Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.
# SentencePiece - Language-Independent Tokenization Unsupervised tokenizer that works on raw text without language-specific preprocessing. ## When to use SentencePiece **Use SentencePiece when:** - Building multilingual models (no language-specific rules) - Working with CJK languages (Chinese, Japanese, Korean) - Need reproducible tokenization (deterministic vocabulary) - Want to train on raw text (no pre-tokenization needed) - Require lightweight deployment (6MB memory, 50k sentences/sec) **Performance**: - **Speed**: 50,000 sentences/sec - **Memory**: ~6MB for loaded model - **Languages**: All (language-independent) **Use alternatives instead**: - **HuggingFace Tokenizers**: Faster training, more flexibility - **tiktoken**: OpenAI models (GPT-3.5/4) - **BERT WordPiece**: English-centric tasks ## Quick start
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