readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - f1
  - accuracy
  model-index:
  - name: tuned_cair_five_classes
    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. -->
  
  # tuned_cair_five_classes
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0976
  - F1: 0.9767
  - Roc Auc: 0.9815
  - Accuracy: 0.9325
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
  | No log        | 1.0   | 250  | 0.1394          | 0.9480 | 0.9577  | 0.845    |
  | 0.1037        | 2.0   | 500  | 0.1221          | 0.95   | 0.9577  | 0.87     |
  | 0.1037        | 3.0   | 750  | 0.1107          | 0.9598 | 0.9680  | 0.8875   |
  | 0.0593        | 4.0   | 1000 | 0.0965          | 0.9662 | 0.9728  | 0.905    |
  | 0.0593        | 5.0   | 1250 | 0.0872          | 0.9734 | 0.9787  | 0.92     |
  | 0.0352        | 6.0   | 1500 | 0.0824          | 0.9753 | 0.9802  | 0.925    |
  | 0.0352        | 7.0   | 1750 | 0.0906          | 0.9701 | 0.9759  | 0.915    |
  | 0.02          | 8.0   | 2000 | 0.0900          | 0.9734 | 0.9787  | 0.925    |
  | 0.02          | 9.0   | 2250 | 0.0930          | 0.9727 | 0.9776  | 0.9225   |
  | 0.0141        | 10.0  | 2500 | 0.0932          | 0.9734 | 0.9787  | 0.9175   |
  | 0.0141        | 11.0  | 2750 | 0.0925          | 0.9760 | 0.9808  | 0.9275   |
  | 0.0098        | 12.0  | 3000 | 0.0964          | 0.9741 | 0.9798  | 0.93     |
  | 0.0098        | 13.0  | 3250 | 0.0964          | 0.9747 | 0.9798  | 0.9275   |
  | 0.0069        | 14.0  | 3500 | 0.0981          | 0.9734 | 0.9787  | 0.925    |
  | 0.0069        | 15.0  | 3750 | 0.0930          | 0.9767 | 0.9815  | 0.9325   |
  | 0.0058        | 16.0  | 4000 | 0.0939          | 0.9767 | 0.9815  | 0.9325   |
  | 0.0058        | 17.0  | 4250 | 0.0959          | 0.9767 | 0.9815  | 0.935    |
  | 0.0048        | 18.0  | 4500 | 0.0972          | 0.9753 | 0.9799  | 0.925    |
  | 0.0048        | 19.0  | 4750 | 0.0971          | 0.9767 | 0.9815  | 0.9325   |
  | 0.0042        | 20.0  | 5000 | 0.0976          | 0.9767 | 0.9815  | 0.9325   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  