mirror of
https://github.com/huggingface/transformers.git
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205 lines
7.3 KiB
Python
205 lines
7.3 KiB
Python
# coding=utf-8
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# Copyright 2018 The Open AI 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 for OpenAI GPT."""
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from __future__ import (absolute_import, division, print_function,
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unicode_literals)
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import json
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import logging
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import os
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import re
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from io import open
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from .tokenization_utils import PreTrainedTokenizer
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from .tokenization_bert import BasicTokenizer
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logger = logging.getLogger(__name__)
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VOCAB_FILES_NAMES = {
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'vocab_file': 'vocab.json',
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'merges_file': 'merges.txt',
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}
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PRETRAINED_VOCAB_FILES_MAP = {
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'vocab_file':
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{
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'openai-gpt': "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-vocab.json",
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},
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'merges_file':
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{
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'openai-gpt': "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-merges.txt",
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},
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}
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
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'openai-gpt': 512,
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}
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def get_pairs(word):
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"""
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Return set of symbol pairs in a word.
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word is represented as tuple of symbols (symbols being variable-length strings)
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"""
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pairs = set()
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prev_char = word[0]
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for char in word[1:]:
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pairs.add((prev_char, char))
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prev_char = char
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return pairs
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def text_standardize(text):
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"""
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fixes some issues the spacy tokenizer had on books corpus
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also does some whitespace standardization
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"""
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text = text.replace('—', '-')
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text = text.replace('–', '-')
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text = text.replace('―', '-')
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text = text.replace('…', '...')
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text = text.replace('´', "'")
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text = re.sub(r'''(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)''', r' \1 ', text)
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text = re.sub(r'\s*\n\s*', ' \n ', text)
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text = re.sub(r'[^\S\n]+', ' ', text)
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return text.strip()
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class OpenAIGPTTokenizer(PreTrainedTokenizer):
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"""
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BPE tokenizer. Peculiarities:
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- lower case all inputs
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- uses SpaCy tokenizer and ftfy for pre-BPE tokenization if they are installed, fallback to BERT's BasicTokenizer if not.
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
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max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
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def __init__(self, vocab_file, merges_file, unk_token="<unk>", **kwargs):
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super(OpenAIGPTTokenizer, self).__init__(unk_token=unk_token, **kwargs)
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try:
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import ftfy
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import spacy
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self.nlp = spacy.load('en', disable=['parser', 'tagger', 'ner', 'textcat'])
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self.fix_text = ftfy.fix_text
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except ImportError:
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logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.")
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self.nlp = BasicTokenizer(do_lower_case=True)
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self.fix_text = None
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self.encoder = json.load(open(vocab_file, encoding="utf-8"))
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self.decoder = {v:k for k,v in self.encoder.items()}
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merges = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
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merges = [tuple(merge.split()) for merge in merges]
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self.bpe_ranks = dict(zip(merges, range(len(merges))))
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self.cache = {}
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@property
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def vocab_size(self):
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return len(self.encoder)
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def bpe(self, token):
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word = tuple(token[:-1]) + (token[-1] + '</w>',)
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if token in self.cache:
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return self.cache[token]
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pairs = get_pairs(word)
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if not pairs:
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return token+'</w>'
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while True:
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bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))
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if bigram not in self.bpe_ranks:
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break
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first, second = bigram
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new_word = []
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i = 0
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while i < len(word):
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try:
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j = word.index(first, i)
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new_word.extend(word[i:j])
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i = j
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except:
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new_word.extend(word[i:])
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break
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if word[i] == first and i < len(word)-1 and word[i+1] == second:
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new_word.append(first+second)
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i += 2
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else:
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new_word.append(word[i])
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i += 1
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new_word = tuple(new_word)
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word = new_word
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if len(word) == 1:
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break
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else:
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pairs = get_pairs(word)
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word = ' '.join(word)
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if word == '\n </w>':
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word = '\n</w>'
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self.cache[token] = word
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return word
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def _tokenize(self, text):
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""" Tokenize a string. """
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split_tokens = []
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if self.fix_text is None:
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# Using BERT's BasicTokenizer
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text = self.nlp.tokenize(text)
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for token in text:
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split_tokens.extend([t for t in self.bpe(token).split(' ')])
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else:
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# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
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text = self.nlp(text_standardize(self.fix_text(text)))
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for token in text:
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split_tokens.extend([t for t in self.bpe(token.text.lower()).split(' ')])
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return split_tokens
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def _convert_token_to_id(self, token):
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""" Converts a token (str/unicode) in an id using the vocab. """
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return self.encoder.get(token, self.encoder.get(self.unk_token))
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def _convert_id_to_token(self, index):
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"""Converts an id in a token (BPE) using the vocab."""
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return self.decoder.get(index, self.unk_token)
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def convert_tokens_to_string(self, tokens):
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""" Converts a sequence of tokens (string) in a single string. """
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out_string = ''.join(tokens).replace('</w>', ' ').strip()
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return out_string
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def save_vocabulary(self, save_directory):
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"""Save the tokenizer vocabulary and merge files to a directory."""
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if not os.path.isdir(save_directory):
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logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
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return
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vocab_file = os.path.join(save_directory, VOCAB_FILES_NAMES['vocab_file'])
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merge_file = os.path.join(save_directory, VOCAB_FILES_NAMES['merges_file'])
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with open(vocab_file, 'w', encoding='utf-8') as f:
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f.write(json.dumps(self.encoder, ensure_ascii=False))
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index = 0
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with open(merge_file, "w", encoding="utf-8") as writer:
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writer.write(u'#version: 0.2\n')
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for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
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if index != token_index:
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logger.warning("Saving vocabulary to {}: BPE merge indices are not consecutive."
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" Please check that the tokenizer is not corrupted!".format(merge_file))
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index = token_index
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writer.write(' '.join(bpe_tokens) + u'\n')
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index += 1
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return vocab_file, merge_file
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