huggingface-tokenizers

You can tokenize text quickly using Rust-backed tokenizers with Python bindings. This approach delivers high throughput and low latency, supporting BPE, WordPiece, and Unigram models for large-scale data preparation and inference.

Alignment tracking maps tokens back to their original text positions. This feature is essential for downstream tasks requiring precise character-level mapping, ensuring accurate post-processing during Transformer integration and batch encoding.

Yes, you can train custom BPE, WordPiece, and Unigram tokenizers using the provided training tools. The Rust core exposes normalizers, pre-tokenizers, post-processors, and decoders through Python bindings for seamless pipeline integration.

Fast tokenization integrates directly with Transformers for model training and inference. It provides batch encoding for large datasets while maintaining alignment tracking and supporting various tokenization models like BPE and Unigram.

Using Rust-backed tokenizers with Python bindings is the best way to preprocess large-scale NLP data efficiently. They deliver high throughput and low latency, supporting batch encoding and training flows for BPE, WordPiece, and Unigram models.

No, you do not need Rust installed. The tokenizers are distributed via pip with a pre-compiled Rust core, exposing normalizers, pre-tokenizers, post-processors, and decoders through Python bindings for immediate integration.