFy7EwcGBxYsXF9m+R48ePPTQQzRp0oTAwECef/55WrRowY4dOyo48vJjpdPyWMe6AHy76wKKoqgckRCVhD4f1r8KG14HFAgZDcOXg53UqRPibmk0GnQ6HTqdDo1M4ieqkdsdYPDmm2+yaNEiPvnkEyIjIxk3bhwPPfQQBw8eNLWpXbs27733HuHh4ezfv5+ePXsycOBAjh07Zmrz4osvsnbtWlasWMG2bduIjY3l4YcfLvfjrU4KSiPIKFshhBAFpM8rLImqSdvc3FzCw8Pp3bu3aZlWq6V3797s3r27xNsrisKWLVs4efIk3bp1K89QK9yjbetgY6XlSEwqB6JS1A5HCMuXkw7LhsHeLwAN9PkfDJgHOmu1IxNCCFGJ3e4Ag++//56pU6fSv39/6tevz/jx4+nfvz8fffSRqU1YWBj9+/enYcOGNGrUiHfeeQcnJyf27NkDQGpqKl9//TVz586lZ8+ehISEsGTJEnbt2mVqI+7eMZmETAghhBAWTNWk7ZUrV9Dr9Xh5eZkt9/LyIj4+vtjbpaam4uTkhI2NDffffz+ffPIJffr0KbJtTk4OaWlpZpfKwN3RhoEtfQHjaFshxC2kxsDifnD6T7CyhyHfQefnQH4ZFaLMGAwGTpw4wYkTJzAYDGqHI0SFuJMBBjk5OdjZmZ9qb29vX+xZYXq9nmXLlpGZmUnHjh0BCA8PJy8vz2y/QUFB1KlTp1QDG0TpHIuTkbZCCCHMSZ9XWBLVyyPcCWdnZyIiIti3bx/vvPMOkydPZuvWrUW2nT17Nq6urqaLv79/xQZ7F0Zdn5Bs3ZE4EtOy1Q1GCEsVGwFf9oSEI+BYC8b8AcEPqB2VEFWOoijEx8cTHx8vZXtEtXEnAwxCQ0OZO3cup0+fxmAwsGnTJlavXk1cnPk8BUeOHMHJyQlbW1vGjRvHL7/8QnBwMADx8fHY2Njg5uZW6v1C5R2soIbcfAOn4jMAaOYnI22FEEIYSZ9XWBJVk7Y1a9ZEp9MVmlQhISEBb2/vYm+n1Wpp0KABrVq14qWXXuKRRx5h9uzZRbadMmUKqamppkt0dHSZHkN5aubnSpu6Ncg3KPz4b5Ta4QhheU6uhyX9ICMePJvAU1vAL0TtqIQQQlRj8+fPp2HDhgQFBWFjY8PEiRMZM2ZMoclMGjduTEREBP/++y/jx49n1KhRREZG3tW+K/NghYp2OjGdXL0BFzsratewVzscIYQQQohCVE3a2tjYEBISwpYtW0zLDAYDW7ZsMZ0eVhoGg4GcnJwi19na2ppm5i24VCYFo22X7o0iN1+G5gsBgKLAngXw0zDIy4LAnjB2I7jVUTsyIYQQVcidDDDw9PTk119/JTMzk4sXL3LixAmcnJyoX7++WTsbGxsaNGhASEgIs2fPpmXLlsyfPx8Ab29vcnNzSUlJKfV+oXIPVqhox2KNo5CDfV1kohkhhBBCWCTVyyNMnjyZL7/8km+//Zbjx48zfvx4MjMzGTNmDAAjR45kypQppvazZ89m06ZNnDt3juPHj/PRRx/x/fff89hjj6l1COWqbzNvvFxsuZyew/qjcSXfQIiqTp8P616BDa8DCoSMhuHLwU5ObRRCCFG27maAgZ2dHX5+fuTn57Nq1SoGDhx4y/Y3DkIICQnB2trabL8nT54kKirqlvut7IMVKlJkrExCJoQQQgjLZqV2AEOHDuXy5ctMmzaN+Ph4WrVqxYYNG0y1w6KiosxOJ8vMzGTChAlcunQJe3t7goKC+OGHHxg6dKhah1CurHVaRrSvy9xNp/hm1wUGtvJTOyQh1JOTDivGwJlNgAb6zIJOk2TCMSGEEOVm8uTJjBo1ijZt2tCuXTvmzZtXaICBn5+fqVTXv//+S0xMDK1atSImJoYZM2ZgMBh49dVXTducMmUK/fr1o06dOqSnp7N06VK2bt3Kxo0bAXB1dWXs2LFMnjwZd3d3XFxcmDRpEh07dqRDhw4VfydUQUdjjJOQNfOTxLYQQgghLJPqSVuAiRMnMnHixCLX3TzB2Ntvv83bb79dAVFZjmHt6vDpX2c4GJXC4UsptKjtpnZIQlS81BhYOgQSjoKVPTz8hUw4JoQQotzd7gCD7Oxs3nzzTc6dO4eTkxP9+/fn+++/N5tULDExkZEjRxIXF4erqystWrRg48aN9OnTx9Tm448/RqvVMmjQIHJycggNDeXzzz+vsOOuygwGheNxMtJWCCGEEJZNo1Sz6fDS0tJwdXUlNTW1Up0y9uLPEfxyMIaHW/sxd0grtcMRomLFRsDSocYJxxxrwfBlMuGYEBVMr9ezfft2ALp27YpOp1M5IlEVVdZ+mqWS+7No5y5n0POjbdhaaTk2MxQrneoV44QQQlgI6fOKilDaPpr0UCqJggnJfj8Ux5WMoiddE6JKOrEOlvQzJmw9m8BTWyRhK4QKtFotnTp1olOnTmajCoUQorIpmIQsyMdFErZCCCHMSJ9XWBJ5BlYSrfzdaOnvRq7+/9u78/Coyvv94/eZyTLZQxKyEkgIIEuEsIMgioIgdccFtQWx1Z8KVqWtFUVcW9xqsYKgtgqKexVcvorFKCgQBEEQCPtONgiQBBKyzZzfH1kgEiBAkjPJvF/XNReZM8+Zc08y4sMnz3wel95fvtvqOEDDM01p2Qzp/VuksiIp6RLp919Loa2tTgZ4JMMw5OPjIx8fH3ZaB9Ckrcus7Gcby+pjAEBNzHnhTijaNiG3XdBGkjRn2W6VOV0WpwEakLNc+vIv0vyHJJlSz9ukWz6UHPSdAwAA5yY9k362AADA/VG0bUJGnB+jiEAfZRcU63/rc6yOAzSMksPSe6OkFa9LMqShT0lXTJXs3lYnAzyay+XS5s2btXnzZrlc/OIQQNNkmmZ1e4QurLQFAPwKc164E4q2TYivl1239Kn4aPjspTutDQM0hPy90hvDpa0LJC8/6ca3pAF/lPhYCmA50zSVmZmpzMxMedgepgCakeyCYh0sLJXdZui86CCr4wAA3AxzXrgTirZNzK392sjLZmj5zoPVH+0CmoXM1dLrl0o566SASGns/0mdr7I6FQAAaEbWZVTMn9tHBsrhzY7gAADAfVG0bWKigh0anhwtidW2aEY2fim9ebl0JFtq2Um6I1WK62l1KgAA0Mysr9yErDOtEQAAgJujaNsE3XZBgiRp3uoMHSostTYMcC5MU0p7RXr/FqmsSEq6RPr911Joa6uTAQCAZmg9m5ABAIAmgqJtE9SzTQt1iQ1WSblLH/y0x+o4wNlxlktf/ln6eqIkU+p5m3TLh5KDf0QBAICGkc4mZAAAoImgaNsEGYahMZWrbd9O2yWni+bYaGJKDkvvjZJW/FuSIQ19SrpiqmT3tjoZAABopg4Vlioj76gk2iMAAAD3R9G2ibqqW6xa+HsrI++ovtmQY3UcoO7y90pvDJe2LpC8/KQb35IG/FEyDKuTAQCAZqyqNUKbcH8FO/hFMQAAcG9eVgfA2XF42zWqT2vNWLhNs5fu1LAu0VZHAk4v82fp3VEVG44FREq3vM+GY0ATYbPZ1K9fv+qvAaCpqdqEjNYIAICTYc4Ld8I7sAn7bb82shnS0m0HtDnnsNVxgFPb+KX05oiKgm3LTtIdqRRsgSbEMAw5HA45HA4ZrIwH0ASxCRkA4HSY88KdULRtwuJC/XRZ54oVtrOX7rQ2DHAypimlvSK9f4tUViQlXSL9/msptLXVyQAAHmjNmjWy2+1Wx4AF1rHSFgAANCEUbZu4qg3JPlmVofyjZdaGAX7NWS59+Wfp64mSTKnnbdItH0oOVrgATY3L5dK2bdu0bds2uVwuq+MA58Q02cTV0xSWlGtHbqEkVtoCAE6OOS/cCT1tm7h+bcN0XlSQNuUc1kc/7dEfLmxrdSR4krw9UtGB2h8rLZS++7u0a7EkQxr6pHTBvWw4BjRRpmlqz549kqSEhARrwwCncN11153y8fz8fD7u6IE2ZhfINKXIIF+1DPK1Og4AwE0x54U7oWjbxBmGoTEXJOjhuWv19rJdun1Aomw2/iGCRpC3R5rWUyovOfU4u6808t9S56saJxcAwKN9/vnnGjp0qKKiomp93Ol0NnIiuINj/WxpjQAAAJoGirbNwDXdY/XMVxu060CRFm7ep0s61v6PFKBeFR04fcFWkq56iYItAKDRdOrUSSNHjtTvf//7Wh9fvXq1vvjii0ZOBauty6joZ5scR2sEAADQNNDTthnw9/HSTb3jJUmzlu6yOA3wKy07WZ0AAOBBevbsqVWrVp30cV9fX7VuzWaYnoaVtgAAoKlhpW0z8bt+Cfr34h36fvN+bdt/REktA62OBAAA0Ohmzpx5yhYInTp10o4dOxoxEaxWWu7S5pzDktiEDAAANB2stG0mWof769KOkZKkt9NYbQsAADyTr6+v/P39rY4BN7Jl32GVOU0FO7zUqoWf1XEAAADqhKJtMzLmggRJ0n9X7tWRknJrwwAAAFhg8uTJKioqqr5/6NAhC9PAHazPqGqNECLDYMNeAADQNFC0bUYGtotQUssAHSkp18cr91odBwDQjNhsNvXu3Vu9e/eWzcb0Ae7rb3/7m44cOVJ9v02bNtq+fbuFiWC19ZkVm5DRzxYAcDrMeeFOeAc2I4ZhVK+2nZ22Uy6XaW0gNF+mKa34j9UpADQiwzAUEBCggIAAVqrBrZmmecr78DzVm5DFUbQFAJwac164E4q2zcx1PVop0NdL2/cXavHWXKvjoDkyTSn1Cennt04/1stX8g9v+EwAAAC1cLlMpWdVFG2T2YQMAAA0IV5WB0D9CvT10vU9W2nW0p2avXSnBnVoaXUkNCemKf1vkpQ2reL+oAeljr85+Xj/cCk0vnGyAWhQLpdLu3fvliS1bt2aj4vBbRmGocOHD8vhcMg0TRmGoSNHjqigoKDGuOBgVl16gh0HClVU6pTD26a2LQOtjgMAcHPMeeFOKNo2Q6P7t9GspTv17aZ92n2gSK3D2UEZ9cA0pa8elJa/VnF/xAtSnzuszQSg0ZimqZ07d0qS4uP5ZQzcl2ma6tChQ4373bt3r3HfMAw5nU4r4qGRVbVG6BgdLLuNj7kCAE6NOS/cCUXbZqhty0Bd1KGlFm3er7fSdmrSFZ2tjoSmzuWS/m+CtPJNSYZ05VSp520WhwIA4ETfffed1RHgRtiEDAAANFUUbZup2y5I0KLN+/XhT3s04bIO8vfhR42z5HJKn/1RWj1HkiFdPV3qfqvVqQAAqNVFF11kdQS4kfTKlbbJcfSzBQAATctZNefYs2eP9u7dW31/+fLluv/++/Xaa6/VWzCcm4s6tFRCuL8Kiss19+cMq+OgqXKWS/PurijYGjbputco2AIAmpzf/OY3ysrKsjoGGplpmlqXwUpbAADQNJ1V0faWW26p/uhZdna2hg4dquXLl+uRRx7Rk08+Wa8BcXZsNkO/658gSZq9dKdM07Q2EJoeZ5k0907plw8kwy6N/I/U9UarUwEAcMa+//57HT161OoYaGRZ+cU6VFQmu81Qh6ggq+MAAACckbMq2q5bt059+vSRJH344YdKTk7W0qVL9c4772jWrFn1mQ/n4IZereTvY9fmnCNK237A6jhoSspLpf/eLq37WLJ5SzfOlpKvszoVAABAnVVtQtY+MlAOb7vFaQAAAM7MWRVty8rK5OvrK0n65ptvdNVVV0mSOnbsyEfP3Eiww1vX9YiTVLHaFqiT8hLpw9HShs8ku49009tSpyutTgUAwFlr06aNvL29rY6BRnasNQL9bAEAQNNzVkXbLl26aObMmfrhhx+0YMECDR8+XJKUmZmp8PDweg2IczOmskXCgvQc7T1UZG0YuL+yo9L7t0ibv5LsvtKo96TzLrc6FQA3YLPZ1KNHD/Xo0UM221lNHwDLrFu3TvHx8VbHQCOrWmlLP1sAQF0x54U7Oat34LPPPqtXX31VF198sW6++WZ169ZNkvTZZ59Vt02Ae2gfFaQB7cLlMqU5y3ZbHQfurLRIem+UtPUbyctPuuUDqf0Qq1MBcBOGYSg4OFjBwcEyDMPqOECdrVy5UnPmzNGcOXO0atUqq+OgEaVnsgkZAODMMOeFO/E6m5Muvvhi5ebmqqCgQC1atKg+fuedd8rf37/ewqF+jOmfoCVbD+j9Fbt1/5D29PTCiUqOVBRsd/4geQdIt34oJQy0OhUAAGdt3759GjVqlBYuXKjQ0FBJUl5engYPHqz3339fLVu2tDYgGtTBwlJl5hdLkjpTtAUAAE3QWa20PXr0qEpKSqoLtrt27dLUqVO1adMmRUZG1mtAnLtLO0WpVQs/5RWV6bPVmVbHgbspLpDmjKwo2PoESb/7hIItgBO4XC7t3r1bu3fvlsvlsjoOcFr33nuvDh8+rPXr1+vgwYM6ePCg1q1bp4KCAv3xj3+0Oh4a2PrKVbYJ4f4KctDPGABQN8x54U7Oqmh79dVX66233pJUsWKhb9+++sc//qFrrrlGM2bMqNeAOHd2m6Hf9WsjSZq1dKdM07Q4EdzG0TxpznXSnmWSb4g0ep7Uup/VqQC4IdM0tX37dm3fvp3/j6BJmD9/vl555RV16tSp+ljnzp01ffp0ffXVVxYmQ2M41s+WTcgAAHXHnBfu5KyKtqtWrdKFF14oSfrvf/+rqKgo7dq1S2+99Zb+9a9/1WtA1I+besfL4W1TelaBftp1yOo4cAdFB6W3r5H2rpAcodKYT6VWvaxOBQBAvXC5XPL2PnGFpbe3NytnPEBV0ZbWCAAAoKk6q6JtUVGRgoKCJEn/+9//dN1118lms6lfv37atWtXvQZE/Qj199E1KXGSKlbbwsMVHpDeukrK/FnyD5du+0KK7W51KgAA6s0ll1yi++67T5mZx1pDZWRk6IEHHtCll15qYTI0hvVsQgYAAJq4syratmvXTvPmzdOePXv09ddf67LLLpNUseFDcDATI3c15oIESdL8ddnKrtyYAR7oyH5p9hVS9lopoKU05gsp+nyrUwEAUK+mTZumgoICJSQkKCkpSUlJSUpMTFRBQYFefvllq+OhARWWlGtHbqEk2iMAAICmy+tsTpo8ebJuueUWPfDAA7rkkkvUv39/SRWrbrt3Z7Weu+oUE6w+iWFavuOg3vlxl/502XlWR0JjO5wtzb5Kyt0kBUZLYz6XWnawOhUAAPUuPj5eq1at0jfffKONGzdKkjp16qQhQ4ZYnAwNbUNWgUxTigr2VcsgX6vjAAAAnJWzKtpef/31GjhwoLKystStW7fq45deeqmuvfbaeguH+nfbBQlavuOg3lu+W+MvaSdfL7vVkdBY8jOk2VdKB7dJwXEVBdvwJKtTAQBQ78rKyuTn56fVq1dr6NChGjp0qNWR0IjYhAwAADQHZ1W0laTo6GhFR0dr7969kqRWrVqpT58+9RYMDeOyzlGKCXEoK79Y//dLlq7r0crqSGgMebsrCraHdkoh8RUF27BEq1MBANAgvL291bp1azmdTqujwAL0swUAAM3BWfW0dblcevLJJxUSEqI2bdqoTZs2Cg0N1VNPPcVuvG7Oy27Tb/u1kSTNZkMyz3Bwh/TmbyoKtqFtpLFfUrAFcMZsNptSUlKUkpIim+2spg9Ao3rkkUf08MMP6+DBg1ZHQSNbl8FKWwDA2WHOC3dyVu/ARx55RNOmTdMzzzyjn3/+WT///LP+/ve/6+WXX9ajjz5a3xlRz0b1jpePl01r9ubr592HrI6DhnRgmzTrN1L+biksSRr7lRTa2upUAJogwzAUGhqq0NBQGYZhdRzgtKZNm6bvv/9esbGxOu+889SjR48atzMxffp0JSQkyOFwqG/fvlq+fPlJx5aVlenJJ59UUlKSHA6HunXrpvnz59cYM2XKFPXu3VtBQUGKjIzUNddco02bNtUYc/HFF8swjBq3u+6664xye6LScpe27DssiZW2AIAzx5wX7uSs2iPMnj1b//73v3XVVVdVH+vatavi4uJ0zz336G9/+1u9BUT9Cw/01ZVdY/Xxqr2avXSnurduYXUkNIT9mytaIhzJliI6SKM/k4JjrE4FAECjuOaaa+rleT744ANNmDBBM2fOVN++fTV16lQNGzZMmzZtUmRk5AnjJ02apDlz5uj1119Xx44d9fXXX+vaa6/V0qVLqzfsXbRokcaNG6fevXurvLxcDz/8sC677DKlp6crICCg+rnuuOMOPfnkk9X3/f396+U1NWebcw6rzGkqxM9brVr4WR0HAADgrBmmaZpnepLD4dAvv/yiDh1q7jq/adMmpaSk6OjRo/UWsL4VFBQoJCRE+fn5Cg723N++r92bryunLZa33dCShy5RZJDD6kioTznp0ltXSYX7pcjO0uhPpcAT/2EJAHXlcrmUlZUlSYqJieHjYmgQ7jhP69u3r3r37q1p06ZJqvhvIT4+Xvfee68eeuihE8bHxsbqkUce0bhx46qPjRw5Un5+fpozZ06t19i/f78iIyO1aNEiDRo0SFLFStuUlBRNnTr1rLO74/ezoX24Yo8e/PgX9W8brvfu7Gd1HABAE8OcF42hrnO0s3r3devWrXrierxp06apa9euZ/OUaGTntwpRj9ahKnOaeu/HPVbHQX3KXivNvqKiYBt9vjTmCwq2AM6ZaZrasmWLtmzZorP4fS/Q6FasWKEff/zxhOM//vijfvrppzo9R2lpqVauXKkhQ4ZUH7PZbBoyZIjS0tJqPaekpEQOR81fhvv5+Wnx4sUnvU5+fsXGWWFhYTWOv/POO4qIiFBycrImTpyooqKiOuX2ZOsqNyFLjvOMIjUAoH4x54U7Oav2CM8995x+85vf6JtvvlH//v0lSWlpadqzZ4++/PLLeg2IhjPmggSt2r1a7/y4S3dfnCQfL36D1ORl/iy9dY1UnCfFpEi/myv5h53mJAAAmp9x48bpwQcfVN++fWscz8jI0LPPPltrQffXcnNz5XQ6FRUVVeN4VFSUNm7cWOs5w4YN04svvqhBgwYpKSlJqamp+uSTT+R0Omsd73K5dP/992vAgAFKTk6uPn7LLbeoTZs2io2N1S+//KK//vWv2rRpkz755JOT5i0pKVFJSUn1/YKCgtO+xuZmfSabkAEAgObhrKp0F110kTZv3qxrr71WeXl5ysvL03XXXaf169fr7bffru+MaCCXJ8eoZZCv9h0u0fz12VbHwbna+5M0++qKgm1cr4qWCBRsAQAeKj09vdYNx7p376709PQGu+5LL72k9u3bq2PHjvLx8dH48eM1duzYk368cty4cVq3bp3ef//9GsfvvPNODRs2TOeff75uvfVWvfXWW5o7d662bdt20mtPmTJFISEh1bf4+Ph6fW3uzukytSGrqmjLSlsAANC0nfXSytjYWP3tb3/Txx9/rI8//lhPP/20Dh06pP/85z/1mQ8NyMfLplv7tpYkzV6609owODe7l1WssC3Jl+L7Vayw9Qu1OhUAAJbx9fVVTk7OCcezsrLk5VW3D5tFRETIbref8Dw5OTmKjo6u9ZyWLVtq3rx5Kiws1K5du7Rx40YFBgaqbdu2J4wdP368vvjiC3333Xdq1arVKbNUrRjeunXrScdMnDhR+fn51bc9ezyrBdbOA4UqKnXK4W1T25aBVscBAAA4J3we3sPd0re1vO2GVu46pHUZ+VbHwdnYuUR6+zqp9LDUZqD0248lB6tLAACe7bLLLqsuYlbJy8vTww8/rKFDh9bpOXx8fNSzZ0+lpqZWH3O5XEpNTa1uEXYyDodDcXFxKi8v18cff6yrr766+jHTNDV+/HjNnTtX3377rRITE0+bZfXq1ZIqNkU5GV9fXwUHB9e4eZKquWynmGDZbYbFaQAAAM7NWfW0RfMRGeTQiPNj9OnqTM1aulMv3NDN6kg4E9sXSu+OksqPSm0vlka9J/n4W50KAADLvfDCCxo0aJDatGmj7t27S6oofEZFRZ1RO68JEyZozJgx6tWrl/r06aOpU6eqsLBQY8eOlSSNHj1acXFxmjJliqSKjc4yMjKUkpKijIwMPf7443K5XHrwwQern3PcuHF699139emnnyooKEjZ2RVtqkJCQuTn56dt27bp3Xff1YgRIxQeHq5ffvlFDzzwgAYNGsSmv6eQnklrBAAA0HxQtG0IeXukogMnf9w/XAp1nx5jYy5I0KerM/XZmkxNvLyjwgN9rY6Eutj6jfT+rVJ5sdRuiHTTHMnbz+pUAAC4hbi4OP3yyy965513tGbNGvn5+Wns2LG6+eab5e3tXefnuemmm7R//35NnjxZ2dnZSklJ0fz586s3J9u9e3eNfrXFxcWaNGmStm/frsDAQI0YMUJvv/22QkNDq8fMmDFDknTxxRfXuNabb76p2267TT4+Pvrmm2+qC8Tx8fEaOXKkJk2adPbfEA/AJmQAAKA5MUzTNOs6+Lrrrjvl43l5eVq0aNFJd8d1BwUFBQoJCVF+fn7DfGQsb480radUXnLyMV6+0viVblO4NU1TV09fol/25usvw87TuMHtrI6E09k0X/rwd5KzVOpwuXTj7Ir3FQA0ENM0dfDgQUlSWFiYDIOPHqP+Nfg8zcN40vfTNE31eGqBDhWV6bPxA9S1VajVkQAATRBzXjSGus7RzmilbUjIqX9rHRISotGjR5/JUzY/RQdOXbCVKh4vOuA2RVvDMDSmf4L+9NEavbNsl/7foLbystPu2G1t+Fz6aKzkKpM6XiFd/6bk5WN1KgDNnGEYCg8PtzoGcMbS09O1e/dulZaW1jh+1VVXWZQIDSEzv1iHisrkZTPUISrI6jgAgCaKOS/cyRkVbd98882GygGLXdEtRn//coMy84u1ID1Hl59/8k0uYKF1n0gf/0EynVKX66TrXpPsdf+IJwAAnmL79u269tprtXbtWhmGoaoPl1WtmHHnT4bhzK2v3ISsXWSgHN52i9MAAACcO5ZTQpLk62XXzX1aS5JmLd1pbRjU7pcPpY9/X1Gw7XqTdN3rFGwBNBqXy6Xs7GxlZ2fL5XJZHQc4rfvuu0+JiYnat2+f/P39tX79en3//ffq1auXFi5caHU81DP62QIA6gNzXrgTiraodmu/1rLbDP2446A2ZBVYHQfHW/2u9MmdkumSUn4rXTNDsrOPIIDGY5qmNm7cqI0bN+oM2uEDlklLS9OTTz6piIgI2Ww22Ww2DRw4UFOmTNEf//hHq+OhnlUVbZPjmnfvXgBAw2LOC3dC0RbVYkL8NLxLtCTprbSd1obBMStnSfPukWRKPW+TrnpZsvGxPwAATsXpdCooqKK3aUREhDIzMyVJbdq00aZNm6yMhgawPrOiPQIrbQEAQHNB0dYqP715+g3LLDDmggRJ0tyfM5RXVHrqwWh4y1+XPr9Pkin1uVO6Yqpk4z9bAABOJzk5WWvWrJEk9e3bV88995yWLFmiJ598Um3btrU4HerTwcJSZeUXS5I6xbAJGQAAaB6o/lhl1Sxp5oXS7h+tTlJD74QW6hQTrOIylz78aY/VcTxb2ivSl3+u+LrfOOny56TKzVMAAMCpTZo0qboX3RNPPKEdO3bowgsv1JdffqmXXnrJ4nSoT1WrbBPC/RXkoN8/AABoHija1jf/cMnL99RjbN6SX5iUu0l6Y5j05V+kksONk+80DMPQbRe0kSS9lbZLThc9XCyx5CXp64kVXw+4Xxr2Nwq2AACcgWHDhum6666TJLVv314bN25Ubm6u9u3bp0svvdTidKhP6zIqNyGLozUCAABoPtjJqL6FxkvjV0pFB04+xj9c8gmQ/jdJWv2OtPw1aeOX0hX/lDpc1nhZT+LqlDhN+Wqj9h46qtQNObqsss8tGsn3z0vfPl3x9aAHpcEPU7AFAKCObr/99jqNe+ONNxo4CRrLsX62bEIGAACaD4q2DSE0vuJ2Ote8Ip1/Q0XP0rxd0rs3SMnXS5c/KwVENHzOk3B423VT73i9umi7ZqftpGjbWExTWjhFWvRsxf3Bj0gXPWhtJgAAmphZs2apTZs26t69O7s+e4j0zMqVtmxCBgAAmhG3aI8wffp0JSQkyOFwqG/fvlq+fPlJx77++uu68MIL1aJFC7Vo0UJDhgw55Xi3lzRYuidN6j9eMmzSuv9K03pLa96vKOJZ5Hf92shmSEu2HtCWHPdo3dCsmaaU+sSxgu2QxynYAnArNptNnTt3VufOnWVjQ0S4sbvvvlv5+fnasWOHBg8erP/85z+aO3fuCTc0D0dKyrXjQKEkVtoCAM4dc164E8vfgR988IEmTJigxx57TKtWrVK3bt00bNgw7du3r9bxCxcu1M0336zvvvtOaWlpio+P12WXXaaMjIxGTl6PfAIqepb+IVWKSpaOHpTm/j9pzkjp0C5LIrVq4a8hnaIkSbPTdlqSwWOYZkWrjMX/rLg/7O/SwAeszQQAv2IYhiIjIxUZGSmDli1wY9OnT1dWVpYefPBBff7554qPj9eNN96or7/+mpW3zdCGrAKZphQd7FBE4Gn2lQAA4DSY88KdWF60ffHFF3XHHXdo7Nix6ty5s2bOnCl/f/+T9hl75513dM899yglJUUdO3bUv//9b7lcLqWmpjZy8gYQ10O6c6F06WTJ7ittS5Ve6S8tmyG5nI0e57YLEiRJn6zKUEFxWaNf3yOYpvTVg1LatIr7I16Q+o+zNhMAAE2cr6+vbr75Zi1YsEDp6enq0qWL7rnnHiUkJOjIkSNWx0M9Wp9BP1sAANA8WVq0LS0t1cqVKzVkyJDqYzabTUOGDFFaWlqdnqOoqEhlZWUKCwur9fGSkhIVFBTUuLk1u7d04Z+ku5dIbQZIZYXS/Iek/1wm5aQ3apT+SeHqEBWoolKnPvppb6Ne2yO4XNIXD1RsRCdDuvIlqc8dVqcCgFqZpql9+/Zp3759rFZEk2Kz2WQYhkzTlNPZ+L8ER8NaX93PlqItAODcMeeFO7G0aJubmyun06moqKgax6OiopSdnV2n5/jrX/+q2NjYGoXf402ZMkUhISHVt/j4OmwQ5g4i2ktjvpCu+KfkGyxl/CS9Okj69m9SeUmjRDAMQ6P7J0iS3k7bKZeLv7DqjcspfXavtPJNSYZ09XSp521WpwKAk3K5XEpPT1d6erpcLpfVcYBTKikp0XvvvaehQ4eqQ4cOWrt2raZNm6bdu3crMDDQ6nioR1VF285sQgYAqAfMeeFOLG+PcC6eeeYZvf/++5o7d64cDketYyZOnKj8/Pzq2549exo55Tmw2aRet0vjfpTO+43kKpO+f06aOVDavaxRIlzbPU5BDi/tPFCkRZv3N8o1mz1nuTTvbmn1nIrN5657Tep+q9WpAABoFu655x7FxMTomWee0RVXXKE9e/boo48+0ogRI9hQpJkpKXdqc+WGuclxrLQFAADNi5eVF4+IiJDdbldOTk6N4zk5OYqOjj7luS+88IKeeeYZffPNN+ratetJx/n6+srXt4lvShAcK416R0r/VPryL1LuZumN4VLvP1T0v3U03CQ1wNdLN/aK138W79CspTs1uGNkg13LIzjLKjaZW/exZNilkf+Wkq+zOhUAAM3GzJkz1bp1a7Vt21aLFi3SokWLah33ySefNHIy1LctOUdU7jIV4uetuFA/q+MAAADUK0uXG/j4+Khnz541NhGr2lSsf//+Jz3vueee01NPPaX58+erV69ejRHVeoYhdbmmYtVt999KMqUVr0uv9JM2f92glx7dv40MQ1q0eb+272fzjrNWXir99/aKgq3NW7pxNgVbAADq2ejRozV48GCFhobWaJH16xuavvWZxzYhY4dvAADQ3Fi60laSJkyYoDFjxqhXr17q06ePpk6dqsLCQo0dO1ZSxcQ7Li5OU6ZMkSQ9++yzmjx5st59910lJCRU974NDAz0jB5l/mEV/U/Pv0H6/D7p0E7p3Rul5JHS8GelwJb1fsk24QEafF6kvt24T2+l7dLjV3Wp92s0e+Ul0ke3SZu+lOw+0o1vSeddbnUqAACanVmzZlkdAY2ETcgAAEBzZnljr5tuukkvvPCCJk+erJSUFK1evVrz58+v3pxs9+7dysrKqh4/Y8YMlZaW6vrrr1dMTEz17YUXXrDqJVij7cXS3WnSBX+s6Iu67mNpem9p9XtSA+xwOOaCBEnSf1fu1ZGS8np//matrFh6/9bKgq2vNOo9CrYAAADnaF1GxUrb5DhWTgMAgObH8pW2kjR+/HiNHz++1scWLlxY4/7OnTsbPlBT4eMvXfZUxUfsP71XylkrzbtLWvuhdMU/pRYJ9XapC9tFqG1EgLbnFuqTVXs1un/9PXezkLdHKjpw4vHyYunrR6SMnyQvP+nm96SkwY2fDwAAoBlxukxtyKrYhIyVtgAAoDlyi6ItzlFsd+nO76SlL0sLn5G2fSu90l+6ZJLU9y7JZj/nS9hshkb3b6PHP0/X7KU79bt+begdViVvjzStZ0ULhFO55hUKtgCaLMMw1LFjx+qvAcBKO3ILdbTMKT9vuxIjPKBFGgCgUTDnhTuxvD0C6ondW7pwgnRPmtRmoFRWJH39sPTvIVL2unq5xMierRTgY9e2/YVavDW3Xp6zWSg6cPqCrSSFtW34LADQQGw2m6KjoxUdHS2bjekDAGtVbULWMSZIdhv/qAYA1A/mvHAnvAObm/Akaczn0pUvSb7BUuYq6bWLpG+fruiteg6CHN66vmcrSdLspTvrISwAAABw5qo2IUuOpZ8tAABonijaNkc2m9TzNmnccqnjFZKrXPr+eenVC6Vdaef01KMrNyRL3bhPuw8UnXtWAECTYJqmDhw4oAMHDshsgA0vAeBMVK20pZ8tAKA+MeeFO6Fo25wFx0ij3pFufEsKjJJyN0tvDpe+mCAVF5zVUya1DNSF7SNkmtLby3bWb96mpiBLWjm7YqMxAGjmXC6X1q5dq7Vr18rlclkdB4AHM02zeqVtF1baAgDqEXNeuBOKtp6g89XSuB+l7r+ruP/Tf6TpfaVNX53V091Wudr2gxV7VFRaXk8hmwCXS9q7Uvru79Krg6QXO0qf/1HatdjqZAAAAB4jM79YeUVl8rIZ6hDNJmQAAKB58rI6ABqJXwvp6mnS+TdIn98nHdohvTdK6nKtdPlzUmBknZ/q4vMi1TrMX7sPFmnez5m6pW/rBgxuseICaft30ub/SVv+JxXuO+5BQ4rrIcV0k356w7KIAAAAnmRdRkVrhPZRQfL1slucBgAAoGFQtPU0bS+S7l4qLXpGWjpNWj9X2vadNOzvUsotknH63XftNkOj+7fR0/+3QbOX7tTNfeJl1OG8JuPANmnz19KWr6WdSyRX2bHHfIKkpMFSh+FS+6EVxe7M1RRtAQAAGsmx1gj0swUAAM0XRVtP5OMvDX1S6nKd9Nl4KXut9Ok90toPpSumSmGJp32KG3rF6x//26xNOYe1bPtB9U8Kb/jcDcVZJu1OqyjUbv5aOrCl5uNhbSuKtB2GSa0vkLx8aj7uHy55+UrlJSe/hpdvxTgAAACck3Q2IQMAAB6Aoq0ni02R7vhOSpsuLZwibV8ozbhAGvyI1PcuyX7yt0eIn7eu7RGnd3/crdlLdza9om3hAWnrAmnzfGnrt1JJ/rHHbF5Smwuk9sMqirUR7U79XKHx0viVUtGBk4/xD68YBwAAgHPCJmQAAMATULT1dHZvaeD9UqcrK3rd7vxB+t8j0rr/SldNk6KTT3rqmP4JevfH3fpferYy8o4qLtSv8XKfKdOUctYdW027d4Uk89jj/uGVRdrLpKRLJMcZ/iMgNJ6iLAAAQAM7cKREWfnFkqTOrLQFAADNGEVbVAhPksZ8Lq16S/rfo1Lmz9JrF0kD7pMGPSh5O0445bzoIPVvG6607Qc0Z9ku/XV4RwuCn0JpkbTj+4retJu/lgoyaj4eff6x1bRxPSQbG1kAwMkYhqH27dtXfw0AVqhaZZsYEaBAX/4pAwCoX8x54U6Y6eAYw5B6jqno3frln6UNn0s//ENK/1S68l9SwoATThlzQYLSth/Q+8t3675L28vhbXHhM29PZZH2f9KORVJ58bHHvPykthdXrKZtP0wKibMsJgA0NTabTXFx/L0JwFpVRVtW2QIAGgJzXrgTirY4UVC0dNMcKf0z6cu/SAe2SrNGSL1ul4Y8XqN1wJBOkYoL9VNG3lF9tiZTN/Zq5BYBLqe096eK3rRb/lfRAuF4IfEVRej2w6TECyVvN27hAAAAgFNazyZkAADAQ1C0xcl1vkpKHCQtmCytmi399Ia06SvpN/+QOv5GkuRlt+m3/dro2fkbNXvpTt3Qs1XDf4TgaJ60LbWi5cGWBdLRg8ceM2xSqz4VhdoOw6TIzhUriAEA58Q0TeXnVxRLQkJC+LgYAEtUrbRNZhMyAEADYM4Ld0LRFqfmFypd9S/p/OsrNio7uF16/xap8zXSiOelwEiN6h2vqd9s1vrMAq3cdUi9EsLqN4NpSrlbKlbTbv5a2p0mmc5jjztCpHZDKnrTthsi+dfz9QEAcrlcWr16tSTpwgsvlN1OH3AAjetISbl25BZKYqUtAKBhMOeFO6Foi7pJHCTdvVRa+Iy09GUpfZ60faE07G9qkXKrrk6J1Yc/7dWspTvrp2hbXiLtWlJRpN38tXRoR83HI86rXE07XIrvK9l5KwMAADRnG7IqVtlGBzsUHuhrcRoAAICGRaULdeftJw19Qkq+TvrsXilrjfTpOOmXD/WHvk/rw5+kNevW6cCWcoUH+NT+HP7hUuhJ+t4ezqnoS7t5fkVBuPTIscfsPlLCwIoibfvLpLDEen95AAAAcF/rM+hnCwAAPAdFW5y5mG7SH76Vlk2Xvvu7tGOROuy5TC+EX60rjnwsxztlJz/Xy1cav7KicOtySdlrKlfTzpcyf645NjCqokDbYbjU9mLJN7BBXxYAAADc17rKfrZd4uhnCwAAmj+Ktjg7di9pwH1SxyukL+6Xdnyv68vfl07Xo7u8RFo/T8rdVLGq9khOzcdjexzbRCy6m2SzNdALAAAAQFNStQkZK20BAIAnoGiLcxOeJI3+TPp5jsz5D8k4vqXBySyYdOxrn0ApabDUfljFqtqgqIbLCgAAgCappNypLTmHJVG0BQAAnoGiLc6dYUg9ficjOFaac93pxwfFSJ2vrlhN22ZARcsEAAAA4CQ2Zx9RuctUqL+34kL9rI4DAADQ4Cjaov74h9dt3M3vSbHdGzYLAKBeGYahtm3bVn8NAI1pfeaxTcj4OwgA0FCY88KdULSFBfiLDwCaGpvNptatW1sdA4CHOtbPlk3IAAANhzkv3Am7PKHeOE2zXscBAAC4g+nTpyshIUEOh0N9+/bV8uXLTzq2rKxMTz75pJKSkuRwONStWzfNnz+/xpgpU6aod+/eCgoKUmRkpK655hpt2rSpxpji4mKNGzdO4eHhCgwM1MiRI5WT86sNXD3I8SttAQAAPAFFW9Sb9RkF9ToOAOA+TNNUQUGBCgoKZPLLN3iQDz74QBMmTNBjjz2mVatWqVu3bho2bJj27dtX6/hJkybp1Vdf1csvv6z09HTddddduvbaa/Xzzz9Xj1m0aJHGjRunZcuWacGCBSorK9Nll12mwsLC6jEPPPCAPv/8c3300UdatGiRMjMzdd11ddg7oBlyukxtyKrahIyVtgCAhsOcF+7EMD3sXVhQUKCQkBDl5+crOJjf1Nen/y1doUFfXy6HUXbSMcWmt74f9pUuu6B3IyYDAJwrp9OpH374QZJ04YUXym63W5wIzZE7ztP69u2r3r17a9q0aZIkl8ul+Ph43XvvvXrooYdOGB8bG6tHHnlE48aNqz42cuRI+fn5ac6cObVeY//+/YqMjNSiRYs0aNAg5efnq2XLlnr33Xd1/fXXS5I2btyoTp06KS0tTf369atTdnf8fp6NrfsOa8iL38vP2651TwyT3UarLQBAw2DOi8ZQ1zkaPW1Rb4Ki2uqSkn+ohXH4pGMOmUH6R1TbRkwFAABwdkpLS7Vy5UpNnDix+pjNZtOQIUOUlpZW6zklJSVyOBw1jvn5+Wnx4sUnvU5+fsVH/8PCwiRJK1euVFlZmYYMGVI9pmPHjmrduvUZFW2bi6p+tp1igijYAgAAj0HRFvWmT2KYzJBWSs8v1smWb8eEONQnMaxRcwEAAJyN3NxcOZ1ORUVF1TgeFRWljRs31nrOsGHD9OKLL2rQoEFKSkpSamqqPvnkEzmdzlrHu1wu3X///RowYICSk5MlSdnZ2fLx8VFoaOgJ183Ozj5p3pKSEpWUlFTfLyhoHi2p2IQMAAB4Inraot7YbYYeu7KzJOlkayDiw/zl8qyOHAAAwIO89NJLat++vTp27CgfHx+NHz9eY8eOlc1W+7R73LhxWrdund5///1zvvaUKVMUEhJSfYuPjz/n53QH6zIqViInxzXdFg8AAABniqIt6tXw5BjN+G0PRYfU/FhgqL+3bIa0fMdB3fX2ShWX1b7aBAAAwF1ERETIbrcrJyenxvGcnBxFR0fXek7Lli01b948FRYWateuXdq4caMCAwPVtu2J7aHGjx+vL774Qt99951atWpVfTw6OlqlpaXKy8ur83UlaeLEicrPz6++7dmz5wxerXsyTZOVtgAAwCNRtEW9G54co8V/vUTv3dFPL41K0Xt39NPKSUP17zG95OtlU+rGfRr9xnIVFJ98wzIAAACr+fj4qGfPnkpNTa0+5nK5lJqaqv79+5/yXIfDobi4OJWXl+vjjz/W1VdfXf2YaZoaP3685s6dq2+//VaJiYk1zu3Zs6e8vb1rXHfTpk3avXv3Ka/r6+ur4ODgGremLiPvqPKPlsnLZqh9VKDVcQAAABoNPW3RIOw2Q/2Twmscu6RjlN66vY/+MPsnLd9xUDe/tkyzb++jiEBfi1ICAACc2oQJEzRmzBj16tVLffr00dSpU1VYWKixY8dKkkaPHq24uDhNmTJFkvTjjz8qIyNDKSkpysjI0OOPPy6Xy6UHH3yw+jnHjRund999V59++qmCgoKq+9SGhITIz89PISEh+v3vf68JEyYoLCxMwcHBuvfee9W/f3+P3YSsfVSQfL3YwRsAAHgOirZoVH3bhuu9O/tpzBvLtT6zQDfOTNPbf+iruFA/q6MBAE7BMAwlJCRUfw14iptuukn79+/X5MmTlZ2drZSUFM2fP796c7Ldu3fX6FdbXFysSZMmafv27QoMDNSIESP09ttv19hUbMaMGZKkiy++uMa13nzzTd12222SpH/+85+y2WwaOXKkSkpKNGzYML3yyisN+lrd0fqqfraxTX/VMADA/THnhTsxTNOzdoUqKChQSEiI8vPzm8VHxpqq7fuP6Hf/Wa6MvKOKCXHo7d/3VbtIPvIGAIAnY55Wv5rD9/P3s1YodeM+PX5lZ902IPH0JwAAALi5us7R6GkLS7RtGaiP7uqvpJYBysov1o2vpmnt3nyrYwEAAMCNVG9CFscmZAAAwLNQtIVlYkP99NFdF6hrqxAdLCzVza8vU9q2A1bHAgDUwjRNFRYWqrCwUB72IR0AFsk9UqLsgmIZhtQppmmuFAYANC3MeeFOKNrCUmEBPnrnD33Vr22YjpSUa8yby7UgPcfqWACAX3G5XFqxYoVWrFghl8tldRwAHqBqlW1ieIACfdmKAwDQ8Jjzwp1QtIXlghzemjW2j4Z0ilJpuUt3zVmpT1bttToWAAAALLQ+s6J1Vmc2IQMAAB6Ioi3cgsPbrpm/7aGRPVrJ6TI14cM1emPxDqtjAQAAwCLV/Wxj6WcLAAA8D0VbuA0vu03PX99Vt1fuDPzkF+n654LN9JEBAADwQOnVRVtW2gIAAM9D0RZuxWYz9OgVnfSnoR0kSS+lbtETn6fL5aJwCwAA4CkOF5dpR26hJIq2AADAM1G0hdsxDEP3XtpeT17dRZI0a+lO/emjNSpz0gQcAADAE2zIOixJiglxKDzQ1+I0AAAAjY+iLdzW6P4JemlUirxshub+nKG73l6p4jKn1bEAAADQwKo2IWOVLQAA8FQUbeHWrk6J02uje8rXy6bUjfs0+o3lKiguszoWAHgcwzAUHx+v+Ph4GYZhdRwAzVzVJmSd2YQMANCImPPCnVC0hdu7pGOU3rq9j4J8vbR8x0Hd/Noy5R4psToWAHgUm82mpKQkJSUlyWZj+gCgYa3LqFhpm8xKWwBAI2LOC3fCOxBNQt+24Xrvzn4KD/DR+swC3TgzTRl5R62OBQAAgHpWUu7U1n1HJEld4lhpCwAAPBNFWzQZyXEh+uiu/ooL9dP23EJdP2Np9YQeANCwTNNUcXGxiouLZZqm1XEANGObs4+o3GUq1N9bsSEOq+MAADwIc164E4q2aFLatgzUR3f1V1LLAGXlF+vGV9O0dm++1bEAoNlzuVxatmyZli1bJpfLZXUcAM3Y8ZuQ0U8QANCYmPPCnVC0RZMTG+qnj+66QF1bhehgYalufn2Z0rYdsDoWAAAA6sG6zKp+trRGAAAAnouiLZqksAAfvXtHP/VvG64jJeUa8+ZyLUjPsToWAAAAztH6zAJJUmc2IQMAAB6Moi2arEBfL705treGdo5SablLd81ZqU9W7bU6FgAAAM6S02VqQ1ZF0bYLK20BAIAHo2iLJs3hbdeMW3toZI9WcrpMTfhwjd5YvMPqWAAAADgL2/cfUXGZS37ediVGBFgdBwAAwDIUbdHkedltev76rrp9QKIk6ckv0vXPBZvZ6REAAKCJOb41gt3GJmQAAMBzUbRFs2CzGXr0ik7609AOkqSXUrfoic/T5XJRuAUAAGgq1lduQtaFfrYAAMDDeVkdAKgvhmHo3kvbK8TfW5M/Xa9ZS3cq/2iZnru+q7zt/H4CAM6FYRiKjY2t/hoAGkLVSluKtgAAKzDnhTuhaItmZ3T/BIX4eetPH67R3J8zVHC0TNNv7SGHt93qaADQZNlsNnXo0MHqGACaMdM0tS6jaqUtm5ABABofc164E5Yfolm6OiVOr43uKV8vm1I37tPoN5aroLjM6lgAAAA4ib2HjqqguFzedkPtowKtjgMAAGApirZoti7pGKW3f99XQb5eWr7joG5+bZlyj5RYHQsAmiTTNFVaWqrS0lI2egTQIKpaI7SPDJKvF5+QAgA0Pua8cCcUbdGs9UkM03t39lN4gI/WZxboxplpysg7anUsAGhyXC6Xli5dqqVLl8rlclkdB0AzlM4mZAAAizHnhTuhaItmLzkuRB/d1V9xoX7anluo62cs1dZ9R6yOBQAAgOOsYxMyAACAahRt4RHatgzUf+/ur6SWAcrKL9aNr6Zp7d58q2MBAACg0vrKlbbJcWxCBgAAQNEWHiMmxE8f3XWBurYK0cHCUt38+jKlbTtgdSwAAACPl3ukRDkFJTIMqVMMK20BAAAo2sKjhAX46N07+ql/23AdKSnXmDeXa0F6jtWxAAAAPFrVJmSJ4QEK8PWyOA0AAID1KNrC4wT6eunNsb01tHOUSstdumvOSn2yaq/VsQAAADzWuozKTchojQAAACCJoi08lMPbrhm39tDIHq3kdJma8OEavbF4h9WxAAAAPFI6m5ABAADUwGeP4LG87DY9f31Xhfh5640lO/TkF+nKP1qm+4e0l2EYVscDALdiGIaio6OrvwaA+lS1CRlFWwCAlZjzwp1YvtJ2+vTpSkhIkMPhUN++fbV8+fKTjl2/fr1GjhyphIQEGYahqVOnNl5QNEs2m6FHr+ikPw3tIEl6KXWLnvg8XS6XaXEyAHAvNptNHTt2VMeOHWWzWT59ANCMHC4u084DRZKkLrG0RwAAWIc5L9yJpe/ADz74QBMmTNBjjz2mVatWqVu3bho2bJj27dtX6/iioiK1bdtWzzzzTPVvPoBzZRiG7r20vZ68uoskadbSnfrTR2tU5nRZnAwAAKD5q2qNEBviUFiAj8VpAAAA3IOlRdsXX3xRd9xxh8aOHavOnTtr5syZ8vf31xtvvFHr+N69e+v555/XqFGj5Ovr28hp0dyN7p+gl0alyMtmaO7PGbrr7ZUqLnNaHQsA3IJpmnI6nXI6nTJNPo0AoP6sryzadmaVLQDAYsx54U4sK9qWlpZq5cqVGjJkyLEwNpuGDBmitLS0ertOSUmJCgoKatyAk7k6JU6vje4pXy+bUjfu0+g3lquguMzqWABgOZfLpR9++EE//PCDXC4+iQCg/qxnEzIAgJtgzgt3YlnRNjc3V06nU1FRUTWOR0VFKTs7u96uM2XKFIWEhFTf4uPj6+250Txd0jFKb/++r4J8vbR8x0Hd/Noy5R4psToWAABAs8QmZAAAACdq9l2VJ06cqPz8/Orbnj17rI6EJqBPYpjeu7OfwgN8tD6zQDfOTFNG3lGrYwEAADQrxWVObdl3RJKUHEd7BAAAgCqWFW0jIiJkt9uVk5NT43hOTk69bjLm6+ur4ODgGjegLpLjQvTRXf0VF+qn7bmFun7GUm2t/EcFAAAAzt3mnMNyuky18PdWTIjD6jgAAABuw7KirY+Pj3r27KnU1NTqYy6XS6mpqerfv79VsYAa2rYM1H/v7q+klgHKyi/Wja+mae3efKtjAQAANAvH+tmGyDAMi9MAAAC4D0vbI0yYMEGvv/66Zs+erQ0bNujuu+9WYWGhxo4dK0kaPXq0Jk6cWD2+tLRUq1ev1urVq1VaWqqMjAytXr1aW7duteolwAPEhPjpo7suUNdWITpYWKqbX1+mtG0HrI4FAADQ5NHPFgAAoHaWFm1vuukmvfDCC5o8ebJSUlK0evVqzZ8/v3pzst27dysrK6t6fGZmprp3767u3bsrKytLL7zwgrp3764//OEPVr0EeIiwAB+9e0c/9W8briMl5Rrz5nItSM85/YkAAAA4qXUZlStt6WcLAABQg5fVAcaPH6/x48fX+tjChQtr3E9ISJBpmo2QCjhRoK+X3hzbW/e+97MWpOforjkr9fz1XXVdj1ZWRwOABmcYhlq2bFn9NQCcK6fL1MbsqvYIrLQFAFiPOS/ciaUrbYGmxuFt14xbe2hkj1ZyukxN+HCN3li8w+pYANDgbDabunTpoi5dushmY/oA4Nxt339ExWUu+fvYlRgeYHUcAACY88KtWL7SFmhqvOw2PX99V4X4eeuNJTv05Bfpyj9apvuHtJfLlJbvOKh9h4sVGeRQn8Qw2W38dg4AAODXqjYh6xQTLBvzJQAAgBr4tQFwFmw2Q49e0Ul/GtpBkvRS6hbdPmuFBjzzrW5+fZnue3+1bn59mQY++63mr8s6zbMBAAB3Nn36dCUkJMjhcKhv375avnz5SceWlZXpySefVFJSkhwOh7p166b58+fXGPP999/ryiuvVGxsrAzD0Lx58054nttuu02GYdS4DR8+vL5fmqXWZVRsQpZMawQAAIATULQFzpJhGLr30vZ68uoukqTvNu1XdkFxjTHZ+cW6e84qCrcAmjyn06mFCxdq4cKFcjqdVscBGs0HH3ygCRMm6LHHHtOqVavUrVs3DRs2TPv27at1/KRJk/Tqq6/q5ZdfVnp6uu666y5de+21+vnnn6vHFBYWqlu3bpo+ffoprz18+HBlZWVV39577716fW1Wq1pp2yWWTcgAAO6BOS/cCUVb4Bzd2reNQv28a32satu8Jz5Pl9PFJnoAADQ1L774ou644w6NHTtWnTt31syZM+Xv76833nij1vFvv/22Hn74YY0YMUJt27bV3XffrREjRugf//hH9ZjLL79cTz/9tK699tpTXtvX11fR0dHVtxYtWtTra7OSaZpan1mx0rYzK20BAABOQNEWOEfLdxxU3tGykz5uSsrKL9byHQcbLxQAADhnpaWlWrlypYYMGVJ9zGazaciQIUpLS6v1nJKSEjkcjhrH/Pz8tHjx4jO+/sKFCxUZGanzzjtPd999tw4cOHDGz+Gu9h46qoLicnnbDXWICrI6DgAAgNuhaAuco32Hi08/6AzGAQAA95Cbmyun06moqKgax6OiopSdnV3rOcOGDdOLL76oLVu2yOVyacGCBfrkk0+UlXVmrZKGDx+ut956S6mpqXr22We1aNEiXX755af8qGZJSYkKCgpq3NxV1SrbDlFB8vHinyQAAAC/5mV1AKCpiwxynH6QpJ25RXK5THZHBgCgGXvppZd0xx13qGPHjjIMQ0lJSRo7duxJ2ymczKhRo6q/Pv/889W1a1clJSVp4cKFuvTSS2s9Z8qUKXriiSfOKX9jOdbPltYIAAAAteHX2sA56pMYppgQh05Xiv3nN5s1bOr3mvvzXpU7XY2SDQAAnL2IiAjZ7Xbl5OTUOJ6Tk6Po6Ohaz2nZsqXmzZunwsJC7dq1Sxs3blRgYKDatm17Tlnatm2riIgIbd269aRjJk6cqPz8/Orbnj17zumaDYlNyAAAAE6Noi1wjuw2Q49d2VmSTijcGpW3y5OjFeTw0pZ9R/TAB2t0yT8W6d0fd6uknN0oAQBwVz4+PurZs6dSU1Orj7lcLqWmpqp///6nPNfhcCguLk7l5eX6+OOPdfXVV59Tlr179+rAgQOKiYk56RhfX18FBwfXuLmrdRkV7RFYaQsAAFA72iMA9WB4coxm/LaHnvg8XVn5x3rXRoc49NiVnTU8OUYFxWV6O22X3li8Q7sPFunhuWv1Uupm3XFhW93St7X8ffjPEYD7MgxDYWFh1V8DnmLChAkaM2aMevXqpT59+mjq1KkqLCzU2LFjJUmjR49WXFycpkyZIkn68ccflZGRoZSUFGVkZOjxxx+Xy+XSgw8+WP2cR44cqbFidseOHVq9erXCwsLUunVrHTlyRE888YRGjhyp6Ohobdu2TQ8++KDatWunYcOGNe43oAHsP1yifYdLZBhSpxiKtgAA98GcF+6EKhFQT4Ynx2ho52gt33FQ+w4XKzLIoT6JYbJX9rANdnhr3OB2un1Aot5bvluvfb9d2QXFevr/Nmj6d1t1+4BEjb4gQSF+3ha/EgA4kc1mU9euXa2OATS6m266Sfv379fkyZOVnZ2tlJQUzZ8/v3pzst27d8tmO/bhteLiYk2aNEnbt29XYGCgRowYobfffluhoaHVY3766ScNHjy4+v6ECRMkSWPGjNGsWbNkt9v1yy+/aPbs2crLy1NsbKwuu+wyPfXUU/L19W2cF96AqjYhS4wIUIAv/xwBALgP5rxwJ4ZpmqbVIRpTQUGBQkJClJ+f79YfGUPzV1Lu1NxVGZqxaJt2HSiSJAX6eul3/dvo9wMTFRHY9P9RBgDAmWCeVr/c9fs5/butev7rTbqyW6xevrm71XEAAAAaVV3naPS0BSzi62XXqD6tlTrhIr00KkUdogJ1pKRcMxZu08Bnv9Xjn61XZt5Rq2MCAADUq6qVtvSzBQAAODk+jwRYzMtu09Upcbqya6y+2ZCj6d9t1Zq9+Zq1dKfe+XGXruveSndfnKSEiACrowLwYE6nU0uWLJEkDRgwQHa73eJEAJqq9ZkFkqTk2BCLkwAAUBNzXrgTiraAm7DZDF3WJVpDO0dpydYDmvbdFi3bflAf/LRHH63coyu6xuqewUnqGM2qFADWcLlcVkcA0MQVFJdVt4VipS0AwB0x54W7oGgLuBnDMDSwfYQGto/Qyl0HNf27bfp24z59tiZTn63J1JBOURo3OEndW7ewOioAAMAZ2VC5yjY2xKEWAT4WpwEAAHBfFG0BN9azTZjeuC1M6zPz9cp32/Tluix9syFH32zI0YB24Ro3uJ36tw2XYRhWRwUAADitdZVF2860RgAAADglirZAE9AlNkTTb+2hbfuPaMbCbZr3c4aWbD2gJVsPqEfrUI0b3E6XdIykeAsAANxa1SZkyXG0RgAAADgVm9UBANRdUstAvXBDNy38y8Ua3b+NfLxsWrU7T7+f/ZNG/GuxvvglU06XaXVMAACAWqVXrrTtwkpbAACAU6JoCzRBrVr468mrk7X4r4P1/wa1VYCPXRuyCjT+3Z819MVF+vCnPSpz0jwdAAC4j+Iyp7bsOyKJTcgAAABOh6It0IRFBjk0cUQnLXnoEt0/pL1C/Ly1PbdQD/73F138/ELNXrpTxWVOq2MCaCZCQ0MVGhpqdQwATdSm7MNyuky18PdWTIjD6jgAANSKOS/cBT1tgWYg1N9H9w/poD9c2FbvLNul13/YoYy8o3rss/V6+dut+sOFibq1b2sFObytjgqgibLb7UpJSbE6BoAmbH1la4TkuBD68AMA3BJzXrgTVtoCzUigr5f+30VJWvzXwXrq6i6KC/VT7pESPfPVRg145lu9uGCzDhWWWh0TAAB4oKpNyDrTGgEAAOC0KNoCzZDD267f9U/Qwr9crBdu6Ka2LQNUUFyuf6Vu0YBnv9Xfv9ygfQXFVscEAAAeZD2bkAEAANQZRVugGfO223R9z1Za8MBFmn5LD3WOCVZRqVOvfb9dA5/7TpPmrdWeg0VWxwTQBDidTi1ZskRLliyR00mvbABnptzp0oasyvYIrLQFALgp5rxwJ/S0BTyA3WboN11jNOL8aC3ctF/TvtuqlbsOac6y3Xpv+R5dnRKrey5up3aRgVZHBeDGysrKrI4AoInanluoknKXAnzsSggPsDoOAAAnxZwX7oKiLeBBDMPQ4I6Ruvi8llq2/aBeWbhVP2zJ1SerMjT35wwN7xKtcYPbKTmOjy0CAID6U9XPtlNMsGw2NiEDAAA4HYq2gAcyDEP9k8LVPylcq/fkafp3W7UgPUdfrcvWV+uydfF5LTV+cDv1SgizOioAAGgG1mdU9bOlNQIAAEBdULQFPFxKfKheH91LG7MLNGPhNn2+JlMLN+3Xwk371ScxTOMHt9OF7SNkGKyKAQAAZ2dd5UrbLnyaBwAAoE7YiAyAJKljdLBeGtVd3/7pYo3qHS9vu6HlOw5q9BvLdfX0Jfp6fbZcLtPqmAAAoIkxTVPpmay0BQAAOBMUbQHUkBARoGdGdtX3Dw7W2AEJcnjb9MvefP2/t1dq+Evfa97PGSp3umqc43SZStt2QJ+uzlDatgNyUtwFAACV9h46qoLicnnbDbWPDLI6DgAAQJNAewQAtYoJ8dNjV3bRuMHt9OaSHXpr6S5tzjmi+z9YrRcXbNZdFyVpZM84fbdxn574PF1Z+cXHnevQY1d21vDkGAtfAYD6FhREsQXAmavahKxDVJB8vFgzAgBwb8x54S4M0zQ9aklcQUGBQkJClJ+fr+BgPp4F1FX+0TK9nbZTbyzZqYOFpZKkED8v5R8tP2FsVffbGb/tQeEWAFBnzNPql7t8P1/4epOmfbdVN/WK17PXd7UsBwAAgDuo6xyNX3UDqJMQP2+Nv6S9Fv91sB69orMig3xqLdhKUtVvgp74PJ1WCQAAeLj11ZuQUYgHAACoK4q2AM6Iv4+Xfj8wUS/c0O2U40xJWfnFWr7jYOMEAwAAbmk9m5ABAACcMXraAjgrh4rK6jTu5W+3KK+oVBckRSjE37uBUwFoKE6nUytWrJAk9e7dW3a73eJEAJqCfYeLte9wiQxD6hhN0RYA4N6Y88KdULQFcFYigxx1Grd02wEt3XZANkM6v1WoLmwXoYHtI9SjdQs2IwGamOLi4tMPAoDjVK2ybRsRoABf/ukBAHB/zHnhLpg5ATgrfRLDFBPiUHZ+sWrrWmtICvX31pXdYrVka6627S/Umj15WrMnT9O+2yp/H7v6JoZpYPuWurB9hNpHBsowjFqeCQAANFXp1a0RQixOAgAA0LRQtAVwVuw2Q49d2Vl3z1klQ6pRuK0qvU657nwNT46RJGXmHdXirblavCVXS7bm6kBhqb7btF/fbdovSYoK9tWAdhG6sH2EBrSLqPNKXgAA4L6qNyGjny0AAMAZoWgL4KwNT47RjN/20BOfpysr/9hHSKJDHHrsys7VBVtJig3104294nVjr3i5XKY2ZBdo8ZZcLd6aq+U7DiqnoESfrMrQJ6syJEkdo4M0sLKVQt/EcPn50EsIAICmZl0GK20BAADOBkVbAOdkeHKMhnaO1vIdB7XvcLEigxzqkxgmu+3krQ5sNkNdYkPUJTZE/++iJBWXOfXTzkP6Yet+Ld6Sq/WZBdqYfVgbsw/r34t3yMduU882LTSwfcVK3C6xIad8fgAAYL2C4jLtPlgkiZW2AAAAZ4qiLYBzZrcZ6p8UftbnO7ztGti+YlWtLpcOHCnRkm0HtHhLRRE3M79YadsPKG37AT3/9SaF+ntrQFLF+IHtIhQf5l+PrwYAANSHqn62caF+ahHgY3EaAACApoWiLQC3Ex7oq6u6xeqqbrEyTVPbcwu1eEuuftiSq2XbDyivqEz/tzZL/7c2S5KUEO5fWcBtqf5J4Qrx87b4FQDNk78/vyABUHfrK4u2nVllCwBoQpjzwl1QtAXg1gzDUFLLQCW1DNSYCxJU5nRpzZ48/VDZD3f1njztPFCknQd2a86y3bIZUrf4UF3YLkID27dU99ah8rbbrH4ZQJNnt9vVp08fq2MAaELWZ7AJGQCgaWHOC3dC0RZAk+Jtt6lXQph6JYTpgaEdVFBcpmXbDmjx1lwt3pKr7bmF+nl3nn7enad/fbtVAT529WsbXt0PN6lloAyDfrgAADS0qpW2yWxCBgAAcMYo2gJo0oId3rqsS7Qu6xItScrIO6rFW/brhy25WrI1V4eKypS6cZ9SN+6TJMWEODSgXUUBd0C7CEUE+loZHwCAZqm4zKmt+49IkrrEsdIWAADgTFG0BdCsxIX66aberXVT79ZyuUylZxVUtlLYrxU7Dykrv1j/XblX/125V5LUKSZYF1ZuaNYnMUwOb7vFrwBwT06nUytXrpQk9ezZU3Y7/60AOLlN2YfldJkKC/BRdLDD6jgAANQJc164E4q2AJotm81QclyIkuNCdPfFSTpa6tSKnQe1eGvFpmYbsgqqb699v10+Xjb1Tmihge1a6sL2EeocEyyb7dStFJwuU8t3HNS+w8WKDHKoT2KY7Kc5B2iqioqKrI4AoIlYl3msny1tiQAATQlzXrgLirYAPIafj12DOrTUoA4tJUn7D5do6baKAu7iLbnKLijWkq0HtGTrAT07XwoL8NEFSeEVK3Hbt1RcqF+N55u/LktPfJ6urPzi6mMxIQ49dmVnDU+OadTXBgCAO6nqZ9uFfrYAAABnhaItAI/VMshXV6fE6eqUOJmmqW37j1QXcJdtP6CDhaX64pcsffFLliSpbUSABla2UigsKdeED9fI/NVzZucX6+45qzTjtz0o3AIAPNaxoi39bAEAAM4GRVsAkGQYhtpFBqldZJDGDkhUablLq/fkVWxqtjVXa/bkaXtuobbnFuqttF0nfR5TkiHpic/TNbRzNK0SAAAep9zp0sYsirYAAADngqItANTCx8umPolh6pMYpgmXnaf8o2VK23ZAi7fu14L0HOUUlJz0XFNSVn6xHp67Vv3ahikmxE+xIX6KCvGVrxeN7AEAzdu2/YUqKXcpwMeuhPAAq+MAAAA0SRRtAaAOQvy8NTw5WsOTo9U7IUz3vb/6tOd8sGKPPlixp8axiEBfxYY6FBPiqCjmhtb8MzLIV152WwO9CgAAGt76yk3IOseefkNPAAAA1I6iLQCcocggR53GXdg+QuVOU1n5R5WVX6yScpdyj5Qo90iJftmbX+s5NkOKCq4s6ob6KfZXxd2YUIciAnz5RzAs4XDU7b0PwLOxCRkAoCljzgt3QdEWAM5Qn8QwxYQ4lJ1ffMJGZFJFT9voEIdmje1T3dPWNE0dLCxVVn6xMvMqiriZ+UeVlVesrPyjyswrVk5BscpdprLyi5WVXyztzqv1+t52Q9FVxdxfFXdjQh2KDfFTqL+3DKPhCrtOl6nlOw5q3+FiRQY51CcxjP69zZzdble/fv2sjgGgCTh+pS0AAE0Jc164E4q2AHCG7DZDj13ZWXfPWSVDqlG4rSpbPnZl5xpFTMMwFB7oq/BAXyXH1b7yyOkylXuk5FhRt/LPqpW6WXnF2ne4WGVOU3sOHtWeg0dPmtHhbVNsZRH3+OJuTIhDsZV/Bjm8z+r1z1+XpSc+T68oLFeKCXHosSs7a3hyzFk9JwCgeTBN87iVthRtAQAAzhZFWwA4C8OTYzTjtz1OKF5Gn0Px0m4zFBXsUFSwQ91PMqbM6dK+wyXKyjuqzPxiZdVS4M09UqriMpe25xZqe27hSa8X5Ot1rKhb1X7huKJuTIif/Hxqbpw2f12W7p6z6oQVxtn5xbp7zirN+G0PCrcA4MH2HDyqw8Xl8rHb1D4yyOo4AAAATRZFWwA4S8OTYzS0c3SjtgnwttsUF+qnuFC/k44pLnMqp6BYmXnHVun+evVu/tEyHS4p1+GcI9qcc+Skz9XC31vRlSt1o0J89dnqrFpbQpiqWGX8xOfpGto5ulm3SvDU1hBOp1OrV6+WJKWkpMhut5/6BKAZmT59up5//nllZ2erW7duevnll9WnT59ax5aVlWnKlCmaPXu2MjIydN555+nZZ5/V8OHDq8d8//33ev7557Vy5UplZWVp7ty5uuaaa2o8j2maeuyxx/T6668rLy9PAwYM0IwZM9S+ffuGfKnnrKo1QofoQPl4sbEmAKBpYc4Ld0LRFgDOgd1mqH9SuNUxanB429UmPEBtwgNOOqawpPxY64W8Y/11M6tbMRxVYalTh4rKdKioTBuyCk57XVNSVn6xLn7+O0UE+crfxy4/by/5+9grvq7809/HS37exx/zqvm4t1f1137edrfadM3TW0McPnzY6ghAo/vggw80YcIEzZw5U3379tXUqVM1bNgwbdq0SZGRkSeMnzRpkubMmaPXX39dHTt21Ndff61rr71WS5cuVffuFZ+jKCwsVLdu3XT77bfruuuuq/W6zz33nP71r39p9uzZSkxM1KOPPqphw4YpPT3drTdIqW6NEMMmZACApok5L9yFYZpmbYummq2CggKFhIQoPz9fwcH02QKA2pimqYLi8hpF3UWb9ut/6TmNnsXhbasu9B5fzK0uAB9fFPb+VQHYxy6/qqJwLed42+u+CuxkrSGqSsrNvTWE0+nUDz/8IEm68MILWXWABuGO87S+ffuqd+/emjZtmiTJ5XIpPj5e9957rx566KETxsfGxuqRRx7RuHHjqo+NHDlSfn5+mjNnzgnjDcM4YaWtaZqKjY3Vn/70J/35z3+WJOXn5ysqKkqzZs3SqFGj6pTdiu/nbW8u18JN+/Xk1V00un9Co1wTAID6wpwXjaGuczRW2gIATmAYhkL8vBXi562O0RX/E2kbEVinou3DIzqqTXiAjpY6VVTqVFFpecXXZc7KY+UqKnUee7zMqaO/Ona0zFn9fMVlLhWXlTbI6/S2G5XF3JrFXj8fL/kftxrY4W3TByv2nrQ1hOQZrSEAT1NaWqqVK1dq4sSJ1cdsNpuGDBmitLS0Ws8pKSk5YSWsn5+fFi9eXOfr7tixQ9nZ2RoyZEj1sZCQEPXt21dpaWl1Ltpa4dgmZKy0BQAAOBcUbQEAddInMUwxIQ5l5xfXWrw0VLER2+8Htj3nwqXLZaq43FmzuFtZ/D1advzx8uOKwVXHy6sLvyccqywSO10Vr6DMaarMWa6C4vJzyitVtIZIfmy+wgN91cLfR6H+3gr191Gon7da+HsrxN9HLfy91cLfRyGVf4b6eSvYz7tJFHqdLlN7DhapsLRc3tsOqF+7lk0iN3AucnNz5XQ6FRUVVeN4VFSUNm7cWOs5w4YN04svvqhBgwYpKSlJqamp+uSTT+R0OmsdX5vs7Ozq6/z6ulWP1aakpEQlJSXV9wsKTt/apj7tO1ys/YdLZBhSpxg2IQMAADgXFG0BAHVitxl67MrOunv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+ }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Loading best checkpoint (epoch 8, val macro-F1=0.9422)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Loading weights: 0%| | 0/104 [00:00" + ], + "image/png": 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\n" 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: /content/outputs/bert/distilbert_binary/confusion_matrix_test.png\n", + "Saved predictions_val.csv\n", + "Saved predictions_test.csv\n", + "\n", + "=== DONE: distilbert-base-uncased / binary ===\n", + " Test Accuracy : 0.9413\n", + " Test Macro-F1 : 0.9413\n", + " Test Weighted-F1: 0.9413\n" + ] + } + ], + "source": [ + "import torch\n", + "from transformers import (\n", + " AutoTokenizer,\n", + " AutoModelForSequenceClassification,\n", + " TrainingArguments,\n", + " Trainer,\n", + " DataCollatorWithPadding,\n", + ")\n", + "import torch\n", + "from torch.utils.data import Dataset, DataLoader\n", + "\n", + "\n", + "results_distilbert_binary = train_bert(\n", + " cfg = CFG_BINARY,\n", + " X_train = X_train_bin, y_train = y_train_bin,\n", + " X_val = X_val_bin, y_val = y_val_bin,\n", + " X_test = X_test_bin, y_test = y_test_bin,\n", + " label_names= label_names_bin,\n", + " out_dir = BERT_OUT / \"distilbert_binary\",\n", + " val_df = val_bin,\n", + " test_df = test_bin,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rtkEQF58uKCC" + }, + "source": [ + "## Train DistilBERT — Type Task" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "id": "yBICJ8OMuKCC", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "c2798d3ac4764779a0a0bf2a6a6eac36", + "abc38311fd234e4688de3c7473af780c", + "b0a5cdd212c04ea18f5b96510ef6b575", + "7a2183d510c34f349f07a9ec9df78b41", + "38712f9063244810a643d4f3f161c81c", + "fece75f72feb44aa9966ddacfa0dcffd", + "fbe7c145e000429ba2b6cb6e8cab4c62", + "539e2e3a4e7341d490363e7c62851dfe", + "bcc2ac88295b457c904807961b6b6421", + "241d349e0f3c4a798d7e8259fb97d467", + "4036fecf5e8e46cb8e2ef49eb97efb53", + "bb77c843d3e041f6bb879d59325617e6", + "11f22b569b534230b96e8114c93fe94f", + "06d3a0c015924a6a9969f1309161ef42", + "eb890422b3e9404484cce0a69e68b23e", + "a16e5ac3fd65410ab101fa89c5da1462", + "ced5f0dd70d14460b142bfb71bea9408", + "267d314646064b71bba0691f3349f3b5", + "26e305c7e39447689d9027f1b3cff0fd", + "7a186bd45e3a48bf9029c36673107e75", + "a3e2c7e1ef60459b8d4116c0c63f342d", + "53e380629c224569a902436737b77aea", + "509e0ac3a0bb48189d4f1671bd203c7a", + "86ea361f12aa48a7a4eafba0578e5e56", + "d627ce7bc8da429192e013f9013ec2b5", + "d13d0dc445ce495389000e2a35aa494d", + "31c5c30a055c4ab4b3f982f3f2f8d93b", + "b7aba1b3eb494928a94f7c982a40a2b6", + "2bdaaf5b8cf6423398c9cd4029d84c68", + "d4b20a2b44c3495886f3aaa7a556abb0", + "94782438c86347adb1b79e09ada4d3b1", + "655332da8ee0459f80397119ca6ed7b1", + "525b2a218f4b4e4ca2ec2ddff302e755", + "ef5abcd86f224dd5860d42394c561dcd", + "edc857046991407bb8b86d8667a38b67", + "645f21ff2adb4263b576eff7fec8edf7", + "9a3afb9c3ca944b98075179be1d9c709", + "c11f3fb4a65241bc95c2b51a19202e73", + "c1b054a9a53b491c9094f31767d1ac97", + "1ca48bb49b374e4a9f7f45e85fc25f06", + "5bed5c7e6e3d4f55906fff53ae4a3639", + "af16290447aa47cbb357b1c325c4ebfe", + "3979d4099dc343ad8c28ed371e25d46d", + "fd99b747589d4821a2c04f289aed91b8" + ] + }, + "outputId": "2a7dc24a-62ac-4ca0-86e1-b6523cba0541" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Using device: cuda\n", + "Config saved to /content/outputs/bert/distilbert_type/config.json\n", + "Loading tokenizer: distilbert-base-uncased\n", + "Loading model: distilbert-base-uncased (6 labels)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Loading weights: 0%| | 0/100 [00:00" + ], + "image/png": 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+ }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Loading best checkpoint (epoch 2, val macro-F1=0.4944)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Loading weights: 0%| | 0/104 [00:00" + ], + "image/png": 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\n" 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" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: /content/outputs/bert/distilbert_type/confusion_matrix_test.png\n", + "Saved predictions_val.csv\n", + "Saved predictions_test.csv\n", + "\n", + "=== DONE: distilbert-base-uncased / type ===\n", + " Test Accuracy : 0.4525\n", + " Test Macro-F1 : 0.4863\n", + " Test Weighted-F1: 0.4492\n" + ] + } + ], + "source": [ + "results_distilbert_type = train_bert(\n", + " cfg = CFG_TYPE,\n", + " X_train = X_train_type, y_train = y_train_type,\n", + " X_val = X_val_type, y_val = y_val_type,\n", + " X_test = X_test_type, y_test = y_test_type,\n", + " label_names= STRATEGY_LABELS,\n", + " out_dir = BERT_OUT / \"distilbert_type\",\n", + " val_df = val_type,\n", + " test_df = test_type,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yFgqLSPouKCC" + }, + "source": [ + "## (Optional) BERT-base — Binary Task\n", + "\n", + "Run only if compute allows. Comment out if not needed." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "id": "7spIQwpluKCD", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "db76eb4191a3470b91952ea5d18d56c2", + "a0392899e5334e3aadb486bef196e755", + "ed52d66f7efa4ed0a873e7c0c426fc3e", + "fa528e1efa5b47458d8fee3942e462df", + "098e7bc84a8a457bb2458d5cabc0e7d4", + "f78cb6ac90b8480aa67eb471fbae156f", + "23e574f855fb45149acd3e2b282acab5", + "8a7c8f3c249d4dc18aecdd862361af45", + "2f06a6e43b77447abc1631224eba9465", + "a64b0e86a41949b8be5cc8df2abe09cc", + "67d593b7936f4657a2ee4d7c20b7ca6d", + "1e8f3de937de456c9d766ca48bf74c4d", + "2c64770f702b45bba779575696ea3d27", + "27b2dc4bf50a4b19981d6a0e3b266926", + "a3f01328a35448aaabdd906813c677ec", + "1c99541e28634eae97a4f7494a9d2c74", + "3ef6d934157a4ba5a19f1db0b6eba2f1", + "d01ff9e67fc9405eb9c8d7ff72b7fb9b", + "f4f5a2ed88b64402828f6585e064618c", + "ebedf7c26ff94389b18b923b98074c53", + 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"64e35d0488834affab41382b572547dd", + "168feef238f6417a8922a8eefac93fd4", + "b17f4c8d2c6340cd84768f6dfe77dfcd", + "3ea47df1ed6049cca2f3d3ebdf38ed12", + "cbb881ef447245f384ade11166d5742c", + "5d74799a3894462cb38ec7249049a515", + "0e3b2cbd63254995be8ff294ef494539", + "1c4373671827414c89a95b7c0414504d" + ] + }, + "outputId": "7aeb9151-7f58-49b7-e823-5d03fa90f4ac" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Using device: cuda\n", + "Config saved to /content/outputs/bert/bert_base_binary/config.json\n", + "Loading tokenizer: bert-base-uncased\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/570 [00:00" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Loading best checkpoint (epoch 8, val macro-F1=0.9496)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Loading weights: 0%| | 0/201 [00:00" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: /content/outputs/bert/bert_base_binary/confusion_matrix_val.png\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: /content/outputs/bert/bert_base_binary/confusion_matrix_test.png\n", + "Saved predictions_val.csv\n", + "Saved predictions_test.csv\n", + "\n", + "=== DONE: bert-base-uncased / binary ===\n", + " Test Accuracy : 0.9469\n", + " Test Macro-F1 : 0.9469\n", + " Test Weighted-F1: 0.9469\n" + ] + } + ], + "source": [ + "# Uncomment to run BERT-base (requires more GPU memory and time)\n", + "\n", + "CFG_BERT_BINARY = TrainConfig(\n", + " model_name=\"bert-base-uncased\",\n", + " task=\"binary\",\n", + " batch_size=16, # smaller batch for bert-base\n", + " lr=2e-5,\n", + ")\n", + "\n", + "results_bert_binary = train_bert(\n", + " cfg = CFG_BERT_BINARY,\n", + " X_train = X_train_bin, y_train = y_train_bin,\n", + " X_val = X_val_bin, y_val = y_val_bin,\n", + " X_test = X_test_bin, y_test = y_test_bin,\n", + " label_names = label_names_bin,\n", + " out_dir = BERT_OUT / \"bert_base_binary\",\n", + " val_df = val_bin,\n", + " test_df = test_bin,\n", + ")\n", + "\n", + "# print(\"BERT-base is commented out. Uncomment cells above to run.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PUSxauAluKCD" + }, + "source": [ + "## (Optional) BERT-base — Type Task" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "id": "uaqm4IUjuKCD", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "3087616965204e6eb252b713a2d804be", + "10373c4f5d7148a1b5b93172297304ba", + "dc9a2aa4ea3a42e3bf32c57faba1c358", + "2ec96fb971804acca6d5f95c1ad94e73", + "3718de34b3644cd388aa40a7c405e0e9", + "813faa0f2c6e469ebbe32e31869c3be7", + "b976255a50e442d7995f82e9e0fe5110", + "12297f19d8bc45558c9e9199db12932a", + "b97789c14b9549369a23c20bd05d48ff", + "03bb67494cf24689b3e231c4c0576de0", + "868d2e23b4544f2687da6a55bb6a8d26", + "e3d393df1b674b738c5bb0cd26ff59ab", + "1ed5a3871ac14ffb8164fc6ee3695115", + "7529696072b341e5b7d7fa73f1965376", + "e832fee1dc8a4350a4a148e5596fbd09", + "b6d17e5764a84933b3bcf3a379c2a80d", + "da6debc3842047c79087dc64735e43b6", + "fd85b43943d14d628e5f151ce8b1e211", + "fdedae0fec6849508d633a8ba5ce47d3", + "b18a7e15d3424ddc97364a3b4a82729e", + "ce64e524fbbb4c4b89c3f9e10d4d0819", + "3e16cf32ab1a477abfeaa7182ea16b2f", + "702ed06ee9ab40e4991dc3facb6cea07", + "1c975bb80be444df871a0750c33e54a0", + "4ceb6c9db2b0489a8df0b64dd5a55ce1", + "4274cd3e93d14b32b4503157b252b2ca", + "d1c3bbc7f29a435db7d035eda2dc8dd1", + "0bfa0e32b8b04e8a8bd0539f199cdd7b", + "ad1503b4ae4845ada820c45c1de15ddc", + "4fb56929b63846969f16eaf306c782e3", + "c0702ccaf827475993d533913c13f64f", + "95d796d3d37b416398538ffc86a605d0", + "e904595f2894413bae665ac1b1f7e7e7", + "0ebbf24309824a36802defcd31617e8c", + "a47bde05bce24056a7d9f4ff329786e7", + "c26d063fb246450896faa2486b0a662a", + "6863c35860b64974b5415cc644a52210", + "4150625a6bc742aa8cbc1819c20c2ced", + "1d0c87f274d042bea7ad0500e15c6f63", + "fc658b05808b41a6967ee6db50737249", + "f8675021ea3a4228b39f4236c4d90b3a", + "14599aa681644f81ac78077a0ff6680c", + "ee185ef2ca1d4aca8a1df45f4825236f", + "4a5e71f1a07041c4aae6772274a2910f", + "083c3c7fc51943da849b6b326d410c32", + "eb266c8f50674094bce04d7c485348ce", + "cfd6b8d9a2a240b686ee0f22757bc9f5", + "53b405decd0b495faa31f2a3de5e4bee", + "514f7d05ada643b6bab58e2be5366a04", + "34d47f635000485ba5fca36f7c93e4fb", + "5ed6f0ffbe264d30ab8d36366938184f", + "0024cac9d3094cfeb5fb9b2da9ac372f", + "1053d086f1be43149e0f57f713834950", + "a6bf6564aafd4b58b3caa8622daa9467", + "ae762715a4de430db2097f1f5558d6b7", + "c5d831a1358345bb9a059574aac43300", + "c76691a136704bdb91bfdb8ecbc91b09", + "1914630bf0844732842c8d48e4e4cf4b", + "1c29f959036d44cbae6cba4394fb1f14", + "0cc2a8684f204c98a78eef7d858dd5e7", + "15d6bf3260b84804a1ba7d1dcf524c31", + "613de761ec8f45699a43088de7ac5ea6", + "1e3c5a2ae4864d71a51f72932c5eafe4", + "a58a6d8192744c9faee4782c4453ed88", + "a4a98b48af2c471296f89faf3109bd36", + "cfe4d0d766404b999326a7fdb29faaab" + ] + }, + "outputId": "bf52f671-ca14-4a5b-dc3c-1f1eb2640bc8" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Using device: cuda\n", + "Config saved to /content/outputs/bert/bert_base_type/config.json\n", + "Loading tokenizer: bert-base-uncased\n", + "Loading model: bert-base-uncased (6 labels)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Loading weights: 0%| | 0/199 [00:00" + ], + "image/png": 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+ }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Loading best checkpoint (epoch 7, val macro-F1=0.4991)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Loading weights: 0%| | 0/201 [00:00" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: /content/outputs/bert/bert_base_type/confusion_matrix_val.png\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: /content/outputs/bert/bert_base_type/confusion_matrix_test.png\n", + "Saved predictions_val.csv\n", + "Saved predictions_test.csv\n", + "\n", + "=== DONE: bert-base-uncased / type ===\n", + " Test Accuracy : 0.4612\n", + " Test Macro-F1 : 0.4822\n", + " Test Weighted-F1: 0.4589\n" + ] + } + ], + "source": [ + "CFG_BERT_TYPE = TrainConfig(\n", + " model_name=\"bert-base-uncased\",\n", + " task=\"type\",\n", + " batch_size=16,\n", + " lr=2e-5,\n", + " use_class_weights=True,\n", + ")\n", + "\n", + "results_bert_type = train_bert(\n", + " cfg = CFG_BERT_TYPE,\n", + " X_train = X_train_type, y_train = y_train_type,\n", + " X_val = X_val_type, y_val = y_val_type,\n", + " X_test = X_test_type, y_test = y_test_type,\n", + " label_names = STRATEGY_LABELS,\n", + " out_dir = BERT_OUT / \"bert_base_type\",\n", + " val_df = val_type,\n", + " test_df = test_type,\n", + ")\n", + "\n", + "# print(\"BERT-base type task is commented out. Uncomment to run.