| wandb: Detected [huggingface_hub.inference, openai] in use. |
| wandb: Use W&B Weave for improved LLM call tracing. Install Weave with `pip install weave` then add `import weave` to the top of your script. |
| wandb: For more information, check out the docs at: https://weave-docs.wandb.ai/ |
| Initial GPU Memory Stats: |
| Allocated: 0.00 MB |
| Cached: 0.00 MB |
| Max Allocated: 0.00 MB |
|
|
| Initializing bidirectional cross-attention model abhinand/MedEmbed-large-v0.1... |
|
|
| ============================================================ |
| 🦥 UNSLOTH MODE: Loading encoder with optimized kernels |
| ============================================================ |
|
|
| 🦥 Unsloth Status: |
| Available: ✅ Yes |
| FastSentenceTransformer: ✅ Yes (preferred) |
| Version: 2026.1.4 |
| CUDA: ✅ Yes |
| GPU: NVIDIA GeForce RTX 4080 SUPER |
| GPU Memory: 16.71 GB |
|
|
| API: FastSentenceTransformer (specialized for embeddings) ✅ |
| Target modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj'] |
| [INFO] Auto-detected max_seq_length for abhinand/MedEmbed-large-v0.1: 512 |
|
|
| ============================================================ |
| 🦥 UNSLOTH MODE: Creating optimized sentence encoder |
| ============================================================ |
| API: FastSentenceTransformer (specialized for embeddings) |
| Model: abhinand/MedEmbed-large-v0.1 |
| Max seq length: 512 |
| Full finetuning: False |
| Unsloth: Using fast encoder path for bert (torch.compile + SDPA) |
| `torch_dtype` is deprecated! Use `dtype` instead! |
| Unsloth: Enabled gradient checkpointing |
| ⚠️ Unsloth loading failed: Target modules {'down_proj', 'v_proj', 'up_proj', 'k_proj', 'o_proj', 'q_proj', 'gate_proj'} not found in the base model. Please check the target modules and try again. |
| Traceback (most recent call last): |
| File "/home/dtim/Ataur/LOKI/Cross_Attention/run_cross_attention.py", line 847, in main |
| sentence_encoder = create_unsloth_sentence_encoder( |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
| File "/home/dtim/Ataur/LOKI/Cross_Attention/unsloth_encoder.py", line 462, in create_unsloth_sentence_encoder |
| sentence_encoder = FastSentenceTransformer.get_peft_model( |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
| File "/home/dtim/anaconda3/envs/LOKI/lib/python3.12/site-packages/unsloth/models/sentence_transformer.py", line 1685, in get_peft_model |
| peft_model = peft_get_peft_model(inner_model, lora_config) |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
| File "/home/dtim/anaconda3/envs/LOKI/lib/python3.12/site-packages/peft/mapping_func.py", line 122, in get_peft_model |
| return MODEL_TYPE_TO_PEFT_MODEL_MAPPING[peft_config.task_type]( |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
| File "/home/dtim/anaconda3/envs/LOKI/lib/python3.12/site-packages/peft/peft_model.py", line 2966, in __init__ |
| super().__init__(model, peft_config, adapter_name, **kwargs) |
| File "/home/dtim/anaconda3/envs/LOKI/lib/python3.12/site-packages/peft/peft_model.py", line 129, in __init__ |
| self.base_model = cls(model, {adapter_name: peft_config}, adapter_name) |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
| File "/home/dtim/anaconda3/envs/LOKI/lib/python3.12/site-packages/peft/tuners/tuners_utils.py", line 298, in __init__ |
| self.inject_adapter(self.model, adapter_name, low_cpu_mem_usage=low_cpu_mem_usage, state_dict=state_dict) |
| File "/home/dtim/anaconda3/envs/LOKI/lib/python3.12/site-packages/peft/tuners/tuners_utils.py", line 880, in inject_adapter |
| raise ValueError(error_msg) |
| ValueError: Target modules {'down_proj', 'v_proj', 'up_proj', 'k_proj', 'o_proj', 'q_proj', 'gate_proj'} not found in the base model. Please check the target modules and try again. |
| Falling back to standard SentenceTransformer loading... |
| Encoder fine-tuning enabled (trainable_encoder=True). No encoder LoRA adapters are attached by default. |
| Initialized model without Flash Attention |
| Embedding dimension: 1024 |
| Max sequence length: 512 |
|
|
| Loading datasets... |
| Loaded 19516 training examples |
| ⚡ Sampled 10000 training examples (deterministic, seed=42) |
| Loaded 2444 evaluation examples |
| ⚡ Sampled 1000 evaluation examples (deterministic) |
| Using top_k_sparse attention mechanism |
| Initializing Top-K Sparse Attention (k=5) |
| Initializing top-k sparse attention with method: zeros |
| Successfully applied zeros initialization to top-k sparse attention |
| Initializing top-k sparse attention with method: zeros |
| Successfully applied zeros initialization to top-k sparse attention |
| Using cosine pair scoring method |
| Bidirectional model initialized with top_k=5, pair_score_method=cosine, share_weights=True, use_refinement=False |
| Using initialization method: zeros |
| Initialization parameters: {'bias_value': 0.0} |
| Sentence encoder is trainable |
| Sentence encoder dtype detected: torch.float32 |
| Model initialized with standard cross-attention layers |
|
|
| 📊 Model Parameter Statistics: |
| Total parameters: 354,040,343 |
| Trainable parameters: 354,040,343 |
| Frozen parameters: 0 |
| Trainable percentage: 100.00% |
|
|
| 🎯 Loss and Aggregation Configuration: |
| Architecture: Bidirectional |
| Loss type: bidirectional_triplet |
| Aggregation method: top_k_pairs |
| Top-k value: 5 |
| Norm type: rmsnorm |
| Q/K RMSNorm: False |
| Embedding caching: ❌ Disabled |
| Triplet batching: 🔒 Isolated examples |
| Triplet strategy: LIMITED |
| Max triplets/example: 2 |
|
|
| 🎲 Initialization Configuration: |
| Method: zeros |
| Description: Initialize all weights to zero (tests uniform attention baseline) |
| Parameters: {'bias_value': 0.0} |
|
|
| 📊 Loss Component Weights (will be normalized): |
| Triplet weight: 0.5 |
| Attention weight: 0.0 |
| Pair weight: 0.3 |
|
|
| ⚙️ Loss Parameters: |
| Margin: 0.3 |
| Scale: 10.0 |
| Pair margin: 0.3 |
| Pair score method: cosine |
| Share attention weights: True |
| Join path extraction: True |
| Join path threshold: 0.15 |
| Self-attention enabled: False |
| Attention mechanism: top_k_sparse |
| Sparse top-k: 5 |
|
|
| 🎯 Attention Mechanism Configuration: |
| Attention type: top_k_sparse |
| Sparse top-k: 5 |
| Initialization method: zeros |
|
|
| 🔄 Bidirectional-Specific Configuration: |
| Pair margin: 0.3 |
| Pair score method: cosine |
| Share attention weights: True |
| Join path extraction: True |
| Join path threshold: 0.15 |
| Self-attention enabled: False |
| Output will be saved to: output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945 |
|
|
| Training configuration saved to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/training_config.json |
|
|
| Starting Model Training... |
| ================================================== |
|
|
| Starting training with ID-based triplet batches... |
| 🎯 Training curves tracker initialized: |
| 📁 Output directory: output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945 |
| 📊 Tracking batch losses: True |
| 📈 Tracking validation loss: True |
| 🔍 Tracking row-sentence metrics: True |
| 💾 Auto-save: True |
| 🎨 Auto-plot: False |
| 🎯 Training curves tracking enabled |
| 🏆 Test-based model saving enabled - will save best models by test metrics |
|
|
| 🔍 Loading row-sentence evaluation data... |
| 📊 Row-sentence evaluation data: |
| Test examples: 36 |
| Annotations: 0 |
| Examples with annotations: 0 |
| ℹ️ Row-sentence data in MIMIC format - will sync with MIMIC loader |
|
|
| 🔍 Loading MIMIC evaluation data... |
| [INFO] Loaded 18 MIMIC annotations |
| Diagnosis row groundings: 114 |
| Medication row groundings: 162 |
| [INFO] MIMIC evaluation data loaded: |
| Test examples: 36 |
| Annotations: 18 admissions |
| Matched examples: 36 |
| ✅ MIMIC evaluation enabled (all 36 test examples) |
| 🔄 Cache disabled: MIMIC test will be re-encoded each epoch |
| 🔗 Syncing generic row-sentence evaluation with loaded MIMIC data for consistent AP tracking... |
| Preparing triplet batches... |
| Triplet sampling strategy: LIMITED |
| Max triplets per example: 2 |
| Using ISOLATED triplet batching (triplets from the same example stay together) |
| Processing examples (isolated batches): 100%|██████████| 10000/10000 [00:01<00:00, 5335.99it/s] |
|
|
| 📊 Triplet Generation Statistics (Isolated Batching): |
| Total examples: 10000 |
| Total triplets: 16502 |
| Avg triplets/example: 1.65 |
| Min triplets/example: 1 |
| Max triplets/example: 2 |
| Examples with 0 triplets: 0 |
| Created 10000 triplet batches for training (all triplets included, adaptive batching) |
| Scheduler configured for 200000 steps with 20000 warmup steps |
|
|
| 🔥 STAGE 0: FROZEN ENCODER ONLY EVALUATION... |
| Evaluating with ONLY the frozen sentence encoder (no cross-attention at all)... |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:10:05<00:00, 4.21s/it] |
| 🔥 FROZEN ENCODER ONLY Accuracy: 0.806 |
| (Total comparisons: 24296) |
| 🔍 Stage 0: Evaluating row-sentence alignment... |
| ℹ️ Using MIMIC evaluator (Level A)... |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.185 |
| Medication: 18 examples, F1=0.242 |
| 🔥 Stage 0 Row-Sent Avg Precision: 0.419 |
| 🔥 Stage 0 Row-Sent Overall Accuracy: 0.213 |
