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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.
import json
import os
import datasets
_DESCRIPTION = """\
The `tldr_news` dataset was constructed by collecting a daily tech newsletter (available at
https://tldr.tech/newsletter). Then for every piece of news, the "headline" and its corresponding "content" were
collected. Such a dataset can be used to train a model to generate a headline from a input piece of text.
"""
# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {"all": "https://github.com/JulesBelveze/tldr_news/blob/main/1.2.0.tar.gz?raw=true"}
class TLDRNewsConfig(datasets.BuilderConfig):
"""BuilderConfig for TLDRNews."""
def __init__(self, **kwargs):
"""BuilderConfig for TLDRNews.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(TLDRNewsConfig, self).__init__(**kwargs)
class TLDRNewsDataset(datasets.GeneratorBasedBuilder):
"""Dataset containing headline & content of pieces of news from the tldr tech newsletter."""
VERSION = datasets.Version("1.2.0")
BUILDER_CONFIGS = [
TLDRNewsConfig(name="all", version=VERSION, description="This contains all the existing newsletter"),
]
DEFAULT_CONFIG_NAME = "all"
def _info(self):
features = datasets.Features(
{
"headline": datasets.Value("string"),
"content": datasets.Value("string"),
"category": datasets.ClassLabel(
num_classes=5,
names=['Sponsor', 'Big Tech & Startups', 'Science and Futuristic Technology',
'Programming, Design & Data Science', 'Miscellaneous']
)
}
)
return datasets.DatasetInfo(description=_DESCRIPTION, features=features)
def _split_generators(self, dl_manager):
urls = _URLS[self.config.name]
data_dir = dl_manager.download_and_extract(urls)
data_dir = os.path.join(data_dir, str(self.config.version))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, "train.json"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": os.path.join(data_dir, "test.json"), "split": "test"},
),
]
def _generate_examples(self, filepath, split):
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for key, row in enumerate(data):
yield key, {"headline": row["headline"], "content": row["content"], "category": row["category"]}
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