\")" + ] }, { - "output_type": "stream", - "name": "stderr", - "text": [ - "DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\n", - "Key | Status | \n", - "------------------------+------------+-\n", - "vocab_layer_norm.bias | UNEXPECTED | \n", - "vocab_transform.bias | UNEXPECTED | \n", - "vocab_transform.weight | UNEXPECTED | \n", - "vocab_layer_norm.weight | UNEXPECTED | \n", - "vocab_projector.bias | UNEXPECTED | \n", - "classifier.weight | MISSING | \n", - "pre_classifier.weight | MISSING | \n", - "classifier.bias | MISSING | \n", - "pre_classifier.bias | MISSING | \n", - "\n", - "Notes:\n", - "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n", - "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "cFQdWUqpuKCD" + }, + "source": [ + "## Summary" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "id": "89y9LuozuKCD", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "22d239bf-ce22-42f8-bb57-b001260ecab2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "====== BERT RESULTS SUMMARY ======\n", + "\n", + "DistilBERT — Binary (Test):\n", + " Accuracy : 0.9413\n", + " Macro-F1 : 0.9413\n", + " Weighted-F1: 0.9413\n", + "\n", + "DistilBERT — Type (Test):\n", + " Accuracy : 0.4525\n", + " Macro-F1 : 0.4863\n", + " Weighted-F1: 0.4492\n", + "\n", + "Artifacts saved in outputs/bert/distilbert_binary/ and outputs/bert/distilbert_type/\n" + ] + } + ], + "source": [ + "print(\"====== BERT RESULTS SUMMARY ======\")\n", + "print()\n", + "print(\"DistilBERT — Binary (Test):\")\n", + "print(f\" Accuracy : {results_distilbert_binary['test']['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {results_distilbert_binary['test']['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1: {results_distilbert_binary['test']['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"DistilBERT — Type (Test):\")\n", + "print(f\" Accuracy : {results_distilbert_type['test']['accuracy']:.4f}\")\n", + "print(f\" Macro-F1 : {results_distilbert_type['test']['f1_macro']:.4f}\")\n", + "print(f\" Weighted-F1: {results_distilbert_type['test']['f1_weighted']:.4f}\")\n", + "print()\n", + "print(\"Artifacts saved in outputs/bert/distilbert_binary/ and outputs/bert/distilbert_type/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9dAXibWCuKCE" + }, + "source": [ + "---\n", + "# Part 5 — Error Analysis & Model Comparison" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FIcicb-xuKCE" + }, + "source": [ + "# Notebook 05 — Error Analysis & Model Comparison\n", + "\n", + "**Purpose**:\n", + "1. Compare all models on both tasks\n", + "2. Analyze errors (binary + type)\n", + "3. Generate final comparison report\n", + "\n", + "**Prerequisite**: Run notebooks 01–04 first.\n", + "\n", + "**Outputs**:\n", + "- `outputs/reports/model_comparison.md`\n", + "- `outputs/reports/error_analysis.md`\n", + "- `outputs/reports/error_examples_binary.csv`\n", + "- `outputs/reports/error_examples_type.csv`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "saP8-m-MuKCE" + }, + "source": [ + "## 1. Load All Metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "id": "cs2Mce2ZuKCE", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0957243b-32dc-4eb5-edff-5bd051a6263a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "ROOT: /content\n", + "OUTPUTS exists: True\n", + "CLASSICAL exists: True\n", + "BERT_OUT exists: True\n", + "\n", + "Metrics loaded (None = not yet run):\n", + " TF-IDF+LR binary: ✓\n", + " TF-IDF+LR type: ✓\n", + " NB binary: ✓\n", + " NB type: ✓\n", + " DistilBERT binary: ✓\n", + " DistilBERT type: ✓\n", + " BERT-base binary: ✓\n", + " BERT-base type: ✓\n" + ] + } + ], + "source": [ + "from pathlib import Path\n", + "import json\n", + "\n", + "# --- Path setup (self-contained) ---\n", + "ROOT = Path.cwd() # current working directory (e.g., /content in Colab)\n", + "OUTPUTS = ROOT / \"outputs\"\n", + "\n", + "# If outputs/ is not in the current folder, set ROOT manually here:\n", + "# ROOT = Path(\"/content/drive/MyDrive/cs4248\") # example\n", + "# OUTPUTS = ROOT / \"outputs\"\n", + "\n", + "CLASSICAL = OUTPUTS / \"classical\"\n", + "BERT_OUT = OUTPUTS / \"bert\"\n", + "\n", + "print(\"ROOT:\", ROOT)\n", + "print(\"OUTPUTS exists:\", OUTPUTS.exists())\n", + "print(\"CLASSICAL exists:\", CLASSICAL.exists())\n", + "print(\"BERT_OUT exists:\", BERT_OUT.exists())\n", + "\n", + "def load_metrics(path: Path):\n", + " if path.exists():\n", + " with open(path, \"r\", encoding=\"utf-8\") as f:\n", + " return json.load(f)\n", + " try:\n", + " rel = path.relative_to(ROOT)\n", + " except Exception:\n", + " rel = path\n", + " print(f\" [WARNING] Not found: {rel}\")\n", + " return None\n", + "\n", + "# --- All metrics ---\n", + "tfidf_lr_binary = load_metrics(CLASSICAL / \"tfidf_lr\" / \"metrics_binary.json\")\n", + "tfidf_lr_type = load_metrics(CLASSICAL / \"tfidf_lr\" / \"metrics_type.json\")\n", + "nb_binary = load_metrics(CLASSICAL / \"naive_bayes\" / \"metrics_binary.json\")\n", + "nb_type = load_metrics(CLASSICAL / \"naive_bayes\" / \"metrics_type.json\")\n", + "distilbert_bin = load_metrics(BERT_OUT / \"distilbert_binary\" / \"metrics.json\")\n", + "distilbert_type = load_metrics(BERT_OUT / \"distilbert_type\" / \"metrics.json\")\n", + "bert_base_bin = load_metrics(BERT_OUT / \"bert_base_binary\" / \"metrics.json\") # optional\n", + "bert_base_type = load_metrics(BERT_OUT / \"bert_base_type\" / \"metrics.json\") # optional\n", + "\n", + "print(\"\\nMetrics loaded (None = not yet run):\")\n", + "for name, m in [\n", + " (\"TF-IDF+LR binary\", tfidf_lr_binary),\n", + " (\"TF-IDF+LR type\", tfidf_lr_type),\n", + " (\"NB binary\", nb_binary),\n", + " (\"NB type\", nb_type),\n", + " (\"DistilBERT binary\", distilbert_bin),\n", + " (\"DistilBERT type\", distilbert_type),\n", + " (\"BERT-base binary\", bert_base_bin),\n", + " (\"BERT-base type\", bert_base_type),\n", + "]:\n", + " print(f\" {name}: {'✓' if m else '✗'}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H_xQm80nuKCE" + }, + "source": [ + "## 2. Model Comparison Tables" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "id": "2RsVxgMtuKCE", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d6f77046-f85d-4ef6-b636-70d223ba04ed" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "=== BINARY TASK — Test Metrics ===\n", + " accuracy f1_macro f1_weighted precision_macro recall_macro\n", + "Model \n", + "TF-IDF + LR 0.8189 0.8189 0.8189 0.8190 0.8189\n", + "Naive Bayes 0.7905 0.7902 0.7902 0.7919 0.7905\n", + "DistilBERT 0.9413 0.9413 0.9413 0.9413 0.9413\n", + "BERT-base 0.9469 0.9469 0.9469 0.9471 0.9469\n", + "\n", + "=== TYPE TASK — Test Metrics ===\n", + " accuracy f1_macro f1_weighted precision_macro recall_macro\n", + "Model \n", + "TF-IDF + LR 0.3953 0.4047 0.3942 0.3905 0.4275\n", + "Naive Bayes 0.3953 0.3953 0.3929 0.4058 0.3876\n", + "DistilBERT 0.4525 0.4863 0.4492 0.4687 0.5256\n", + "BERT-base 0.4612 0.4822 0.4589 0.4911 0.4817\n" + ] + } + ], + "source": [ + "def extract_test_metrics(metrics_dict: dict | None, split: str = \"test\") -> dict:\n", + " \"\"\"Extract test-split metrics from a metrics dict, handling different formats.\"\"\"\n", + " if metrics_dict is None:\n", + " return {\"accuracy\": None, \"f1_macro\": None, \"f1_weighted\": None,\n", + " \"precision_macro\": None, \"recall_macro\": None}\n", + " test_m = metrics_dict.get(split, metrics_dict) # BERT format uses 'test' key directly\n", + " return {\n", + " \"accuracy\" : test_m.get(\"accuracy\"),\n", + " \"f1_macro\" : test_m.get(\"f1_macro\"),\n", + " \"f1_weighted\" : test_m.get(\"f1_weighted\"),\n", + " \"precision_macro\" : test_m.get(\"precision_macro\"),\n", + " \"recall_macro\" : test_m.get(\"recall_macro\"),\n", + " }\n", + "\n", + "\n", + "binary_rows = []\n", + "type_rows = []\n", + "\n", + "model_map_bin = [\n", + " (\"TF-IDF + LR\", tfidf_lr_binary),\n", + " (\"Naive Bayes\", nb_binary),\n", + " (\"DistilBERT\", distilbert_bin),\n", + " (\"BERT-base\", bert_base_bin),\n", + "]\n", + "\n", + "model_map_type = [\n", + " (\"TF-IDF + LR\", tfidf_lr_type),\n", + " (\"Naive Bayes\", nb_type),\n", + " (\"DistilBERT\", distilbert_type),\n", + " (\"BERT-base\", bert_base_type),\n", + "]\n", + "\n", + "for name, m in model_map_bin:\n", + " row = {\"Model\": name}\n", + " row.update(extract_test_metrics(m))\n", + " binary_rows.append(row)\n", + "\n", + "for name, m in model_map_type:\n", + " row = {\"Model\": name}\n", + " row.update(extract_test_metrics(m))\n", + " type_rows.append(row)\n", + "\n", + "binary_comp = pd.DataFrame(binary_rows).set_index(\"Model\")\n", + "type_comp = pd.DataFrame(type_rows).set_index(\"Model\")\n", + "\n", + "print(\"=== BINARY TASK — Test Metrics ===\")\n", + "print(binary_comp.to_string(float_format=lambda x: f\"{x:.4f}\" if x is not None else \"N/A\"))\n", + "print()\n", + "print(\"=== TYPE TASK — Test Metrics ===\")\n", + "print(type_comp.to_string(float_format=lambda x: f\"{x:.4f}\" if x is not None else \"N/A\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "id": "n35eLHCCuKCF", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 555 + }, + "outputId": "a568caec-eaff-46e0-d0e4-04ff50be1820" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/reports/model_comparison_chart.png\n" + ] + } + ], + "source": [ + "# ── Comparison bar charts ─────────────────────────────────────────────────────\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n", + "\n", + "for ax, comp, title in [\n", + " (axes[0], binary_comp, \"Binary Task\"),\n", + " (axes[1], type_comp, \"Type Task\"),\n", + "]:\n", + " valid = comp.dropna(subset=[\"f1_macro\"])\n", + " if len(valid) == 0:\n", + " ax.set_title(f\"{title} — No data\")\n", + " continue\n", + " models = valid.index.tolist()\n", + " f1_vals = valid[\"f1_macro\"].tolist()\n", + " acc_vals = valid[\"accuracy\"].tolist()\n", + " x = range(len(models))\n", + " w = 0.35\n", + " bars1 = ax.bar([i - w/2 for i in x], f1_vals, w, label=\"Macro-F1\", color=\"steelblue\")\n", + " bars2 = ax.bar([i + w/2 for i in x], acc_vals, w, label=\"Accuracy\", color=\"coral\")\n", + " ax.set_xticks(list(x)); ax.set_xticklabels(models, rotation=20, ha=\"right\")\n", + " ax.set_ylim(0, 1.05)\n", + " ax.set_ylabel(\"Score\")\n", + " ax.set_title(f\"{title} — Model Comparison (Test)\", fontsize=13)\n", + " ax.legend()\n", + " for bar in bars1:\n", + " ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n", + " f\"{bar.get_height():.3f}\", ha=\"center\", fontsize=8)\n", + " for bar in bars2:\n", + " ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n", + " f\"{bar.get_height():.3f}\", ha=\"center\", fontsize=8)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(REPORTS_DIR / \"model_comparison_chart.png\", dpi=150, bbox_inches=\"tight\")\n", + "plt.show()\n", + "print(\"Saved: outputs/reports/model_comparison_chart.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GC0rNp0auKCF" + }, + "source": [ + "## 3. Error Analysis — Binary Task" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "id": "jokvmFh9uKCF", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "164f83d7-4c74-4c7d-a043-8f2ce6f2720d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Using DistilBERT predictions for binary error analysis\n" + ] + } + ], + "source": [ + "def load_predictions(path: Path) -> pd.DataFrame | None:\n", + " if path.exists():\n", + " return pd.read_csv(path)\n", + " print(f\" [WARNING] Not found: {path}\")\n", + " return None\n", + "\n", + "\n", + "# Use best available binary model predictions for error analysis\n", + "# Priority: BERT > TF-IDF+LR > NB\n", + "pred_paths_bin = [\n", + " (\"DistilBERT\", BERT_OUT / \"distilbert_binary\" / \"predictions_test.csv\"),\n", + " (\"TF-IDF + LR\", CLASSICAL / \"tfidf_lr\" / \"predictions_test_binary.csv\"),\n", + " (\"Naive Bayes\", CLASSICAL / \"naive_bayes\" / \"predictions_test_binary.csv\"),\n", + "]\n", + "\n", + "error_source_bin = None\n", + "error_model_bin = None\n", + "for model_name, path in pred_paths_bin:\n", + " df = load_predictions(path)\n", + " if df is not None:\n", + " error_source_bin = df\n", + " error_model_bin = model_name\n", + " print(f\"Using {model_name} predictions for binary error analysis\")\n", + " break\n", + "\n", + "if error_source_bin is None:\n", + " print(\"No binary predictions found. Run at least one model first.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "id": "0qFoK11OuKCF", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3788d97d-6ce5-4034-de12-1b69e1b607ea" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Total binary test errors: 499\n", + " False Positives (non-sarcastic predicted as sarcastic) : 233\n", + " False Negatives (sarcastic predicted as non-sarcastic) : 266\n", + "\n", + "Error sample size: 24 examples\n", + "\n", + "--- False Positives (non-sarcastic → predicted sarcastic) ---\n", + " The global-warming crisis contributed to a delightful mid-February afternoon.\n", + " Slovenian eighth-graders outperform U.S. students in science, surprising themselves.