huggingface-tokenizers

Fast NLP tokenization for large text corpora is achieved by using a Rust-backed core with Python bindings, enabling high-throughput data pipelines and streaming tokenization. This supports preprocessing for BERT, GPT, and T5 models.

Fast tokenizers work seamlessly with existing Transformers pipelines through compatibility with AutoTokenizer and PreTrainedTokenizerFast. This allows integration into model training and deployment without changing your preprocessing logic.

Alignment tracking during multi-process encoding maps generated tokens back to their original character spans in the text. This ensures accurate alignment mapping across high-throughput data pipelines and multi-process encoding tasks.

Production-ready tokenization pipelines are built using fast, Rust-backed tokenizers that provide multi-process encoding and alignment tracking. This approach handles large datasets efficiently while maintaining compatibility with Transformer models.

Rust is not required to be installed locally to use these tokenizers in Python projects. The package provides Python bindings that wrap the fast Rust core, allowing you to run tokenization directly within your Python environment.