Datasets:
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# coding=utf-8
# Copyright 2020 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
"""MIHVG TOTAWARPAD dataset."""
import os
import datasets
_CITATION = """\
@misc{
author={Laszlo, David},
title={totag-arpad},
year={2023},
howpublished={\\url{https://github.com/stedko/totagarpad}}
}"""
_DESCRIPTION = """\
Train dataset Tota W. Arpad cikkei a hvg-rol \
"""
class TotaGArpad(datasets.GeneratorBasedBuilder):
"""Tota Gep Arpad dataset builder."""
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({"text": datasets.Value("string")}),
supervised_keys=None,
homepage="https://github.com/stedko/totagarpad/blob/main/totawarpad-articles.txt",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
download_path = dl_manager.download_and_extract(
"https://raw.githubusercontent.com/stedko/totagarpad/main/totawarpad-articles.txt"
)
if os.path.isdir(download_path):
txt_path = os.path.join(download_path, "input.txt")
else:
txt_path = download_path
with open(txt_path, "r", encoding="utf-8") as f:
text = f.read()
i = int(len(text) * 0.9)
train_text, text = text[:i], text[i:]
i = int(len(text) * 0.5)
validation_text, text = text[:i], text[i:]
test_text = text
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"split_key": "train", "split_text": train_text},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"split_key": "validation", "split_text": validation_text},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"split_key": "test", "split_text": test_text},
),
]
def _generate_examples(self, split_key, split_text):
data_key = split_key
feature_dict = {"text": split_text}
yield data_key, feature_dict |