readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - ncbi_disease
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: biobert-base-cased-v1.2_ncbi_disease-softmax-labelall-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: ncbi_disease
        type: ncbi_disease
        args: ncbi_disease
      metrics:
      - name: Precision
        type: precision
        value: 0.8288508557457213
      - name: Recall
        type: recall
        value: 0.8614993646759848
      - name: F1
        type: f1
        value: 0.8448598130841122
      - name: Accuracy
        type: accuracy
        value: 0.9861487755016897
  ---
  
  <!-- 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_ncbi_disease-softmax-labelall-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 ncbi_disease dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0629
  - Precision: 0.8289
  - Recall: 0.8615
  - F1: 0.8449
  - Accuracy: 0.9861
  
  ## 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: 4
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0554        | 1.0   | 1359 | 0.0659          | 0.7814    | 0.8132 | 0.7970 | 0.9825   |
  | 0.0297        | 2.0   | 2718 | 0.0445          | 0.8284    | 0.8895 | 0.8578 | 0.9876   |
  | 0.0075        | 3.0   | 4077 | 0.0629          | 0.8289    | 0.8615 | 0.8449 | 0.9861   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.2+cu102
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  