wehub-resource-sync
631 lines
24 KiB
Python
631 lines
24 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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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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import os
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import unicodedata
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from ..tokenizer_utils import (
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PretrainedTokenizer,
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_is_control,
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_is_punctuation,
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_is_symbol,
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_is_whitespace,
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convert_to_unicode,
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whitespace_tokenize,
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)
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__all__ = [
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"BasicTokenizer",
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"BertTokenizer",
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"WordpieceTokenizer",
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]
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class BasicTokenizer(object):
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"""
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Runs basic tokenization (punctuation splitting, lower casing, etc.).
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Args:
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do_lower_case (bool):
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Whether to lowercase the input when tokenizing.
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Defaults to `True`.
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never_split (Iterable):
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Collection of tokens which will never be split during tokenization. Only has an effect when
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`do_basic_tokenize=True`
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tokenize_chinese_chars (bool):
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Whether to tokenize Chinese characters.
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strip_accents: (bool):
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Whether to strip all accents. If this option is not specified, then it will be determined by the
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value for `lowercase` (as in the original BERT).
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"""
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def __init__(self, do_lower_case=True, never_split=None, tokenize_chinese_chars=True, strip_accents=None):
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"""Constructs a BasicTokenizer."""
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if never_split is None:
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never_split = []
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self.do_lower_case = do_lower_case
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self.never_split = set(never_split)
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self.tokenize_chinese_chars = tokenize_chinese_chars
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self.strip_accents = strip_accents
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def tokenize(self, text, never_split=None):
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"""
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Tokenizes a piece of text using basic tokenizer.
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Args:
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text (str): A piece of text.
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never_split (List[str]): List of token not to split.
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Returns:
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list(str): A list of tokens.
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Examples:
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.. code-block::
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from paddlenlp.transformers import BasicTokenizer
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basictokenizer = BasicTokenizer()
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tokens = basictokenizer.tokenize('He was a puppeteer')
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'''
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['he', 'was', 'a', 'puppeteer']
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'''
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"""
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text = convert_to_unicode(text)
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never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
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text = self._clean_text(text)
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if self.tokenize_chinese_chars:
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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 token not in never_split:
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if self.do_lower_case:
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token = token.lower()
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if self.strip_accents is not False:
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token = self._run_strip_accents(token)
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elif self.strip_accents:
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token = self._run_strip_accents(token)
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split_tokens.extend(self._run_split_on_punc(token, never_split))
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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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"""
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Strips accents from a piece of text.
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"""
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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, never_split=None):
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"""
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Splits punctuation on a piece of text.
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"""
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if never_split is not None and text in never_split:
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return [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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# punctuation and symbol should be treat as single char.
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if _is_punctuation(char) or _is_symbol(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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"""
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Adds whitespace around any CJK character.
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"""
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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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"""
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Checks whether CP is the codepoint of a CJK character.
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"""
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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 the other languages.
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if (
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(cp >= 0x4E00 and cp <= 0x9FFF)
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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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): #
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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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"""
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Performs invalid character removal and whitespace cleanup on text.
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"""
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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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"""
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Runs WordPiece tokenization.
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Args:
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vocab (Vocab|dict):
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Vocab of the word piece tokenizer.
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unk_token (str):
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A specific token to replace all unknown tokens.
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max_input_chars_per_word (int):
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If a word's length is more than
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max_input_chars_per_word, it will be dealt as unknown word.
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Defaults to 100.
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"""
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def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
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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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"""
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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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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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list (str): A list of wordpiece tokens.
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Examples:
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.. code-block::
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from paddlenlp.transformers import BertTokenizer, WordpieceTokenizer
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berttokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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vocab = berttokenizer.vocab
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unk_token = berttokenizer.unk_token
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wordpiecetokenizer = WordpieceTokenizer(vocab,unk_token)
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inputs = wordpiecetokenizer.tokenize("unaffable")
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print(inputs)
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'''
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["un", "##aff", "##able"]
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'''
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"""
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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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class BertTokenizer(PretrainedTokenizer):
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"""
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Constructs a BERT tokenizer. It uses a basic tokenizer to do punctuation
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splitting, lower casing and so on, and follows a WordPiece tokenizer to
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tokenize as subwords.
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Args:
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vocab_file (str):
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The vocabulary file path (ends with '.txt') required to instantiate
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a `WordpieceTokenizer`.
