Datasets:

lmqg
/

qg_itquad

	"""
	gsplit -l 1500 -d --additional-suffix=.jsonl test.jsonl test
	gsplit -l 1500 -d --additional-suffix=.jsonl train.jsonl train
	gsplit -l 1500 -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
	import spacy
	from random import seed, shuffle
	from tqdm import tqdm
	from datasets import load_dataset

	DATASET_NAME = "squad_it"
	DATASET_TYPES = None
	HIGHLIGHT_TOKEN = '<hl>'
	GENERATE_TEST_SPLIT = True
	SPLITTER = spacy.load('it_core_news_sm')


	def get_sentence(document: str): return [str(sent) for sent in SPLITTER(document).sents]


	def process_single_data(question: str, paragraph: str, answer: str):
	""" Convert single raw json data into QG format """
	example = {'question': question, 'paragraph': paragraph, 'answer': answer}
	start = example['paragraph'].find(example['answer'])
	end = start + len(answer)
	assert paragraph[start:end] == answer
	# get sentence
	before_tmp = get_sentence(example['paragraph'][:start])
	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]
	before_sentence = before_sentence if before_sentence.endswith(' ') else f'{before_sentence} '
	after_tmp = get_sentence(example['paragraph'][start + len(example['answer']):])
	if len(after_tmp) == 0:
	after = ''
	after_sentence = ''
	else:
	after = ' '.join(after_tmp[1:])
	after_sentence = after_tmp[0]
	after_sentence = after_sentence if after_sentence.startswith(' ') else f' {after_sentence}'
	example['sentence'] = f"{before_sentence}{example['answer']}{after_sentence}"

	# get paragraph_sentence
	before = '' if before == '' else f'{before} '
	after = '' if after == '' else f' {after}'
	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'][:start], HIGHLIGHT_TOKEN, example['answer'],
	example['paragraph'][start + 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 before_tmp[-1].endswith(' ') else f'{before_tmp[-1]} '
	if len(after_tmp) == 0:
	after = ''
	else:
	after = after_tmp[0] if after_tmp[0].startswith(' ') else f' {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)

	return example


	if __name__ == '__main__':
	output = './data/processed'
	os.makedirs(output, exist_ok=True)
	if DATASET_TYPES is not None:
	dataset = load_dataset(DATASET_NAME, DATASET_TYPES)
	else:
	dataset = load_dataset(DATASET_NAME)
	for _split in dataset.keys():
	tmp_dataset = dataset[_split]
	with open(f'{output}/{_split}.jsonl', 'w') as f:
	for single_data in tqdm(tmp_dataset):
	question_str = single_data['question']
	paragraph_str = single_data['context']
	answer_str = single_data['answers']['text']
	if type(answer_str) == list:
	answer_str = answer_str[0]
	assert type(answer_str) is str, answer_str
	assert type(question_str) is str, question_str
	assert type(paragraph_str) is str, paragraph_str
	tmp_data = process_single_data(question=question_str, paragraph=paragraph_str, answer=answer_str)
	tmp_data['paragraph_id'] = single_data['id']
	f.write(json.dumps(tmp_data) + '\n')
	if GENERATE_TEST_SPLIT:
	# randomly sample for validation set
	with open(f'{output}/train.jsonl') as f:
	lines_train = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
	with open(f'{output}/test.jsonl') as f:
	size = len([i for i in f.read().split('\n') if len(i) > 0])
	paragraph_ids = list(set([i['paragraph_id'] for i in lines_train]))
	data_train = {p: [i for i in lines_train if i['paragraph_id'] == p] for p in paragraph_ids}
	seed(0)
	shuffle(paragraph_ids)
	data_test = []
	data_train_new = []
	for i in paragraph_ids:
	if len(data_test) < size:
	data_test += data_train[i]
	else:
	data_train_new += data_train[i]
	with open(f'{output}/train.jsonl', 'w') as f:
	f.write('\n'.join([json.dumps(i) for i in data_train_new]))
	with open(f'{output}/validation.jsonl', 'w') as f:
	f.write('\n'.join([json.dumps(i) for i in data_test]))