Datasets:
Tasks:
Text Generation
Modalities:
Text
Sub-tasks:
language-modeling
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
question-generation
License:
| """ 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])) | |