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

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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.

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  1. README.md +37 -9
README.md CHANGED
@@ -1,23 +1,28 @@
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  ---
 
 
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  license: cc-by-4.0
 
 
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  task_categories:
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  - sentence-similarity
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  - text-retrieval
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- language:
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- - mr
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  tags:
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  - Marathi NLP
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  - Sentence Similarity
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  - Marathi STS
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- pretty_name: MahaSTS
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- size_categories:
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- - 10K<n<100K
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  ---
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  # MahaSTS Dataset
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- **Paper**: [L3Cube-MahaSTS: A Marathi Sentence Similarity Dataset and Models](Coming soon)
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- **Code**: [https://github.com/l3cube-pune/MarathiNLP](https://github.com/l3cube-pune/MarathiNLP)
 
 
 
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  ## Overview:
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  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:
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  ## Model Benchmarks:
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  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.
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  ## Citation:
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- If you use this dataset, please cite the following:
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  ```bibtex
 
 
 
 
 
 
 
 
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  @article{joshi2022l3cube,
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  title={L3cube-mahanlp: Marathi natural language processing datasets, models, and library},
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  author={Joshi, Raviraj},
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  journal={arXiv preprint arXiv:2205.14728},
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  year={2022}
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- }
 
 
 
 
 
 
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  ---
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+ language:
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+ - mr
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  license: cc-by-4.0
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+ size_categories:
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+ - 10K<n<100K
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  task_categories:
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  - sentence-similarity
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  - text-retrieval
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+ - text-ranking
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+ pretty_name: MahaSTS
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  tags:
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  - Marathi NLP
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  - Sentence Similarity
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  - Marathi STS
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+ - low-resource
 
 
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  ---
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  # MahaSTS Dataset
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+ 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.
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+
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+ **Paper**: [L3Cube-MahaSTS: A Marathi Sentence Similarity Dataset and Models](https://huggingface.co/papers/2508.21569)
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+ **Code**: [https://github.com/l3cube-pune/MarathiNLP](https://github.com/l3cube-pune/MarathiNLP)
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+ **Project page**: [https://github.com/l3cube-pune/MarathiNLP](https://github.com/l3cube-pune/MarathiNLP)
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  ## Overview:
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  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.
 
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  ## Model Benchmarks:
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  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.
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+ ## Sample Usage
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+
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+ The `L3Cube-MahaNLP` library, which includes resources related to this dataset, can be installed via pip:
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+
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+ ```bash
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+ pip install mahaNLP
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+ ```
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+
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+ Usage examples are provided in this demo [Colab notebook](https://colab.research.google.com/drive/1POx3Bi1cML6-s3Z3u8g8VpqzpoYCyv2q).
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+
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  ## Citation:
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+ If you use this dataset, please cite the following paper:
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  ```bibtex
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+ @article{joshi2025l3cubemahasts,
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+ title={L3Cube-MahaSTS: A Marathi Sentence Similarity Dataset and Models},
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+ author={Joshi, Raviraj and others},
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+ journal={arXiv preprint arXiv:2508.21569},
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+ year={2025},
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+ url={https://huggingface.co/papers/2508.21569}
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+ }
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+
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  @article{joshi2022l3cube,
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  title={L3cube-mahanlp: Marathi natural language processing datasets, models, and library},
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  author={Joshi, Raviraj},
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  journal={arXiv preprint arXiv:2205.14728},
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  year={2022}
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+ }
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+ ```
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+
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+ ## License
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+
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+ This dataset is licensed under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).