422 lines
14 KiB
Python
422 lines
14 KiB
Python
# Copyright 2018 The Google AI Language Team Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tokenization classes."""
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from __future__ import absolute_import, division, print_function
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import collections
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import re
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import unicodedata
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import six
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def validate_case_matches_checkpoint(do_lower_case, init_checkpoint):
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"""Checks whether the casing config is consistent with the checkpoint name."""
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# The casing has to be passed in by the user and there is no explicit check
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# as to whether it matches the checkpoint. The casing information probably
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# should have been stored in the bert_config.json file, but it's not, so
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# we have to heuristically detect it to validate.
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if not init_checkpoint:
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return
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m = re.match('^.*?([A-Za-z0-9_-]+)/bert_model.ckpt', init_checkpoint)
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if m is None:
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return
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model_name = m.group(1)
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lower_models = [
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'uncased_L-24_H-1024_A-16', 'uncased_L-12_H-768_A-12',
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'multilingual_L-12_H-768_A-12', 'chinese_L-12_H-768_A-12'
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]
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cased_models = [
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'cased_L-12_H-768_A-12', 'cased_L-24_H-1024_A-16',
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'multi_cased_L-12_H-768_A-12'
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]
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is_bad_config = False
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if model_name in lower_models and not do_lower_case:
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is_bad_config = True
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actual_flag = 'False'
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case_name = 'lowercased'
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opposite_flag = 'True'
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if model_name in cased_models and do_lower_case:
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is_bad_config = True
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actual_flag = 'True'
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case_name = 'cased'
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opposite_flag = 'False'
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if is_bad_config:
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raise ValueError(
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'You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. '
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'However, `%s` seems to be a %s model, so you '
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'should pass in `--do_lower_case=%s` so that the fine-tuning matches '
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'how the model was pre-training. If this error is wrong, please '
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'just comment out this check.' %
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(actual_flag, init_checkpoint, model_name, case_name,
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opposite_flag))
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def convert_to_unicode(text):
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"""Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
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if six.PY3:
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if isinstance(text, str):
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return text
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elif isinstance(text, bytes):
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return text.decode('utf-8', 'ignore')
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else:
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raise ValueError('Unsupported string type: %s' % (type(text)))
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elif six.PY2:
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if isinstance(text, str):
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return text.decode('utf-8', 'ignore')
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elif isinstance(text, unicode):
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return text
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else:
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raise ValueError('Unsupported string type: %s' % (type(text)))
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else:
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raise ValueError('Not running on Python2 or Python 3?')
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def printable_text(text):
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"""Returns text encoded in a way suitable for print or `tf.logging`."""
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# These functions want `str` for both Python2 and Python3, but in one case
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# it's a Unicode string and in the other it's a byte string.
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if six.PY3:
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if isinstance(text, str):
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return text
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elif isinstance(text, bytes):
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return text.decode('utf-8', 'ignore')
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else:
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raise ValueError('Unsupported string type: %s' % (type(text)))
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elif six.PY2:
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if isinstance(text, str):
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return text
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elif isinstance(text, unicode):
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return text.encode('utf-8')
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else:
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raise ValueError('Unsupported string type: %s' % (type(text)))
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else:
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raise ValueError('Not running on Python2 or Python 3?')
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def load_vocab(vocab_file):
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"""Loads a vocabulary file into a dictionary."""
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vocab = collections.OrderedDict()
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index = 0
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with open(vocab_file, 'r', encoding='utf-8') as reader:
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while True:
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token = convert_to_unicode(reader.readline())
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if not token:
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break
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token = token.strip()
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vocab[token] = index
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index += 1
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return vocab
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def convert_by_vocab(vocab, items):
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"""Converts a sequence of [tokens|ids] using the vocab."""
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output = []
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for item in items:
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output.append(vocab[item])
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return output
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def convert_tokens_to_ids(vocab, tokens):
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return convert_by_vocab(vocab, tokens)
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def convert_ids_to_tokens(inv_vocab, ids):
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return convert_by_vocab(inv_vocab, ids)
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def whitespace_tokenize(text):
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"""Runs basic whitespace cleaning and splitting on a piece of text."""
