Update README.md

README.md

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----
-license: cc-by-4.0
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+---
+license: cc-by-4.0
+task_categories:
+- sentence-similarity
+- text-retrieval
+language:
+- mr
+tags:
+- Marathi NLP
+- Sentence Similarity
+- Marathi STS
+pretty_name: MahaSTS
+size_categories:
+- 10K<n<100K
+---
+
+# MahaSTS Dataset
+
+**Paper**: [L3Cube-MahaSTS: A Marathi Sentence Similarity Dataset and Models](Coming soon)  
+**Code**: [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** 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:
+
+```bibtex
+@article{joshi2022l3cube,
+  title={L3cube-mahanlp: Marathi natural language processing datasets, models, and library},
+  author={Joshi, Raviraj},
+  journal={arXiv preprint arXiv:2205.14728},
+  year={2022}
+}