Datasets:

lmqg
/

qg_subjqa

	""" Script to process raw SQuAD file for Question Generation format
	You need to run `python -m spacy download en_core_web_sm`.
	Split when uploading to dataset hub by
	```
	gsplit -l 1500 -d --additional-suffix=.jsonl default.train.jsonl default.train
	```
	"""
	import json
	import os
	import re

	import pandas as pd
	import spacy


	SPLITTER = spacy.load('en_core_web_sm')
	HIGHLIGHT_TOKEN = '<hl>'


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


	def process_single_data(question, paragraph, answer):
	""" 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 '{} '.format(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 ' {}'.format(after_sentence)
	example['sentence'] = '{}{}{}'.format(before_sentence, example['answer'], after_sentence)

	# get paragraph_sentence
	before = '' if before == '' else '{} '.format(before)
	after = '' if after == '' else ' {}'.format(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 '{} '.format(before_tmp[-1])
	if len(after_tmp) == 0:
	after = ''
	else:
	after = after_tmp[0] if after_tmp[0].startswith(' ') else ' {}'.format(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__':
	os.makedirs('./data/processed', exist_ok=True)
	for i in ["books", "electronics", "grocery", "movies", "restaurants", "tripadvisor"]:
	for s in ["dev.csv", "test.csv", "train.csv"]:
	df = pd.read_csv(f'SubjQA/SubjQA/{i}/splits/{s}')
	df = df[[x != 'ANSWERNOTFOUND' for x in df['human_ans_spans']]]
	df['review'] = [x.replace('ANSWERNOTFOUND', '') for x in df['review']]
	output = []
	for _, _g in df.groupby('q_review_id'):
	if any(i == 'ANSWERNOTFOUND' for i in _g['human_ans_spans']):
	continue
	_len = [len(i) for i in _g["human_ans_spans"]]
	_df = _g.iloc[_len.index(max(_len))]
	start, end = eval(_df['human_ans_indices'])
	out = process_single_data(question=re.sub(r'\s+\?', '?', _df['question']),
	answer=_df['review'][start:end],
	paragraph=_df['review'])
	out['question_subj_level'] = int(_df['question_subj_level'])
	out['answer_subj_level'] = int(_df['answer_subj_level'])
	out['paragraph_id'] = _df['review_id']
	out['domain'] = _df['domain']
	output.append(out)
	with open(f'./data/processed/{i}.{s.replace(".csv", ".jsonl")}', 'w') as f:
	f.write('\n'.join([json.dumps(i) for i in output]))