readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - jnlpba
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: biobert-base-cased-v1.2-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: jnlpba
        type: jnlpba
        args: jnlpba
      metrics:
      - name: Precision
        type: precision
        value: 0.7150627220423177
      - name: Recall
        type: recall
        value: 0.8300729927007299
      - name: F1
        type: f1
        value: 0.7682875335686659
      - name: Accuracy
        type: accuracy
        value: 0.90497239665345
  ---
  
  <!-- 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-finetuned-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 jnlpba dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3655
  - Precision: 0.7151
  - Recall: 0.8301
  - F1: 0.7683
  - Accuracy: 0.9050
  
  ## 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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.257         | 1.0   | 1160 | 0.2889          | 0.7091    | 0.8222 | 0.7615 | 0.9021   |
  | 0.1962        | 2.0   | 2320 | 0.3009          | 0.7154    | 0.8259 | 0.7667 | 0.9048   |
  | 0.158         | 3.0   | 3480 | 0.3214          | 0.7098    | 0.8228 | 0.7621 | 0.9031   |
  | 0.131         | 4.0   | 4640 | 0.3385          | 0.7174    | 0.8292 | 0.7692 | 0.9055   |
  | 0.1081        | 5.0   | 5800 | 0.3655          | 0.7151    | 0.8301 | 0.7683 | 0.9050   |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.9.1+cu102
  - Datasets 1.13.2
  - Tokenizers 0.10.3
  