Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
extractive-qa
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
conversational-qa
License:
File size: 3,202 Bytes
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"""CoQA dataset."""
import json
import datasets
_HOMEPAGE = "https://stanfordnlp.github.io/coqa/"
_CITATION = """\
@article{reddy-etal-2019-coqa,
title = "{C}o{QA}: A Conversational Question Answering Challenge",
author = "Reddy, Siva and
Chen, Danqi and
Manning, Christopher D.",
journal = "Transactions of the Association for Computational Linguistics",
volume = "7",
year = "2019",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q19-1016",
doi = "10.1162/tacl_a_00266",
pages = "249--266",
}
"""
_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):
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
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"),
}
),
}
),
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
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."""
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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