\n", + " Denny's launches omelet with 3,000 spider eggs\n", + " Study shows 83% of web content is unsuitable for humans\n", + " Aphasia study discovers that empty fullness is facilitated by happy yellow ideas\n", + " Cindy Crawford will have pure silk flowing from her backside.\n", + " Kerry's face shows joy over the latest polls.\n", + " Trump weeps into a pillow, questioning why he can't say the right thing.\n", + " Only you have been invited to Dave's debate party, which is considered creepy.\n", + " Entertainment news helicopters are covering a shocking red carpet outfit.\n", + " Ophthalmologist recommends eye rest before surgery\n", + " Cat experiences minor choking incident\n", + "\n", + "--- False Negatives (sarcastic → predicted non-sarcastic) ---\n", + " hollywood's biggest stars endure long lines at oscars security screening\n", + " officials warn consumers of counterfeit tickets ahead of solar eclipse\n", + " non-priest arrested on charges of child molestation\n", + " argentina tightens security in anticipation of numerous criminals arriving for g20\n", + " now that man has heard about barack obama, he sees references to him all over the place\n", + " semiotics department accuses university administration of anti-semiotism\n", + " diabetic, gout-ridden kim jong-un by far healthiest person in north korea\n", + " thousands dead in indonesia again\n", + " gerber: feeding formula to baby helps infant bond with parent corporation\n", + " marco rubio climbs over garden wall for forbidden midnight meeting with super pac\n", + " group that makes dodge truck commercials called 'creative team'\n", + " hate-crime bill stalled by pro-hate lobby\n" + ] + } + ], + "source": [ + "if error_source_bin is not None:\n", + " pred_col = \"predicted\" if \"predicted\" in error_source_bin.columns else \"predicted_id\"\n", + " true_col = \"binary_label\"\n", + " err_bin = error_source_bin[error_source_bin[\"correct\"] == 0].copy()\n", + "\n", + " # Categorize\n", + " fp_bin = err_bin[err_bin[true_col] == 0].copy() # False Positives (predicted sarcastic)\n", + " fn_bin = err_bin[err_bin[true_col] == 1].copy() # False Negatives (predicted not-sarcastic)\n", + "\n", + " print(f\"Total binary test errors: {len(err_bin)}\")\n", + " print(f\" False Positives (non-sarcastic predicted as sarcastic) : {len(fp_bin)}\")\n", + " print(f\" False Negatives (sarcastic predicted as non-sarcastic) : {len(fn_bin)}\")\n", + "\n", + " # Select 20+ errors: mix of FP and FN\n", + " n_each = 12\n", + " sample_fp = fp_bin.head(n_each)\n", + " sample_fn = fn_bin.head(n_each)\n", + " error_sample_bin = pd.concat([sample_fp, sample_fn], ignore_index=True)\n", + " error_sample_bin[\"error_type\"] = [\"FP\"] * len(sample_fp) + [\"FN\"] * len(sample_fn)\n", + "\n", + " print(f\"\\nError sample size: {len(error_sample_bin)} examples\")\n", + " print(\"\\n--- False Positives (non-sarcastic → predicted sarcastic) ---\")\n", + " for _, row in sample_fp.iterrows():\n", + " print(f\" {row['text']}\")\n", + "\n", + " print(\"\\n--- False Negatives (sarcastic → predicted non-sarcastic) ---\")\n", + " for _, row in sample_fn.iterrows():\n", + " print(f\" {row['text']}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "id": "gAh7r-JhuKCF", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7f6a7810-1732-41d5-b001-cef2cebcf2f1" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Failure mode distribution:\n", + "failure_mode\n", + "lexical_ambiguity 19\n", + "neutral_reporting_style_confused 3\n", + "very_short_text 2\n", + "Name: count, dtype: int64\n", + "\n", + "Saved: outputs/reports/error_examples_binary.csv\n" + ] + } + ], + "source": [ + "if error_source_bin is not None:\n", + " # Failure mode categorization (heuristic rules)\n", + " def categorize_binary_error(text: str, error_type: str) -> str:\n", + " text_lower = text.lower()\n", + " if \"?\" in text and error_type == \"FP\":\n", + " return \"rhetorical_phrasing\"\n", + " if any(w in text_lower for w in [\"report\", \"study\", \"survey\", \"finds\", \"shows\"]):\n", + " return \"neutral_reporting_style_confused\"\n", + " if any(w in text_lower for w in [\"best\", \"great\", \"amazing\", \"wonderful\", \"perfect\"]):\n", + " return \"positive_framing_confused\"\n", + " if \"onion\" in text_lower:\n", + " return \"domain_leak\"\n", + " if len(text.split()) <= 5:\n", + " return \"very_short_text\"\n", + " return \"lexical_ambiguity\"\n", + "\n", + " error_sample_bin[\"failure_mode\"] = error_sample_bin.apply(\n", + " lambda r: categorize_binary_error(r[\"text\"], r[\"error_type\"]), axis=1\n", + " )\n", + "\n", + " print(\"Failure mode distribution:\")\n", + " print(error_sample_bin[\"failure_mode\"].value_counts())\n", + "\n", + " # Save\n", + " error_sample_bin.to_csv(REPORTS_DIR / \"error_examples_binary.csv\", index=False)\n", + " print(\"\\nSaved: outputs/reports/error_examples_binary.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3Ybue8H2uKCF" + }, + "source": [ + "## 4. Error Analysis — Type Task" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "id": "-yBp16Z6uKCG", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "35cd044d-13ae-497c-83dd-3b15c0a6b56f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Using DistilBERT predictions for type error analysis\n" + ] + } + ], + "source": [ + "pred_paths_type = [\n", + " (\"DistilBERT\", BERT_OUT / \"distilbert_type\" / \"predictions_test.csv\"),\n", + " (\"TF-IDF + LR\", CLASSICAL / \"tfidf_lr\" / \"predictions_test_type.csv\"),\n", + " (\"Naive Bayes\", CLASSICAL / \"naive_bayes\" / \"predictions_test_type.csv\"),\n", + "]\n", + "\n", + "error_source_type = None\n", + "error_model_type = None\n", + "for model_name, path in pred_paths_type:\n", + " df = load_predictions(path)\n", + " if df is not None:\n", + " error_source_type = df\n", + " error_model_type = model_name\n", + " print(f\"Using {model_name} predictions for type error analysis\")\n", + " break\n", + "\n", + "if error_source_type is None:\n", + " print(\"No type predictions found.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "id": "r21XN805uKCG", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "95d63a98-acaa-4eaa-8b58-3f05f5be5426" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Total type errors: 2327\n", + "\n", + "Top confusion pairs (true → predicted):\n", + "type_label predicted_label\n", + "sarcasm irony 271\n", + " satire 193\n", + "irony sarcasm 182\n", + "satire irony 172\n", + "sarcasm overstatement 167\n", + "satire overstatement 140\n", + "irony overstatement 132\n", + " satire 124\n", + "sarcasm understatement 121\n", + "satire sarcasm 116\n", + "irony understatement 102\n", + "understatement irony 84\n", + "overstatement irony 78\n", + " satire 70\n", + "understatement overstatement 61\n", + "\n", + "Error sample size: 25 examples\n", + "\n", + "--- Sample type misclassifications ---\n", + " [True=irony Pred=overstatement ] The family talisman: that mysterious object that definitely brings luck and isn't just a d\n", + " [True=sarcasm Pred=irony ] concert security guard would willingly give his life to protect coldplay\n", + " [True=sarcasm Pred=overstatement ] Beyoncé's mom dares to exist near her daughter at an event, steals show. The horror.\n", + " [True=irony Pred=sarcasm ] Court extends voter registration after Scott refuses: Democracy in action.\n", + " [True=irony Pred=overstatement ] Wow, turning 77 is the peak of ferocity, who knew?\n", + " [True=sarcasm Pred=overstatement ] stormy daniels '60 minutes' interview leads to spike in pornhub searches for anderson coop\n", + " [True=sarcasm Pred=overstatement ] Tom Cruise holds breath for 6 minutes for a stunt – contributing so much to society.\n", + " [True=sarcasm Pred=overstatement ] 6 things you need to know now about Obamacare's Cadillac tax – spoiler: it's not about lux\n", + " [True=irony Pred=overstatement ] Six architectural projects that revitalized Muslim communities—as if architecture solves e\n", + " [True=satire Pred=sarcasm ] Why I launched a platform to empower survivors of bullying: So I could feel good about mys\n", + " [True=sarcasm Pred=overstatement ] Nothing says 'hit game' like being crowned the summer's dumbest – total favorite material!\n", + " [True=sarcasm Pred=irony ] US-backed rebels launch Raqqa operation: proxy wars with a side of American weaponry.\n", + " [True=satire Pred=overstatement ] Denée Benton heads to Broadway: Ruby's story hits the big stage\n", + " [True=sarcasm Pred=satire ] cancer researchers develop highly promising new pink consumer item\n", + " [True=satire Pred=understatement ] new cereal for poor stays crunchy in water\n" + ] + } + ], + "source": [ + "if error_source_type is not None:\n", + " err_type = error_source_type[error_source_type[\"correct\"] == 0].copy()\n", + "\n", + " # Identify label columns\n", + " true_col_type = \"type_label\"\n", + " pred_col_type = \"predicted_label\" if \"predicted_label\" in err_type.columns else \"predicted\"\n", + "\n", + " print(f\"Total type errors: {len(err_type)}\")\n", + " print(\"\\nTop confusion pairs (true → predicted):\")\n", + " if pred_col_type in err_type.columns:\n", + " pairs = err_type.groupby([true_col_type, pred_col_type]).size().sort_values(ascending=False).head(15)\n", + " print(pairs.to_string())\n", + "\n", + " # Sample 20+ errors\n", + " error_sample_type = err_type.sample(min(25, len(err_type)), random_state=42)\n", + "\n", + " print(f\"\\nError sample size: {len(error_sample_type)} examples\")\n", + " print(\"\\n--- Sample type misclassifications ---\")\n", + " display_cols = [\"text\", true_col_type]\n", + " if pred_col_type in error_sample_type.columns:\n", + " display_cols.append(pred_col_type)\n", + " for _, row in error_sample_type.head(15).iterrows():\n", + " true_l = row[true_col_type]\n", + " pred_l = row.get(pred_col_type, \"?\")\n", + " print(f\" [True={true_l:20s} Pred={pred_l:20s}] {row['text'][:90]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "id": "IzgDVYhfuKCG", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1652dad1-b55b-4bfc-8f86-0c37d3d02947" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Failure mode distribution:\n", + "failure_mode\n", + "lexical_ambiguity 14\n", + "strategy_semantic_overlap 9\n", + "rhetorical_vs_other_with_question 1\n", + "generation_artifact_formal_phrasing 1\n", + "Name: count, dtype: int64\n", + "\n", + "Saved: outputs/reports/error_examples_type.csv\n" + ] + } + ], + "source": [ + "if error_source_type is not None:\n", + " def categorize_type_error(text: str, true_label: str, pred_label: str) -> str:\n", + " \"\"\"Heuristic failure mode categorization for type task.\"\"\"\n", + " text_lower = text.lower()\n", + " # Strategy overlap: sarcasm ↔ irony ↔ satire are frequently confused\n", + " overlap_pairs = {\n", + " frozenset({\"sarcasm\", \"irony\"}),\n", + " frozenset({\"sarcasm\", \"satire\"}),\n", + " frozenset({\"irony\", \"satire\"}),\n", + " }\n", + " if frozenset({true_label, pred_label}) in overlap_pairs:\n", + " return \"strategy_semantic_overlap\"\n", + " if \"rhetorical_question\" in [true_label, pred_label]:\n", + " if \"?\" in text:\n", + " return \"rhetorical_vs_other_with_question\"\n", + " return \"rhetorical_without_question_mark\"\n", + " if true_label in [\"overstatement\", \"understatement\"] and pred_label in [\"sarcasm\", \"irony\"]:\n", + " return \"scale_confusion_with_sarcasm\"\n", + " if \"report\" in text_lower or \"according\" in text_lower:\n", + " return \"generation_artifact_formal_phrasing\"\n", + " return \"lexical_ambiguity\"\n", + "\n", + " if pred_col_type in error_sample_type.columns:\n", + " error_sample_type[\"failure_mode\"] = error_sample_type.apply(\n", + " lambda r: categorize_type_error(r[\"text\"], r[true_col_type], r[pred_col_type]),\n", + " axis=1\n", + " )\n", + " print(\"Failure mode distribution:\")\n", + " print(error_sample_type[\"failure_mode\"].value_counts())\n", + "\n", + " error_sample_type.to_csv(REPORTS_DIR / \"error_examples_type.csv\", index=False)\n", + " print(\"\\nSaved: outputs/reports/error_examples_type.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NEmnBPTtuKCG" + }, + "source": [ + "## 5. Generate Final Reports" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "id": "xH7naLCouKCG", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "aba66ed6-4ae9-4d90-c013-e116adc6f8ef" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/reports/model_comparison.md\n", + "\n", + "Preview:\n", + "# Model Comparison Report\n", + "\n", + "> Auto-generated from outputs of notebooks 02–04.\n", + "\n", + "## Task A — Binary Classification (Test Set)\n", + "\n", + "| Model | Accuracy | Precision (M) | Recall (M) | F1 (Macro) | F1 (Weighted) |\n", + "|-------|----------|--------------|------------|------------|--------------|\n", + "| TF-IDF + LR | 0.8189 | 0.8190 | 0.8189 | 0.8189 | 0.8189 |\n", + "| Naive Bayes | 0.7905 | 0.7919 | 0.7905 | 0.7902 | 0.7902 |\n", + "| DistilBERT | 0.9413 | 0.9413 | 0.9413 | 0.9413 | 0.9413 |\n", + "| BERT-base | 0.9469 | 0.9471 | 0.9469 | 0.9469 | 0.9469 |\n", + "\n", + "_Primary metric: Macro-F1. Best value bolded (manually after review)._\n", + "\n", + "## Task B — Sarcasm Type Classification (Test Set)\n", + "\n", + "| Model | Accuracy | Precision (M) | Recall (M) | F1 (Macro) | F1 (Weighted) |\n", + "|-------|----------|--------------|------------|------------|--------------|\n", + "| TF-IDF + LR | 0.3953 | 0.3905 | 0.4275 | 0.4047 | 0.3942 |\n", + "| Naive Bayes | 0.3953 | 0.4058 | 0.3876 | 0.3953 | 0.3929 |\n", + "| DistilBERT | 0.4525 | 0.4687 | 0.5256 | 0.4863 | 0.4492 |\n", + "| BERT-base | 0.4612 | 0.4911 | 0.4817 | 0.4822 | 0.4589 |\n", + "\n", + "## Recommendation\n", + "\n", + "### Best model for Binary Task: **BERT-base** (Macro-F1 = 0.9469)\n", + "\n", + "### Best model for Type Task: **DistilBERT** (Macro-F1 = 0.4863)\n", + "\n", + "### Trade-offs\n", + "\n", + "| Aspect | TF-IDF + LR | Naive Bayes | DistilBERT |\n", + "|--------|------------|-------------|-----------|\n", + "| Training speed | Fast (minutes) | Very fast (seconds) | Slow (hours) |\n", + "| Inference speed | Very fast | Very fast | Moderate |\n", + "| Interpretability | High (feature weights) | High (log-probs) | Low (black-box) |\n", + "| Handles context | No | No | Yes (self-attention) |\n", + "| Handles rare words | Via TF-IDF | Via smoothing | Via sub-word tokenization |\n", + "| GPU required | No | No | Recommended |\n", + "\n", + "**Deployment recommendation**: Use TF-IDF+LR if real-time speed and interpretability are priorities.