README.md

---
language:
- mr
license: cc-by-4.0
size_categories:
- 10K<n<100K
task_categories:
- sentence-similarity
- text-retrieval
- text-ranking
pretty_name: MahaSTS
tags:
- Marathi NLP
- Sentence Similarity
- Marathi STS
- low-resource
---

# MahaSTS Dataset

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.

Alongside the dataset, the **MahaSBERT-STS-v2** model is fine-tuned for regression-based similarity scoring, providing a baseline for Marathi sentence similarity tasks.

## Language:
- **Primary Language**: Marathi (Low-resource Indic Language)

## Dataset Size:
- **Total Sentence Pairs**: 16,860
  - **Train**: 14,328 sentence pairs
  - **Validation**: 840 sentence pairs
  - **Test**: 1,692 sentence pairs
- **Bucket Distribution**: 
  - 6 similarity buckets (0-5)
  - 2,810 sentence pairs per bucket

## Annotation:
Each sentence pair is labeled with a continuous similarity score in the range of 0 to 5. The labels represent the degree of similarity between the two sentences, with 0 indicating no similarity and 5 indicating high similarity.

## Intended Use:
The dataset is intended for:
- **Sentence Similarity**
- **Regression Tasks**
- **Sentence Embeddings**
- **Marathi Embedding Model Benchmarking**

## Model Benchmarks:
The [**MahaSBERT-STS-v2**](https://huggingface.co/l3cube-pune/marathi-sentence-similarity-sbert-v2) model, fine-tuned on this dataset, provides a performance baseline. Other models like **MahaBERT**, **MuRIL**, **IndicBERT**, and **IndicSBERT** can be benchmarked for comparison.

## Citation:
If you use this dataset, please cite the following paper:

```bibtex
@article{mirashi2025l3cube,
  title={L3Cube-MahaSTS: A Marathi Sentence Similarity Dataset and Models},
  author={Mirashi, Aishwarya and Joshi, Ananya and Joshi, Raviraj},
  journal={arXiv preprint arXiv:2508.21569},
  year={2025}
}

@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/).