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do_lower_case (bool, optional):
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Whether to lowercase the input when tokenizing.
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Defaults to `True`.
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do_basic_tokenize (bool, optional):
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Whether to use a basic tokenizer before a WordPiece tokenizer.
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Defaults to `True`.
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never_split (Iterable, optional):
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Collection of tokens which will never be split during tokenization. Only has an effect when
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`do_basic_tokenize=True`. Defaults to `None`.
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unk_token (str, optional):
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A special token representing the *unknown (out-of-vocabulary)* token.
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An unknown token is set to be `unk_token` inorder to be converted to an ID.
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Defaults to "[UNK]".
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sep_token (str, optional):
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A special token separating two different sentences in the same input.
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Defaults to "[SEP]".
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pad_token (str, optional):
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A special token used to make arrays of tokens the same size for batching purposes.
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Defaults to "[PAD]".
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cls_token (str, optional):
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A special token used for sequence classification. It is the last token
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of the sequence when built with special tokens. Defaults to "[CLS]".
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mask_token (str, optional):
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A special token representing a masked token. This is the token used
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in the masked language modeling task which the model tries to predict the original unmasked ones.
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Defaults to "[MASK]".
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tokenize_chinese_chars (bool, optional):
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Whether to tokenize Chinese characters.
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Defaults to `True`.
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strip_accents: (bool, optional):
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Whether to strip all accents. If this option is not specified, then it will be determined by the
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value for `lowercase` (as in the original BERT).
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Defaults to `None`.
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Examples:
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.. code-block::
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from paddlenlp.transformers import BertTokenizer
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tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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inputs = tokenizer('He was a puppeteer')
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print(inputs)
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'''
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{'input_ids': [101, 2002, 2001, 1037, 13997, 11510, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0]}
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'''
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"""
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resource_files_names = {"vocab_file": "vocab.txt"} # for save_pretrained
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pretrained_resource_files_map = {
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"vocab_file": {
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"bert-base-uncased": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-uncased-vocab.txt",
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"bert-large-uncased": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-large-uncased-vocab.txt",
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"bert-base-cased": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-cased-vocab.txt",
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"bert-large-cased": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-large-cased-vocab.txt",
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"bert-base-multilingual-uncased": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-multilingual-uncased-vocab.txt",
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"bert-base-multilingual-cased": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-multilingual-cased-vocab.txt",
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"bert-base-chinese": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-chinese-vocab.txt",
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"bert-wwm-chinese": "http://bj.bcebos.com/paddlenlp/models/transformers/bert/bert-wwm-chinese-vocab.txt",
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"bert-wwm-ext-chinese": "http://bj.bcebos.com/paddlenlp/models/transformers/bert/bert-wwm-ext-chinese-vocab.txt",
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"macbert-large-chinese": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-chinese-vocab.txt",
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"macbert-base-chinese": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-chinese-vocab.txt",
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"simbert-base-chinese": "https://bj.bcebos.com/paddlenlp/models/transformers/simbert/vocab.txt",
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"uer/chinese-roberta-base": "https://bj.bcebos.com/paddlenlp/models/transformers/uer/chinese_roberta_vocab.txt",
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"uer/chinese-roberta-medium": "https://bj.bcebos.com/paddlenlp/models/transformers/uer/chinese_roberta_vocab.txt",
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"uer/chinese-roberta-6l-768h": "https://bj.bcebos.com/paddlenlp/models/transformers/uer/chinese_roberta_vocab.txt",
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"uer/chinese-roberta-small": "https://bj.bcebos.com/paddlenlp/models/transformers/uer/chinese_roberta_vocab.txt",
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"uer/chinese-roberta-mini": "https://bj.bcebos.com/paddlenlp/models/transformers/uer/chinese_roberta_vocab.txt",
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"uer/chinese-roberta-tiny": "https://bj.bcebos.com/paddlenlp/models/transformers/uer/chinese_roberta_vocab.txt",
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}
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}
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pretrained_init_configuration = {
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"bert-base-uncased": {"do_lower_case": True},
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"bert-large-uncased": {"do_lower_case": True},
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"bert-base-cased": {"do_lower_case": False},
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"bert-large-cased": {"do_lower_case": False},
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"bert-base-multilingual-uncased": {"do_lower_case": True},
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"bert-base-multilingual-cased": {"do_lower_case": False},
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"bert-base-chinese": {"do_lower_case": False},
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"bert-wwm-chinese": {"do_lower_case": False},