\n", + "Use DistilBERT for maximum accuracy when GPU inference is available.\n" + ] + } + ], + "source": [ + "def fmt(v) -> str:\n", + " \"\"\"Format a metric value for markdown.\"\"\"\n", + " if v is None:\n", + " return \"N/A\"\n", + " return f\"{v:.4f}\"\n", + "\n", + "\n", + "def build_model_comparison_md() -> str:\n", + " lines = []\n", + " lines.append(\"# Model Comparison Report\")\n", + " lines.append(\"\")\n", + " lines.append(\"> Auto-generated from outputs of notebooks 02–04.\")\n", + " lines.append(\"\")\n", + "\n", + " # Binary task table\n", + " lines.append(\"## Task A — Binary Classification (Test Set)\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Model | Accuracy | Precision (M) | Recall (M) | F1 (Macro) | F1 (Weighted) |\")\n", + " lines.append(\"|-------|----------|--------------|------------|------------|--------------|\")\n", + " for name, m in model_map_bin:\n", + " r = extract_test_metrics(m)\n", + " lines.append(\n", + " f\"| {name} | {fmt(r['accuracy'])} | {fmt(r['precision_macro'])} | \"\n", + " f\"{fmt(r['recall_macro'])} | {fmt(r['f1_macro'])} | {fmt(r['f1_weighted'])} |\"\n", + " )\n", + " lines.append(\"\")\n", + " lines.append(\"_Primary metric: Macro-F1. Best value bolded (manually after review)._\")\n", + " lines.append(\"\")\n", + "\n", + " # Type task table\n", + " lines.append(\"## Task B — Sarcasm Type Classification (Test Set)\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Model | Accuracy | Precision (M) | Recall (M) | F1 (Macro) | F1 (Weighted) |\")\n", + " lines.append(\"|-------|----------|--------------|------------|------------|--------------|\")\n", + " for name, m in model_map_type:\n", + " r = extract_test_metrics(m)\n", + " lines.append(\n", + " f\"| {name} | {fmt(r['accuracy'])} | {fmt(r['precision_macro'])} | \"\n", + " f\"{fmt(r['recall_macro'])} | {fmt(r['f1_macro'])} | {fmt(r['f1_weighted'])} |\"\n", + " )\n", + " lines.append(\"\")\n", + "\n", + " # Recommendation\n", + " lines.append(\"## Recommendation\")\n", + " lines.append(\"\")\n", + "\n", + " # Find best binary model\n", + " best_bin_name, best_bin_f1 = \"N/A\", -1\n", + " for name, m in model_map_bin:\n", + " r = extract_test_metrics(m)\n", + " if r[\"f1_macro\"] is not None and r[\"f1_macro\"] > best_bin_f1:\n", + " best_bin_f1 = r[\"f1_macro\"]\n", + " best_bin_name = name\n", + "\n", + " best_type_name, best_type_f1 = \"N/A\", -1\n", + " for name, m in model_map_type:\n", + " r = extract_test_metrics(m)\n", + " if r[\"f1_macro\"] is not None and r[\"f1_macro\"] > best_type_f1:\n", + " best_type_f1 = r[\"f1_macro\"]\n", + " best_type_name = name\n", + "\n", + " lines.append(f\"### Best model for Binary Task: **{best_bin_name}** (Macro-F1 = {fmt(best_bin_f1)})\")\n", + " lines.append(\"\")\n", + " lines.append(f\"### Best model for Type Task: **{best_type_name}** (Macro-F1 = {fmt(best_type_f1)})\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Trade-offs\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Aspect | TF-IDF + LR | Naive Bayes | DistilBERT |\")\n", + " lines.append(\"|--------|------------|-------------|-----------|\")\n", + " lines.append(\"| Training speed | Fast (minutes) | Very fast (seconds) | Slow (hours) |\")\n", + " lines.append(\"| Inference speed | Very fast | Very fast | Moderate |\")\n", + " lines.append(\"| Interpretability | High (feature weights) | High (log-probs) | Low (black-box) |\")\n", + " lines.append(\"| Handles context | No | No | Yes (self-attention) |\")\n", + " lines.append(\"| Handles rare words | Via TF-IDF | Via smoothing | Via sub-word tokenization |\")\n", + " lines.append(\"| GPU required | No | No | Recommended |\")\n", + " lines.append(\"\")\n", + " lines.append(\"**Deployment recommendation**: Use TF-IDF+LR if real-time speed and interpretability are priorities.\")\n", + " lines.append(\"Use DistilBERT for maximum accuracy when GPU inference is available.\")\n", + "\n", + " return \"\\n\".join(lines)\n", + "\n", + "\n", + "comparison_md = build_model_comparison_md()\n", + "with open(REPORTS_DIR / \"model_comparison.md\", \"w\", encoding=\"utf-8\") as f:\n", + " f.write(comparison_md)\n", + "\n", + "print(\"Saved: outputs/reports/model_comparison.md\")\n", + "print(\"\\nPreview:\")\n", + "print(comparison_md[:2000])" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "id": "CmgZF4S1uKCH", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1db31eb7-bbb6-4d50-80ed-f76dba280f10" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saved: outputs/reports/error_analysis.md\n" + ] + } + ], + "source": [ + "def build_error_analysis_md() -> str:\n", + " lines = []\n", + " lines.append(\"# Error Analysis Report\")\n", + " lines.append(\"\")\n", + " lines.append(\"> Auto-generated from model predictions. Examples drawn from test set.\")\n", + " lines.append(\"\")\n", + "\n", + " # ── Binary ────────────────────────────────────────────────────────────────\n", + " lines.append(\"## Task A — Binary Classification Errors\")\n", + " lines.append(\"\")\n", + "\n", + " if error_source_bin is not None:\n", + " err_df = error_source_bin[error_source_bin[\"correct\"] == 0]\n", + " n_fp = len(err_df[err_df[\"binary_label\"] == 0])\n", + " n_fn = len(err_df[err_df[\"binary_label\"] == 1])\n", + " lines.append(f\"**Model**: {error_model_bin} | **Total errors**: {len(err_df):,} | FP: {n_fp:,} | FN: {n_fn:,}\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Failure Mode Summary\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Failure Mode | Description |\")\n", + " lines.append(\"|-------------|-------------|\")\n", + " lines.append(\"| Lexical ambiguity | Sarcasm/irony requires pragmatic context beyond lexical cues |\")\n", + " lines.append(\"| Neutral reporting style confused | Formal generated text mimics neutral news, but still classified as sarcastic |\")\n", + " lines.append(\"| Positive framing confused | Genuine positive news shares superlative vocabulary with ironic praise |\")\n", + " lines.append(\"| Rhetorical phrasing | Questions that look rhetorical but are literal |\")\n", + " lines.append(\"| Very short text | Too little context for confident classification |\")\n", + " lines.append(\"\")\n", + " lines.append(\"### False Positive Examples (Non-sarcastic predicted as sarcastic)\")\n", + " lines.append(\"\")\n", + " fp_examples = error_source_bin[\n", + " (error_source_bin[\"correct\"] == 0) & (error_source_bin[\"binary_label\"] == 0)\n", + " ].head(10)\n", + " for _, row in fp_examples.iterrows():\n", + " lines.append(f\"- `{row['text']}`\")\n", + " lines.append(\"\")\n", + " lines.append(\"### False Negative Examples (Sarcastic predicted as non-sarcastic)\")\n", + " lines.append(\"\")\n", + " fn_examples = error_source_bin[\n", + " (error_source_bin[\"correct\"] == 0) & (error_source_bin[\"binary_label\"] == 1)\n", + " ].head(10)\n", + " for _, row in fn_examples.iterrows():\n", + " lines.append(f\"- `{row['text']}`\")\n", + " else:\n", + " lines.append(\"_Binary predictions not available. Run at least one model._\")\n", + "\n", + " lines.append(\"\")\n", + "\n", + " # ── Type ──────────────────────────────────────────────────────────────────\n", + " lines.append(\"## Task B — Sarcasm Type Classification Errors\")\n", + " lines.append(\"\")\n", + "\n", + " if error_source_type is not None:\n", + " err_df_t = error_source_type[error_source_type[\"correct\"] == 0]\n", + " lines.append(f\"**Model**: {error_model_type} | **Total errors**: {len(err_df_t):,}\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Failure Mode Summary\")\n", + " lines.append(\"\")\n", + " lines.append(\"| Failure Mode | Description |\")\n", + " lines.append(\"|-------------|-------------|\")\n", + " lines.append(\"| Strategy semantic overlap | sarcasm/irony/satire share similar surface forms; labels conflated |\")\n", + " lines.append(\"| Scale confusion | overstatement/understatement confused with sarcasm due to exaggeration cues |\")\n", + " lines.append(\"| Rhetorical vs. other | rhetorical_question confused with irony/sarcasm when ? is absent |\")\n", + " lines.append(\"| Generation artifact | formal paraphrase style shifts text away from original strategy markers |\")\n", + " lines.append(\"| Lexical ambiguity | strategy relies on world knowledge rather than surface vocabulary |\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Key Insight\")\n", + " lines.append(\"\")\n", + " lines.append(\"The primary failure mode is **strategy semantic overlap**: sarcasm, irony, and satire\")\n", + " lines.append(\"are conceptually related and frequently share similar linguistic surface patterns.\")\n", + " lines.append(\"The label `sarcasm` as a strategy within a sarcasm dataset introduces circularity.\")\n", + " lines.append(\"The `rhetorical_question` class is syntactically distinctive (ends with ?) and should\")\n", + " lines.append(\"be easier to classify; errors in this class suggest the classifier may ignore punctuation.\")\n", + " lines.append(\"\")\n", + " lines.append(\"### Sample Misclassifications\")\n", + " lines.append(\"\")\n", + " sample_t = error_source_type[error_source_type[\"correct\"] == 0].head(20)\n", + " true_c = \"type_label\"\n", + " pred_c = \"predicted_label\" if \"predicted_label\" in sample_t.columns else \"predicted\"\n", + " for _, row in sample_t.iterrows():\n", + " true_l = row[true_c]\n", + " pred_l = row.get(pred_c, \"?\")\n", + " lines.append(f\"- **True**: {true_l} | **Pred**: {pred_l} | `{row['text'][:100]}`\")\n", + " else:\n", + " lines.append(\"_Type predictions not available. Run at least one model._\")\n", + "\n", + " lines.append(\"\")\n", + " lines.append(\"## Summary of Observations\")\n", + " lines.append(\"\")\n", + " lines.append(\"1. **Binary task** is relatively tractable — TheOnion writing style has strong lexical signatures\")\n", + " lines.append(\"2. **Generated headlines** (is_generated=1) may fool classifiers trained mainly on original text\")\n", + " lines.append(\"3. **Type task** is fundamentally harder because strategies are not mutually exclusive\")\n", + " lines.append(\"4. **Class imbalance** (rhetorical_question = 3.7%) is a significant challenge\")\n", + " lines.append(\"5. **BERT** should better capture contextual incongruence; classical models rely on surface vocabulary\")\n", + "\n", + " return \"\\n\".join(lines)\n", + "\n", + "\n", + "error_md = build_error_analysis_md()\n", + "with open(REPORTS_DIR / \"error_analysis.md\", \"w\", encoding=\"utf-8\") as f:\n", + " f.write(error_md)\n", + "\n", + "print(\"Saved: outputs/reports/error_analysis.md\")" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "id": "fdUb27BQuKCH", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "09cecd0a-a889-497b-fbd3-e7f20bcd817b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "====== ERROR ANALYSIS COMPLETE ======\n", + "\n", + "Saved artifacts:\n", + " outputs/reports/error_analysis.md\n", + " outputs/reports/error_examples_binary.csv\n", + " outputs/reports/error_examples_type.csv\n", + " outputs/reports/model_comparison.md\n", + " outputs/reports/model_comparison_chart.png\n", + "\n", + "Pipeline complete. See outputs/reports/model_comparison.md for final results.\n" + ] + } + ], + "source": [ + "print(\"====== ERROR ANALYSIS COMPLETE ======\")\n", + "print()\n", + "print(\"Saved artifacts:\")\n", + "for p in sorted(REPORTS_DIR.iterdir()):\n", + " print(f\" {p.relative_to(ROOT)}\")\n", + "print()\n", + "print(\"Pipeline complete. See outputs/reports/model_comparison.md for final results.\")" + ] + }, + { + "cell_type": "code", + "source": [ + "# Copy the whole outputs/ directory into Google Drive under the first folder named \"cs4248\" (case-insensitive)\n", + "# Change MATCH_INDEX if multiple cs4248 folders are found.\n", + "\n", + "from google.colab import drive\n", + "import os, shutil\n", + "\n", + "drive.mount('/content/drive')\n", + "\n", + "DRIVE_ROOT = \"/content/drive/MyDrive\"\n", + "TARGET_NAME = \"cs4248\"\n", + "MATCH_INDEX = 0 # change if needed\n", + "\n", + "# Find all folders named cs4248\n", + "matches = []\n", + "for root, dirs, files in os.walk(DRIVE_ROOT):\n", + " for d in dirs:\n", + " if d.lower() == TARGET_NAME.lower():\n", + " matches.append(os.path.join(root, d))\n", + "\n", + "print(\"Matches found:\")\n", + "for i, m in enumerate(matches):\n", + " print(f\"[{i}] {m}\")\n", + "\n", + "if not matches:\n", + " raise FileNotFoundError(\"No folder named 'cs4248' found in Google Drive.\")\n", + "\n", + "target_dir = matches[MATCH_INDEX]\n", + "\n", + "src = \"outputs\"\n", + "if not os.path.exists(src):\n", + " raise FileNotFoundError(f\"Missing source folder: {src}\")\n", + "\n", + "dst = os.path.join(target_dir, \"outputs\")\n", + "shutil.copytree(src, dst, dirs_exist_ok=True)\n", + "\n", + "print(f\"\\nDone. Copied '{src}' to:\")\n", + "print(dst)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DWK25WXwZ07s", + "outputId": "c88caf63-32cf-4a4f-dd40-84e9e503a976" + }, + "execution_count": 71, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n", + "Matches found:\n", + "[0] /content/drive/MyDrive/CS4248\n", + "\n", + "Done. Copied 'outputs' to:\n", + "/content/drive/MyDrive/CS4248/outputs\n" + ] + } + ] } - ], - "source": [ - "import torch\n", - "from transformers import (\n", - " AutoTokenizer,\n", - " AutoModelForSequenceClassification,\n", - " TrainingArguments,\n", - " Trainer,\n", - " DataCollatorWithPadding,\n", - ")\n", - "import torch\n", - "from torch.utils.data import Dataset, DataLoader\n", - "\n", - "\n", - "results_distilbert_binary = train_bert(\n", - " cfg = CFG_BINARY,\n", - " X_train = X_train_bin, y_train = y_train_bin,\n", - " X_val = X_val_bin, y_val = y_val_bin,\n", - " X_test = X_test_bin, y_test = y_test_bin,\n", - " label_names= label_names_bin,\n", - " out_dir = BERT_OUT / \"distilbert_binary\",\n", - " val_df = val_bin,\n", - " test_df = test_bin,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rtkEQF58uKCC" - }, - "source": [ - "## Train DistilBERT — Type Task" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "yBICJ8OMuKCC" - }, - "outputs": [], - "source": [ - "results_distilbert_type = train_bert(\n", - " cfg = CFG_TYPE,\n", - " X_train = X_train_type, y_train = y_train_type,\n", - " X_val = X_val_type, y_val = y_val_type,\n", - " X_test = X_test_type, y_test = y_test_type,\n", - " label_names= STRATEGY_LABELS,\n", - " out_dir = BERT_OUT / \"distilbert_type\",\n", - " val_df = val_type,\n", - " test_df = test_type,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yFgqLSPouKCC" - }, - "source": [ - "## (Optional) BERT-base — Binary Task\n", - "\n", - "Run only if compute allows. Comment out if not needed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "7spIQwpluKCD" - }, - "outputs": [], - "source": [ - "# Uncomment to run BERT-base (requires more GPU memory and time)\n", - "\n", - "CFG_BERT_BINARY = TrainConfig(\n", - " model_name=\"bert-base-uncased\",\n", - " task=\"binary\",\n", - " batch_size=16, # smaller batch for bert-base\n", - " lr=2e-5,\n", - ")\n", - "\n", - "results_bert_binary = train_bert(\n", - " cfg = CFG_BERT_BINARY,\n", - " X_train = X_train_bin, y_train = y_train_bin,\n", - " X_val = X_val_bin, y_val = y_val_bin,\n", - " X_test = X_test_bin, y_test = y_test_bin,\n", - " label_names = label_names_bin,\n", - " out_dir = BERT_OUT / \"bert_base_binary\",\n", - " val_df = val_bin,\n", - " test_df = test_bin,\n", - ")\n", - "\n", - "# print(\"BERT-base is commented out. Uncomment cells above to run.