def __repr__(self):
s = ""
s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
s += ", question_text: %s" % (
tokenization.printable_text(self.question_text))
s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
if self.start_position:
s += ", start_position: %d" % (self.start_position)
if self.start_position:
s += ", end_position: %d" % (self.end_position)
return s
def __str__(self):
s = ""
s += "tokens: %s\n" % (" ".join(
[tokenization.printable_text(x) for x in self.tokens]))
s += "segment_ids: %s\n" % (" ".join([str(x) for x in self.segment_ids]))
s += "is_random_next: %s\n" % self.is_random_next
s += "masked_lm_positions: %s\n" % (" ".join(
[str(x) for x in self.masked_lm_positions]))
s += "masked_lm_labels: %s\n" % (" ".join(
[tokenization.printable_text(x) for x in self.masked_lm_labels]))
s += "\n"
return s
def __repr__(self):
s = ""
s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
s += ", question_text: %s" % (
tokenization.printable_text(self.question_text))
s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
if self.start_position:
s += ", start_position: %d" % (self.start_position)
if self.start_position:
s += ", end_position: %d" % (self.end_position)
if self.history_answer_marker:
s += ', history_answer_marker: {}'.format(json.dumps(self.history_answer_marker))
if self.metadata:
s += ', metadata: ' + json.dumps(self.metadata)
return s
def convert_single_example(ex_index, example, label_list, max_seq_length,
tokenizer):
"""Converts a single `InputExample` into a single `InputFeatures`."""
labellist = example.label.split(' ')
#注意这里从1开始，因为句子长度不够时要进行填充其对应的label填充为0
label_map = {}
for (i, label) in enumerate(label_list,1):
label_map[label] = i
#样本向字id的转换
if len(example.text_a)>max_seq_length-2:
example.text_a=example.text_a[0:(max_seq_length-2)]
labellist=labellist[0:(max_seq_length-2)]
tokens_a = tokenizer.tokenize(example.text_a)
# 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 unambiguously 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 the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
#若输入的是句子对要加入特殊字符
tokens = []
segment_ids = []
label_ids=[]
tokens.append("[CLS]")
segment_ids.append(0)
label_ids.append(label_map["[CLS]"])
for i,token in enumerate(tokens_a):
tokens.append(token)
segment_ids.append(0)
label_ids.append(label_map[labellist[i]])
tokens.append("[SEP]")
segment_ids.append(0)
label_ids.append(label_map["[SEP]"])
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)
# we don't concerned about it!
label_ids.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
assert len(label_ids) == max_seq_length
if ex_index < 5:
tf.logging.info("*** Example ***")
tf.logging.info("guid: %s" % (example.guid))
tf.logging.info("tokens: %s" % " ".join(
[tokenization.printable_text(x) for x in tokens]))
tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
tf.logging.info("label_ids: %s" % " ".join([str(x) for x in label_ids]))
#创建InputFeatures的一个实例化对象并返回n
feature = InputFeatures(
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_ids=label_ids)
return feature
def convert_examples_to_features(examples, tokenizer, max_seq_length,
doc_stride, max_query_length, is_training,
output_fn):
"""Loads a data file into a list of `InputBatch`s."""
unique_id = 1000000000
for (example_index, example) in enumerate(examples):
query_tokens = tokenizer.tokenize(example.question_text)
if len(query_tokens) > max_query_length:
query_tokens = query_tokens[0:max_query_length]
tok_to_orig_index = []
orig_to_tok_index = []
all_doc_tokens = []
for (i, token) in enumerate(example.doc_tokens):
orig_to_tok_index.append(len(all_doc_tokens))
sub_tokens = tokenizer.tokenize(token)
for sub_token in sub_tokens:
tok_to_orig_index.append(i)
all_doc_tokens.append(sub_token)
tok_start_position = None
tok_end_position = None
if is_training and example.is_impossible:
tok_start_position = -1
tok_end_position = -1
if is_training and not example.is_impossible:
tok_start_position = orig_to_tok_index[example.start_position]
if example.end_position < len(example.doc_tokens) - 1:
tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
else:
tok_end_position = len(all_doc_tokens) - 1
(tok_start_position, tok_end_position) = _improve_answer_span(
all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
example.orig_answer_text)
# The -3 accounts for [CLS], [SEP] and [SEP]
max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
# We can have documents that are longer than the maximum sequence length.
# To deal with this we do a sliding window approach, where we take chunks
# of the up to our max length with a stride of `doc_stride`.
_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
"DocSpan", ["start", "length"])
doc_spans = []
start_offset = 0
while start_offset < len(all_doc_tokens):
length = len(all_doc_tokens) - start_offset
if length > max_tokens_for_doc:
length = max_tokens_for_doc
doc_spans.append(_DocSpan(start=start_offset, length=length))
if start_offset + length == len(all_doc_tokens):
break
start_offset += min(length, doc_stride)
for (doc_span_index, doc_span) in enumerate(doc_spans):
tokens = []
token_to_orig_map = {}
token_is_max_context = {}
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for token in query_tokens:
tokens.append(token)
segment_ids.append(0)
tokens.append("[SEP]")
segment_ids.append(0)
for i in range(doc_span.length):
split_token_index = doc_span.start + i
token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
is_max_context = _check_is_max_context(doc_spans, doc_span_index,
split_token_index)
token_is_max_context[len(tokens)] = is_max_context
tokens.append(all_doc_tokens[split_token_index])
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)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
start_position = None
end_position = None
if is_training and not example.is_impossible:
# For training, if our document chunk does not contain an annotation
# we throw it out, since there is nothing to predict.
doc_start = doc_span.start
doc_end = doc_span.start + doc_span.length - 1
out_of_span = False
if not (tok_start_position >= doc_start and
tok_end_position <= doc_end):
out_of_span = True
if out_of_span:
start_position = 0
end_position = 0
else:
doc_offset = len(query_tokens) + 2
start_position = tok_start_position - doc_start + doc_offset
end_position = tok_end_position - doc_start + doc_offset
if is_training and example.is_impossible:
start_position = 0
end_position = 0
if example_index < 20:
tf.logging.info("*** Example ***")
tf.logging.info("unique_id: %s" % (unique_id))
tf.logging.info("example_index: %s" % (example_index))
tf.logging.info("doc_span_index: %s" % (doc_span_index))
tf.logging.info("tokens: %s" % " ".join(
[tokenization.printable_text(x) for x in tokens]))
tf.logging.info("token_to_orig_map: %s" % " ".join(
["%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)]))
tf.logging.info("token_is_max_context: %s" % " ".join([
"%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context)
]))
tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
tf.logging.info(
"input_mask: %s" % " ".join([str(x) for x in input_mask]))
tf.logging.info(
"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
if is_training and example.is_impossible:
tf.logging.info("impossible example")
if is_training and not example.is_impossible:
answer_text = " ".join(tokens[start_position:(end_position + 1)])
tf.logging.info("start_position: %d" % (start_position))
tf.logging.info("end_position: %d" % (end_position))
tf.logging.info(
"answer: %s" % (tokenization.printable_text(answer_text)))
feature = InputFeatures(
unique_id=unique_id,
example_index=example_index,
doc_span_index=doc_span_index,
tokens=tokens,
token_to_orig_map=token_to_orig_map,
token_is_max_context=token_is_max_context,
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
start_position=start_position,
end_position=end_position,
is_impossible=example.is_impossible)
# Run callback
output_fn(feature)
unique_id += 1
def convert_single_example(ex_index, example, label_list, max_seq_length,
tokenizer,
mode): ######################## important
label_map = {}
for (i, label) in enumerate(label_list, 1):
label_map[label] = i
label2idpath = './output/label2id.pkl'
if not os.path.exists(label2idpath):
with open(label2idpath, 'wb') as w:
pickle.dump(label_map, w)
textlist = list(example.text)
labellist = list(example.label)
tokens = []
labels = []
unknow_index = [] #记录下标记为[UNK]在textlist当中的位置，用于还原
for i, word in enumerate(textlist):
token = tokenizer.tokenize(word)
tokens.extend(token)
label_1 = labellist[i]
for m in range(len(token)):
if m == 0:
labels.append(label_1)
else:
labels.append("X")
if token[m] == "[UNK]":
unknow_index.append(i)
assert len(tokens) == len(labels)
if len(tokens) >= max_seq_length - 1:
tokens = tokens[0:(max_seq_length - 2)]
labels = labels[0:(max_seq_length - 2)]
ntokens = []
segment_ids = []
label_ids = []
ntokens.append("[CLS]")
segment_ids.append(0)
label_ids.append(label_map["[CLS]"])
for i, token in enumerate(tokens):
ntokens.append(token)
segment_ids.append(0)
label_ids.append(label_map[labels[i]])
ntokens.append("[SEP]")
segment_ids.append(0)
label_ids.append(label_map["[SEP]"])
input_ids = tokenizer.convert_tokens_to_ids(ntokens)
input_mask = [1] * len(input_ids)
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
label_ids.append(0)
ntokens.append("**NULL**")
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
assert len(label_ids) == max_seq_length
if ex_index < 5:
tf.logging.info("*** Example ***")
tf.logging.info("guid: %s" % (example.guid))
tf.logging.info(
"tokens: %s" %
" ".join([tokenization.printable_text(x) for x in tokens]))
tf.logging.info("input_ids: %s" % " ".join([str(x)
for x in input_ids]))
tf.logging.info("input_mask: %s" %
" ".join([str(x) for x in input_mask]))
tf.logging.info("segment_ids: %s" %
" ".join([str(x) for x in segment_ids]))
tf.logging.info("label_ids: %s" % " ".join([str(x)
for x in label_ids]))
feature = InputFeatures(
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_ids=label_ids,
)
# 还原[UNK]的数据
output_tokens = []
for i, each in enumerate(ntokens):
if each != "[UNK]":
output_tokens.append(each)
else:
index = unknow_index[0]
output_tokens.append(textlist[index])
unknow_index = unknow_index[1:]
write_tokens(output_tokens, mode)
return feature #InputFeature实例
def convert_single_example(ex_index, example, label_list, max_seq_length,
tokenizer):
"""Converts a single `InputExample` into a single `InputFeatures`."""
