| # Multi-Model Indian Address NER Demo | |
| This is a Gradio-based demo that allows you to compare three different Indian Address NER models: | |
| - [TinyBERT](https://huggingface.co/shiprocket-ai/open-tinybert-indian-address-ner) - Lightweight and fast | |
| - [ModernBERT](https://huggingface.co/shiprocket-ai/open-modernbert-indian-address-ner) - Modern architecture | |
| - [IndicBERT](https://huggingface.co/shiprocket-ai/open-indicbert-indian-address-ner) - Indic language optimized | |
| ## What it does | |
| This application allows you to: | |
| 1. **Single Model Analysis**: Choose one model and extract entities from Indian addresses | |
| 2. **Model Comparison**: Compare how all three models perform on the same address | |
| 3. **Interactive Testing**: Use sample addresses or input your own | |
| The models can identify: | |
| - Building names | |
| - Floor numbers | |
| - House details | |
| - Roads | |
| - Sub-localities | |
| - Localities | |
| - Landmarks | |
| - Cities | |
| - States | |
| - Countries | |
| - Pincodes | |
| ## How to use | |
| ### Single Model Analysis | |
| 1. Select a model from the dropdown (TinyBERT, ModernBERT, or IndicBERT) | |
| 2. Enter an Indian address in the text box | |
| 3. Click "Extract Entities" or press Enter | |
| 4. View the extracted entities with confidence scores | |
| ### Model Comparison | |
| 1. Go to the "Model Comparison" tab | |
| 2. Enter an address | |
| 3. Click "Compare All Models" | |
| 4. See how each model performs on the same input | |
| ## Example addresses | |
| - Shop No 123, Sunshine Apartments, Andheri West, Mumbai, 400058 | |
| - DLF Cyber City, Sector 25, Gurgaon, Haryana | |
| - Flat 201, MG Road, Bangalore, Karnataka, 560001 | |
| ## Model Information | |
| ### TinyBERT | |
| - **Parameters**: ~66.4M | |
| - **Advantages**: Fastest inference, lowest memory | |
| - **Best for**: Real-time applications, mobile deployment | |
| ### ModernBERT | |
| - **Parameters**: ~599MB model | |
| - **Advantages**: Modern architecture, balanced performance | |
| - **Best for**: High accuracy with reasonable speed | |
| ### IndicBERT | |
| - **Parameters**: ~131MB model | |
| - **Advantages**: Optimized for Indian languages/contexts | |
| - **Best for**: Mixed language addresses, regional contexts | |
| **Framework**: PyTorch + Transformers |