huggingface-tokenizers

To train a custom tokenizer for a large text corpus, you can configure and train BPE, WordPiece, or Unigram algorithms with full pipeline settings including normalization and pre-tokenization. This generates a model-ready vocabulary from your raw text quickly.

Offset mapping in tokenization tracks token-to-text alignments, mapping generated token IDs back to their original text spans. You need it for downstream NLP tasks like span labeling, entity recognition, and aligning model predictions with source text.

You can accelerate text tokenization for large-scale corpus preprocessing by using Rust-backed tokenizers. This provides production-grade speed suitable for processing huge text volumes where performance is critical.

Yes, you can load custom trained tokenizers using AutoTokenizer in Transformers. The process exports your trained tokenizer to a Transformers-compatible format, allowing seamless integration and loading within production pipelines via PreTrainedTokenizerFast.

Choosing between BPE, WordPiece, and Unigram depends on your model family and language needs. BPE is versatile for many models, WordPiece is standard for BERT-like architectures, and Unigram suits multilingual or probabilistic tokenization requirements.

Yes, Rust-backed tokenization supports full pipeline configuration. You can define and apply custom normalization, pre-tokenization, and post-processing steps alongside your chosen tokenization algorithm to prepare text exactly as your model requires.