代码拉取完成，页面将自动刷新
# coding=utf-8
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
# 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, unicode_literals
import collections
import logging
import os
import unicodedata
import six
from io import open
import pickle
import sentencepiece as spm
import jieba
import oknlp
from file_utils import cached_path
logger = logging.getLogger(__name__)
PRETRAINED_VOCAB_ARCHIVE_MAP = {
'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt",
'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt",
'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt",
'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
}
PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
'bert-base-uncased': 512,
'bert-large-uncased': 512,
'bert-base-cased': 512,
'bert-large-cased': 512,
'bert-base-multilingual-uncased': 512,
'bert-base-multilingual-cased': 512,
'bert-base-chinese': 512,
}
VOCAB_NAME = 'vocab.txt'
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 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 = reader.readline()
if not token:
break
token = token.strip()
vocab[token] = index
index += 1
return vocab
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
def load_vocab_spm(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 = reader.readline()
if not token:
break
token = token.strip().split()[0].strip()
vocab[token] = index
index += 1
return vocab
# WUBI2CH = "/mnt/nfs/home/scl/LanguageModeling/BERT/data/wubi_to_chinese_unique.pkl"
# CH2WUBI = "/mnt/nfs/home/scl/LanguageModeling/BERT/data/chinese_to_wubi_unique.pkl"
# ENCODE2CH = "/home/ubuntu/WubiBERT/data/cangjie_to_chinese.pkl"
# CH2ENCODE = "/home/ubuntu/WubiBERT/data/chinese_to_cangjie.pkl"
cangjie2ch = "data/cangjie_to_chinese.pkl"
ch2cangjie = "data/chinese_to_cangjie.pkl"
stroke2ch = "data/stroke_to_chinese.pkl"
ch2stroke = "data/chinese_to_stroke.pkl"
zhengma2ch = "data/zhengma_to_chinese.pkl"
ch2zhengma = "data/chinese_to_zhengma.pkl"
wubi2ch = "data/wubi_to_chinese.pkl"
ch2wubi = "data/chinese_to_wubi.pkl"
pinyin2ch = "data/pinyin_to_chinese.pkl"
ch2pinyin = "data/chinese_to_pinyin.pkl"
zhuyin2ch = "data/zhuyin_to_chinese.pkl"
ch2zhuyin = "data/chinese_to_zhuyin.pkl"
# shuffle_map = "data/wubi_shuffle_dict.pkl"
# with open(shuffle_map, 'rb') as f:
# shuffle_map = pickle.load(f)
control_char = u'0123456789abcdefghijklmnopqrstuvwxyz'
control_uni = [chr(ord(c)+50000) for c in control_char]
CH2EN_PUNC = {f: t
for f, t in zip(
u'，。！？【】（）％＃＠＆１２３４５６７８９０；：',
u',.!?[]()%#@&1234567890;:')}
def load_dict(dict_path):
return pickle.load(open(dict_path, "rb"))
## load some preprocessed dicts
with open("byte_char_map.pkl", "rb") as f:
ch_chars = pickle.load(f)
SEP = chr(ord('_')+50000)
with open("random_index_map.pkl", 'rb') as f:
random_index_map = pickle.load(f)
# map_dict = load_dict(CH2ENCODE)
class ByteTokenizer(object):
def __init__(self, vocab_file, model_file, max_len=None,
never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
if (not os.path.isfile(vocab_file)) or (not os.path.isfile(model_file)):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained ".format(vocab_file))
self.vocab = load_vocab_spm(vocab_file)
self.spm_tokenizer = spm.SentencePieceProcessor(model_file=model_file)
self.ids_to_tokens = collections.OrderedDict(
[(ids, tok) for tok, ids in self.vocab.items()])
self.max_len = max_len if max_len is not None else int(1e12)
def convert_line(self, text):
out_line = ""
for ch_char in text.strip():
c = bytes(ch_char, 'utf-8')
for byte_index in c:
# print (byte_index)
ch = ch_chars[byte_index]
out_line += ch
out_line += SEP
return out_line
def tokenize(self, text):
out_line = self.convert_line(text)
return self.spm_tokenizer.encode(out_line, out_type=str)
def convert_tokens_to_ids(self, tokens):
"""Converts a sequence of tokens into ids using the vocab."""
