Update paper link, add task category, sample usage, and citation for MahaSTS

This PR updates the dataset card for MahaSTS with several improvements:

* The paper link has been updated from "Coming soon" to the official Hugging Face Papers link: `https://huggingface.co/papers/2508.21569`.
* The `text-ranking` task category has been added to the metadata, reflecting the dataset's use in Sentence Textual Similarity (STS) tasks.
* A `low-resource` tag has been added to the metadata, as the paper abstract highlights the dataset's utility in low-resource settings.
* A "Project page" link has been included, pointing to the associated GitHub repository for comprehensive project information.
* A "Sample Usage" section has been added, including instructions for installing the `mahaNLP` library via pip and a link to a demo Colab notebook, directly sourced from the GitHub README.
* The "Citation" section has been updated to include a specific BibTeX entry for the "L3Cube-MahaSTS: A Marathi Sentence Similarity Dataset and Models" paper.
* An explicit "License" section has been added to the markdown content, confirming the dataset is licensed under `Creative Commons Attribution 4.0 International License (CC-BY-4.0)`, aligning with the existing metadata.

README.md

@@ -1,23 +1,28 @@
 ---
+language:
+- mr
 license: cc-by-4.0
+size_categories:
+- 10K<n<100K
 task_categories:
 - sentence-similarity
 - text-retrieval
-language:
-- mr
+- text-ranking
+pretty_name: MahaSTS
 tags:
 - Marathi NLP
 - Sentence Similarity
 - Marathi STS
-pretty_name: MahaSTS
-size_categories:
-- 10K<n<100K
+- low-resource
 ---
 
 # MahaSTS Dataset
 
-**Paper**: [L3Cube-MahaSTS: A Marathi Sentence Similarity Dataset and Models](Coming soon)  
-**Code**: [https://github.com/l3cube-pune/MarathiNLP](https://github.com/l3cube-pune/MarathiNLP)
+The MahaSTS dataset is a human-annotated Sentence Textual Similarity (STS) dataset for Marathi, consisting of 16,860 sentence pairs labeled with continuous similarity scores in the range of 0-5. It is designed to enable effective training for sentence similarity tasks in Marathi, particularly in low-resource settings.
+
+**Paper**: [L3Cube-MahaSTS: A Marathi Sentence Similarity Dataset and Models](https://huggingface.co/papers/2508.21569)  
+**Code**: [https://github.com/l3cube-pune/MarathiNLP](https://github.com/l3cube-pune/MarathiNLP)  
+**Project page**: [https://github.com/l3cube-pune/MarathiNLP](https://github.com/l3cube-pune/MarathiNLP)
 
 ## Overview:
 The **MahaSTS Dataset** is a human-annotated dataset for Sentence Textual Similarity (STS) in **Marathi**, designed to train and evaluate models on sentence similarity tasks. The dataset contains 16,860 Marathi sentence pairs, each labeled with a continuous similarity score in the range of 0–5. The dataset is split into training, validation, and test sets with a ratio of 85:10:5, ensuring balanced supervision.
@@ -49,13 +54,36 @@ The dataset is intended for:
 ## Model Benchmarks:
 The **MahaSBERT-STS-v2** model, fine-tuned on this dataset, provides a performance baseline. Other models like **MahaBERT**, **MuRIL**, **IndicBERT**, and **IndicSBERT** can be benchmarked for comparison.
 
+## Sample Usage
+
+The `L3Cube-MahaNLP` library, which includes resources related to this dataset, can be installed via pip:
+
+```bash
+pip install mahaNLP
+```
+
+Usage examples are provided in this demo [Colab notebook](https://colab.research.google.com/drive/1POx3Bi1cML6-s3Z3u8g8VpqzpoYCyv2q).
+
 ## Citation:
-If you use this dataset, please cite the following:
+If you use this dataset, please cite the following paper:
 
 ```bibtex
+@article{joshi2025l3cubemahasts,
+  title={L3Cube-MahaSTS: A Marathi Sentence Similarity Dataset and Models},
+  author={Joshi, Raviraj and others},
+  journal={arXiv preprint arXiv:2508.21569},
+  year={2025},
+  url={https://huggingface.co/papers/2508.21569}
+}
+
 @article{joshi2022l3cube,
   title={L3cube-mahanlp: Marathi natural language processing datasets, models, and library},
   author={Joshi, Raviraj},
   journal={arXiv preprint arXiv:2205.14728},
   year={2022}
-}
+}
+```
+
+## License
+
+This dataset is licensed under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).