Spaces:
Runtime error
Runtime error
File size: 8,584 Bytes
4121bec |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 |
import logging
import random
class InputExample(object):
"""A single training/test example for the language model."""
def __init__(self, guid, tokens_a, tokens_b=None, is_next=None,
lm_labels=None, img_id=None, is_img_match=None,
img_label=None):
"""Constructs a InputExample.
Args:
guid: Unique id for the example.
tokens_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
tokens_b: (Optional) string. The untokenized text of the second sequence.
Only must be specified for sequence pair tasks.
"""
self.guid = guid
self.tokens_a = tokens_a
self.tokens_b = tokens_b
self.is_next = is_next # nextSentence
self.lm_labels = lm_labels # masked words for language model
self.img_id = img_id
self.is_img_match = is_img_match
self.img_label = img_label
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self, input_ids, input_mask, segment_ids, is_next,
lm_label_ids, img_feat_len, is_img_match):
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
self.is_next = is_next
self.lm_label_ids = lm_label_ids
self.img_feat_len = img_feat_len
self.is_img_match = is_img_match
def random_word(tokens, tokenizer):
"""
Masking some random tokens for Language Model task with probabilities as in the original BERT paper.
:param tokens: list of str, tokenized sentence.
:param tokenizer: Tokenizer, object used for tokenization (we need it's vocab here)
:return: (list of str, list of int), masked tokens and related labels for LM prediction
"""
output_label = []
for i, token in enumerate(tokens):
prob = random.random()
# mask token with 15% probability
if prob < 0.15:
prob /= 0.15
# 80% randomly change token to mask token
if prob < 0.8:
tokens[i] = "[MASK]"
# 10% randomly change token to random token
elif prob < 0.9:
tokens[i] = random.choice(list(tokenizer.vocab.items()))[0]
# -> rest 10% randomly keep current token
# append current token to output (we will predict these later)
try:
output_label.append(tokenizer.vocab[token])
except KeyError:
# For unknown words (should not occur with BPE vocab)
output_label.append(tokenizer.vocab["[UNK]"])
logging.warning(
"Cannot find token '{}' in vocab. Using [UNK] insetad".format(
token))
else:
# no masking token (will be ignored by loss function later)
output_label.append(-1)
return tokens, output_label
def convert_example_to_features(args, example, max_seq_length, tokenizer,
img_feat_len):
"""
Convert a raw sample (pair of sentences as tokenized strings) into a proper training sample with
IDs, LM labels, input_mask, CLS and SEP tokens etc.
:param args: parameter settings
:param img_feat_len: lens of actual img features
:param example: InputExample, containing sentence input as strings and is_next label
:param max_seq_length: int, maximum length of sequence.
:param tokenizer: Tokenizer
:return: InputFeatures, containing all inputs and labels of one sample as IDs (as used for model training)
"""
tokens_a = example.tokens_a
tokens_b = None
if example.tokens_b:
tokens_b = example.tokens_b
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for [CLS], [SEP], [SEP] with "- 3"
_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
else:
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[:(max_seq_length - 2)]
tokens_a, t1_label = random_word(tokens_a, tokenizer)
if tokens_b:
tokens_b, t2_label = random_word(tokens_b, tokenizer)
# concatenate lm labels and account for CLS, SEP, SEP
if tokens_b:
lm_label_ids = ([-1] + t1_label + [-1] + t2_label + [-1])
else:
lm_label_ids = ([-1] + t1_label + [-1])
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambigiously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens = []
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for token in tokens_a:
tokens.append(token)
segment_ids.append(0)
tokens.append("[SEP]")
segment_ids.append(0)
if tokens_b:
assert len(tokens_b) > 0
for token in tokens_b:
tokens.append(token)
segment_ids.append(1)
tokens.append("[SEP]")
segment_ids.append(1)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real tokens are attended to.
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
lm_label_ids.append(-1)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
assert len(lm_label_ids) == max_seq_length
# image features
if args.max_img_seq_length > 0:
if img_feat_len > args.max_img_seq_length:
input_mask = input_mask + [1] * img_feat_len
else:
input_mask = input_mask + [1] * img_feat_len
pad_img_feat_len = args.max_img_seq_length - img_feat_len
input_mask = input_mask + ([0] * pad_img_feat_len)
lm_label_ids = lm_label_ids + [-1] * args.max_img_seq_length
if example.guid < 1:
logging.info("*** Example ***")
logging.info("guid: %s" % example.guid)
logging.info("tokens: %s" % " ".join([str(x) for x in tokens]))
logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
logging.info("LM label: %s " % lm_label_ids)
logging.info("Is next sentence label: %s " % example.is_next)
features = InputFeatures(input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
lm_label_ids=lm_label_ids,
is_next=example.is_next,
img_feat_len=img_feat_len,
is_img_match=example.is_img_match)
return features
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
"""Truncates a sequence pair in place to the maximum length."""
# This is a simple heuristic which will always truncate the longer sequence
# one token at a time. This makes more sense than truncating an equal percent
# of tokens from each, since if one sequence is very short then each token
# that's truncated likely contains more information than a longer sequence.
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_length:
break
if len(tokens_a) > len(tokens_b):
tokens_a.pop()
else:
tokens_b.pop() |