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Fix a few errors in the README.md

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Most notably, the convention for representing Python code (using ">>>" for code, without for output) was used the wrong way round.
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benjamin-ny
2020-01-13 09:38:38 +01:00
committed by GitHub
parent fc9e81d4ab
commit 743d66340d
1 changed files with 16 additions and 16 deletions
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README.md
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@ -19,7 +19,7 @@ versatility.
## Main features:
- Train new vocabularies and tokenize, using todays most used tokenizers.
- 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.
@ -34,29 +34,29 @@ Start using in a matter of seconds:
```python
# Tokenizers provides ultra-fast implementations of most current tokenizers:
from tokenizers import (ByteLevelBPETokenizer,
>>> 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],
>>> 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),
>>> 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]]
>>> '😁'
>>> output.original_str[output.offsets[10]]
'😁'
```
And training an new vocabulary is just as easy: