Datasets:
Tasks:
Text Generation
Sub-tasks:
language-modeling
Languages:
English
Multilinguality:
monolingual
Size Categories:
1k<n<10K
Source Datasets:
tweet_qa
ArXiv:
License:
| import json | |
| import os | |
| from random import seed, shuffle | |
| import re | |
| from tqdm import tqdm | |
| from typing import Dict | |
| from datasets import load_dataset | |
| SEP_TOKEN = " | " | |
| def create_data(hf_data): | |
| df = hf_data.to_pandas() | |
| output = [] | |
| for tweet, g in df.groupby("Tweet"): | |
| example = { | |
| 'paragraph': tweet.replace(SEP_TOKEN, " "), | |
| "paragraph_id": '-'.join(g['qid']), | |
| 'questions': [_g.replace(SEP_TOKEN, " ") for _g in g['Question']], | |
| 'answers': [_g[0].replace(SEP_TOKEN, " ") for _g in g['Answer']], | |
| } | |
| example["questions_answers"] = SEP_TOKEN.join([f"question: {q}, answer: {a}" for q, a in zip(example["questions"], example["answers"])]) | |
| output.append(example) | |
| return output | |
| if __name__ == '__main__': | |
| tweet_qa = load_dataset("tweet_qa") | |
| data_valid = create_data(tweet_qa['validation']) | |
| data_train = create_data(tweet_qa['train']) | |
| seed(1) | |
| test_len = len(data_valid) | |
| shuffle(data_train) | |
| data_test = data_train[:test_len] | |
| data_train = data_train[test_len:] | |
| data_all = {'train': data_train, 'validation': data_valid, '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): | |
| f.write(json.dumps(single_data) + '\n') |