if isinstance(example, PaddingInputExample):
return InputFeatures(input_ids=[0] * max_seq_length,
input_mask=[0] * max_seq_length,
segment_ids=[0] * max_seq_length,
label_id=0,
is_real_example=False)
label_map = {}
for (i, label) in enumerate(label_list):
label_map[label] = i
tokens_a = tokenizer.tokenize(example.text_a)
tokens_b = None
if example.text_b:
tokens_b = tokenizer.tokenize(example.text_b)
if 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:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[0:(max_seq_length - 2)]
# 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 unambiguously 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 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:
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)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
label_id = label_map[example.label]
if ex_index < 5:
tf.logging.info("*** Example ***")
tf.logging.info("guid: %s" % (example.guid))
tf.logging.info(
"tokens: %s" %
" ".join([tokenization.printable_text(x) for x in tokens]))
tf.logging.info("input_ids: %s" % " ".join([str(x)
for x in input_ids]))
tf.logging.info("input_mask: %s" %
" ".join([str(x) for x in input_mask]))
tf.logging.info("segment_ids: %s" %
" ".join([str(x) for x in segment_ids]))
tf.logging.info("label: %s (id = %d)" % (example.label, label_id))
feature = InputFeatures(input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_id=label_id,
is_real_example=True)
return feature
def convert_single_example(ex_index, example, label_map, max_seq_length,
tokenizer, mode):
textlist = example.text.split(' ')
labellist = example.label.split(' ')
tokens = []
labels = []
# print(textlist)
for i, word in enumerate(textlist):
token = tokenizer.tokenize(word)
# print(token)
tokens.extend(token)
label_1 = labellist[i]
# print(label_1)
for m in range(len(token)):
if m == 0:
labels.append(label_1)
else:
labels.append("X")
# print(tokens, labels)
# tokens = tokenizer.tokenize(example.text)
if len(tokens) >= max_seq_length - 1:
tokens = tokens[0:(max_seq_length - 2)]
labels = labels[0:(max_seq_length - 2)]
ntokens = []
segment_ids = []
label_ids = []
ntokens.append("[CLS]")
segment_ids.append(0)
# append("O") or append("[CLS]") not sure!
label_ids.append(label_map["[CLS]"])
for i, token in enumerate(tokens):
ntokens.append(token)
segment_ids.append(0)
label_ids.append(label_map[labels[i]])
ntokens.append("[SEP]")
segment_ids.append(0)
# append("O") or append("[SEP]") not sure!
label_ids.append(label_map["[SEP]"])
input_ids = tokenizer.convert_tokens_to_ids(ntokens)
input_mask = [1] * len(input_ids)
# label_mask = [1] * len(input_ids)
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
# we don't concerned about it!
label_ids.append(0)
ntokens.append("**NULL**")
# label_mask.append(0)
# print(len(input_ids))
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
assert len(label_ids) == max_seq_length
# assert len(label_mask) == max_seq_length
if ex_index < 5:
tf.logging.info("*** Example ***")
tf.logging.info("guid: %s" % (example.guid))
tf.logging.info(
"tokens: %s" %
" ".join([tokenization.printable_text(x) for x in tokens]))
tf.logging.info("input_ids: %s" % " ".join([str(x)
for x in input_ids]))
tf.logging.info("input_mask: %s" %
" ".join([str(x) for x in input_mask]))
tf.logging.info("segment_ids: %s" %
" ".join([str(x) for x in segment_ids]))
tf.logging.info("label_ids: %s" % " ".join([str(x)
for x in label_ids]))
# tf.logging.info("label_mask: %s" % " ".join([str(x) for x in label_mask]))
feature = InputFeatures(
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_ids=label_ids,
# label_mask = label_mask
)
write_tokens(ntokens, mode)
return feature
def convert_examples_to_features(examples,
tokenizer,
max_seq_length,
doc_stride,
max_query_length,
return_answers,
skip_no_answer,
verbose=False,
save_with_prob=False,
msg="Converting examples"):
"""Loads a data file into a list of `InputBatch`s."""
unique_id = 1000000000
features = []
question_features = []
for (example_index, example) in enumerate(tqdm(examples, desc=msg)):
# Tokenize query into (sub)tokens
query_tokens = tokenizer.tokenize(example.question_text)
if len(query_tokens) > max_query_length:
query_tokens = query_tokens[0:max_query_length]
# Creating a map between word <=> (sub)token
tok_to_word_index = []
word_to_tok_index = [] # word to (start of) subtokens
all_doc_tokens = []
for (i, word) in enumerate(example.doc_words):
word_to_tok_index.append(len(all_doc_tokens))
sub_tokens = tokenizer.tokenize(word)
for sub_token in sub_tokens:
tok_to_word_index.append(i)
all_doc_tokens.append(sub_token)
# The -2 accounts for [CLS], [SEP]
max_tokens_for_doc = max_seq_length - 2
# Split sequence by max_seq_len with doc_stride, _DocSpan is based on tokens without [CLS], [SEP]
_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
"DocSpan", ["start", "length"])
doc_spans = []
start_tok_offset = 0 # From all_doc_tokens
# Get doc_spans with stride and offset
while start_tok_offset < len(all_doc_tokens):
length = len(all_doc_tokens) - start_tok_offset
if length > max_tokens_for_doc:
length = max_tokens_for_doc
doc_spans.append(_DocSpan(start=start_tok_offset, length=length))
if start_tok_offset + length == len(all_doc_tokens):
break
start_tok_offset += min(
length, doc_stride) # seems to prefer doc_stride always
assert doc_stride < length, "length is no larger than doc_stride for {}".format(
doc_spans)
# Iterate each doc_span and make out_tokens
for (doc_span_index, doc_span) in enumerate(doc_spans):
# Find answer position based on new out_tokens
start_position = None
end_position = None
# For no_answer, same (-1, -1) applies
if example.start_position is not None and example.start_position < 0:
assert example.start_position == -1 and example.end_position == -1
start_position, end_position = NO_ANS, NO_ANS
# For existing answers, find answers if exist
elif return_answers:
# Get token-level start/end position
tok_start_position = word_to_tok_index[example.start_position]
if example.end_position < len(example.doc_words) - 1:
tok_end_position = word_to_tok_index[
example.end_position +
1] - 1 # By backwarding from next word
else:
assert example.end_position == len(example.doc_words) - 1
tok_end_position = len(all_doc_tokens) - 1
# Improve answer span by subword-level
(tok_start_position, tok_end_position) = _improve_answer_span(
all_doc_tokens, tok_start_position, tok_end_position,
tokenizer, example.orig_answer_text)
# Throw away training samples without answers (due to doc_span split)
doc_start = doc_span.start
doc_end = doc_span.start + doc_span.length - 1
if (tok_start_position < doc_start
or tok_end_position < doc_start
or tok_start_position > doc_end
or tok_end_position > doc_end):
if skip_no_answer:
continue
else:
# For NQ, only add this in 2% (50 times downsample)
if save_with_prob:
if np.random.randint(100) < 2:
start_position, end_position = NO_ANS, NO_ANS
else:
continue
else:
start_position, end_position = NO_ANS, NO_ANS
# Training samples with answers
else:
doc_offset = 1 # For [CLS]
start_position = tok_start_position - doc_start + doc_offset
end_position = tok_end_position - doc_start + doc_offset
assert start_position >= 0 and end_position >= 0, (
start_position, end_position)
out_tokens = [] # doc
out_tokens_ = [] # quesry
out_tokens.append("[CLS]")
out_tokens_.append("[CLS]")
token_to_word_map = {
} # The difference with tok_to_word_index is it includes special tokens
token_is_max_context = {}
# For query tokens, just copy and add [SEP]
for token in query_tokens:
out_tokens_.append(token)
out_tokens_.append("[SEP]")
# For each doc token, create token_to_word_map and is_max_context, and add to out_tokens
for i in range(doc_span.length):
split_token_index = doc_span.start + i
token_to_word_map[len(
out_tokens)] = tok_to_word_index[split_token_index]
