readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - bc2gm_corpus
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: bc2gm_corpus
        type: bc2gm_corpus
        args: bc2gm_corpus
      metrics:
      - name: Precision
        type: precision
        value: 0.7853881278538812
      - name: Recall
        type: recall
        value: 0.8158102766798419
      - name: F1
        type: f1
        value: 0.8003101977510663
      - name: Accuracy
        type: accuracy
        value: 0.9758965601366187
  ---
  
  <!-- 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. -->
  
  # bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner
  
  This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the bc2gm_corpus dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1505
  - Precision: 0.7854
  - Recall: 0.8158
  - F1: 0.8003
  - Accuracy: 0.9759
  
  ## 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: 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: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0981        | 1.0   | 782  | 0.0712          | 0.7228    | 0.7948 | 0.7571 | 0.9724   |
  | 0.0509        | 2.0   | 1564 | 0.0687          | 0.7472    | 0.8199 | 0.7818 | 0.9746   |
  | 0.0121        | 3.0   | 2346 | 0.0740          | 0.7725    | 0.8011 | 0.7866 | 0.9747   |
  | 0.0001        | 4.0   | 3128 | 0.1009          | 0.7618    | 0.8251 | 0.7922 | 0.9741   |
  | 0.0042        | 5.0   | 3910 | 0.1106          | 0.7757    | 0.8185 | 0.7965 | 0.9754   |
  | 0.0015        | 6.0   | 4692 | 0.1182          | 0.7812    | 0.8111 | 0.7958 | 0.9758   |
  | 0.0001        | 7.0   | 5474 | 0.1283          | 0.7693    | 0.8275 | 0.7973 | 0.9753   |
  | 0.0072        | 8.0   | 6256 | 0.1376          | 0.7863    | 0.8158 | 0.8008 | 0.9762   |
  | 0.0045        | 9.0   | 7038 | 0.1468          | 0.7856    | 0.8180 | 0.8015 | 0.9761   |
  | 0.0           | 10.0  | 7820 | 0.1505          | 0.7854    | 0.8158 | 0.8003 | 0.9759   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  