Huggingface Tokenizers

This is the Rust-based tokenization library that powers HuggingFace transformers under the hood. It processes a gigabyte of text in under 20 seconds on CPU, which matters when you're training custom tokenizers or building production pipelines. You get all three major algorithms: BPE for GPT-style models, WordPiece for BERT, and Unigram for multilingual work. The alignment tracking is genuinely useful since you can map tokens back to their original character positions. If you're just loading a pretrained tokenizer for inference, transformers.AutoTokenizer is easier. But if you need to train from scratch, handle massive corpora, or squeeze performance out of tokenization, this is the tool. The Python bindings hide all the Rust complexity while keeping the speed.