|
- """
- bert_tokenizer.py is modified from huggingface/pytorch-pretrained-BERT, which is licensed under the Apache License 2.0.
- """
- import collections
- import os
- import unicodedata
- from io import open
-
-
- PRETRAINED_VOCAB_ARCHIVE_MAP = {
- 'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
- 'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
- 'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt",
- 'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt",
- 'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt",
- 'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
- 'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
- }
- PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
- 'bert-base-uncased': 512,
- 'bert-large-uncased': 512,
- 'bert-base-cased': 512,
- 'bert-large-cased': 512,
- 'bert-base-multilingual-uncased': 512,
- 'bert-base-multilingual-cased': 512,
- 'bert-base-chinese': 512,
- }
- VOCAB_NAME = 'vocab.txt'
-
-
- def load_vocab(vocab_file):
- """Loads a vocabulary file into a dictionary."""
- vocab = collections.OrderedDict()
- index = 0
- with open(vocab_file, "r", encoding="utf-8") as reader:
- while True:
- token = reader.readline()
- if not token:
- break
- token = token.strip()
- vocab[token] = index
- index += 1
- return vocab
-
-
- def whitespace_tokenize(text):
- """Runs basic whitespace cleaning and splitting on a piece of text."""
- text = text.strip()
- if not text:
- return []
- tokens = text.split()
- return tokens
-
-
- class BertTokenizer(object):
- """Runs end-to-end tokenization: punctuation splitting + wordpiece"""
-
- def __init__(self, vocab_file, do_lower_case=True, max_len=None, do_basic_tokenize=True,
- never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
- """Constructs a BertTokenizer.
- Args:
- vocab_file: Path to a one-wordpiece-per-line vocabulary file
- do_lower_case: Whether to lower case the input
- Only has an effect when do_wordpiece_only=False
- do_basic_tokenize: Whether to do basic tokenization before wordpiece.
- max_len: An artificial maximum length to truncate tokenized sequences to;
- Effective maximum length is always the minimum of this
- value (if specified) and the underlying BERT model's
- sequence length.
- never_split: List of tokens which will never be split during tokenization.
- Only has an effect when do_wordpiece_only=False
- """
- if not os.path.isfile(vocab_file):
- raise ValueError(
- "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
- "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file))
- self.vocab = load_vocab(vocab_file)
- self.ids_to_tokens = collections.OrderedDict(
- [(ids, tok) for tok, ids in self.vocab.items()])
- self.do_basic_tokenize = do_basic_tokenize
- if do_basic_tokenize:
- self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
- never_split=never_split)
- self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
- self.max_len = max_len if max_len is not None else int(1e12)
-
- def tokenize(self, text):
- split_tokens = []
- if self.do_basic_tokenize:
- for token in self.basic_tokenizer.tokenize(text):
- for sub_token in self.wordpiece_tokenizer.tokenize(token):
- split_tokens.append(sub_token)
- else:
- split_tokens = self.wordpiece_tokenizer.tokenize(text)
- return split_tokens
-
- def convert_tokens_to_ids(self, tokens):
- """Converts a sequence of tokens into ids using the vocab."""
- ids = []
- for token in tokens:
- ids.append(self.vocab[token])
- if len(ids) > self.max_len:
- print(
- "WARNING!\n\""
- "Token indices sequence length is longer than the specified maximum "
- "sequence length for this BERT model ({} > {}). Running this"
- " sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
- )
- return ids
-
- def convert_ids_to_tokens(self, ids):
- """Converts a sequence of ids in wordpiece tokens using the vocab."""
- tokens = []
- for i in ids:
- tokens.append(self.ids_to_tokens[i])
- return tokens
-
- def save_vocabulary(self, vocab_path):
- """Save the tokenizer vocabulary to a directory or file."""
- index = 0
- if os.path.isdir(vocab_path):
- vocab_file = os.path.join(vocab_path, VOCAB_NAME)
- with open(vocab_file, "w", encoding="utf-8") as writer:
- for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
- if index != token_index:
- print("Saving vocabulary to {}: vocabulary indices are not consecutive."