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text = text.strip()
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if not text:
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return []
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tokens = text.split()
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return tokens
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class FullTokenizer(object):
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"""Runs end-to-end tokenization."""
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def __init__(self, vocab_file, do_lower_case=True):
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self.vocab = load_vocab(vocab_file)
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self.inv_vocab = {v: k for k, v in self.vocab.items()}
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self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
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self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
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def tokenize(self, text):
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split_tokens = []
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for token in self.basic_tokenizer.tokenize(text):
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for sub_token in self.wordpiece_tokenizer.tokenize(token):
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split_tokens.append(sub_token)
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return split_tokens
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def convert_tokens_to_ids(self, tokens):
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return convert_by_vocab(self.vocab, tokens)
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def convert_ids_to_tokens(self, ids):
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return convert_by_vocab(self.inv_vocab, ids)
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@staticmethod
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def convert_tokens_to_string(tokens, clean_up_tokenization_spaces=True):
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""" Converts a sequence of tokens (string) in a single string. """
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def clean_up_tokenization(out_string):
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""" Clean up a list of simple English tokenization artifacts
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like spaces before punctuations and abreviated forms.
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"""
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out_string = (
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out_string.replace(' .', '.').replace(' ?', '?').replace(
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' !', '!').replace(' ,', ',').replace(" ' ", "'").replace(
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" n't", "n't").replace(" 'm", "'m").replace(
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" 's", "'s").replace(" 've",
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"'ve").replace(" 're", "'re"))
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return out_string
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text = ' '.join(tokens).replace(' ##', '').strip()
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if clean_up_tokenization_spaces:
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clean_text = clean_up_tokenization(text)
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return clean_text
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else:
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return text
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def vocab_size(self):
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return len(self.vocab)
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class BasicTokenizer(object):
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"""Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
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def __init__(self, do_lower_case=True):
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"""Constructs a BasicTokenizer.
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Args:
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do_lower_case: Whether to lower case the input.
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"""
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self.do_lower_case = do_lower_case
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def tokenize(self, text):
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"""Tokenizes a piece of text."""
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text = convert_to_unicode(text)
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text = self._clean_text(text)
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# This was added on November 1st, 2018 for the multilingual and Chinese
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# models. This is also applied to the English models now, but it doesn't
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# matter since the English models were not trained on any Chinese data
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# and generally don't have any Chinese data in them (there are Chinese
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# characters in the vocabulary because Wikipedia does have some Chinese
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# words in the English Wikipedia.).
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text = self._tokenize_chinese_chars(text)
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orig_tokens = whitespace_tokenize(text)
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split_tokens = []
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for token in orig_tokens:
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if self.do_lower_case:
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token = token.lower()
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token = self._run_strip_accents(token)
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split_tokens.extend(self._run_split_on_punc(token))
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output_tokens = whitespace_tokenize(' '.join(split_tokens))
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return output_tokens
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def _run_strip_accents(self, text):
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"""Strips accents from a piece of text."""
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text = unicodedata.normalize('NFD', text)
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output = []
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for char in text:
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cat = unicodedata.category(char)
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if cat == 'Mn':
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continue
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output.append(char)
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return ''.join(output)
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def _run_split_on_punc(self, text):
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"""Splits punctuation on a piece of text."""
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chars = list(text)
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i = 0
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start_new_word = True
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output = []
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while i < len(chars):
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char = chars[i]
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if _is_punctuation(char):
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output.append([char])
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start_new_word = True
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else:
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if start_new_word:
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output.append([])
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start_new_word = False
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output[-1].append(char)
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i += 1
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return [''.join(x) for x in output]
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def _tokenize_chinese_chars(self, text):
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"""Adds whitespace around any CJK character."""
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output = []
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for char in text:
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cp = ord(char)
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if self._is_chinese_char(cp):
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output.append(' ')
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output.append(char)
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output.append(' ')
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else:
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output.append(char)
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return ''.join(output)
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def _is_chinese_char(self, cp):
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"""Checks whether CP is the codepoint of a CJK character."""
