Datasets:
Languages:
French
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
License:
File size: 3,886 Bytes
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"""FQuAD dataset."""
import json
import os
from textwrap import dedent
import datasets
_HOMEPAGE = "https://fquad.illuin.tech/"
_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%.
"""
_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}
}
"""
class Fquad(datasets.GeneratorBasedBuilder):
"""FQuAD dataset."""
VERSION = datasets.Version("1.0.0")
@property
def manual_download_instructions(self):
return dedent("""\
To access the data for this dataset, you need to request it at:
https://fquad.illuin.tech/#download
Unzip the downloaded file with `unzip download-form-fquad1.0.zip -d <path/to/directory>`, into a destination
directory <path/to/directory>, which will contain the two data files train.json and valid.json.
To load the dataset, pass the full path to the destination directory
in your call to the loading function: `datasets.load_dataset("fquad", data_dir="<path/to/directory>")`
""")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
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 ...
}
),
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "train.json")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "valid.json")},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
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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