readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: AgitationTextV3
    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. -->
  
  # AgitationTextV3
  
  This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5199
  - Accuracy: 0.8
  - Precision: 0.9636
  - Recall: 0.7465
  - F1: 0.8413
  
  ## 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     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.6797        | 1.0   | 50   | 0.6650          | 0.57     | 0.85      | 0.4789 | 0.6126 |
  | 0.599         | 2.0   | 100  | 0.6180          | 0.65     | 0.86      | 0.6056 | 0.7107 |
  | 0.5121        | 3.0   | 150  | 0.5714          | 0.75     | 0.8833    | 0.7465 | 0.8092 |
  | 0.4049        | 4.0   | 200  | 0.5187          | 0.81     | 0.9194    | 0.8028 | 0.8571 |
  | 0.3091        | 5.0   | 250  | 0.5034          | 0.77     | 0.9444    | 0.7183 | 0.816  |
  | 0.2303        | 6.0   | 300  | 0.4673          | 0.78     | 0.9298    | 0.7465 | 0.8281 |
  | 0.1773        | 7.0   | 350  | 0.4802          | 0.8      | 0.9322    | 0.7746 | 0.8462 |
  | 0.1396        | 8.0   | 400  | 0.5260          | 0.8      | 0.9636    | 0.7465 | 0.8413 |
  | 0.1204        | 9.0   | 450  | 0.5317          | 0.8      | 0.9636    | 0.7465 | 0.8413 |
  | 0.0982        | 10.0  | 500  | 0.5199          | 0.8      | 0.9636    | 0.7465 | 0.8413 |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  