Tokenizers is a library that implements state-of-the-art tokenization algorithms optimized for both research and production use.
The library addresses the need for fast, flexible tokenization in natural language processing pipelines. It provides implementations of widely-used tokenization methods including Byte-Pair Encoding, WordPiece, and Unigram algorithms. The core implementation is written in Rust to achieve high performance, enabling tokenization of a gigabyte of text in under twenty seconds on a standard CPU. The library handles the full preprocessing pipeline, including normalization with alignment tracking that lets you map tokens back to their original source text, as well as truncation, padding, and special token insertion.
The tool suits projects that need to train custom vocabularies or apply existing tokenization schemes with minimal latency. It works well for teams building language model infrastructure where performance matters, whether in research settings or production systems. The library provides bindings for Python, Node.js, and Rust, making it accessible across different technology stacks. The straightforward API allows basic tokenization in a single line of code while supporting extensive customization of pre-tokenization behavior and training parameters.
Development activity shows consistent maintenance with regular updates to the codebase. The project maintains active continuous integration workflows for the Rust implementation. The library receives ongoing improvements and bug fixes across its multiple language bindings. Documentation is actively maintained with examples and detailed guides available through the project's homepage.
