Datasets:
Languages:
Persian
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
found
Source Datasets:
original
ArXiv:
License:
# coding=utf-8 | |
# 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. | |
"""pn_summary""" | |
from __future__ import absolute_import, division, print_function | |
import csv | |
import os | |
import datasets | |
_CITATION = """\ | |
@article{pnSummary, title={Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization}, | |
author={Mehrdad Farahani, Mohammad Gharachorloo, Mohammad Manthouri}, | |
year={2020}, | |
eprint={2012.11204}, | |
archivePrefix={arXiv}, | |
primaryClass={cs.CL} | |
} | |
""" | |
_DESCRIPTION = """\ | |
A well-structured summarization dataset for the Persian language consists of 93,207 records. It is prepared for Abstractive/Extractive tasks (like cnn_dailymail for English). It can also be used in other scopes like Text Generation, Title Generation, and News Category Classification. | |
It is imperative to consider that the newlines were replaced with the `[n]` symbol. Please interpret them into normal newlines (for ex. `t.replace("[n]", "\n")`) and then use them for your purposes. | |
""" | |
_HOMEPAGE = "https://github.com/hooshvare/pn-summary" | |
_LICENSE = "MIT License" | |
_URLs = { | |
"1.0.0": { | |
"data": "https://drive.google.com/u/0/uc?id=16OgJ_OrfzUF_i3ftLjFn9kpcyoi7UJeO&export=download", | |
"features": [ | |
{"name": "id", "type": datasets.Value("string")}, | |
{"name": "title", "type": datasets.Value("string")}, | |
{"name": "article", "type": datasets.Value("string")}, | |
{"name": "summary", "type": datasets.Value("string")}, | |
{ | |
"name": "category", | |
"type": datasets.ClassLabel( | |
names=[ | |
"Economy", | |
"Roads-Urban", | |
"Banking-Insurance", | |
"Agriculture", | |
"International", | |
"Oil-Energy", | |
"Industry", | |
"Transportation", | |
"Science-Technology", | |
"Local", | |
"Sports", | |
"Politics", | |
"Art-Culture", | |
"Society", | |
"Health", | |
"Research", | |
"Education-University", | |
"Tourism", | |
] | |
), | |
}, | |
{"name": "categories", "type": datasets.Value("string")}, | |
{ | |
"name": "network", | |
"type": datasets.ClassLabel(names=["Tahlilbazaar", "Imna", "Shana", "Mehr", "Irna", "Khabaronline"]), | |
}, | |
{"name": "link", "type": datasets.Value("string")}, | |
], | |
} | |
} | |
class PnSummaryConfig(datasets.BuilderConfig): | |
"""BuilderConfig for pn_summary.""" | |
def __init__(self, **kwargs): | |
"""BuilderConfig for pn_summary.""" | |
super(PnSummaryConfig, self).__init__(**kwargs) | |
class PnSummary(datasets.GeneratorBasedBuilder): | |
"""A well-structured summarization dataset for the Persian language: pn_summary""" | |
BUILDER_CONFIGS = [ | |
PnSummaryConfig( | |
name="1.0.0", version=datasets.Version("1.0.0"), description="The first version of pn_summary" | |
), | |
] | |
def _info(self): | |
feature_names_types = _URLs[self.config.name]["features"] | |
features = datasets.Features({f["name"]: f["type"] for f in feature_names_types}) | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, citation=_CITATION | |
) | |
def _split_generators(self, dl_manager): | |
my_urls = _URLs[self.config.name] | |
data_dir = dl_manager.download_and_extract(my_urls["data"]) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"filepath": os.path.join(data_dir, "pn_summary", "train.csv"), | |
"split": "train", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"filepath": os.path.join(data_dir, "pn_summary", "dev.csv"), | |
"split": "validation", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"filepath": os.path.join(data_dir, "pn_summary", "test.csv"), | |
"split": "test", | |
}, | |
), | |
] | |
def _generate_examples(self, filepath, split): | |
feature_names_types = _URLs[self.config.name]["features"] | |
features = [f["name"] for f in feature_names_types] | |
with open(filepath, encoding="utf-8") as csv_file: | |
reader = csv.DictReader(csv_file, quotechar='"', delimiter="\t", quoting=csv.QUOTE_MINIMAL) | |
for _id, row in enumerate(reader): | |
if len(row) == len(features): | |
yield _id, {f: row[f] for f in features} | |