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toktokenizer is a BPE tokenizer implemented in rust and exposed in python using pyo3 bindings.
import toktokenizer as tok
bpe = tok.BPETokenizer.from_pretrained("wikibpe.json")
assert bpe.decode(bpe.encode("rust is pretty fun 🦀"))
Install toktokenizer
from PyPI with the following
pip install toktokenizer
Note: if you want to build from source make sure rust is installed!
The only class toktokenizer
exposes is BPETokenizer
. The class itself is pretty minimalistic, with all major methods being showed below:
from toktokenizer import BPETokenizer
bpe = BPETokenizer()
# train a byte-pair tokenizer on some corpus
train_corpus = "this is some training data. any dumped string will do!"
vocab_size = 8
bpe.train(train_corpus, vocab_size)
# save tokenizer state
bpe.save_encoder("8word.json")
# load tokenizer from dumped file
bpe.load_encoder("8word.json")
# encode and decode
input_ids = bpe.encode("some data")
decoded = bpe.decode(input_ids)
slightly faster than openai & a lot quicker than 🤗!
Performance measured on 2.5MB from the wikitext test split using openai's tiktoken gpt2 tokenizer with tiktoken==0.6.0
and the implementation from 🤗 tokenizers at tokenizers==0.19.1
FAQs
Unknown package
We found that toktokenizer demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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