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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.
"""HuffPost Dataset."""
import csv
import json
import os
import datasets
_CITATION = """\
@book{book,
author = {Misra, Rishabh and Grover, Jigyasa},
year = {2021},
month = {01},
pages = {},
title = {Sculpting Data for ML: The first act of Machine Learning},
isbn = {978-0-578-83125-1}
}
@dataset{dataset,
author = {Misra, Rishabh},
year = {2018},
month = {06},
pages = {},
title = {News Category Dataset},
doi = {10.13140/RG.2.2.20331.18729}
}
"""
_DESCRIPTION = """\
A dataset of approximately 200K news headlines from the year 2012 to 2018 collected from HuffPost."""
_HOMEPAGE = "https://www.kaggle.com/datasets/rmisra/news-category-dataset"
_LICENSE = "CC0: Public Domain"
_URLS = "https://huggingface.co/datasets/khalidalt/HuffPost/resolve/main/News_Category_Dataset_v2.json"
class HuffPost(datasets.GeneratorBasedBuilder):
"""HuffPost Dataset."""
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="default", version=VERSION, description="Default config"),
]
DEFAULT_CONFIG_NAME = "default"
def _info(self):
features = datasets.Features(
{
"category": datasets.Value("string"),
"headline": datasets.Value("string"),
"authors": datasets.Value("string"),
"link": datasets.Value("string"),
"short_description": datasets.Value("string"),
"date": datasets.Value("string"),
"label": datasets.ClassLabel(names=["POLITICS","WELLNESS","ENTERTAINMENT","TRAVEL","STYLE & BEAUTY",
"PARENTING","HEALTHY LIVING","QUEER VOICES","FOOD & DRINK",
"BUSINESS","COMEDY","SPORTS","BLACK VOICES","HOME & LIVING","PARENTS",
"THE WORLDPOST","WEDDINGS","WOMEN","IMPACT","DIVORCE","CRIME","MEDIA",
"WEIRD NEWS","GREEN","WORLDPOST","RELIGION","STYLE","SCIENCE",
"WORLD NEWS","TASTE","TECH","MONEY","ARTS","FIFTY","GOOD NEWS",
"ARTS & CULTURE","ENVIRONMENT","COLLEGE","LATINO VOICES","CULTURE & ARTS",
"EDUCATION"]),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": data_dir},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath):
with open(filepath, encoding="utf-8") as f:
for key, row in enumerate(f):
data = json.loads(row)
data['label'] = data['category']
# Yields examples as (key, example) tuples
yield key, data
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