Datasets:

strombergnlp
/

rumoureval_2019

	# Copyright 2022 Mads Kongsbak and Leon Derczynski
	#
	# 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.
	# TODO: Address all TODOs and remove all explanatory comments
	"""RumourEval 2019: Stance Prediction"""


	import csv
	import json
	import os

	import datasets


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@inproceedings{gorrell-etal-2019-semeval,
	title = "{S}em{E}val-2019 Task 7: {R}umour{E}val, Determining Rumour Veracity and Support for Rumours",
	author = "Gorrell, Genevieve and
	Kochkina, Elena and
	Liakata, Maria and
	Aker, Ahmet and
	Zubiaga, Arkaitz and
	Bontcheva, Kalina and
	Derczynski, Leon",
	booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
	month = jun,
	year = "2019",
	address = "Minneapolis, Minnesota, USA",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/S19-2147",
	doi = "10.18653/v1/S19-2147",
	pages = "845--854",
	}

	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\

	Stance prediction task in English. The goal is to predict whether a given reply to a claim either supports, denies, questions, or simply comments on the claim. Ran as a SemEval task in 2019.
	"""

	# 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 = "cc-by-4.0"

	class RumourEval2019Config(datasets.BuilderConfig):

	def __init__(self, **kwargs):
	super(RumourEval2019Config, self).__init__(**kwargs)

	class RumourEval2019(datasets.GeneratorBasedBuilder):
	"""RumourEval2019 Stance Detection Dataset formatted in triples of (source_text, reply_text, label)"""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	RumourEval2019Config(name="RumourEval2019", version=VERSION, description="Stance Detection Dataset"),
	]

	def _info(self):
	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"source_text": datasets.Value("string"),
	"reply_text": datasets.Value("string"),
	"label": datasets.features.ClassLabel(
	names=[
	"support",
	"deny",
	"query",
	"comment"
	]
	)
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	train_text = dl_manager.download_and_extract("rumoureval2019_train.csv")
	validation_text = dl_manager.download_and_extract("rumoureval2019_val.csv")
	test_text = dl_manager.download_and_extract("rumoureval2019_test.csv")

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_text, "split": "train"}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": validation_text, "split": "validation"}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_text, "split": "test"}),
	]

	def _generate_examples(self, filepath, split):
	with open(filepath, encoding="utf-8") as f:
	reader = csv.DictReader(f, delimiter=",")
	guid = 0
	for instance in reader:
	instance["source_text"] = instance.pop("source_text")
	instance["reply_text"] = instance.pop("reply_text")
	instance["label"] = instance.pop("label")
	instance['id'] = str(guid)
	yield guid, instance
	guid += 1