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"bert-wwm-ext-chinese": {"do_lower_case": False},
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"macbert-large-chinese": {"do_lower_case": False},
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"macbert-base-chinese": {"do_lower_case": False},
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"simbert-base-chinese": {"do_lower_case": True},
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"uer/chinese-roberta-base": {"do_lower_case": True},
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"uer/chinese-roberta-medium": {"do_lower_case": True},
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"uer/chinese-roberta-6l-768h": {"do_lower_case": True},
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"uer/chinese-roberta-small": {"do_lower_case": True},
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"uer/chinese-roberta-mini": {"do_lower_case": True},
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"uer/chinese-roberta-tiny": {"do_lower_case": True},
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}
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max_model_input_sizes = {
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"bert-base-uncased": 512,
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"bert-large-uncased": 512,
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"bert-base-cased": 512,
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"bert-large-cased": 512,
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"bert-base-multilingual-uncased": 512,
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"bert-base-multilingual-cased": 512,
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"bert-base-chinese": 512,
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"bert-wwm-chinese": 512,
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"bert-wwm-ext-chinese": 512,
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"macbert-large-chinese": 512,
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"macbert-base-chinese": 512,
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"simbert-base-chinese": 512,
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"uer/chinese-roberta-base": 512,
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"uer/chinese-roberta-medium": 512,
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"uer/chinese-roberta-6l-768h": 512,
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"uer/chinese-roberta-small": 512,
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"uer/chinese-roberta-mini": 512,
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"uer/chinese-roberta-tiny": 512,
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}
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padding_side = "right"
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def __init__(
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self,
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vocab_file,
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do_lower_case=True,
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do_basic_tokenize=True,
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never_split=None,
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unk_token="[UNK]",
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sep_token="[SEP]",
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pad_token="[PAD]",
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cls_token="[CLS]",
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mask_token="[MASK]",
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tokenize_chinese_chars=True,
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strip_accents=None,
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**kwargs
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):
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if not os.path.isfile(vocab_file):
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raise ValueError(
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"Can't find a vocabulary file at path '{}'. To load the "
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"vocabulary from a pretrained model please use "
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"`tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file)
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)
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self.do_lower_case = do_lower_case
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self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token)
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self.do_basic_tokenize = do_basic_tokenize
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if do_basic_tokenize:
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self.basic_tokenizer = BasicTokenizer(
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do_lower_case=do_lower_case,
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never_split=never_split,
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tokenize_chinese_chars=tokenize_chinese_chars,
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strip_accents=strip_accents,
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)
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self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=unk_token)
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@property
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def vocab_size(self):
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"""
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Return the size of vocabulary.
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Returns:
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int: The size of vocabulary.
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"""
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return len(self.vocab)
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def get_vocab(self):
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return dict(self.vocab.token_to_idx, **self.added_tokens_encoder)
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def _tokenize(self, text):
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"""
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End-to-end tokenization for BERT models.
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Args:
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text (str): The text to be tokenized.
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Returns:
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list: A list of string representing converted tokens.
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"""
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split_tokens = []
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|
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)
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|
else:
|
|
split_tokens = self.wordpiece_tokenizer.tokenize(text)
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|
return split_tokens
|
|
|
|
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
|
|
|
|
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))
|
|
|
|
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
|
|
"""
|
|
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
|
|
adding special tokens.
|
|
|
|
A BERT sequence has the following format:
|
|
|
|
- single sequence: ``[CLS] X [SEP]``
|
|
- pair of sequences: ``[CLS] A [SEP] B [SEP]``
|
|
|
|
Args:
|
|
token_ids_0 (List[int]):
|
|
List of IDs to which the special tokens will be added.
|
|
token_ids_1 (List[int], optional):
|
|
Optional second list of IDs for sequence pairs. Defaults to None.
|
|
|
|
Returns:
|
|
List[int]: List of input_id with the appropriate special tokens.
|
|
"""
|
|
if token_ids_1 is None:
|
|
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
|
|
_cls = [self.cls_token_id]
|
|
_sep = [self.sep_token_id]
|
|
return _cls + token_ids_0 + _sep + token_ids_1 + _sep
|
|
|
|
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)]
|
|
|
|
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]
|
|
|
|
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)
|