Datasets:

ai4bharat
/

IndicQA

	"""TODO(xquad): Add a description here."""


	import json

	import datasets
	from datasets.tasks import QuestionAnsweringExtractive


	_CITATION = """\

	"""

	_DESCRIPTION = """\

	"""

	_URL = "https://huggingface.co/datasets/ai4bharat/IndicQA/resolve/main/data/"
	_LANG = ["as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"]


	class IndicqaConfig(datasets.BuilderConfig):

	"""BuilderConfig for Indicqa"""

	def __init__(self, lang, **kwargs):
	"""

	Args:
	lang: string, language for the input text
	**kwargs: keyword arguments forwarded to super.
	"""
	super(IndicqaConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
	self.lang = lang


	class Xquad(datasets.GeneratorBasedBuilder):
	"""TODO(indicqa): Short description of my dataset."""

	# TODO(indicqa): Set up version.
	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [IndicqaConfig(name=f"indicqa.{lang}", description=_DESCRIPTION, lang=lang) for lang in _LANG]

	def _info(self):
	# TODO(indicqa): 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"),
	"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(indicqa): 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 = {lang: _URL + f"indicqa.{lang}.json" for lang in _LANG}
	downloaded_files = dl_manager.download_and_extract(urls_to_download)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": downloaded_files[self.config.lang]},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	# TODO(indicqa): Yields (key, example) tuples from the dataset
	with open(filepath, encoding="utf-8") as f:
	indicqa = json.load(f)
	id_ = 0
	for article in indicqa["data"]:
	for paragraph in article["paragraphs"]:
	context = paragraph["context"].strip()
	for qa in paragraph["qas"]:
	question = qa["question"].strip()
	answer_starts = [answer["answer_start"] for answer in qa["answers"]]
	answers = [answer["text"].strip() for answer in qa["answers"]]

	# Features currently used are "context", "question", and "answers".
	# Others are extracted here for the ease of future expansions.
	yield id_, {
	"context": context,
	"question": question,
	"id": qa["id"],
	"answers": {
	"answer_start": answer_starts,
	"text": answers,
	},
	}
	id_ += 1