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# L3Cube-MahaParaphrase Dataset
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Paper: [MahaParaphrase: A Marathi Paraphrase Detection Corpus and BERT-based Models](https://huggingface.co/papers/2508.17444)
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Code: https://github.com/l3cube-pune/MarathiNLP
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## Overview:
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## Model Benchmarks:
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Standard transformer-based models like **BERT** have been evaluated on this dataset, providing a performance baseline for future research.
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## Sample Usage
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This dataset is part of the `mahaNLP` library. You can install it via pip:
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```bash
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pip install mahaNLP
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```
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For usage examples, please refer to the [L3Cube-MahaNLP Colab demo](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 original work as follows:
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```bibtex
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@article{
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title={MahaParaphrase: A Marathi Paraphrase Detection Corpus and BERT-based Models},
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author={Joshi, Raviraj},
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journal={arXiv preprint arXiv:2508.17444},
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year={2025}
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}
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# L3Cube-MahaParaphrase Dataset
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Paper: [MahaParaphrase: A Marathi Paraphrase Detection Corpus and BERT-based Models](https://huggingface.co/papers/2508.17444) <br>
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Code: https://github.com/l3cube-pune/MarathiNLP
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## Overview:
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## Model Benchmarks:
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Standard transformer-based models like **BERT** have been evaluated on this dataset, providing a performance baseline for future research.
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## Citation:
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If you use this dataset, please cite the original work as follows:
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```bibtex
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@article{jadhav2025mahaparaphrase,
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title={MahaParaphrase: A Marathi Paraphrase Detection Corpus and BERT-based Models},
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author={Jadhav, Suramya and Shanbhag, Abhay and Thakurdesai, Amogh and Sinare, Ridhima and Joshi, Ananya and Joshi, Raviraj},
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journal={arXiv preprint arXiv:2508.17444},
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year={2025}
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}
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