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Browse files- farsi_news.py +0 -93
farsi_news.py
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# Copyright 2020 the HuggingFace Datasets Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Lint as: python3
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"""Farsi News Datasets: Hamshahri and RadioFarda"""
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import json
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import datasets
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_CITATION = """\
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"""
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_DESCRIPTION = """\
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Contains Farsi (Persian) datasets for Machine Learning tasks, particularly NLP.
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These datasets have been extracted from the RSS feed of two Farsi news agency websites:
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- Hamshahri
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- RadioFarda
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"""
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_URL = "https://raw.githubusercontent.com/sci2lab/Farsi-datasets/master/farsi_news/"
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_URLS = {
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"hamshahri": _URL + "hamshahri.json",
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"radiofarda": _URL + "radiofarda.json",
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}
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class FarsiNews(datasets.GeneratorBasedBuilder):
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"""Farsi News Datasets: Hamshahri and RadioFarda"""
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VERSION = datasets.Version("1.0.0")
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def _info(self):
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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# datasets.features.FeatureConnectors
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features=datasets.Features(
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{
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"title": datasets.Value("string"),
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"summary": datasets.Value("string"),
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"link": datasets.Value("string"),
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"tags": datasets.features.Sequence(datasets.Value("string")),
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}
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),
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# If there's a common (input, target) tuple from the features,
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# specify them here. They'll be used if as_supervised=True in
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# builder.as_dataset.
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supervised_keys=None,
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# Homepage of the dataset for documentation
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homepage="https://github.com/sci2lab/Farsi-datasets",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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# dl_manager is a datasets.download.DownloadManager that can be used to
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# download and extract URLs
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urls_to_download = _URLS
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dl_dir = dl_manager.download_and_extract(urls_to_download)
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return [
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datasets.SplitGenerator(
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name="hamshahri",
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"filepath": dl_dir["hamshahri"], "split": "hamshahri"},
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),
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datasets.SplitGenerator(
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name="radiofarda",
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"filepath": dl_dir["radiofarda"], "split": "radiofarda"},
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),
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]
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def _generate_examples(self, filepath, split):
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"""Yields examples."""
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with open(filepath, encoding="utf-8") as f:
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data = json.load(f)
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for id_, example in enumerate(data):
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yield id_, example
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