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-# ESGenius
-The code and dataset are coming soon...
+# ESGenius Benchmark
+
+This repository contains the dataset and evaluation code that accompany the **ESGenius** paper. The benchmark focuses on question answering within the domains of Environmental, Social and Governance (ESG) and sustainability. The full paper is provided as [`ESGenius_arxiv_v1.pdf`](ESGenius_arxiv_v1.pdf).
+
+## Dataset
+
+The dataset consists of 1,136 multiple-choice questions covering a range of ESG frameworks such as IPCC, GRI, SASB, ISO, IFRS/ISSB, TCFD and CDP. Each question provides four answer options (A–D) plus a fallback choice `Z` for "Not sure". The plain question set is stored in [`data/ESGenius_1136q.csv`](data/ESGenius_1136q.csv) and [`data/ESGenius_1136q.json`](data/ESGenius_1136q.json). A version with references is available as [`data/ESGenius_w_ref_1136q.csv`](data/ESGenius_w_ref_1136q.csv) and includes page numbers, document names and the original text snippet.
+
+Example header from the reference file:
+
+```csv
+"query_id","new_id","query","answer","A","B","C","D","Z","ref_page","ref_doc","source_text"
+```
+【F:data/ESGenius_w_ref_1136q.csv†L1-L1】
+
+## Repository Layout
+
+- **`evaluation_utils.py`** – common utilities for loading data, building prompts, computing metrics and saving results. It also loads API keys from a `.env` file:
+
+  ```python
+  load_dotenv()
+  HF_TOKEN = os.getenv("HF_TOKEN")
+  DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY")
+  DASHSCOPE_API_KEY = os.getenv("DASHSCOPE_API_KEY")
+  OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+  ```
+  【F:evaluation_utils.py†L36-L46】
+
+- **`eval_opensource.py`** – evaluates open‑source Hugging Face models locally using GPU acceleration.
+- **`eval_opensource_rag.py`** – adds a minimal retrieval‑augmented generation (RAG) step before generation.
+- **`eval_qwen_api.py`** – evaluates Qwen models through the Dashscope API.
+- **`index.html`** – interactive heatmap visualising results for 1,136 questions:
+
+  ```html
+  <h1>ESGenius_1136q - Interactive Evaluation Heatmap</h1>
+  <p>
+      This report visualizes the evaluation results for <b>50</b> models tested on <b>1136</b> questions from the <b>ESGenius_1136q</b> benchmark dataset.
+  ```
+  【F:index.html†L27-L35】
+
+- **`figures/`** – images of word clouds and summary plots used in the paper.
+
+## Figures
+
+Several figures from the paper are provided for quick reference in the
+[`figures/`](figures/) directory. They visualise the question content and the
+overall evaluation results.
+
+![Question word cloud](figures/ESGenius_QA_question_wordcloud.png)
+![Answer option word cloud](figures/ESGenius_QA_option_wordcloud.png)
+![Source text word cloud](figures/ESGenius_Source_Text_wordcloud.png)
+
+![Question distribution](figures/num_questions_distribution_pie.png)
+![Page distribution](figures/pages_distribution_pie.png)
+
+![Main results](figures/main_results.png)
+![Accuracy vs model size](figures/acc_vs_model_size.png)
+
+## Running Evaluations
+
+1. Install dependencies (PyTorch, Transformers, pandas, etc.).
+2. Create a `.env` file with the required API tokens (`HF_TOKEN`, `DASHSCOPE_API_KEY`, etc.).
+3. Run one of the evaluation scripts:
+
+```bash
+python eval_opensource.py        # evaluate local open‑source models
+python eval_opensource_rag.py    # evaluate with RAG retrieval
+python eval_qwen_api.py          # evaluate Qwen via Dashscope API
+```
+
+Results are written to the `results/` directory as Excel workbooks containing a summary sheet and detailed predictions.
+
+## License
+
+This project is licensed under the [Apache 2.0 License](LICENSE).
+
+For full details of the benchmark and experimental setup, please refer to the accompanying paper `ESGenius_arxiv_v1.pdf`.