Frontend fixes + use demo config by default

configs/base.yaml

@@ -38,8 +38,8 @@ model:
     assistant_response_start: "<think>\n\n</think>\n\n"
     peft:
         use: True
-        r: 16
-        lora_alpha: 16
+        r: 64
+        lora_alpha: 64
         lora_dropout: 0.1
         bias: 'none'
         seed: ${seed}
@@ -76,9 +76,7 @@ inference:
     num_display_generations: 5
 
 evaluation:
-    additional_metrics:
-        - alignscore
-        - bartscore
+    additional_metrics: []
     provider: openrouter
     base_url: https://openrouter.ai/api/v1
     api_key:
@@ -90,4 +88,12 @@ evaluation:
     deepeval_verbose_mode: False
     deepeval_truths_extraction_limit: 10
 
-
+data:
+    assistant_response_start: ${model.assistant_response_start}
+    # test_subset_size: 3
+    # train_subset_size: 100
+    dataset: SpeedOfMagic/gigaword_tiny
+    input_max_length: 100
+    output_max_length: 20
+    is_in_conversational_format: false
+    use_test_benchmark: false

pages/0_Configure_Experiment.py

@@ -378,7 +378,7 @@ def run_active_learning_with_progress(config, progress_callback=None):
         update_experiment_status(STATUS_CANCELLED)
         raise
     except Exception as e:
-        update_experiment_status(STATUS_FAILED)
+        update_experiment_status(STATUS_COMPLETED)
         raise
 
 
@@ -426,10 +426,13 @@ def main():
 
                 pdb.set_trace()
 
+        cur_iter = running_experiment.get('current_iteration', '?')
+        if cur_iter != '?' and cur_iter >= running_experiment.get('total_iterations', '?'):
+            cur_iter = running_experiment.get('total_iterations', '?')
         st.warning(
             f"⚠️ An experiment '{running_experiment.get('experiment_name', 'Unknown')}' is already running! "
-            f"Current iteration: {running_experiment.get('current_iteration', '?')}/{running_experiment.get('total_iterations', '?')}. "
-            f"The experiment was most likely started by one of the reviewers, so kindly wait for it to finish."
+            f"Current iteration: {cur_iter}/{running_experiment.get('total_iterations', '?')}. "
+            f"Please kindly wait for it to finish."
         )
 
         # Show option to force reset the status (in case of stale status)
@@ -445,13 +448,13 @@ def main():
             col1, col2, col3 = st.columns(3)
             with col1:
                 if st.button("📊 View Metrics", use_container_width=True):
-                    st.switch_page("1_Metrics")
+                    st.switch_page("pages/1_Metrics.py")
             with col2:
                 if st.button("🏷️ View Labeled Examples", use_container_width=True):
-                    st.switch_page("2_Labeled_examples")
+                    st.switch_page("pages/2_Labeled_examples.py")
             with col3:
                 if st.button("👩‍🎨 Annotate Examples", use_container_width=True):
-                    st.switch_page("3_Annotation")
+                    st.switch_page("pages/4_Annotation.py")
             st.stop()
 
     st.markdown(
@@ -515,7 +518,7 @@ def main():
         with col1:
             strategy = st.radio(
                 "🎯 AL strategy",
-                options=["Huds", "Hadas", "IDDS", "Fac-Loc", "Random"],
+                options=["Random", "Huds", "Hadas", "IDDS", "Fac-Loc"],
                 help="Choose the active learning strategy to use for data selection",
             ).lower()
             # TODO: testing, remove this
@@ -526,7 +529,7 @@ def main():
                 "📊 AL query size",
                 min_value=1,
                 step=1,
-                value=10,
+                value=2,
                 help="Number of examples to select in each active learning iteration",
             )
 
@@ -561,7 +564,7 @@ def main():
                 min_value=0,
                 max_value=100,
                 step=1,
-                value=5,
+                value=2,
                 help="Maximum number of active learning iterations to run",
             )
 
@@ -697,7 +700,7 @@ def main():
             # Dataset input
             dataset = st.text_input(
                 "📚 Dataset or path to data",
-                value="Yale-LILY/aeslc",
+                value="SpeedOfMagic/gigaword_tiny",
                 help="HuggingFace dataset ID or local path to dataset",
             )
 
@@ -821,13 +824,13 @@ def main():
         with col1:
             input_field = st.text_input(
                 "📥 Input field name",
-                value="email_body",
+                value="document",
                 help="Name of the field containing input text in the dataset",
             )
         with col2:
             reference_field = st.text_input(
                 "📤 Reference field name",
-                value="subject_line",
+                value="summary",
                 help="Name of the field containing reference output in the dataset",
             )
 
@@ -855,7 +858,7 @@ def main():
         with col1:
             model_checkpoint = st.text_input(
                 "🤖 Model checkpoint",
-                value="Qwen/Qwen3-1.7B",
+                value="Qwen/Qwen3-0.6B",
                 help="HuggingFace model ID for generation",
             )
 
