tokenizers

Provides an implementation of today's most used tokenizers, with a focus on performance and versatility.

Main features:

• Train new vocabularies and tokenize, using today's most used tokenizers.
• Extremely fast (both training and tokenization), thanks to the Rust implementation. Takes less than 20 seconds to tokenize a GB of text on a server's CPU.
• Easy to use, but also extremely versatile.
• Designed for research and production.
• Normalization comes with alignments tracking. It's always possible to get the part of the original sentence that corresponds to a given token.
• Does all the pre-processing: Truncate, Pad, add the special tokens your model needs.

Bindings

We provide bindings to the following languages (more to come!):

• Rust (Original implementation)
• Python
• Node.js
• Ruby (Contributed by @ankane, external repo)

Quick example using Python:

Choose your model between Byte-Pair Encoding, WordPiece or Unigram and instantiate a tokenizer:

You can customize how pre-tokenization (e.g., splitting into words) is done:

Then training your tokenizer on a set of files just takes two lines of codes:

Once your tokenizer is trained, encode any text with just one line:

Check the documentation or the quicktour to learn more!