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* implement a simple max_sentencepiece_length into BPE Add a way for the BPE trainer to behave like the unigram trainer where tokens longer than a certain lenght(default 16 in SPM) to be skipped. this is implemented in unigram trainer but in a different way. If this code were to be actually integrated some works to be done Documentation describing the behavior and how it should be set. Set default==0 so it doesnt act unless set provide ways in the python binding for the user to set max token length I was trying to find a way to implement max_sentencepiece_length through pretokenizer split rules and to be honest, its very difficult and regexes can be real slow when operating on the whole training corpus. * implement a simple max_sentencepiece_length into BPE Add a way for the BPE trainer to behave like the unigram trainer where tokens longer than a certain lenght(default 16 in SPM) to be skipped. this is implemented in unigram trainer but in a different way. If this code were to be actually integrated some works to be done Documentation describing the behavior and how it should be set. Set default==0 so it doesnt act unless set provide ways in the python binding for the user to set max token length I was trying to find a way to implement max_sentencepiece_length through pretokenizer split rules and to be honest, its very difficult and regexes can be real slow when operating on the whole training corpus. * utilize Option<u16> for safer code. * Other version. * Update trainer.rs clarify with type usize propagate max_length option * change max_length into more descriptive name in the documentation https://huggingface.co/docs/tokenizers/api/trainers unigramtrainer uses max_piece_length for similar function. since BPE the underlying concept is merges, using max_merge_length as the variable name could prove more descriptive. * change variable name in trainer.rs change max_merge_length into max_token_length * Update trainer.rs add several max_token_length declaration that were missing. impl BpeTrainerBuilder struct BpeTrainer Add explanation for variable shadowing. * Update trainer.rs Move default definition of max_token_length to proper location. adjust downstream variable initializations accordingly. * add max_token_length test * Add bpe direct assert test * Update trainer.rs clarified test documentation * Creating the bindings. * Fix the default. * Re-adding missing package-lock which I accidentally removed. * .. * Fixing trainer test. * Fix. --------- Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.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.
Bindings
We provide bindings to the following languages (more to come!):
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 python documentation or the python quicktour to learn more!