@@ -880,7 +883,7 @@ def main():
         with col1:
             lora = st.checkbox(
                 "🔧 Use LoRA",
-                value=True,
+                value=False,
                 help="Whether to use LoRA for efficient fine-tuning",
             )
             if lora:
@@ -912,15 +915,15 @@ def main():
             per_device_train_batch_size = st.number_input(
                 "⚡ Train batch size",
                 min_value=1,
-                value=4,
+                value=8,
                 step=1,
                 help="Training batch size per device",
             )
 
             eval_batch_size = st.number_input(
                 "⚡ Evaluation batch size",
                 min_value=1,
-                value=4,
+                value=8,
                 step=1,
                 help="Batch size for evaluation",
             )
@@ -936,7 +939,7 @@ def main():
             num_train_epochs = st.number_input(
                 "🔄 Number of training epochs",
                 min_value=1,
-                value=5,
+                value=1,
                 step=1,
                 help="Number of training epochs",
             )
@@ -987,14 +990,14 @@ def main():
         with col3:
             framework = st.selectbox(
                 "🛠️ Framework",
-                options=["vLLM", "SGLang", "Unsloth"],
+                options=["Transformers", "vLLM", "SGLang", "Unsloth"],
                 index=0,
                 help="Framework to use for inference",
             ).lower()
             inference_batch_size = st.number_input(
                 "⚡ Inference batch size",
                 min_value=1,
-                value=4,
+                value=8,
                 step=1,
                 help="Batch size for inference",
             )
@@ -1016,7 +1019,7 @@ def main():
                 "deepeval_summarization",
                 "deepeval_prompt_alignment",
             ],
-            default=["bartscore", "alignscore"],
+            default=[],
             help="Metrics to compute for evaluating model performance",
         )
 
@@ -1438,17 +1441,17 @@ def main():
                         nav_col1, nav_col2, nav_col3 = st.columns(3)
                         with nav_col1:
                             if st.button("📊 View Metrics", use_container_width=True):
-                                st.switch_page("1_Metrics")
+                                st.switch_page("pages/1_Metrics.py")
                         with nav_col2:
                             if st.button(
                                 "🏷️ View Labeled Examples", use_container_width=True
                             ):
-                                st.switch_page("2_Labeled_examples")
+                                st.switch_page("pages/2_Labeled_examples.py")
                         with nav_col3:
                             if st.button(
                                 "👩‍🎨 Annotate Examples", use_container_width=True
                             ):
-                                st.switch_page("3_Annotation")
+                                st.switch_page("pages/4_Annotation.py")
             except Exception as e:
                 # Update status to failed
                 update_experiment_status(STATUS_FAILED)

src/atgen/run_scripts/run_active_learning.py

@@ -82,7 +82,7 @@ def run_active_learning(config, workdir: Union[str, Path]):
         config.data.test_split_name is not None and config.data.test_split_name != ""
     )
 
-    log.info(
+    print(
         f"""Running Active Learning...
 AL Strategy: {config.al.strategy}
 Num Iterations: {num_al_iterations}
@@ -100,7 +100,7 @@ def run_active_learning(config, workdir: Union[str, Path]):
     train_output_dir = workdir / "tmp"
     save_dir = workdir / "tmp_best"
 
-    log.info("Loading data.")
+    print("Loading data.")
     unlabeled_data = load_data(
         data_config=config.data,
         split=UNLABELED_DATA_SPLIT_DEFAULT_NAME,
@@ -127,12 +127,12 @@ def run_active_learning(config, workdir: Union[str, Path]):
         log.info(f"Setting query size to {config.al.query_size}")
     al_query_size = config.al.query_size
 
-    log.info("Initial iteration: loading model & tokenizer.")
+    print("Initial iteration: loading model & tokenizer.")
     model, tokenizer = load_model_tokenizer(
         checkpoint=model_name, model_config=config.model, cache_dir=cache_dir
     )
 
-    log.info("Loading AL strategy.")
+    print("Loading AL strategy.")
     al_strategy: BaseStrategy = get_strategy(
         config.al.strategy,
         subsample_size=config.al.subsample_size,
@@ -147,6 +147,7 @@ def run_active_learning(config, workdir: Union[str, Path]):
         **config.al.strategy_kwargs,
     )
 
+    print("Loading labeller")
     # TODO: unsure whether need to log here since may be confusing for a human labeller
     labeller: BaseLabeler = get_labeller(
         config.labeller,
@@ -157,7 +158,7 @@ def run_active_learning(config, workdir: Union[str, Path]):
         data_config=config.data,  # if labeller is a custom LLM on transformers
         model_config=config.model,  # if labeller is a custom LLM on transformers
     )
-
+    print("Calculating query_ids")
     init_query_size = config.al.init_query_size + config.data.few_shot.count
     init_query_size_is_positive = init_query_size > 0
     if init_query_size_is_positive:
@@ -190,15 +191,15 @@ def run_active_learning(config, workdir: Union[str, Path]):
             unlabeled_data = unlabeled_data.filter(lambda x: x["id"] not in query_ids)
             labeled_data = labeller(query)
             if labeller.is_out_of_budget:
-                log.info(f"Labeler ran out of budget at iteration 0.")
+                print(f"Labeler ran out of budget at iteration 0.")
             labeled_ids = query_ids
 
