Datasets:
Tasks:
Text Generation
Sub-tasks:
language-modeling
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Source Datasets:
squad
ArXiv:
License:
| """ 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 3300 -d --additional-suffix=.jsonl train.jsonl train | |
| gsplit -l 3300 -d --additional-suffix=.jsonl test.jsonl test | |
| gsplit -l 3300 -d --additional-suffix=.jsonl dev.jsonl dev | |
| ``` | |
| """ | |
| import json | |
| import os | |
| import re | |
| from glob import glob | |
| from tqdm import tqdm | |
| from typing import List, Dict | |
| 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 jsonline_reader(filename: str): | |
| with open(filename, 'r') as f_reader: | |
| examples = [json.loads(i) for i in f_reader.read().split('\n') if len(i) > 0] | |
| return examples | |
| def process_single_data(data: Dict): | |
| """ Convert single raw json data into QG format """ | |
| example = {'question': data["question"], 'paragraph': data["context"], 'answer': data["answer"]} | |
| # 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] | |
| before_sentence = before_sentence if before_sentence.endswith(' ') else '{} '.format(before_sentence) | |
| 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] | |
| 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'][: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 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__': | |
| output = './data/processed' | |
| os.makedirs(output, exist_ok=True) | |
| path = {'train': 'data/raw/train*.jsonl', 'dev': 'data/raw/dev.jsonl', 'test': 'data/raw/test.jsonl'} | |
| for k, v in path.items(): | |
| json_data = [] | |
| for _file in sorted(glob(v)): | |
| json_data += jsonline_reader(_file) | |
| with open('{}/{}.jsonl'.format(output, k), 'w') as f: | |
| for single_data in tqdm(json_data): | |
| single_data = process_single_data(single_data) | |
| f.write(json.dumps(single_data) + '\n') | |