def tokenize(self, text):
"""
:param text:
:return:
"""
text = convert_to_unicode(text)
split_tokens = text.split(self.split_char)
return split_tokens
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.vocabulary.vocab_dict:
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 tokenize(self, text):
"""Tokenizes a piece of text into its word pieces.
Returns:
A list of wordpiece tokens.
"""
text = text.lower() if self.do_lower_case else text
text = convert_to_unicode(text)
output_tokens = []
for token in text.split(self.split_token):
if token in self.vocab:
output_tokens.append(token)
else:
sp_tokens = self.tokenizer.EncodeAsPieces(token)
for sp_token in sp_tokens:
if sp_token in self.vocab:
output_tokens.append(sp_token)
return output_tokens
def tokenize(self, text):
"""Tokenizes a piece of text into its word pieces.
Returns:
A list of wordpiece tokens.
"""
text = text.lower() if self.do_lower_case else text
text = convert_to_unicode(text.replace("\1", " "))
tokens = self.tokenizer.EncodeAsPieces(text)
output_tokens = []
for token in tokens:
if token == self.sp_unk_token:
token = self.unk_token
if token in self.vocab:
output_tokens.append(token)
else:
output_tokens.append(self.unk_token)
return output_tokens
def load_vocab(self):
"""
:return:
"""
vocab_dict = collections.OrderedDict()
id_dict = collections.OrderedDict()
file_vocab = open(self.vocab_path)
for num, line in enumerate(file_vocab):
items = convert_to_unicode(line.strip()).split("\t")
if len(items) > 2:
break
token = items[0]
if len(items) == 2:
index = items[1]
else:
index = num
token = token.strip()
vocab_dict[token] = int(index)
id_dict[index] = token
return vocab_dict, id_dict
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 tokenize(self, text):
"""
:param text:
:return:
"""
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 = u'\u2581' + substr
if substr in self.vocabulary.vocab_dict:
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