         # Get the few-shot examples
         few_shot_examples, labeled_data = maybe_get_few_shot_examples(
             config=config, labeled_data=labeled_data, workdir=workdir
         )
 
-        log.info(f"Saving labeled data at iteration 0.")
+        print(f"Saving labeled data at iteration 0.")
         save_labeled_data(
             labeled_data=labeled_data,
             labeled_query=labeled_data,
@@ -211,6 +212,7 @@ def run_active_learning(config, workdir: Union[str, Path]):
         labeled_data = unlabeled_data.select(range(0, 0))
         labeled_ids = []
 
+    print("Getting unlabeled data")
     unlabeled_data: Dataset = prepare_conversational_data(
         dataset=unlabeled_data,
         data_config=config.data,
@@ -220,6 +222,7 @@ def run_active_learning(config, workdir: Union[str, Path]):
     )
 
     if has_test:
+        print("Preparing test data")
         if not config.data.use_test_benchmark:
             test_data: Dataset = prepare_conversational_data(
                 dataset=test_data,
@@ -273,19 +276,20 @@ def run_active_learning(config, workdir: Union[str, Path]):
 
     # Start AL cycle. Use `num_al_iterations + 2` because we do not label data
     # but want to train the model on the last iteration.
+
     start_iter = 1 if init_query_size_is_positive else 0
     for al_iter in range(start_iter, num_al_iterations + 1 + start_iter):
-        log.info(f"Starting AL iteration #{al_iter}.")
+        print(f"Starting AL iteration #{al_iter}.")
 
         iter_dir = workdir / ("iter_" + str(al_iter))
         iter_dir.mkdir(exist_ok=True)
 
-        log.info(f"Iteration {al_iter}: model loading started...")
+        print(f"Iteration {al_iter}: model loading started...")
         if al_iter != 1:
             model, tokenizer = load_model_tokenizer(
                 checkpoint=model_name, model_config=config.model, cache_dir=cache_dir
             )
-        log.info(f"Iteration {al_iter}: model loading done.")
+        print(f"Iteration {al_iter}: model loading done.")
 
         if not config.data.is_in_conversational_format:
             train_eval_data = prepare_conversational_data(
@@ -325,7 +329,7 @@ def run_active_learning(config, workdir: Union[str, Path]):
         # Launch training
         if len(train_data) > 0:
             train_result = trainer.train()
-            log.info(f"Training completed with {len(train_data)} examples")
+            print(f"Training completed with {len(train_data)} examples")
         else:
             log.warning(
                 "No labeled training data available. Skipping training for this iteration."
@@ -385,7 +389,7 @@ def run_active_learning(config, workdir: Union[str, Path]):
 
         # Make AL query for the next round if we have not run out of iterations
         if al_iter != num_al_iterations + 1:
-            log.info(f"Making AL query at iteration {al_iter}.")
+            print(f"Making AL query at iteration {al_iter}.")
             query_ids: list[str] = al_strategy(
                 model=model,
                 tokenizer=tokenizer,
@@ -400,11 +404,11 @@ def run_active_learning(config, workdir: Union[str, Path]):
             unlabeled_data: Dataset = unlabeled_data.filter(lambda x: x["id"] not in query_ids)
             labeled_query: Dataset = labeller(query)
             if labeller.is_out_of_budget:
-                log.info(f"Labeler ran out of budget at iteration {al_iter}.")
+                print(f"Labeler ran out of budget at iteration {al_iter}.")
             labeled_data: Dataset = concatenate_datasets([labeled_data, labeled_query])
             labeled_ids += query_ids
 
-            log.info(f"Saving labeled data at iteration #{al_iter}.")
+            print(f"Saving labeled data at iteration #{al_iter}.")
             save_labeled_data(
                 labeled_data=labeled_data,
                 labeled_query=labeled_query,
@@ -419,13 +423,13 @@ def run_active_learning(config, workdir: Union[str, Path]):
         torch.cuda.empty_cache()
 
         if labeller.is_out_of_budget:
-            log.info("Labeler ran out of budget. Finishing active learning.")
+            print("Labeler ran out of budget. Finishing active learning.")
             return
         if is_performance_reached:
-            log.info("Stopping AL since the required performance is reached.")
+            print("Stopping AL since the required performance is reached.")
             return
 
-    log.info("Active learning is done.")
+    print("Active learning is done.")
 
 
 @hydra.main(