Fake-News-Detection-Roberta

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Fake-News-Detection-Roberta

This model is a fine-tuned version of smallbenchnlp/roberta-small on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0119
• Accuracy: 0.9981
• Precision: 0.9994
• Recall: 0.9966
• F1 score: 0.9980
• Auc score: 0.9980

Model description

The model is a fake news classifier which has been fined-tuned using the small version of pre-trained RoBERTa where Label 0 indicates its a Fake News but Label 1 indicates its a Real News.

Intended uses & limitations

To use the Inference API, please give input of word limit=70.

Training and evaluation data

The model was trained and tested using this dataset :https://www.kaggle.com/datasets/sadikaljarif/fake-news-detection-dataset-english

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 0.5
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.27.1
• Pytorch 1.13.1+cu116
• Tokenizers 0.13.2