is_max_context = _check_is_max_context(doc_spans,
doc_span_index,
split_token_index)
token_is_max_context[len(out_tokens)] = is_max_context
out_tokens.append(all_doc_tokens[split_token_index])
out_tokens.append("[SEP]")
# Convert to ids and masks
input_ids = tokenizer.convert_tokens_to_ids(out_tokens)
input_ids_ = tokenizer.convert_tokens_to_ids(out_tokens_)
input_mask = [1] * len(input_ids)
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)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
while len(
input_ids_) < max_query_length + 2: # +2 for [CLS], [SEP]
input_ids_.append(0)
input_mask_.append(0)
assert len(input_ids_) == max_query_length + 2
assert len(input_mask_) == max_query_length + 2
# Printing for debug
if example_index < 1 and verbose:
logger.info("*** Example ***")
logger.info("unique_id: %s" % (unique_id))
logger.info("example_index: %s" % (example_index))
logger.info("doc_span_index: %s" % (doc_span_index))
logger.info("tokens: %s" % " ".join(
[tokenization.printable_text(x) for x in out_tokens]))
logger.info("q tokens: %s" % " ".join(
[tokenization.printable_text(x) for x in out_tokens_]))
logger.info("token_to_word_map: %s" % " ".join([
"%d:%d" % (x, y)
for (x, y) in six.iteritems(token_to_word_map)
]))
logger.info("token_is_max_context: %s" % " ".join([
"%d:%s" % (x, y)
for (x, y) in six.iteritems(token_is_max_context)
]))
logger.info("input_ids: %s" %
" ".join([str(x) for x in input_ids]))
logger.info("input_mask: %s" %
" ".join([str(x) for x in input_mask]))
if return_answers:
answer_text = " ".join(
out_tokens[start_position:(end_position + 1)])
logger.info("start_position: %d" % (start_position))
logger.info("end_position: %d" % (end_position))
logger.info("answer: %s" %
(tokenization.printable_text(answer_text)))
# Append feature
features.append(
ContextFeatures(unique_id=unique_id,
example_index=example_index,
doc_span_index=doc_span_index,
tokens=out_tokens,
token_to_word_map=token_to_word_map,
token_is_max_context=token_is_max_context,
input_ids=input_ids,
input_mask=input_mask,
start_position=start_position,
end_position=end_position))
question_features.append(
QuestionFeatures(unique_id=unique_id,
example_index=example_index,
tokens_=out_tokens_,
input_ids=input_ids_,
input_mask=input_mask_))
# Check validity of answer
if return_answers:
if start_position <= NO_ANS:
assert start_position == NO_ANS and end_position == NO_ANS, (
start_position, end_position)
else:
assert out_tokens[start_position:end_position+1] == \
all_doc_tokens[tok_start_position:tok_end_position+1]
orig_text, start_pos, end_pos = get_final_text_(
example, features[-1], start_position, end_position,
True, False)
phrase = orig_text[start_pos:end_pos]
try:
assert phrase == example.orig_answer_text
except Exception as e:
# print('diff ans [%s]/[%s]'%(phrase, example.orig_answer_text))
pass
unique_id += 1
return features, question_features
def write_instance_to_example_files(instances, tokenizer, max_seq_length,
max_predictions_per_seq, outputs, log_fn,
report_fn):
'''Create TF example files from `TrainingInstance`s'''
tf_examples = []
writers = []
for _output in outputs:
writers.append(tf.io.TFRecordWriter(_output))
writer_index = 0
total_written = 0
for (inst_index, instance) in enumerate(instances):
input_ids = tokenizer.convert_tokens_to_ids(instance.tokens)
input_mask = [1] * len(input_ids)
segment_ids = list(instance.segment_ids)
assert len(input_ids) <= max_seq_length
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
masked_lm_positions = list(instance.masked_lm_positions)
masked_lm_ids = tokenizer.convert_tokens_to_ids(
instance.masked_lm_labels)
masked_lm_weights = [1.0] * len(masked_lm_ids)
while len(masked_lm_positions) < max_predictions_per_seq:
masked_lm_positions.append(0)
masked_lm_ids.append(0)
masked_lm_weights.append(0.0)
next_sentence_label = 1 if instance.is_random_next else 0
features = collections.OrderedDict()
features['input_ids'] = create_int_feature(input_ids)
features['input_mask'] = create_int_feature(input_mask)
features['segment_ids'] = create_int_feature(segment_ids)
features['masked_lm_positions'] = create_int_feature(
masked_lm_positions)
features['masked_lm_ids'] = create_int_feature(masked_lm_ids)
features['masked_lm_weights'] = create_float_feature(masked_lm_weights)
features['next_sentence_labels'] = create_int_feature(
[next_sentence_label])
tf_example = tf.train.Example(features=tf.train.Features(
feature=features))
tf_examples.append(tf_example)
writers[writer_index].write(tf_example.SerializeToString())
writer_index = (writer_index + 1) % len(writers)
total_written += 1
# DEMO
if inst_index < constants.INSTANCE_DEMO_SIZE:
log_fn('### Example')
log_fn('tokens: {}'.format(' '.join(
[tokenization.printable_text(x) for x in instance.tokens])))
for feature_name in features.keys():
feature = features[feature_name]
values = []
if feature.int64_list.value:
values = feature.int64_list.value
elif feature.float_list.value:
values = feature.float_list.value
log_fn('{}: {}'.format(feature_name,
' '.join([str(x) for x in values])))
for writer in writers:
writer.close()
log_fn('### Generate TF example')
log_fn('# {} instances'.format(total_written))
report_fn('[INFO] TF example: {} instances'.format(total_written))
return tf_examples
def convert_single_example(ex_index, example, label_list, max_seq_length,
tokenizer, mode):
label_map = {}
for (i, label) in enumerate(label_list, 1):
label_map[label] = i
with open(os.path.join(FLAGS.output_dir, 'label2id.pkl'), 'wb') as w:
pickle.dump(label_map, w)
textlist = example.text.split(' ')
labellist = example.label.split(' ')
gztrs_list = example.gazetteer.split(' ')
tokens = []
labels = []
gazetteers = []
for i, word in enumerate(textlist):
token = tokenizer.tokenize(word)
tokens.extend(token)
label_1 = labellist[i]
gztr = gztrs_list[i]
for m in range(len(token)):
if token[m] == '': assert False == True
if m == 0:
labels.append(label_1)
gazetteers.append(gztr)
else:
labels.append("X")
gazetteers.append("X")
if len(tokens) >= max_seq_length - 1:
tokens = tokens[0:(max_seq_length - 2)]
labels = labels[0:(max_seq_length - 2)]
gazetteers = gazetteers[0:(max_seq_length - 2)]
ntokens = []
segment_ids = []
label_ids = []
gazetteer_ids = []
ntokens.append("[CLS]")
segment_ids.append(0)
label_ids.append(label_map["[CLS]"])
gazetteer_ids.append(label_map["[CLS]"])
for i, token in enumerate(tokens):
ntokens.append(token)
segment_ids.append(0)
label_ids.append(label_map[labels[i]])
gazetteer_ids.append(label_map[gazetteers[i]])
ntokens.append("[SEP]")
segment_ids.append(0)
label_ids.append(label_map["[SEP]"])
gazetteer_ids.append(label_map["[SEP]"])
input_ids = tokenizer.convert_tokens_to_ids(ntokens)
input_mask = [1] * len(input_ids)
length = min(FLAGS.max_seq_length, len(label_ids))
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
label_ids.append(0)
gazetteer_ids.append(0)
ntokens.append("**NULL**")
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
assert len(label_ids) == max_seq_length
if ex_index < 5:
tf.logging.info("*** Example ***")
tf.logging.info("guid: %s" % (example.guid))
tf.logging.info(
"tokens: %s" %
" ".join([tokenization.printable_text(x) for x in tokens]))
tf.logging.info("input_ids: %s" % " ".join([str(x)
for x in input_ids]))
tf.logging.info("input_mask: %s" %
" ".join([str(x) for x in input_mask]))
tf.logging.info("segment_ids: %s" %
" ".join([str(x) for x in segment_ids]))
tf.logging.info("label_ids: %s" % " ".join([str(x)
for x in label_ids]))
feature = InputFeatures(input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_ids=label_ids,
gazetteer=gazetteer_ids,
length=length)
write_tokens(ntokens, mode)
return feature
def convert_examples_to_features(examples, label_list, max_seq_length,
tokenizer, output_file):
"""Loads a data file into a list of `InputBatch`s."""
