structure saas with tools
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Davidson Gomes
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Metadata-Version: 2.4
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Name: tokenizers
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Version: 0.21.1
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Classifier: Development Status :: 5 - Production/Stable
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Classifier: Intended Audience :: Developers
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Classifier: Intended Audience :: Education
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Classifier: Intended Audience :: Science/Research
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Classifier: License :: OSI Approved :: Apache Software License
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Classifier: Operating System :: OS Independent
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Classifier: Programming Language :: Python :: 3
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Classifier: Programming Language :: Python :: 3.9
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Classifier: Programming Language :: Python :: 3.10
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Classifier: Programming Language :: Python :: 3.11
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Classifier: Programming Language :: Python :: 3.12
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Classifier: Programming Language :: Python :: 3.13
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Classifier: Programming Language :: Python :: 3 :: Only
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Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
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Requires-Dist: huggingface-hub>=0.16.4,<1.0
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Requires-Dist: pytest ; extra == 'testing'
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Requires-Dist: requests ; extra == 'testing'
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Requires-Dist: numpy ; extra == 'testing'
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Requires-Dist: datasets ; extra == 'testing'
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Requires-Dist: black==22.3 ; extra == 'testing'
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Requires-Dist: ruff ; extra == 'testing'
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Requires-Dist: sphinx ; extra == 'docs'
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Requires-Dist: sphinx-rtd-theme ; extra == 'docs'
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Requires-Dist: setuptools-rust ; extra == 'docs'
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Requires-Dist: tokenizers[testing] ; extra == 'dev'
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Provides-Extra: testing
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Provides-Extra: docs
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Provides-Extra: dev
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Keywords: NLP,tokenizer,BPE,transformer,deep learning
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Author: Anthony MOI <m.anthony.moi@gmail.com>
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Author-email: Nicolas Patry <patry.nicolas@protonmail.com>, Anthony Moi <anthony@huggingface.co>
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Requires-Python: >=3.9
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Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
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Project-URL: Homepage, https://github.com/huggingface/tokenizers
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Project-URL: Source, https://github.com/huggingface/tokenizers
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<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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<a href="https://badge.fury.io/py/tokenizers">
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<img alt="Build" src="https://badge.fury.io/py/tokenizers.svg">
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</a>
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<a href="https://github.com/huggingface/tokenizers/blob/master/LICENSE">
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<img alt="GitHub" src="https://img.shields.io/github/license/huggingface/tokenizers.svg?color=blue">
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</a>
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</p>
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<br>
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# Tokenizers
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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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Bindings over the [Rust](https://github.com/huggingface/tokenizers/tree/master/tokenizers) implementation.
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If you are interested in the High-level design, you can go check it there.
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Otherwise, let's dive in!
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## Main features:
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- Train new vocabularies and tokenize using 4 pre-made tokenizers (Bert WordPiece and the 3
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most common BPE versions).
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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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### Installation
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#### With pip:
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```bash
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pip install tokenizers
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```
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#### From sources:
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To use this method, you need to have the Rust installed:
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```bash
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# Install with:
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curl https://sh.rustup.rs -sSf | sh -s -- -y
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export PATH="$HOME/.cargo/bin:$PATH"
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```
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Once Rust is installed, you can compile doing the following
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```bash
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git clone https://github.com/huggingface/tokenizers
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cd tokenizers/bindings/python
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# Create a virtual env (you can use yours as well)
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python -m venv .env
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source .env/bin/activate
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# Install `tokenizers` in the current virtual env
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pip install -e .
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```
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### Load a pretrained tokenizer from the Hub
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```python
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from tokenizers import Tokenizer
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tokenizer = Tokenizer.from_pretrained("bert-base-cased")
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```
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### Using the provided Tokenizers
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We provide some pre-build tokenizers to cover the most common cases. You can easily load one of
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these using some `vocab.json` and `merges.txt` files:
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```python
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from tokenizers import CharBPETokenizer
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# Initialize a tokenizer
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vocab = "./path/to/vocab.json"
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merges = "./path/to/merges.txt"
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tokenizer = CharBPETokenizer(vocab, merges)
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# And then encode:
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encoded = tokenizer.encode("I can feel the magic, can you?")
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print(encoded.ids)
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print(encoded.tokens)
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```
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And you can train them just as simply:
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```python
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from tokenizers import CharBPETokenizer
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# Initialize a tokenizer
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tokenizer = CharBPETokenizer()
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# Then train it!
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tokenizer.train([ "./path/to/files/1.txt", "./path/to/files/2.txt" ])
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# Now, let's use it:
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encoded = tokenizer.encode("I can feel the magic, can you?")
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# And finally save it somewhere
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tokenizer.save("./path/to/directory/my-bpe.tokenizer.json")
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```
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#### Provided Tokenizers
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- `CharBPETokenizer`: The original BPE
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- `ByteLevelBPETokenizer`: The byte level version of the BPE
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- `SentencePieceBPETokenizer`: A BPE implementation compatible with the one used by SentencePiece
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- `BertWordPieceTokenizer`: The famous Bert tokenizer, using WordPiece
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All of these can be used and trained as explained above!
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### Build your own
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Whenever these provided tokenizers don't give you enough freedom, you can build your own tokenizer,
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by putting all the different parts you need together.
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You can check how we implemented the [provided tokenizers](https://github.com/huggingface/tokenizers/tree/master/bindings/python/py_src/tokenizers/implementations) and adapt them easily to your own needs.
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#### Building a byte-level BPE
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Here is an example showing how to build your own byte-level BPE by putting all the different pieces
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together, and then saving it to a single file:
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```python
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from tokenizers import Tokenizer, models, pre_tokenizers, decoders, trainers, processors
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# Initialize a tokenizer
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tokenizer = Tokenizer(models.BPE())
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# Customize pre-tokenization and decoding
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tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=True)
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tokenizer.decoder = decoders.ByteLevel()
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tokenizer.post_processor = processors.ByteLevel(trim_offsets=True)
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# And then train
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trainer = trainers.BpeTrainer(
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vocab_size=20000,
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min_frequency=2,
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initial_alphabet=pre_tokenizers.ByteLevel.alphabet()
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)
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tokenizer.train([
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"./path/to/dataset/1.txt",
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"./path/to/dataset/2.txt",
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"./path/to/dataset/3.txt"
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], trainer=trainer)
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# And Save it
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tokenizer.save("byte-level-bpe.tokenizer.json", pretty=True)
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```
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Now, when you want to use this tokenizer, this is as simple as:
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```python
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from tokenizers import Tokenizer
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tokenizer = Tokenizer.from_file("byte-level-bpe.tokenizer.json")
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encoded = tokenizer.encode("I can feel the magic, can you?")
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```
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