|
|
| 🚀 STAGE 1: SOPHISTICATED MODEL EVALUATION (PRE-TRAINING)... |
| Evaluating sophisticated model BEFORE training... |
| 🚀 Using CUDA device for evaluation |
|
|
| 🎯 TRAINING CONFIGURATION: Starting from Stage 0 |
| 📋 Configuration detected: |
| Encoder trainable: True |
| Use cache: False |
| Use LoRA: False |
| Encoder-only training: True |
| Mode: Encoder-only training (cross-attention heads frozen) |
| Encoder tuning mode: gradual unfreezing |
| 🔓 Gradual unfreezing initialized: 2/24 top layers trainable (layers 22..23) |
| Enabling gradient checkpointing for memory efficiency |
| Optimizer groups configured: head=0 @ 1.00e-04, encoder=391 @ 1.00e-05 |
| Initialized EncoderOnlyTripletLoss with margin=0.3, scale=10.0, ranking=infonce |
| Initialized EncoderOnlyTripletLoss with margin=0.3, scale=10.0 |
| Using cosine LR schedule with warmup |
| 🔧 Using bidirectional evaluation for bidirectional model |
| Evaluating bidirectional model with join path extraction... |
| Evaluating examples: 100%|██████████| 1000/1000 [1:10:13<00:00, 4.21s/it] |
| 🚀 SOPHISTICATED MODEL Accuracy (Pre-training): 0.806 |
| 🔍 Stage 1: Evaluating row-sentence alignment (Sophisticated Untrained)... |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.185 |
| Medication: 18 examples, F1=0.242 |
| 🚀 Stage 1 Row-Sent Avg Precision: 0.418 |
| 🚀 Stage 1 Row-Sent Overall Accuracy: 0.213 |
|
|
| 📊 ARCHITECTURE BENEFIT: |
| 🔥→🚀 Sophisticated model benefit: +0.000 (0.0%) |
|
|
| 📈 ROW-SENTENCE PROGRESSION (Avg Precision): |
| 🔥 Stage 0 (Frozen): 0.419 |
| 🚀 Stage 1 (Sophisticated): 0.418 (+-0.000) |
| ⚠️ WARNING: Sophisticated model barely improves over frozen encoder! |
| Consider if the architecture complexity is necessary for this task. |
| 📊 Logging initial stage metrics to wandb... |
| ✅ Baseline metrics logged to wandb (epoch 0) |
|
|
| 📊 Adding initial stage metrics to training curves... |
| 📊 Initial stage metrics recorded: |
| 🔥 Stage 0 (Frozen): 0.81 acc, 0.42 row-sent AP |
| 🚀 Stage 1 (Sophisticated Untrained): 0.81 acc, 0.42 row-sent AP |
| 🎯 Stage 2 (Initial): 0.00 acc, 0.00 row-sent AP |
| 📊 Adding Epoch 0 (untrained model) baseline to training curves... |
|
|
| 🎓 STARTING TRAINING OF ENCODER ONLY (NO HEADS) |
| Training model type: Encoder Only (No Heads) |
| Training from Stage: 0 |
|
|
| ==================== Epoch 1/20 ==================== |
| Training model has 26242048 trainable parameters |
| Epoch 1: 100%|██████████| 10000/10000 [4:48:51<00:00, 1.73s/it, batch_loss=0.687] |
| Current learning rates: 5.00e-06 | margin=0.300 |
| Epoch 1 loss statistics - Mean: 0.689, Min: 0.520, Max: 0.854 |
| Evaluating after epoch... |
| 🔎 Validation evaluator: Stage 0 (frozen encoder baseline) |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:09:57<00:00, 4.20s/it] |
| Epoch 1 Accuracy: 0.806 |
| Collecting tables and contexts for test_row_sent_epoch split... |
| Processing test_row_sent_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 36305.59it/s] |
| Found 36 unique tables and 39 unique contexts in test_row_sent_epoch split |
| Using device: cuda for embedding cache |
| Encoding test_row_sent_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 61.58it/s] |
| Encoding test_row_sent_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.19it/s] |
| 🔎 Row-sent evaluator: Stage 0 (frozen encoder baseline) |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.203 |
| Medication: 18 examples, F1=0.244 |
| Row-Sent Overall Acc: 0.224 |
| Row-Sent Avg Precision: 0.435 |
| Examples evaluated: 36 |
| Collecting tables and contexts for mimic_test_epoch split... |
| Processing mimic_test_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 37328.79it/s] |
| Found 36 unique tables and 39 unique contexts in mimic_test_epoch split |
| Using device: cuda for embedding cache |
| Encoding mimic_test_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 59.44it/s] |
| Encoding mimic_test_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.19it/s] |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.203 |
| Medication: 18 examples, F1=0.244 |
| MIMIC Diagnosis - P: 0.119, R: 0.847, F1: 0.203 |
| MIMIC Medication - P: 0.153, R: 0.840, F1: 0.244 |
|
|
| 📊 Epoch 1 Summary: |
| 🔥 Train Loss: 0.69 ± 0.04 |
| 🎯 Val Accuracy: 0.81 |
| 🏆 Best Accuracy: 0.81 (Epoch 1) |
| ⏱️ Epoch Time: 21544.8s |
| 📈 Learning Rate: 5.00e-06 |
| 🔍 Row-Sent Overall Acc: 0.22 |
| 🏆 Best Test Overall Acc: 0.22 (Epoch 1) |
| 🔍 Row-Sent Avg Precision: 0.44 |
| 🏆 Best Test Avg Precision: 0.44 (Epoch 1) |
| Stage 0 note: encoder fine-tuning active; eval uses on-the-fly embeddings (no cache) |
| New best model! Accuracy: 0.806 |
| New best test overall accuracy! 0.224 (Epoch 1) |
| New best test average precision! 0.435 (Epoch 1) |
|
|
| ==================== Epoch 2/20 ==================== |
| 🔓 Gradual unfreezing: enabled layers 21..21 (total 3/24) |
| Epoch 2: 100%|██████████| 10000/10000 [4:49:35<00:00, 1.74s/it, batch_loss=0.683] |
| Current learning rates: 1.00e-05 | margin=0.300 |
| Epoch 2 loss statistics - Mean: 0.689, Min: 0.523, Max: 0.871 |
| Evaluating after epoch... |
| 🔎 Validation evaluator: Stage 0 (frozen encoder baseline) |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:10:21<00:00, 4.22s/it] |
| Epoch 2 Accuracy: 0.806 |
| Collecting tables and contexts for test_row_sent_epoch split... |
| Processing test_row_sent_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 36314.32it/s] |
| Found 36 unique tables and 39 unique contexts in test_row_sent_epoch split |
| Using device: cuda for embedding cache |
| Encoding test_row_sent_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 61.60it/s] |
| Encoding test_row_sent_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.18it/s] |
| 🔎 Row-sent evaluator: Stage 0 (frozen encoder baseline) |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.203 |
| Medication: 18 examples, F1=0.244 |
| Row-Sent Overall Acc: 0.223 |
| Row-Sent Avg Precision: 0.435 |
| Examples evaluated: 36 |
| Collecting tables and contexts for mimic_test_epoch split... |
| Processing mimic_test_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 37748.74it/s] |
| Found 36 unique tables and 39 unique contexts in mimic_test_epoch split |
| Using device: cuda for embedding cache |
| Encoding mimic_test_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 59.77it/s] |
| Encoding mimic_test_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.17it/s] |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.203 |
| Medication: 18 examples, F1=0.244 |
| MIMIC Diagnosis - P: 0.118, R: 0.847, F1: 0.203 |
| MIMIC Medication - P: 0.153, R: 0.840, F1: 0.244 |
|
|
| 📊 Epoch 2 Summary: |
| 🔥 Train Loss: 0.69 ± 0.04 |
| 🎯 Val Accuracy: 0.81 |
| 🏆 Best Accuracy: 0.81 (Epoch 1) |
| ⏱️ Epoch Time: 21613.4s |
| 📈 Learning Rate: 1.00e-05 |
| 🔍 Row-Sent Overall Acc: 0.22 |
| 🏆 Best Test Overall Acc: 0.22 (Epoch 1) |
| 🔍 Row-Sent Avg Precision: 0.44 |
| 🏆 Best Test Avg Precision: 0.44 (Epoch 1) |
| Stage 0 note: encoder fine-tuning active; eval uses on-the-fly embeddings (no cache) |
|
|
| ==================== Epoch 3/20 ==================== |
| 🔓 Gradual unfreezing: enabled layers 20..20 (total 4/24) |
| Epoch 3: 100%|██████████| 10000/10000 [4:50:32<00:00, 1.74s/it, batch_loss=0.683] |
| Current learning rates: 9.92e-06 | margin=0.300 |
| Epoch 3 loss statistics - Mean: 0.689, Min: 0.504, Max: 0.875 |
| Loss not decreasing. Patience: 1/1 |
| Reset learning rate to 5.00e-05 |
| Evaluating after epoch... |
| 🔎 Validation evaluator: Stage 0 (frozen encoder baseline) |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:10:17<00:00, 4.22s/it] |
| Epoch 3 Accuracy: 0.805 |
| Collecting tables and contexts for test_row_sent_epoch split... |
| Processing test_row_sent_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 35511.51it/s] |
| Found 36 unique tables and 39 unique contexts in test_row_sent_epoch split |
| Using device: cuda for embedding cache |
| Encoding test_row_sent_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 61.69it/s] |
| Encoding test_row_sent_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.19it/s] |
| 🔎 Row-sent evaluator: Stage 0 (frozen encoder baseline) |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.201 |
| Medication: 18 examples, F1=0.243 |
| Row-Sent Overall Acc: 0.222 |
| Row-Sent Avg Precision: 0.435 |
| Examples evaluated: 36 |
| Collecting tables and contexts for mimic_test_epoch split... |
| Processing mimic_test_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 36846.01it/s] |
| Found 36 unique tables and 39 unique contexts in mimic_test_epoch split |
| Using device: cuda for embedding cache |
| Encoding mimic_test_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 59.21it/s] |
| Encoding mimic_test_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.17it/s] |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.201 |
| Medication: 18 examples, F1=0.244 |
| MIMIC Diagnosis - P: 0.117, R: 0.847, F1: 0.201 |
| MIMIC Medication - P: 0.153, R: 0.840, F1: 0.244 |
|
|
| 📊 Epoch 3 Summary: |
| 🔥 Train Loss: 0.69 ± 0.04 |
| 🎯 Val Accuracy: 0.81 |
