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CATIE-AQ
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frenchQA

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Question Answering

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frenchQA / frenchQA.py

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Update frenchQA.py

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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,
	},
	}