label_map = {}
for (i, label) in enumerate(label_list):
label_map[label] = i
writer = tf.python_io.TFRecordWriter(output_file)
for (ex_index, example) in enumerate(examples):
tokens_a = tokenizer.tokenize(example.text_a)
if ex_index % 10000 == 0:
tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
tokens_b = None
if example.text_b:
tokens_b = tokenizer.tokenize(example.text_b)
if 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:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[0:(max_seq_length - 2)]
# 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 unambiguously 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:
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)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
label_id = label_map[example.label]
if ex_index < 5:
tf.logging.info("*** Example ***")
tf.logging.info("guid: %s" % (example.guid))
tf.logging.info("tokens: %s" % " ".join(
[tokenization.printable_text(x) for x in tokens]))
tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
tf.logging.info(
"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
tf.logging.info("label: %s (id = %d)" % (example.label, label_id))
def create_int_feature(values):
feature = tf.train.Feature(
int64_list=tf.train.Int64List(value=list(values)))
return feature
features = collections.OrderedDict()
features["input_ids"] = create_int_feature(input_ids)
features["input_mask"] = create_int_feature(input_mask)
features["segment_ids"] = create_int_feature(segment_ids)
features["label_ids"] = create_int_feature([label_id])
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
writer.write(tf_example.SerializeToString())
def predict_from_model(self, task):
ans = []
text = task["text"]
text = text.replace("?", ".")
text = text.replace("!", ".")
text = text.replace("<…>", "[MASK]")
text = text.replace("<...>", "[MASK]")
text = text.replace("...", "[MASK]")
text = text.replace("…", "[MASK]")
key = " "
for var in self.option:
if isinstance(var, list):
if var[0] in text and var[1] in text:
key = var[0] + var[1]
break
else:
if var in text:
key = var
break
sentences = text.split('.')
second_sen = ""
cnt = 0
for sen in sentences:
if "[MASK]" in sen:
second_sen = sen[4:]
break
cnt += 1
first_sen = sentences[cnt - 1][4:]
first_sen += '.'
second_sen += '.'
sentence = first_sen + ' ' + second_sen
sentence = sentence.replace(' [MASK] ', '[MASK]')
sentence = sentence.replace('[MASK] ', '[MASK]')
sentence = sentence.replace(' [MASK]',
'[MASK]') # удаляем лишние пробелы
sentence = sentence.split('[MASK]')
tokens = ['[CLS]']
upper_case = False
for i in range(len(sentence)):
if i == 0:
tokens = tokens + self.tokenizer.tokenize(sentence[i])
else:
if tokens[-1] == '.':
upper_case = True
tokens = tokens + ['[MASK]'] + self.tokenizer.tokenize(
sentence[i])
tokens = tokens + ['[SEP]']
token_input = self.tokenizer.convert_tokens_to_ids(tokens)
token_input = token_input + [0] * (512 - len(token_input))
mask_input = [0] * 512
for i in range(len(mask_input)):
if token_input[i] == 103:
mask_input[i] = 1
seg_input = [0] * 512
token_input = np.asarray([token_input])
mask_input = np.asarray([mask_input])
seg_input = np.asarray([seg_input])
predicts = self.model.predict([token_input, seg_input, mask_input])
predicts = predicts[0]
if key == " ":
vals = np.amax(predicts, axis=-1)
predicts = np.argmax(predicts, axis=-1)
predicts = predicts[0][:len(tokens)]
out = []
for i in range(len(mask_input[0])):
if mask_input[0][
i] == 1: # [0][i], т.к. сеть возвращает batch с формой (1,512), где в первом элементе наш результат
out.append(predicts[i])
out = self.tokenizer.convert_ids_to_tokens(
out) # индексы в текстовые токены
out = ' '.join(out) # объединяем токены в строку с пробелами
out = tokenization.printable_text(out) # в удобочитаемый текст
out = out.replace(' ##', '')
return out.lower()
else:
word_list = self.option_to_list[key]
new_word_list = []
if upper_case:
for word in word_list:
new_word_list.append(word[0].upper() + word[1:])
else:
new_word_list = word_list
#print(new_word_list)
id_word_list = self.tokenizer.convert_tokens_to_ids(new_word_list)
ID_prob = []
for i in range(len(mask_input[0])):
if mask_input[0][i] == 1:
for ID in id_word_list:
ID_prob.append([
predicts[0][i][ID],
self.tokenizer.convert_ids_to_tokens([ID])
])
ID_prob = sorted(ID_prob, key=lambda x: x[0], reverse=True)
return ID_prob[0][1][0].lower()
def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer):
"""Loads a data file into a list of `InputBatch`s."""
label_map = dict()
for label in label_list:
label_map[label] = len(label_map)
reverse_label_map = dict([(v,k) for (k,v) in label_map.items()])
max_len_in_data = 0
features = []
for (ex_index, example) in enumerate(examples):
tokens_a, tokens_map_a = tokenizer.tokenize_with_map(example.text_a)
example.text_a_map = tokens_map_a
if len(tokens_a) > max_len_in_data:
max_len_in_data = len(tokens_a)
max_len_in_data_tokens = tokens_a
tokens_b = None
if example.text_b:
tokens_b, tokens_map_b = tokenizer.tokenize_with_map(example.text_b)
example.text_b_map = tokens_map_b
if 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:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[0:(max_seq_length - 2)]
# Account for [SEP] with "-1"
# if len(tokens_a) > max_seq_length - 1:
# tokens_a = tokens_a[0:(max_seq_length - 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 = []
# try to not add [CLS]
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:
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)
label_ids = [label_map["[CLS]"]] # `[CLS]' symbol
# label_ids = [] # no `[CLS]' symbol
for ori_pos in tokens_map_a:
t_l = example.label[ori_pos]
label_ids.append(label_map[t_l])
if len(label_ids) == len(input_ids) - 1: # exclude last [SEP]
break
label_ids.append(label_map["[SEP]"]) # `[SEP]' symbol
assert len(label_ids) == len(input_ids), "Label and sent len diff: {} and {}".format(len(label_ids), 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)
label_ids.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
if ex_index < 3:
logger.info("*** Example ***")
logger.info("guid: %s" % (example.guid))
logger.info("tokens: %s" % " ".join(
[tokenization.printable_text(x) for x in tokens]))
logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
logger.info(
"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
logger.info("labels: %s (id = %s)" % (' '.join([reverse_label_map[ll] for ll in label_ids]), label_ids))
features.append(
InputFeatures(
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_id=label_ids))
logger.info("Max length in data {}: {}".format(max_len_in_data, ' '.join(max_len_in_data_tokens)))
return features
def convert_examples_to_features(examples, seq_length, tokenizer):
"""Loads a data file into a list of `InputBatch`s."""
features = []
for (ex_index, example) in enumerate(examples):
tokens_a = tokenizer.tokenize(example.text_a)
tokens_b = None
if example.text_b:
tokens_b = tokenizer.tokenize(example.text_b)
if 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, seq_length - 3)
else:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > seq_length - 2:
tokens_a = tokens_a[0:(seq_length - 2)]
# 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 unambiguously 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 = []
input_type_ids = []
tokens.append("[CLS]")
input_type_ids.append(0)
for token in tokens_a:
tokens.append(token)
input_type_ids.append(0)
tokens.append("[SEP]")
input_type_ids.append(0)
if tokens_b:
for token in tokens_b:
tokens.append(token)
input_type_ids.append(1)
tokens.append("[SEP]")
input_type_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) < seq_length:
input_ids.append(0)
input_mask.append(0)
input_type_ids.append(0)
assert len(input_ids) == seq_length
assert len(input_mask) == seq_length
assert len(input_type_ids) == seq_length
if ex_index < 5:
tf.logging.info("*** Example ***")
tf.logging.info("unique_id: %s" % (example.unique_id))
tf.logging.info("tokens: %s" % " ".join(
[tokenization.printable_text(x) for x in tokens]))
tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
tf.logging.info(
"input_type_ids: %s" % " ".join([str(x) for x in input_type_ids]))
features.append(
InputFeatures(
unique_id=example.unique_id,
tokens=tokens,
input_ids=input_ids,
input_mask=input_mask,
input_type_ids=input_type_ids))
return features
def write_instance_to_example_files(instances, tokenizer, max_seq_length,
max_predictions_per_seq, output_files):
"""Create TF example files from `TrainingInstance`s."""
writers = []
for output_file in output_files:
writers.append(tf.python_io.TFRecordWriter(output_file))
writer_index = 0
total_written = 0
for (inst_index, instance) in enumerate(instances):
input_ids = tokenizer.convert_tokens_to_ids(instance.tokens)
input_mask = [1] * len(input_ids)
segment_ids = list(instance.segment_ids)
assert len(input_ids) <= max_seq_length
#把每一个句子都弄成一样长的.如果不到max_seq_length就补0.
