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"""TODO(wiqa): Add a description here."""
import json
import os
import datasets
# TODO(wiqa): BibTeX citation
_CITATION = """\
@article{wiqa,
author = {Niket Tandon and Bhavana Dalvi Mishra and Keisuke Sakaguchi and Antoine Bosselut and Peter Clark}
title = {WIQA: A dataset for "What if..." reasoning over procedural text},
journal = {arXiv:1909.04739v1},
year = {2019},
}
"""
# TODO(wiqa):
_DESCRIPTION = """\
The WIQA dataset V1 has 39705 questions containing a perturbation and a possible effect in the context of a paragraph.
The dataset is split into 29808 train questions, 6894 dev questions and 3003 test questions.
"""
_URL = "https://public-aristo-processes.s3-us-west-2.amazonaws.com/wiqa_dataset_no_explanation_v2/wiqa-dataset-v2-october-2019.zip"
URl = "s3://ai2-s2-research-public/open-corpus/2020-04-10/"
class Wiqa(datasets.GeneratorBasedBuilder):
"""TODO(wiqa): Short description of my dataset."""
# TODO(wiqa): Set up version.
VERSION = datasets.Version("0.1.0")
def _info(self):
# TODO(wiqa): 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_stem": datasets.Value("string"),
"question_para_step": datasets.features.Sequence(datasets.Value("string")),
"answer_label": datasets.Value("string"),
"answer_label_as_choice": datasets.Value("string"),
"choices": datasets.features.Sequence(
{"text": datasets.Value("string"), "label": datasets.Value("string")}
),
"metadata_question_id": datasets.Value("string"),
"metadata_graph_id": datasets.Value("string"),
"metadata_para_id": datasets.Value("string"),
"metadata_question_type": datasets.Value("string"),
"metadata_path_len": datasets.Value("int32"),
}
),
# 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/wiqa",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(wiqa): 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)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(dl_dir, "train.jsonl")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(dl_dir, "test.jsonl")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(dl_dir, "dev.jsonl")},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
# TODO(wiqa): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
data = json.loads(row)
yield id_, {
"question_stem": data["question"]["stem"],
"question_para_step": data["question"]["para_steps"],
"answer_label": data["question"]["answer_label"],
"answer_label_as_choice": data["question"]["answer_label_as_choice"],
"choices": {
"text": [choice["text"] for choice in data["question"]["choices"]],
"label": [choice["label"] for choice in data["question"]["choices"]],
},
"metadata_question_id": data["metadata"]["ques_id"],
"metadata_graph_id": data["metadata"]["graph_id"],
"metadata_para_id": data["metadata"]["para_id"],
"metadata_question_type": data["metadata"]["question_type"],
"metadata_path_len": data["metadata"]["path_len"],
}
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