ids = []
for token in tokens:
if token in self.vocab:
ids.append(self.vocab[token])
else:
ids.append(self.vocab['[UNK]'])
if len(ids) > self.max_len:
raise ValueError(
"Token indices sequence length is longer than the specified maximum "
" sequence length for this BERT model ({} > {}). Running this"
" sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
)
return ids
def convert_ids_to_tokens(self, ids):
"""Converts a sequence of ids in wordpiece tokens using the vocab."""
tokens = []
for i in ids:
tokens.append(self.ids_to_tokens[i])
return tokens
class RandomIndexTokenizer(object):
def __init__(self, vocab_file, model_file, max_len=None,
never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
if (not os.path.isfile(vocab_file)) or (not os.path.isfile(model_file)):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained ".format(vocab_file))
self.vocab = load_vocab_spm(vocab_file)
self.spm_tokenizer = spm.SentencePieceProcessor(model_file=model_file)
self.ids_to_tokens = collections.OrderedDict(
[(ids, tok) for tok, ids in self.vocab.items()])
self.max_len = max_len if max_len is not None else int(1e12)
def convert_line(self, text):
out_line = ""
for ch_char in text.strip():
if ch_char in random_index_map:
out_line += str(random_index_map[ch_char])
else:
out_line += ch_char
out_line += SEP
return out_line
def tokenize(self, text):
out_line = self.convert_line(text)
return self.spm_tokenizer.encode(out_line, out_type=str)
def convert_tokens_to_ids(self, tokens):
"""Converts a sequence of tokens into ids using the vocab."""
ids = []
for token in tokens:
if token in self.vocab:
ids.append(self.vocab[token])
else:
ids.append(self.vocab['[UNK]'])
if len(ids) > self.max_len:
raise ValueError(
"Token indices sequence length is longer than the specified maximum "
" sequence length for this BERT model ({} > {}). Running this"
" sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
)
return ids
def convert_ids_to_tokens(self, ids):
"""Converts a sequence of ids in wordpiece tokens using the vocab."""
tokens = []
for i in ids:
tokens.append(self.ids_to_tokens[i])
return tokens
class BertZhTokenizer(object):
"for bert_chinese_uncased_22675 tokenization"
def __init__(self, vocab_file, model_file=None, do_lower_case=True, max_len=None,
never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
if (not os.path.isfile(vocab_file)):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained ".format(vocab_file))
self.vocab = load_vocab_spm(vocab_file)
self.ids_to_tokens = collections.OrderedDict(
[(ids, tok) for tok, ids in self.vocab.items()])
self.max_len = max_len if max_len is not None else int(1e12)
def tokenize(self, line):
line = line.lower().replace(' ', '')
line = list(line.strip())
return line
def convert_tokens_to_ids(self, tokens):
"""Converts a sequence of tokens into ids using the vocab."""
ids = []
for token in tokens:
if token in self.vocab:
ids.append(self.vocab[token])
else:
ids.append(self.vocab['[UNK]'])
if len(ids) > self.max_len:
raise ValueError(
"Token indices sequence length is longer than the specified maximum "
" sequence length for this BERT model ({} > {}). Running this"
" sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
)
return ids
def convert_ids_to_tokens(self, ids):
"""Converts a sequence of ids in wordpiece tokens using the vocab."""
tokens = []
for i in ids:
tokens.append(self.ids_to_tokens[i])
return tokens
class RawZhTokenizer(object):
"for sp_raw_zh tokenization"
def __init__(self, vocab_file, model_file, do_lower_case=True, max_len=None,
never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
if (not os.path.isfile(vocab_file)) or (not os.path.isfile(model_file)):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained ".format(vocab_file))
self.vocab = load_vocab_spm(vocab_file)
self.spm_tokenizer = spm.SentencePieceProcessor(model_file=model_file)
self.ids_to_tokens = collections.OrderedDict(
[(ids, tok) for tok, ids in self.vocab.items()])
self.do_lower_case = do_lower_case
# self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
# never_split=never_split)
# self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
self.max_len = max_len if max_len is not None else int(1e12)
def tokenize(self, text):
if self.do_lower_case:
text = text.lower() ## lowercasing doesn't matter much here
return self.spm_tokenizer.encode(text, out_type=str)
def convert_tokens_to_ids(self, tokens):
"""Converts a sequence of tokens into ids using the vocab."""