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
masked_lm_positions = list(instance.masked_lm_positions)
masked_lm_ids = tokenizer.convert_tokens_to_ids(
instance.masked_lm_labels)
masked_lm_weights = [1.0] * len(masked_lm_ids)
while len(masked_lm_positions) < max_predictions_per_seq:
masked_lm_positions.append(0)
masked_lm_ids.append(0)
masked_lm_weights.append(0.0)
next_sentence_label = 1 if instance.is_random_next else 0
features = collections.OrderedDict()
features["input_ids"] = create_int_feature(input_ids)
features["input_mask"] = create_int_feature(input_mask)
features["segment_ids"] = create_int_feature(segment_ids)
features["masked_lm_positions"] = create_int_feature(
masked_lm_positions)
features["masked_lm_ids"] = create_int_feature(masked_lm_ids)
features["masked_lm_weights"] = create_float_feature(masked_lm_weights)
features["next_sentence_labels"] = create_int_feature(
[next_sentence_label])
tf_example = tf.train.Example(features=tf.train.Features(
feature=features))
writers[writer_index].write(tf_example.SerializeToString())
writer_index = (writer_index + 1) % len(writers)
total_written += 1
if inst_index < 20:
tf.logging.info("*** Example ***")
tf.logging.info("tokens: %s" % " ".join(
[tokenization.printable_text(x) for x in instance.tokens]))
for feature_name in features.keys():
feature = features[feature_name]
values = []
if feature.int64_list.value:
values = feature.int64_list.value
elif feature.float_list.value:
values = feature.float_list.value
tf.logging.info(
"%s: %s" %
(feature_name, " ".join([str(x) for x in values])))
for writer in writers:
writer.close()
tf.logging.info("Wrote %d total instances", total_written)
def convert_documents_to_features(examples, tokenizer, max_seq_length,
doc_stride):
"""Loads a data file into a list of `InputBatch`s."""
unique_id = 1000000000
features = []
for (example_index,
example) in enumerate(tqdm(examples, desc='Converting documents')):
# Creating a map between word <=> (sub)token
tok_to_word_index = []
word_to_tok_index = [] # word to (start of) subtokens
all_doc_tokens = []
for (i, word) in enumerate(example.doc_words):
word_to_tok_index.append(len(all_doc_tokens))
sub_tokens = tokenizer.tokenize(word)
for sub_token in sub_tokens:
tok_to_word_index.append(i)
all_doc_tokens.append(sub_token)
# The -2 accounts for [CLS], [SEP]
max_tokens_for_doc = max_seq_length - 2
# Split sequence by max_seq_len with doc_stride, _DocSpan is based on tokens without [CLS], [SEP]
_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
"DocSpan", ["start", "length"])
doc_spans = []
start_tok_offset = 0 # From all_doc_tokens
# Get doc_spans with stride and offset
while start_tok_offset < len(all_doc_tokens):
length = len(all_doc_tokens) - start_tok_offset
if length > max_tokens_for_doc:
length = max_tokens_for_doc
doc_spans.append(_DocSpan(start=start_tok_offset, length=length))
if start_tok_offset + length == len(all_doc_tokens):
break
start_tok_offset += min(
length, doc_stride) # seems to prefer doc_stride always
assert doc_stride < length, "length is no larger than doc_stride for {}".format(
doc_spans)
# Iterate each doc_span and make out_tokens
for (doc_span_index, doc_span) in enumerate(doc_spans):
out_tokens = [] # doc
out_tokens.append("[CLS]")
token_to_word_map = {
} # The difference with tok_to_word_index is it includes special tokens
token_is_max_context = {}
# For each doc token, create token_to_word_map and is_max_context, and add to out_tokens
for i in range(doc_span.length):
split_token_index = doc_span.start + i
token_to_word_map[len(
out_tokens)] = tok_to_word_index[split_token_index]
is_max_context = _check_is_max_context(doc_spans,
doc_span_index,
split_token_index)
token_is_max_context[len(out_tokens)] = is_max_context
out_tokens.append(all_doc_tokens[split_token_index])
out_tokens.append("[SEP]")
# Convert to ids and masks
input_ids = tokenizer.convert_tokens_to_ids(out_tokens)
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)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
# Printing for debug
if example_index < 1 and doc_span_index < 1:
logger.info("*** Example ***")
logger.info("unique_id: %s" % (unique_id))
logger.info("example_index: %s" % (example_index))
logger.info("doc_span_index: %s" % (doc_span_index))
logger.info("tokens: %s" % " ".join(
[tokenization.printable_text(x) for x in out_tokens]))
logger.info("token_to_word_map: %s" % " ".join([
"%d:%d" % (x, y)
for (x, y) in six.iteritems(token_to_word_map)
]))
logger.info("token_is_max_context: %s" % " ".join([
"%d:%s" % (x, y)
for (x, y) in six.iteritems(token_is_max_context)
]))
logger.info("input_ids: %s" %
" ".join([str(x) for x in input_ids]))
logger.info("input_mask: %s" %
" ".join([str(x) for x in input_mask]))
# Append feature
features.append(
ContextFeatures(unique_id=unique_id,
example_index=example_index,
doc_span_index=doc_span_index,
tokens=out_tokens,
token_to_word_map=token_to_word_map,
token_is_max_context=token_is_max_context,
input_ids=input_ids,
input_mask=input_mask))
unique_id += 1
return features
def convert_single_example(ex_index, example, label_list, max_seq_length,
tokenizer, mode):
label_map = {}
for (i, label) in enumerate(label_list, 1):
label_map[label] = i
with open(os.path.join(FLAGS.output_dir, 'label2id.pkl'), 'wb') as w:
pickle.dump(label_map, w)
textlist = example.text.split(' ')
labellist = example.label.split(' ')
tokens = []
labels = []
orig_to_tok = []
for i, word in enumerate(textlist):
token = tokenizer.tokenize(word)
orig_to_tok.append(len(tokens) + 1) # +1 for CLS
tokens.extend(token)
label_1 = labellist[i]
for m in range(len(token)):
if m == 0:
labels.append(label_1)
else:
labels.append("X")
# tokens = tokenizer.tokenize(example.text)
if len(tokens) >= max_seq_length - 1:
tokens = tokens[0:(max_seq_length - 2)]
labels = labels[0:(max_seq_length - 2)]
ntokens = []
segment_ids = []
label_ids = []
my_labels = []
ntokens.append("[CLS]")
segment_ids.append(0)
# append("O") or append("[CLS]") not sure!
label_ids.append(label_map["[CLS]"])
my_labels.append("[CLS]")
for i, token in enumerate(tokens):
ntokens.append(token)
segment_ids.append(0)
label_ids.append(label_map[labels[i]])
my_labels.append(labels[i])
ntokens.append("[SEP]")
segment_ids.append(0)
# append("O") or append("[SEP]") not sure!
label_ids.append(label_map["[SEP]"])
my_labels.append("[SEP]")
input_ids = tokenizer.convert_tokens_to_ids(ntokens)
input_mask = [1] * len(input_ids)
#label_mask = [1] * len(input_ids)
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
# we don't concerned about it!
label_ids.append(0)
my_labels.append(0)
ntokens.append("**NULL**")
#label_mask.append(0)
# print(len(input_ids))
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
assert len(label_ids) == max_seq_length
#assert len(label_mask) == max_seq_length
if ex_index < 5:
tf.logging.info("*** Example ***")
tf.logging.info("guid: %s" % (example.guid))
tf.logging.info(
"tokens: %s" %
" ".join([tokenization.printable_text(x) for x in tokens]))
tf.logging.info("input_ids: %s" % " ".join([str(x)
for x in input_ids]))
tf.logging.info("input_mask: %s" %
" ".join([str(x) for x in input_mask]))
tf.logging.info("segment_ids: %s" %
" ".join([str(x) for x in segment_ids]))
tf.logging.info("label_ids: %s" % " ".join([str(x)
for x in label_ids]))
#tf.logging.info("label_mask: %s" % " ".join([str(x) for x in label_mask]))
feature = InputFeatures(
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_ids=label_ids,
#label_mask = label_mask
)
#write_tokens(ntokens,mode)
my_write_tokens(ntokens, my_labels, mode)
return feature
def convert_single_example(ex_index, example, label_list, max_seq_length,
tokenizer):
"""Converts a single `RaceExample` into a single `InputFeatures`."""
