mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-15 10:38:23 +06:00
188 lines
7.3 KiB
Python
188 lines
7.3 KiB
Python
# coding=utf-8
|
||
# Copyright 2018 The Open AI Team Authors and The HugginFace Inc. team.
|
||
#
|
||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||
# you may not use this file except in compliance with the License.
|
||
# You may obtain a copy of the License at
|
||
#
|
||
# http://www.apache.org/licenses/LICENSE-2.0
|
||
#
|
||
# Unless required by applicable law or agreed to in writing, software
|
||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
# See the License for the specific language governing permissions and
|
||
# limitations under the License.
|
||
"""Tokenization classes for OpenAI GPT."""
|
||
import os
|
||
import re
|
||
import json
|
||
from tqdm import tqdm
|
||
import logging
|
||
|
||
from .file_utils import cached_path
|
||
|
||
logger = logging.getLogger(__name__)
|
||
|
||
PRETRAINED_VOCAB_ARCHIVE_MAP = {
|
||
'openai-gpt': "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-vocab.json",
|
||
}
|
||
PRETRAINED_MERGES_ARCHIVE_MAP = {
|
||
'openai-gpt': "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-merges.txt",
|
||
}
|
||
PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
|
||
'openai-gpt': 512,
|
||
}
|
||
VOCAB_NAME = 'vocab.json'
|
||
MERGES_NAME = 'merges.txt'
|
||
|
||
def get_pairs(word):
|
||
"""
|
||
Return set of symbol pairs in a word.
|
||
word is represented as tuple of symbols (symbols being variable-length strings)
|
||
"""
|
||
pairs = set()
|
||
prev_char = word[0]
|
||
for char in word[1:]:
|
||
pairs.add((prev_char, char))
|
||
prev_char = char
|
||
return pairs
|
||
|
||
def text_standardize(text):
|
||
"""
|
||
fixes some issues the spacy tokenizer had on books corpus
|
||
also does some whitespace standardization
|
||
"""
|
||
text = text.replace('—', '-')
|
||
text = text.replace('–', '-')
|
||
text = text.replace('―', '-')
|
||
text = text.replace('…', '...')
|
||
text = text.replace('´', "'")
|
||
text = re.sub(r'''(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)''', r' \1 ', text)
|
||
text = re.sub(r'\s*\n\s*', ' \n ', text)
|
||
text = re.sub(r'[^\S\n]+', ' ', text)
|
||
return text.strip()
|
||
|
||
class OpenAIGPTTokenizer(object):
|
||
"""
|
||
mostly a wrapper for a public python bpe tokenizer
|
||
"""
|
||
@classmethod
|
||
def from_pretrained(cls, pretrained_model_name, 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 in PRETRAINED_VOCAB_ARCHIVE_MAP:
|
||
vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name]
|
||
merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[pretrained_model_name]
|
||
else:
|
||
vocab_file = pretrained_model_name
|
||
if os.path.isdir(vocab_file):
|
||
vocab_file = os.path.join(vocab_file, VOCAB_NAME)
|
||
merges_file = os.path.join(vocab_file, MERGES_NAME)
|
||
# redirect to the cache, if necessary
|
||
try:
|
||
resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
|
||
resolved_merges_file = cached_path(merges_file, cache_dir=cache_dir)
|
||
except FileNotFoundError:
|
||
logger.error(
|
||
"Model name '{}' was not found in model name list ({}). "
|
||
"We assumed '{}' was a path or url but couldn't find any file "
|
||
"associated to this path or url.".format(
|
||
pretrained_model_name,
|
||
', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
|
||
vocab_file))
|
||
return None
|
||
if resolved_vocab_file == vocab_file and resolved_merges_file == merges_file:
|
||
logger.info("loading vocabulary file {}".format(vocab_file))
|
||
logger.info("loading merges file {}".format(merges_file))
|
||
else:
|
||
logger.info("loading vocabulary file {} from cache at {}".format(
|
||
vocab_file, resolved_vocab_file))
|
||
logger.info("loading merges file {} from cache at {}".format(
|
||
merges_file, resolved_merges_file))
|
||
if pretrained_model_name 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]
|
||
kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
|
||
# Instantiate tokenizer.
|
||
tokenizer = cls(resolved_vocab_file, resolved_merges_file, *inputs, **kwargs)
|
||
return tokenizer
|
||
|
||
def __init__(self, vocab_file, merges_file):
|
||
try:
|
||
import ftfy
|
||
import spacy
|
||
except ImportError:
|
||
raise ImportError("Please install ftfy and spacy to use OpenAI GPT tokenizer.")
|
||
|
||
self.nlp = spacy.load('en', disable=['parser', 'tagger', 'ner', 'textcat'])
|
||
self.encoder = json.load(open(vocab_file))
|
||
self.decoder = {v:k for k,v in self.encoder.items()}
|
||
merges = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
|
||
merges = [tuple(merge.split()) for merge in merges]
|
||
self.bpe_ranks = dict(zip(merges, range(len(merges))))
|
||
self.cache = {}
|
||
|
||
def bpe(self, token):
|
||
word = tuple(token[:-1]) + ( token[-1] + '</w>',)
|
||
if token in self.cache:
|
||
return self.cache[token]
|
||
pairs = get_pairs(word)
|
||
|
||
if not pairs:
|
||
return token+'</w>'
|
||
|
||
while True:
|
||
bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
|
||
if bigram not in self.bpe_ranks:
|
||
break
|
||
first, second = bigram
|
||
new_word = []
|
||
i = 0
|
||
while i < len(word):
|
||
try:
|
||
j = word.index(first, i)
|
||
new_word.extend(word[i:j])
|
||
i = j
|
||
except:
|
||
new_word.extend(word[i:])
|
||
break
|
||
|
||
if word[i] == first and i < len(word)-1 and word[i+1] == second:
|
||
new_word.append(first+second)
|
||
i += 2
|
||
else:
|
||
new_word.append(word[i])
|
||
i += 1
|
||
new_word = tuple(new_word)
|
||
word = new_word
|
||
if len(word) == 1:
|
||
break
|
||
else:
|
||
pairs = get_pairs(word)
|
||
word = ' '.join(word)
|
||
if word == '\n </w>':
|
||
word = '\n</w>'
|
||
self.cache[token] = word
|
||
return word
|
||
|
||
def tokenize(self, texts, verbose=True):
|
||
texts_tokens = []
|
||
if verbose:
|
||
for text in tqdm(texts, ncols=80, leave=False):
|
||
text = self.nlp(text_standardize(ftfy.fix_text(text)))
|
||
text_tokens = []
|
||
for token in text:
|
||
text_tokens.extend([self.encoder.get(t, 0) for t in self.bpe(token.text.lower()).split(' ')])
|
||
texts_tokens.append(text_tokens)
|
||
else:
|
||
for text in texts:
|
||
text = self.nlp(text_standardize(ftfy.fix_text(text)))
|
||
text_tokens = []
|
||
for token in text:
|
||
text_tokens.extend([self.encoder.get(t, 0) for t in self.bpe(token.text.lower()).split(' ')])
|
||
texts_tokens.append(text_tokens)
|
||
return texts_tokens
|