readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - bc2gm_corpus
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: biobert-base-cased-v1.2-bc2gm-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: bc2gm_corpus
        type: bc2gm_corpus
        config: bc2gm_corpus
        split: train
        args: bc2gm_corpus
      metrics:
      - name: Precision
        type: precision
        value: 0.7988356059445381
      - name: Recall
        type: recall
        value: 0.8243478260869566
      - name: F1
        type: f1
        value: 0.8113912231559292
      - name: Accuracy
        type: accuracy
        value: 0.9772069842818806
  ---
  
  <!-- 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. -->
  
  # biobert-base-cased-v1.2-bc2gm-ner
  
  This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the bc2gm_corpus dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1528
  - Precision: 0.7988
  - Recall: 0.8243
  - F1: 0.8114
  - Accuracy: 0.9772
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.057         | 1.0   | 782  | 0.0670          | 0.7446    | 0.8051 | 0.7736 | 0.9738   |
  | 0.0586        | 2.0   | 1564 | 0.0689          | 0.7689    | 0.8106 | 0.7892 | 0.9755   |
  | 0.0123        | 3.0   | 2346 | 0.0715          | 0.7846    | 0.8076 | 0.7959 | 0.9750   |
  | 0.0002        | 4.0   | 3128 | 0.0896          | 0.7942    | 0.8199 | 0.8068 | 0.9767   |
  | 0.0004        | 5.0   | 3910 | 0.1119          | 0.7971    | 0.8201 | 0.8084 | 0.9765   |
  | 0.0004        | 6.0   | 4692 | 0.1192          | 0.7966    | 0.8337 | 0.8147 | 0.9768   |
  | 0.013         | 7.0   | 5474 | 0.1274          | 0.7932    | 0.8266 | 0.8095 | 0.9773   |
  | 0.0236        | 8.0   | 6256 | 0.1419          | 0.7976    | 0.8213 | 0.8093 | 0.9771   |
  | 0.0004        | 9.0   | 7038 | 0.1519          | 0.8004    | 0.8261 | 0.8130 | 0.9772   |
  | 0.0           | 10.0  | 7820 | 0.1528          | 0.7988    | 0.8243 | 0.8114 | 0.9772   |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  