| --- |
| license: apache-2.0 |
| base_model: PlanTL-GOB-ES/bsc-bio-ehr-es |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: vih_explainability2 |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # vih_explainability2 |
| |
| This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3811 |
| - Roc Auc: 0.8939 |
| - Ap Score: 0.7760 |
| - Precision: 0.9146 |
| - Recall: 0.8065 |
| - F1: 0.8571 |
| |
| ## Model description |
| |
| More information needed |
| |
| ## Intended uses & limitations |
| |
| More information needed |
| |
| ## Training and evaluation data |
| |
| More information needed |
| |
| ## Training procedure |
| |
| ### Training hyperparameters |
| |
| The following hyperparameters were used during training: |
| - learning_rate: 3e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Roc Auc | Ap Score | Precision | Recall | F1 | |
| |:-------------:|:------:|:----:|:---------------:|:-------:|:--------:|:---------:|:------:|:------:| |
| | 0.3644 | 0.8475 | 100 | 0.2350 | 0.8402 | 0.6907 | 0.9028 | 0.6989 | 0.7879 | |
| | 0.1848 | 1.6949 | 200 | 0.2358 | 0.8765 | 0.7168 | 0.8588 | 0.7849 | 0.8202 | |
| | 0.1462 | 2.5424 | 300 | 0.2215 | 0.9021 | 0.7509 | 0.8571 | 0.8387 | 0.8478 | |
| | 0.1105 | 3.3898 | 400 | 0.2671 | 0.8778 | 0.7504 | 0.9114 | 0.7742 | 0.8372 | |
| | 0.079 | 4.2373 | 500 | 0.3124 | 0.8630 | 0.7338 | 0.92 | 0.7419 | 0.8214 | |
| | 0.0248 | 5.0847 | 600 | 0.3464 | 0.8765 | 0.7416 | 0.9 | 0.7742 | 0.8324 | |
| | 0.0127 | 5.9322 | 700 | 0.3822 | 0.8617 | 0.7248 | 0.9079 | 0.7419 | 0.8166 | |
| | 0.0089 | 6.7797 | 800 | 0.3625 | 0.8885 | 0.7674 | 0.9136 | 0.7957 | 0.8506 | |
| | 0.0042 | 7.6271 | 900 | 0.3643 | 0.8885 | 0.7674 | 0.9136 | 0.7957 | 0.8506 | |
| | 0.0052 | 8.4746 | 1000 | 0.3772 | 0.8939 | 0.7760 | 0.9146 | 0.8065 | 0.8571 | |
| | 0.0031 | 9.3220 | 1100 | 0.3811 | 0.8939 | 0.7760 | 0.9146 | 0.8065 | 0.8571 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.41.0 |
| - Pytorch 2.3.0+cu121 |
| - Datasets 2.19.1 |
| - Tokenizers 0.19.1 |
| |
