readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: ADE-Bio_ClinicalBERT-NER
    results: []
  ---
  
  <!-- 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. -->
  
  # ADE-Bio_ClinicalBERT-NER
  
  This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1926
  - Precision: 0.7830
  - Recall: 0.8811
  - F1: 0.8291
  - Accuracy: 0.9437
  
  ## 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: 1e-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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.2389        | 1.0   | 201  | 0.2100          | 0.7155    | 0.8292 | 0.7681 | 0.9263   |
  | 0.0648        | 2.0   | 402  | 0.1849          | 0.7716    | 0.8711 | 0.8183 | 0.9392   |
  | 0.2825        | 3.0   | 603  | 0.1856          | 0.7834    | 0.8788 | 0.8284 | 0.9422   |
  | 0.199         | 4.0   | 804  | 0.1875          | 0.7796    | 0.8781 | 0.8259 | 0.9430   |
  | 0.0404        | 5.0   | 1005 | 0.1926          | 0.7830    | 0.8811 | 0.8291 | 0.9437   |
  
  
  ### Framework versions
  
  - Transformers 4.21.0
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  