readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - nubes
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bsc-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: nubes
        type: nubes
        config: Nubes
        split: train
        args: Nubes
      metrics:
      - name: Precision
        type: precision
        value: 0.8846931894807822
      - name: Recall
        type: recall
        value: 0.9190893169877408
      - name: F1
        type: f1
        value: 0.9015633052740079
      - name: Accuracy
        type: accuracy
        value: 0.9765241847383958
  ---
  
  <!-- 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. -->
  
  # bsc-finetuned-ner
  
  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 nubes dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1548
  - Precision: 0.8847
  - Recall: 0.9191
  - F1: 0.9016
  - Accuracy: 0.9765
  
  ## 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: 5e-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: 7
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1179        | 1.0   | 1726  | 0.1078          | 0.8223    | 0.8673 | 0.8442 | 0.9682   |
  | 0.0775        | 2.0   | 3452  | 0.1152          | 0.8474    | 0.8872 | 0.8669 | 0.9722   |
  | 0.0424        | 3.0   | 5178  | 0.1096          | 0.8677    | 0.9054 | 0.8862 | 0.9753   |
  | 0.023         | 4.0   | 6904  | 0.1301          | 0.8740    | 0.9040 | 0.8888 | 0.9753   |
  | 0.0098        | 5.0   | 8630  | 0.1352          | 0.8829    | 0.9194 | 0.9008 | 0.9778   |
  | 0.0088        | 6.0   | 10356 | 0.1483          | 0.8903    | 0.9121 | 0.9010 | 0.9756   |
  | 0.0045        | 7.0   | 12082 | 0.1548          | 0.8847    | 0.9191 | 0.9016 | 0.9765   |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  