BlingFire is a high-performance library for finite state machine and regular expression manipulation, primarily focused on Natural Language Processing (NLP) tasks. It offers state-of-the-art tokenization algorithms, including WordPiece, SentencePiece (Unigram LM and BPE), and pattern-based methods, designed for speed and ease of use across multiple programming languages.

How It Works

BlingFire leverages highly optimized C++ implementations of finite state machines and tokenization algorithms. It provides a unified interface for various tokenization models (e.g., BERT, XLNET, RoBERTa), allowing users to load external model files. The library emphasizes minimal configuration and zero-shot learning capabilities for tokenization, with optional normalization and support for custom model creation.

Quick Start & Requirements

• Install via pip: pip install -U blingfire
• Requires Python.
• Precompiled models for various tokenizers (BERT, XLNET, RoBERTa, etc.) are included or can be loaded from external files.
• Official documentation and examples are available in the repository.

Highlighted Details

• Claims to be 4-5x faster than Hugging Face Tokenizers and ~2x faster than SentencePiece implementations.
• Offers a uniform interface for multiple tokenization algorithms, simplifying integration with models like BERT and XLNET.
• Supports tokenization, sentence breaking, multi-word expression matching, stemming/lemmatization, and induced syllabification.
• Provides C#, Rust, and JavaScript (WASM) APIs, enabling cross-platform and web-based applications.

Maintenance & Community

• Developed by the "Bling" team at Microsoft, contributing to Bing's NLP capabilities.
• Contributions are welcome, subject to a Contributor License Agreement (CLA).
• Security issues should be reported to Microsoft Security Response Center (MSRC).

Licensing & Compatibility

• Licensed under the MIT License.
• Permissive license suitable for commercial use and integration into closed-source projects.

Limitations & Caveats

The JavaScript integration is noted as "still in progress" with potential for future changes. While supporting multiple languages, the primary focus and most extensive examples are Python-based.