fix NER output; fix BERT Tokenizer
Kai Labusch
5 years ago
parent
b24687c484
commit
9bf2e6f51b
@ -0,0 +1,419 @@
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# coding=utf-8
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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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"""Tokenization classes."""
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from __future__ import absolute_import, division, print_function, unicode_literals
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import collections
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import logging
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import os
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import unicodedata
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from io import open
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from pytorch_pretrained_bert.file_utils import cached_path
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logger = logging.getLogger(__name__)
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PRETRAINED_VOCAB_ARCHIVE_MAP = {
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'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
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'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
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'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt",
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'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt",
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'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt",
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'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
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'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
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}
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PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
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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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}
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VOCAB_NAME = 'vocab.txt'
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def load_vocab(vocab_file):
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"""Loads a vocabulary file into a dictionary."""
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vocab = collections.OrderedDict()
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index = 0
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with open(vocab_file, "r", encoding="utf-8") as reader:
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while True:
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token = reader.readline()
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if not token:
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break
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token = token.strip()
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vocab[token] = index
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index += 1
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return vocab
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def whitespace_tokenize(text):
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"""Runs basic whitespace cleaning and splitting on a piece of text."""
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text = text.strip()
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if not text:
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return []
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tokens = text.split()
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return tokens
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class BertTokenizer(object):
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"""Runs end-to-end tokenization: punctuation splitting + wordpiece"""
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def __init__(self, vocab_file, do_lower_case=True, max_len=None, do_basic_tokenize=True,
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never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
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"""Constructs a BertTokenizer.
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Args:
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vocab_file: Path to a one-wordpiece-per-line vocabulary file
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do_lower_case: Whether to lower case the input
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Only has an effect when do_wordpiece_only=False
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do_basic_tokenize: Whether to do basic tokenization before wordpiece.
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max_len: An artificial maximum length to truncate tokenized sequences to;
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Effective maximum length is always the minimum of this
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value (if specified) and the underlying BERT model's
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sequence length.
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never_split: List of tokens which will never be split during tokenization.
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Only has an effect when do_wordpiece_only=False
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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 vocabulary from a Google pretrained "
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"model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file))
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self.vocab = load_vocab(vocab_file)
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self.ids_to_tokens = collections.OrderedDict(
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[(ids, tok) for tok, ids in self.vocab.items()])
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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(do_lower_case=do_lower_case,
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never_split=never_split)
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self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
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self.max_len = max_len if max_len is not None else int(1e12)
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def tokenize(self, text):
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split_tokens = []
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if self.do_basic_tokenize:
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for token in self.basic_tokenizer.tokenize(text):
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for sub_token in self.wordpiece_tokenizer.tokenize(token):
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split_tokens.append(sub_token)
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else:
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split_tokens = self.wordpiece_tokenizer.tokenize(text)
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return split_tokens
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def convert_tokens_to_ids(self, tokens):
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"""Converts a sequence of tokens into ids using the vocab."""
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ids = []
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for token in tokens:
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ids.append(self.vocab[token])
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if len(ids) > self.max_len:
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logger.warning(
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"Token indices sequence length is longer than the specified maximum "
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" sequence length for this BERT model ({} > {}). Running this"
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" sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
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)
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return ids
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def convert_ids_to_tokens(self, ids):
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"""Converts a sequence of ids in wordpiece tokens using the vocab."""
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tokens = []
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for i in ids:
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tokens.append(self.ids_to_tokens[i])
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return tokens
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def save_vocabulary(self, vocab_path):
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"""Save the tokenizer vocabulary to a directory or file."""
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index = 0
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if os.path.isdir(vocab_path):
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vocab_file = os.path.join(vocab_path, VOCAB_NAME)
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with open(vocab_file, "w", encoding="utf-8") as writer:
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for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
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if index != token_index:
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logger.warning("Saving vocabulary to {}: vocabulary indices are not consecutive."
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" Please check that the vocabulary is not corrupted!".format(vocab_file))
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index = token_index
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writer.write(token + u'\n')
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index += 1
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return vocab_file
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
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"""
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Instantiate a PreTrainedBertModel from a pre-trained model file.
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Download and cache the pre-trained model file if needed.
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"""
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if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
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vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
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if '-cased' in pretrained_model_name_or_path and kwargs.get('do_lower_case', True):
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logger.warning("The pre-trained model you are loading is a cased model but you have not set "
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"`do_lower_case` to False. We are setting `do_lower_case=False` for you but "
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"you may want to check this behavior.")
