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Datasets:
rahular
/
varta

Tasks:
Summarization
Feature Extraction
Languages:
Assamese
Bihari
Bengali
Size:
1B<n<10B
ArXiv:
License:
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1
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v1.0

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  2. README.md +89 -0
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README.md ADDED
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+ ---
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+ license: cc
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+ task_categories:
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+ - summarization
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+ - feature-extraction
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+ language:
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+ - as
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+ - bh
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+ - bn
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+ - en
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+ - gu
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+ - hi
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+ - kn
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+ - ml
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+ - mr
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+ - ne
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+ - or
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+ - pa
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+ - ta
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+ - te
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+ - ur
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+ pretty_name: varta
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+ size_categories:
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+ - 1B<n<10B
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+ ---
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+
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+ ## Dataset Description
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+
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+ - **Repository:** https://github.com/rahular/varta
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+ - **Paper:** https://arxiv.org/abs/2305.05858
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+
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+ ### Dataset Summary
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+
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+ Varta is a diverse, challenging, large-scale, multilingual, and high-quality headline-generation dataset containing 41.8 million news articles in 14 Indic languages and English.
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+ The data is crawled from DailyHunt, a popular news aggregator in India that pulls high-quality articles from multiple trusted and reputed news publishers.
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+
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+ ### Languages
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+
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+ Assamese, Bhojpuri, Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Tamil, Telugu, and Urdu.
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+
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+ ## Dataset Structure
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+
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+ ### Data Fields
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+
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+ - id: unique identifier for the artilce on DailyHunt. This id will be used to recreate the dataset.
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+ - langCode: ISO 639-1 language code
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+ - source_url: the url that points to the article on the website of the original publisher
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+ - dh_url: the url that points to the article on DailyHunt
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+
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+ - id: unique identifier for the artilce on DailyHunt.
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+ - url: the url that points to the article on DailyHunt
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+ - headline: headline of the article
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+ - publication_date: date of publication
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+ - text: main body of the article
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+ - tags: main topics related to the article
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+ - reactions: user likes, dislikes, etc.
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+ - source_media: original publisher name
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+ - source_url: the url that points to the article on the website of the original publisher
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+ - word_count: number of words in the article
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+ - langCode: language of the article
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+
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+ ### Data Splits
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+
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+ From every language, we randomly sample 10,000 articles each for validation and testing. We also ensure that at least 80% of a language’s data is available for training.
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+ Therefore, if a language has less than 100,000 articles, we restrict its validation and test splits to 10% of its size.
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+
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+ We also create a `small` training set by limiting the number of articles from each language to 100K.
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+ This `small` training set with a size of 1.3M is used in all our fine-tuning experiments.
69
+ You can find the `small` training set [here](https://huggingface.co/datasets/rahular/varta/blob/main/varta/train/train_100k.json)
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+
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+ ## Data Recreation
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+ To recreate the dataset, follow this [README file](https://github.com/rahular/varta/tree/main/crawler#README.md).
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+
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+ ## Misc
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+ - Original source: https://m.dailyhunt.in/
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+ - License: CC-BY 4.0
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+
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+ ## Citation Information
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+
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+ ```
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+ @misc{aralikatte2023varta,
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+ title={V\=arta: A Large-Scale Headline-Generation Dataset for Indic Languages},
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+ author={Rahul Aralikatte and Ziling Cheng and Sumanth Doddapaneni and Jackie Chi Kit Cheung},
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+ year={2023},
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+ eprint={2305.05858},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL}
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+ }
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+ ```
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