https://github.com/huggingface/tokenizers

💥 Fast State-of-the-Art Tokenizers optimized for Research and Production
https://github.com/huggingface/tokenizers

bert gpt language-model natural-language-processing natural-language-understanding nlp transformers

Last synced: 22 days ago
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💥 Fast State-of-the-Art Tokenizers optimized for Research and Production

• Host: GitHub
• URL: https://github.com/huggingface/tokenizers
• Owner: huggingface
• License: apache-2.0
• Created: 2019-11-01T17:52:20.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2025-03-18T16:33:44.000Z (about 1 month ago)
• Last Synced: 2025-03-25T07:38:46.614Z (24 days ago)
• Topics: bert, gpt, language-model, natural-language-processing, natural-language-understanding, nlp, transformers
• Language: Rust
• Homepage: https://huggingface.co/docs/tokenizers
• Size: 10.2 MB
• Stars: 9,520
• Watchers: 121
• Forks: 867
• Open Issues: 80
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION.cff

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README

        


    


    

    






    

    

        

    

    

        

    



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](bindings/python/benches/test_tiktoken.py) should give the following on a g6 aws instance:

![image](https://github.com/user-attachments/assets/2b913d4b-e488-4cbc-b542-f90a6c40643d)

## Bindings

We provide bindings to the following languages (more to come!):

  - [Rust](https://github.com/huggingface/tokenizers/tree/main/tokenizers) (Original implementation)

  - [Python](https://github.com/huggingface/tokenizers/tree/main/bindings/python)

  - [Node.js](https://github.com/huggingface/tokenizers/tree/main/bindings/node)

  - [Ruby](https://github.com/ankane/tokenizers-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

```

our 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](https://huggingface.co/docs/tokenizers/index)

or the [quicktour](https://huggingface.co/docs/tokenizers/quicktour) to learn more!