readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: L_Roberta3
    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. -->
  
  # L_Roberta3
  
  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: 0.2095
  - Accuracy: 0.9555
  - F1: 0.9555
  - Precision: 0.9555
  - Recall: 0.9555
  - C Report:               precision    recall  f1-score   support
  
             0       0.97      0.95      0.96       876
             1       0.94      0.97      0.95       696
  
      accuracy                           0.96      1572
     macro avg       0.95      0.96      0.96      1572
  weighted avg       0.96      0.96      0.96      1572
  
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|
  | 0.2674        | 1.0   | 329  | 0.2436          | 0.9389   | 0.9389 | 0.9389    | 0.9389 |               precision    recall  f1-score   support
  
             0       0.94      0.95      0.95       876
             1       0.94      0.92      0.93       696
  
      accuracy                           0.94      1572
     macro avg       0.94      0.94      0.94      1572
  weighted avg       0.94      0.94      0.94      1572
   | None     |
  | 0.1377        | 2.0   | 658  | 0.1506          | 0.9408   | 0.9408 | 0.9408    | 0.9408 |               precision    recall  f1-score   support
  
             0       0.97      0.92      0.95       876
             1       0.91      0.96      0.94       696
  
      accuracy                           0.94      1572
     macro avg       0.94      0.94      0.94      1572
  weighted avg       0.94      0.94      0.94      1572
   | None     |
  | 0.0898        | 3.0   | 987  | 0.1491          | 0.9548   | 0.9548 | 0.9548    | 0.9548 |               precision    recall  f1-score   support
  
             0       0.96      0.96      0.96       876
             1       0.95      0.95      0.95       696
  
      accuracy                           0.95      1572
     macro avg       0.95      0.95      0.95      1572
  weighted avg       0.95      0.95      0.95      1572
   | None     |
  | 0.0543        | 4.0   | 1316 | 0.1831          | 0.9561   | 0.9561 | 0.9561    | 0.9561 |               precision    recall  f1-score   support
  
             0       0.97      0.95      0.96       876
             1       0.94      0.96      0.95       696
  
      accuracy                           0.96      1572
     macro avg       0.95      0.96      0.96      1572
  weighted avg       0.96      0.96      0.96      1572
   | None     |
  | 0.0394        | 5.0   | 1645 | 0.2095          | 0.9555   | 0.9555 | 0.9555    | 0.9555 |               precision    recall  f1-score   support
  
             0       0.97      0.95      0.96       876
             1       0.94      0.97      0.95       696
  
      accuracy                           0.96      1572
     macro avg       0.95      0.96      0.96      1572
  weighted avg       0.96      0.96      0.96      1572
   | None     |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.2+cu102
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  