Datasets:
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
import csv
import json
import os
import datasets
_DESCRIPTION = """\
This new dataset is designed to solve persian long summarization tasks.
"""
_URLS = {
"train": "https://huggingface.co/datasets/zedfum/long-summarization-persian/blob/main/train.csv",
"validation": "https://huggingface.co/datasets/zedfum/long-summarization-persian/blob/main/validation.csv",
"test": "https://huggingface.co/datasets/zedfum/long-summarization-persian/blob/main/test.csv",
}
class long_summarization_persianConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
"""BuilderConfig for long_summarization_persian.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(long_summarization_persianConfig, self).__init__(**kwargs)
class long_summarization_persian(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
long_summarization_persianConfig(
name="long_summarization_persian",
version=datasets.Version("1.0.0"),
description="long_summarization_persian dataset",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"article": datasets.Value("string"),
"summary": datasets.Value("string")
}
),
)
def _split_generators(self, dl_manager):
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"]}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": downloaded_files["validation"]},
),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for id_, article in enumerate(data):
article = article["article"]
summary = article["summary"]
yield id_, {
"article": article,
"summary": summary,
} |