Datasets:
Tasks:
Question Answering
Sub-tasks:
closed-domain-qa
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
crowdsourced
Annotations Creators:
no-annotation
Source Datasets:
original
Tags:
License:
File size: 3,562 Bytes
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"""TODO(sciQ): Add a description here."""
import json
import os
import datasets
# TODO(sciQ): BibTeX citation
_CITATION = """\
@inproceedings{SciQ,
title={Crowdsourcing Multiple Choice Science Questions},
author={Johannes Welbl, Nelson F. Liu, Matt Gardner},
year={2017},
journal={arXiv:1707.06209v1}
}
"""
# TODO(sciQ):
_DESCRIPTION = """\
The SciQ dataset contains 13,679 crowdsourced science exam questions about Physics, Chemistry and Biology, among others. The questions are in multiple-choice format with 4 answer options each. For the majority of the questions, an additional paragraph with supporting evidence for the correct answer is provided.
"""
_URL = "https://s3-us-west-2.amazonaws.com/ai2-website/data/SciQ.zip"
class Sciq(datasets.GeneratorBasedBuilder):
"""TODO(sciQ): Short description of my dataset."""
# TODO(sciQ): Set up version.
VERSION = datasets.Version("0.1.0")
def _info(self):
# TODO(sciQ): 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 ...
"question": datasets.Value("string"),
"distractor3": datasets.Value("string"),
"distractor1": datasets.Value("string"),
"distractor2": datasets.Value("string"),
"correct_answer": datasets.Value("string"),
"support": 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://allenai.org/data/sciq",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(sciQ): Downloads the data and defines the splits
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
dl_dir = dl_manager.download_and_extract(_URL)
data_dir = os.path.join(dl_dir, "SciQ dataset-2 3")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "train.json")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "valid.json")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "test.json")},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
# TODO(sciQ): 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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