Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
open-domain-qa
Languages:
English
Size:
10K - 100K
License:
File size: 3,789 Bytes
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"""TODO(wiki_qa): Add a description here."""
import csv
import os
import datasets
# TODO(wiki_qa): BibTeX citation
_CITATION = """\
@InProceedings{YangYihMeek:EMNLP2015:WikiQA,
author = {{Yi}, Yang and {Wen-tau}, Yih and {Christopher} Meek},
title = "{WikiQA: A Challenge Dataset for Open-Domain Question Answering}",
journal = {Association for Computational Linguistics},
year = 2015,
doi = {10.18653/v1/D15-1237},
pages = {2013–2018},
}
"""
# TODO(wiki_qa):
_DESCRIPTION = """\
Wiki Question Answering corpus from Microsoft
"""
_DATA_URL = "https://download.microsoft.com/download/E/5/f/E5FCFCEE-7005-4814-853D-DAA7C66507E0/WikiQACorpus.zip" # 'https://www.microsoft.com/en-us/download/confirmation.aspx?id=52419'
class WikiQa(datasets.GeneratorBasedBuilder):
"""TODO(wiki_qa): Short description of my dataset."""
# TODO(wiki_qa): Set up version.
VERSION = datasets.Version("0.1.0")
def _info(self):
# TODO(wiki_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(
{
"question_id": datasets.Value("string"),
"question": datasets.Value("string"),
"document_title": datasets.Value("string"),
"answer": datasets.Value("string"),
"label": datasets.features.ClassLabel(num_classes=2),
# 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://www.microsoft.com/en-us/download/details.aspx?id=52419",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(wiki_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_dir = dl_manager.download_and_extract(_DATA_URL)
dl_dir = os.path.join(dl_dir, "WikiQACorpus")
# dl_dir = os.path.join(dl_dir, '')
return [
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(dl_dir, "WikiQA-test.tsv")}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(dl_dir, "WikiQA-dev.tsv")}
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(dl_dir, "WikiQA-train.tsv")},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
# TODO(wiki_qa): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for idx, row in enumerate(reader):
yield idx, {
"question_id": row["QuestionID"],
"question": row["Question"],
"document_title": row["DocumentTitle"],
"answer": row["Sentence"],
"label": row["Label"],
}
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