huggingface-tokenizers

Speed up text tokenization for NLP pipelines by using Rust-based HuggingFace tokenizers, which provide a high-performance core with Python and Node.js bindings for high-throughput preprocessing. This ensures fast and reliable tokenization across research and production settings.

Yes, you can train a custom vocabulary using BPE, WordPiece, and Unigram algorithms. The Rust-based tokenizers include training utilities that allow you to build and train custom vocabularies tailored to your specific NLP model requirements.

Yes, alignment tracking is fully supported. The tokenizers provide token-to-text alignment tracking alongside common preprocessing pipelines, ensuring you can map generated tokens back to their exact positions in the original input text.

Integrate custom tokenizers with HuggingFace Transformers by leveraging seamless integration support built into the library. This allows you to apply trained custom vocabularies and preprocessing pipelines directly within your Transformers research or production workflows.

The best way to handle high-throughput text preprocessing for production NLP is using a Rust-based tokenizer core. It delivers production-ready reliability and speed, easily handling large-scale preprocessing workloads via Python or Node.js bindings.