| 🏆 Best Accuracy: 0.81 (Epoch 1) |
| ⏱️ Epoch Time: 21666.8s |
| 📈 Learning Rate: 5.00e-05 |
| 🔍 Row-Sent Overall Acc: 0.22 |
| 🏆 Best Test Overall Acc: 0.22 (Epoch 1) |
| 🔍 Row-Sent Avg Precision: 0.44 |
| 🏆 Best Test Avg Precision: 0.44 (Epoch 1) |
| Stage 0 note: encoder fine-tuning active; eval uses on-the-fly embeddings (no cache) |
|
|
| ==================== Epoch 4/20 ==================== |
| 🔓 Gradual unfreezing: enabled layers 19..19 (total 5/24) |
| Epoch 4: 100%|██████████| 10000/10000 [4:50:41<00:00, 1.74s/it, batch_loss=0.683] |
| Current learning rates: 9.70e-06 | margin=0.300 |
| Epoch 4 loss statistics - Mean: 0.689, Min: 0.525, Max: 0.854 |
| Loss not decreasing. Patience: 1/1 |
| Reset learning rate to 5.00e-05 |
| Evaluating after epoch... |
| 🔎 Validation evaluator: Stage 0 (frozen encoder baseline) |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:10:11<00:00, 4.21s/it] |
| Epoch 4 Accuracy: 0.805 |
| Collecting tables and contexts for test_row_sent_epoch split... |
| Processing test_row_sent_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 36756.32it/s] |
| Found 36 unique tables and 39 unique contexts in test_row_sent_epoch split |
| Using device: cuda for embedding cache |
| Encoding test_row_sent_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 61.76it/s] |
| Encoding test_row_sent_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.17it/s] |
| 🔎 Row-sent evaluator: Stage 0 (frozen encoder baseline) |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.201 |
| Medication: 18 examples, F1=0.244 |
| Row-Sent Overall Acc: 0.222 |
| Row-Sent Avg Precision: 0.435 |
| Examples evaluated: 36 |
| Collecting tables and contexts for mimic_test_epoch split... |
| Processing mimic_test_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 18810.88it/s] |
| Found 36 unique tables and 39 unique contexts in mimic_test_epoch split |
| Using device: cuda for embedding cache |
| Encoding mimic_test_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 56.02it/s] |
| Encoding mimic_test_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.19it/s] |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.201 |
| Medication: 18 examples, F1=0.244 |
| MIMIC Diagnosis - P: 0.117, R: 0.847, F1: 0.201 |
| MIMIC Medication - P: 0.153, R: 0.840, F1: 0.244 |
|
|
| 📊 Epoch 4 Summary: |
| 🔥 Train Loss: 0.69 ± 0.04 |
| 🎯 Val Accuracy: 0.81 |
| 🏆 Best Accuracy: 0.81 (Epoch 1) |
| ⏱️ Epoch Time: 21669.1s |
| 📈 Learning Rate: 5.00e-05 |
| 🔍 Row-Sent Overall Acc: 0.22 |
| 🏆 Best Test Overall Acc: 0.22 (Epoch 1) |
| 🔍 Row-Sent Avg Precision: 0.43 |
| 🏆 Best Test Avg Precision: 0.44 (Epoch 1) |
| Stage 0 note: encoder fine-tuning active; eval uses on-the-fly embeddings (no cache) |
|
|
| ==================== Epoch 5/20 ==================== |
| 🔓 Gradual unfreezing: enabled layers 18..18 (total 6/24) |
| Epoch 5: 100%|██████████| 10000/10000 [4:50:47<00:00, 1.74s/it, batch_loss=0.686] |
| Current learning rates: 9.33e-06 | margin=0.300 |
| Epoch 5 loss statistics - Mean: 0.689, Min: 0.535, Max: 0.854 |
| Loss not decreasing. Patience: 1/1 |
| Reset learning rate to 5.00e-05 |
| Evaluating after epoch... |
| 🔎 Validation evaluator: Stage 0 (frozen encoder baseline) |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:09:56<00:00, 4.20s/it] |
| Epoch 5 Accuracy: 0.806 |
| Collecting tables and contexts for test_row_sent_epoch split... |
| Processing test_row_sent_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 36054.19it/s] |
| Found 36 unique tables and 39 unique contexts in test_row_sent_epoch split |
| Using device: cuda for embedding cache |
| Encoding test_row_sent_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 61.85it/s] |
| Encoding test_row_sent_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.19it/s] |
| 🔎 Row-sent evaluator: Stage 0 (frozen encoder baseline) |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.201 |
| Medication: 18 examples, F1=0.243 |
| Row-Sent Overall Acc: 0.222 |
| Row-Sent Avg Precision: 0.434 |
| Examples evaluated: 36 |
| Collecting tables and contexts for mimic_test_epoch split... |
| Processing mimic_test_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 39097.60it/s] |
| Found 36 unique tables and 39 unique contexts in mimic_test_epoch split |
| Using device: cuda for embedding cache |
| Encoding mimic_test_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 59.19it/s] |
| Encoding mimic_test_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.19it/s] |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.201 |
| Medication: 18 examples, F1=0.243 |
| MIMIC Diagnosis - P: 0.117, R: 0.849, F1: 0.201 |
| MIMIC Medication - P: 0.153, R: 0.840, F1: 0.243 |
|
|
| 📊 Epoch 5 Summary: |
| 🔥 Train Loss: 0.69 ± 0.04 |
| 🎯 Val Accuracy: 0.81 |
| 🏆 Best Accuracy: 0.81 (Epoch 1) |
| ⏱️ Epoch Time: 21660.5s |
| 📈 Learning Rate: 5.00e-05 |
| 🔍 Row-Sent Overall Acc: 0.22 |
| 🏆 Best Test Overall Acc: 0.22 (Epoch 1) |
| 🔍 Row-Sent Avg Precision: 0.43 |
| 🏆 Best Test Avg Precision: 0.44 (Epoch 1) |
| Stage 0 note: encoder fine-tuning active; eval uses on-the-fly embeddings (no cache) |
|
|
| ==================== Epoch 6/20 ==================== |
| Epoch 6: 100%|██████████| 10000/10000 [4:50:51<00:00, 1.75s/it, batch_loss=0.688] |
| Current learning rates: 8.83e-06 | margin=0.300 |
| Epoch 6 loss statistics - Mean: 0.689, Min: 0.532, Max: 0.857 |
| Evaluating after epoch... |
| 🔎 Validation evaluator: Stage 0 (frozen encoder baseline) |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:10:18<00:00, 4.22s/it] |
| Epoch 6 Accuracy: 0.806 |
| Collecting tables and contexts for test_row_sent_epoch split... |
| Processing test_row_sent_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 35603.62it/s] |
| Found 36 unique tables and 39 unique contexts in test_row_sent_epoch split |
| Using device: cuda for embedding cache |
| Encoding test_row_sent_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 61.66it/s] |
| Encoding test_row_sent_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.18it/s] |
| 🔎 Row-sent evaluator: Stage 0 (frozen encoder baseline) |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.201 |
| Medication: 18 examples, F1=0.244 |
| Row-Sent Overall Acc: 0.222 |
| Row-Sent Avg Precision: 0.434 |
| Examples evaluated: 36 |
| Collecting tables and contexts for mimic_test_epoch split... |
| Processing mimic_test_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 36756.32it/s] |
| Found 36 unique tables and 39 unique contexts in mimic_test_epoch split |
| Using device: cuda for embedding cache |
| Encoding mimic_test_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 58.97it/s] |
| Encoding mimic_test_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.17it/s] |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.201 |
| Medication: 18 examples, F1=0.244 |
| MIMIC Diagnosis - P: 0.117, R: 0.851, F1: 0.201 |
| MIMIC Medication - P: 0.153, R: 0.840, F1: 0.244 |
|
|
| 📊 Epoch 6 Summary: |
| 🔥 Train Loss: 0.69 ± 0.04 |
| 🎯 Val Accuracy: 0.81 |
| 🏆 Best Accuracy: 0.81 (Epoch 1) |
| ⏱️ Epoch Time: 21686.2s |
| 📈 Learning Rate: 8.83e-06 |
| 🔍 Row-Sent Overall Acc: 0.22 |
| 🏆 Best Test Overall Acc: 0.22 (Epoch 1) |
| 🔍 Row-Sent Avg Precision: 0.43 |
| 🏆 Best Test Avg Precision: 0.44 (Epoch 1) |
| Stage 0 note: encoder fine-tuning active; eval uses on-the-fly embeddings (no cache) |
|
|
| ==================== Epoch 7/20 ==================== |
| Epoch 7: 100%|██████████| 10000/10000 [4:51:28<00:00, 1.75s/it, batch_loss=0.682] |
| Current learning rates: 8.21e-06 | margin=0.300 |
| Epoch 7 loss statistics - Mean: 0.689, Min: 0.512, Max: 0.866 |
| Evaluating after epoch... |
| 🔎 Validation evaluator: Stage 0 (frozen encoder baseline) |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:10:17<00:00, 4.22s/it] |
| Epoch 7 Accuracy: 0.806 |
| Collecting tables and contexts for test_row_sent_epoch split... |
| Processing test_row_sent_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 37504.95it/s] |
| Found 36 unique tables and 39 unique contexts in test_row_sent_epoch split |
| Using device: cuda for embedding cache |
| Encoding test_row_sent_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 61.92it/s] |
| Encoding test_row_sent_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.16it/s] |
| 🔎 Row-sent evaluator: Stage 0 (frozen encoder baseline) |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.201 |
| Medication: 18 examples, F1=0.243 |
| Row-Sent Overall Acc: 0.222 |
| Row-Sent Avg Precision: 0.435 |
| Examples evaluated: 36 |
| Collecting tables and contexts for mimic_test_epoch split... |
| Processing mimic_test_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 16710.37it/s] |
| Found 36 unique tables and 39 unique contexts in mimic_test_epoch split |
| Using device: cuda for embedding cache |
| Encoding mimic_test_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 57.66it/s] |
| Encoding mimic_test_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.18it/s] |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.201 |
| Medication: 18 examples, F1=0.243 |
| MIMIC Diagnosis - P: 0.117, R: 0.851, F1: 0.201 |
