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https://github.com/mii443/tokenizers.git
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80 lines
3.1 KiB
Markdown
80 lines
3.1 KiB
Markdown
<p align="center">
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<br>
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<img src="https://huggingface.co/landing/assets/tokenizers/tokenizers-logo.png" width="600"/>
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<br>
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<p>
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<p align="center">
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<img alt="Build" src="https://github.com/huggingface/tokenizers/workflows/Rust/badge.svg">
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<a href="https://github.com/huggingface/tokenizers/blob/main/LICENSE">
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<img alt="GitHub" src="https://img.shields.io/github/license/huggingface/tokenizers.svg?color=blue&cachedrop">
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</a>
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<a href="https://pepy.tech/project/tokenizers">
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<img src="https://pepy.tech/badge/tokenizers/week" />
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</a>
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</p>
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Provides an implementation of today's most used tokenizers, with a focus on performance and
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versatility.
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## Main features:
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- Train new vocabularies and tokenize, using today's most used tokenizers.
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- Extremely fast (both training and tokenization), thanks to the Rust implementation. Takes
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less than 20 seconds to tokenize a GB of text on a server's CPU.
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- Easy to use, but also extremely versatile.
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- Designed for research and production.
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- Normalization comes with alignments tracking. It's always possible to get the part of the
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original sentence that corresponds to a given token.
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- Does all the pre-processing: Truncate, Pad, add the special tokens your model needs.
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## Performances
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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:
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## Bindings
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We provide bindings to the following languages (more to come!):
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- [Rust](https://github.com/huggingface/tokenizers/tree/main/tokenizers) (Original implementation)
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- [Python](https://github.com/huggingface/tokenizers/tree/main/bindings/python)
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- [Node.js](https://github.com/huggingface/tokenizers/tree/main/bindings/node)
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- [Ruby](https://github.com/ankane/tokenizers-ruby) (Contributed by @ankane, external repo)
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## Quick example using Python:
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Choose your model between Byte-Pair Encoding, WordPiece or Unigram and instantiate a tokenizer:
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```python
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from tokenizers import Tokenizer
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from tokenizers.models import BPE
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tokenizer = Tokenizer(BPE())
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```
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You can customize how pre-tokenization (e.g., splitting into words) is done:
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```python
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from tokenizers.pre_tokenizers import Whitespace
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tokenizer.pre_tokenizer = Whitespace()
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```
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Then training your tokenizer on a set of files just takes two lines of codes:
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```python
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from tokenizers.trainers import BpeTrainer
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trainer = BpeTrainer(special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
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tokenizer.train(files=["wiki.train.raw", "wiki.valid.raw", "wiki.test.raw"], trainer=trainer)
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```
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Once your tokenizer is trained, encode any text with just one line:
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```python
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output = tokenizer.encode("Hello, y'all! How are you 😁 ?")
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print(output.tokens)
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# ["Hello", ",", "y", "'", "all", "!", "How", "are", "you", "[UNK]", "?"]
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```
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Check the [documentation](https://huggingface.co/docs/tokenizers/index)
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or the [quicktour](https://huggingface.co/docs/tokenizers/quicktour) to learn more!
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