Datasets:

lmqg
/

qg_jaquad

	""" Script to process raw SQuAD file for Question Generation format
	cd data/processed
	gsplit -l 500 -d --additional-suffix=.jsonl train.jsonl train
	gsplit -l 500 -d --additional-suffix=.jsonl test.jsonl test
	gsplit -l 1000 -d --additional-suffix=.jsonl validation.jsonl validation
	rm -rf test.jsonl
	rm -rf train.jsonl
	rm -rf validation.jsonl
	"""
	import json
	import os
	import re
	from tqdm import tqdm
	from typing import Dict
	from datasets import load_dataset
	from ja_sentence_split import JASplitter

	HIGHLIGHT_TOKEN = '<hl>'
	SPLITTER = JASplitter()


	def get_sentence(document: str):
	return [str(s) for s in SPLITTER(document)]


	def process_single_data(data: Dict):
	""" Convert single raw json data into QG format """
	example = {'question': data["question"], 'paragraph': data["context"]}

	# check answer
	answer_text = data['answers']['text'][0]
	answer_start = data['answers']['answer_start'][0]
	answer_end = answer_start + len(answer_text)
	assert example['paragraph'][answer_start: answer_end] == answer_text
	example['answer'] = answer_text

	# get sentence
	position = example['paragraph'].find(example['answer'])
	assert position != -1
	before_tmp = get_sentence(example['paragraph'][:position])
	if len(before_tmp) == 0:
	before = ''
	before_sentence = ''
	else:
	if before_tmp[-1].endswith('。'):
	before = ' '.join(before_tmp)
	before_sentence = ''
	else:
	before = ' '.join(before_tmp[:-1])
	before_sentence = before_tmp[-1]
	after_tmp = get_sentence(example['paragraph'][position + len(example['answer']):])
	if len(after_tmp) == 0:
	after = ''
	after_sentence = ''
	else:
	after = ' '.join(after_tmp[1:])
	after_sentence = after_tmp[0]
	example['sentence'] = '{}{}{}'.format(before_sentence, example['answer'], after_sentence)

	# get paragraph_sentence
	source_text = '{0}{1}{2}{1}{3}'.format(before, HIGHLIGHT_TOKEN, example['sentence'], after)
	example['paragraph_sentence'] = re.sub(r'\s+', ' ', source_text)

	# get paragraph_answer
	source_text = '{0}{1}{2}{1}{3}'.format(
	example['paragraph'][:position], HIGHLIGHT_TOKEN, example['answer'],
	example['paragraph'][position + len(example['answer']):])
	example['paragraph_answer'] = re.sub(r'\s+', ' ', source_text)

	# get sentence_answer
	if len(before_tmp) == 0 or before_tmp[-1].endswith('。'):
	before = ''
	else:
	before = before_tmp[-1]
	if len(after_tmp) == 0:
	after = ''
	else:
	after = after_tmp[0]
	source_text = '{0}{1}{2}{1}{3}'.format(before, HIGHLIGHT_TOKEN, example['answer'], after)
	example['sentence_answer'] = re.sub(r'\s+', ' ', source_text)
	for _k in example.keys():
	example[_k] = example[_k].replace('。\n\n', '。').replace('。\n', '。')
	return example


	if __name__ == '__main__':
	jaquad_data = load_dataset("SkelterLabsInc/JaQuAD")
	data_dev = jaquad_data['validation']

	# create test set from training
	data_train = jaquad_data['train']
	context = sorted(list(set(data_train['context'])))
	data_test = [data_train[i] for i in range(len(data_train)) if data_train[i]['context'] in context[:927]]
	data_train = [data_train[i] for i in range(len(data_train)) if data_train[i]['context'] in context[927:]]
	print(f'train ({len(data_train)}, test ({len(data_test)}), dev ({len(data_dev)})')
	data_all = {'train': data_train, 'validation': data_dev, 'test': data_test}
	output = './data/processed'
	os.makedirs(output, exist_ok=True)
	for k, _data in data_all.items():
	with open('{}/{}.jsonl'.format(output, k), 'w') as f:
	for single_data in tqdm(_data):
	single_data = process_single_data(single_data)
	f.write(json.dumps(single_data) + '\n')