ids = []
for token in tokens:
if token in self.vocab:
ids.append(self.vocab[token])
else:
ids.append(self.vocab['[UNK]'])
if len(ids) > self.max_len:
raise ValueError(
"Token indices sequence length is longer than the specified maximum "
" sequence length for this BERT model ({} > {}). Running this"
" sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
)
return ids
def convert_ids_to_tokens(self, ids):
"""Converts a sequence of ids in wordpiece tokens using the vocab."""
tokens = []
for i in ids:
tokens.append(self.ids_to_tokens[i])
return tokens
class CommonZhNoIndexTokenizer(object):
"for cangjie_zh, wubi_zh, ... all such tokenization"
def __init__(self, vocab_file, model_file, do_lower_case=True, max_len=None,
never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
if (not os.path.isfile(vocab_file)) or (not os.path.isfile(model_file)):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained ".format(vocab_file))
if 'cangjie' in vocab_file:
self.map_dict = load_dict(ch2cangjie)
elif 'stroke' in vocab_file:
self.map_dict = load_dict(ch2stroke)
elif 'zhengma' in vocab_file:
self.map_dict = load_dict(ch2zhengma)
elif 'wubi' in vocab_file:
self.map_dict = load_dict(ch2wubi)
elif 'pinyin' in vocab_file:
self.map_dict = load_dict(ch2pinyin)
elif 'zhuyin' in vocab_file:
self.map_dict = load_dict(ch2zhuyin)
self.vocab = load_vocab_spm(vocab_file)
self.spm_tokenizer = spm.SentencePieceProcessor(model_file=model_file)
self.ids_to_tokens = collections.OrderedDict(
[(ids, tok) for tok, ids in self.vocab.items()])
# self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
# never_split=never_split)
# self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
self.max_len = max_len if max_len is not None else int(1e12)
def convert_line(self, text):
text = text.lower() # always lowercasing
out_line = ""
for ch_word in text:
ch_char = ch_word.strip()
if len(ch_char) == 0:
continue
## all convert to EN punctuations,
## to avoid mixture of different punctuations
if ch_char in CH2EN_PUNC:
ch_char = CH2EN_PUNC[ch_char]
if ch_char in self.map_dict:
mapped = ''.join([c for c in self.map_dict[ch_char].strip() if not c.isdigit()])
out_line += mapped + chr(ord('_')+50000) ## for sp_concat
else:
if ch_char in control_char:
ch_char = chr(ord(ch_char)+50000)
out_line += ch_char ## sp_concat
return out_line
def tokenize(self, text):
out_line = self.convert_line(text)
return self.spm_tokenizer.encode(out_line, out_type=str)
def convert_tokens_to_ids(self, tokens):
"""Converts a sequence of tokens into ids using the vocab."""
ids = []
for token in tokens:
if token in self.vocab:
ids.append(self.vocab[token])
else:
ids.append(self.vocab['[UNK]'])
if len(ids) > self.max_len:
raise ValueError(
"Token indices sequence length is longer than the specified maximum "
" sequence length for this BERT model ({} > {}). Running this"
" sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
)
return ids
def convert_ids_to_tokens(self, ids):
"""Converts a sequence of ids in wordpiece tokens using the vocab."""
tokens = []
for i in ids:
tokens.append(self.ids_to_tokens[i])
return tokens
## TODO: implement the detokenizer!
class PinyinConcatWubiTokenizer(object):
"for cangjie_zh, wubi_zh, ... all such tokenization"
def __init__(self, vocab_file, model_file, do_lower_case=True, max_len=None,
never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
if (not os.path.isfile(vocab_file)) or (not os.path.isfile(model_file)):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained ".format(vocab_file))
self.map_dict_pinyin = load_dict(ch2pinyin)
self.map_dict_wubi = load_dict(ch2wubi)
self.vocab = load_vocab_spm(vocab_file)
self.spm_tokenizer = spm.SentencePieceProcessor(model_file=model_file)
self.ids_to_tokens = collections.OrderedDict(
[(ids, tok) for tok, ids in self.vocab.items()])
# self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
# never_split=never_split)
# self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
self.max_len = max_len if max_len is not None else int(1e12)
def convert_line(self, text):
text = text.lower() # always lowercasing
out_line = ""
for ch_word in text:
ch_char = ch_word.strip()
if len(ch_char) == 0:
continue
## all convert to EN punctuations,
## to avoid mixture of different punctuations
if ch_char in CH2EN_PUNC:
ch_char = CH2EN_PUNC[ch_char]
if (ch_char in self.map_dict_pinyin) or (ch_char in self.map_dict_wubi):
mapped = ''
if ch_char in self.map_dict_pinyin:
mapped += ''.join([c for c in self.map_dict_pinyin[ch_char].strip() if not c.isdigit()])
if ch_char in self.map_dict_wubi:
mapped += ''.join([c for c in self.map_dict_wubi[ch_char].strip() if not c.isdigit()])
out_line += mapped + chr(ord('_')+50000) ## for sp_concat
else:
if ch_char in control_char:
ch_char = chr(ord(ch_char)+50000)
out_line += ch_char ## sp_concat
return out_line
def tokenize(self, text):
out_line = self.convert_line(text)
return self.spm_tokenizer.encode(out_line, out_type=str)
def convert_tokens_to_ids(self, tokens):
"""Converts a sequence of tokens into ids using the vocab."""
