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

LIAR Dataset

• This model is finetuned on a large dataset of hand-labeled short statements from politifact.com's API.
• Data went through a series of text cleaning stages such as:
  1. Lower-case standardization for improved 'uncased' model performance.
  2. Mixed letter/digit word removal.
  3. Stopword removal.
  4. Extra space trimming.

DistilBERT Uncased Tokenizer

• The text is tokenized using the 'distilbert-base-uncased' 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 Uncased Model

• The model that is finetuned is the DistilBERT model, 'distilbert-base-uncased'.
• 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.
• This model outperforms the finetuned 'distilbert-base-cased' by over 5% average F1-score.
  • This improvement comes mainly from the slower learning rate and improved data preprocessing.
  • These modifications allow for a smoother training curve and convergence.