readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: CTEBMSP_ANAT_DISO
    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. -->
  
  # CTEBMSP_ANAT_DISO
  
  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: 0.0909
  - Anat Precision: 0.7522
  - Anat Recall: 0.7147
  - Anat F1: 0.7330
  - Anat Number: 361
  - Diso Precision: 0.8915
  - Diso Recall: 0.8919
  - Diso F1: 0.8917
  - Diso Number: 2645
  - Overall Precision: 0.8755
  - Overall Recall: 0.8706
  - Overall F1: 0.8731
  - Overall Accuracy: 0.9873
  
  ## 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: 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: 8
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Anat Precision | Anat Recall | Anat F1 | Anat Number | Diso Precision | Diso Recall | Diso F1 | Diso Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 0.0592        | 1.0   | 2133  | 0.0506          | 0.6950         | 0.4986      | 0.5806  | 361         | 0.8635         | 0.8609      | 0.8622  | 2645        | 0.8484            | 0.8174         | 0.8326     | 0.9843           |
  | 0.0323        | 2.0   | 4266  | 0.0583          | 0.7899         | 0.6039      | 0.6845  | 361         | 0.8780         | 0.8817      | 0.8798  | 2645        | 0.8697            | 0.8483         | 0.8589     | 0.9858           |
  | 0.0201        | 3.0   | 6399  | 0.0580          | 0.6565         | 0.7147      | 0.6844  | 361         | 0.8598         | 0.8764      | 0.8680  | 2645        | 0.8339            | 0.8570         | 0.8453     | 0.9851           |
  | 0.0121        | 4.0   | 8532  | 0.0758          | 0.7240         | 0.6759      | 0.6991  | 361         | 0.8976         | 0.8752      | 0.8863  | 2645        | 0.8776            | 0.8513         | 0.8642     | 0.9863           |
  | 0.0078        | 5.0   | 10665 | 0.0814          | 0.7219         | 0.7119      | 0.7169  | 361         | 0.8776         | 0.8975      | 0.8875  | 2645        | 0.8595            | 0.8752         | 0.8673     | 0.9862           |
  | 0.0031        | 6.0   | 12798 | 0.0974          | 0.7599         | 0.6399      | 0.6947  | 361         | 0.8895         | 0.8915      | 0.8905  | 2645        | 0.8761            | 0.8613         | 0.8686     | 0.9867           |
  | 0.002         | 7.0   | 14931 | 0.0980          | 0.7143         | 0.6787      | 0.6960  | 361         | 0.8813         | 0.8957      | 0.8884  | 2645        | 0.8624            | 0.8696         | 0.8660     | 0.9860           |
  | 0.0005        | 8.0   | 17064 | 0.0909          | 0.7522         | 0.7147      | 0.7330  | 361         | 0.8915         | 0.8919      | 0.8917  | 2645        | 0.8755            | 0.8706         | 0.8731     | 0.9873           |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  