Datasets:
Tasks:
Summarization
Languages:
Russian
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Source Datasets:
original
ArXiv:
Tags:
License:
File size: 3,010 Bytes
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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and Ilya Gusev
#
# 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.
# Lint as: python3
"""Gazeta: Dataset for Automatic Summarization of Russian News"""
import json
import os
import datasets
_CITATION = """
@InProceedings{10.1007/978-3-030-59082-6_9,
author="Gusev, Ilya",
editor="Filchenkov, Andrey and Kauttonen, Janne and Pivovarova, Lidia",
title="Dataset for Automatic Summarization of Russian News",
booktitle="Artificial Intelligence and Natural Language",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="122--134",
isbn="978-3-030-59082-6"
}
"""
_DESCRIPTION = "Dataset for automatic summarization of Russian news"
_HOMEPAGE = "https://github.com/IlyaGusev/gazeta"
_URLS = {
"train": "gazeta_train.jsonl",
"val": "gazeta_val.jsonl",
"test": "gazeta_test.jsonl"
}
_DOCUMENT = "text"
_SUMMARY = "summary"
class GazetaDataset(datasets.GeneratorBasedBuilder):
"""Gazeta Dataset"""
VERSION = datasets.Version("2.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="default", version=VERSION, description=""),
]
DEFAULT_CONFIG_NAME = "default"
def _info(self):
features = datasets.Features(
{
_DOCUMENT: datasets.Value("string"),
_SUMMARY: datasets.Value("string"),
"title": datasets.Value("string"),
"date": datasets.Value("string"),
"url": datasets.Value("string")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=(_DOCUMENT, _SUMMARY),
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["val"]}),
]
def _generate_examples(self, filepath):
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
data = json.loads(row)
yield id_, data
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