# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace 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.
import os
import unicodedata
from ..tokenizer_utils import (
PretrainedTokenizer,
_is_control,
_is_punctuation,
_is_symbol,
_is_whitespace,
convert_to_unicode,
whitespace_tokenize,
)
__all__ = [
"BasicTokenizer",
"BertTokenizer",
"WordpieceTokenizer",
]
[docs]class BasicTokenizer(object):
"""
Runs basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (bool):
Whether to lowercase the input when tokenizing.
Defaults to `True`.
never_split (Iterable):
Collection of tokens which will never be split during tokenization. Only has an effect when
`do_basic_tokenize=True`
tokenize_chinese_chars (bool):
Whether to tokenize Chinese characters.
strip_accents: (bool):
Whether to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original BERT).
"""
def __init__(self, do_lower_case=True, never_split=None, tokenize_chinese_chars=True, strip_accents=None):
"""Constructs a BasicTokenizer."""
if never_split is None:
never_split = []
self.do_lower_case = do_lower_case
self.never_split = set(never_split)
self.tokenize_chinese_chars = tokenize_chinese_chars
self.strip_accents = strip_accents
[docs] def tokenize(self, text, never_split=None):
"""
Tokenizes a piece of text using basic tokenizer.
Args:
text (str): A piece of text.
never_split (List[str]): List of token not to split.
Returns:
list(str): A list of tokens.
Examples:
.. code-block::
from paddlenlp.transformers import BasicTokenizer
basictokenizer = BasicTokenizer()
tokens = basictokenizer.tokenize('He was a puppeteer')
'''
['he', 'was', 'a', 'puppeteer']
'''
"""
text = convert_to_unicode(text)
never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
text = self._clean_text(text)
if self.tokenize_chinese_chars:
text = self._tokenize_chinese_chars(text)
orig_tokens = whitespace_tokenize(text)
split_tokens = []
for token in orig_tokens:
if token not in never_split:
if self.do_lower_case:
token = token.lower()
if self.strip_accents is not False:
token = self._run_strip_accents(token)
elif self.strip_accents:
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token, never_split))
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, never_split=None):
"""
Splits punctuation on a piece of text.
"""
if never_split is not None and text in never_split:
return [text]
chars = list(text)
i = 0
start_new_word = True
output = []
while i < len(chars):
char = chars[i]
# punctuation and symbol should be treat as single char.
if _is_punctuation(char) or _is_symbol(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 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)
[docs]class WordpieceTokenizer(object):
"""
Runs WordPiece tokenization.
Args:
vocab (Vocab|dict):
Vocab of the word piece tokenizer.
unk_token (str):
A specific token to replace all unknown tokens.
max_input_chars_per_word (int):
If a word's length is more than
max_input_chars_per_word, it will be dealt as unknown word.
Defaults to 100.
"""
def __init__(self, vocab, unk_token, 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
[docs] 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.
Args:
text: A single token or whitespace separated tokens. This should have
already been passed through `BasicTokenizer`.
Returns:
list (str): A list of wordpiece tokens.
Examples:
.. code-block::
from paddlenlp.transformers import BertTokenizer, WordpieceTokenizer
berttokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
vocab = berttokenizer.vocab
unk_token = berttokenizer.unk_token
wordpiecetokenizer = WordpieceTokenizer(vocab,unk_token)
inputs = wordpiecetokenizer.tokenize("unaffable")
print(inputs)
'''
["un", "##aff", "##able"]
'''
"""
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
[docs]class BertTokenizer(PretrainedTokenizer):
"""
Constructs a BERT tokenizer. It uses a basic tokenizer to do punctuation
splitting, lower casing and so on, and follows a WordPiece tokenizer to
tokenize as subwords.
Args:
vocab_file (str):
The vocabulary file path (ends with '.txt') required to instantiate
a `WordpieceTokenizer`.
do_lower_case (bool, optional):
Whether to lowercase the input when tokenizing.
Defaults to `True`.
do_basic_tokenize (bool, optional):
Whether to use a basic tokenizer before a WordPiece tokenizer.
Defaults to `True`.
never_split (Iterable, optional):
Collection of tokens which will never be split during tokenization. Only has an effect when
`do_basic_tokenize=True`. Defaults to `None`.
unk_token (str, optional):
A special token representing the *unknown (out-of-vocabulary)* token.
An unknown token is set to be `unk_token` inorder to be converted to an ID.
Defaults to "[UNK]".
sep_token (str, optional):
A special token separating two different sentences in the same input.
Defaults to "[SEP]".
pad_token (str, optional):
A special token used to make arrays of tokens the same size for batching purposes.
