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* Adding rust audit. * Update clap version + derive_builder (they clashed). * Ignoring specific CVE which can be ignored https://github.com/Azure/iot-identity-service/issues/481 * Updating python lock. * Revert `derive-builder` update. * Adding back help msg.
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