Datasets:
lmqg
/

Languages:
Spanish
Multilinguality:
monolingual
Size Categories:
1k<n<10K
Source Datasets:
lmqg/qg_esquad
ArXiv:
Tags:
question-generation
License:
File size: 1,300 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 paragraph, g in df.groupby("paragraph"):
        example = {
            'paragraph': paragraph.replace(SEP_TOKEN, " "),
            'questions': [_g.replace(SEP_TOKEN, " ") for _g in g['question']],
            'answers': [_g.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__':
    qg_squad = load_dataset("lmqg/qg_esquad")
    data_valid = create_data(qg_squad['validation'])
    data_train = create_data(qg_squad['train'])
    data_test = create_data(qg_squad['test'])
    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')