Datasets:

lmqg
/

qg_squadshifts

	""" Script to process raw SQuADshift file for Question Generation format
	cd data/processed
	gsplit -l 1500 -d --additional-suffix=.jsonl new_wiki.test.jsonl new_wiki.test
	gsplit -l 1500 -d --additional-suffix=.jsonl nyt.test.jsonl nyt.test
	gsplit -l 1500 -d --additional-suffix=.jsonl reddit.test.jsonl reddit.test
	gsplit -l 1500 -d --additional-suffix=.jsonl amazon.test.jsonl amazon.test

	gsplit -l 1500 -d --additional-suffix=.jsonl new_wiki.train.jsonl new_wiki.train
	gsplit -l 1500 -d --additional-suffix=.jsonl nyt.train.jsonl nyt.train
	gsplit -l 1500 -d --additional-suffix=.jsonl reddit.train.jsonl reddit.train
	gsplit -l 1500 -d --additional-suffix=.jsonl amazon.train.jsonl amazon.train

	gsplit -l 1500 -d --additional-suffix=.jsonl new_wiki.validation.jsonl new_wiki.validation
	gsplit -l 1500 -d --additional-suffix=.jsonl nyt.validation.jsonl nyt.validation
	gsplit -l 1500 -d --additional-suffix=.jsonl reddit.validation.jsonl reddit.validation
	gsplit -l 1500 -d --additional-suffix=.jsonl amazon.validation.jsonl amazon.validation

	rm -rf new_wiki.test.jsonl
	rm -rf nyt.test.jsonl
	rm -rf reddit.test.jsonl
	rm -rf amazon.test.jsonl

	rm -rf new_wiki.train.jsonl
	rm -rf nyt.train.jsonl
	rm -rf reddit.train.jsonl
	rm -rf amazon.train.jsonl

	rm -rf new_wiki.validation.jsonl
	rm -rf nyt.validation.jsonl
	rm -rf reddit.validation.jsonl
	rm -rf amazon.validation.jsonl

	"""
	import json
	import os
	import re
	from random import shuffle, seed
	from tqdm import tqdm

	import spacy
	from datasets import load_dataset

	DATASET_NAME = "squadshifts"
	DATASET_TYPES = ['new_wiki', 'nyt', 'reddit', 'amazon']
	HIGHLIGHT_TOKEN = '<hl>'
	GENERATE_TEST_SPLIT = True
	SPLITTER = spacy.load('en_core_web_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)
	for data_type in DATASET_TYPES:
	dataset = load_dataset(DATASET_NAME, data_type)
	_split = 'test'
	tmp_dataset = dataset[_split]
	full_data = []
	for single_data in tqdm(tmp_dataset):
	question_str = single_data['question'] #.replace("\n", ".").replace('"', "'")
	paragraph_str = single_data['context'] #.replace("\n", ".").replace('"', "'")
	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']
	full_data.append(tmp_data)

	# split test into train/valid/test
	test_size = int(len(full_data)/2)
	train_size = int((len(full_data) - test_size) * 2/3)
	# train_size = 2500
	valid_size = len(full_data) - train_size - test_size
	assert train_size + test_size + valid_size == len(full_data), f"{train_size}, {test_size}, {valid_size}"
	paragraph_ids = list(set([i['paragraph_id'] for i in full_data]))
	data_dict = {p: [i for i in full_data if i['paragraph_id'] == p] for p in paragraph_ids}
	seed(0)
	shuffle(paragraph_ids)
	lines_train = []
	lines_test = []
	lines_valid = []

	for i in paragraph_ids:
	if len(lines_test) < test_size:
	lines_test += data_dict[i]
	elif len(lines_train) < train_size:
	lines_train += data_dict[i]
	else:
	lines_valid += data_dict[i]
	print(f'STATS(train/valid/test): {data_type}\| {len(lines_train)}/{len(lines_valid)}/{len(lines_test)}')
	with open(f'{output}/{data_type}.test.jsonl', 'w') as f:
	f.write('\n'.join([json.dumps(i) for i in lines_test]))
	with open(f'{output}/{data_type}.train.jsonl', 'w') as f:
	f.write('\n'.join([json.dumps(i) for i in lines_train]))
	with open(f'{output}/{data_type}.validation.jsonl', 'w') as f:
	f.write('\n'.join([json.dumps(i) for i in lines_valid]))