Datasets:
ai2_arc

Languages: English
Multilinguality: monolingual
Size Categories: 1K<n<10K
Language Creators: found
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Source Datasets: original
ai2_arc / ai2_arc.py
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Update files from the datasets library (from 1.6.0)
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"""TODO(arc): Add a description here."""
import json
import os
import datasets
# TODO(ai2_arc): BibTeX citation
_CITATION = """\
@article{allenai:arc,
author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and
Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
journal = {arXiv:1803.05457v1},
year = {2018},
}
"""
# TODO(ai2_arc):
_DESCRIPTION = """\
A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in
advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains
only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also
including a corpus of over 14 million science sentences relevant to the task, and an implementation of three neural baseline models for this dataset. We pose ARC as a challenge to the community.
"""
_URL = "https://s3-us-west-2.amazonaws.com/ai2-website/data/ARC-V1-Feb2018.zip"
class Ai2ArcConfig(datasets.BuilderConfig):
"""BuilderConfig for Ai2ARC."""
def __init__(self, **kwargs):
"""BuilderConfig for Ai2Arc.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(Ai2ArcConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
class Ai2Arc(datasets.GeneratorBasedBuilder):
"""TODO(arc): Short description of my dataset."""
# TODO(arc): Set up version.
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
Ai2ArcConfig(
name="ARC-Challenge",
description="""\
Challenge Set of 2590 “hard” questions (those that both a retrieval and a co-occurrence method fail to answer correctly)
""",
),
Ai2ArcConfig(
name="ARC-Easy",
description="""\
Easy Set of 5197 questions
""",
),
]
def _info(self):
# TODO(ai2_arc): 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(
{
"id": datasets.Value("string"),
"question": datasets.Value("string"),
"choices": datasets.features.Sequence(
{"text": datasets.Value("string"), "label": datasets.Value("string")}
),
"answerKey": datasets.Value("string")
# These are the features of your dataset like images, labels ...
}
),
# 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/arc",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(ai2_arc): 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, "ARC-V1-Feb2018-2")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, self.config.name, self.config.name + "-Train.jsonl")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, self.config.name, self.config.name + "-Test.jsonl")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, self.config.name, self.config.name + "-Dev.jsonl")},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
# TODO(ai2_arc): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
for row in f:
data = json.loads(row)
answerkey = data["answerKey"]
id_ = data["id"]
question = data["question"]["stem"]
choices = data["question"]["choices"]
text_choices = [choice["text"] for choice in choices]
label_choices = [choice["label"] for choice in choices]
yield id_, {
"id": id_,
"answerKey": answerkey,
"question": question,
"choices": {"text": text_choices, "label": label_choices},
}