readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: clinicalAgitationTextClassificationModelV1
    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. -->
  
  # clinicalAgitationTextClassificationModelV1
  
  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: 0.7664
  - Accuracy: 0.86
  - Precision: 0.8986
  - Recall: 0.8986
  - F1: 0.8986
  
  ## 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: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.6593        | 1.0   | 50   | 0.5648          | 0.74     | 0.8525    | 0.7536 | 0.8    |
  | 0.5082        | 2.0   | 100  | 0.4731          | 0.76     | 0.8814    | 0.7536 | 0.8125 |
  | 0.3744        | 3.0   | 150  | 0.4516          | 0.84     | 0.8732    | 0.8986 | 0.8857 |
  | 0.2618        | 4.0   | 200  | 0.5435          | 0.83     | 0.8514    | 0.9130 | 0.8811 |
  | 0.2145        | 5.0   | 250  | 0.4750          | 0.85     | 0.95      | 0.8261 | 0.8837 |
  | 0.1426        | 6.0   | 300  | 0.5433          | 0.86     | 0.8986    | 0.8986 | 0.8986 |
  | 0.0971        | 7.0   | 350  | 0.5997          | 0.85     | 0.9355    | 0.8406 | 0.8855 |
  | 0.0741        | 8.0   | 400  | 0.7534          | 0.86     | 0.8986    | 0.8986 | 0.8986 |
  | 0.0513        | 9.0   | 450  | 0.8293          | 0.83     | 0.8611    | 0.8986 | 0.8794 |
  | 0.0456        | 10.0  | 500  | 0.7664          | 0.86     | 0.8986    | 0.8986 | 0.8986 |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  