Datasets:
Languages:
Finnish
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
machine translated
Annotations Creators:
found
Source Datasets:
xlsum
License:
File size: 3,924 Bytes
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"""XL-Sum-FI Finnish abstractive summarization dataset based on machine translation of the XL-Sum dataset"""
import json
import os
import datasets
_CITATION = """\
Please cite the article and also acknowledge Filip Ginter / TurkuNLP for the machine translated version
@inproceedings{hasan-etal-2021-xl,
title = "{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages",
author = "Hasan, Tahmid and
Bhattacharjee, Abhik and
Islam, Md. Saiful and
Mubasshir, Kazi and
Li, Yuan-Fang and
Kang, Yong-Bin and
Rahman, M. Sohel and
Shahriyar, Rifat",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.413",
pages = "4693--4703",
}
"""
_DESCRIPTION = """\
This dataset is a DeepL -based machine translation of a part of the English section of the XLSum dataset:[https://github.com/csebuetnlp/xl-sum](https://github.com/csebuetnlp/xl-sum) In the present version, only examples where the full version is at most 10x the summary in length are included. We might translate more later.
"""
_HOMEPAGE = "https://github.com/TurkuNLP/xlsum-fi"
_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)"
_URL = "https://huggingface.co/datasets/TurkuNLP/xlsum-fi/resolve/main/data/{}_XLSum-fi_v{}.tar.bz2"
_LANGUAGES = [
"finnish",
]
class Xlsum(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("2.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="{}".format(lang),
version=datasets.Version("2.0.0")
)
for lang in _LANGUAGES
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"url": datasets.Value("string"),
"title": datasets.Value("string"),
"summary": datasets.Value("string"),
"text": datasets.Value("string"),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE,
version=self.VERSION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
lang = str(self.config.name)
url = _URL.format(lang, self.VERSION.version_str[:-2])
data_dir = dl_manager.download_and_extract(url)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(data_dir, lang + "_train.jsonl"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(data_dir, lang + "_test.jsonl"),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(data_dir, lang + "_val.jsonl"),
},
),
]
def _generate_examples(self, filepath):
"""Yields examples as (key, example) tuples."""
with open(filepath, encoding="utf-8") as f:
for idx_, row in enumerate(f):
data = json.loads(row)
yield idx_, {
"id": data["id"],
"url": data["url"],
"title": data["title"],
"summary": data["summary"],
"text": data["text"],
}
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