| MIMIC Medication - P: 0.152, R: 0.840, F1: 0.243 |
|
|
| 📊 Epoch 7 Summary: |
| 🔥 Train Loss: 0.69 ± 0.04 |
| 🎯 Val Accuracy: 0.81 |
| 🏆 Best Accuracy: 0.81 (Epoch 1) |
| ⏱️ Epoch Time: 21722.1s |
| 📈 Learning Rate: 8.21e-06 |
| 🔍 Row-Sent Overall Acc: 0.22 |
| 🏆 Best Test Overall Acc: 0.22 (Epoch 1) |
| 🔍 Row-Sent Avg Precision: 0.43 |
| 🏆 Best Test Avg Precision: 0.44 (Epoch 1) |
| Stage 0 note: encoder fine-tuning active; eval uses on-the-fly embeddings (no cache) |
|
|
| ==================== Epoch 8/20 ==================== |
| Epoch 8: 100%|██████████| 10000/10000 [4:51:15<00:00, 1.75s/it, batch_loss=0.682] |
| Current learning rates: 7.50e-06 | margin=0.300 |
| Epoch 8 loss statistics - Mean: 0.689, Min: 0.536, Max: 0.865 |
| Loss not decreasing. Patience: 1/1 |
| Reset learning rate to 5.00e-05 |
| Evaluating after epoch... |
| 🔎 Validation evaluator: Stage 0 (frozen encoder baseline) |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:10:14<00:00, 4.21s/it] |
| Epoch 8 Accuracy: 0.806 |
| Collecting tables and contexts for test_row_sent_epoch split... |
| Processing test_row_sent_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 37099.49it/s] |
| Found 36 unique tables and 39 unique contexts in test_row_sent_epoch split |
| Using device: cuda for embedding cache |
| Encoding test_row_sent_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 61.28it/s] |
| Encoding test_row_sent_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.18it/s] |
| 🔎 Row-sent evaluator: Stage 0 (frozen encoder baseline) |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.201 |
| Medication: 18 examples, F1=0.243 |
| Row-Sent Overall Acc: 0.222 |
| Row-Sent Avg Precision: 0.434 |
| Examples evaluated: 36 |
| Collecting tables and contexts for mimic_test_epoch split... |
| Processing mimic_test_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 36587.10it/s] |
| Found 36 unique tables and 39 unique contexts in mimic_test_epoch split |
| Using device: cuda for embedding cache |
| Encoding mimic_test_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 59.93it/s] |
| Encoding mimic_test_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.18it/s] |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.200 |
| Medication: 18 examples, F1=0.243 |
| MIMIC Diagnosis - P: 0.117, R: 0.851, F1: 0.200 |
| MIMIC Medication - P: 0.152, R: 0.840, F1: 0.243 |
|
|
| 📊 Epoch 8 Summary: |
| 🔥 Train Loss: 0.69 ± 0.04 |
| 🎯 Val Accuracy: 0.81 |
| 🏆 Best Accuracy: 0.81 (Epoch 1) |
| ⏱️ Epoch Time: 21706.6s |
| 📈 Learning Rate: 5.00e-05 |
| 🔍 Row-Sent Overall Acc: 0.22 |
| 🏆 Best Test Overall Acc: 0.22 (Epoch 1) |
| 🔍 Row-Sent Avg Precision: 0.43 |
| 🏆 Best Test Avg Precision: 0.44 (Epoch 1) |
| Stage 0 note: encoder fine-tuning active; eval uses on-the-fly embeddings (no cache) |
|
|
| ==================== Epoch 9/20 ==================== |
| Epoch 9: 100%|██████████| 10000/10000 [4:51:10<00:00, 1.75s/it, batch_loss=0.681] |
| Current learning rates: 6.71e-06 | margin=0.300 |
| Epoch 9 loss statistics - Mean: 0.689, Min: 0.522, Max: 0.859 |
| Loss not decreasing. Patience: 1/1 |
| Reset learning rate to 5.00e-05 |
| Evaluating after epoch... |
| 🔎 Validation evaluator: Stage 0 (frozen encoder baseline) |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:10:14<00:00, 4.21s/it] |
| Epoch 9 Accuracy: 0.806 |
| Collecting tables and contexts for test_row_sent_epoch split... |
| Processing test_row_sent_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 33149.27it/s] |
| Found 36 unique tables and 39 unique contexts in test_row_sent_epoch split |
| Using device: cuda for embedding cache |
| Encoding test_row_sent_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 61.62it/s] |
| Encoding test_row_sent_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.18it/s] |
| 🔎 Row-sent evaluator: Stage 0 (frozen encoder baseline) |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.200 |
| Medication: 18 examples, F1=0.241 |
| Row-Sent Overall Acc: 0.221 |
| Row-Sent Avg Precision: 0.434 |
| Examples evaluated: 36 |
| Collecting tables and contexts for mimic_test_epoch split... |
| Processing mimic_test_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 35704.65it/s] |
| Found 36 unique tables and 39 unique contexts in mimic_test_epoch split |
| Using device: cuda for embedding cache |
| Encoding mimic_test_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 58.34it/s] |
| Encoding mimic_test_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.18it/s] |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.200 |
| Medication: 18 examples, F1=0.241 |
| MIMIC Diagnosis - P: 0.116, R: 0.851, F1: 0.200 |
| MIMIC Medication - P: 0.151, R: 0.840, F1: 0.241 |
|
|
| 📊 Epoch 9 Summary: |
| 🔥 Train Loss: 0.69 ± 0.04 |
| 🎯 Val Accuracy: 0.81 |
| 🏆 Best Accuracy: 0.81 (Epoch 1) |
| ⏱️ Epoch Time: 21701.2s |
| 📈 Learning Rate: 5.00e-05 |
| 🔍 Row-Sent Overall Acc: 0.22 |
| 🏆 Best Test Overall Acc: 0.22 (Epoch 1) |
| 🔍 Row-Sent Avg Precision: 0.43 |
| 🏆 Best Test Avg Precision: 0.44 (Epoch 1) |
| Stage 0 note: encoder fine-tuning active; eval uses on-the-fly embeddings (no cache) |
|
|
| ==================== Epoch 10/20 ==================== |
| Epoch 10: 100%|██████████| 10000/10000 [4:51:09<00:00, 1.75s/it, batch_loss=0.678] |
| Current learning rates: 5.87e-06 | margin=0.300 |
| Epoch 10 loss statistics - Mean: 0.689, Min: 0.531, Max: 0.860 |
| Evaluating after epoch... |
| 🔎 Validation evaluator: Stage 0 (frozen encoder baseline) |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:10:17<00:00, 4.22s/it] |
| Epoch 10 Accuracy: 0.806 |
| Collecting tables and contexts for test_row_sent_epoch split... |
| Processing test_row_sent_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 34735.44it/s] |
| Found 36 unique tables and 39 unique contexts in test_row_sent_epoch split |
| Using device: cuda for embedding cache |
| Encoding test_row_sent_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 61.24it/s] |
| Encoding test_row_sent_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.18it/s] |
| 🔎 Row-sent evaluator: Stage 0 (frozen encoder baseline) |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.200 |
| Medication: 18 examples, F1=0.241 |
| Row-Sent Overall Acc: 0.221 |
| Row-Sent Avg Precision: 0.434 |
| Examples evaluated: 36 |
| Collecting tables and contexts for mimic_test_epoch split... |
| Processing mimic_test_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 38101.17it/s] |
| Found 36 unique tables and 39 unique contexts in mimic_test_epoch split |
| Using device: cuda for embedding cache |
| Encoding mimic_test_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 59.52it/s] |
| Encoding mimic_test_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.18it/s] |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.200 |
| Medication: 18 examples, F1=0.241 |
| MIMIC Diagnosis - P: 0.116, R: 0.851, F1: 0.200 |
| MIMIC Medication - P: 0.151, R: 0.840, F1: 0.241 |
|
|
| 📊 Epoch 10 Summary: |
| 🔥 Train Loss: 0.69 ± 0.04 |
| 🎯 Val Accuracy: 0.81 |
| 🏆 Best Accuracy: 0.81 (Epoch 10) |
| ⏱️ Epoch Time: 21704.0s |
| 📈 Learning Rate: 5.87e-06 |
| 🔍 Row-Sent Overall Acc: 0.22 |
| 🏆 Best Test Overall Acc: 0.22 (Epoch 1) |
| 🔍 Row-Sent Avg Precision: 0.43 |
| 🏆 Best Test Avg Precision: 0.44 (Epoch 1) |
| Stage 0 note: encoder fine-tuning active; eval uses on-the-fly embeddings (no cache) |
| New best model! Accuracy: 0.806 |
|
|
| ==================== Epoch 11/20 ==================== |
| Epoch 11: 100%|██████████| 10000/10000 [4:51:28<00:00, 1.75s/it, batch_loss=0.678] |
| Current learning rates: 5.00e-06 | margin=0.300 |
| Epoch 11 loss statistics - Mean: 0.689, Min: 0.530, Max: 0.859 |
| Loss not decreasing. Patience: 1/1 |
| Reset learning rate to 5.00e-05 |
| Evaluating after epoch... |
| 🔎 Validation evaluator: Stage 0 (frozen encoder baseline) |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:10:21<00:00, 4.22s/it] |
| Epoch 11 Accuracy: 0.806 |
| Collecting tables and contexts for test_row_sent_epoch split... |
| Processing test_row_sent_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 35229.80it/s] |
| Found 36 unique tables and 39 unique contexts in test_row_sent_epoch split |
| Using device: cuda for embedding cache |
| Encoding test_row_sent_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 61.53it/s] |
| Encoding test_row_sent_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.17it/s] |
| 🔎 Row-sent evaluator: Stage 0 (frozen encoder baseline) |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.200 |
| Medication: 18 examples, F1=0.241 |
| Row-Sent Overall Acc: 0.221 |
| Row-Sent Avg Precision: 0.433 |
| Examples evaluated: 36 |
| Collecting tables and contexts for mimic_test_epoch split... |
| Processing mimic_test_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 34976.82it/s] |
| Found 36 unique tables and 39 unique contexts in mimic_test_epoch split |
| Using device: cuda for embedding cache |
| Encoding mimic_test_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 59.47it/s] |
| Encoding mimic_test_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.17it/s] |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.200 |
