---
license: apache-2.0
language:
- ur
- ar
base_model:
- distilbert/distilbert-base-multilingual-cased
tags:
- chatbot
- urdu
- urduchatbot
- ai
---

# LughaatBERT

LughaatBERT is a transformer-based model fine-tuned for question-answering tasks, designed to power intelligent chatbots and other conversational AI applications. Developed using the Hugging Face ecosystem, it excels at understanding natural language queries and providing accurate, context-aware responses.

## Model Details

- **Model Name:** LughaatBERT
- **Version:** 1.0
- **Author:** [muhammadnoman76](https://huggingface.co/muhammadnoman76)
- **Hugging Face Model Hub:** [LughaatBERT](https://huggingface.co/muhammadnoman76/LughaatBERT)

## Key Features

- **Natural Language Understanding:** Leverages transformer architecture for precise intent and context comprehension.
- **Question-Answering Proficiency:** Optimized to provide relevant and accurate answers.
- **Versatility:** Suitable for a wide range of tasks, including domain-specific applications.
- **Seamless Integration:** Easily deployable in APIs and chatbot frameworks.

## Applications

1. Interactive question-answering chatbots.
2. Knowledge-base retrieval systems.
3. Language learning and educational tools.
4. Automated customer support solutions.

## How to Use LughaatBERT

You can use LughaatBERT with the Hugging Face Transformers library. Below is an example demonstrating how to use it for question-answering:

```python
from transformers import DistilBertTokenizer, DistilBertModel

model_name = "muhammadnoman76/LughaatBERT"

tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertModel.from_pretrained(model_name)
```

## Training Details

The model was fine-tuned on a diverse question-answering dataset using the Hugging Face Transformers library. It is designed to handle queries with contextual understanding and produce accurate results across various domains.

## Deployment

LughaatBERT can be integrated into real-world applications via:

- **Hugging Face Pipelines:** Use the simple interface for rapid prototyping.
- **Custom API Integration:** Load the model in your custom backend for full control.

## Citation

If you use LughaatBERT in your research or applications, please cite it as:

```
@model{LughaatBERT,
  author = {muhammadnoman76},
  title = {LughaatBERT: A Question-Answering Model},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/muhammadnoman76/LughaatBERT}
}
```

## License

This model is available under the Apache 2.0 License. See the [LICENSE](LICENSE) file for details.

---

For any questions or issues, feel free to contact [muhammadnoman76](https://huggingface.co/muhammadnoman76).