Datasets:
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
Code: https://github.com/l3cube-pune/MarathiNLP
Project page: 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 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:
@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).