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raima2001/
Fake-News-Detection-Roberta

Name: Fake-News-Detection-Roberta
Availability: InStock

generated_from_trainer

autotrain_compatible

endpoints_compatible

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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

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1 score	Auc score
0.0206	0.49	500	0.0119	0.9981	0.9994	0.9966	0.9980	0.9980

Framework versions

Transformers 4.27.1
Pytorch 1.13.1+cu116
Tokenizers 0.13.2

raima2001/Fake-News-Detection-Roberta