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"""TODO(fquad): Add a description here."""
from __future__ import absolute_import, division, print_function
import json
import os
import datasets
# TODO(fquad): BibTeX citation
_CITATION = """\
@ARTICLE{2020arXiv200206071
author = {Martin, d'Hoffschmidt and Maxime, Vidal and
Wacim, Belblidia and Tom, Brendlé},
title = "{FQuAD: French Question Answering Dataset}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language},
year = "2020",
month = "Feb",
eid = {arXiv:2002.06071},
pages = {arXiv:2002.06071},
archivePrefix = {arXiv},
eprint = {2002.06071},
primaryClass = {cs.CL}
}
"""
# TODO(fquad):
_DESCRIPTION = """\
FQuAD: French Question Answering Dataset
We introduce FQuAD, a native French Question Answering Dataset. FQuAD contains 25,000+ question and answer pairs.
Finetuning CamemBERT on FQuAD yields a F1 score of 88% and an exact match of 77.9%.
"""
_URL = "https://storage.googleapis.com/illuin/fquad/"
_URLS = {
"train": _URL + "train.json.zip",
"valid": _URL + "valid.json.zip",
}
class Fquad(datasets.GeneratorBasedBuilder):
"""TODO(fquad): Short description of my dataset."""
# TODO(fquad): Set up version.
VERSION = datasets.Version("0.1.0")
def _info(self):
# TODO(fquad): 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(
{
"context": datasets.Value("string"),
"questions": datasets.features.Sequence(datasets.Value("string")),
"answers": datasets.features.Sequence(
{"texts": datasets.Value("string"), "answers_starts": datasets.Value("int32")}
),
# 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://fquad.illuin.tech/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(fquad): Downloads the data and defines the splits
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
download_urls = _URLS
dl_dir = dl_manager.download_and_extract(download_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(dl_dir["train"], "train.json")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(dl_dir["valid"], "valid.json")},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
# TODO(fquad): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for id1, examples in enumerate(data["data"]):
for id2, example in enumerate(examples["paragraphs"]):
questions = [question["question"] for question in example["qas"]]
answers = [answer["answers"] for answer in example["qas"]]
texts = [answer[0]["text"] for answer in answers]
answers_starts = [answer[0]["answer_start"] for answer in answers]
yield str(id1) + "_" + str(id2), {
"context": example["context"],
"questions": questions,
"answers": {"texts": texts, "answers_starts": answers_starts},
}
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