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# Copyright 2020 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
"""Farsi News Datasets: Hamshahri and RadioFarda"""
import json
import datasets
_CITATION = """\
"""
_DESCRIPTION = """\
Contains Farsi (Persian) datasets for Machine Learning tasks, particularly NLP.
These datasets have been extracted from the RSS feed of two Farsi news agency websites:
- Hamshahri
- RadioFarda
"""
_URL = "https://raw.githubusercontent.com/sci2lab/Farsi-datasets/master/farsi_news/"
_URLS = {
"hamshahri": _URL + "hamshahri.json",
"radiofarda": _URL + "radiofarda.json",
}
class FarsiNews(datasets.GeneratorBasedBuilder):
"""Farsi News Datasets: Hamshahri and RadioFarda"""
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# datasets.features.FeatureConnectors
features=datasets.Features(
{
"title": datasets.Value("string"),
"summary": datasets.Value("string"),
"link": datasets.Value("string"),
"tags": datasets.features.Sequence(datasets.Value("string")),
}
),
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage="https://github.com/sci2lab/Farsi-datasets",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
urls_to_download = _URLS
dl_dir = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(
name="hamshahri",
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": dl_dir["hamshahri"], "split": "hamshahri"},
),
datasets.SplitGenerator(
name="radiofarda",
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": dl_dir["radiofarda"], "split": "radiofarda"},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for id_, example in enumerate(data):
yield id_, example
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