Preventing Over-Specialization in Fine-Tuning: A Practical Tip for Transformer Models
When fine-tuning a pre-trained transformer model, one common pitfall is over-specialization. This occurs when the model becomes overly reliant on specific token classes, such as punctuation, which can negatively impact its generalizability. To mitigate this issue, consider masking out specific token classes during the fine-tuning process.
Why Masking Token Classes Matters
Punctuation marks, such as periods, commas, and semicolons, can provide valuable contextual information for a model. However, if the model over-relies on these tokens, it may struggle to generalize to new, unseen data. By masking out these tokens, you can encourage the model to focus on more meaningful features, such as word embeddings and semantic relationships.
Benefits of Masking Token Classes
- Improved Generalizability: By preventing over-specialization, masked token classes can enhance the model's abili...
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