readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - ncbi_disease
  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: ncbi_disease
        type: ncbi_disease
        args: ncbi_disease
      metrics:
      - name: Precision
        type: precision
        value: 0.8396334478808706
      - name: Recall
        type: recall
        value: 0.8731387730792138
      - name: F1
        type: f1
        value: 0.856058394160584
      - name: Accuracy
        type: accuracy
        value: 0.9824805769647444
  ---
  
  <!-- 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 ncbi_disease dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0706
  - Precision: 0.8396
  - Recall: 0.8731
  - F1: 0.8561
  - Accuracy: 0.9825
  
  ## 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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 340  | 0.0691          | 0.8190    | 0.7868 | 0.8026 | 0.9777   |
  | 0.101         | 2.0   | 680  | 0.0700          | 0.8334    | 0.8553 | 0.8442 | 0.9807   |
  | 0.0244        | 3.0   | 1020 | 0.0706          | 0.8396    | 0.8731 | 0.8561 | 0.9825   |
  
  
  ### Framework versions
  
  - Transformers 4.19.4
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.0
  - Tokenizers 0.12.1
  