Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
crowdsourced
ArXiv:
License:
File size: 3,817 Bytes
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"""TODO(coqa): Add a description here."""
import json
import datasets
# TODO(coqa): BibTeX citation
_CITATION = """\
@InProceedings{SivaAndAl:Coca,
author = {Siva, Reddy and Danqi, Chen and Christopher D., Manning},
title = {WikiQA: A Challenge Dataset for Open-Domain Question Answering},
journal = { arXiv},
year = {2018},
}
"""
# TODO(coqa):
_DESCRIPTION = """\
CoQA: A Conversational Question Answering Challenge
"""
_TRAIN_DATA_URL = "https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json"
_DEV_DATA_URL = "https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json"
class Coqa(datasets.GeneratorBasedBuilder):
"""TODO(coqa): Short description of my dataset."""
# TODO(coqa): Set up version.
VERSION = datasets.Version("1.0.0")
def _info(self):
# TODO(coqa): 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(
{
"source": datasets.Value("string"),
"story": datasets.Value("string"),
"questions": datasets.features.Sequence(datasets.Value("string")),
"answers": datasets.features.Sequence(
{
"input_text": datasets.Value("string"),
"answer_start": datasets.Value("int32"),
"answer_end": 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://stanfordnlp.github.io/coqa/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(coqa): Downloads the data and defines the splits
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
urls_to_download = {"train": _TRAIN_DATA_URL, "dev": _DEV_DATA_URL}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"], "split": "train"}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"], "split": "validation"}
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
# TODO(coqa): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for row in data["data"]:
questions = [question["input_text"] for question in row["questions"]]
story = row["story"]
source = row["source"]
answers_start = [answer["span_start"] for answer in row["answers"]]
answers_end = [answer["span_end"] for answer in row["answers"]]
answers = [answer["input_text"] for answer in row["answers"]]
yield row["id"], {
"source": source,
"story": story,
"questions": questions,
"answers": {"input_text": answers, "answer_start": answers_start, "answer_end": answers_end},
}
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