SentencePiece is a text segmentation engine and tokenization library designed for machine learning workflows. It provides a comprehensive toolkit for transforming raw text into subword units or numerical identifiers, enabling consistent data representation for neural network training and inference. The library supports the training of segmentation models from raw text, allowing for the creation of custom vocabularies tailored to specific domain requirements.
The project distinguishes itself through its byte-level encoding and fallback mechanisms, which ensure that every input can be represented without relying on unknown tokens. It employs probabilistic subword modeling and stochastic sampling to improve model robustness during training. To handle large-scale datasets, the engine utilizes memory-mapped model loading and thread-safe, parallelized processing, which distributes encoding and decoding tasks across multiple CPU cores.
Beyond core segmentation, the library includes a deterministic normalization pipeline that manages Unicode transformations and whitespace formatting to ensure consistent text representation. It also provides granular control over vocabulary composition, including the reservation of special control symbols, the enforcement of atomic token definitions, and the ability to map tokens back to their original character positions for precise alignment.
