Project Title

tokenizers — Fast State-of-the-Art Tokenizers for Research and Production

Overview

tokenizers is a Rust-based library providing implementations of today's most used tokenizers, optimized for both performance and versatility. It is designed for use in research and production environments, offering fast training and tokenization, easy-to-use interfaces, and comprehensive pre-processing capabilities.

Key Features

Train new vocabularies and tokenize using popular tokenizers like Byte-Pair Encoding, WordPiece, and Unigram.
Extremely fast tokenization and training, capable of processing a GB of text in under 20 seconds on a server's CPU.
Versatile and easy-to-use design, suitable for both research and production.
Alignment tracking for normalization, allowing retrieval of original sentence parts corresponding to given tokens.
Pre-processing capabilities including truncation, padding, and addition of special tokens required by models.

Use Cases

Researchers and data scientists using natural language processing models can utilize tokenizers for efficient text pre-processing.
Developers in production environments can integrate tokenizers to ensure fast and accurate text tokenization for machine learning models.
Educational purposes, where understanding and implementing different tokenization algorithms is required.

Advantages

Rust implementation ensures high performance and efficiency.
Supports multiple languages through bindings, including Python, Node.js, and Ruby.
Comprehensive documentation and quick tour available for ease of use.

Limitations / Considerations

The project's performance may vary depending on the hardware it is run on.
The license is currently unknown, which might affect its use in certain commercial applications.

NLTK: A leading platform for building Python programs to work with human language data, offering a different set of tools and a Pythonic approach.
spaCy: An industrial-strength natural language processing library for Python, which also includes tokenization among its features but focuses more broadly on NLP tasks.
Apache OpenNLP: A machine learning-based toolkit for the processing of natural language text, which includes tokenization but is part of a larger suite of NLP tools.

Basic Information

GitHub: https://github.com/huggingface/tokenizers
Stars: 10,093
License: Unknown
Last Commit: 2025-09-21

📊 Project Information

Project Name: tokenizers
GitHub URL: https://github.com/huggingface/tokenizers
Programming Language: Rust
⭐ Stars: 10,093
🍴 Forks: 968
📅 Created: 2019-11-01
🔄 Last Updated: 2025-09-21

🏷️ Project Topics

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📚 Documentation

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tokenizers

Project Description