ids = []
for token in tokens:
if token in self.vocab:
ids.append(self.vocab[token])
else:
ids.append(self.vocab['[UNK]'])
if len(ids) > self.max_len:
raise ValueError(
"Token indices sequence length is longer than the specified maximum "
" sequence length for this BERT model ({} > {}). Running this"
" sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
)
return ids
def convert_ids_to_tokens(self, ids):
"""Converts a sequence of ids in wordpiece tokens using the vocab."""
tokens = []
for i in ids:
tokens.append(self.ids_to_tokens[i])
return tokens
## TODO: implement the detokenizer!
class ShuffledTokenizer(object):
"for cangjie_zh, wubi_zh, ... all such tokenization"
def __init__(self, vocab_file, model_file, do_lower_case=True, max_len=None,
never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
if (not os.path.isfile(vocab_file)) or (not os.path.isfile(model_file)):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained ".format(vocab_file))
if 'cangjie' in vocab_file:
self.map_dict = load_dict(ch2cangjie)
elif 'stroke' in vocab_file:
self.map_dict = load_dict(ch2stroke)
elif 'zhengma' in vocab_file:
self.map_dict = load_dict(ch2zhengma)
elif 'wubi' in vocab_file:
self.map_dict = load_dict(ch2wubi)
shuffle_map = "data/wubi_shuffle_dict.pkl"
elif 'pinyin' in vocab_file:
self.map_dict = load_dict(ch2pinyin)
shuffle_map = "data/pinyin_shuffle_dict.pkl"
elif 'zhuyin' in vocab_file:
self.map_dict = load_dict(ch2zhuyin)
self.vocab = load_vocab_spm(vocab_file)
self.spm_tokenizer = spm.SentencePieceProcessor(model_file=model_file)
self.ids_to_tokens = collections.OrderedDict(
[(ids, tok) for tok, ids in self.vocab.items()])
self.max_len = max_len if max_len is not None else int(1e12)
with open(shuffle_map, 'rb') as f:
self.shuffle_map = pickle.load(f)
def convert_line(self, line):
# text = text.lower() # always lowercasing
text = ""
for c in line.strip():
if c in self.shuffle_map:
newc = self.shuffle_map[c]
# print (c, newc)
else:
newc = c
text += newc
# print (text)
out_line = ""
for ch_word in text:
ch_char = ch_word.strip()
if len(ch_char) == 0:
continue
## all convert to EN punctuations,
## to avoid mixture of different punctuations
if ch_char in CH2EN_PUNC:
ch_char = CH2EN_PUNC[ch_char]
if ch_char in self.map_dict:
# add _ at the end of each ZH char as seperation
out_line += self.map_dict[ch_char].strip() + chr(ord('_')+50000) ## for sp_concat
else:
if ch_char in control_char:
ch_char = chr(ord(ch_char)+50000)
out_line += ch_char ## sp_concat
return out_line
def tokenize(self, text):
out_line = self.convert_line(text)
return self.spm_tokenizer.encode(out_line, out_type=str)
def convert_tokens_to_ids(self, tokens):
"""Converts a sequence of tokens into ids using the vocab."""
