README.md

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title: IndicVerse
emoji: 🌏
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colorTo: red
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# 🌏 IndicVerse

IndicVerse is dedicated to advancing natural language processing (NLP) capabilities for Indic languages. Our mission is to bridge the gap in NLP research for low-resource Indic languages by providing high-quality datasets, pre-trained models, and tools tailored for diverse linguistic needs.

## 🚀 What We Do
- **Datasets**: Creation and publication of datasets for various NLP tasks, including translation, classification, and generation, with a focus on Indic languages.
- **Models**: Development of state-of-the-art NLP models fine-tuned for Indic languages, leveraging techniques like PEFT and LoRA.
- **Research**: Conducting and sharing research to solve key challenges in Indic NLP, including transliteration, low-resource learning, and domain-specific applications.

## 📚 Featured Projects
- **Hellaswag-Telugu**: A Telugu version of the Hellaswag dataset for advanced evaluation.
- **Indic Language Translation and Transliteration**: Custom tools and APIs for translation and mixed transliteration (Telugu-English).

## 🛠️ How to Contribute
We welcome contributions! Whether you’re interested in annotating data, building models, or sharing insights, feel free to get in touch.

## 🌐 Links
- [Hugging Face Hub](https://huggingface.co./IndicVerse)


## 📜 Citation
If you use our datasets or models in your research, please cite us as follows:

```
@misc{IndicVerse2024,  
  author = {Nikhil Chowdary Paleti and Divi Eswar Chowdary},  
  title = {Indic Verse: Datasets and Models for Advancing Indic Languages in NLP},  
  year = {2024},  
  publisher = {Hugging Face},  
  url = {https://huggingface.co./IndicVerse}  
}
```


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