def featurize(self, example, is_training):
"""Turn an InputExample into a dict of features."""
if is_training and self.config.distill and self._distill_inputs is None:
self._distill_inputs = utils.load_pickle(
self.config.distill_inputs(self.name))
input_ids = example.text_a
input_mask = example.mask
segment_ids = []
while len(segment_ids) < self.config.max_seq_length:
segment_ids.append(0)
assert len(input_ids) == self.config.max_seq_length
assert len(input_mask) == self.config.max_seq_length
assert len(segment_ids) == self.config.max_seq_length
eid = example.eid
features = {
"input_ids": input_ids,
"input_mask": input_mask,
"segment_ids": segment_ids,
"task_id": self.config.task_names.index(self.name),
self.name + "_eid": eid,
}
self._add_features(features, example,
None if self._distill_inputs is None else
self._distill_inputs[eid])
return features
def featurize(self, example, is_training):
"""Turn an InputExample into a dict of features."""
if is_training and self.config.distill and self._distill_inputs is None:
self._distill_inputs = utils.load_pickle(
self.config.distill_inputs(self.name))
tokens_a = self._tokenizer.tokenize(example.text_a)
tokens_b = None
if example.text_b:
tokens_b = self._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,
self.config.max_seq_length - 3)
else:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > self.config.max_seq_length - 2:
tokens_a = tokens_a[0:(self.config.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 = self._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) < self.config.max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
assert len(input_ids) == self.config.max_seq_length
assert len(input_mask) == self.config.max_seq_length
assert len(segment_ids) == self.config.max_seq_length
eid = example.eid
features = {
"input_ids": input_ids,
"input_mask": input_mask,
"segment_ids": segment_ids,
"task_id": self.config.task_names.index(self.name),
self.name + "_eid": eid,
}
self._add_features(
features, example, None
if self._distill_inputs is None else self._distill_inputs[eid])
return features
