c45aebd102
* current updates * simplify * set_item works, but `tokenizer._tokenizer.post_processor[1].single = ["$0", "</s>"]` does not ! * fix: `normalizers` deserialization and other refactoring * fix: `pre_tokenizer` deserialization * feat: add `__len__` implementation for `normalizer::PySequence` * feat: add `__setitem__` impl for `normalizers::PySequence` * feat: add `__setitem__` impl to `pre_tokenizer::PySequence` * feat: add `__setitem__` impl to `post_processor::PySequence` * test: add normalizer sequence setter check * refactor: allow unused `processors::setter` macro * test: add `__setitem__` test for processors & pretok * refactor: `unwrap` -> `PyException::new_err()?` * refactor: fmt * refactor: remove unnecessary `pub` * feat(bindings): add missing getters & setters for pretoks * feat(bindings): add missing getters & setters for processors * refactor(bindings): rewrite RwLock poison error msg * refactor: remove debug print * feat(bindings): add description as to why custom deser is needed * feat: make post proc sequence elements mutable * fix(binding): serialization --------- Co-authored-by: Luc Georges <luc.sydney.georges@gmail.com>
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 should give the following on a g6 aws instance:
Bindings
We provide bindings to the following languages (more to come!):
Installation
You can install from source using:
pip install git+https://github.com/huggingface/tokenizers.git#subdirectory=bindings/python
our 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!