- " Please check that the vocabulary is not corrupted!".format(vocab_file))
- index = token_index
- writer.write(token + u'\n')
- index += 1
- return vocab_file
-
- @classmethod
- def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
- """
- Instantiate a PreTrainedBertModel from a pre-trained model file.
- Download and cache the pre-trained model file if needed.
- """
- if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
- vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
- if '-cased' in pretrained_model_name_or_path and kwargs.get('do_lower_case', True):
- print("The pre-trained model you are loading is a cased model but you have not set "
- "`do_lower_case` to False. We are setting `do_lower_case=False` for you but "
- "you may want to check this behavior.")
- kwargs['do_lower_case'] = False
- elif '-cased' not in pretrained_model_name_or_path and not kwargs.get('do_lower_case', True):
- print("The pre-trained model you are loading is an uncased model but you have set "
- "`do_lower_case` to False. We are setting `do_lower_case=True` for you "
- "but you may want to check this behavior.")
- kwargs['do_lower_case'] = True
- else:
- vocab_file = pretrained_model_name_or_path
- if os.path.isdir(vocab_file):
- vocab_file = os.path.join(vocab_file, VOCAB_NAME)
- # redirect to the cache, if necessary
- resolved_vocab_file = vocab_file
- print("loading vocabulary file {}".format(vocab_file))
- if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
- # if we're using a pretrained model, ensure the tokenizer wont index sequences longer
- # than the number of positional embeddings
- max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path]
- kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
- # Instantiate tokenizer.
- tokenizer = cls(resolved_vocab_file, *inputs, **kwargs)
- return tokenizer
-
-
- class BasicTokenizer(object):
- """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
-
- def __init__(self,
- do_lower_case=True,
- never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
- """Constructs a BasicTokenizer.
- Args:
- do_lower_case: Whether to lower case the input.
- """
- self.do_lower_case = do_lower_case
- self.never_split = never_split
-
- def tokenize(self, text):
- """Tokenizes a piece of text."""
- text = self._clean_text(text)
- # This was added on November 1st, 2018 for the multilingual and Chinese
- # models. This is also applied to the English models now, but it doesn't
- # matter since the English models were not trained on any Chinese data
- # and generally don't have any Chinese data in them (there are Chinese
- # characters in the vocabulary because Wikipedia does have some Chinese
- # words in the English Wikipedia.).
- text = self._tokenize_chinese_chars(text)
- orig_tokens = whitespace_tokenize(text)
- split_tokens = []
- for token in orig_tokens:
- if self.do_lower_case and token not in self.never_split:
- token = token.lower()
- token = self._run_strip_accents(token)
- split_tokens.extend(self._run_split_on_punc(token))
-
- output_tokens = whitespace_tokenize(" ".join(split_tokens))
- return output_tokens
-
- def _run_strip_accents(self, text):
- """Strips accents from a piece of text."""
- text = unicodedata.normalize("NFD", text)
- output = []
- for char in text:
- cat = unicodedata.category(char)
- if cat == "Mn":
- continue
- output.append(char)
- return "".join(output)
-
- def _run_split_on_punc(self, text):
- """Splits punctuation on a piece of text."""
- if text in self.never_split:
- return [text]
- chars = list(text)
- i = 0
- start_new_word = True
- output = []
- while i < len(chars):
- char = chars[i]
- if _is_punctuation(char):
- output.append([char])
- start_new_word = True
- else:
- if start_new_word:
- output.append([])
- start_new_word = False
- output[-1].append(char)
- i += 1
-
- return ["".join(x) for x in output]
-
- def _tokenize_chinese_chars(self, text):
- """Adds whitespace around any CJK character."""
- output = []
- for char in text:
- cp = ord(char)
- if self._is_chinese_char(cp):
- output.append(" ")
- output.append(char)
- output.append(" ")
- else:
- output.append(char)
- return "".join(output)
-
- def _is_chinese_char(self, cp):
- """Checks whether CP is the codepoint of a CJK character."""
