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