ids = []
for token in tokens:
if token in self.vocab:
ids.append(self.vocab[token])
else:
ids.append(self.vocab['[UNK]'])
if len(ids) > self.max_len:
raise ValueError(
"Token indices sequence length is longer than the specified maximum "
" sequence length for this BERT model ({} > {}). Running this"
" sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
)
return ids
def convert_ids_to_tokens(self, ids):
"""Converts a sequence of ids in wordpiece tokens using the vocab."""
tokens = []
for i in ids:
tokens.append(self.ids_to_tokens[i])
return tokens
## TODO: implement the detokenizer!
class RawEnTokenizer(object):
"for sp_raw_zh tokenization"
def __init__(self, vocab_file, model_file, do_lower_case=True, max_len=None,
never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
if (not os.path.isfile(vocab_file)) or (not os.path.isfile(model_file)):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained ".format(vocab_file))
self.vocab = load_vocab_spm(vocab_file)
self.spm_tokenizer = spm.SentencePieceProcessor(model_file=model_file)
self.ids_to_tokens = collections.OrderedDict(
[(ids, tok) for tok, ids in self.vocab.items()])
# self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
# never_split=never_split)
# self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
self.do_lower_case = do_lower_case
self.max_len = max_len if max_len is not None else int(1e12)
def tokenize(self, text):
if self.do_lower_case:
text = text.lower()
return self.spm_tokenizer.encode(text, out_type=str)
def convert_tokens_to_ids(self, tokens):
"""Converts a sequence of tokens into ids using the vocab."""
ids = []
for token in tokens:
if token in self.vocab:
ids.append(self.vocab[token])
else:
ids.append(self.vocab['[UNK]'])
if len(ids) > self.max_len:
raise ValueError(
"Token indices sequence length is longer than the specified maximum "
" sequence length for this BERT model ({} > {}). Running this"
" sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
)
return ids
def convert_ids_to_tokens(self, ids):
"""Converts a sequence of ids in wordpiece tokens using the vocab."""
tokens = []
for i in ids:
tokens.append(self.ids_to_tokens[i])
return tokens
class WubiZhTokenizer(object):
"for sp_wubi_zh (also have char sep) tokenization"
def __init__(self, vocab_file, model_file, do_lower_case=True, max_len=None,
never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
if (not os.path.isfile(vocab_file)) or (not os.path.isfile(model_file)):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained ".format(vocab_file))
self.vocab = load_vocab_spm(vocab_file)
self.spm_tokenizer = spm.SentencePieceProcessor(model_file=model_file)
self.ids_to_tokens = collections.OrderedDict(
[(ids, tok) for tok, ids in self.vocab.items()])
# self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
# never_split=never_split)
# self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
self.max_len = max_len if max_len is not None else int(1e12)
def convert_line(self, text):
text = text.lower() # always lowercasing
out_line = " " # actually space doesn't matter
for ch_word in text:
ch_word = ch_word.strip()
for ch_char in ch_word:
if ch_char in map_dict:
out_line += map_dict[ch_char].strip() + chr(ord('_')+50000)
else:
if ch_char in control_char:
ch_char = chr(ord(ch_char)+50000)
out_line += ch_char + ''
# out_line += ' '
return out_line
def tokenize(self, text):
out_line = self.convert_line(text)
return self.spm_tokenizer.encode(out_line, out_type=str)
def convert_tokens_to_ids(self, tokens):
"""Converts a sequence of tokens into ids using the vocab."""
ids = []
for token in tokens:
if token in self.vocab:
ids.append(self.vocab[token])
else:
ids.append(self.vocab['[UNK]'])
if len(ids) > self.max_len:
raise ValueError(
"Token indices sequence length is longer than the specified maximum "
" sequence length for this BERT model ({} > {}). Running this"
" sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
)
return ids
def convert_ids_to_tokens(self, ids):
"""Converts a sequence of ids in wordpiece tokens using the vocab."""
