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"""FrenchQA: One French QA Dataset to rule them all"""
import csv
import datasets
from datasets.tasks import QuestionAnsweringExtractive
# TODO(squad_v2): BibTeX citation
_CITATION = """\
"""
_DESCRIPTION = """\
One French QA Dataset to rule them all, One French QA Dataset to find them, One French QA Dataset to bring them all, and in the darkness bind them.
"""
_URLS = {
"train": "train.csv",
"dev": "valid.csv",
"test": "test.csv"
}
class FrenchQAConfig(datasets.BuilderConfig):
"""BuilderConfig for frenchQA."""
def __init__(self, **kwargs):
"""BuilderConfig for FrenchQA.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(FrenchQAConfig, self).__init__(**kwargs)
class FrenchQA(datasets.GeneratorBasedBuilder):
"""TODO(squad_v2): Short description of my dataset."""
# TODO(squad_v2): Set up version.
BUILDER_CONFIGS = [
FrenchQAConfig(name="frenchQA", version=datasets.Version("1.0.0"), description="frenchQA"),
]
def _info(self):
# TODO(squad_v2): 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(
{
"id": datasets.Value("string"),
"title": datasets.Value("string"),
"context": datasets.Value("string"),
"question": datasets.Value("string"),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string"),
"answer_start": 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="",
citation=_CITATION,
task_templates=[
QuestionAnsweringExtractive(
question_column="question", context_column="context", answers_column="answers"
)
],
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(squad_v2): 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 = _URLS
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
"""Yields examples."""
# TODO(squad_v2): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
squad = csv.DictReader(f, delimiter = ";")
for id_, row in enumerate(squad):
answer_start = []
text = []
if row["answer_start"] != "-1":
answer_start = [row["answer_start"]]
text = [row["answer"]]
yield id_, {
"title": row["dataset"],
"context": row["context"],
"question": row["question"],
"id": id_,
"answers": {
"answer_start": answer_start,
"text": text,
},
} |