F_Roberta_classifier2

This model is a fine-tuned version of distilroberta-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1317

• 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