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Update parquet files
Browse files- .gitignore +0 -1
- README.md +0 -35
- persian_daily.zip → default/persian_daily_news-train-00000-of-00016.parquet +2 -2
- default/persian_daily_news-train-00001-of-00016.parquet +3 -0
- default/persian_daily_news-train-00002-of-00016.parquet +3 -0
- default/persian_daily_news-train-00003-of-00016.parquet +3 -0
- default/persian_daily_news-train-00004-of-00016.parquet +3 -0
- default/persian_daily_news-train-00005-of-00016.parquet +3 -0
- default/persian_daily_news-train-00006-of-00016.parquet +3 -0
- default/persian_daily_news-train-00007-of-00016.parquet +3 -0
- default/persian_daily_news-train-00008-of-00016.parquet +3 -0
- default/persian_daily_news-train-00009-of-00016.parquet +3 -0
- default/persian_daily_news-train-00010-of-00016.parquet +3 -0
- default/persian_daily_news-train-00011-of-00016.parquet +3 -0
- default/persian_daily_news-train-00012-of-00016.parquet +3 -0
- default/persian_daily_news-train-00013-of-00016.parquet +3 -0
- default/persian_daily_news-train-00014-of-00016.parquet +3 -0
- default/persian_daily_news-train-00015-of-00016.parquet +3 -0
- persian_daily_news.py +0 -64
.gitignore
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*.lock
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README.md
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---
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pretty_name: Persian Daily News
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language:
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- fa
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source_datasets:
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- original
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task_categories:
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- Summarization
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- sequence-modeling
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---
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# Persian Daily News
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# Dataset Summary
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persian_daily_news is a collection of 2 million of unique news articles with the headline for each article. dataset can be used in abstractive summarization and paraphrasing tasks.
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This effort is part of a bigger perspective to have several datasets in Persian language(and other low resources languages) for different tasks that have two important factors: `free` and `easy-to-use`. Here is a quick HOW-TO for using this dataset in datasets library:[Demo-datasets](https://saied71.github.io/RohanAiLab/2021/09/03/Demo-datasets.html)
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# Description
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As discussed before, this dataset contains 2M news articles. Each article has these two attributes: text and summary. Here is a sample of dataset:
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```
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text: به گزارش گروه بین الملل ، خبرگزاری رسمی قطر اعلام کرد، بعد از امضای موافقتنامه همکاری نظامی بین قطر و روسیه این امکان فراهم شده است تا نظامیان قطری برای تکمیل آموزشهای نظامی خود عازم روسیه شده و در آنجا تعلیم ببینند.در چارچوب این قرارداد که امروز یک شنبه توسط سرتیپ ستاد عبدالعزیز صالح السلیطی رییس هییت همکاریهای بین المللی نظامی قطر و سرلشکر ویکتور جوریمیکین رییس اداره عمومی نیروی انسانی وزارت دفاع روسیه به امضا رسید، روابط نظامی بین دوحه و مسکو در زمینه موسسات آموزشهای نظامی شاهد توسه قابل توجهی خواهد شد.به نوشته این خبرگزاری روابط قطر و روسیه در حال گسترش بوده و به سوی شکلگیری مشارکت راهبردی در تمامی زمینهها پیش میرود.
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summary: از این پس نظامیان قطری برای آموزش عازم روسیه شده و در موسسات آموزش نظامی این کشور تعلیم خواهند دید.
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```
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# Citation
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```
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title={persian_daily_news},
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author={Saied Alimoradi},
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year={2021}
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}
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```
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persian_daily.zip → default/persian_daily_news-train-00000-of-00016.parquet
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default/persian_daily_news-train-00010-of-00016.parquet
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default/persian_daily_news-train-00012-of-00016.parquet
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default/persian_daily_news-train-00014-of-00016.parquet
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version https://git-lfs.github.com/spec/v1
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default/persian_daily_news-train-00015-of-00016.parquet
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version https://git-lfs.github.com/spec/v1
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size 8280157
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persian_daily_news.py
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import datasets
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import csv
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import os
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import sys
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csv.field_size_limit(sys.maxsize)
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_DESCRIPTION = """Persian Daily News dataset is a collection of 2 million news articles with the headline of each news article.
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This dataset contains news articles and their summaries for the last 10 years.
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This dataset is provided by Rohan AI lab for research purposes.
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"""
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_PROJECT_URL = """"""
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_CITATION = """
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https://saied71.github.io/RohanAiLab/,
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author={Saied Alimoradi},
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year={2021}
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}
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"""
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_URL = "persian_daily.zip"
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class Persian_news(datasets.GeneratorBasedBuilder):
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"text": datasets.Value("string"),
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"summary": datasets.Value("string"),
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}
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),
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homepage=_PROJECT_URL,
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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_dir = dl_manager.download_and_extract(_URL)
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data_dir = os.path.join(dl_dir, "persian_daily.csv")
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"filepath": data_dir,
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},),]
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def _generate_examples(self, filepath):
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"""Yields examples."""
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with open(filepath, encoding="utf-8") as f:
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reader = csv.reader(f)
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for id_, row in enumerate(reader):
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if id_ == 0:
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continue
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yield id_, {
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"text": row[1],
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"summary": row[0]
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}
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