Datasets:

boun-tabi
/

squad_tr

	# 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.
	#
	# This file is based off of the dataset loader script for the original SQuAD2.0
	# dataset.
	#
	# https://huggingface.co/datasets/squad_v2

	# Lint as: python3
	"""SQuAD-TR Dataset"""


	import json

	import datasets
	from datasets.tasks import QuestionAnsweringExtractive


	logger = datasets.logging.get_logger(__name__)

	_HOMEPAGE = "https://github.com/boun-tabi/squad-tr"

	_CITATION = """\
	@article{
	budur2023squadtr,
	title={Building Efficient and Effective OpenQA Systems for Low-Resource Languages},
	author={todo},
	journal={todo},
	year={2023}
	}
	"""

	_DESCRIPTION = """\
	SQuAD-TR is a machine translated version of the original SQuAD2.0 dataset into
	Turkish.
	"""

	_VERSION = "1.0.0"

	_DATA_URL = _HOMEPAGE + "/raw/beta/data"
	_DATA_URLS = {
	"default": {
	"train": f"{_DATA_URL}/squad-tr-train-v{_VERSION}.json.gz",
	"dev": f"{_DATA_URL}/squad-tr-dev-v{_VERSION}.json.gz",
	},
	"excluded": {
	"train": f"{_DATA_URL}/squad-tr-train-v{_VERSION}-excluded.json.gz",
	"dev": f"{_DATA_URL}/squad-tr-dev-v{_VERSION}-excluded.json.gz",
	}
	}


	class SquadTRConfig(datasets.BuilderConfig):
	"""BuilderConfig for SQuAD-TR."""

	def __init__(self, **kwargs):
	"""BuilderConfig for SQuAD-TR.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(SquadTRConfig, self).__init__(**kwargs)


	class SquadTR(datasets.GeneratorBasedBuilder):
	"""SQuAD-TR: Machine translated version of the original SQuAD2.0 dataset into Turkish."""

	VERSION = datasets.Version(_VERSION)

	BUILDER_CONFIGS = [
	SquadTRConfig(
	name="default",
	version=datasets.Version(_VERSION),
	description="SQuAD-TR default version.",
	),
	SquadTRConfig(
	name="excluded",
	version=datasets.Version(_VERSION),
	description="SQuAD-TR excluded version.",
	),
	SquadTRConfig(
	name="openqa",
	version=datasets.Version(_VERSION),
	description="SQuAD-TR OpenQA version.",
	),
	]

	DEFAULT_CONFIG_NAME = "default"

	def _info(self):

	# We change the contents of the "answers" field based on the
	# configuration selected. Specifically, we are excluding the
	# "answer_start" field for the "excluded" and "openqa" configurations.
	if self.config.name in ["excluded", "openqa"]:
	answers_feature = datasets.features.Sequence({
	"text": datasets.Value("string"),
	})
	else:
	answers_feature = datasets.features.Sequence({
	"text": datasets.Value("string"),
	"answer_start": datasets.Value("int32"),
	})

	# Constructing our dataset features.
	features = datasets.Features({
	"id": datasets.Value("string"),
	"title": datasets.Value("string"),
	"context": datasets.Value("string"),
	"question": datasets.Value("string"),
	"answers": answers_feature
	})

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	task_templates=[
	QuestionAnsweringExtractive(question_column="question", context_column="context", answers_column="answers")
	],
	)

	def _split_generators(self, dl_manager):

	# If the configuration selected is "default" or "excluded", we directly
	# load the files from the URLs in _DATA_URLS. For the "openqa"
	# configuration, we combine the datapints from the two different files
	# used in the "default" and "excluded" configurations.
	if self.config.name == "openqa":
	default_files = dl_manager.download_and_extract(_DATA_URLS["default"])
	excluded_files = dl_manager.download_and_extract(_DATA_URLS["excluded"])
	train_file_paths = [default_files["train"], excluded_files["train"]]
	dev_file_paths = [default_files["dev"], excluded_files["dev"]]

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath_list": train_file_paths}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath_list": dev_file_paths}),
	]
	else:
	config_urls = _DATA_URLS[self.config.name]
	downloaded_files = dl_manager.download_and_extract(config_urls)

	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"]}),
	]


	def _generate_examples(self, filepath=None, filepath_list=None):
	"""This function returns the examples in the raw (text) form."""
	assert filepath or filepath_list
	if filepath:
	filepath_list = [filepath]

	# Combining the generators for the different filepaths
	generators = [self._generate_examples_from_filepath(f) for f in filepath_list]
	for generator in generators:
	for element in generator:
	yield element

	def _generate_examples_from_filepath(self, filepath):
	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"]:
	# Constructing our answers dictonary. Note that the
	# answers_dictionary won't include the answer_start
	# field in the "excluded" and "openqa" modes.
	answers_dictionary = {
	"text": [answer["text"] for answer in qa["answers"]],
	}
	if self.config.name not in ["excluded", "openqa"]:
	answers_dictionary["answer_start"] = [answer["answer_start"] for answer in qa["answers"]]

	# Constructing our datapoint
	datapoint = {
	"title": title,
	"context": context,
	"question": qa["question"],
	"id": qa["id"],
	"answers": answers_dictionary,
	}

	# Features currently used are "context", "question", and "answers".
	# Others are extracted here for the ease of future expansions.
	yield key, datapoint
	key += 1