![]() ![]() Subword-nmt that uses the number of merge operations. Note that SentencePiece specifies the final vocabulary size for training, which is different from That the final vocabulary size is fixed, e.g., 8k, 16k, or 32k. Unlike most unsupervised word segmentation algorithms, whichĪssume an infinite vocabulary, SentencePiece trains the segmentation model such Neural Machine Translation models typically operate with a fixed The number of unique tokens is predetermined Here are the high level differences from other implementations. SentencePiece supports two segmentation algorithms, byte-pair-encoding (BPE) and unigram language model. SentencePiece is a re-implementation of sub-word units, an effective way to alleviate the open vocabulary Note that BPE algorithm used in WordPiece is slightly different from the original BPE. Comparisons with other implementations Feature NFKC-based normalization: SentencePiece performs NFKC-based text normalization.įor those unfamiliar with SentencePiece as a software/algorithm, one can read a gentle introduction here.Direct vocabulary id generation: SentencePiece manages vocabulary to id mapping and can directly generate vocabulary id sequences from raw sentences.Self-contained: The same tokenization/detokenization is obtained as long as the same model file is used.Fast and lightweight: Segmentation speed is around 50k sentences/sec, and memory footprint is around 6MB.Subword regularization: SentencePiece implements subword sampling for subword regularization and BPE-dropout which help to improve the robustness and accuracy of NMT models.Multiple subword algorithms: BPE and unigram language model are supported.Language independent: SentencePiece treats the sentences just as sequences of Unicode characters. ![]() Pre-tokenization ( Moses tokenizer/ MeCab/ KyTea) is not always required. Purely data driven: SentencePiece trains tokenization and detokenization.SentencePiece allows us to make a purely end-to-end system that does not depend on language-specific pre/postprocessing. With the extension of direct training from raw sentences. Subword units (e.g., byte-pair-encoding (BPE) ) and Is predetermined prior to the neural model training. Neural Network-based text generation systems where the vocabulary size SentencePiece is an unsupervised text tokenizer and detokenizer mainly for ![]()
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