🔤 Tokenizer Comparison
Compare BPE, WordPiece, SentencePiece, and Unigram tokenization methods
Your Progress
0 / 5 completedIntroduction to Tokenization
🎯 Why Tokenization Matters
Tokenization converts text into discrete units that language models can process. The choice of tokenizer impacts vocabulary size, model efficiency, and multilingual performance.
Subword tokenization balances vocabulary size with coverage, enabling models to handle rare words and new languages efficiently.
Simple but huge vocabulary. Struggles with rare words and morphology.
Small vocabulary but long sequences. Loses semantic meaning.
Optimal balance: moderate vocabulary, handles rare words, preserves meaning.
🏆 Major Tokenizers in Use
Merges frequent character pairs iteratively. Simple and effective.
Similar to BPE but uses likelihood-based scoring. Common in Google models.
Language-agnostic, treats text as raw UTF-8. Excellent for multilingual.
Probabilistic model that finds optimal subword vocabulary.
✅ Benefits
- •Handle unlimited vocabulary with fixed size
- •Better compression than word-level
- •Capture morphological patterns
⚠️ Trade-offs
- •Vocabulary size impacts memory
- •Different tokenizers not interchangeable
- •Longer sequences for some languages