readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: F_Roberta_classifier2
    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. -->
  
  # F_Roberta_classifier2
  
  This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1317
  - Accuracy: 0.9751
  - F1: 0.9751
  - Precision: 0.9751
  - Recall: 0.9751
  - C Report:               precision    recall  f1-score   support
  
             0       0.97      0.98      0.98      1467
             1       0.98      0.97      0.98      1466
  
      accuracy                           0.98      2933
     macro avg       0.98      0.98      0.98      2933
  weighted avg       0.98      0.98      0.98      2933
  
  - C Matrix: None
  
  ## 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: 5e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | C Report                                                                                                                                                                                                                                                                                                                               | C Matrix |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|
  | 0.1626        | 1.0   | 614  | 0.0936          | 0.9707   | 0.9707 | 0.9707    | 0.9707 |               precision    recall  f1-score   support
  
             0       0.97      0.97      0.97      1467
             1       0.97      0.97      0.97      1466
  
      accuracy                           0.97      2933
     macro avg       0.97      0.97      0.97      2933
  weighted avg       0.97      0.97      0.97      2933
   | None     |
  | 0.0827        | 2.0   | 1228 | 0.0794          | 0.9731   | 0.9731 | 0.9731    | 0.9731 |               precision    recall  f1-score   support
  
             0       0.96      0.98      0.97      1467
             1       0.98      0.96      0.97      1466
  
      accuracy                           0.97      2933
     macro avg       0.97      0.97      0.97      2933
  weighted avg       0.97      0.97      0.97      2933
   | None     |
  | 0.0525        | 3.0   | 1842 | 0.1003          | 0.9737   | 0.9737 | 0.9737    | 0.9737 |               precision    recall  f1-score   support
  
             0       0.97      0.98      0.97      1467
             1       0.98      0.97      0.97      1466
  
      accuracy                           0.97      2933
     macro avg       0.97      0.97      0.97      2933
  weighted avg       0.97      0.97      0.97      2933
   | None     |
  | 0.0329        | 4.0   | 2456 | 0.1184          | 0.9751   | 0.9751 | 0.9751    | 0.9751 |               precision    recall  f1-score   support
  
             0       0.98      0.97      0.98      1467
             1       0.97      0.98      0.98      1466
  
      accuracy                           0.98      2933
     macro avg       0.98      0.98      0.98      2933
  weighted avg       0.98      0.98      0.98      2933
   | None     |
  | 0.0179        | 5.0   | 3070 | 0.1317          | 0.9751   | 0.9751 | 0.9751    | 0.9751 |               precision    recall  f1-score   support
  
             0       0.97      0.98      0.98      1467
             1       0.98      0.97      0.98      1466
  
      accuracy                           0.98      2933
     macro avg       0.98      0.98      0.98      2933
  weighted avg       0.98      0.98      0.98      2933
   | None     |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.0
  - Tokenizers 0.12.1
  