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jblitzar
/
github-python

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"than this will be padded.")
flags.DEFINE_integer(
"max_num_segments_perdoc", 8,
"The maximum number of segments for each document"
)
class PointwiseInstance(object):
"""A single training instance (sentence pair)."""
def __init__(self, exampleid, tokens_a, tokens_b_list, relation_label):
self.exampleid = exampleid
self.tokens_a = tokens_a
self.tokens_b_list = tokens_b_list
self.relation_label = relation_label
def __str__(self):
s = ""
s += "example id: %s\n" % self.exampleid
s += "tokens a: %s\n" % (" ".join(
[tokenization.printable_text(x) for x in self.tokens_a]))
s += "tokens b: %s\n" % (" ".join(
[tokenization.printable_text(x) for x in self.tokens_b_list]))
s += "relation label: %s\n" % self.relation_label
s += "\n"
return s
def __repr__(self):
return self.__str__()
def create_int_feature(values):
feature = tf.train.Feature(int64_list=tf.train.Int64List(value=values))
return feature
def convert_data_pointwise(writer, tokenizer, qid_list, relevance_dict, corpus_dict, query_dict, is_eval=False):
if is_eval:
max_num_example = FLAGS.rerank_threshold
else:
max_num_example = FLAGS.max_num_train_instance_perquery
instances = []
idx = 0
for qid in qid_list:
tf.logging.info("Generating data for query {}".format(qid))
relevance = relevance_dict.get(qid)
judged_docno_list = relevance.get_judged_docno_list()
supervised_docno_list = relevance.get_supervised_docno_list() # initial ranking
# training data from the judged docno, built from bm25 top1000 result
relevant_docno_list = set()
if judged_docno_list is not None:
relevant_docno_list = judged_docno_list[1] + judged_docno_list[2]
relevant_docno_list = set(relevant_docno_list)
for docno in supervised_docno_list[:max_num_example]:
relation_label = 1 if docno in relevant_docno_list else 0
query = query_dict[qid]
doc = corpus_dict[docno]
instance = create_instance_pointwise(tokenizer, FLAGS.max_seq_length, qid, docno, query, doc, relation_label)
# append and shuffle on training set
if not is_eval:
instances.append(instance)
else:
write_instance_to_example_files(writer, tokenizer, instance, idx)
idx += 1
tf.logging.info("Totally {} examples".format(len(instances)))
if not is_eval:
random.shuffle(instances)
for idx, instance in enumerate(instances):
write_instance_to_example_files(writer, tokenizer, instance, idx)
if is_eval:
write_padding_instance_to_example_files(writer)
writer.close()
print("Distribution of length. Key is length, Val is count.")
for key, val in stats.items():
print("{}\t{}".format(key, val))
def create_instance_pointwise(tokenizer, max_seq_length, qid, docno, query, doc, label):
query = tokenization.convert_to_unicode(query)
doc = tokenization.convert_to_unicode(doc)
passages = get_passages(doc, FLAGS.plen, FLAGS.overlap)
if len(passages) == 0:
tf.logging.warn("Passage length is 0 in qid {} docno {}".format(qid, docno))
query = tokenization.convert_to_bert_input(
text=query,
max_seq_length=64,
tokenizer=tokenizer,
add_cls=True,
convert_to_id=False
)
passages = [tokenization.convert_to_bert_input(
text=p,
max_seq_length=max_seq_length-len(query),
tokenizer=tokenizer,
add_cls=False,
convert_to_id=False