Provides an implementation of today's most used tokenizers, with a focus on performance and versatility.
- 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.
We provide bindings to the following languages (more to come!):
Start using in a matter of seconds:
# Tokenizers provides ultra-fast implementations of most current tokenizers: >>> from tokenizers import (ByteLevelBPETokenizer, CharBPETokenizer, 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 π ?") 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 corresponding to the 10th token: >>> output.original_str[output.offsets[10]] 'π'
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 ββββββββββββββββββββββββββββββββββββββββ 20993/20993 [00:00:00] Count pairs ββββββββββββββββββββββββββββββββββββββββ 20993/20993 [00:00:03] Compute merges ββββββββββββββββββββββββββββββββββββββββ 19375/19375