readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: ner_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. -->
  
  # ner_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: 0.0746
  - Anat Precision: 0.6512
  - Anat Recall: 0.6573
  - Anat F1: 0.6542
  - Anat Number: 534
  - Diso Precision: 0.8727
  - Diso Recall: 0.8844
  - Diso F1: 0.8785
  - Diso Number: 2915
  - Overall Precision: 0.8385
  - Overall Recall: 0.8492
  - Overall F1: 0.8438
  - Overall Accuracy: 0.9838
  
  ## 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: 2e-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: 4
  
  ### 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 0.0625        | 1.0   | 1682 | 0.0591          | 0.5407         | 0.6723      | 0.5993  | 534         | 0.8516         | 0.8624      | 0.8570  | 2915        | 0.7945            | 0.8330         | 0.8133     | 0.9808           |
  | 0.0397        | 2.0   | 3364 | 0.0633          | 0.6237         | 0.6798      | 0.6505  | 534         | 0.8576         | 0.8820      | 0.8696  | 2915        | 0.8196            | 0.8507         | 0.8348     | 0.9826           |
  | 0.0181        | 3.0   | 5046 | 0.0698          | 0.6452         | 0.6948      | 0.6691  | 534         | 0.8670         | 0.8878      | 0.8773  | 2915        | 0.8312            | 0.8579         | 0.8443     | 0.9833           |
  | 0.0121        | 4.0   | 6728 | 0.0746          | 0.6512         | 0.6573      | 0.6542  | 534         | 0.8727         | 0.8844      | 0.8785  | 2915        | 0.8385            | 0.8492         | 0.8438     | 0.9838           |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  