| --- |
| 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_explainability3 |
| 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_explainability3 |
| |
| 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: 0.3951 |
| - Roc Auc: 0.8213 |
| - Ap Score: 0.7049 |
| - Precision: 0.9836 |
| - Recall: 0.6452 |
| - F1: 0.7792 |
| |
| ## 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: 1e-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 | |
| |:-------------:|:------:|:----:|:---------------:|:-------:|:--------:|:---------:|:------:|:------:| |
| | 0.4261 | 0.8475 | 100 | 0.3832 | 0.6129 | 0.3793 | 1.0 | 0.2258 | 0.3684 | |
| | 0.2405 | 1.6949 | 200 | 0.4736 | 0.6344 | 0.4138 | 1.0 | 0.2688 | 0.4237 | |
| | 0.2088 | 2.5424 | 300 | 0.3452 | 0.7729 | 0.6274 | 0.9808 | 0.5484 | 0.7034 | |
| | 0.2196 | 3.3898 | 400 | 0.3644 | 0.7151 | 0.5431 | 1.0 | 0.4301 | 0.6015 | |
| | 0.2068 | 4.2373 | 500 | 0.5156 | 0.6344 | 0.4138 | 1.0 | 0.2688 | 0.4237 | |
| | 0.1374 | 5.0847 | 600 | 0.3988 | 0.7944 | 0.6619 | 0.9821 | 0.5914 | 0.7383 | |
| | 0.1098 | 5.9322 | 700 | 0.3629 | 0.8051 | 0.6791 | 0.9828 | 0.6129 | 0.7550 | |
| | 0.0914 | 6.7797 | 800 | 0.3394 | 0.8240 | 0.6934 | 0.9531 | 0.6559 | 0.7771 | |
| | 0.088 | 7.6271 | 900 | 0.3612 | 0.8334 | 0.7009 | 0.9403 | 0.6774 | 0.7875 | |
| | 0.0787 | 8.4746 | 1000 | 0.3801 | 0.8213 | 0.7049 | 0.9836 | 0.6452 | 0.7792 | |
| | 0.0588 | 9.3220 | 1100 | 0.3951 | 0.8213 | 0.7049 | 0.9836 | 0.6452 | 0.7792 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.41.0 |
| - Pytorch 2.3.0+cu121 |
| - Datasets 2.19.1 |
| - Tokenizers 0.19.1 |
| |