if isinstance(example, PaddingInputExample):
four_options = []
for i in range(len(label_list)):
option = Option(input_ids=[0] * max_seq_length,
input_mask=[0] * max_seq_length,
segment_ids=[0] * max_seq_length)
four_options.append(option)
return InputFeature(four_options=four_options,
label_id=0,
is_real_example=False)
label_map = {'A': 0, 'B': 1, 'C': 2, 'D': 3}
tokens_article = tokenizer.tokenize(example.article)
tokens_question = tokenizer.tokenize(example.question)
four_options = []
for option in example.four_options:
tokens_option = tokenizer.tokenize(option)
max_article_length = max_seq_length - len(tokens_question) - len(
tokens_option) - 4
tokens_article_temp = tokens_article[:max_article_length]
# 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 unambiguously 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 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_article_temp:
tokens.append(token)
segment_ids.append(0)
tokens.append("[SEP]")
segment_ids.append(0)
for token in tokens_question:
tokens.append(token)
segment_ids.append(1)
tokens.append("[SEP]")
segment_ids.append(1)
for token in tokens_option:
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)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
label_id = label_map[example.label]
if ex_index < 1:
tf.logging.info("*** Example ***")
tf.logging.info("id: %s" % example.id)
tf.logging.info(
"tokens: %s" %
" ".join([tokenization.printable_text(x) for x in tokens]))
tf.logging.info("input_ids: %s" %
" ".join([str(x) for x in input_ids]))
tf.logging.info("input_mask: %s" %
" ".join([str(x) for x in input_mask]))
tf.logging.info("segment_ids: %s" %
" ".join([str(x) for x in segment_ids]))
tf.logging.info("label: %s (id = %d)" % (example.label, label_id))
token = Option(input_ids, input_mask, segment_ids)
four_options.append(token)
feature = InputFeature(four_options=four_options,
label_id=label_id,
is_real_example=True)
return feature
def write_instance_to_example_files(instances, tokenizer, max_seq_length,
max_predictions_per_seq, output_files):
"""Create TF example files from `TrainingInstance`s."""
writers = []
for output_file in output_files:
writers.append(tf.io.TFRecordWriter(output_file))
writer_index = 0
total_written = 0
for (inst_index, instance) in enumerate(instances):
input_ids = tokenizer.convert_tokens_to_ids(instance.tokens)
input_mask = [1] * len(input_ids)
segment_ids = list(instance.segment_ids)
token_boundary = list(instance.token_boundary)
assert len(input_ids) <= max_seq_length
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
token_boundary.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
masked_lm_positions = list(instance.masked_lm_positions)
masked_lm_ids = tokenizer.convert_tokens_to_ids(
instance.masked_lm_labels)
masked_lm_weights = [1.0] * len(masked_lm_ids)
multiplier = 1 + int(FLAGS.do_permutation)
while len(masked_lm_positions) < max_predictions_per_seq * multiplier:
masked_lm_positions.append(0)
masked_lm_ids.append(0)
masked_lm_weights.append(0.0)
sentence_order_label = 1 if instance.is_random_next else 0
features = collections.OrderedDict()
features["input_ids"] = create_int_feature(input_ids)
features["input_mask"] = create_int_feature(input_mask)
features["segment_ids"] = create_int_feature(segment_ids)
features["token_boundary"] = create_int_feature(token_boundary)
features["masked_lm_positions"] = create_int_feature(
masked_lm_positions)
features["masked_lm_ids"] = create_int_feature(masked_lm_ids)
features["masked_lm_weights"] = create_float_feature(masked_lm_weights)
# Note: We keep this feature name `next_sentence_labels` to be compatible
# with the original data created by lanzhzh@. However, in the ALBERT case
# it does contain sentence_order_label.
features["next_sentence_labels"] = create_int_feature(
[sentence_order_label])
tf_example = tf.train.Example(features=tf.train.Features(
feature=features))
writers[writer_index].write(tf_example.SerializeToString())
writer_index = (writer_index + 1) % len(writers)
total_written += 1
if inst_index < 20:
logging.info("*** Example ***")
logging.info("tokens: %s" % " ".join(
[tokenization.printable_text(x) for x in instance.tokens]))
for feature_name in features.keys():
feature = features[feature_name]
values = []
if feature.int64_list.value:
values = feature.int64_list.value
elif feature.float_list.value:
values = feature.float_list.value
logging.info("%s: %s" %
(feature_name, " ".join([str(x)
for x in values])))
for writer in writers:
writer.close()
meta_data = {
"task_type": "albert_pretraining",
"train_data_size": total_written,
"max_seq_length": max_seq_length,
"max_predictions_per_seq": FLAGS.max_predictions_per_seq
}
with tf.io.gfile.GFile(FLAGS.meta_data_file_path, "w") as writer:
writer.write(json.dumps(meta_data, indent=4) + "\n")
logging.info("Wrote %d total instances", total_written)
def convert_examples_to_features(examples, tokenizer, max_seq_length,
doc_stride, max_query_length, is_training):
"""Loads a data file into a list of `InputBatch`s."""
unique_id = 1000000000
features = []
for (example_index, example) in enumerate(examples):
query_tokens = tokenizer.tokenize(example.question_text)
if len(query_tokens) > max_query_length:
query_tokens = query_tokens[0:max_query_length]
tok_to_orig_index = []
orig_to_tok_index = []
all_doc_tokens = []
for (i, token) in enumerate(example.doc_tokens):
orig_to_tok_index.append(len(all_doc_tokens))
sub_tokens = tokenizer.tokenize(token)
for sub_token in sub_tokens:
tok_to_orig_index.append(i)
all_doc_tokens.append(sub_token)
tok_start_position = None
tok_end_position = None
if is_training:
tok_start_position = orig_to_tok_index[example.start_position]
if example.end_position < len(example.doc_tokens) - 1:
tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
else:
tok_end_position = len(all_doc_tokens) - 1
(tok_start_position, tok_end_position) = _improve_answer_span(
all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
example.orig_answer_text)
# The -3 accounts for [CLS], [SEP] and [SEP]
max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
# We can have documents that are longer than the maximum sequence length.
# To deal with this we do a sliding window approach, where we take chunks
# of the up to our max length with a stride of `doc_stride`.
_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
"DocSpan", ["start", "length"])
doc_spans = []
start_offset = 0
while start_offset < len(all_doc_tokens):
length = len(all_doc_tokens) - start_offset
if length > max_tokens_for_doc:
length = max_tokens_for_doc
doc_spans.append(_DocSpan(start=start_offset, length=length))
if start_offset + length == len(all_doc_tokens):
break
start_offset += min(length, doc_stride)
for (doc_span_index, doc_span) in enumerate(doc_spans):
tokens = []
token_to_orig_map = {}
token_is_max_context = {}
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for token in query_tokens:
tokens.append(token)
segment_ids.append(0)
tokens.append("[SEP]")
segment_ids.append(0)
for i in range(doc_span.length):
split_token_index = doc_span.start + i
token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
is_max_context = _check_is_max_context(doc_spans, doc_span_index,
split_token_index)
token_is_max_context[len(tokens)] = is_max_context
tokens.append(all_doc_tokens[split_token_index])
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)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
start_position = None
end_position = None
if is_training:
# For training, if our document chunk does not contain an annotation
# we throw it out, since there is nothing to predict.
doc_start = doc_span.start
doc_end = doc_span.start + doc_span.length - 1
if (example.start_position < doc_start or
example.end_position < doc_start or
example.start_position > doc_end or example.end_position > doc_end):
continue
doc_offset = len(query_tokens) + 2
start_position = tok_start_position - doc_start + doc_offset
end_position = tok_end_position - doc_start + doc_offset
if example_index < 20:
tf.logging.info("*** Example ***")
tf.logging.info("unique_id: %s" % (unique_id))
tf.logging.info("example_index: %s" % (example_index))
tf.logging.info("doc_span_index: %s" % (doc_span_index))
tf.logging.info("tokens: %s" % " ".join(
[tokenization.printable_text(x) for x in tokens]))
tf.logging.info("token_to_orig_map: %s" % " ".join(
["%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)]))
tf.logging.info("token_is_max_context: %s" % " ".join([
"%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context)
]))
tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
tf.logging.info(
"input_mask: %s" % " ".join([str(x) for x in input_mask]))
tf.logging.info(
"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
if is_training:
answer_text = " ".join(tokens[start_position:(end_position + 1)])
tf.logging.info("start_position: %d" % (start_position))
tf.logging.info("end_position: %d" % (end_position))
tf.logging.info(
"answer: %s" % (tokenization.printable_text(answer_text)))
features.append(
InputFeatures(
unique_id=unique_id,
example_index=example_index,
doc_span_index=doc_span_index,
tokens=tokens,
token_to_orig_map=token_to_orig_map,
token_is_max_context=token_is_max_context,
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
start_position=start_position,
end_position=end_position))
unique_id += 1
return features
def convert_examples_to_features(examples, seq_length, tokenizer):
"""Loads a data file into a list of `InputBatch`s."""