tokens = []
for i in ids:
tokens.append(self.ids_to_tokens[i])
return tokens
## TODO: implement the detokenizer!
class CangjieTokenizer(object):
"for cangjie_zh tokenization"
def __init__(self, vocab_file, model_file, do_lower_case=True, max_len=None,
never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
if (not os.path.isfile(vocab_file)) or (not os.path.isfile(model_file)):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained ".format(vocab_file))
self.vocab = load_vocab_spm(vocab_file)
self.spm_tokenizer = spm.SentencePieceProcessor(model_file=model_file)
self.ids_to_tokens = collections.OrderedDict(
[(ids, tok) for tok, ids in self.vocab.items()])
# self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
# never_split=never_split)
# self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
self.max_len = max_len if max_len is not None else int(1e12)
def convert_line(self, text):
text = text.lower() # always lowercasing
out_line = ""
for ch_word in text:
ch_char = ch_word.strip()
if len(ch_char) == 0:
continue
## all convert to EN punctuations,
## to avoid mixture of different punctuations
if ch_char in CH2EN_PUNC:
ch_char = CH2EN_PUNC[ch_char]
if ch_char in map_dict:
# add _ at the end of each ZH char as seperation
out_line += map_dict[ch_char].strip() + chr(ord('_')+50000) ## for sp_concat
else:
if ch_char in control_char:
ch_char = chr(ord(ch_char)+50000)
out_line += ch_char ## sp_concat
return out_line
def tokenize(self, text):
out_line = self.convert_line(text)
return self.spm_tokenizer.encode(out_line, out_type=str)
def convert_tokens_to_ids(self, tokens):
"""Converts a sequence of tokens into ids using the vocab."""
ids = []
for token in tokens:
if token in self.vocab:
ids.append(self.vocab[token])
else:
ids.append(self.vocab['[UNK]'])
if len(ids) > self.max_len:
raise ValueError(
"Token indices sequence length is longer than the specified maximum "
" sequence length for this BERT model ({} > {}). Running this"
" sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
)
return ids
def convert_ids_to_tokens(self, ids):
"""Converts a sequence of ids in wordpiece tokens using the vocab."""
tokens = []
for i in ids:
tokens.append(self.ids_to_tokens[i])
return tokens
## TODO: implement the detokenizer!
class CommonZhTokenizer(object):
"for cangjie_zh, wubi_zh, ... all such tokenization"
def __init__(self, vocab_file, model_file, do_lower_case=True, max_len=None,
never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
if (not os.path.isfile(vocab_file)) or (not os.path.isfile(model_file)):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained ".format(vocab_file))
if 'cangjie' in vocab_file:
self.map_dict = load_dict(ch2cangjie)
elif 'stroke' in vocab_file:
self.map_dict = load_dict(ch2stroke)
elif 'zhengma' in vocab_file:
self.map_dict = load_dict(ch2zhengma)
elif 'wubi' in vocab_file:
self.map_dict = load_dict(ch2wubi)
elif 'pinyin' in vocab_file:
self.map_dict = load_dict(ch2pinyin)
elif 'zhuyin' in vocab_file:
self.map_dict = load_dict(ch2zhuyin)
self.vocab = load_vocab_spm(vocab_file)
self.spm_tokenizer = spm.SentencePieceProcessor(model_file=model_file)
self.ids_to_tokens = collections.OrderedDict(
[(ids, tok) for tok, ids in self.vocab.items()])
# self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
# never_split=never_split)
# self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
self.max_len = max_len if max_len is not None else int(1e12)
def convert_line(self, text):
text = text.lower() # always lowercasing
out_line = ""
for ch_word in text:
ch_char = ch_word.strip()
if len(ch_char) == 0:
continue
## all convert to EN punctuations,
## to avoid mixture of different punctuations
if ch_char in CH2EN_PUNC:
ch_char = CH2EN_PUNC[ch_char]
if ch_char in self.map_dict:
# add _ at the end of each ZH char as seperation
out_line += self.map_dict[ch_char].strip() + chr(ord('_')+50000) ## for sp_concat
else:
if ch_char in control_char:
ch_char = chr(ord(ch_char)+50000)
out_line += ch_char ## sp_concat
return out_line
def tokenize(self, text):
out_line = self.convert_line(text)
return self.spm_tokenizer.encode(out_line, out_type=str)
def convert_tokens_to_ids(self, tokens):
"""Converts a sequence of tokens into ids using the vocab."""
ids = []
for token in tokens:
if token in self.vocab:
ids.append(self.vocab[token])
else:
ids.append(self.vocab['[UNK]'])
if len(ids) > self.max_len:
raise ValueError(
"Token indices sequence length is longer than the specified maximum "
" sequence length for this BERT model ({} > {}). Running this"
" sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
)
return ids
def convert_ids_to_tokens(self, ids):
"""Converts a sequence of ids in wordpiece tokens using the vocab."""
