Datasets:

alexandrainst
/

scandi-qa

	# Copyright 2022 The HuggingFace Datasets Authors and Dan Saattrup Nielsen.
	#
	# 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.
	"""Python build script for the ScandiQA dataset."""


	import json
	from pathlib import Path
	from typing import List

	from datasets import Version
	from datasets.builder import BuilderConfig, GeneratorBasedBuilder
	from datasets.download import DownloadManager
	from datasets.features import Features, Value
	from datasets.info import DatasetInfo
	from datasets.splits import SplitGenerator

	_DESCRIPTION = """
	ScandiQA is a dataset of questions and answers in the Danish, Norwegian, and Swedish
	languages. All samples come from the Natural Questions (NQ) dataset, which is a large
	question answering dataset from Google searches. The Scandinavian questions and answers
	come from the MKQA dataset, where 10,000 NQ samples were manually translated into,
	among others, Danish, Norwegian, and Swedish. However, this did not include a
	translated context, hindering the training of extractive question answering models.

	We merged the NQ dataset with the MKQA dataset, and extracted contexts as either "long
	answers" from the NQ dataset, being the paragraph in which the answer was found, or
	otherwise we extract the context by locating the paragraphs which have the largest
	cosine similarity to the question, and which contains the desired answer.

	Further, many answers in the MKQA dataset were "language normalised": for instance, all
	date answers were converted to the format "YYYY-MM-DD", meaning that in most cases
	these answers are not appearing in any paragraphs. We solve this by extending the MKQA
	answers with plausible "answer candidates", being slight perturbations or translations
	of the answer.

	With the contexts extracted, we translated these to Danish, Swedish and Norwegian using
	the DeepL translation service for Danish and Swedish, and the Google Translation
	service for Norwegian. After translation we ensured that the Scandinavian answers do
	indeed occur in the translated contexts.

	As we are filtering the MKQA samples at both the "merging stage" and the "translation
	stage", we are not able to fully convert the 10,000 samples to the Scandinavian
	languages, and instead get roughly 8,000 samples per language. These have further been
	split into a training, validation and test split, with the former two containing
	roughly 750 samples. The splits have been created in such a way that the proportion of
	samples without an answer is roughly the same in each split.
	"""

	_HOMEPAGE = "https://huggingface.co/alexandrainst/scandiqa"
	_LICENSE = "CC BY 4.0"
	_URLS = {
	"da": [
	"https://huggingface.co/datasets/alexandrainst/scandi-qa/resolve/main/data/da/train.jsonl",
	"https://huggingface.co/datasets/alexandrainst/scandi-qa/resolve/main/data/da/val.jsonl",
	"https://huggingface.co/datasets/alexandrainst/scandi-qa/resolve/main/data/da/test.jsonl",
	],
	"sv": [
	"https://huggingface.co/datasets/alexandrainst/scandi-qa/resolve/main/data/sv/train.jsonl",
	"https://huggingface.co/datasets/alexandrainst/scandi-qa/resolve/main/data/sv/val.jsonl",
	"https://huggingface.co/datasets/alexandrainst/scandi-qa/resolve/main/data/sv/test.jsonl",
	],
	"no": [
	"https://huggingface.co/datasets/alexandrainst/scandi-qa/resolve/main/data/no/train.jsonl",
	"https://huggingface.co/datasets/alexandrainst/scandi-qa/resolve/main/data/no/val.jsonl",
	"https://huggingface.co/datasets/alexandrainst/scandi-qa/resolve/main/data/no/test.jsonl",
	],
	}

	# _CITATION = """
	# @InProceedings{huggingface:dataset,
	# title = {ScandiQA: A Scandinavian Question Answering Dataset},
	# author={Dan Saattrup Nielsen},
	# year={2022}
	# }
	# """


	class ScandiQA(GeneratorBasedBuilder):
	"""Scandinavian question answering dataset."""

	VERSION = Version("1.0.0")

	BUILDER_CONFIGS = [
	BuilderConfig(
	name="da",
	version=VERSION,
	description="The Danish part of the ScandiQA dataset.",
	),
	BuilderConfig(
	name="sv",
	version=VERSION,
	description="The Swedish part of the ScandiQA dataset.",
	),
	BuilderConfig(
	name="no",
	version=VERSION,
	description="The Norwegian part of the ScandiQA dataset.",
	),
	]

	def _info(self) -> DatasetInfo:
	features = Features(
	{
	"id": Value("string"),
	"question": Value("string"),
	"answers": {
	"text": [Value("string")],
	"answer_start": [Value("int64")],
	},
	"context": Value("string"),
	"answers_en": {
	"text": [Value("string")],
	"answer_start": [Value("int64")],
	},
	"context_en": Value("string"),
	"title_en": Value("string"),
	}
	)
	return DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	# citation=_CITATION,
	)

	def _split_generators(self, dl_manager: DownloadManager) -> List[SplitGenerator]:
	urls = _URLS[self.config.name]
	downloaded_files = dl_manager.download_and_extract(urls)
	return [
	SplitGenerator(
	name="train",
	gen_kwargs=dict(
	filepath=downloaded_files[0],
	split="train",
	),
	),
	SplitGenerator(
	name="val",
	gen_kwargs=dict(
	filepath=downloaded_files[1],
	split="val",
	),
	),
	SplitGenerator(
	name="test",
	gen_kwargs=dict(
	filepath=downloaded_files[2],
	split="test"
	),
	),
	]

	def _generate_examples(self, filepath: str, split):
	with Path(filepath).open(encoding="utf-8") as f:
	for key, row in enumerate(f):
	data = json.loads(row)
	yield key, {
	"id": str(data["id"]),
	"question": data["question"],
	"answers": {
	"text": [data["answer"]],
	"answer_start": [data["answer_start"]],
	},
	"context": data["context"],
	"answers_en": {
	"text": [data["answer_en"]],
	"answer_start": [data["answer_start_en"]],
	},
	"context_en": data["context_en"],
	"title_en": data["title_en"],
	}