Datasets:
selqa

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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.
	"""SelQA: A New Benchmark for Selection-Based Question Answering"""


	import csv
	import json

	import datasets


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@InProceedings{7814688,
	author={T. {Jurczyk} and M. {Zhai} and J. D. {Choi}},
	booktitle={2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)},
	title={SelQA: A New Benchmark for Selection-Based Question Answering},
	year={2016},
	volume={},
	number={},
	pages={820-827},
	doi={10.1109/ICTAI.2016.0128}
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	The SelQA dataset provides crowdsourced annotation for two selection-based question answer tasks,
	answer sentence selection and answer triggering.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = ""

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = ""

	# TODO: Add link to the official dataset URLs here
	# The HuggingFace dataset library don't host the datasets but only point to the original files
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	types = {
	"answer_selection": "ass",
	"answer_triggering": "at",
	}

	modes = {"analysis": "json", "experiments": "tsv"}


	class SelqaConfig(datasets.BuilderConfig):
	""" "BuilderConfig for SelQA Dataset"""

	def __init__(self, mode, type_, **kwargs):
	super(SelqaConfig, self).__init__(**kwargs)
	self.mode = mode
	self.type_ = type_


	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class Selqa(datasets.GeneratorBasedBuilder):
	"""A New Benchmark for Selection-based Question Answering."""

	VERSION = datasets.Version("1.1.0")

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	BUILDER_CONFIG_CLASS = SelqaConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	BUILDER_CONFIGS = [
	SelqaConfig(
	name="answer_selection_analysis",
	mode="analysis",
	type_="answer_selection",
	version=VERSION,
	description="This part covers answer selection analysis",
	),
	SelqaConfig(
	name="answer_selection_experiments",
	mode="experiments",
	type_="answer_selection",
	version=VERSION,
	description="This part covers answer selection experiments",
	),
	SelqaConfig(
	name="answer_triggering_analysis",
	mode="analysis",
	type_="answer_triggering",
	version=VERSION,
	description="This part covers answer triggering analysis",
	),
	SelqaConfig(
	name="answer_triggering_experiments",
	mode="experiments",
	type_="answer_triggering",
	version=VERSION,
	description="This part covers answer triggering experiments",
	),
	]

	DEFAULT_CONFIG_NAME = "answer_selection_analysis" # It's not mandatory to have a default configuration. Just use one if it make sense.

	def _info(self):
	if (
	self.config.mode == "experiments"
	): # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"question": datasets.Value("string"),
	"candidate": datasets.Value("string"),
	"label": datasets.ClassLabel(names=["0", "1"]),
	}
	)
	else:
	if self.config.type_ == "answer_selection":
	features = datasets.Features(
	{
	"section": datasets.Value("string"),
	"question": datasets.Value("string"),
	"article": datasets.Value("string"),
	"is_paraphrase": datasets.Value("bool"),
	"topic": datasets.ClassLabel(
	names=[
	"MUSIC",
	"TV",
	"TRAVEL",
	"ART",
	"SPORT",
	"COUNTRY",
	"MOVIES",
	"HISTORICAL EVENTS",
	"SCIENCE",
	"FOOD",
	]
	),
	"answers": datasets.Sequence(datasets.Value("int32")),
	"candidates": datasets.Sequence(datasets.Value("string")),
	"q_types": datasets.Sequence(
	datasets.ClassLabel(names=["what", "why", "when", "who", "where", "how", ""])
	),
	}
	)
	else:
	features = datasets.Features(
	{
	"section": datasets.Value("string"),
	"question": datasets.Value("string"),
	"article": datasets.Value("string"),
	"is_paraphrase": datasets.Value("bool"),
	"topic": datasets.ClassLabel(
	names=[
	"MUSIC",
	"TV",
	"TRAVEL",
	"ART",
	"SPORT",
	"COUNTRY",
	"MOVIES",
	"HISTORICAL EVENTS",
	"SCIENCE",
	"FOOD",
	]
	),
	"q_types": datasets.Sequence(
	datasets.ClassLabel(names=["what", "why", "when", "who", "where", "how", ""])
	),
	"candidate_list": datasets.Sequence(
	{
	"article": datasets.Value("string"),
	"section": datasets.Value("string"),
	"candidates": datasets.Sequence(datasets.Value("string")),
	"answers": datasets.Sequence(datasets.Value("int32")),
	}
	),
	}
	)
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# 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=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
	# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	urls = {
	"train": f"https://raw.githubusercontent.com/emorynlp/selqa/master/{types[self.config.type_]}/selqa-{types[self.config.type_]}-train.{modes[self.config.mode]}",
	"dev": f"https://raw.githubusercontent.com/emorynlp/selqa/master/{types[self.config.type_]}/selqa-{types[self.config.type_]}-dev.{modes[self.config.mode]}",
	"test": f"https://raw.githubusercontent.com/emorynlp/selqa/master/{types[self.config.type_]}/selqa-{types[self.config.type_]}-test.{modes[self.config.mode]}",
	}
	data_dir = dl_manager.download_and_extract(urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir["train"],
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": data_dir["test"], "split": "test"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir["dev"],
	"split": "dev",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	"""Yields examples."""
	# TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
	# It is in charge of opening the given file and yielding (key, example) tuples from the dataset
	# The key is not important, it's more here for legacy reason (legacy from tfds)
	with open(filepath, encoding="utf-8") as f:
	if self.config.mode == "experiments":
	csv_reader = csv.DictReader(
	f, delimiter="\t", quoting=csv.QUOTE_NONE, fieldnames=["question", "candidate", "label"]
	)
	for id_, row in enumerate(csv_reader):
	yield id_, row
	else:
	if self.config.type_ == "answer_selection":
	for row in f:
	data = json.loads(row)
	for id_, item in enumerate(data):
	yield id_, {
	"section": item["section"],
	"question": item["question"],
	"article": item["article"],
	"is_paraphrase": item["is_paraphrase"],
	"topic": item["topic"],
	"answers": item["answers"],
	"candidates": item["candidates"],
	"q_types": item["q_types"],
	}
	else:
	for row in f:
	data = json.loads(row)
	for id_, item in enumerate(data):
	candidate_list = []
	for entity in item["candidate_list"]:
	candidate_list.append(
	{
	"article": entity["article"],
	"section": entity["section"],
	"answers": entity["answers"],
	"candidates": entity["candidates"],
	}
	)
	yield id_, {
	"section": item["section"],
	"question": item["question"],
	"article": item["article"],
	"is_paraphrase": item["is_paraphrase"],
	"topic": item["topic"],
	"q_types": item["q_types"],
	"candidate_list": candidate_list,
	}