features = []
for (ex_index, example) in enumerate(examples):
tokens_a = tokenizer.tokenize(example.text_a)
tokens_b = None
if example.text_b:
tokens_b = tokenizer.tokenize(example.text_b)
if 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, seq_length - 3)
else:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > seq_length - 2:
tokens_a = tokens_a[0:(seq_length - 2)]
# 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 unambiguously 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 = []
input_type_ids = []
tokens.append("[CLS]")
input_type_ids.append(0)
for token in tokens_a:
tokens.append(token)
input_type_ids.append(0)
tokens.append("[SEP]")
input_type_ids.append(0)
if tokens_b:
for token in tokens_b:
tokens.append(token)
input_type_ids.append(1)
tokens.append("[SEP]")
input_type_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) < seq_length:
input_ids.append(0)
input_mask.append(0)
input_type_ids.append(0)
assert len(input_ids) == seq_length
assert len(input_mask) == seq_length
assert len(input_type_ids) == seq_length
if ex_index < 5:
tf.logging.info("*** Example ***")
tf.logging.info("unique_id: %s" % (example.unique_id))
tf.logging.info("tokens: %s" % " ".join(
[tokenization.printable_text(x) for x in tokens]))
tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
tf.logging.info(
"input_type_ids: %s" % " ".join([str(x) for x in input_type_ids]))
features.append(
InputFeatures(
unique_id=example.unique_id,
tokens=tokens,
input_ids=input_ids,
input_mask=input_mask,
input_type_ids=input_type_ids))
return features
def convert_single_example(ex_index, example, slot_list, class_types,
max_seq_length, tokenizer):
"""Converts a single `InputExample` into a single `InputFeatures`."""
if isinstance(example, run_classifier.PaddingInputExample):
return InputFeatures(input_ids=[0] * max_seq_length,
input_mask=[0] * max_seq_length,
segment_ids=[0] * max_seq_length,
start_pos={slot: 0
for slot in slot_list},
end_pos={slot: 0
for slot in slot_list},
class_label_id={slot: 0
for slot in slot_list},
is_real_example=False,
guid="NONE")
class_label_id_dict = {}
start_pos_dict = {}
end_pos_dict = {}
for slot in slot_list:
tokens_a, token_labels_a = tokenize_text_and_label(
example.text_a, example.text_a_label, slot, tokenizer)
tokens_b, token_labels_b = tokenize_text_and_label(
example.text_b, example.text_b_label, slot, tokenizer)
input_text_too_long = util.truncate_length_and_warn(
tokens_a, tokens_b, max_seq_length, example.guid)
if input_text_too_long:
if ex_index < 10:
if len(token_labels_a) > len(tokens_a):
tf.logging.info(' tokens_a truncated labels: %s' %
str(token_labels_a[len(tokens_a):]))
if len(token_labels_b) > len(tokens_b):
tf.logging.info(' tokens_b truncated labels: %s' %
str(token_labels_b[len(tokens_b):]))
token_labels_a = token_labels_a[:len(tokens_a)]
token_labels_b = token_labels_b[:len(tokens_b)]
assert len(token_labels_a) == len(tokens_a)
assert len(token_labels_b) == len(tokens_b)
token_label_ids = util.get_token_label_ids(token_labels_a,
token_labels_b,
max_seq_length)
class_label_id_dict[slot] = class_types.index(
example.class_label[slot])
start_pos_dict[slot], end_pos_dict[slot] = util.get_start_end_pos(
example.class_label[slot], token_label_ids, max_seq_length)
tokens, input_ids, input_mask, segment_ids = util.get_bert_input(
tokens_a, tokens_b, max_seq_length, tokenizer)
if ex_index < 10:
tf.logging.info("*** Example ***")
tf.logging.info("guid: %s" % (example.guid))
tf.logging.info(
"tokens: %s" %
" ".join([tokenization.printable_text(x) for x in tokens]))
tf.logging.info("input_ids: %s" % " ".join([str(x)
for x in input_ids]))
tf.logging.info("input_mask: %s" %
" ".join([str(x) for x in input_mask]))
tf.logging.info("segment_ids: %s" %
" ".join([str(x) for x in segment_ids]))
tf.logging.info("start_pos: %s" % str(start_pos_dict))
tf.logging.info("end_pos: %s" % str(end_pos_dict))
tf.logging.info("class_label_id: %s" % str(class_label_id_dict))
feature = InputFeatures(input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
start_pos=start_pos_dict,
end_pos=end_pos_dict,
class_label_id=class_label_id_dict,
is_real_example=True,
guid=example.guid)
return feature, input_text_too_long
def convert_single_example(ex_index, example, label_list, max_seq_length,
tokenizer):
"""Converts a single `InputExample` into a single `InputFeatures`."""
if isinstance(example, PaddingInputExample):
return InputFeatures(
input_ids=[0] * max_seq_length,
input_mask=[0] * max_seq_length,
segment_ids=[0] * max_seq_length,
label_id=0,
is_real_example=False,
)
label_map = {}
for (i, label) in enumerate(label_list):
label_map[label] = i
tokens_a = tokenizer.tokenize(example.text_a)
tokens_b = None
if example.text_b:
tokens_b = tokenizer.tokenize(example.text_b)
if 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:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[0:(max_seq_length - 2)]
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:
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)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
label_id = label_map[str(example.label)]
if ex_index < 5:
tf.logging.info("*** Example ***")
tf.logging.info("guid: %s" % (example.guid))
tf.logging.info(
"tokens: %s" %
" ".join([tokenization.printable_text(x) for x in tokens]))
tf.logging.info("input_ids: %s" % " ".join([str(x)
for x in input_ids]))
tf.logging.info("input_mask: %s" %
" ".join([str(x) for x in input_mask]))
tf.logging.info("segment_ids: %s" %
" ".join([str(x) for x in segment_ids]))
tf.logging.info("label: %s (id = %d)" % (example.label, label_id))
tf.logging.info("meta: %s" % (example.meta))
feature = InputFeatures(input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_id=label_id,
is_real_example=True,
meta=example.meta)
return feature
def convert_single_example(ex_index, example, label_list, max_seq_length,
tokenizer):
"""Converts a single `InputExample` into a single `InputFeatures`."""
if isinstance(example, PaddingInputExample):
return InputFeatures(input_ids=[0] * max_seq_length,
input_mask=[0] * max_seq_length,
segment_ids=[0] * max_seq_length,
label_id=0,
is_real_example=False)
label_map = {}
for (i, label) in enumerate(label_list):
label_map[label] = i
input_tokens = example.text.split(" ")
token_labels = example.label.split(" ")
pre_tokens = []
pre_labels = []
for i, word in enumerate(input_tokens):
# word tokenize, if not in vocab.txt of bert, it will use WordPiece. For word being tokenized, add label 'X'
token = tokenizer.tokenize(word)
pre_tokens.extend(token)
pre_label = token_labels[i]
for m in range(len(token)):
pre_labels.append(pre_label)
# if m == 0:
# pre_labels.append(pre_label)
# else:
# pre_labels.append(pre_label)
assert len(pre_tokens) == len(pre_labels), "{} \t {}".format(
pre_tokens, pre_labels)
# Account for [CLS] and [SEP] with "- 2"
if len(pre_tokens) > max_seq_length - 2:
pre_tokens = pre_tokens[0:(max_seq_length - 2)]
pre_labels = pre_labels[0:(max_seq_length - 2)]
# 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 unambiguously 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 the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens = []
segment_ids = []
label_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
label_ids.append(label_map["[CLS]"])
for i, token in enumerate(pre_tokens):
tokens.append(token)
segment_ids.append(0)
label_ids.append(label_map[pre_labels[i]])
tokens.append("[SEP]")
segment_ids.append(0)
label_ids.append(label_map["[SEP]"])
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)
label_ids.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
assert len(label_ids) == max_seq_length
# label_id = label_map[example.label]
if ex_index < 5:
tf.logging.info("*** Example ***")
tf.logging.info("guid: %s" % (example.guid))
tf.logging.info(
"tokens: %s" %
" ".join([tokenization.printable_text(x) for x in tokens]))
tf.logging.info("input_ids: %s" % " ".join([str(x)
for x in input_ids]))
tf.logging.info("input_mask: %s" %
" ".join([str(x) for x in input_mask]))
tf.logging.info("segment_ids: %s" %
" ".join([str(x) for x in segment_ids]))
tf.logging.info("label: %s " % " ".join([str(x) for x in label_ids]))
feature = InputFeatures(input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_ids=label_ids,
is_real_example=True)
return feature
def convert_single_example(ex_index, example, label_list, max_seq_length,
tokenizer):
"""Converts a single `InputExample` into a single `InputFeatures`."""
