Datasets:
humicroedit

	# 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.
	"""This is humorous headline dataset called Humicroedit introduced in the Task-7 of SemEval 2020."""


	import csv
	import os

	import datasets


	_CITATION = """\
	@article{hossain2019president,
	title={" President Vows to Cut< Taxes> Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines},
	author={Hossain, Nabil and Krumm, John and Gamon, Michael},
	journal={arXiv preprint arXiv:1906.00274},
	year={2019}
	}
	"""

	_DESCRIPTION = """\
	This new dataset is designed to assess the funniness of edited news headlines.
	"""

	_HOMEPAGE = "https://www.cs.rochester.edu/u/nhossain/humicroedit.html"
	_LICENSE = ""

	_URL = "https://cs.rochester.edu/u/nhossain/semeval-2020-task-7-dataset.zip"


	class Humicroedit(datasets.GeneratorBasedBuilder):
	"""This is humorous headline dataset called Humicroedit introduced in the Task-7 of SemEval 2020."""

	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 = MyBuilderConfig

	# 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 = [
	datasets.BuilderConfig(name="subtask-1", description="This part of the dataset covers the data for subtask-1"),
	datasets.BuilderConfig(name="subtask-2", description="This part of the dataset covers the data for subtask-2"),
	]

	def _info(self):
	if self.config.name == "subtask-1":
	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"original": datasets.Value("string"),
	"edit": datasets.Value("string"),
	"grades": datasets.Value("string"),
	"meanGrade": datasets.Value("float"),
	# These are the features of your dataset like images, labels ...
	}
	)
	else:
	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"original1": datasets.Value("string"),
	"edit1": datasets.Value("string"),
	"grades1": datasets.Value("string"),
	"meanGrade1": datasets.Value("float"),
	"original2": datasets.Value("string"),
	"edit2": datasets.Value("string"),
	"grades2": datasets.Value("string"),
	"meanGrade2": datasets.Value("float"),
	"label": datasets.ClassLabel(names=["equal", "sentence1", "sentence2"]),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_dir = dl_manager.download_and_extract(_URL)
	ROOT = "semeval-2020-task-7-dataset"

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, ROOT, self.config.name, "train.csv"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, ROOT, self.config.name, "test.csv"), "split": "test"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, ROOT, self.config.name, "dev.csv"),
	"split": "dev",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split("funlines"),
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, ROOT, self.config.name, "train_funlines.csv"),
	"split": "funlines",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	"""Yields examples."""
	label_names = ["equal", "sentence1", "sentence2"]

	with open(filepath, encoding="utf-8") as csv_file:
	csv_reader = csv.reader(
	csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
	)
	next(csv_reader)

	for id_, row in enumerate(csv_reader):
	if self.config.name == "subtask-1":
	row_id, original, edit, grades, meanGrade = row
	yield id_, {
	"id": row_id,
	"original": original,
	"edit": edit,
	"grades": grades,
	"meanGrade": meanGrade,
	}
	else:
	row_id, original1, edit1, grades1, meanGrade1, original2, edit2, grades2, meanGrade2, label = row
	yield id_, {
	"id": row_id,
	"original1": original1,
	"edit1": edit1,
	"grades1": grades1,
	"meanGrade1": meanGrade1,
	"original2": original2,
	"edit2": edit2,
	"grades2": grades2,
	"meanGrade2": meanGrade2,
	"label": label_names[int(label)],
	}