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Commit 0517e7a1 authored by thomwolf's avatar thomwolf
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Fix GPT2 and RoBERTa tokenizer to beging with a space - update Roberta tokenizer

parent 55f69a11
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Showing with 9 additions and 116 deletions
pytorch_transformers/modeling_gpt2.py
View file @ 0517e7a1
......@@ -682,7 +682,7 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2DoubleHeadsModel.from_pretrained('gpt2')
tokenizer.add_special_tokens({'cls_token': '[CLS]'}) # Add a [CLS] to the vocabulary (we should train it also!)
model.resize_token_embeddings(tokenizer.vocab_size + 1)
model.resize_token_embeddings(len(tokenizer)) # Update the model embeddings to the new vocabulary size (add a vector at the end)
choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0) # Batch size 1, 2 choices
mc_token_ids = torch.tensor([input_ids.size(-1), input_ids.size(-1)]).unsqueeze(0) # Batch size 1
......
pytorch_transformers/tokenization_gpt2.py
View file @ 0517e7a1
......@@ -109,11 +109,11 @@ class GPT2Tokenizer(PreTrainedTokenizer):
bos_token="<|endoftext|>", eos_token="<|endoftext|>", **kwargs):
super(GPT2Tokenizer, self).__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs)
self.encoder = json.load(open(vocab_file))
self.decoder = {v:k for k,v in self.encoder.items()}
self.errors = errors # how to handle errors in decoding
self.encoder = json.load(open(vocab_file, encoding="utf-8"))
self.decoder = {v: k for k, v in self.encoder.items()}
self.errors = errors # how to handle errors in decoding
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v:k for k, v in self.byte_encoder.items()}
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
bpe_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
bpe_merges = [tuple(merge.split()) for merge in bpe_data]
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
......@@ -169,6 +169,7 @@ class GPT2Tokenizer(PreTrainedTokenizer):
def _tokenize(self, text):
""" Tokenize a string. """
text = ' ' + text # GPT-2 (and RoBERTa) tokenizers need at least one space to begin the sentence with.
bpe_tokens = []
for token in re.findall(self.pat, text):
if sys.version_info[0] == 2:
......@@ -214,4 +215,4 @@ class GPT2Tokenizer(PreTrainedTokenizer):
writer.write(' '.join(bpe_tokens) + u'\n')
index += 1
return vocab_file, merge_file
return vocab_file, merge_file
\ No newline at end of file
pytorch_transformers/tokenization_roberta.py
View file @ 0517e7a1
......@@ -23,8 +23,7 @@ import os
import regex as re
from io import open
from .tokenization_gpt2 import bytes_to_unicode, get_pairs
from .tokenization_utils import PreTrainedTokenizer
from .tokenization_gpt2 import GPT2Tokenizer
try:
from functools import lru_cache
......@@ -63,7 +62,7 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
}
class RobertaTokenizer(PreTrainedTokenizer):
class RobertaTokenizer(GPT2Tokenizer):
"""
RoBERTa BPE tokenizer, derived from the GPT-2 tokenizer. Peculiarities: Byte-level BPE
"""
......@@ -77,89 +76,6 @@ class RobertaTokenizer(PreTrainedTokenizer):
sep_token=sep_token, cls_token=cls_token, pad_token=pad_token,
mask_token=mask_token, **kwargs)
self.encoder = json.load(open(vocab_file, encoding="utf-8"))
self.decoder = {v: k for k, v in self.encoder.items()}
self.errors = errors # how to handle errors in decoding
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
bpe_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
bpe_merges = [tuple(merge.split()) for merge in bpe_data]
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
self.cache = {}
# Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
@property
def vocab_size(self):
return len(self.encoder)
def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token)
pairs = get_pairs(word)
if not pairs:
return token
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)
self.cache[token] = word
return word
def _tokenize(self, text):
""" Tokenize a string. """
bpe_tokens = []
for token in re.findall(self.pat, text):
if sys.version_info[0] == 2:
token = ''.join(self.byte_encoder[ord(b)] for b in token)
else:
token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' '))
return bpe_tokens
def _convert_token_to_id(self, token):
""" Converts a token (str/unicode) in an id using the vocab. """
return self.encoder.get(token, self.encoder.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (string/unicode) using the vocab."""
return self.decoder.get(index)
def convert_tokens_to_string(self, tokens):
""" Converts a sequence of tokens (string) in a single string. """
text = ''.join(tokens)
text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
return text
def add_special_tokens_single_sentence(self, token_ids):
"""
Adds special tokens to a sequence for sequence classification tasks.
......@@ -175,27 +91,3 @@ class RobertaTokenizer(PreTrainedTokenizer):
sep = [self._convert_token_to_id(self.sep_token)]
cls = [self._convert_token_to_id(self.cls_token)]
return cls + token_ids_0 + sep + sep + token_ids_1 + sep
def save_vocabulary(self, save_directory):
"""Save the tokenizer vocabulary and merge files to a directory."""
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
return
vocab_file = os.path.join(save_directory, VOCAB_FILES_NAMES['vocab_file'])
merge_file = os.path.join(save_directory, VOCAB_FILES_NAMES['merges_file'])
with open(vocab_file, 'w', encoding='utf-8') as f:
f.write(json.dumps(self.encoder, ensure_ascii=False))
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
writer.write(u'#version: 0.2\n')
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning("Saving vocabulary to {}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!".format(merge_file))
index = token_index
writer.write(' '.join(bpe_tokens) + u'\n')
index += 1
return vocab_file, merge_file
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