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diff --git a/README.md b/README.md
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index 0000000000000000000000000000000000000000..fa58e78a4f9ffcf7d5d399b20e3d994ed8b19235
--- /dev/null
+++ b/README.md
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+---
+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}
+}
+```
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