Your language generation model uses an outdated tokenizer causing missing context How do you address this

Question

With the help of proper example can you tell me if Your language generation model uses an outdated tokenizer causing missing context. How do you address this?

Answer 1

To address missing context due to an outdated tokenizer, update to a modern subword-based tokenizer like SentencePiece, BPE, or WordPiece and retrain tokenized datasets accordingly.

Here is the code snippet you can refer to:

In the above, we are using the following key points::

• SentencePiece Training: Uses a modern tokenizer to handle subword segmentation effectively.
• Vocab Update: Trains a new tokenizer with a vocabulary size of 32,000 for better generalization.
• Hugging Face Compatibility: Wraps the trained tokenizer for easy integration with transformers.
• Tokenization & Decoding: Ensures correct tokenization and reversibility of generated text.
• Improved Context Retention: Reduces missing context issues caused by outdated tokenization methods.

Hence, by upgrading to a modern subword-based tokenizer like SentencePiece and integrating it with the model, we enhance context retention and improve the quality of generated language outputs.