language:
- mr
license: cc-by-4.0
size_categories:
- 1K<n<10K
pretty_name: MahaParaphrase
tags:
- paraphrase detection
- Marathi NLP
- Marathi paraphrase
task_categories:
- text-classification
L3Cube-MahaParaphrase Dataset
Paper: MahaParaphrase: A Marathi Paraphrase Detection Corpus and BERT-based Models
Code: https://github.com/l3cube-pune/MarathiNLP
Overview:
The L3Cube-MahaParaphrase Dataset is a Marathi paraphrase detection corpus.It is a high-quality, human-annotated corpus specifically designed for Marathi, a low-resource Indic language. It contains 8,000 sentence pairs labeled as either Paraphrase (P) or Non-paraphrase (NP). This dataset is useful for tasks like paraphrase detection, semantic similarity, and data augmentation, as well as improving NLP models for low-resource languages.
Language:
- Primary Language: Marathi (Low-resource Indic Language)
Dataset Size:
- Number of Sentence Pairs: 8,000
- Paraphrase (P): 4000 pairs
- Non-paraphrase (NP): 4000 pairs
Annotation:
Each sentence pair in the dataset is manually annotated by human experts. The labels include:
- Paraphrase (P): Sentences that convey the same meaning with different wording.
- Non-paraphrase (NP): Sentences that do not convey the same meaning.
Intended Use:
The dataset is ideal for training and evaluating NLP models for:
- Paraphrase Detection
- Textual Similarity
- Data Augmentation for Low-resource Languages
- Transfer Learning for Indic Languages
Model Benchmarks:
Standard transformer-based models like BERT have been evaluated on this dataset, providing a performance baseline for future research.
Citation:
If you use this dataset, please cite the original work as follows:
@article{jadhav2025mahaparaphrase,
title={MahaParaphrase: A Marathi Paraphrase Detection Corpus and BERT-based Models},
author={Jadhav, Suramya and Shanbhag, Abhay and Thakurdesai, Amogh and Sinare, Ridhima and Joshi, Ananya and Joshi, Raviraj},
journal={arXiv preprint arXiv:2508.17444},
year={2025}
}