Datasets:
circa

	# 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.
	"""Dataset containing polar questions and indirect answers."""


	import csv

	import datasets


	_CITATION = """\
	@InProceedings{louis_emnlp2020,
	author = "Annie Louis and Dan Roth and Filip Radlinski",
	title = ""{I}'d rather just go to bed": {U}nderstanding {I}ndirect {A}nswers",
	booktitle = "Proceedings of the 2020 Conference on Empirical Methods
	in Natural Language Processing",
	year = "2020",
	}
	"""

	_DESCRIPTION = """\
	The Circa (meaning ‘approximately’) dataset aims to help machine learning systems
	to solve the problem of interpreting indirect answers to polar questions.

	The dataset contains pairs of yes/no questions and indirect answers, together with
	annotations for the interpretation of the answer. The data is collected in 10
	different social conversational situations (eg. food preferences of a friend).

	NOTE: There might be missing labels in the dataset and we have replaced them with -1.
	The original dataset contains no train/dev/test splits.
	"""

	_LICENSE = "Creative Commons Attribution 4.0 License"

	_DATA_URL = "https://raw.githubusercontent.com/google-research-datasets/circa/main/circa-data.tsv"


	class Circa(datasets.GeneratorBasedBuilder):
	"""Dataset containing polar questions and indirect answers."""

	VERSION = datasets.Version("1.1.0")

	def _info(self):
	features = datasets.Features(
	{
	"context": datasets.Value("string"),
	"question-X": datasets.Value("string"),
	"canquestion-X": datasets.Value("string"),
	"answer-Y": datasets.Value("string"),
	"judgements": datasets.Value("string"),
	"goldstandard1": datasets.features.ClassLabel(
	names=[
	"Yes",
	"No",
	"In the middle, neither yes nor no",
	"Probably yes / sometimes yes",
	"Probably no",
	"Yes, subject to some conditions",
	"Other",
	"I am not sure how X will interpret Y’s answer",
	]
	),
	"goldstandard2": datasets.features.ClassLabel(
	names=[
	"Yes",
	"No",
	"In the middle, neither yes nor no",
	"Yes, subject to some conditions",
	"Other",
	]
	),
	}
	)
	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="https://github.com/google-research-datasets/circa",
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	train_path = dl_manager.download_and_extract(_DATA_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": train_path,
	"split": datasets.Split.TRAIN,
	},
	),
	]

	def _generate_examples(self, filepath, split):
	with open(filepath, encoding="utf-8") as f:
	goldstandard1_labels = [
	"Yes",
	"No",
	"In the middle, neither yes nor no",
	"Probably yes / sometimes yes",
	"Probably no",
	"Yes, subject to some conditions",
	"Other",
	"I am not sure how X will interpret Y’s answer",
	]
	goldstandard2_labels = [
	"Yes",
	"No",
	"In the middle, neither yes nor no",
	"Yes, subject to some conditions",
	"Other",
	]
	data = csv.reader(f, delimiter="\t")
	next(data, None) # skip the headers
	for id_, row in enumerate(data):
	row = [x if x != "nan" else -1 for x in row]
	_, context, question_X, canquestion_X, answer_Y, judgements, goldstandard1, goldstandard2 = row
	if goldstandard1 not in goldstandard1_labels:
	goldstandard1 = -1
	if goldstandard2 not in goldstandard2_labels:
	goldstandard2 = -1

	yield id_, {
	"context": context,
	"question-X": question_X,
	"canquestion-X": canquestion_X,
	"answer-Y": answer_Y,
	"judgements": judgements,
	"goldstandard1": goldstandard1,
	"goldstandard2": goldstandard2,
	}