Defaults to "[PAD]".
cls_token (str, optional):
A special token used for sequence classification. It is the last token
of the sequence when built with special tokens. Defaults to "[CLS]".
mask_token (str, optional):
A special token representing a masked token. This is the token used
in the masked language modeling task which the model tries to predict the original unmasked ones.
Defaults to "[MASK]".
tokenize_chinese_chars (bool, optional):
Whether to tokenize Chinese characters.
Defaults to `True`.
strip_accents: (bool, optional):
Whether to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original BERT).
Defaults to `None`.
Examples:
.. code-block::
from paddlenlp.transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
inputs = tokenizer('He was a puppeteer')
print(inputs)
'''
{'input_ids': [101, 2002, 2001, 1037, 13997, 11510, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0]}
'''
"""
resource_files_names = {"vocab_file": "vocab.txt"} # for save_pretrained
pretrained_resource_files_map = {
"vocab_file": {
"bert-base-uncased": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-uncased-vocab.txt",
"bert-large-uncased": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-large-uncased-vocab.txt",
"bert-base-cased": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-cased-vocab.txt",
"bert-large-cased": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-large-cased-vocab.txt",
"bert-base-multilingual-uncased": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-multilingual-uncased-vocab.txt",
"bert-base-multilingual-cased": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-multilingual-cased-vocab.txt",
"bert-base-chinese": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-chinese-vocab.txt",
"bert-wwm-chinese": "http://bj.bcebos.com/paddlenlp/models/transformers/bert/bert-wwm-chinese-vocab.txt",
"bert-wwm-ext-chinese": "http://bj.bcebos.com/paddlenlp/models/transformers/bert/bert-wwm-ext-chinese-vocab.txt",
"macbert-large-chinese": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-chinese-vocab.txt",
"macbert-base-chinese": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-chinese-vocab.txt",
"simbert-base-chinese": "https://bj.bcebos.com/paddlenlp/models/transformers/simbert/vocab.txt",
"uer/chinese-roberta-base": "https://bj.bcebos.com/paddlenlp/models/transformers/uer/chinese_roberta_vocab.txt",
"uer/chinese-roberta-medium": "https://bj.bcebos.com/paddlenlp/models/transformers/uer/chinese_roberta_vocab.txt",
"uer/chinese-roberta-6l-768h": "https://bj.bcebos.com/paddlenlp/models/transformers/uer/chinese_roberta_vocab.txt",
"uer/chinese-roberta-small": "https://bj.bcebos.com/paddlenlp/models/transformers/uer/chinese_roberta_vocab.txt",
"uer/chinese-roberta-mini": "https://bj.bcebos.com/paddlenlp/models/transformers/uer/chinese_roberta_vocab.txt",
"uer/chinese-roberta-tiny": "https://bj.bcebos.com/paddlenlp/models/transformers/uer/chinese_roberta_vocab.txt",
}
}
pretrained_init_configuration = {
"bert-base-uncased": {"do_lower_case": True},
"bert-large-uncased": {"do_lower_case": True},
"bert-base-cased": {"do_lower_case": False},
"bert-large-cased": {"do_lower_case": False},
"bert-base-multilingual-uncased": {"do_lower_case": True},
"bert-base-multilingual-cased": {"do_lower_case": False},
"bert-base-chinese": {"do_lower_case": False},
"bert-wwm-chinese": {"do_lower_case": False},
"bert-wwm-ext-chinese": {"do_lower_case": False},
"macbert-large-chinese": {"do_lower_case": False},
"macbert-base-chinese": {"do_lower_case": False},
"simbert-base-chinese": {"do_lower_case": True},
"uer/chinese-roberta-base": {"do_lower_case": True},
"uer/chinese-roberta-medium": {"do_lower_case": True},
"uer/chinese-roberta-6l-768h": {"do_lower_case": True},
"uer/chinese-roberta-small": {"do_lower_case": True},
"uer/chinese-roberta-mini": {"do_lower_case": True},
"uer/chinese-roberta-tiny": {"do_lower_case": True},
}
max_model_input_sizes = {
"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,
"bert-wwm-chinese": 512,
"bert-wwm-ext-chinese": 512,
"macbert-large-chinese": 512,
"macbert-base-chinese": 512,
"simbert-base-chinese": 512,
"uer/chinese-roberta-base": 512,
"uer/chinese-roberta-medium": 512,
"uer/chinese-roberta-6l-768h": 512,
"uer/chinese-roberta-small": 512,
"uer/chinese-roberta-mini": 512,
"uer/chinese-roberta-tiny": 512,
}
padding_side = "right"
def __init__(
self,
vocab_file,
do_lower_case=True,
do_basic_tokenize=True,
never_split=None,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
tokenize_chinese_chars=True,
strip_accents=None,
**kwargs
):
if not os.path.isfile(vocab_file):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the "
"vocabulary from a pretrained model please use "
"`tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file)
)
self.do_lower_case = do_lower_case
self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token)
self.do_basic_tokenize = do_basic_tokenize
if do_basic_tokenize:
self.basic_tokenizer = BasicTokenizer(
do_lower_case=do_lower_case,
never_split=never_split,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=unk_token)
@property
def vocab_size(self):
"""
Return the size of vocabulary.
