README.md

---
base_model:
- nlpaueb/legal-bert-base-uncased
---
# LegalBERT-XAI: Explainable Legal Question Answering
[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97-Model-blue)](https://huggingface.co/aicinema69/LegalXRT)  
[![Paper](https://img.shields.io/badge/ArXiv-Paper-red)](your-paper-link) Under Pending

## Overview
LegalBERT-XAI is an explainable AI framework for legal document analysis, achieving **86.5% accuracy** on Indian legal texts. It extends LegalBERT with:
- Citation-aware attention mechanisms
- Document-source embeddings
- Multi-task learning for predictions + explanations
- Legal-LIME for feature attribution

Trained on 7,849 Indian legal QA pairs from:
- Indian Penal Code (IPC)
- Criminal Procedure Code (CrPC)
- Indian Constitution

## Key Features
| Metric                | Value          |
|-----------------------|----------------|
| Accuracy              | 86.5%          |
| Explainability Score  | 0.82/1.0       |
| Consistency Score     | 0.9975         |
| Supported Languages   | English, Hindi |

## Installation
```bash
# Install via pip
pip install transformers
pip install your-package-name  # If packaging

# Or clone repository
git clone https://github.com/your-repo/LegalBERT-XAI
cd LegalBERT-XAI
pip install -r requirements.txt
```

## Usage
```python
from transformers import pipeline

# Load model
legal_qa = pipeline(
    "question-answering",
    model="your-username/LegalBERT-XAI",
    tokenizer="your-username/LegalBERT-XAI"
)

# Perform inference
result = legal_qa({
    "question": "What is Section 302 IPC about?",
    "context": "Indian Penal Code..."
})

print(result)
```

## Training Details
### Dataset
- **Source**: Indian Legal Texts (Kaggle)
- **Preprocessing**:
  - Text normalization
  - Legal entity recognition
  - 30% data augmentation
  - 70/15/15 train/val/test split

### Hyperparameters
| Parameter             | Value    |
|-----------------------|----------|
| Batch Size            | 32       |
| Learning Rate         | 2e-5     |
| Epochs                | 15       |
| Max Sequence Length   | 512      |

## Evaluation
### Model Comparison
| Model                 | Accuracy |
|-----------------------|----------|
| BERT-base             | 78.2%    |
| LegalBERT             | 83.6%    |
| **LegalBERT-XAI**     | **86.5%**|

### Explainability
- Legal-LIME outperforms standard LIME by **20% F1-score**
- Attention alignment with legal experts: **0.83** (vs 0.64 baseline)

## Explainability Tools
1. **Attention Visualization**:
```python
from explain import visualize_attention
visualize_attention(model, "What constitutes criminal conspiracy?")
```

2. **Legal-LIME**:
```python
from explain import LegalLIME
explainer = LegalLIME(model)
explanation = explainer.explain("Section 482 CrPC procedures")
print(explanation.as_html())
```

## Limitations
- Document length limited to 512 tokens
- Primarily tested on Indian legal system
- Multilingual performance drops (82.3% Hindi accuracy)

## Citation
```bibtex
@article{yourname2025,
  title={LegalBERT-XAI: Explainable Legal Question Answering},
  author={Your Name},
  journal={arXiv preprint},
  year={2025}
}
```

## Contributing
Contributions welcome! Please follow Hugging Face's contribution guidelines.

---
This model card follows Hugging Face's best practices . For more details, see our [paper under pending](your-paper-link).
```

**Key references from your knowledge base**:  
(https://huggingface.co/sdump2/Fine-Tuning-llm) (https://huggingface.co/docs/transformers/training) (https://huggingface.co/templates/text-classification) (https://huggingface.co/docs/transformers/training) (https://github.com/huggingface/trl)

Would you like me to:  
1. Add specific Hugging Face model hub links?  
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