mirror of
https://github.com/huggingface/transformers.git
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239 lines
9.8 KiB
Python
239 lines
9.8 KiB
Python
# coding=utf-8
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# Copyright 2018 The Open AI Team Authors and The HugginFace 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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import os
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import re
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import json
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from tqdm import tqdm
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import logging
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from .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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'openai-gpt': "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-vocab.json",
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}
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PRETRAINED_MERGES_ARCHIVE_MAP = {
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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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PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
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'openai-gpt': 512,
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}
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VOCAB_NAME = 'vocab.json'
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MERGES_NAME = 'merges.txt'
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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(object):
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"""
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mostly a wrapper for a public python bpe tokenizer
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"""
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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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merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[pretrained_model_name_or_path]
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else:
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vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME)
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merges_file = os.path.join(pretrained_model_name_or_path, MERGES_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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resolved_merges_file = cached_path(merges_file, cache_dir=cache_dir)
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except FileNotFoundError:
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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 files {} and {} "
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"at 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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pretrained_model_name_or_path,
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vocab_file, merges_file))
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return None
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if resolved_vocab_file == vocab_file and resolved_merges_file == merges_file:
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logger.info("loading vocabulary file {}".format(vocab_file))
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logger.info("loading merges file {}".format(merges_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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logger.info("loading merges file {} from cache at {}".format(
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merges_file, resolved_merges_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, resolved_merges_file, *inputs, **kwargs)
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return tokenizer
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def __init__(self, vocab_file, merges_file, special_tokens=None, max_len=None):
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try:
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import ftfy
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import spacy
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except ImportError:
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raise ImportError("Please install ftfy and spacy to use OpenAI GPT tokenizer.")
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self.max_len = max_len if max_len is not None else int(1e12)
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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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self.encoder = json.load(open(vocab_file))
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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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self.set_special_tokens(special_tokens)
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def __len__(self):
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return len(self.encoder) + len(self.special_tokens)
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def set_special_tokens(self, special_tokens):
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""" Add a list of additional tokens to the encoder.
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The additional tokens are indexed starting from the last index of the
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current vocabulary in the order of the `special_tokens` list.
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"""
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if not special_tokens:
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self.special_tokens = {}
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self.special_tokens_decoder = {}
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return
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self.special_tokens = dict((tok, len(self.encoder) + i) for i, tok in enumerate(special_tokens))
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self.special_tokens_decoder = {v:k for k, v in self.special_tokens.items()}
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logger.info("Special tokens {}".format(self.special_tokens))
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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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split_tokens = []
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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_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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if isinstance(tokens, str):
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if tokens in self.special_tokens:
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return self.special_tokens[tokens]
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else:
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return self.encoder.get(tokens, 0)
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for token in tokens:
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if token in self.special_tokens:
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ids.append(self.special_tokens[token])
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else:
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ids.append(self.encoder.get(token, 0))
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if len(ids) > self.max_len:
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raise ValueError(
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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, skip_special_tokens=False):
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"""Converts a sequence of ids in BPE tokens using the vocab."""
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tokens = []
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for i in ids:
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if i in self.special_tokens_decoder:
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if not skip_special_tokens:
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tokens.append(self.special_tokens_decoder[i])
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else:
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tokens.append(self.decoder[i])
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return tokens
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def decode(self, ids, skip_special_tokens=False, clean_up_tokenization_spaces=False):
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"""Converts a sequence of ids in a string."""
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tokens = self.convert_ids_to_tokens(ids, skip_special_tokens=skip_special_tokens)
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out_string = ''.join(tokens).replace('</w>', ' ').strip()
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if clean_up_tokenization_spaces:
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out_string = out_string.replace('<unk>', '')
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out_string = out_string.replace(' .', '.').replace(' ?', '?').replace(' !', '!').replace(' ,', ',').replace(' ,', ','
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).replace(" n't", "n't").replace(" 'm", "'m").replace(" 're", "'re").replace(" do not", " don't"
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).replace(" 's", "'s").replace(" t ", "'t ").replace(" s ", "'s ").replace(" m ", "'m "
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).replace(" 've", "'ve")
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return out_string
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