vih_explainability / README.md
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vih_explainability
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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_explainability
    results: []

vih_explainability

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: 0.3980
  • 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