readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: AgitationTextV4
    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. -->
  
  # AgitationTextV4
  
  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: 0.5320
  - Accuracy: 0.76
  - Precision: 0.8507
  - Recall: 0.8028
  - F1: 0.8261
  
  ## 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: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.6223        | 1.0   | 50   | 0.6247          | 0.66     | 0.7403    | 0.8028 | 0.7703 |
  | 0.5431        | 2.0   | 100  | 0.5636          | 0.66     | 0.9302    | 0.5634 | 0.7018 |
  | 0.4361        | 3.0   | 150  | 0.5346          | 0.7      | 0.8475    | 0.7042 | 0.7692 |
  | 0.3617        | 4.0   | 200  | 0.5255          | 0.72     | 0.8413    | 0.7465 | 0.7910 |
  | 0.2878        | 5.0   | 250  | 0.5009          | 0.74     | 0.8358    | 0.7887 | 0.8116 |
  | 0.2282        | 6.0   | 300  | 0.5116          | 0.76     | 0.8615    | 0.7887 | 0.8235 |
  | 0.1747        | 7.0   | 350  | 0.5235          | 0.75     | 0.8966    | 0.7324 | 0.8062 |
  | 0.1454        | 8.0   | 400  | 0.5663          | 0.79     | 0.8472    | 0.8592 | 0.8531 |
  | 0.125         | 9.0   | 450  | 0.5371          | 0.77     | 0.8529    | 0.8169 | 0.8345 |
  | 0.1188        | 10.0  | 500  | 0.5320          | 0.76     | 0.8507    | 0.8028 | 0.8261 |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  