Fake News Classifier - Finetuned: 'distilbert-base-cased'

LIAR Dataset

• This model is finetuned on a large dataset of hand-labeled short statements from politifact.com's API.
• Relevant columns of the data (speaker, statement, etc.) are concatenated and tokenized to create the model input.

DistilBERT Cased Tokenizer

• The text is tokenized using the 'distilbert-base-cased' HuggingFace tokenizer.
• For training, the text is cut to a block-size of 200.
• Max length padding is used to maintain consistent input data shape.

DistilBERT Cased Model

• The model that is finetuned is the DistilBERT model, 'distilbert-base-cased'.
• This is a small and fast text classifier, perfect for real-time inference!
  • 40% less parameters than the base BERT model.
  • 60% faster while preserving 95% performance of the base BERT model.
• The intuition for using the cased model is to capture some patterns in the writing style (capitalization, punctuation).
  • This information may be relevant for detecting fake news sources.
  • Writing styles may be relevant (as we see in clickbait titles with capitalization).
• This model performs well in flagging misinformation (fake news), especially if the format is similar to the training distribution.
• Overall, the performance is worse than the finetuned 'distilbert-base-uncased,' as the training data is less clean.