| Medication: 18 examples, F1=0.241 |
| MIMIC Diagnosis - P: 0.116, R: 0.851, F1: 0.200 |
| MIMIC Medication - P: 0.151, R: 0.840, F1: 0.241 |
|
|
| 📊 Epoch 11 Summary: |
| 🔥 Train Loss: 0.69 ± 0.04 |
| 🎯 Val Accuracy: 0.81 |
| 🏆 Best Accuracy: 0.81 (Epoch 10) |
| ⏱️ Epoch Time: 21726.3s |
| 📈 Learning Rate: 5.00e-05 |
| 🔍 Row-Sent Overall Acc: 0.22 |
| 🏆 Best Test Overall Acc: 0.22 (Epoch 1) |
| 🔍 Row-Sent Avg Precision: 0.43 |
| 🏆 Best Test Avg Precision: 0.44 (Epoch 1) |
| Stage 0 note: encoder fine-tuning active; eval uses on-the-fly embeddings (no cache) |
|
|
| ==================== Epoch 12/20 ==================== |
| Epoch 12: 100%|██████████| 10000/10000 [4:51:23<00:00, 1.75s/it, batch_loss=0.685] |
| Current learning rates: 4.13e-06 | margin=0.300 |
| Epoch 12 loss statistics - Mean: 0.689, Min: 0.508, Max: 0.865 |
| Evaluating after epoch... |
| 🔎 Validation evaluator: Stage 0 (frozen encoder baseline) |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:10:16<00:00, 4.22s/it] |
| Epoch 12 Accuracy: 0.806 |
| Collecting tables and contexts for test_row_sent_epoch split... |
| Processing test_row_sent_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 33391.19it/s] |
| Found 36 unique tables and 39 unique contexts in test_row_sent_epoch split |
| Using device: cuda for embedding cache |
| Encoding test_row_sent_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 61.82it/s] |
| Encoding test_row_sent_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.18it/s] |
| 🔎 Row-sent evaluator: Stage 0 (frozen encoder baseline) |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.200 |
| Medication: 18 examples, F1=0.240 |
| Row-Sent Overall Acc: 0.220 |
| Row-Sent Avg Precision: 0.433 |
| Examples evaluated: 36 |
| Collecting tables and contexts for mimic_test_epoch split... |
| Processing mimic_test_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 37588.98it/s] |
| Found 36 unique tables and 39 unique contexts in mimic_test_epoch split |
| Using device: cuda for embedding cache |
| Encoding mimic_test_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 59.78it/s] |
| Encoding mimic_test_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.11it/s] |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.200 |
| Medication: 18 examples, F1=0.240 |
| MIMIC Diagnosis - P: 0.116, R: 0.847, F1: 0.200 |
| MIMIC Medication - P: 0.149, R: 0.840, F1: 0.240 |
|
|
| 📊 Epoch 12 Summary: |
| 🔥 Train Loss: 0.69 ± 0.04 |
| 🎯 Val Accuracy: 0.81 |
| 🏆 Best Accuracy: 0.81 (Epoch 10) |
| ⏱️ Epoch Time: 21717.2s |
| 📈 Learning Rate: 4.13e-06 |
| 🔍 Row-Sent Overall Acc: 0.22 |
| 🏆 Best Test Overall Acc: 0.22 (Epoch 1) |
| 🔍 Row-Sent Avg Precision: 0.43 |
| 🏆 Best Test Avg Precision: 0.44 (Epoch 1) |
| Stage 0 note: encoder fine-tuning active; eval uses on-the-fly embeddings (no cache) |
|
|
| ==================== Epoch 13/20 ==================== |
| Epoch 13: 100%|██████████| 10000/10000 [4:51:05<00:00, 1.75s/it, batch_loss=0.684] |
| Current learning rates: 3.29e-06 | margin=0.300 |
| Epoch 13 loss statistics - Mean: 0.689, Min: 0.528, Max: 0.890 |
| Loss not decreasing. Patience: 1/1 |
| Reset learning rate to 5.00e-05 |
| Evaluating after epoch... |
| 🔎 Validation evaluator: Stage 0 (frozen encoder baseline) |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:09:57<00:00, 4.20s/it] |
| Epoch 13 Accuracy: 0.806 |
| Collecting tables and contexts for test_row_sent_epoch split... |
| Processing test_row_sent_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 36711.63it/s] |
| Found 36 unique tables and 39 unique contexts in test_row_sent_epoch split |
| Using device: cuda for embedding cache |
| Encoding test_row_sent_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 61.63it/s] |
| Encoding test_row_sent_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.19it/s] |
| 🔎 Row-sent evaluator: Stage 0 (frozen encoder baseline) |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.198 |
| Medication: 18 examples, F1=0.240 |
| Row-Sent Overall Acc: 0.219 |
| Row-Sent Avg Precision: 0.433 |
| Examples evaluated: 36 |
| Collecting tables and contexts for mimic_test_epoch split... |
| Processing mimic_test_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 39808.84it/s] |
| Found 36 unique tables and 39 unique contexts in mimic_test_epoch split |
| Using device: cuda for embedding cache |
| Encoding mimic_test_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 59.23it/s] |
| Encoding mimic_test_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.19it/s] |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.198 |
| Medication: 18 examples, F1=0.240 |
| MIMIC Diagnosis - P: 0.115, R: 0.847, F1: 0.198 |
| MIMIC Medication - P: 0.149, R: 0.840, F1: 0.240 |
|
|
| 📊 Epoch 13 Summary: |
| 🔥 Train Loss: 0.69 ± 0.04 |
| 🎯 Val Accuracy: 0.81 |
| 🏆 Best Accuracy: 0.81 (Epoch 10) |
| ⏱️ Epoch Time: 21679.7s |
| 📈 Learning Rate: 5.00e-05 |
| 🔍 Row-Sent Overall Acc: 0.22 |
| 🏆 Best Test Overall Acc: 0.22 (Epoch 1) |
| 🔍 Row-Sent Avg Precision: 0.43 |
| 🏆 Best Test Avg Precision: 0.44 (Epoch 1) |
| Stage 0 note: encoder fine-tuning active; eval uses on-the-fly embeddings (no cache) |
|
|
| ==================== Epoch 14/20 ==================== |
| Epoch 14: 100%|██████████| 10000/10000 [4:50:45<00:00, 1.74s/it, batch_loss=0.686] |
| Current learning rates: 2.50e-06 | margin=0.300 |
| Epoch 14 loss statistics - Mean: 0.689, Min: 0.542, Max: 0.859 |
| Loss not decreasing. Patience: 1/1 |
| Reset learning rate to 5.00e-05 |
| Evaluating after epoch... |
| 🔎 Validation evaluator: Stage 0 (frozen encoder baseline) |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:09:57<00:00, 4.20s/it] |
| Epoch 14 Accuracy: 0.806 |
| Collecting tables and contexts for test_row_sent_epoch split... |
| Processing test_row_sent_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 36114.55it/s] |
| Found 36 unique tables and 39 unique contexts in test_row_sent_epoch split |
| Using device: cuda for embedding cache |
| Encoding test_row_sent_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 61.32it/s] |
| Encoding test_row_sent_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.19it/s] |
| 🔎 Row-sent evaluator: Stage 0 (frozen encoder baseline) |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.198 |
| Medication: 18 examples, F1=0.240 |
| Row-Sent Overall Acc: 0.219 |
| Row-Sent Avg Precision: 0.433 |
| Examples evaluated: 36 |
| Collecting tables and contexts for mimic_test_epoch split... |
| Processing mimic_test_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 38796.23it/s] |
| Found 36 unique tables and 39 unique contexts in mimic_test_epoch split |
| Using device: cuda for embedding cache |
| Encoding mimic_test_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 59.26it/s] |
| Encoding mimic_test_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.19it/s] |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.198 |
| Medication: 18 examples, F1=0.240 |
| MIMIC Diagnosis - P: 0.115, R: 0.847, F1: 0.198 |
| MIMIC Medication - P: 0.149, R: 0.840, F1: 0.240 |
|
|
| 📊 Epoch 14 Summary: |
| 🔥 Train Loss: 0.69 ± 0.04 |
| 🎯 Val Accuracy: 0.81 |
| 🏆 Best Accuracy: 0.81 (Epoch 10) |
| ⏱️ Epoch Time: 21660.1s |
| 📈 Learning Rate: 5.00e-05 |
| 🔍 Row-Sent Overall Acc: 0.22 |
| 🏆 Best Test Overall Acc: 0.22 (Epoch 1) |
| 🔍 Row-Sent Avg Precision: 0.43 |
| 🏆 Best Test Avg Precision: 0.44 (Epoch 1) |
| Stage 0 note: encoder fine-tuning active; eval uses on-the-fly embeddings (no cache) |
|
|
| ==================== Epoch 15/20 ==================== |
| Epoch 15: 100%|██████████| 10000/10000 [4:50:47<00:00, 1.74s/it, batch_loss=0.688] |
| Current learning rates: 1.79e-06 | margin=0.300 |
| Epoch 15 loss statistics - Mean: 0.689, Min: 0.532, Max: 0.869 |
| Loss not decreasing. Patience: 1/1 |
| Reset learning rate to 5.00e-05 |
| Evaluating after epoch... |
| 🔎 Validation evaluator: Stage 0 (frozen encoder baseline) |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:10:03<00:00, 4.20s/it] |
| Epoch 15 Accuracy: 0.806 |
| Collecting tables and contexts for test_row_sent_epoch split... |
| Processing test_row_sent_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 37356.49it/s] |
| Found 36 unique tables and 39 unique contexts in test_row_sent_epoch split |
| Using device: cuda for embedding cache |
| Encoding test_row_sent_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 61.65it/s] |
| Encoding test_row_sent_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.18it/s] |
| 🔎 Row-sent evaluator: Stage 0 (frozen encoder baseline) |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.198 |
| Medication: 18 examples, F1=0.240 |
| Row-Sent Overall Acc: 0.219 |
| Row-Sent Avg Precision: 0.433 |
| Examples evaluated: 36 |
| Collecting tables and contexts for mimic_test_epoch split... |
| Processing mimic_test_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 36428.21it/s] |
| Found 36 unique tables and 39 unique contexts in mimic_test_epoch split |
| Using device: cuda for embedding cache |
| Encoding mimic_test_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 59.69it/s] |
