SentencePiece

SentencePiece is a language-independent subword tokenization library that treats input text as raw byte sequences without relying on pre-tokenization or whitespace assumptions, enabling robust multilingual text processing. This unsupervised tokenization approach combines Byte-Pair Encoding (BPE) and unigram language model algorithms to create optimal vocabularies that handle diverse scripts, languages, and writing systems uniformly. SentencePiece operates directly on raw Unicode text, eliminating preprocessing dependencies and language-specific tokenization rules that can introduce biases or errors. The library supports vocabulary size optimization, regularization techniques, and reversible tokenization that preserves original text formatting including spaces and punctuation. Advanced features include sampling-based subword regularization, vocabulary pruning, and cross-lingual tokenization consistency that enhances model robustness across multilingual applications. SentencePiece serves as the standard tokenization framework for many modern language models, providing reliable text representation for global AI deployment scenarios.