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huggingface-tokenizers

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Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.

When to use

• Need extremely fast tokenization (<20s per GB of text)
• Training custom tokenizers from scratch
• Want alignment tracking (token → original text position)
• Building production NLP pipelines
• Need to tokenize large corpora efficiently
• **Speed**: <20 seconds to tokenize 1GB on CPU
• **Implementation**: Rust core with Python/Node.js bindings
• **Efficiency**: 10-100× faster than pure Python implementations

When not to use

• loading pretrained tokenizers only
• language-independent tokenization
• OpenAI GPT model tokenization

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