Datasets:
Tasks:
Question Answering
Sub-tasks:
multiple-choice-qa
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Annotations Creators:
crowdsourced
Source Datasets:
extended|aqua_rat
Tags:
License:
File size: 3,251 Bytes
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"""TODO(math_qa): Add a description here."""
import json
import os
import datasets
# TODO(math_qa): BibTeX citation
_CITATION = """
"""
# TODO(math_qa):
_DESCRIPTION = """
Our dataset is gathered by using a new representation language to annotate over the AQuA-RAT dataset. AQuA-RAT has provided the questions, options, rationale, and the correct options.
"""
_URL = "https://math-qa.github.io/math-QA/data/MathQA.zip"
class MathQa(datasets.GeneratorBasedBuilder):
"""TODO(math_qa): Short description of my dataset."""
# TODO(math_qa): Set up version.
VERSION = datasets.Version("0.1.0")
def _info(self):
# TODO(math_qa): Specifies the datasets.DatasetInfo object
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# datasets.features.FeatureConnectors
features=datasets.Features(
{
# These are the features of your dataset like images, labels ...
"Problem": datasets.Value("string"),
"Rationale": datasets.Value("string"),
"options": datasets.Value("string"),
"correct": datasets.Value("string"),
"annotated_formula": datasets.Value("string"),
"linear_formula": datasets.Value("string"),
"category": datasets.Value("string"),
}
),
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage="https://math-qa.github.io/math-QA/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(math_qa): Downloads the data and defines the splits
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
dl_path = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(dl_path, "train.json")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(dl_path, "test.json")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(dl_path, "dev.json")},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
# TODO(math_qa): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for id_, row in enumerate(data):
yield id_, row
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