Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
fact-checking
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
stance-detection
License:
File size: 3,810 Bytes
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# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""
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{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""
# 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 = ""
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("0.9.0")
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",
"query",
"deny",
"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 |