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# This defines a "chinese character" as anything in the CJK Unicode block:
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# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
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#
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# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
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# despite its name. The modern Korean Hangul alphabet is a different block,
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# as is Japanese Hiragana and Katakana. Those alphabets are used to write
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# space-separated words, so they are not treated specially and handled
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# like the all of the other languages.
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if ((cp >= 0x4E00 and cp <= 0x9FFF) or (cp >= 0x3400 and cp <= 0x4DBF)
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or (cp >= 0x20000 and cp <= 0x2A6DF)
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or (cp >= 0x2A700 and cp <= 0x2B73F)
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or (cp >= 0x2B740 and cp <= 0x2B81F)
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or (cp >= 0x2B820 and cp <= 0x2CEAF)
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or (cp >= 0xF900 and cp <= 0xFAFF)
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or (cp >= 0x2F800 and cp <= 0x2FA1F)):
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return True
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return False
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def _clean_text(self, text):
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"""Performs invalid character removal and whitespace cleanup on text."""
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output = []
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for char in text:
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cp = ord(char)
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if cp == 0 or cp == 0xfffd or _is_control(char):
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continue
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if _is_whitespace(char):
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output.append(' ')
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else:
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output.append(char)
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return ''.join(output)
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class WordpieceTokenizer(object):
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"""Runs WordPiece tokenization."""
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def __init__(self, vocab, unk_token='[UNK]', max_input_chars_per_word=200):
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self.vocab = vocab
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self.unk_token = unk_token
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self.max_input_chars_per_word = max_input_chars_per_word
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def tokenize(self, text):
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"""Tokenizes a piece of text into its word pieces.
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This uses a greedy longest-match-first algorithm to perform tokenization
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using the given vocabulary.
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For example:
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input = "unaffable"
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output = ["un", "##aff", "##able"]
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Args:
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text: A single token or whitespace separated tokens. This should have
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already been passed through `BasicTokenizer.
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Returns:
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A list of wordpiece tokens.
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"""
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text = convert_to_unicode(text)
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output_tokens = []
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for token in whitespace_tokenize(text):
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chars = list(token)
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if len(chars) > self.max_input_chars_per_word:
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output_tokens.append(self.unk_token)
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continue
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is_bad = False
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start = 0
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sub_tokens = []
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while start < len(chars):
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end = len(chars)
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cur_substr = None
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while start < end:
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substr = ''.join(chars[start:end])
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if start > 0:
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substr = '##' + substr
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if substr in self.vocab:
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cur_substr = substr
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break
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end -= 1
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if cur_substr is None:
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is_bad = True
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break
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sub_tokens.append(cur_substr)
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start = end
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if is_bad:
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output_tokens.append(self.unk_token)
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else:
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output_tokens.extend(sub_tokens)
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return output_tokens
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def _is_whitespace(char):
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"""Checks whether `chars` is a whitespace character."""
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# \t, \n, and \r are technically contorl characters but we treat them
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# as whitespace since they are generally considered as such.
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if char == ' ' or char == '\t' or char == '\n' or char == '\r':
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return True
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cat = unicodedata.category(char)
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if cat == 'Zs':
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return True
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return False
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def _is_control(char):
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"""Checks whether `chars` is a control character."""
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# These are technically control characters but we count them as whitespace
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# characters.
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if char == '\t' or char == '\n' or char == '\r':
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return False
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cat = unicodedata.category(char)
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if cat in ('Cc', 'Cf'):
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return True
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return False
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def _is_punctuation(char):
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"""Checks whether `chars` is a punctuation character."""
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cp = ord(char)
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# We treat all non-letter/number ASCII as punctuation.
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# Characters such as "^", "$", and "`" are not in the Unicode
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# Punctuation class but we treat them as punctuation anyways, for
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# consistency.
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if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64)
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or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
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return True
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cat = unicodedata.category(char)
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if cat.startswith('P'):
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return True
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return False
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