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 += "token_boundary: %s\n" % (" ".join(
[str(x) for x in self.token_boundary]))
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 example_to_token_ids_segment_ids_label_ids(
ex_index,
example,
max_seq_length,
tokenizer):
"""Converts an ``InputExample`` to token ids and segment ids."""
if ex_index < 5:
tf.logging.info("*** Example {ex_index} ***")
tf.logging.info("qid: %s" % (example.qid))
question_tokens = tokenizer.tokenize(example.question)
answers_tokens = map(tokenizer.tokenize, example.answers)
token_ids = []
segment_ids = []
for choice_idx, answer_tokens in enumerate(answers_tokens):
truncated_question_tokens = question_tokens[
:max((max_seq_length - 3)//2, max_seq_length - (len(answer_tokens) + 3))]
truncated_answer_tokens = answer_tokens[
:max((max_seq_length - 3)//2, max_seq_length - (len(question_tokens) + 3))]
choice_tokens = []
choice_segment_ids = []
choice_tokens.append("[CLS]")
choice_segment_ids.append(0)
for question_token in truncated_question_tokens:
choice_tokens.append(question_token)
choice_segment_ids.append(0)
choice_tokens.append("[SEP]")
choice_segment_ids.append(0)
for answer_token in truncated_answer_tokens:
choice_tokens.append(answer_token)
choice_segment_ids.append(1)
choice_tokens.append("[SEP]")
choice_segment_ids.append(1)
choice_token_ids = tokenizer.convert_tokens_to_ids(choice_tokens)
token_ids.append(choice_token_ids)
segment_ids.append(choice_segment_ids)
if ex_index < 5:
tf.logging.info("choice %s" % choice_idx)
tf.logging.info("tokens: %s" % " ".join(
[tokenization.printable_text(t) for t in choice_tokens]))
tf.logging.info("token ids: %s" % " ".join(
[str(x) for x in choice_token_ids]))
tf.logging.info("segment ids: %s" % " ".join(
[str(x) for x in choice_segment_ids]))
label_ids = [example.label]
if ex_index < 5:
tf.logging.info("label: %s (id = %d)" % (example.label, label_ids[0]))
return token_ids, segment_ids, 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)
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:
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_single_example(ex_index, example, label_list, max_seq_length,
tokenizer, task_name):
"""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)
if task_name != "sts-b":
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 ALBERT 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
# 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
if task_name != "sts-b":
label_id = label_map[example.label]
else:
label_id = 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_examples_to_features(self, seq_length, tokenizer):
"""Loads a data file into a list of `InputBatch`s."""
# features = []
input_masks = []
examples = self._to_example(self.input_queue.get())
for (ex_index, example) in enumerate(examples):
tokens_a = tokenizer.tokenize(example.text_a)
# if the sentences's length is more than seq_length, only use sentence's left part
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 = []
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)
# Where "input_ids" are tokens's index in vocabulary
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)
input_masks.append(input_mask)
# Zero-pad up to the sequence length.
while len(input_ids) < seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
assert len(input_ids) == seq_length
assert len(input_mask) == seq_length
assert len(segment_ids) == 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]))
yield InputFeatures(guid=example.guid,
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids)