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kwargs['do_lower_case'] = False
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elif '-cased' not in pretrained_model_name_or_path and not kwargs.get('do_lower_case', True):
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logger.warning("The pre-trained model you are loading is an uncased model but you have set "
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"`do_lower_case` to False. We are setting `do_lower_case=True` for you "
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"but you may want to check this behavior.")
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kwargs['do_lower_case'] = True
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else:
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vocab_file = pretrained_model_name_or_path
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if os.path.isdir(vocab_file):
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vocab_file = os.path.join(vocab_file, VOCAB_NAME)
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# redirect to the cache, if necessary
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try:
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resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
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except EnvironmentError:
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logger.error(
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"Model name '{}' was not found in model name list ({}). "
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"We assumed '{}' was a path or url but couldn't find any file "
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"associated to this path or url.".format(
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pretrained_model_name_or_path,
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', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
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vocab_file))
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return None
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if resolved_vocab_file == vocab_file:
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logger.info("loading vocabulary file {}".format(vocab_file))
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else:
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logger.info("loading vocabulary file {} from cache at {}".format(
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vocab_file, resolved_vocab_file))
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if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
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# if we're using a pretrained model, ensure the tokenizer wont index sequences longer
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# than the number of positional embeddings
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max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path]
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kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
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# Instantiate tokenizer.
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tokenizer = cls(resolved_vocab_file, *inputs, **kwargs)
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return tokenizer
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class BasicTokenizer(object):
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"""Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
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def __init__(self,
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do_lower_case=True,
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never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
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"""Constructs a BasicTokenizer.
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Args:
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do_lower_case: Whether to lower case the input.
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"""
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self.do_lower_case = do_lower_case
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self.never_split = never_split
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def tokenize(self, text):
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"""Tokenizes a piece of text."""
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text = self._clean_text(text)
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# This was added on November 1st, 2018 for the multilingual and Chinese
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# models. This is also applied to the English models now, but it doesn't
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# matter since the English models were not trained on any Chinese data
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# and generally don't have any Chinese data in them (there are Chinese
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# characters in the vocabulary because Wikipedia does have some Chinese
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# words in the English Wikipedia.).
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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 self.do_lower_case and token not in self.never_split:
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token = token.lower()
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token = self._run_strip_accents(token)
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split_tokens.extend(self._run_split_on_punc(token))
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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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"""Strips accents from a piece of text."""
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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):
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"""Splits punctuation on a piece of text."""
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if text in self.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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if _is_punctuation(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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"""Adds whitespace around any CJK character."""
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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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"""Checks whether CP is the codepoint of a CJK character."""
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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 of the other languages.
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if ((cp >= 0x4E00 and cp <= 0x9FFF) or #
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(cp >= 0x3400 and cp <= 0x4DBF) or #
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(cp >= 0x20000 and cp <= 0x2A6DF) or #
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(cp >= 0x2A700 and cp <= 0x2B73F) or #
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(cp >= 0x2B740 and cp <= 0x2B81F) or #
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(cp >= 0x2B820 and cp <= 0x2CEAF) or
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(cp >= 0xF900 and cp <= 0xFAFF) or #
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(cp >= 0x2F800 and cp <= 0x2FA1F)): #
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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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"""Performs invalid character removal and whitespace cleanup on text."""
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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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"""Runs WordPiece tokenization."""
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def __init__(self, vocab, unk_token="[UNK]", 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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"""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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For example:
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input = "unaffable"
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output = ["un", "##aff", "##able"]
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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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A list of wordpiece tokens.
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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(self.unk_token)
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start += 1
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else:
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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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def _is_whitespace(char):
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"""Checks whether `chars` is a whitespace character."""
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# \t, \n, and \r are technically contorl characters but we treat them
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# as whitespace since they are generally considered as such.
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if char == " " or char == "\t" or char == "\n" or char == "\r":
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return True
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cat = unicodedata.category(char)
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if cat == "Zs":
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return True
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return False
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def _is_control(char):
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"""Checks whether `chars` is a control character."""
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# These are technically control characters but we count them as whitespace
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# characters.
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if char == "\t" or char == "\n" or char == "\r":
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return False
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cat = unicodedata.category(char)
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if cat.startswith("C"):
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return True
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return False
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def _is_punctuation(char):
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"""Checks whether `chars` is a punctuation character."""
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cp = ord(char)
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# We treat all non-letter/number ASCII as punctuation.
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# Characters such as "^", "$", and "`" are not in the Unicode
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# Punctuation class but we treat them as punctuation anyways, for
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# consistency.
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if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
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(cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
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return True
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cat = unicodedata.category(char)
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if cat.startswith("P"):
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return True
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return False
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