This model is a multiclass classification model trained on the Liar Dataset using the BERT (bert-base-uncased) architecture. The primary task is to classify news articles into different categories, making it suitable for fake news detection. BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based model known for its effectiveness in natural language processing tasks.
This model is designed for categorizing news articles into one of 6 target classes.
The model is intended for classifying news articles into multiple categories, aiding in the detection of fake news.
The model may inherit biases present in the training data, and it's important to be aware of potential biases in the predictions.
