Datasets:

TUKE-DeutscheTelekom
/

skquad

	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	# Lint as: python3
	"""SQUAD: The Stanford Question Answering Dataset."""


	import json

	import datasets
	from datasets.tasks import QuestionAnsweringExtractive


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	TBD
	"""

	_DESCRIPTION = """\
	Slovak Question Answering Dataset
	"""

	_URL = "https://files.kemt.fei.tuke.sk/corpora/sk-quad/sk-quad-220614.tar.gz"

	_FILES = {
	"dev": "sk-quad-220614/sk-quad-220614-dev.json",
	"train": "sk-quad-220614/sk-quad-220614-train.json",
	}

	class SkQuadConfig(datasets.BuilderConfig):
	"""BuilderConfig for SQUAD."""

	def __init__(self, **kwargs):
	"""BuilderConfig for SQUAD.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(SkQuadConfig, self).__init__(**kwargs)


	class SkQuad(datasets.GeneratorBasedBuilder):
	"""SQUAD: The Stanford Question Answering Dataset. Version 1.1."""

	BUILDER_CONFIGS = [
	SkQuadConfig(
	name="plain_text",
	version=datasets.Version("1.1.1", ""),
	description="Plain text",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	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"),
	}
	),
	}
	),
	# No default supervised_keys (as we have to pass both question
	# and context as input).
	supervised_keys=None,
	homepage="https://rajpurkar.github.io/SQuAD-explorer/",
	citation=_CITATION,
	task_templates=[
	QuestionAnsweringExtractive(
	question_column="question", context_column="context", answers_column="answers"
	)
	],
	)

	def _split_generators(self, dl_manager):
	downloaded_dir = dl_manager.download_and_extract(_URL)
	print(downloaded_dir)
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_dir + "/" + _FILES["train"]}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_dir+ "/" + _FILES["dev"]}),
	]

	def _generate_examples(self, filepath):
	"""This function returns the examples in the raw (text) form."""
	logger.info("generating examples from = %s", filepath)
	key = 0
	with open(filepath, encoding="utf-8") as f:
	squad = json.load(f)
	for article in squad["data"]:
	title = article.get("title", "")
	for paragraph in article["paragraphs"]:
	context = paragraph["context"] # do not strip leading blank spaces GH-2585
	for qa in paragraph["qas"]:
	answer_starts = [answer["answer_start"] for answer in qa["answers"]]
	assert len(qa["question"]) > 0
	#if len(answer_starts) == 0:
	# continue
	answers = [answer["text"] for answer in qa["answers"]]
	assert len(answer_starts) == len(answers)
	# Features currently used are "context", "question", and "answers".
	# Others are extracted here for the ease of future expansions.
	yield key, {
	"title": title,
	"context": context,
	"question": qa["question"],
	"id": qa["id"],
	"answers": {
	"answer_start": answer_starts,
	"text": answers,
	},
	}
	key += 1