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* implement a simple max_sentencepiece_length into BPE Add a way for the BPE trainer to behave like the unigram trainer where tokens longer than a certain lenght(default 16 in SPM) to be skipped. this is implemented in unigram trainer but in a different way. If this code were to be actually integrated some works to be done Documentation describing the behavior and how it should be set. Set default==0 so it doesnt act unless set provide ways in the python binding for the user to set max token length I was trying to find a way to implement max_sentencepiece_length through pretokenizer split rules and to be honest, its very difficult and regexes can be real slow when operating on the whole training corpus. * implement a simple max_sentencepiece_length into BPE Add a way for the BPE trainer to behave like the unigram trainer where tokens longer than a certain lenght(default 16 in SPM) to be skipped. this is implemented in unigram trainer but in a different way. If this code were to be actually integrated some works to be done Documentation describing the behavior and how it should be set. Set default==0 so it doesnt act unless set provide ways in the python binding for the user to set max token length I was trying to find a way to implement max_sentencepiece_length through pretokenizer split rules and to be honest, its very difficult and regexes can be real slow when operating on the whole training corpus. * utilize Option<u16> for safer code. * Other version. * Update trainer.rs clarify with type usize propagate max_length option * change max_length into more descriptive name in the documentation https://huggingface.co/docs/tokenizers/api/trainers unigramtrainer uses max_piece_length for similar function. since BPE the underlying concept is merges, using max_merge_length as the variable name could prove more descriptive. * change variable name in trainer.rs change max_merge_length into max_token_length * Update trainer.rs add several max_token_length declaration that were missing. impl BpeTrainerBuilder struct BpeTrainer Add explanation for variable shadowing. * Update trainer.rs Move default definition of max_token_length to proper location. adjust downstream variable initializations accordingly. * add max_token_length test * Add bpe direct assert test * Update trainer.rs clarified test documentation * Creating the bindings. * Fix the default. * Re-adding missing package-lock which I accidentally removed. * .. * Fixing trainer test. * Fix. --------- Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
NodeJS implementation of today's most used tokenizers, with a focus on performance and versatility. Bindings over the Rust implementation. If you are interested in the High-level design, you can go check it there.
Main features
- Train new vocabularies and tokenize using 4 pre-made tokenizers (Bert WordPiece and the 3 most common BPE versions).
- Extremely fast (both training and tokenization), thanks to the Rust implementation. Takes less than 20 seconds to tokenize a GB of text on a server's CPU.
- Easy to use, but also extremely versatile.
- Designed for research and production.
- Normalization comes with alignments tracking. It's always possible to get the part of the original sentence that corresponds to a given token.
- Does all the pre-processing: Truncate, Pad, add the special tokens your model needs.
Installation
npm install tokenizers@latest
Basic example
import { BertWordPieceTokenizer } from "tokenizers";
const wordPieceTokenizer = await BertWordPieceTokenizer.fromOptions({ vocabFile: "./vocab.txt" });
const wpEncoded = await wordPieceTokenizer.encode("Who is John?", "John is a teacher");
console.log(wpEncoded.length);
console.log(wpEncoded.tokens);
console.log(wpEncoded.ids);
console.log(wpEncoded.attentionMask);
console.log(wpEncoded.offsets);
console.log(wpEncoded.overflowing);
console.log(wpEncoded.specialTokensMask);
console.log(wpEncoded.typeIds);
console.log(wpEncoded.wordIndexes);
Provided Tokenizers
BPETokenizer: The original BPEByteLevelBPETokenizer: The byte level version of the BPESentencePieceBPETokenizer: A BPE implementation compatible with the one used by SentencePieceBertWordPieceTokenizer: The famous Bert tokenizer, using WordPiece