readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - recall
  - precision
  model-index:
  - name: Bio_ClinicalBERT-finetuned-skinwound
    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. -->
  
  # Bio_ClinicalBERT-finetuned-skinwound
  
  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: 0.3435
  - Accuracy: 0.8938
  - F1: 0.8884
  - Recall: 0.8938
  - Precision: 0.8857
  
  ## 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 | Accuracy | F1     | Recall | Precision |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
  | 0.5905        | 1.0   | 154  | 0.3423          | 0.8828   | 0.8416 | 0.8828 | 0.8064    |
  | 0.3472        | 2.0   | 308  | 0.2942          | 0.8901   | 0.8753 | 0.8901 | 0.8800    |
  | 0.2651        | 3.0   | 462  | 0.2977          | 0.8974   | 0.8858 | 0.8974 | 0.8889    |
  | 0.2203        | 4.0   | 616  | 0.3224          | 0.9011   | 0.8945 | 0.9011 | 0.8930    |
  | 0.164         | 5.0   | 770  | 0.3435          | 0.8938   | 0.8884 | 0.8938 | 0.8857    |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  