tokenizers

开源

针对研究和生产优化的先进快速标记器

10.9K stars0 点赞0 次浏览

工具介绍

针对研究和生产优化的先进快速标记器

<br>

<a href="https://github.com/huggingface/tokenizers/blob/main/LICENSE">
    
</a>
<a href="https://pepy.tech/project/tokenizers">
    
</a>

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

Performances can vary depending on hardware, but running the ~/bindings/python/benches/test_tiktoken.py should give the following on a g6 aws instance:

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:

bash

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

or install the released versions with

bash

pip install tokenizers

Quick example using Python:

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

python

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:

python

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:

python

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:

python

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!