---
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
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# 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