readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - ncbi_disease
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: biobert-finetuned-ncbi
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: ncbi_disease
        type: ncbi_disease
        config: ncbi_disease
        split: train
        args: ncbi_disease
      metrics:
      - name: Precision
        type: precision
        value: 0.8192771084337349
      - name: Recall
        type: recall
        value: 0.8640406607369758
      - name: F1
        type: f1
        value: 0.8410636982065552
      - name: Accuracy
        type: accuracy
        value: 0.9856218100336114
  ---
  
  <!-- 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-finetuned-ncbi
  
  This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the ncbi_disease dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0590
  - Precision: 0.8193
  - Recall: 0.8640
  - F1: 0.8411
  - Accuracy: 0.9856
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1049        | 1.0   | 680  | 0.0588          | 0.7826    | 0.7776 | 0.7801 | 0.9806   |
  | 0.0362        | 2.0   | 1360 | 0.0539          | 0.8084    | 0.8577 | 0.8323 | 0.9852   |
  | 0.0109        | 3.0   | 2040 | 0.0590          | 0.8193    | 0.8640 | 0.8411 | 0.9856   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  