Datasets:
mlsum

Tasks:

Text Classification

Sub-tasks: news-articles-summarization multi-class-classification multi-label-classification

Languages: German Spanish French

Multilinguality: multilingual

Size Categories: 100K<n<1M 10K<n<100K

Language Creators: found

Annotations Creators: found

Source Datasets: extended|cnn_dailymail original

Tags:

License:

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mlsum / mlsum.py

albertvillanova's picture

albertvillanova HF staff

Fix JSONDecodeError (#3)

af3364b over 1 year ago

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	import json

	import datasets


	_CITATION = """\
	@article{scialom2020mlsum,
	title={MLSUM: The Multilingual Summarization Corpus},
	author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo},
	journal={arXiv preprint arXiv:2004.14900},
	year={2020}
	}
	"""

	_DESCRIPTION = """\
	We present MLSUM, the first large-scale MultiLingual SUMmarization dataset.
	Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish.
	Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community.
	We report cross-lingual comparative analyses based on state-of-the-art systems.
	These highlight existing biases which motivate the use of a multi-lingual dataset.
	"""

	_URL = "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM"
	_LANG = ["de", "es", "fr", "ru", "tu"]


	class Mlsum(datasets.GeneratorBasedBuilder):

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name=lang,
	version=datasets.Version("1.0.0"),
	description="",
	)
	for lang in _LANG
	]

	def _info(self):
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# datasets.features.FeatureConnectors
	features=datasets.Features(
	{
	"text": datasets.Value("string"),
	"summary": datasets.Value("string"),
	"topic": datasets.Value("string"),
	"url": datasets.Value("string"),
	"title": datasets.Value("string"),
	"date": datasets.Value("string")
	# These are the features of your dataset like images, labels ...
	}
	),
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage="",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs

	lang = self.config.name
	urls_to_download = {
	"train": f"{_URL}/{lang}_train.jsonl?inline=false",
	"validation": f"{_URL}/{lang}_val.jsonl?inline=false",
	"test": f"{_URL}/{lang}_test.jsonl?inline=false",
	}
	downloaded_files = dl_manager.download(urls_to_download)

	return [
	datasets.SplitGenerator(
	name=split,
	gen_kwargs={
	"filepath": downloaded_files[split],
	},
	)
	for split in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as f:
	for id_, line in enumerate(f):
	data = json.loads(line)
	yield id_, {
	"text": data["text"],
	"summary": data["summary"],
	"topic": data["topic"],
	"url": data["url"],
	"title": data["title"],
	"date": data["date"],
	}