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# coding=utf-8
# 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.
"""Cleaned Dutch split of the mC4 corpus."""
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_HOMEPAGE = "https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset"
_CITATION = """
@article{Narayan2018DontGM,
title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization},
author={Shashi Narayan and Shay B. Cohen and Mirella Lapata},
journal={ArXiv},
year={2018},
volume={abs/1808.08745}
}
"""
_DESCRIPTION = """
Extreme Summarization (XSum) Dataset.
There are three features:
- document: Input news article.
- summary: One sentence summary of the article.
- id: BBC ID of the article.
"""
_DATA_URL_NL = "https://huggingface.co/datasets/yhavinga/xsum_dutch/resolve/main/{config}/{split}.json.gz"
_DOCUMENT = "document"
_SUMMARY = "summary"
_ID = "id"
_SUPPORTED_VERSIONS = [
datasets.Version("1.0.0", "Default version."),
]
class XsumDutchConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super().__init__(**kwargs)
class XsumDutch(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
XsumDutchConfig(
name=str(version), description=version.description
)
for version in _SUPPORTED_VERSIONS
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
_DOCUMENT: datasets.Value("string"),
_SUMMARY: datasets.Value("string"),
"id": datasets.Value("string"),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
result = [
datasets.SplitGenerator(
name=split,
gen_kwargs={
"filepath": dl_manager.download_and_extract(
_DATA_URL_NL.format(split=str(split), config=str(self.config.name))
)
},
)
for split in [
datasets.Split.TRAIN,
datasets.Split.VALIDATION,
datasets.Split.TEST,
]
]
return result
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form by iterating on all the files."""
logger.info(f"Generating examples from {filepath}")
with open(filepath, "r") as file:
for _id, line in enumerate(file):
example = json.loads(line)
yield _id, example
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