readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - bc4chemd_ner
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bc4chemd_ner-Bio_ClinicalBERT-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: bc4chemd_ner
        type: bc4chemd_ner
        args: bc4chemd
      metrics:
      - name: Precision
        type: precision
        value: 0.8944236722550557
      - name: Recall
        type: recall
        value: 0.8777321865383098
      - name: F1
        type: f1
        value: 0.8859993229654115
      - name: Accuracy
        type: accuracy
        value: 0.9908228496683563
  ---
  
  <!-- 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. -->
  
  # bc4chemd_ner-Bio_ClinicalBERT-finetuned-ner
  
  This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the bc4chemd_ner dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0641
  - Precision: 0.8944
  - Recall: 0.8777
  - F1: 0.8860
  - Accuracy: 0.9908
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.006         | 1.0   | 1918  | 0.0310          | 0.8697    | 0.8510 | 0.8602 | 0.9894   |
  | 0.0097        | 2.0   | 3836  | 0.0345          | 0.8855    | 0.8637 | 0.8745 | 0.9898   |
  | 0.0058        | 3.0   | 5754  | 0.0359          | 0.8733    | 0.8836 | 0.8784 | 0.9902   |
  | 0.0014        | 4.0   | 7672  | 0.0440          | 0.8723    | 0.8842 | 0.8782 | 0.9903   |
  | 0.0005        | 5.0   | 9590  | 0.0539          | 0.8862    | 0.8673 | 0.8766 | 0.9903   |
  | 0.0001        | 6.0   | 11508 | 0.0558          | 0.8939    | 0.8628 | 0.8781 | 0.9904   |
  | 0.0001        | 7.0   | 13426 | 0.0558          | 0.8846    | 0.8729 | 0.8787 | 0.9903   |
  | 0.0012        | 8.0   | 15344 | 0.0635          | 0.8935    | 0.8696 | 0.8814 | 0.9905   |
  | 0.0           | 9.0   | 17262 | 0.0624          | 0.8897    | 0.8831 | 0.8864 | 0.9908   |
  | 0.0002        | 10.0  | 19180 | 0.0641          | 0.8944    | 0.8777 | 0.8860 | 0.9908   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  