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+# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
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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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+
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+import os
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+import unicodedata
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ Returns:
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+ list(str): A list of tokens.
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+
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+ Examples:
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+ .. code-block::
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+
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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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+
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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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+
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+ output_tokens = whitespace_tokenize(" ".join(split_tokens))
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+ return output_tokens
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+
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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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+
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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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+
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+ return ["".join(x) for x in output]
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+
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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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+
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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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+
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+ return False
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+
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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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+
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+
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+class WordpieceTokenizer(object):
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+ """
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+ Runs WordPiece tokenization.
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+
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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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+
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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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+
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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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+
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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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+
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+ Returns:
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+ list (str): A list of wordpiece tokens.
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+
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+ Examples:
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+ .. code-block::
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+
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+ from paddlenlp.transformers import BertTokenizer, WordpieceTokenizer
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+
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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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+
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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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+
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+ Examples:
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+ .. code-block::
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+
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+ from paddlenlp.transformers import BertTokenizer
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+ tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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+
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+ inputs = tokenizer('He was a puppeteer')
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+ print(inputs)
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+
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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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+
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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},
|
|
|
+ "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)
|
|
|
+
|
|
|
+ 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
|
|
|
+
|
|
|
+ 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)
|
Zhang Zelun