huggingface/tokenizers

---

huggingface/tokenizers.json

{
  "createdAt": "2019-11-01T17:52:20Z",
  "defaultBranch": "main",
  "description": "💥 Fast State-of-the-Art Tokenizers optimized for Research and Production",
  "fullName": "huggingface/tokenizers",
  "homepage": "https://huggingface.co/docs/tokenizers",
  "language": "Rust",
  "name": "tokenizers",
  "pushedAt": "2025-10-16T09:22:48Z",
  "stargazersCount": 10248,
  "topics": [
    "bert",
    "gpt",
    "language-model",
    "natural-language-processing",
    "natural-language-understanding",
    "nlp",
    "transformers"
  ],
  "updatedAt": "2025-11-25T19:27:02Z",
  "url": "https://github.com/huggingface/tokenizers"
}

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:

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!