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# coding=utf-8
# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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
"""CST Wikinews classification dataset."""
import csv
from typing import List, Tuple, Dict, Generator
import datasets
_DESCRIPTION = """CST Wikinews dataset."""
_URLS = {
"train": "https://huggingface.co/datasets/clarin-pl/cst-wikinews/resolve/main/train.csv",
"test": "https://huggingface.co/datasets/clarin-pl/cst-wikinews/resolve/main/test.csv",
}
_HOMEPAGE = "https://clarin-pl.eu/dspace/handle/11321/305"
_LABELS=[
"Brak_relacji",
"Dalsze_informacje",
"Krzyżowanie_się",
"Opis",
"Parafraza",
"Spełnienie",
"Streszczenie",
"Tożsamość",
"Tło_historyczne",
"Uszczegółowienie",
"Zawieranie",
"Źródło",
]
class CSTWikinews(datasets.GeneratorBasedBuilder):
def _info(self) -> datasets.DatasetInfo:
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"sentence_1": datasets.Value("string"),
"sentence_2": datasets.Value("string"),
"label": datasets.features.ClassLabel(
names=_LABELS,
num_classes=len(_LABELS)
),
}
),
homepage=_HOMEPAGE,
)
def _split_generators(
self, dl_manager: datasets.DownloadManager
) -> List[datasets.SplitGenerator]:
urls_to_download = _URLS
downloaded_files = dl_manager.download_and_extract(urls_to_download)
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"]},
),
]
def _generate_examples(
self, filepath: str
) -> Generator[Tuple[int, Dict[str, str]], None, None]:
with open(filepath, mode="r", encoding="utf-8") as csv_file:
csv_reader = csv.reader(
csv_file,
delimiter=",",
quoting=csv.QUOTE_ALL,
skipinitialspace=True,
)
next(csv_reader, None) # skip the headers
for row_id, (s1, s2, label) in enumerate(csv_reader):
label = int(label)
yield row_id, {
"sentence_1": s1,
"sentence_2": s2,
"label": label,
}
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