File size: 3,683 Bytes
c2e5397 cbeab7f c2e5397 cbeab7f c2e5397 cbeab7f c2e5397 cbeab7f c2e5397 cbeab7f c2e5397 cbeab7f c2e5397 cbeab7f c2e5397 cbeab7f c2e5397 cbeab7f c2e5397 cbeab7f c2e5397 |
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 |
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",
"validation": f"{_URL}/{lang}_val.jsonl",
"test": f"{_URL}/{lang}_test.jsonl",
}
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"],
}
|