Datasets:
Tasks:
Text Generation
Sub-tasks:
language-modeling
Languages:
English
Multilinguality:
monolingual
Size Categories:
1k<n<10K
Source Datasets:
tweet_qa
ArXiv:
License:
File size: 1,430 Bytes
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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') |