| Encoding mimic_test_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.18it/s] |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.198 |
| Medication: 18 examples, F1=0.240 |
| MIMIC Diagnosis - P: 0.115, R: 0.847, F1: 0.198 |
| MIMIC Medication - P: 0.149, R: 0.840, F1: 0.240 |
|
|
| 📊 Epoch 15 Summary: |
| 🔥 Train Loss: 0.69 ± 0.04 |
| 🎯 Val Accuracy: 0.81 |
| 🏆 Best Accuracy: 0.81 (Epoch 10) |
| ⏱️ Epoch Time: 21666.9s |
| 📈 Learning Rate: 5.00e-05 |
| 🔍 Row-Sent Overall Acc: 0.22 |
| 🏆 Best Test Overall Acc: 0.22 (Epoch 1) |
| 🔍 Row-Sent Avg Precision: 0.43 |
| 🏆 Best Test Avg Precision: 0.44 (Epoch 1) |
| Stage 0 note: encoder fine-tuning active; eval uses on-the-fly embeddings (no cache) |
|
|
| ==================== Epoch 16/20 ==================== |
| Epoch 16: 100%|██████████| 10000/10000 [4:50:44<00:00, 1.74s/it, batch_loss=0.689] |
| Current learning rates: 1.17e-06 | margin=0.300 |
| Epoch 16 loss statistics - Mean: 0.689, Min: 0.534, Max: 0.860 |
| Evaluating after epoch... |
| 🔎 Validation evaluator: Stage 0 (frozen encoder baseline) |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:09:57<00:00, 4.20s/it] |
| Epoch 16 Accuracy: 0.806 |
| Collecting tables and contexts for test_row_sent_epoch split... |
| Processing test_row_sent_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 36393.09it/s] |
| Found 36 unique tables and 39 unique contexts in test_row_sent_epoch split |
| Using device: cuda for embedding cache |
| Encoding test_row_sent_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 61.83it/s] |
| Encoding test_row_sent_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.19it/s] |
| 🔎 Row-sent evaluator: Stage 0 (frozen encoder baseline) |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.198 |
| Medication: 18 examples, F1=0.240 |
| Row-Sent Overall Acc: 0.219 |
| Row-Sent Avg Precision: 0.433 |
| Examples evaluated: 36 |
| Collecting tables and contexts for mimic_test_epoch split... |
| Processing mimic_test_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 39250.05it/s] |
| Found 36 unique tables and 39 unique contexts in mimic_test_epoch split |
| Using device: cuda for embedding cache |
| Encoding mimic_test_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 59.32it/s] |
| Encoding mimic_test_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.19it/s] |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.198 |
| Medication: 18 examples, F1=0.240 |
| MIMIC Diagnosis - P: 0.115, R: 0.847, F1: 0.198 |
| MIMIC Medication - P: 0.149, R: 0.840, F1: 0.240 |
|
|
| 📊 Epoch 16 Summary: |
| 🔥 Train Loss: 0.69 ± 0.04 |
| 🎯 Val Accuracy: 0.81 |
| 🏆 Best Accuracy: 0.81 (Epoch 10) |
| ⏱️ Epoch Time: 21658.9s |
| 📈 Learning Rate: 1.17e-06 |
| 🔍 Row-Sent Overall Acc: 0.22 |
| 🏆 Best Test Overall Acc: 0.22 (Epoch 1) |
| 🔍 Row-Sent Avg Precision: 0.43 |
| 🏆 Best Test Avg Precision: 0.44 (Epoch 1) |
| Stage 0 note: encoder fine-tuning active; eval uses on-the-fly embeddings (no cache) |
|
|
| ==================== Epoch 17/20 ==================== |
| Epoch 17: 100%|██████████| 10000/10000 [4:50:45<00:00, 1.74s/it, batch_loss=0.686] |
| Current learning rates: 1.00e-06 | margin=0.300 |
| Epoch 17 loss statistics - Mean: 0.689, Min: 0.534, Max: 0.849 |
| Loss not decreasing. Patience: 1/1 |
| Reset learning rate to 5.00e-05 |
| Evaluating after epoch... |
| 🔎 Validation evaluator: Stage 0 (frozen encoder baseline) |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:09:57<00:00, 4.20s/it] |
| Epoch 17 Accuracy: 0.806 |
| Collecting tables and contexts for test_row_sent_epoch split... |
| Processing test_row_sent_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 37467.73it/s] |
| Found 36 unique tables and 39 unique contexts in test_row_sent_epoch split |
| Using device: cuda for embedding cache |
| Encoding test_row_sent_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 61.80it/s] |
| Encoding test_row_sent_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.19it/s] |
| 🔎 Row-sent evaluator: Stage 0 (frozen encoder baseline) |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.198 |
| Medication: 18 examples, F1=0.240 |
| Row-Sent Overall Acc: 0.219 |
| Row-Sent Avg Precision: 0.433 |
| Examples evaluated: 36 |
| Collecting tables and contexts for mimic_test_epoch split... |
| Processing mimic_test_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 37035.80it/s] |
| Found 36 unique tables and 39 unique contexts in mimic_test_epoch split |
| Using device: cuda for embedding cache |
| Encoding mimic_test_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 59.21it/s] |
| Encoding mimic_test_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.19it/s] |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.198 |
| Medication: 18 examples, F1=0.240 |
| MIMIC Diagnosis - P: 0.115, R: 0.847, F1: 0.198 |
| MIMIC Medication - P: 0.149, R: 0.840, F1: 0.240 |
|
|
| 📊 Epoch 17 Summary: |
| 🔥 Train Loss: 0.69 ± 0.04 |
| 🎯 Val Accuracy: 0.81 |
| 🏆 Best Accuracy: 0.81 (Epoch 10) |
| ⏱️ Epoch Time: 21660.2s |
| 📈 Learning Rate: 5.00e-05 |
| 🔍 Row-Sent Overall Acc: 0.22 |
| 🏆 Best Test Overall Acc: 0.22 (Epoch 1) |
| 🔍 Row-Sent Avg Precision: 0.43 |
| 🏆 Best Test Avg Precision: 0.44 (Epoch 1) |
| Stage 0 note: encoder fine-tuning active; eval uses on-the-fly embeddings (no cache) |
|
|
| ==================== Epoch 18/20 ==================== |
| Epoch 18: 100%|██████████| 10000/10000 [4:50:43<00:00, 1.74s/it, batch_loss=0.679] |
| Current learning rates: 1.00e-06 | margin=0.300 |
| Epoch 18 loss statistics - Mean: 0.689, Min: 0.532, Max: 0.872 |
| Loss not decreasing. Patience: 1/1 |
| Reset learning rate to 5.00e-05 |
| Evaluating after epoch... |
| 🔎 Validation evaluator: Stage 0 (frozen encoder baseline) |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:09:58<00:00, 4.20s/it] |
| Epoch 18 Accuracy: 0.806 |
| Collecting tables and contexts for test_row_sent_epoch split... |
| Processing test_row_sent_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 37090.38it/s] |
| Found 36 unique tables and 39 unique contexts in test_row_sent_epoch split |
| Using device: cuda for embedding cache |
| Encoding test_row_sent_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 61.83it/s] |
| Encoding test_row_sent_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.19it/s] |
| 🔎 Row-sent evaluator: Stage 0 (frozen encoder baseline) |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.198 |
| Medication: 18 examples, F1=0.240 |
| Row-Sent Overall Acc: 0.219 |
| Row-Sent Avg Precision: 0.433 |
| Examples evaluated: 36 |
| Collecting tables and contexts for mimic_test_epoch split... |
| Processing mimic_test_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 34647.76it/s] |
| Found 36 unique tables and 39 unique contexts in mimic_test_epoch split |
| Using device: cuda for embedding cache |
| Encoding mimic_test_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 59.39it/s] |
| Encoding mimic_test_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.19it/s] |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.198 |
| Medication: 18 examples, F1=0.240 |
| MIMIC Diagnosis - P: 0.115, R: 0.847, F1: 0.198 |
| MIMIC Medication - P: 0.149, R: 0.840, F1: 0.240 |
|
|
| 📊 Epoch 18 Summary: |
| 🔥 Train Loss: 0.69 ± 0.04 |
| 🎯 Val Accuracy: 0.81 |
| 🏆 Best Accuracy: 0.81 (Epoch 10) |
| ⏱️ Epoch Time: 21658.0s |
| 📈 Learning Rate: 5.00e-05 |
| 🔍 Row-Sent Overall Acc: 0.22 |
| 🏆 Best Test Overall Acc: 0.22 (Epoch 1) |
| 🔍 Row-Sent Avg Precision: 0.43 |
| 🏆 Best Test Avg Precision: 0.44 (Epoch 1) |
| Stage 0 note: encoder fine-tuning active; eval uses on-the-fly embeddings (no cache) |
|
|
| ==================== Epoch 19/20 ==================== |
| Epoch 19: 100%|██████████| 10000/10000 [4:50:59<00:00, 1.75s/it, batch_loss=0.687] |
| Current learning rates: 1.00e-06 | margin=0.300 |
| Epoch 19 loss statistics - Mean: 0.689, Min: 0.518, Max: 0.866 |
| Evaluating after epoch... |
| 🔎 Validation evaluator: Stage 0 (frozen encoder baseline) |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:10:17<00:00, 4.22s/it] |
| Epoch 19 Accuracy: 0.806 |
| Collecting tables and contexts for test_row_sent_epoch split... |
| Processing test_row_sent_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 32640.50it/s] |
| Found 36 unique tables and 39 unique contexts in test_row_sent_epoch split |
| Using device: cuda for embedding cache |
| Encoding test_row_sent_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 61.64it/s] |
| Encoding test_row_sent_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.18it/s] |
| 🔎 Row-sent evaluator: Stage 0 (frozen encoder baseline) |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.198 |
| Medication: 18 examples, F1=0.240 |
| Row-Sent Overall Acc: 0.219 |
| Row-Sent Avg Precision: 0.433 |
| Examples evaluated: 36 |
| Collecting tables and contexts for mimic_test_epoch split... |
| Processing mimic_test_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 34799.48it/s] |
| Found 36 unique tables and 39 unique contexts in mimic_test_epoch split |
| Using device: cuda for embedding cache |
| Encoding mimic_test_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 52.03it/s] |
