Datasets:
Tasks:
Text Generation
Sub-tasks:
language-modeling
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Source Datasets:
subjqa
ArXiv:
License:
File size: 6,605 Bytes
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""" 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]))
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