Provides an implementation of today's most used tokenizers, with a focus on performance and versatility.

Main features:

- Train new vocabularies and tokenize, using today's most used tokenizers. - 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.

Performances

Bindings

We provide bindings to the following languages (more to come!): - Rust (Original implementation) - Python - Node.js - Ruby (Contributed by @ankane, external repo)

Installation

You can install from source using:

pip install git+https://github.com/huggingface/tokenizers.git#subdirectory=bindings/python

or install the released versions with

pip install tokenizers

Quick example using Python:

Choose your model between Byte-Pair Encoding, WordPiece or Unigram and instantiate a tokenizer:

from tokenizers import Tokenizer
from tokenizers.models import BPE
tokenizer = Tokenizer(BPE())

You can customize how pre-tokenization (e.g., splitting into words) is done:

from tokenizers.pre_tokenizers import Whitespace
tokenizer.pre_tokenizer = Whitespace()

Then training your tokenizer on a set of files just takes two lines of codes:

from tokenizers.trainers import BpeTrainer
trainer = BpeTrainer(special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"]) tokenizer.train(files=["wiki.train.raw", "wiki.valid.raw", "wiki.test.raw"], trainer=trainer)

Once your tokenizer is trained, encode any text with just one line:

output = tokenizer.encode("Hello, y'all! How are you 😁 ?") print(output.tokens) 
["Hello", ",", "y", "'", "all", "!", "How", "are", "you", "[UNK]", "?"]

Check the documentation or the quicktour to learn more!