tokens = []
for i in ids:
tokens.append(self.ids_to_tokens[i])
return tokens
## TODO: implement the detokenizer!
class ConcatSepTokenizer(object):
"for sp_concat_sep (wubi) tokenization"
def __init__(self, vocab_file, model_file, do_lower_case=True, max_len=None,
never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
if (not os.path.isfile(vocab_file)) or (not os.path.isfile(model_file)):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained ".format(vocab_file))
self.vocab = load_vocab_spm(vocab_file)
self.spm_tokenizer = spm.SentencePieceProcessor(model_file=model_file)
self.ids_to_tokens = collections.OrderedDict(
[(ids, tok) for tok, ids in self.vocab.items()])
# self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
# never_split=never_split)
# self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
self.max_len = max_len if max_len is not None else int(1e12)
def convert_line(self, text):
text = text.lower() # always lowercasing
in_line = jieba.lcut(text)
out_line = " " # actually space doesn't matter
for ch_word in in_line:
ch_word = ch_word.strip()
for ch_char in ch_word:
if ch_char in map_dict:
out_line += map_dict[ch_char].strip() + chr(ord('_')+50000)
else:
if ch_char in control_char:
ch_char = chr(ord(ch_char)+50000)
out_line += ch_char + ''
out_line += ' '
return out_line
def tokenize(self, text):
out_line = self.convert_line(text)
return self.spm_tokenizer.encode(out_line, out_type=str)
def convert_tokens_to_ids(self, tokens):
"""Converts a sequence of tokens into ids using the vocab."""
ids = []
for token in tokens:
if token in self.vocab:
ids.append(self.vocab[token])
else:
ids.append(self.vocab['[UNK]'])
if len(ids) > self.max_len:
raise ValueError(
"Token indices sequence length is longer than the specified maximum "
" sequence length for this BERT model ({} > {}). Running this"
" sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
)
return ids
def convert_ids_to_tokens(self, ids):
"""Converts a sequence of ids in wordpiece tokens using the vocab."""
tokens = []
for i in ids:
tokens.append(self.ids_to_tokens[i])
return tokens
## TODO: implement the detokenizer!
class BertTokenizer(object):
"""Runs end-to-end tokenization: punctuation splitting + wordpiece"""
def __init__(self, vocab_file, model_file=None, do_lower_case=True, max_len=None,
never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
if not os.path.isfile(vocab_file):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
"model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file))
self.vocab = load_vocab(vocab_file)
self.ids_to_tokens = collections.OrderedDict(
[(ids, tok) for tok, ids in self.vocab.items()])
self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
never_split=never_split)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
self.max_len = max_len if max_len is not None else int(1e12)
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):
"""Converts a sequence of tokens into ids using the vocab."""
ids = []
for token in tokens:
ids.append(self.vocab[token])
if len(ids) > self.max_len:
raise ValueError(
"Token indices sequence length is longer than the specified maximum "
" sequence length for this BERT model ({} > {}). Running this"
" sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
)
return ids
def convert_ids_to_tokens(self, ids):
"""Converts a sequence of ids in wordpiece tokens using the vocab."""
tokens = []
for i in ids:
tokens.append(self.ids_to_tokens[i])
return tokens
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
"""
Instantiate a PreTrainedBertModel from a pre-trained model file.
Download and cache the pre-trained model file if needed.
"""
if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
else:
vocab_file = pretrained_model_name_or_path
if os.path.isdir(vocab_file):
vocab_file = os.path.join(vocab_file, VOCAB_NAME)
# redirect to the cache, if necessary
try:
resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
except EnvironmentError:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find any file "
"associated to this path or url.".format(
pretrained_model_name_or_path,
', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
vocab_file))
return None
if resolved_vocab_file == vocab_file:
logger.info("loading vocabulary file {}".format(vocab_file))
else:
logger.info("loading vocabulary file {} from cache at {}".format(
vocab_file, resolved_vocab_file))
if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
# if we're using a pretrained model, ensure the tokenizer wont index sequences longer
# than the number of positional embeddings
max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path]
kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
# Instantiate tokenizer.
tokenizer = cls(resolved_vocab_file, *inputs, **kwargs)
return tokenizer
class BasicTokenizer(object):
"""Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
def __init__(self,
do_lower_case=True,
never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
"""Constructs a BasicTokenizer.
Args:
do_lower_case: Whether to lower case the input.
