Datasets:

JulesBelveze
/

tldr_news

Tasks:

Text2Text Generation

Text Generation

Sub-tasks:

news-articles-headline-generation

text-simplification

language-modeling

Languages:

Multilinguality:

Size Categories:

Language Creators:

Annotations Creators:

Source Datasets:

Dataset card Viewer Files Files and versions

tldr_news / tldr_news.py

JulesBelveze's picture

v1.2.0

92eb294 almost 2 years ago

raw history blame

No virus

3.56 kB

	# 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"]}