readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: DISO_bsc_test16
    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. -->
  
  # DISO_bsc_test16
  
  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: 0.1732
  - Diso Precision: 0.7577
  - Diso Recall: 0.7757
  - Diso F1: 0.7666
  - Diso Number: 4552
  - Overall Precision: 0.7577
  - Overall Recall: 0.7757
  - Overall F1: 0.7666
  - Overall Accuracy: 0.9732
  
  ## 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: 8e-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: 8
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Diso Precision | Diso Recall | Diso F1 | Diso Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 0.0948        | 1.0   | 1400  | 0.0766          | 0.7157         | 0.7594      | 0.7369  | 4552        | 0.7157            | 0.7594         | 0.7369     | 0.9710           |
  | 0.0631        | 2.0   | 2800  | 0.0818          | 0.7442         | 0.7599      | 0.7520  | 4552        | 0.7442            | 0.7599         | 0.7520     | 0.9726           |
  | 0.0454        | 3.0   | 4200  | 0.0842          | 0.7544         | 0.7654      | 0.7599  | 4552        | 0.7544            | 0.7654         | 0.7599     | 0.9728           |
  | 0.0311        | 4.0   | 5600  | 0.1113          | 0.7678         | 0.7700      | 0.7689  | 4552        | 0.7678            | 0.7700         | 0.7689     | 0.9732           |
  | 0.0217        | 5.0   | 7000  | 0.1231          | 0.7745         | 0.7687      | 0.7716  | 4552        | 0.7745            | 0.7687         | 0.7716     | 0.9743           |
  | 0.015         | 6.0   | 8400  | 0.1482          | 0.7651         | 0.7733      | 0.7691  | 4552        | 0.7651            | 0.7733         | 0.7691     | 0.9735           |
  | 0.0101        | 7.0   | 9800  | 0.1498          | 0.7576         | 0.7709      | 0.7642  | 4552        | 0.7576            | 0.7709         | 0.7642     | 0.9730           |
  | 0.0073        | 8.0   | 11200 | 0.1732          | 0.7577         | 0.7757      | 0.7666  | 4552        | 0.7577            | 0.7757         | 0.7666     | 0.9732           |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  