"""
self.do_lower_case = do_lower_case
self.never_split = never_split
def tokenize(self, text):
"""Tokenizes a piece of 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 and token not in self.never_split:
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."""
if text in self.never_split:
return [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=100):
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.
"""
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.startswith("C"):
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
class CWSNewTokenizer(object):
def __init__(self, vocab_file, model_file, cws_vocab_file, do_lower_case=True, max_len=None,
never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
if (not os.path.isfile(vocab_file)) or (not os.path.isfile(model_file)):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained ".format(vocab_file))
if 'cangjie' in vocab_file:
self.map_dict = load_dict(ch2cangjie)
elif 'stroke' in vocab_file:
self.map_dict = load_dict(ch2stroke)
elif 'zhengma' in vocab_file:
self.map_dict = load_dict(ch2zhengma)
elif 'wubi' in vocab_file:
self.map_dict = load_dict(ch2wubi)
elif 'pinyin' in vocab_file:
self.map_dict = load_dict(ch2pinyin)
elif 'zhuyin' in vocab_file:
self.map_dict = load_dict(ch2zhuyin)
self.vocab = load_vocab_spm(vocab_file)
self.spm_tokenizer = spm.SentencePieceProcessor(model_file=model_file)
self.cws_vocab = load_vocab(cws_vocab_file)
self.seg = oknlp.algorithm.cws.get_by_name('thulac')
self.ids_to_tokens = collections.OrderedDict(
[(ids, tok) for tok, ids in self.vocab.items()])
# self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
# never_split=never_split)
# self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
self.max_len = max_len if max_len is not None else int(1e12)
def convert_line(self, text):
text = text.lower() # always lowercasing
out_line = ""
for ch_word in text:
ch_char = ch_word.strip()
if len(ch_char) == 0:
continue
## all convert to EN punctuations,
## to avoid mixture of different punctuations
if ch_char in CH2EN_PUNC:
ch_char = CH2EN_PUNC[ch_char]
if ch_char in self.map_dict:
# add _ at the end of each ZH char as seperation
out_line += self.map_dict[ch_char].strip() + chr(ord('_')+50000) ## for sp_concat
else:
if ch_char in control_char:
ch_char = chr(ord(ch_char)+50000)
out_line += ch_char ## sp_concat
return out_line
def tokenize(self, text):
words = self.seg([text])[0]
# print (words)
tokens = []
for word in words:
if word in self.cws_vocab:
tokens.append(word)
else:
for char in word:
if char in self.cws_vocab:
tokens.append(char)
continue
if char in control_char:
char = chr(ord('_')+50000) + chr(ord(char)+50000)
tokens.append(char)
continue
char = self.map_dict[char] if char in self.map_dict else char
tokens.extend([chr(ord('_')+50000) + x for x in self.spm_tokenizer.encode(char, out_type=str)])
return tokens
def convert_tokens_to_ids(self, tokens):
"""Converts a sequence of tokens into ids using the vocab."""
ids = []
for token in tokens:
if token in self.cws_vocab:
ids.append(self.cws_vocab[token])
elif token[0] == chr(ord('_')+50000) and token[1:] in self.vocab:
ids.append(self.vocab[token[1:]]+len(self.cws_vocab))
else:
ids.append(self.vocab['[UNK]'])
if len(ids) > self.max_len:
raise ValueError(
"Token indices sequence length is longer than the specified maximum "
" sequence length for this BERT model ({} > {}). Running this"
" sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
)
return ids
def convert_ids_to_tokens(self, ids):
"""Converts a sequence of ids in wordpiece tokens using the vocab."""
tokens = []
for i in ids:
tokens.append(self.ids_to_tokens[i])
return tokens
## TODO: implement the detokenizer!
ALL_TOKENIZERS = {
"ConcatSep": ConcatSepTokenizer,
"WubiZh": WubiZhTokenizer,
"RawZh": RawZhTokenizer,
'CommonZh': CommonZhTokenizer,
"BertZh": BertZhTokenizer,
"Bert": BertTokenizer,
"BertHF": BertTokenizer,
'CommonZhNoIndex': CommonZhNoIndexTokenizer,
'Shuffled': ShuffledTokenizer,
'PinyinConcatWubi': PinyinConcatWubiTokenizer,
'CWS': CWSNewTokenizer,
'Byte': ByteTokenizer,
'RandomIndex': RandomIndexTokenizer,
}
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