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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: about 1 month 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 (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2024-07-30T13:27:17.000Z (about 1 month ago)
- Last Synced: 2024-07-31T12:17:17.768Z (about 1 month ago)
- Topics: bert, gpt, language-model, natural-language-processing, natural-language-understanding, nlp, transformers
- Language: Rust
- Homepage: https://huggingface.co/docs/tokenizers
- Size: 10.1 MB
- Stars: 8,759
- Watchers: 121
- Forks: 759
- Open Issues: 34
-
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.
## 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)
## 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!