| Encoding mimic_test_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.18it/s] |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.198 |
| Medication: 18 examples, F1=0.240 |
| MIMIC Diagnosis - P: 0.115, R: 0.847, F1: 0.198 |
| MIMIC Medication - P: 0.149, R: 0.840, F1: 0.240 |
|
|
| 📊 Epoch 19 Summary: |
| 🔥 Train Loss: 0.69 ± 0.04 |
| 🎯 Val Accuracy: 0.81 |
| 🏆 Best Accuracy: 0.81 (Epoch 10) |
| ⏱️ Epoch Time: 21693.0s |
| 📈 Learning Rate: 1.00e-06 |
| 🔍 Row-Sent Overall Acc: 0.22 |
| 🏆 Best Test Overall Acc: 0.22 (Epoch 1) |
| 🔍 Row-Sent Avg Precision: 0.43 |
| 🏆 Best Test Avg Precision: 0.44 (Epoch 1) |
| Stage 0 note: encoder fine-tuning active; eval uses on-the-fly embeddings (no cache) |
|
|
| ==================== Epoch 20/20 ==================== |
| Epoch 20: 100%|██████████| 10000/10000 [4:51:12<00:00, 1.75s/it, batch_loss=0.684] |
| Current learning rates: 1.00e-06 | margin=0.300 |
| Epoch 20 loss statistics - Mean: 0.689, Min: 0.538, Max: 0.871 |
| Loss not decreasing. Patience: 1/1 |
| Reset learning rate to 5.00e-05 |
| Evaluating after epoch... |
| 🔎 Validation evaluator: Stage 0 (frozen encoder baseline) |
| 🔥 Evaluating with FROZEN ENCODER ONLY (no cross-attention, aggregation=top_k_pairs) |
| Evaluating frozen encoder baseline: 100%|██████████| 1000/1000 [1:10:14<00:00, 4.21s/it] |
| Epoch 20 Accuracy: 0.806 |
| Collecting tables and contexts for test_row_sent_epoch split... |
| Processing test_row_sent_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 34560.53it/s] |
| Found 36 unique tables and 39 unique contexts in test_row_sent_epoch split |
| Using device: cuda for embedding cache |
| Encoding test_row_sent_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 61.65it/s] |
| Encoding test_row_sent_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.18it/s] |
| 🔎 Row-sent evaluator: Stage 0 (frozen encoder baseline) |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.198 |
| Medication: 18 examples, F1=0.240 |
| Row-Sent Overall Acc: 0.219 |
| Row-Sent Avg Precision: 0.433 |
| Examples evaluated: 36 |
| Collecting tables and contexts for mimic_test_epoch split... |
| Processing mimic_test_epoch examples: 100%|██████████| 36/36 [00:00<00:00, 38333.32it/s] |
| Found 36 unique tables and 39 unique contexts in mimic_test_epoch split |
| Using device: cuda for embedding cache |
| Encoding mimic_test_epoch tables: 100%|██████████| 36/36 [00:00<00:00, 59.05it/s] |
| Encoding mimic_test_epoch contexts: 100%|██████████| 39/39 [00:07<00:00, 5.14it/s] |
| [INFO] MIMIC Row Grounding evaluation: 36 examples |
| Diagnosis: 18 examples, F1=0.198 |
| Medication: 18 examples, F1=0.240 |
| MIMIC Diagnosis - P: 0.115, R: 0.847, F1: 0.198 |
| MIMIC Medication - P: 0.149, R: 0.840, F1: 0.240 |
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| 📊 Epoch 20 Summary: |
| 🔥 Train Loss: 0.69 ± 0.04 |
| 🎯 Val Accuracy: 0.81 |
| 🏆 Best Accuracy: 0.81 (Epoch 10) |
| ⏱️ Epoch Time: 21702.7s |
| 📈 Learning Rate: 5.00e-05 |
| 🔍 Row-Sent Overall Acc: 0.22 |
| 🏆 Best Test Overall Acc: 0.22 (Epoch 1) |
| 🔍 Row-Sent Avg Precision: 0.43 |
| 🏆 Best Test Avg Precision: 0.44 (Epoch 1) |
| Stage 0 note: encoder fine-tuning active; eval uses on-the-fly embeddings (no cache) |
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| Loading best model from epoch 10 with accuracy 0.806 |
| Best model saved to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/abhinand/MedEmbed-large-v0.1/best_model_epoch_10/model.pt |
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| 🎯 SAVING BEST TEST-BASED MODELS: |
| Loading best test overall accuracy model from epoch 1 (0.224) |
| Best test overall accuracy model saved to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/abhinand/MedEmbed-large-v0.1/best_test_overall_acc_epoch_1/model.pt |
| Loading best test average precision model from epoch 1 (0.435) |
| Best test average precision model saved to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/abhinand/MedEmbed-large-v0.1/best_test_avg_precision_epoch_1/model.pt |
| Reloaded validation-based best model for final analysis |
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| ========================================================================================== |
| 🎯 COMPREHENSIVE 3-STAGE IMPACT ANALYSIS |
| ========================================================================================== |
| 📊 COMPLETE PERFORMANCE BREAKDOWN: |
| 🔥 Stage 0 - Frozen Encoder Only: 0.806 |
| 🚀 Stage 1 - Sophisticated (Pre): 0.806 (+0.000) |
| 🏆 Stage 2 - Trained Model: 0.806 (+0.000) |
| 📈 Total Improvement: +0.000 (0.0%) |
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| 🏆 TEST METRICS SUMMARY: |
| 🎯 Best Test Overall Accuracy: 0.224 (Epoch 1) |
| 🎯 Best Test Average Precision: 0.435 (Epoch 1) |
| 📊 Validation vs Test Performance: |
| - Validation Best: 0.806 (Epoch 10) |
| - Test Overall Acc: 0.224 (Epoch 1) |
| - Test Avg Precision: 0.435 (Epoch 1) |
| ⚠️ NOTE: Best test overall accuracy occurred at epoch 1, not at best validation epoch 10✅ Final summary logged to wandb |
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| 🎨 Skipping 3-STAGE example visualizations (skip_four_stage_viz=True) |
| ========================================================================================== |
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| ====================================================================== |
| 🎯 TRAINING CURVES SUMMARY |
| ====================================================================== |
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| ====================================================================== |
| 🎯 TRAINING SUMMARY - abhinand/MedEmbed-large-v0.1 |
| ====================================================================== |
| 📊 Total Epochs: 20 |
| 🏆 Best Accuracy: 0.81 (Epoch 10) |
| 📈 Final Accuracy: 0.81 |
| 📉 Accuracy Improvement: -0.00 |
| 🔥 Initial Loss: 0.69 |
| 🎯 Final Loss: 0.69 |
| 📉 Loss Reduction: -0.00 |
| ⏱️ Average Epoch Time: 21674.9s |
| 🕐 Total Training Time: 433497.5s (7225.0 min) |
| 🏆 Best Test Metrics: |
| 🔍 Best Overall Accuracy: 0.22 (Epoch 1) |
| 🔍 Best Average Precision: 0.44 (Epoch 1) |
| 📊 Initial Stage Metrics: |
| 🔥 Stage 0 (Frozen): 0.81 acc, 0.42 row-sent AP |
| 🚀 Stage 1 (Sophisticated Untrained): 0.81 acc, 0.42 row-sent AP |
| 🎯 Stage 2 (Initial): 0.00 acc, 0.00 row-sent AP |
| ====================================================================== |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/training_plots/abhinand_MedEmbed-large-v0.1_final_training_curves.png |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/training_plots/abhinand_MedEmbed-large-v0.1_final_training_curves.pdf |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/training_plots/abhinand_MedEmbed-large-v0.1_training_loss.png |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/training_plots/abhinand_MedEmbed-large-v0.1_training_loss.pdf |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/training_plots/abhinand_MedEmbed-large-v0.1_validation_accuracy.png |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/training_plots/abhinand_MedEmbed-large-v0.1_validation_accuracy.pdf |
| 📈 Generating batch-level analysis... |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/training_plots/abhinand_MedEmbed-large-v0.1_batch_losses_heatmap.png |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/training_plots/abhinand_MedEmbed-large-v0.1_batch_losses_heatmap.pdf |
| 🌡️ Batch losses heatmap saved to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/training_plots/abhinand_MedEmbed-large-v0.1_batch_losses_heatmap.png |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/training_plots/abhinand_MedEmbed-large-v0.1_batch_losses_epoch_1.png |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/training_plots/abhinand_MedEmbed-large-v0.1_batch_losses_epoch_1.pdf |
| 📈 Batch losses plot for epoch 1 saved to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/training_plots/abhinand_MedEmbed-large-v0.1_batch_losses_epoch_1.png |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/training_plots/abhinand_MedEmbed-large-v0.1_batch_losses_epoch_21.png |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/training_plots/abhinand_MedEmbed-large-v0.1_batch_losses_epoch_21.pdf |
| 📈 Batch losses plot for epoch 21 saved to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/training_plots/abhinand_MedEmbed-large-v0.1_batch_losses_epoch_21.png |
| ✅ Training curves analysis complete! |
| 📁 All plots saved to: output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/training_plots |
| 💾 Training data saved to: output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/training_data |
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| 🎓 TRAINING COMPLETED: Returning trained Encoder Only (No Heads) |
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| ✅ Training completed! The returned model is the BEST performing model from training. |
| 📁 Check the training logs above to see which epoch achieved the highest validation accuracy. |
| Saved best model to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/MedEmbed-large-v0.1_best.pt |
