readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - bc2gm_corpus
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bioBERT-finrtuned-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: 0.7932528628907459
      - name: Recall
        type: recall
        value: 0.8373080692584123
      - name: F1
        type: f1
        value: 0.8146853146853147
      - name: Accuracy
        type: accuracy
        value: 0.9750375532003672
  widget:
    - text: "JUP, AKT1, and AURKC are examples of genes"
  
  ---
  
  <!-- 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-finrtuned-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: 0.0887
  - Precision: 0.7933
  - Recall: 0.8373
  - F1: 0.8147
  - Accuracy: 0.9750
  
  ## 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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0893        | 1.0   | 1563 | 0.0748          | 0.7447    | 0.8063 | 0.7743 | 0.9722   |
  | 0.0507        | 2.0   | 3126 | 0.0773          | 0.7928    | 0.8275 | 0.8098 | 0.9739   |
  | 0.0286        | 3.0   | 4689 | 0.0887          | 0.7933    | 0.8373 | 0.8147 | 0.9750   |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.6
  