- # This defines a "chinese character" as anything in the CJK Unicode block:
- # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
- #
- # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
- # despite its name. The modern Korean Hangul alphabet is a different block,
- # as is Japanese Hiragana and Katakana. Those alphabets are used to write
- # space-separated words, so they are not treated specially and handled
- # like the all of the other languages.
- if ((cp >= 0x4E00 and cp <= 0x9FFF) or #
- (cp >= 0x3400 and cp <= 0x4DBF) or #
- (cp >= 0x20000 and cp <= 0x2A6DF) or #
- (cp >= 0x2A700 and cp <= 0x2B73F) or #
- (cp >= 0x2B740 and cp <= 0x2B81F) or #
- (cp >= 0x2B820 and cp <= 0x2CEAF) or
- (cp >= 0xF900 and cp <= 0xFAFF) or #
- (cp >= 0x2F800 and cp <= 0x2FA1F)): #
- return True
-
- return False
-
- def _clean_text(self, text):
- """Performs invalid character removal and whitespace cleanup on text."""
- output = []
- for char in text:
- cp = ord(char)
- if cp == 0 or cp == 0xfffd or _is_control(char):
- continue
- if _is_whitespace(char):
- output.append(" ")
- else:
- output.append(char)
- return "".join(output)
-
-
- class WordpieceTokenizer(object):
- """Runs WordPiece tokenization."""
-
- def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100):
- self.vocab = vocab
- self.unk_token = unk_token
- self.max_input_chars_per_word = max_input_chars_per_word
-
- def tokenize(self, text):
- """Tokenizes a piece of text into its word pieces.
- This uses a greedy longest-match-first algorithm to perform tokenization
- using the given vocabulary.
- For example:
- input = "unaffable"
- output = ["un", "##aff", "##able"]
- Args:
- text: A single token or whitespace separated tokens. This should have
- already been passed through `BasicTokenizer`.
- Returns:
- A list of wordpiece tokens.
- """
-
- output_tokens = []
- for token in whitespace_tokenize(text):
- chars = list(token)
- if len(chars) > self.max_input_chars_per_word:
- output_tokens.append(self.unk_token)
- continue
-
- is_bad = False
- start = 0
- sub_tokens = []
- while start < len(chars):
- end = len(chars)
- cur_substr = None
- while start < end:
- substr = "".join(chars[start:end])
- if start > 0:
- substr = "##" + substr
- if substr in self.vocab:
- cur_substr = substr
- break
- end -= 1
- if cur_substr is None:
- is_bad = True
- break
- sub_tokens.append(cur_substr)
- start = end
-
- if is_bad:
- output_tokens.append(self.unk_token)
- else:
- output_tokens.extend(sub_tokens)
- return output_tokens
-
-
- def _is_whitespace(char):
- """Checks whether `chars` is a whitespace character."""
- # \t, \n, and \r are technically contorl characters but we treat them
- # as whitespace since they are generally considered as such.
- if char == " " or char == "\t" or char == "\n" or char == "\r":
- return True
- cat = unicodedata.category(char)
- if cat == "Zs":
- return True
- return False
-
-
- def _is_control(char):
- """Checks whether `chars` is a control character."""
- # These are technically control characters but we count them as whitespace
- # characters.
- if char == "\t" or char == "\n" or char == "\r":
- return False
- cat = unicodedata.category(char)
- if cat.startswith("C"):
- return True
- return False
-
-
- def _is_punctuation(char):
- """Checks whether `chars` is a punctuation character."""
- cp = ord(char)
- # We treat all non-letter/number ASCII as punctuation.
- # Characters such as "^", "$", and "`" are not in the Unicode
- # Punctuation class but we treat them as punctuation anyways, for
- # consistency.
- if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
- (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
- return True
- cat = unicodedata.category(char)
- if cat.startswith("P"):
- return True
- return False
|