Datasets:
Tasks:
Text Generation
Modalities:
Text
Sub-tasks:
language-modeling
Languages:
Japanese
Size:
10K - 100K
ArXiv:
Tags:
question-generation
License:
File size: 3,703 Bytes
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""" Script to process raw SQuAD file for Question Generation format
gsplit -l 1000 -d --additional-suffix=.jsonl train.jsonl train
gsplit -l 1000 -d --additional-suffix=.jsonl test.jsonl test
gsplit -l 1000 -d --additional-suffix=.jsonl validation.jsonl validation
"""
import json
import os
import re
from tqdm import tqdm
from typing import List, 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"], 'passage': 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['passage'][answer_start: answer_end] == answer_text
example['answer'] = answer_text
# get sentence
position = example['passage'].find(example['answer'])
assert position != -1
before_tmp = get_sentence(example['passage'][: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['passage'][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 passage_sentence
source_text = '{0}{1}{2}{1}{3}'.format(before, HIGHLIGHT_TOKEN, example['sentence'], after)
example['passage_sentence'] = re.sub(r'\s+', ' ', source_text)
# get passage_answer
source_text = '{0}{1}{2}{1}{3}'.format(
example['passage'][:position], HIGHLIGHT_TOKEN, example['answer'],
example['passage'][position + len(example['answer']):])
example['passage_answer'] = re.sub(r'\s+', ' ', source_text)
# get sentence_answer
before = get_sentence(example['passage'][:position])
if len(before) == 0 or before[-1].endswith('。'):
before = ''
else:
before = before[-1]
after = get_sentence(example['passage'][position + len(example['answer']):])
if len(after) == 0:
after = ''
else:
after = after[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']
data_train = jaquad_data['train']
data_train = data_train.shuffle(seed=1)
data_test = [data_train[i] for i in range(len(data_dev))]
data_train = [data_train[i] for i in range(len(data_dev), len(data_train))]
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')
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