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SentencePiece is an unsupervised text tokenizer and detokenizer mainly for
Neural Network-based text generation systems where the vocabulary size
is predetermined prior to the neural model training. SentencePiece implements
**subword units** (e.g., **byte-pair-encoding (BPE)** [[Sennrich et al.](http://www.aclweb.org/anthology/P16-1162)]) and
**subword units** (e.g., **byte-pair-encoding (BPE)** [[Sennrich et al.](https://www.aclweb.org/anthology/P16-1162)]) and
**unigram language model** [[Kudo.](https://arxiv.org/abs/1804.10959)])
with the extension of direct training from raw sentences. SentencePiece allows us to make a purely end-to-end system that does not depend on language-specific pre/postprocessing.
@ -23,7 +23,7 @@ with the extension of direct training from raw sentences. SentencePiece allows u
- **Purely data driven**: SentencePiece trains tokenization and detokenization
models from sentences. Pre-tokenization ([Moses tokenizer](https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/tokenizer.perl)/[MeCab](http://taku910.github.io/mecab/)/[KyTea](http://www.phontron.com/kytea/)) is not always required.
- **Language independent**: SentencePiece treats the sentences just as sequences of Unicode characters. There is no language-dependent logic.
- **Multiple subword algorithms**: **BPE** [[Sennrich et al.](http://www.aclweb.org/anthology/P16-1162)] and **unigram language model** [[Kudo.](https://arxiv.org/abs/1804.10959)] are supported.
- **Multiple subword algorithms**: **BPE** [[Sennrich et al.](https://www.aclweb.org/anthology/P16-1162)] and **unigram language model** [[Kudo.](https://arxiv.org/abs/1804.10959)] are supported.
- **Subword regularization**: SentencePiece implements subword sampling for [subword regularization](https://arxiv.org/abs/1804.10959) and [BPE-dropout](https://arxiv.org/abs/1910.13267) which help to improve the robustness and accuracy of NMT models.
- **Fast and lightweight**: Segmentation speed is around 50k sentences/sec, and memory footprint is around 6MB.
- **Self-contained**: The same tokenization/detokenization is obtained as long as the same model file is used.