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "PUSxauAluKCD" - }, - "source": [ - "## (Optional) BERT-base — Type Task" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "uaqm4IUjuKCD" - }, - "outputs": [], - "source": [ - "CFG_BERT_TYPE = TrainConfig(\n", - " model_name=\"bert-base-uncased\",\n", - " task=\"type\",\n", - " batch_size=16,\n", - " lr=2e-5,\n", - " use_class_weights=True,\n", - ")\n", - "\n", - "results_bert_type = train_bert(\n", - " cfg = CFG_BERT_TYPE,\n", - " X_train = X_train_type, y_train = y_train_type,\n", - " X_val = X_val_type, y_val = y_val_type,\n", - " X_test = X_test_type, y_test = y_test_type,\n", - " label_names = STRATEGY_LABELS,\n", - " out_dir = BERT_OUT / \"bert_base_type\",\n", - " val_df = val_type,\n", - " test_df = test_type,\n", - ")\n", - "\n", - "# print(\"BERT-base type task is commented out. Uncomment to run.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cFQdWUqpuKCD" - }, - "source": [ - "## Summary" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "89y9LuozuKCD" - }, - "outputs": [], - "source": [ - "print(\"====== BERT RESULTS SUMMARY ======\")\n", - "print()\n", - "print(\"DistilBERT — Binary (Test):\")\n", - "print(f\" Accuracy : {results_distilbert_binary['test']['accuracy']:.4f}\")\n", - "print(f\" Macro-F1 : {results_distilbert_binary['test']['f1_macro']:.4f}\")\n", - "print(f\" Weighted-F1: {results_distilbert_binary['test']['f1_weighted']:.4f}\")\n", - "print()\n", - "print(\"DistilBERT — Type (Test):\")\n", - "print(f\" Accuracy : {results_distilbert_type['test']['accuracy']:.4f}\")\n", - "print(f\" Macro-F1 : {results_distilbert_type['test']['f1_macro']:.4f}\")\n", - "print(f\" Weighted-F1: {results_distilbert_type['test']['f1_weighted']:.4f}\")\n", - "print()\n", - "print(\"Artifacts saved in outputs/bert/distilbert_binary/ and outputs/bert/distilbert_type/\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9dAXibWCuKCE" - }, - "source": [ - "---\n", - "# Part 5 — Error Analysis & Model Comparison" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FIcicb-xuKCE" - }, - "source": [ - "# Notebook 05 — Error Analysis & Model Comparison\n", - "\n", - "**Purpose**:\n", - "1. Compare all models on both tasks\n", - "2. Analyze errors (binary + type)\n", - "3. Generate final comparison report\n", - "\n", - "**Prerequisite**: Run notebooks 01–04 first.\n", - "\n", - "**Outputs**:\n", - "- `outputs/reports/model_comparison.md`\n", - "- `outputs/reports/error_analysis.md`\n", - "- `outputs/reports/error_examples_binary.csv`\n", - "- `outputs/reports/error_examples_type.csv`" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "saP8-m-MuKCE" - }, - "source": [ - "## 1. Load All Metrics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "cs2Mce2ZuKCE" - }, - "outputs": [], - "source": [ - "def load_metrics(path: Path) -> dict | None:\n", - " if path.exists():\n", - " with open(path) as f:\n", - " return json.load(f)\n", - " print(f\" [WARNING] Not found: {path.relative_to(ROOT)}\")\n", - " return None\n", - "\n", - "\n", - "# All metrics\n", - "tfidf_lr_binary = load_metrics(CLASSICAL / \"tfidf_lr\" / \"metrics_binary.json\")\n", - "tfidf_lr_type = load_metrics(CLASSICAL / \"tfidf_lr\" / \"metrics_type.json\")\n", - "nb_binary = load_metrics(CLASSICAL / \"naive_bayes\" / \"metrics_binary.json\")\n", - "nb_type = load_metrics(CLASSICAL / \"naive_bayes\" / \"metrics_type.json\")\n", - "distilbert_bin = load_metrics(BERT_OUT / \"distilbert_binary\" / \"metrics.json\")\n", - "distilbert_type = load_metrics(BERT_OUT / \"distilbert_type\" / \"metrics.json\")\n", - "bert_base_bin = load_metrics(BERT_OUT / \"bert_base_binary\" / \"metrics.json\") # optional\n", - "bert_base_type = load_metrics(BERT_OUT / \"bert_base_type\" / \"metrics.json\") # optional\n", - "\n", - "print(\"Metrics loaded (None = not yet run):\")\n", - "for name, m in [(\"TF-IDF+LR binary\", tfidf_lr_binary), (\"TF-IDF+LR type\", tfidf_lr_type),\n", - " (\"NB binary\", nb_binary), (\"NB type\", nb_type),\n", - " (\"DistilBERT binary\", distilbert_bin), (\"DistilBERT type\", distilbert_type),\n", - " (\"BERT-base binary\", bert_base_bin), (\"BERT-base type\", bert_base_type)]:\n", - " print(f\" {name}: {'✓' if m else '✗'}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "H_xQm80nuKCE" - }, - "source": [ - "## 2. Model Comparison Tables" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "2RsVxgMtuKCE" - }, - "outputs": [], - "source": [ - "def extract_test_metrics(metrics_dict: dict | None, split: str = \"test\") -> dict:\n", - " \"\"\"Extract test-split metrics from a metrics dict, handling different formats.\"\"\"\n", - " if metrics_dict is None:\n", - " return {\"accuracy\": None, \"f1_macro\": None, \"f1_weighted\": None,\n", - " \"precision_macro\": None, \"recall_macro\": None}\n", - " test_m = metrics_dict.get(split, metrics_dict) # BERT format uses 'test' key directly\n", - " return {\n", - " \"accuracy\" : test_m.get(\"accuracy\"),\n", - " \"f1_macro\" : test_m.get(\"f1_macro\"),\n", - " \"f1_weighted\" : test_m.get(\"f1_weighted\"),\n", - " \"precision_macro\" : test_m.get(\"precision_macro\"),\n", - " \"recall_macro\" : test_m.get(\"recall_macro\"),\n", - " }\n", - "\n", - "\n", - "binary_rows = []\n", - "type_rows = []\n", - "\n", - "model_map_bin = [\n", - " (\"TF-IDF + LR\", tfidf_lr_binary),\n", - " (\"Naive Bayes\", nb_binary),\n", - " (\"DistilBERT\", distilbert_bin),\n", - " (\"BERT-base\", bert_base_bin),\n", - "]\n", - "\n", - "model_map_type = [\n", - " (\"TF-IDF + LR\", tfidf_lr_type),\n", - " (\"Naive Bayes\", nb_type),\n", - " (\"DistilBERT\", distilbert_type),\n", - " (\"BERT-base\", bert_base_type),\n", - "]\n", - "\n", - "for name, m in model_map_bin:\n", - " row = {\"Model\": name}\n", - " row.update(extract_test_metrics(m))\n", - " binary_rows.append(row)\n", - "\n", - "for name, m in model_map_type:\n", - " row = {\"Model\": name}\n", - " row.update(extract_test_metrics(m))\n", - " type_rows.append(row)\n", - "\n", - "binary_comp = pd.DataFrame(binary_rows).set_index(\"Model\")\n", - "type_comp = pd.DataFrame(type_rows).set_index(\"Model\")\n", - "\n", - "print(\"=== BINARY TASK — Test Metrics ===\")\n", - "print(binary_comp.to_string(float_format=lambda x: f\"{x:.4f}\" if x is not None else \"N/A\"))\n", - "print()\n", - "print(\"=== TYPE TASK — Test Metrics ===\")\n", - "print(type_comp.to_string(float_format=lambda x: f\"{x:.4f}\" if x is not None else \"N/A\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "n35eLHCCuKCF" - }, - "outputs": [], - "source": [ - "# ── Comparison bar charts ─────────────────────────────────────────────────────\n", - "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n", - "\n", - "for ax, comp, title in [\n", - " (axes[0], binary_comp, \"Binary Task\"),\n", - " (axes[1], type_comp, \"Type Task\"),\n", - "]:\n", - " valid = comp.dropna(subset=[\"f1_macro\"])\n", - " if len(valid) == 0:\n", - " ax.set_title(f\"{title} — No data\")\n", - " continue\n", - " models = valid.index.tolist()\n", - " f1_vals = valid[\"f1_macro\"].tolist()\n", - " acc_vals = valid[\"accuracy\"].tolist()\n", - " x = range(len(models))\n", - " w = 0.35\n", - " bars1 = ax.bar([i - w/2 for i in x], f1_vals, w, label=\"Macro-F1\", color=\"steelblue\")\n", - " bars2 = ax.bar([i + w/2 for i in x], acc_vals, w, label=\"Accuracy\", color=\"coral\")\n", - " ax.set_xticks(list(x)); ax.set_xticklabels(models, rotation=20, ha=\"right\")\n", - " ax.set_ylim(0, 1.05)\n", - " ax.set_ylabel(\"Score\")\n", - " ax.set_title(f\"{title} — Model Comparison (Test)\", fontsize=13)\n", - " ax.legend()\n", - " for bar in bars1:\n", - " ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n", - " f\"{bar.get_height():.3f}\", ha=\"center\", fontsize=8)\n", - " for bar in bars2:\n", - " ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n", - " f\"{bar.get_height():.3f}\", ha=\"center\", fontsize=8)\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(REPORTS_DIR / \"model_comparison_chart.png\", dpi=150, bbox_inches=\"tight\")\n", - "plt.show()\n", - "print(\"Saved: outputs/reports/model_comparison_chart.png\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GC0rNp0auKCF" - }, - "source": [ - "## 3. Error Analysis — Binary Task" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "jokvmFh9uKCF" - }, - "outputs": [], - "source": [ - "def load_predictions(path: Path) -> pd.DataFrame | None:\n", - " if path.exists():\n", - " return pd.read_csv(path)\n", - " print(f\" [WARNING] Not found: {path}\")\n", - " return None\n", - "\n", - "\n", - "# Use best available binary model predictions for error analysis\n", - "# Priority: BERT > TF-IDF+LR > NB\n", - "pred_paths_bin = [\n", - " (\"DistilBERT\", BERT_OUT / \"distilbert_binary\" / \"predictions_test.csv\"),\n", - " (\"TF-IDF + LR\", CLASSICAL / \"tfidf_lr\" / \"predictions_test_binary.csv\"),\n", - " (\"Naive Bayes\", CLASSICAL / \"naive_bayes\" / \"predictions_test_binary.csv\"),\n", - "]\n", - "\n", - "error_source_bin = None\n", - "error_model_bin = None\n", - "for model_name, path in pred_paths_bin:\n", - " df = load_predictions(path)\n", - " if df is not None:\n", - " error_source_bin = df\n", - " error_model_bin = model_name\n", - " print(f\"Using {model_name} predictions for binary error analysis\")\n", - " break\n", - "\n", - "if error_source_bin is None:\n", - " print(\"No binary predictions found. Run at least one model first.\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "0qFoK11OuKCF" - }, - "outputs": [], - "source": [ - "if error_source_bin is not None:\n", - " pred_col = \"predicted\" if \"predicted\" in error_source_bin.columns else \"predicted_id\"\n", - " true_col = \"binary_label\"\n", - " err_bin = error_source_bin[error_source_bin[\"correct\"] == 0].copy()\n", - "\n", - " # Categorize\n", - " fp_bin = err_bin[err_bin[true_col] == 0].copy() # False Positives (predicted sarcastic)\n", - " fn_bin = err_bin[err_bin[true_col] == 1].copy() # False Negatives (predicted not-sarcastic)\n", - "\n", - " print(f\"Total binary test errors: {len(err_bin)}\")\n", - " print(f\" False Positives (non-sarcastic predicted as sarcastic) : {len(fp_bin)}\")\n", - " print(f\" False Negatives (sarcastic predicted as non-sarcastic) : {len(fn_bin)}\")\n", - "\n", - " # Select 20+ errors: mix of FP and FN\n", - " n_each = 12\n", - " sample_fp = fp_bin.head(n_each)\n", - " sample_fn = fn_bin.head(n_each)\n", - " error_sample_bin = pd.concat([sample_fp, sample_fn], ignore_index=True)\n", - " error_sample_bin[\"error_type\"] = [\"FP\"] * len(sample_fp) + [\"FN\"] * len(sample_fn)\n", - "\n", - " print(f\"\\nError sample size: {len(error_sample_bin)} examples\")\n", - " print(\"\\n--- False Positives (non-sarcastic → predicted sarcastic) ---\")\n", - " for _, row in sample_fp.iterrows():\n", - " print(f\" {row['text']}\")\n", - "\n", - " print(\"\\n--- False Negatives (sarcastic → predicted non-sarcastic) ---\")\n", - " for _, row in sample_fn.iterrows():\n", - " print(f\" {row['text']}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "gAh7r-JhuKCF" - }, - "outputs": [], - "source": [ - "if error_source_bin is not None:\n", - " # Failure mode categorization (heuristic rules)\n", - " def categorize_binary_error(text: str, error_type: str) -> str:\n", - " text_lower = text.lower()\n", - " if \"?\" in text and error_type == \"FP\":\n", - " return \"rhetorical_phrasing\"\n", - " if any(w in text_lower for w in [\"report\", \"study\", \"survey\", \"finds\", \"shows\"]):\n", - " return \"neutral_reporting_style_confused\"\n", - " if any(w in text_lower for w in [\"best\", \"great\", \"amazing\", \"wonderful\", \"perfect\"]):\n", - " return \"positive_framing_confused\"\n", - " if \"onion\" in text_lower:\n", - " return \"domain_leak\"\n", - " if len(text.split()) <= 5:\n", - " return \"very_short_text\"\n", - " return \"lexical_ambiguity\"\n", - "\n", - " error_sample_bin[\"failure_mode\"] = error_sample_bin.apply(\n", - " lambda r: categorize_binary_error(r[\"text\"], r[\"error_type\"]), axis=1\n", - " )\n", - "\n", - " print(\"Failure mode distribution:\")\n", - " print(error_sample_bin[\"failure_mode\"].value_counts())\n", - "\n", - " # Save\n", - " error_sample_bin.to_csv(REPORTS_DIR / \"error_examples_binary.csv\", index=False)\n", - " print(\"\\nSaved: outputs/reports/error_examples_binary.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3Ybue8H2uKCF" - }, - "source": [ - "## 4. Error Analysis — Type Task" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "-yBp16Z6uKCG" - }, - "outputs": [], - "source": [ - "pred_paths_type = [\n", - " (\"DistilBERT\", BERT_OUT / \"distilbert_type\" / \"predictions_test.csv\"),\n", - " (\"TF-IDF + LR\", CLASSICAL / \"tfidf_lr\" / \"predictions_test_type.csv\"),\n", - " (\"Naive Bayes\", CLASSICAL / \"naive_bayes\" / \"predictions_test_type.csv\"),\n", - "]\n", - "\n", - "error_source_type = None\n", - "error_model_type = None\n", - "for model_name, path in pred_paths_type:\n", - " df = load_predictions(path)\n", - " if df is not None:\n", - " error_source_type = df\n", - " error_model_type = model_name\n", - " print(f\"Using {model_name} predictions for type error analysis\")\n", - " break\n", - "\n", - "if error_source_type is None:\n", - " print(\"No type predictions found.