vih_explainability3 / README.md
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vih_explainability3
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metadata
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: []

vih_explainability3

This model is a fine-tuned version of 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