File size: 4,763 Bytes
3db59d9 5b60e55 3db59d9 5b60e55 3db59d9 5b60e55 3db59d9 5b60e55 3db59d9 5b60e55 3db59d9 5b60e55 3db59d9 5b60e55 3db59d9 8e23256 3db59d9 5b60e55 3db59d9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 |
# 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": dataset.Value("string"),
# These are the features of your dataset like images, labels ...
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# Homepage of the dataset for documentation
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.TRAIN,
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)
# Yields examples as (key, example) tuples
yield key, {
"category": data["category"],
"headline": data["headline"],
"authors": data["authors"],
"link": data["link"],
"short_description": data["short_description"],
#"date": data["date"],
}
|