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Menu iconMenu iconUnder the Hood of Large Language Models
Under the Hood of Large Language Models
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Project 2: Train a Custom Domain-Specific Tokenizer (e.g., for legal or medical texts)
0. Setup
1. Gather a Representative Mini-Corpus
2. Train a BPE Tokenizer (🤗 tokenizers)
3. Train a SentencePiece Tokenizer (Unigram or BPE)
4. Wrap Your Tokenizer for Transformers
5. Evaluate Tokenizer Quality
6. Add a User Vocabulary (optional but powerful)
7. Save, Load, and Version
8. Plug Into a Small Model (sanity run)
Pitfalls & Tips
Learning outcomes
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Project 2: Train a Custom Domain-Specific Tokenizer (e.g., for legal or medical texts)

Learning outcomes

  • You trained and evaluated BPE and SentencePiece tokenizers for a niche domain.
  • You measured efficiency (avg tokens, compression) and protected key terms.
  • You packaged artifacts and integrated them with Transformers.

Learning outcomes

  • You trained and evaluated BPE and SentencePiece tokenizers for a niche domain.
  • You measured efficiency (avg tokens, compression) and protected key terms.
  • You packaged artifacts and integrated them with Transformers.

Learning outcomes

  • You trained and evaluated BPE and SentencePiece tokenizers for a niche domain.
  • You measured efficiency (avg tokens, compression) and protected key terms.
  • You packaged artifacts and integrated them with Transformers.

Learning outcomes

  • You trained and evaluated BPE and SentencePiece tokenizers for a niche domain.
  • You measured efficiency (avg tokens, compression) and protected key terms.
  • You packaged artifacts and integrated them with Transformers.
Purchase this book