\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "r21XN805uKCG" - }, - "outputs": [], - "source": [ - "if error_source_type is not None:\n", - " err_type = error_source_type[error_source_type[\"correct\"] == 0].copy()\n", - "\n", - " # Identify label columns\n", - " true_col_type = \"type_label\"\n", - " pred_col_type = \"predicted_label\" if \"predicted_label\" in err_type.columns else \"predicted\"\n", - "\n", - " print(f\"Total type errors: {len(err_type)}\")\n", - " print(\"\\nTop confusion pairs (true → predicted):\")\n", - " if pred_col_type in err_type.columns:\n", - " pairs = err_type.groupby([true_col_type, pred_col_type]).size().sort_values(ascending=False).head(15)\n", - " print(pairs.to_string())\n", - "\n", - " # Sample 20+ errors\n", - " error_sample_type = err_type.sample(min(25, len(err_type)), random_state=42)\n", - "\n", - " print(f\"\\nError sample size: {len(error_sample_type)} examples\")\n", - " print(\"\\n--- Sample type misclassifications ---\")\n", - " display_cols = [\"text\", true_col_type]\n", - " if pred_col_type in error_sample_type.columns:\n", - " display_cols.append(pred_col_type)\n", - " for _, row in error_sample_type.head(15).iterrows():\n", - " true_l = row[true_col_type]\n", - " pred_l = row.get(pred_col_type, \"?\")\n", - " print(f\" [True={true_l:20s} Pred={pred_l:20s}] {row['text'][:90]}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "IzgDVYhfuKCG" - }, - "outputs": [], - "source": [ - "if error_source_type is not None:\n", - " def categorize_type_error(text: str, true_label: str, pred_label: str) -> str:\n", - " \"\"\"Heuristic failure mode categorization for type task.\"\"\"\n", - " text_lower = text.lower()\n", - " # Strategy overlap: sarcasm ↔ irony ↔ satire are frequently confused\n", - " overlap_pairs = {\n", - " frozenset({\"sarcasm\", \"irony\"}),\n", - " frozenset({\"sarcasm\", \"satire\"}),\n", - " frozenset({\"irony\", \"satire\"}),\n", - " }\n", - " if frozenset({true_label, pred_label}) in overlap_pairs:\n", - " return \"strategy_semantic_overlap\"\n", - " if \"rhetorical_question\" in [true_label, pred_label]:\n", - " if \"?\" in text:\n", - " return \"rhetorical_vs_other_with_question\"\n", - " return \"rhetorical_without_question_mark\"\n", - " if true_label in [\"overstatement\", \"understatement\"] and pred_label in [\"sarcasm\", \"irony\"]:\n", - " return \"scale_confusion_with_sarcasm\"\n", - " if \"report\" in text_lower or \"according\" in text_lower:\n", - " return \"generation_artifact_formal_phrasing\"\n", - " return \"lexical_ambiguity\"\n", - "\n", - " if pred_col_type in error_sample_type.columns:\n", - " error_sample_type[\"failure_mode\"] = error_sample_type.apply(\n", - " lambda r: categorize_type_error(r[\"text\"], r[true_col_type], r[pred_col_type]),\n", - " axis=1\n", - " )\n", - " print(\"Failure mode distribution:\")\n", - " print(error_sample_type[\"failure_mode\"].value_counts())\n", - "\n", - " error_sample_type.to_csv(REPORTS_DIR / \"error_examples_type.csv\", index=False)\n", - " print(\"\\nSaved: outputs/reports/error_examples_type.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NEmnBPTtuKCG" - }, - "source": [ - "## 5. Generate Final Reports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "xH7naLCouKCG" - }, - "outputs": [], - "source": [ - "def fmt(v) -> str:\n", - " \"\"\"Format a metric value for markdown.\"\"\"\n", - " if v is None:\n", - " return \"N/A\"\n", - " return f\"{v:.4f}\"\n", - "\n", - "\n", - "def build_model_comparison_md() -> str:\n", - " lines = []\n", - " lines.append(\"# Model Comparison Report\")\n", - " lines.append(\"\")\n", - " lines.append(\"> Auto-generated from outputs of notebooks 02–04.\")\n", - " lines.append(\"\")\n", - "\n", - " # Binary task table\n", - " lines.append(\"## Task A — Binary Classification (Test Set)\")\n", - " lines.append(\"\")\n", - " lines.append(\"| Model | Accuracy | Precision (M) | Recall (M) | F1 (Macro) | F1 (Weighted) |\")\n", - " lines.append(\"|-------|----------|--------------|------------|------------|--------------|\")\n", - " for name, m in model_map_bin:\n", - " r = extract_test_metrics(m)\n", - " lines.append(\n", - " f\"| {name} | {fmt(r['accuracy'])} | {fmt(r['precision_macro'])} | \"\n", - " f\"{fmt(r['recall_macro'])} | {fmt(r['f1_macro'])} | {fmt(r['f1_weighted'])} |\"\n", - " )\n", - " lines.append(\"\")\n", - " lines.append(\"_Primary metric: Macro-F1. Best value bolded (manually after review)._\")\n", - " lines.append(\"\")\n", - "\n", - " # Type task table\n", - " lines.append(\"## Task B — Sarcasm Type Classification (Test Set)\")\n", - " lines.append(\"\")\n", - " lines.append(\"| Model | Accuracy | Precision (M) | Recall (M) | F1 (Macro) | F1 (Weighted) |\")\n", - " lines.append(\"|-------|----------|--------------|------------|------------|--------------|\")\n", - " for name, m in model_map_type:\n", - " r = extract_test_metrics(m)\n", - " lines.append(\n", - " f\"| {name} | {fmt(r['accuracy'])} | {fmt(r['precision_macro'])} | \"\n", - " f\"{fmt(r['recall_macro'])} | {fmt(r['f1_macro'])} | {fmt(r['f1_weighted'])} |\"\n", - " )\n", - " lines.append(\"\")\n", - "\n", - " # Recommendation\n", - " lines.append(\"## Recommendation\")\n", - " lines.append(\"\")\n", - "\n", - " # Find best binary model\n", - " best_bin_name, best_bin_f1 = \"N/A\", -1\n", - " for name, m in model_map_bin:\n", - " r = extract_test_metrics(m)\n", - " if r[\"f1_macro\"] is not None and r[\"f1_macro\"] > best_bin_f1:\n", - " best_bin_f1 = r[\"f1_macro\"]\n", - " best_bin_name = name\n", - "\n", - " best_type_name, best_type_f1 = \"N/A\", -1\n", - " for name, m in model_map_type:\n", - " r = extract_test_metrics(m)\n", - " if r[\"f1_macro\"] is not None and r[\"f1_macro\"] > best_type_f1:\n", - " best_type_f1 = r[\"f1_macro\"]\n", - " best_type_name = name\n", - "\n", - " lines.append(f\"### Best model for Binary Task: **{best_bin_name}** (Macro-F1 = {fmt(best_bin_f1)})\")\n", - " lines.append(\"\")\n", - " lines.append(f\"### Best model for Type Task: **{best_type_name}** (Macro-F1 = {fmt(best_type_f1)})\")\n", - " lines.append(\"\")\n", - " lines.append(\"### Trade-offs\")\n", - " lines.append(\"\")\n", - " lines.append(\"| Aspect | TF-IDF + LR | Naive Bayes | DistilBERT |\")\n", - " lines.append(\"|--------|------------|-------------|-----------|\")\n", - " lines.append(\"| Training speed | Fast (minutes) | Very fast (seconds) | Slow (hours) |\")\n", - " lines.append(\"| Inference speed | Very fast | Very fast | Moderate |\")\n", - " lines.append(\"| Interpretability | High (feature weights) | High (log-probs) | Low (black-box) |\")\n", - " lines.append(\"| Handles context | No | No | Yes (self-attention) |\")\n", - " lines.append(\"| Handles rare words | Via TF-IDF | Via smoothing | Via sub-word tokenization |\")\n", - " lines.append(\"| GPU required | No | No | Recommended |\")\n", - " lines.append(\"\")\n", - " lines.append(\"**Deployment recommendation**: Use TF-IDF+LR if real-time speed and interpretability are priorities.\")\n", - " lines.append(\"Use DistilBERT for maximum accuracy when GPU inference is available.\")\n", - "\n", - " return \"\\n\".join(lines)\n", - "\n", - "\n", - "comparison_md = build_model_comparison_md()\n", - "with open(REPORTS_DIR / \"model_comparison.md\", \"w\", encoding=\"utf-8\") as f:\n", - " f.write(comparison_md)\n", - "\n", - "print(\"Saved: outputs/reports/model_comparison.md\")\n", - "print(\"\\nPreview:\")\n", - "print(comparison_md[:2000])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "CmgZF4S1uKCH" - }, - "outputs": [], - "source": [ - "def build_error_analysis_md() -> str:\n", - " lines = []\n", - " lines.append(\"# Error Analysis Report\")\n", - " lines.append(\"\")\n", - " lines.append(\"> Auto-generated from model predictions. Examples drawn from test set.\")\n", - " lines.append(\"\")\n", - "\n", - " # ── Binary ────────────────────────────────────────────────────────────────\n", - " lines.append(\"## Task A — Binary Classification Errors\")\n", - " lines.append(\"\")\n", - "\n", - " if error_source_bin is not None:\n", - " err_df = error_source_bin[error_source_bin[\"correct\"] == 0]\n", - " n_fp = len(err_df[err_df[\"binary_label\"] == 0])\n", - " n_fn = len(err_df[err_df[\"binary_label\"] == 1])\n", - " lines.append(f\"**Model**: {error_model_bin} | **Total errors**: {len(err_df):,} | FP: {n_fp:,} | FN: {n_fn:,}\")\n", - " lines.append(\"\")\n", - " lines.append(\"### Failure Mode Summary\")\n", - " lines.append(\"\")\n", - " lines.append(\"| Failure Mode | Description |\")\n", - " lines.append(\"|-------------|-------------|\")\n", - " lines.append(\"| Lexical ambiguity | Sarcasm/irony requires pragmatic context beyond lexical cues |\")\n", - " lines.append(\"| Neutral reporting style confused | Formal generated text mimics neutral news, but still classified as sarcastic |\")\n", - " lines.append(\"| Positive framing confused | Genuine positive news shares superlative vocabulary with ironic praise |\")\n", - " lines.append(\"| Rhetorical phrasing | Questions that look rhetorical but are literal |\")\n", - " lines.append(\"| Very short text | Too little context for confident classification |\")\n", - " lines.append(\"\")\n", - " lines.append(\"### False Positive Examples (Non-sarcastic predicted as sarcastic)\")\n", - " lines.append(\"\")\n", - " fp_examples = error_source_bin[\n", - " (error_source_bin[\"correct\"] == 0) & (error_source_bin[\"binary_label\"] == 0)\n", - " ].head(10)\n", - " for _, row in fp_examples.iterrows():\n", - " lines.append(f\"- `{row['text']}`\")\n", - " lines.append(\"\")\n", - " lines.append(\"### False Negative Examples (Sarcastic predicted as non-sarcastic)\")\n", - " lines.append(\"\")\n", - " fn_examples = error_source_bin[\n", - " (error_source_bin[\"correct\"] == 0) & (error_source_bin[\"binary_label\"] == 1)\n", - " ].head(10)\n", - " for _, row in fn_examples.iterrows():\n", - " lines.append(f\"- `{row['text']}`\")\n", - " else:\n", - " lines.append(\"_Binary predictions not available. Run at least one model._\")\n", - "\n", - " lines.append(\"\")\n", - "\n", - " # ── Type ──────────────────────────────────────────────────────────────────\n", - " lines.append(\"## Task B — Sarcasm Type Classification Errors\")\n", - " lines.append(\"\")\n", - "\n", - " if error_source_type is not None:\n", - " err_df_t = error_source_type[error_source_type[\"correct\"] == 0]\n", - " lines.append(f\"**Model**: {error_model_type} | **Total errors**: {len(err_df_t):,}\")\n", - " lines.append(\"\")\n", - " lines.append(\"### Failure Mode Summary\")\n", - " lines.append(\"\")\n", - " lines.append(\"| Failure Mode | Description |\")\n", - " lines.append(\"|-------------|-------------|\")\n", - " lines.append(\"| Strategy semantic overlap | sarcasm/irony/satire share similar surface forms; labels conflated |\")\n", - " lines.append(\"| Scale confusion | overstatement/understatement confused with sarcasm due to exaggeration cues |\")\n", - " lines.append(\"| Rhetorical vs. other | rhetorical_question confused with irony/sarcasm when ? is absent |\")\n", - " lines.append(\"| Generation artifact | formal paraphrase style shifts text away from original strategy markers |\")\n", - " lines.append(\"| Lexical ambiguity | strategy relies on world knowledge rather than surface vocabulary |\")\n", - " lines.append(\"\")\n", - " lines.append(\"### Key Insight\")\n", - " lines.append(\"\")\n", - " lines.append(\"The primary failure mode is **strategy semantic overlap**: sarcasm, irony, and satire\")\n", - " lines.append(\"are conceptually related and frequently share similar linguistic surface patterns.\")\n", - " lines.append(\"The label `sarcasm` as a strategy within a sarcasm dataset introduces circularity.\")\n", - " lines.append(\"The `rhetorical_question` class is syntactically distinctive (ends with ?) and should\")\n", - " lines.append(\"be easier to classify; errors in this class suggest the classifier may ignore punctuation.\")\n", - " lines.append(\"\")\n", - " lines.append(\"### Sample Misclassifications\")\n", - " lines.append(\"\")\n", - " sample_t = error_source_type[error_source_type[\"correct\"] == 0].head(20)\n", - " true_c = \"type_label\"\n", - " pred_c = \"predicted_label\" if \"predicted_label\" in sample_t.columns else \"predicted\"\n", - " for _, row in sample_t.iterrows():\n", - " true_l = row[true_c]\n", - " pred_l = row.get(pred_c, \"?\")\n", - " lines.append(f\"- **True**: {true_l} | **Pred**: {pred_l} | `{row['text'][:100]}`\")\n", - " else:\n", - " lines.append(\"_Type predictions not available. Run at least one model._\")\n", - "\n", - " lines.append(\"\")\n", - " lines.append(\"## Summary of Observations\")\n", - " lines.append(\"\")\n", - " lines.append(\"1. **Binary task** is relatively tractable — TheOnion writing style has strong lexical signatures\")\n", - " lines.append(\"2. **Generated headlines** (is_generated=1) may fool classifiers trained mainly on original text\")\n", - " lines.append(\"3. **Type task** is fundamentally harder because strategies are not mutually exclusive\")\n", - " lines.append(\"4. **Class imbalance** (rhetorical_question = 3.7%) is a significant challenge\")\n", - " lines.append(\"5. **BERT** should better capture contextual incongruence; classical models rely on surface vocabulary\")\n", - "\n", - " return \"\\n\".join(lines)\n", - "\n", - "\n", - "error_md = build_error_analysis_md()\n", - "with open(REPORTS_DIR / \"error_analysis.md\", \"w\", encoding=\"utf-8\") as f:\n", - " f.write(error_md)\n", - "\n", - "print(\"Saved: outputs/reports/error_analysis.md\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "fdUb27BQuKCH" - }, - "outputs": [], - "source": [ - "print(\"====== ERROR ANALYSIS COMPLETE ======\")\n", - "print()\n", - "print(\"Saved artifacts:\")\n", - "for p in sorted(REPORTS_DIR.iterdir()):\n", - " print(f\" {p.relative_to(ROOT)}\")\n", - "print()\n", - "print(\"Pipeline complete. See outputs/reports/model_comparison.md for final results.\")" - ] - } - ] + ] } \ No newline at end of file