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