Datasets:
Tasks:
Summarization
Modalities:
Text
Formats:
parquet
Languages:
Russian
Size:
10K - 100K
ArXiv:
License:
| # 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 | |