Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
extractive-qa
Languages:
Vietnamese
Size:
10K - 100K
ArXiv:
File size: 3,649 Bytes
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"""
Script used to process UIT-ViQuAD 2.0.
Source: https://github.com/tuanbc88/ai_question_answering/tree/master/machine_reading_comprehension/02_datasets
"""
import os
import json
import pandas as pd
from itertools import groupby
from datasets import Dataset, DatasetDict, Features, Sequence, Value
def deduplicate_answers(answers):
answers_sorted = sorted(answers, key=lambda x: (x['text'], x['answer_start']))
unique_answers = [next(group) for _, group in groupby(answers_sorted, key=lambda x: (x['text'], x['answer_start']))]
return unique_answers
data_dir = "UIT-ViQuAD 2.0"
dataset_dict = {}
features = Features({
'id': Value('string'),
'uit_id': Value('string'),
'title': Value('string'),
'context': Value('string'),
'question': Value('string'),
'answers': Sequence(feature={'text': Value('string'), 'answer_start': Value('int32')}),
'is_impossible': Value('bool'),
'plausible_answers': Sequence(feature={'text': Value('string'), 'answer_start': Value('int32')})
})
for split in ["train", "dev", "test"]:
fname = os.path.join(data_dir, f"{split}.json")
data = json.load(open(fname))
ids, uit_ids, titles, contexts, questions, all_answers, impossibles, all_plausible_answers = [], [], [], [], [], [], [], []
title_i = 0
print("-"*20, split, len(data["data"]), "-"*20)
for title_data in data["data"]:
title = title_data["title"]
ctx_i = 0
title_i += 1
for ctx_and_qs in title_data["paragraphs"]:
qas = ctx_and_qs["qas"]
context = ctx_and_qs["context"]
q_i = 0
ctx_i += 1
question_set = set()
for q in qas:
question = q["question"]
answers = q.get("answers", None)
plausible_answers = q.get("plausible_answers", None)
# Dedup answers
if answers:
answers = deduplicate_answers(answers)
if plausible_answers:
plausible_answers = deduplicate_answers(plausible_answers)
uit_id = q["id"]
is_impossible = q.get("is_impossible", False)
# Check duplicate questions
if question in question_set:
print("---Found duplicate question: ", question, "---")
print("Answer: ", answers)
print("Answer plaus: ", plausible_answers)
print("Impossible: ", is_impossible)
continue
q_i += 1
overall_id = f"{title_i:04d}-{ctx_i:04d}-{q_i:04d}"
# Append data to lists
ids.append(overall_id)
uit_ids.append(uit_id)
titles.append(title)
contexts.append(context)
questions.append(question)
all_answers.append(answers)
impossibles.append(is_impossible)
all_plausible_answers.append(plausible_answers)
question_set.add(question)
# Convert to Dataset
dataset = Dataset.from_dict({
'id': ids,
'uit_id': uit_ids,
'title': titles,
'context': contexts,
'question': questions,
'answers': all_answers,
'is_impossible': impossibles,
'plausible_answers': all_plausible_answers
}, features=features)
dataset_dict[split if split!="dev" else "validation"] = dataset
print(dataset_dict)
hf_dataset = DatasetDict(dataset_dict)
hf_name = "UIT-ViQuAD2.0"
hf_dataset.push_to_hub(f"taidng/{hf_name}")
print("Dataset uploaded successfully!")
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