readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: clinical-finetuned-AgitationModel
    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. -->
  
  # clinical-finetuned-AgitationModel
  
  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: 0.9746
  - Accuracy: 0.88
  - Precision: 0.9178
  - Recall: 0.9178
  - F1: 0.9178
  
  ## 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
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.0949        | 1.0   | 50   | 1.0393          | 0.85     | 0.8816    | 0.9178 | 0.8993 |
  | 0.0475        | 2.0   | 100  | 1.0619          | 0.85     | 0.8816    | 0.9178 | 0.8993 |
  | 0.0149        | 3.0   | 150  | 0.9746          | 0.88     | 0.9178    | 0.9178 | 0.9178 |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu113
  - Tokenizers 0.12.1
  