Fix GPT2 and RoBERTa tokenizer to beging with a space - update Roberta tokenizer
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thomwolf
@@ -23,8 +23,7 @@ import os
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import regex as re
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from io import open
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from .tokenization_gpt2 import bytes_to_unicode, get_pairs
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from .tokenization_utils import PreTrainedTokenizer
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from .tokenization_gpt2 import GPT2Tokenizer
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try:
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from functools import lru_cache
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@@ -63,7 +62,7 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
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}
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class RobertaTokenizer(PreTrainedTokenizer):
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class RobertaTokenizer(GPT2Tokenizer):
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"""
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RoBERTa BPE tokenizer, derived from the GPT-2 tokenizer. Peculiarities: Byte-level BPE
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"""
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@@ -77,89 +76,6 @@ class RobertaTokenizer(PreTrainedTokenizer):
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sep_token=sep_token, cls_token=cls_token, pad_token=pad_token,
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mask_token=mask_token, **kwargs)
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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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self.errors = errors # how to handle errors in decoding
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self.byte_encoder = bytes_to_unicode()
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self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
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bpe_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
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bpe_merges = [tuple(merge.split()) for merge in bpe_data]
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self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
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self.cache = {}
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# Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
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self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
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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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if token in self.cache:
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return self.cache[token]
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word = tuple(token)
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pairs = get_pairs(word)
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if not pairs:
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return token
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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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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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bpe_tokens = []
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for token in re.findall(self.pat, text):
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if sys.version_info[0] == 2:
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token = ''.join(self.byte_encoder[ord(b)] for b in token)
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else:
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token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
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bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' '))
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return bpe_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 index (integer) in a token (string/unicode) using the vocab."""
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return self.decoder.get(index)
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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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text = ''.join(tokens)
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text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
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return text
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def add_special_tokens_single_sentence(self, token_ids):
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"""
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Adds special tokens to a sequence for sequence classification tasks.
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@@ -175,27 +91,3 @@ class RobertaTokenizer(PreTrainedTokenizer):
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sep = [self._convert_token_to_id(self.sep_token)]
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cls = [self._convert_token_to_id(self.cls_token)]
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return cls + token_ids_0 + sep + sep + token_ids_1 + sep
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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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