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](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!