text stringlengths 1 93.6k |
|---|
) for p in passages]
|
instance = PointwiseInstance(
|
exampleid="{}-{}".format(qid, docno),
|
tokens_a=query,
|
tokens_b_list=passages,
|
relation_label=label
|
)
|
return instance
|
def write_padding_instance_to_example_files(writer, num_examples=50):
|
# 1-d arrays
|
input_ids = np.zeros((FLAGS.max_seq_length * FLAGS.max_num_segments_perdoc), dtype=np.int)
|
num_segments = FLAGS.max_num_segments_perdoc
|
label = 0
|
features = collections.OrderedDict()
|
features["input_ids"] = create_int_feature(input_ids)
|
features["tokens_a_len"] = create_int_feature([3])
|
features["tokens_ids_lens"] = create_int_feature([24] * FLAGS.max_num_segments_perdoc)
|
features["num_segments"] = create_int_feature([num_segments])
|
features["label"] = create_int_feature([label])
|
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
|
for _ in range(num_examples):
|
writer.write(tf_example.SerializeToString())
|
tf.logging.info("write {} padding instances successfully".format(num_examples))
|
def write_instance_to_example_files(writer, tokenizer, instance, instance_idx):
|
def padding_2d(ids_list, num_tokens_per_segment, padding_value=0):
|
_len = len(ids_list)
|
if padding_value == 0:
|
matrix = np.zeros((_len, num_tokens_per_segment), dtype=np.int)
|
elif padding_value == 1:
|
matrix = np.ones((_len, num_tokens_per_segment), dtype=np.int)
|
else:
|
raise ValueError("Unsupport padding value")
|
for i, _list in enumerate(ids_list):
|
matrix[i, :len(_list)] = _list
|
return matrix.flatten()
|
tokens_a = instance.tokens_a
|
tokens_b_list = instance.tokens_b_list
|
tokens_a_ids = tokenizer.convert_tokens_to_ids(tokens_a)
|
tokens_b_list = [tokenizer.convert_tokens_to_ids(p) for p in tokens_b_list]
|
label = instance.relation_label
|
assert len(tokens_b_list) <= FLAGS.max_num_segments_perdoc
|
num_segments = len(tokens_b_list)
|
input_ids = [tokens_a_ids + tokens_b_passage_ids for tokens_b_passage_ids in tokens_b_list]
|
tokens_a_len = len(tokens_a_ids) # helpful for segment ids
|
input_ids_lens = [len(input_id) for input_id in input_ids] # helpful for input mask
|
input_ids_lens = input_ids_lens + [FLAGS.max_seq_length] * (FLAGS.max_num_segments_perdoc - len(input_ids_lens))
|
input_ids = padding_2d(input_ids,FLAGS.max_seq_length, padding_value=0)
|
# write to tfrecord
|
features = collections.OrderedDict()
|
features["input_ids"] = create_int_feature(input_ids)
|
features["tokens_a_len"] = create_int_feature([tokens_a_len])
|
features["tokens_ids_lens"] = create_int_feature(input_ids_lens)
|
features["num_segments"] = create_int_feature([num_segments])
|
features["label"] = create_int_feature([label])
|
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
|
writer.write(tf_example.SerializeToString())
|
if instance_idx < 5:
|
tf.logging.info("*** Example ***")
|
tf.logging.info("tokens_a: %s" % " ".join(
|
[tokenization.printable_text(x) for x in instance.tokens_a]))
|
tf.logging.info("tokens_b_list: {}".format(instance.tokens_b_list))
|
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])))
|
stats = collections.defaultdict(int)
|
def get_passages(text, plen, overlap):
|
""" Modified from https://github.com/AdeDZY/SIGIR19-BERT-IR/blob/master/tools/gen_passages.py
|
:param text:
|
:param plen:
|
:param overlap:
|
:return:
|
"""
|
words = text.strip().split(' ')
|
s, e = 0, 0
|
passages = []
|
while s < len(words):
|
e = s + plen
|
if e >= len(words):
|
e = len(words)
|
# if the last one is shorter than 'overlap', it is already in the previous passage.
|
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