Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
fact-checking
Languages:
Bulgarian
Size:
1K - 10K
License:
# 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 with clickbait and fake news in Bulgarian. """ | |
from __future__ import absolute_import, division, print_function | |
import openpyxl # noqa: requires this pandas optional dependency for reading xlsx files | |
import pandas as pd | |
import datasets | |
_CITATION = """\ | |
@InProceedings{clickbait_news_bg, | |
title = {Dataset with clickbait and fake news in Bulgarian. Introduced for the Hack the Fake News 2017.}, | |
authors={Data Science Society}, | |
year={2017}, | |
url={https://gitlab.com/datasciencesociety/case_fake_news/} | |
} | |
""" | |
# TODO: Add description of the dataset here | |
# You can copy an official description | |
_DESCRIPTION = """\ | |
Dataset with clickbait and fake news in Bulgarian. Introduced for the Hack the Fake News 2017. | |
""" | |
# TODO: Add a link to an official homepage for the dataset here | |
_HOMEPAGE = "https://gitlab.com/datasciencesociety/case_fake_news/" | |
# 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) | |
_URLs = { | |
"default_train": "https://gitlab.com/datasciencesociety/case_fake_news/-/raw/master/data/FN_Training_Set.xlsx", | |
"default_validation": "https://gitlab.com/datasciencesociety/case_fake_news/-/raw/master/data/FN_Validation_Set.xlsx", | |
} | |
class ClickbaitNewsBG(datasets.GeneratorBasedBuilder): | |
VERSION = datasets.Version("1.1.0") | |
DEFAULT_CONFIG_NAME = "default" | |
def _info(self): | |
if self.config.name == "default": | |
features = datasets.Features( | |
{ | |
"fake_news_score": datasets.features.ClassLabel(names=["legitimate", "fake"]), | |
"click_bait_score": datasets.features.ClassLabel(names=["normal", "clickbait"]), | |
"content_title": datasets.Value("string"), | |
"content_url": datasets.Value("string"), | |
"content_published_time": datasets.Value("string"), | |
"content": datasets.Value("string"), | |
} | |
) | |
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(_URLs) | |
return [ | |
datasets.SplitGenerator( | |
name=spl_enum, | |
gen_kwargs={ | |
"filepath": data_dir[f"{self.config.name}_{spl}"], | |
"split": spl, | |
}, | |
) | |
for spl, spl_enum in [ | |
("train", datasets.Split.TRAIN), | |
("validation", datasets.Split.VALIDATION), | |
] | |
] | |
def _generate_examples(self, filepath, split): | |
""" Yields examples. """ | |
keys = [ | |
"fake_news_score", | |
"click_bait_score", | |
"content_title", | |
"content_url", | |
"content_published_time", | |
"content", | |
] | |
with open(filepath, "rb") as f: | |
data = pd.read_excel(f, engine="openpyxl") | |
for id_, row in enumerate(data.itertuples()): | |
row_dict = dict() | |
for key, value in zip(keys, row[1:]): | |
if key == "fake_news_score": | |
row_dict[key] = "legitimate" if value == 1 else "fake" | |
elif key == "click_bait_score": | |
row_dict[key] = "normal" if value == 1 else "clickbait" | |
else: | |
row_dict[key] = str(value) | |
yield id_, row_dict | |