huggingface-tokenizers

Custom tokenizer training builds domain-adaptive vocabularies from scratch using BPE, WordPiece, or Unigram algorithms. You save the trained tokenizer as a file and reload it for consistent domain-specific text encoding across batches.

Alignment tracking records offset mappings during tokenization, linking generated tokens back to their exact character positions in the source text. This enables span-aware tasks like Named Entity Recognition and Question Answering to highlight accurate text boundaries.

Yes, custom trained tokenizers integrate seamlessly with Transformers via fast backends. You train your tokenizer, save it as a tokenizer file, and load it directly using the AutoTokenizer class for immediate use in Transformers workflows.

Training a custom tokenizer from scratch using Unigram, WordPiece, or BPE is the best way to build domain-adaptive vocabularies. This creates a specialized vocabulary tailored to your specific corpus, improving model accuracy on domain-specific text.

BPE tokenization supports offset and alignment information natively alongside fast encode and decode operations. This provides the exact text spans needed for position-aware tasks like token-span alignment and span extraction.