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Fast and Customizable Tokenizers

author Anthony MOI
author_email anthony@huggingface.co
classifiers
  • Development Status :: 5 - Production/Stable
  • Intended Audience :: Developers
  • Intended Audience :: Education
  • Intended Audience :: Science/Research
  • License :: OSI Approved :: Apache Software License
  • Operating System :: OS Independent
  • Programming Language :: Python :: 3
  • Programming Language :: Python :: 3.5
  • Programming Language :: Python :: 3.6
  • Programming Language :: Python :: 3.7
  • Programming Language :: Python :: 3.8
  • Topic :: Scientific/Engineering :: Artificial Intelligence
description_content_type text/markdown
keywords NLP tokenizer BPE transformer deep learning
license Apache License 2.0
provides_extras testing
requires_dist
  • pytest ; extra == 'testing'

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tokenizers-0.10.0-cp37-cp37m-linux_loongarch64.whl
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Type
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Python
3.7



Build GitHub


Tokenizers

Provides an 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.

Otherwise, let's dive in!

Main features:

Installation

With pip:

pip install tokenizers

From sources:

To use this method, you need to have the Rust installed:

# Install with:
curl https://sh.rustup.rs -sSf | sh -s -- -y
export PATH="$HOME/.cargo/bin:$PATH"

Once Rust is installed, you can compile doing the following

git clone https://github.com/huggingface/tokenizers
cd tokenizers/bindings/python

# Create a virtual env (you can use yours as well)
python -m venv .env
source .env/bin/activate

# Install `tokenizers` in the current virtual env
pip install setuptools_rust
python setup.py install

Using the provided Tokenizers

We provide some pre-build tokenizers to cover the most common cases. You can easily load one of these using some vocab.json and merges.txt files:

from tokenizers import CharBPETokenizer

# Initialize a tokenizer
vocab = "./path/to/vocab.json"
merges = "./path/to/merges.txt"
tokenizer = CharBPETokenizer(vocab, merges)

# And then encode:
encoded = tokenizer.encode("I can feel the magic, can you?")
print(encoded.ids)
print(encoded.tokens)

And you can train them just as simply:

from tokenizers import CharBPETokenizer

# Initialize a tokenizer
tokenizer = CharBPETokenizer()

# Then train it!
tokenizer.train([ "./path/to/files/1.txt", "./path/to/files/2.txt" ])

# Now, let's use it:
encoded = tokenizer.encode("I can feel the magic, can you?")

# And finally save it somewhere
tokenizer.save("./path/to/directory/my-bpe.tokenizer.json")

Provided Tokenizers

All of these can be used and trained as explained above!

Build your own

Whenever these provided tokenizers don't give you enough freedom, you can build your own tokenizer, by putting all the different parts you need together. You can check how we implemented the provided tokenizers and adapt them easily to your own needs.

Building a byte-level BPE

Here is an example showing how to build your own byte-level BPE by putting all the different pieces together, and then saving it to a single file:

from tokenizers import Tokenizer, models, pre_tokenizers, decoders, trainers, processors

# Initialize a tokenizer
tokenizer = Tokenizer(models.BPE())

# Customize pre-tokenization and decoding
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=True)
tokenizer.decoder = decoders.ByteLevel()
tokenizer.post_processor = processors.ByteLevel(trim_offsets=True)

# And then train
trainer = trainers.BpeTrainer(vocab_size=20000, min_frequency=2)
tokenizer.train([
	"./path/to/dataset/1.txt",
	"./path/to/dataset/2.txt",
	"./path/to/dataset/3.txt"
], trainer=trainer)

# And Save it
tokenizer.save("byte-level-bpe.tokenizer.json", pretty=True)

Now, when you want to use this tokenizer, this is as simple as:

from tokenizers import Tokenizer

tokenizer = Tokenizer.from_file("byte-level-bpe.tokenizer.json")

encoded = tokenizer.encode("I can feel the magic, can you?")