Datasets:

L4IO
/

tota_gep_arpad

Tasks:

Text Generation

Languages:

Size Categories:

Tags:

DOI:

License:

Dataset card Viewer Files Files and versions Community

tota_gep_arpad / tota_gep_arpad.py

sted's picture

Update tota_gep_arpad.py

ecf37a7 about 1 year ago

raw history blame contribute delete

No virus

2.79 kB

	# 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