Fake News Detection using BERT (Bidirectional Encoder Representations from Transformers)

This project uses the BERT model, a transformer-based architecture, to perform fake news detection. BERT has revolutionized NLP by providing contextualized word embeddings and enabling models to better understand the nuances of language. This project fine-tunes a pre-trained BERT model on a fake news detection dataset. The model is trained using the transformer architecture, where the input text is tokenized into subwords and passed through the model. The final output is passed through a classification layer to predict the label of the news article.