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πŸ’₯Fast State-of-the-Art Tokenizers optimized for Research and Production
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n1t0 Improve the way BpeTrainer creates a BPE
Before these modifications, the BPE constructed by the BpeTrainer didn't
account for some of its configuration. So if the Trainer was using some
suffix or prefix for example, it didn't instantiate the produced BPE
with these options.
Also any special tokens that are used during training, should be
automatically added to the Tokenizer with the Model. This is now handled
properly.
Latest commit 3895d14 Jan 17, 2020
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LICENSE Create LICENSE Jan 5, 2020
README.md Typo in README.md Jan 14, 2020

README.md



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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.

Quick examples using Python:

Start using in a matter of seconds:

# Tokenizers provides ultra-fast implementations of most current tokenizers:
>>> from tokenizers import (ByteLevelBPETokenizer,
                            BPETokenizer,
                            SentencePieceBPETokenizer,
                            BertWordPieceTokenizer)
# Ultra-fast => they can encode 1GB of text in ~20sec on a standard server's CPU
# Tokenizers can be easily instantiated from standard files
>>> tokenizer = BertWordPieceTokenizer("bert-base-uncased-vocab.txt", lowercase=True)
Tokenizer(vocabulary_size=30522, model=BertWordPiece, add_special_tokens=True, unk_token=[UNK], 
          sep_token=[SEP], cls_token=[CLS], clean_text=True, handle_chinese_chars=True, 
          strip_accents=True, lowercase=True, wordpieces_prefix=##)

# Tokenizers provide exhaustive outputs: tokens, mapping to original string, attention/special token masks.
# They also handle model's max input lengths as well as padding (to directly encode in padded batches)
>>> output = tokenizer.encode("Hello, y'all! How are you 😁 ?")
Encoding(num_tokens=13, attributes=[ids, type_ids, tokens, offsets, attention_mask, special_tokens_mask, overflowing, original_str, normalized_str])
>>> print(output.ids, output.tokens, output.offsets)
[101, 7592, 1010, 1061, 1005, 2035, 999, 2129, 2024, 2017, 100, 1029, 102]
['[CLS]', 'hello', ',', 'y', "'", 'all', '!', 'how', 'are', 'you', '[UNK]', '?', '[SEP]']
[(0, 0), (0, 5), (5, 6), (7, 8), (8, 9), (9, 12), (12, 13), (14, 17), (18, 21), (22, 25), (26, 27),
 (28, 29), (0, 0)]
# Here is an example using the offsets mapping to retrieve the string coresponding to the 10th token:
>>> output.original_str[output.offsets[10]]
'😁'

And training a new vocabulary is just as easy:

# You can also train a BPE/Byte-levelBPE/WordPiece vocabulary on your own files
>>> tokenizer = ByteLevelBPETokenizer()
>>> tokenizer.train(["wiki.test.raw"], vocab_size=20000)
[00:00:00] Tokenize words                 β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ   20993/20993
[00:00:00] Count pairs                    β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ   20993/20993
[00:00:03] Compute merges                 β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ   19375/19375

Bindings

We provide bindings to the following languages (more to come!):

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