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| Evaluating on Test Set... |
| Loaded 36 test examples |
| Cache disabled - evaluation will compute embeddings on-the-fly |
| Evaluating bidirectional model with join path extraction... |
| Evaluating examples: 100%|██████████| 36/36 [00:18<00:00, 1.94it/s] |
| Saved 36 join path examples to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/extracted_join_paths.json |
| Test Accuracy: 0.931 |
| Join Paths Extracted: 36 |
| Evaluation results saved to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/test_metrics.json |
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| 🎨 Generating visualizations using the BEST model (highest validation accuracy)... |
| ✅ CONFIRMED: Using BEST model checkpoint from training |
| 📁 Best model path: output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/MedEmbed-large-v0.1_best.pt |
| 🔧 Using aggregation method: top_k_pairs |
| ✅ Best model file confirmed to exist |
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| Running comprehensive visualizations using top_k_pairs... |
| 🔍 Loading trained model from: output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/MedEmbed-large-v0.1_best.pt |
| 📁 Loading TRAINED model from checkpoint: output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/MedEmbed-large-v0.1_best.pt |
| ✅ CONFIRMED: Loading BEST model checkpoint (highest validation accuracy) |
| 📄 Found configuration file: args.json |
| ✅ Loaded from config: model_type=Bidirectional, embedding_dim=1024, attention_type=top_k_sparse |
| Creating dynamic encoder for 1024 dimensions on cuda |
| 📐 Final embedding dimension: 1024 |
| Creating BidirectionalTableTextModel... |
| 🚀 Using top_k_sparse attention mechanism |
| 📋 Using top-5 sparse attention |
| Using top_k_sparse attention mechanism |
| Initializing Top-K Sparse Attention (k=5) |
| Initializing top-k sparse attention with method: xavier_uniform |
| Successfully applied xavier_uniform initialization to top-k sparse attention |
| Initializing top-k sparse attention with method: xavier_uniform |
| Successfully applied xavier_uniform initialization to top-k sparse attention |
| Using cosine pair scoring method |
| Bidirectional model initialized with top_k=3, pair_score_method=cosine, share_weights=False, use_refinement=True |
| Using initialization method: xavier_uniform |
| Sentence encoder is frozen |
| Sentence encoder dtype detected: torch.float32 |
| Loading trained weights from output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/MedEmbed-large-v0.1_best.pt |
| ✅ Trained weights loaded successfully (custom layers only) |
| 🏆 VERIFICATION: Best model weights are now active for visualization |
| 🎯 Model ready for visualization: BEST TRAINED model on cuda |
| ✅ Trained model loaded with dynamic dimension detection on cuda |
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| Processing example 16 (ID: 2221973625275923611): |
| 8 rows, 69 sentences |
| Using aggregation method: top_k_pairs |
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| Generating comprehensive 4-panel analysis... |
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| 🔍 Creating comprehensive analysis for Example 16... |
| Found 8 rows and 69 sentences |
| Computing comprehensive similarities... |
| Computing raw embedding similarities... |
| Computing bidirectional attention and contextualized similarities... |
| Applying top_k_sparse attention mechanism... |
| Computing contextualized similarities... |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/comprehensive_analysis_example_16.png |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/comprehensive_analysis_example_16.pdf |
| 💾 Saved comprehensive analysis to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/comprehensive_analysis_example_16.png |
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| 📊 Analysis Summary for Example 16: |
| Raw Embeddings - Range: [-0.101, 0.082] |
| Contextualized Similarities - Range: [-0.071, 0.396] |
| Final Model Similarities - Range: [-0.091, 0.082] |
| Cross-Attention - Range: [0.000, 0.200] |
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| 🔝 Top 3 pairs by Raw Embeddings: |
| 1. Row 2 - Sentence 57: 0.082 |
| 2. Row 6 - Sentence 55: 0.079 |
| 3. Row 6 - Sentence 45: 0.074 |
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| 🔝 Top 3 pairs by Contextualized Similarities: |
| 1. Row 1 - Sentence 3: 0.396 |
| 2. Row 3 - Sentence 2: 0.379 |
| 3. Row 5 - Sentence 4: 0.377 |
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| 🔝 Top 3 pairs by Final Model Similarities: |
| 1. Row 4 - Sentence 27: 0.082 |
| 2. Row 5 - Sentence 20: 0.082 |
| 3. Row 4 - Sentence 10: 0.079 |
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| 🔝 Top 3 pairs by Cross-Attention: |
| 1. Row 5 - Sentence 2: 0.200 |
| 2. Row 5 - Sentence 3: 0.200 |
| 3. Row 5 - Sentence 4: 0.200 |
| 📄 Detailed analysis saved to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/example_16_similarity_analysis.txt |
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| All visualizations saved to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations |
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| 🔬 Creating comprehensive model analysis... |
| 🔬 Creating comprehensive analysis for example 16 using BEST model... |
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| 🔍 Creating comprehensive analysis for Example 16... |
| Found 8 rows and 69 sentences |
| Computing comprehensive similarities... |
| Computing raw embedding similarities... |
| Computing bidirectional attention and contextualized similarities... |
| Applying top_k_sparse attention mechanism... |
| Computing contextualized similarities... |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/comprehensive_analysis_example_16.png |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/comprehensive_analysis_example_16.pdf |
| 💾 Saved comprehensive analysis to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/comprehensive_analysis_example_16.png |
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| 📊 Analysis Summary for Example 16: |
| Raw Embeddings - Range: [0.456, 0.725] |
| Contextualized Similarities - Range: [0.834, 0.949] |
| Final Model Similarities - Range: [0.456, 0.725] |
| Cross-Attention - Range: [0.000, 0.200] |
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| 🔝 Top 3 pairs by Raw Embeddings: |
| 1. Row 8 - Sentence 25: 0.725 |
| 2. Row 6 - Sentence 25: 0.723 |
| 3. Row 2 - Sentence 28: 0.717 |
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| 🔝 Top 3 pairs by Contextualized Similarities: |
| 1. Row 4 - Sentence 38: 0.949 |
| 2. Row 1 - Sentence 2: 0.943 |
| 3. Row 4 - Sentence 4: 0.943 |
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| 🔝 Top 3 pairs by Final Model Similarities: |
| 1. Row 8 - Sentence 25: 0.725 |
| 2. Row 6 - Sentence 25: 0.723 |
| 3. Row 2 - Sentence 28: 0.717 |
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| 🔝 Top 3 pairs by Cross-Attention: |
| 1. Row 5 - Sentence 2: 0.200 |
| 2. Row 5 - Sentence 3: 0.200 |
| 3. Row 5 - Sentence 4: 0.200 |
| 📄 Detailed analysis saved to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/example_16_similarity_analysis.txt |
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| 🔬 Generating step-by-step diagnostics for example 16... |
| 🔬 Generating step-by-step diagnostics for example 16... |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/diagnostics_example_16/step1_raw_similarities.png |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/diagnostics_example_16/step1_raw_similarities.pdf |
| Applying top_k_sparse attention mechanism... |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/diagnostics_example_16/step2_forward_attention.png |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/diagnostics_example_16/step2_forward_attention.pdf |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/diagnostics_example_16/step3_reverse_attention.png |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/diagnostics_example_16/step3_reverse_attention.pdf |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/diagnostics_example_16/step4_contextualized_similarities.png |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/diagnostics_example_16/step4_contextualized_similarities.pdf |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/diagnostics_example_16/step5_refined_similarities.png |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/diagnostics_example_16/step5_refined_similarities.pdf |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/diagnostics_example_16/step6_final_pair_scores.png |
| Saved plot to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/diagnostics_example_16/step6_final_pair_scores.pdf |
| Generating validation report... |
| Validation report saved to output_best_model/bidirectional_MedEmbed-large-v0.1_20260128_011945/visualizations/diagnostics_example_16/validation_report_example_16.txt |
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| 🎯 Skipping 4-STAGE example visualizations (--skip_four_stage_viz is set) |