if isinstance(example, PaddingInputExample):
return InputFeatures(
input_ids=[0] * max_seq_length,
input_mask=[0] * max_seq_length,
segment_ids=[0] * max_seq_length,
label_id=0,
label_weight=1.,
is_real_example=False)
label_map = {}
for (i, label) in enumerate(label_list):
label_map[label] = i
tokens_a = tokenizer.tokenize(example.text_a)
tokens_b = None
if example.text_b:
tokens_b = tokenizer.tokenize(example.text_b)
if 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:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[0:(max_seq_length - 2)]
# 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 unambiguously 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 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:
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)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
label_id = label_map[example.label]
if ex_index < 5:
tf.logging.info("*** Example ***")
tf.logging.info("guid: %s" % (example.guid))
tf.logging.info("tokens: %s" % " ".join(
[tokenization.printable_text(x) for x in tokens]))
tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
tf.logging.info("label: %s (id = %d)" % (example.label, label_id))
feature = InputFeatures(
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_id=label_id,
label_weight=example.label_weight,
is_real_example=True)
return feature
def convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer, mode):
"""
:param ex_index: example num
:param example:
:param label_list: all labels
:param max_seq_length:
:param tokenizer: WordPiece tokenization
:param mode:
:return: feature
IN this part we should rebuild input sentences to the following format.
example:[Jim,Hen,##son,was,a,puppet,##eer]
labels: [I-PER,I-PER,X,O,O,O,X]
"""
label_map = {}
#here start with zero this means that "[PAD]" is zero
for (i,label) in enumerate(label_list):
label_map[label] = i
#with open(FLAGS.middle_output+"/label2id.pkl",'wb') as w:
# pickle.dump(label_map,w)
textlist = example.text.split(' ')
labellist = example.label.split(' ')
tokens = []
labels = []
for i,(word,label) in enumerate(zip(textlist,labellist)):
token = tokenizer.tokenize(word)
tokens.extend(token)
for i,_ in enumerate(token):
if i==0:
labels.append(label)
else:
labels.append("X")
# only Account for [CLS] with "- 1".
if len(tokens) >= max_seq_length - 1:
tokens = tokens[0:(max_seq_length - 1)]
labels = labels[0:(max_seq_length - 1)]
ntokens = []
segment_ids = []
label_ids = []
ntokens.append("[CLS]")
segment_ids.append(0)
label_ids.append(label_map["[CLS]"])
for i, token in enumerate(tokens):
ntokens.append(token)
segment_ids.append(0)
label_ids.append(label_map[labels[i]])
# after that we don't add "[SEP]" because we want a sentence don't have
# stop tag, because i think its not very necessary.
# or if add "[SEP]" the model even will cause problem, special the crf layer was used.
input_ids = tokenizer.convert_tokens_to_ids(ntokens)
mask = [1]*len(input_ids)
#use zero to padding and you should
while len(input_ids) < max_seq_length:
input_ids.append(0)
mask.append(0)
segment_ids.append(0)
label_ids.append(0)
ntokens.append("[PAD]")
assert len(input_ids) == max_seq_length
assert len(mask) == max_seq_length
assert len(segment_ids) == max_seq_length
assert len(label_ids) == max_seq_length
assert len(ntokens) == max_seq_length
if ex_index < 3:
logging.info("*** Example ***")
logging.info("guid: %s" % (example.guid))
logging.info("tokens: %s" % " ".join(
[tokenization.printable_text(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 mask]))
logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
logging.info("label_ids: %s" % " ".join([str(x) for x in label_ids]))
feature = InputFeatures(
input_ids=input_ids,
mask=mask,
segment_ids=segment_ids,
label_ids=label_ids,
)
# we need ntokens because if we do predict it can help us return to original token.
return feature,ntokens,label_ids
def write_instance_to_example_files(instances, tokenizer, max_seq_length,
max_predictions_per_seq, output_files):
"""Create TF example files from `TrainingInstance`s."""
writers = []
for output_file in output_files:
writers.append(tf.python_io.TFRecordWriter(output_file))
writer_index = 0
total_written = 0
for (inst_index, instance) in enumerate(instances):
input_ids = tokenizer.convert_tokens_to_ids(instance.tokens)
input_mask = [1] * len(input_ids)
segment_ids = list(instance.segment_ids)
assert len(input_ids) <= max_seq_length
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
masked_lm_positions = list(instance.masked_lm_positions)
masked_lm_ids = tokenizer.convert_tokens_to_ids(instance.masked_lm_labels)
masked_lm_weights = [1.0] * len(masked_lm_ids)
while len(masked_lm_positions) < max_predictions_per_seq:
masked_lm_positions.append(0)
masked_lm_ids.append(0)
masked_lm_weights.append(0.0)
next_sentence_label = 1 if instance.is_random_next else 0
features = collections.OrderedDict()
features["input_ids"] = create_int_feature(input_ids)
features["input_mask"] = create_int_feature(input_mask)
features["segment_ids"] = create_int_feature(segment_ids)
features["masked_lm_positions"] = create_int_feature(masked_lm_positions)
features["masked_lm_ids"] = create_int_feature(masked_lm_ids)
features["masked_lm_weights"] = create_float_feature(masked_lm_weights)
features["next_sentence_labels"] = create_int_feature([next_sentence_label])
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
writers[writer_index].write(tf_example.SerializeToString())
writer_index = (writer_index + 1) % len(writers)
total_written += 1
if inst_index < 20:
tf.logging.info("*** Example ***")
tf.logging.info("tokens: %s" % " ".join(
[tokenization.printable_text(x) for x in instance.tokens]))
for feature_name in features.keys():
feature = features[feature_name]
values = []
if feature.int64_list.value:
values = feature.int64_list.value
elif feature.float_list.value:
values = feature.float_list.value
tf.logging.info(
"%s: %s" % (feature_name, " ".join([str(x) for x in values])))
for writer in writers:
writer.close()
tf.logging.info("Wrote %d total instances", total_written)
def convert_examples_to_features(examples, tokenizer, max_seq_length,
doc_stride, max_query_length, is_training,
output_fn):
"""Loads a data file into a list of `InputBatch`s."""
unique_id = 1000000000
for (example_index, example) in enumerate(examples):
query_tokens = tokenizer.tokenize(example.question_text)
if len(query_tokens) > max_query_length:
query_tokens = query_tokens[0:max_query_length]
tok_to_orig_index = []
orig_to_tok_index = []
all_doc_tokens = []
for (i, token) in enumerate(example.doc_tokens):
orig_to_tok_index.append(len(all_doc_tokens))
sub_tokens = tokenizer.tokenize(token)
for sub_token in sub_tokens:
tok_to_orig_index.append(i)
all_doc_tokens.append(sub_token)
target = example.answer
# The -3 accounts for [CLS], [SEP] and [SEP]
max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
# We can have documents that are longer than the maximum sequence length.
# To deal with this we do a sliding window approach, where we take chunks
# of the up to our max length with a stride of `doc_stride`.
_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
"DocSpan", ["start", "length"])
doc_spans = []
start_offset = 0
while start_offset < len(all_doc_tokens):
length = len(all_doc_tokens) - start_offset
if length > max_tokens_for_doc:
length = max_tokens_for_doc
doc_spans.append(_DocSpan(start=start_offset, length=length))
if start_offset + length == len(all_doc_tokens):
break
start_offset += min(length, doc_stride)
for (doc_span_index, doc_span) in enumerate(doc_spans):
tokens = []
token_to_orig_map = {}
token_is_max_context = {}
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for token in query_tokens:
tokens.append(token)
segment_ids.append(0)
tokens.append("[SEP]")
segment_ids.append(0)
for i in range(doc_span.length):
split_token_index = doc_span.start + i
token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
is_max_context = _check_is_max_context(doc_spans, doc_span_index,
split_token_index)
token_is_max_context[len(tokens)] = is_max_context
tokens.append(all_doc_tokens[split_token_index])
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)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
if example_index < 1:
tf.logging.info("*** Example ***")
tf.logging.info("unique_id: %s" % (unique_id))
# tf.logging.info("example_index: %s" % (example_index))
# tf.logging.info("doc_span_index: %s" % (doc_span_index))
tf.logging.info("tokens: %s" % " ".join(
[tokenization.printable_text(x) for x in tokens]))
# tf.logging.info("token_to_orig_map: %s" % " ".join(
# ["%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)]))
# tf.logging.info("token_is_max_context: %s" % " ".join([
# "%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context)
# ]))
# tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
# tf.logging.info(
# "input_mask: %s" % " ".join([str(x) for x in input_mask]))
# tf.logging.info(
# "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
if is_training:
tf.logging.info(
"target: %d" % (target))
feature = InputFeatures(
unique_id=unique_id,
example_index=example_index,
doc_span_index=doc_span_index,
tokens=tokens,
token_to_orig_map=token_to_orig_map,
token_is_max_context=token_is_max_context,
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
target=target)
# Run callback
output_fn(feature)
unique_id += 1
