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---
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_explainability
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_explainability
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: 0.3980
- Roc Auc: 0.8920
- Ap Score: 0.8575
- Precision: 0.8926
- Recall: 0.8920
- F1: 0.8919
## 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: 9e-06
- train_batch_size: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:------:|:----:|:---------------:|:-------:|:--------:|:---------:|:------:|:------:|
| 0.62 | 0.5376 | 50 | 0.5331 | 0.7789 | 0.7389 | 0.7905 | 0.7789 | 0.7763 |
| 0.5106 | 1.0753 | 100 | 0.4343 | 0.7899 | 0.7614 | 0.8118 | 0.7899 | 0.7856 |
| 0.3762 | 1.6129 | 150 | 0.3364 | 0.8594 | 0.8075 | 0.8596 | 0.8594 | 0.8594 |
| 0.2878 | 2.1505 | 200 | 0.3582 | 0.8597 | 0.8260 | 0.8636 | 0.8597 | 0.8591 |
| 0.2556 | 2.6882 | 250 | 0.3121 | 0.8706 | 0.8440 | 0.8764 | 0.8706 | 0.8698 |
| 0.165 | 3.2258 | 300 | 0.3746 | 0.8652 | 0.8349 | 0.8699 | 0.8652 | 0.8645 |
| 0.2125 | 3.7634 | 350 | 0.3842 | 0.8815 | 0.8629 | 0.8898 | 0.8815 | 0.8805 |
| 0.1923 | 4.3011 | 400 | 0.3178 | 0.9080 | 0.8662 | 0.9086 | 0.9080 | 0.9081 |
| 0.1333 | 4.8387 | 450 | 0.3397 | 0.8704 | 0.8297 | 0.8709 | 0.8704 | 0.8702 |
| 0.137 | 5.3763 | 500 | 0.3369 | 0.9028 | 0.8718 | 0.9034 | 0.9028 | 0.9027 |
| 0.1103 | 5.9140 | 550 | 0.3493 | 0.9025 | 0.8545 | 0.9045 | 0.9025 | 0.9026 |
| 0.0896 | 6.4516 | 600 | 0.4059 | 0.8813 | 0.8507 | 0.8838 | 0.8813 | 0.8809 |
| 0.0573 | 6.9892 | 650 | 0.3956 | 0.8813 | 0.8470 | 0.8826 | 0.8813 | 0.8810 |
| 0.0716 | 7.5269 | 700 | 0.5566 | 0.8815 | 0.8674 | 0.8926 | 0.8815 | 0.8803 |
| 0.0893 | 8.0645 | 750 | 0.3980 | 0.8920 | 0.8575 | 0.8926 | 0.8920 | 0.8919 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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