L_Roberta3

This model is a fine-tuned version of 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