Datasets:
Update paper link, add task category, sample usage, and citation for MahaSTS
Browse filesThis 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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---
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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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tags:
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- Marathi NLP
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- Sentence Similarity
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- Marathi STS
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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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## 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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## 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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**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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The `L3Cube-MahaNLP` library, which includes resources related to this dataset, can be installed via pip:
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```bash
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pip install mahaNLP
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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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## 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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@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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## License
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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/).
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