Returns:
int: The size of vocabulary.
"""
return len(self.vocab)
[docs] def get_vocab(self):
return dict(self.vocab.token_to_idx, **self.added_tokens_encoder)
def _tokenize(self, text):
"""
End-to-end tokenization for BERT models.
Args:
text (str): The text to be tokenized.
Returns:
list: A list of string representing converted tokens.
"""
split_tokens = []
if self.do_basic_tokenize:
for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
# If the token is part of the never_split set
if token in self.basic_tokenizer.never_split:
split_tokens.append(token)
else:
split_tokens += self.wordpiece_tokenizer.tokenize(token)
else:
split_tokens = self.wordpiece_tokenizer.tokenize(text)
return split_tokens
[docs] def convert_tokens_to_string(self, tokens):
"""
Converts a sequence of tokens (list of string) to a single string. Since
the usage of WordPiece introducing `##` to concat subwords, also removes
`##` when converting.
Args:
tokens (list): A list of string representing tokens to be converted.
Returns:
str: Converted string from tokens.
Examples:
.. code-block::
from paddlenlp.transformers import BertTokenizer
berttokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
tokens = berttokenizer.tokenize('He was a puppeteer')
'''
['he', 'was', 'a', 'puppet', '##eer']
'''
strings = tokenizer.convert_tokens_to_string(tokens)
'''
he was a puppeteer
'''
"""
out_string = " ".join(tokens).replace(" ##", "").strip()
return out_string
[docs] def num_special_tokens_to_add(self, pair=False):
"""
Returns the number of added tokens when encoding a sequence with special tokens.
Args:
pair(bool):
Whether the input is a sequence pair or a single sequence.
Defaults to `False` and the input is a single sequence.
Returns:
int: Number of tokens added to sequences.
"""
token_ids_0 = []
token_ids_1 = []
return len(self.build_inputs_with_special_tokens(token_ids_0, token_ids_1 if pair else None))
[docs] def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None):
"""
Build offset map from a pair of offset map by concatenating and adding offsets of special tokens.
A BERT offset_mapping has the following format:
- single sequence: ``(0,0) X (0,0)``
- pair of sequences: ``(0,0) A (0,0) B (0,0)``
Args:
offset_mapping_ids_0 (List[tuple]):
List of wordpiece offsets to which the special tokens will be added.
offset_mapping_ids_1 (List[tuple], optional):
Optional second list of wordpiece offsets for offset mapping pairs. Defaults to None.
Returns:
List[tuple]: A list of wordpiece offsets with the appropriate offsets of special tokens.
"""
if offset_mapping_1 is None:
return [(0, 0)] + offset_mapping_0 + [(0, 0)]
return [(0, 0)] + offset_mapping_0 + [(0, 0)] + offset_mapping_1 + [(0, 0)]
[docs] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task.
A BERT sequence pair mask has the following format:
::
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (List[int]):
A list of `inputs_ids` for the first sequence.
token_ids_1 (List[int], optional):
Optional second list of IDs for sequence pairs. Defaults to None.
Returns:
List[int]: List of token_type_id according to the given sequence(s).
"""
_sep = [self.sep_token_id]
_cls = [self.cls_token_id]
if token_ids_1 is None:
return len(_cls + token_ids_0 + _sep) * [0]
return len(_cls + token_ids_0 + _sep) * [0] + len(token_ids_1 + _sep) * [1]
[docs] def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``encode`` methods.
Args:
token_ids_0 (List[int]):
A list of `inputs_ids` for the first sequence.
token_ids_1 (List[int], optional):
Optional second list of IDs for sequence pairs. Defaults to None.
already_has_special_tokens (bool, optional): Whether or not the token list is already
formatted with special tokens for the model. Defaults to None.
Returns:
List[int]: The list of integers either be 0 or 1: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model."
)
return list(map(lambda x: 1 if x in self.all_special_ids else 0, token_ids_0))
if token_ids_1 is not None:
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1]
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.vocab._idx_to_token.get(index, self.unk_token)