def _convert_example_to_record(self,
example,
max_seq_length,
tokenizer,
phase=None):
tokens = tokenization.convert_to_unicode(example.text_a).split(u"")
if phase != "predict":
labels = tokenization.convert_to_unicode(example.label).split(u"")
tokens, labels = self._reseg_token_label(tokens=tokens,
labels=labels,
tokenizer=tokenizer,
phase=phase)
if len(tokens) > max_seq_length - 2:
tokens = tokens[0:(max_seq_length - 2)]
labels = labels[0:(max_seq_length - 2)]
tokens = ["[CLS]"] + tokens + ["[SEP]"]
token_ids = tokenizer.convert_tokens_to_ids(tokens)
position_ids = list(range(len(token_ids)))
text_type_ids = [0] * len(token_ids)
no_entity_id = len(self.label_map) - 1
label_ids = [no_entity_id
] + [self.label_map[label]
for label in labels] + [no_entity_id]
Record = namedtuple(
'Record',
['token_ids', 'text_type_ids', 'position_ids', 'label_ids'])
record = Record(token_ids=token_ids,
text_type_ids=text_type_ids,
position_ids=position_ids,
label_ids=label_ids)
else:
tokens = self._reseg_token_label(tokens=tokens,
tokenizer=tokenizer,
phase=phase)
if len(tokens) > max_seq_length - 2:
tokens = tokens[0:(max_seq_length - 2)]
tokens = ["[CLS]"] + tokens + ["[SEP]"]
token_ids = tokenizer.convert_tokens_to_ids(tokens)
position_ids = list(range(len(token_ids)))
text_type_ids = [0] * len(token_ids)
Record = namedtuple('Record',
['token_ids', 'text_type_ids', 'position_ids'])
record = Record(
token_ids=token_ids,
text_type_ids=text_type_ids,
position_ids=position_ids,
)
return record
def _convert_text_to_feature(self, text):
"""
Convert the raw text to feature which is needed to run program (feed_vars).
"""
text_a = convert_to_unicode(text)
tokens_a = self.tokenizer.tokenize(text_a)
max_seq_len = 512
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > max_seq_len - 2:
tokens_a = tokens_a[0:(max_seq_len - 2)]
tokens = []
text_type_ids = []
tokens.append("[CLS]")
text_type_ids.append(0)
for token in tokens_a:
tokens.append(token)
text_type_ids.append(0)
tokens.append("[SEP]")
text_type_ids.append(0)
token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
position_ids = list(range(len(token_ids)))
task_ids = [0] * len(token_ids)
padded_token_ids, input_mask = pad_batch_data([token_ids],
max_seq_len=max_seq_len,
pad_idx=self.pad_id,
return_input_mask=True)
padded_text_type_ids = pad_batch_data([text_type_ids],
max_seq_len=max_seq_len,
pad_idx=self.pad_id)
padded_position_ids = pad_batch_data([position_ids],
max_seq_len=max_seq_len,
pad_idx=self.pad_id)
padded_task_ids = pad_batch_data([task_ids],
max_seq_len=max_seq_len,
pad_idx=self.pad_id)
feature = [
padded_token_ids, padded_position_ids, padded_text_type_ids,
input_mask, padded_task_ids
]
return feature
def _convert_example_to_record(self,
example,
max_seq_length,
tokenizer,
phase=None):
"""Converts a single `Example` into a single `Record`."""
text_a = tokenization.convert_to_unicode(example.text_a)
tokens_a = tokenizer.tokenize(text_a)
tokens_b = None
if example.text_b is not None:
#if "text_b" in example._fields:
text_b = tokenization.convert_to_unicode(example.text_b)
tokens_b = tokenizer.tokenize(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"
self._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 = []
text_type_ids = []
tokens.append("[CLS]")
text_type_ids.append(0)
for token in tokens_a:
tokens.append(token)
text_type_ids.append(0)
tokens.append("[SEP]")
text_type_ids.append(0)
if tokens_b:
for token in tokens_b:
tokens.append(token)
text_type_ids.append(1)
tokens.append("[SEP]")
text_type_ids.append(1)
token_ids = tokenizer.convert_tokens_to_ids(tokens)
position_ids = list(range(len(token_ids)))
label_ids = []
for label in example.label:
label_ids.append(int(label))
if phase != "predict":
Record = namedtuple(
'Record',
['token_ids', 'text_type_ids', 'position_ids', 'label_ids'])
record = Record(token_ids=token_ids,
text_type_ids=text_type_ids,
position_ids=position_ids,
label_ids=label_ids)
else:
Record = namedtuple('Record',
['token_ids', 'text_type_ids', 'position_ids'])
record = Record(token_ids=token_ids,
text_type_ids=text_type_ids,
position_ids=position_ids)
return record
def _convert_example_to_record(self,
example,
max_seq_length,
tokenizer,
phase=None):
"""Converts a single `Example` into a single `Record`."""
text_a = tokenization.convert_to_unicode(example.text_a)
tokens_a = tokenizer.tokenize(text_a)
tokens_b = None
if example.text_b is not None:
#if "text_b" in example._fields:
text_b = tokenization.convert_to_unicode(example.text_b)
tokens_b = tokenizer.tokenize(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"
self._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/ERNIE 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 = []
text_type_ids = []
tokens.append("[CLS]")
text_type_ids.append(0)
for token in tokens_a:
tokens.append(token)
text_type_ids.append(0)
tokens.append("[SEP]")
text_type_ids.append(0)
if tokens_b:
for token in tokens_b:
tokens.append(token)
text_type_ids.append(1)
tokens.append("[SEP]")
text_type_ids.append(1)
token_ids = tokenizer.convert_tokens_to_ids(tokens)
position_ids = list(range(len(token_ids)))
if self.label_map:
if example.label not in self.label_map:
raise KeyError("example.label = {%s} not in label" %
example.label)
label_id = self.label_map[example.label]
else:
label_id = example.label
Record = namedtuple(
'Record',
['token_ids', 'text_type_ids', 'position_ids', 'label_id'])
if phase != "predict":
Record = namedtuple(
'Record',
['token_ids', 'text_type_ids', 'position_ids', 'label_id'])
record = Record(token_ids=token_ids,
text_type_ids=text_type_ids,
position_ids=position_ids,
label_id=label_id)
else:
Record = namedtuple('Record',
['token_ids', 'text_type_ids', 'position_ids'])
record = Record(token_ids=token_ids,
text_type_ids=text_type_ids,
position_ids=position_ids)
return record
