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--- |
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title: Multi-Model Indian Address NER |
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emoji: 🏠 |
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colorFrom: blue |
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colorTo: green |
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sdk: gradio |
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sdk_version: 5.35.0 |
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app_file: app.py |
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pinned: false |
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--- |
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# Multi-Model Indian Address NER Demo |
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This is a Gradio-based demo that allows you to compare three different Indian Address NER models: |
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- [TinyBERT](https://huggingface.co/shiprocket-ai/open-tinybert-indian-address-ner) - Lightweight and fast |
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- [ModernBERT](https://huggingface.co/shiprocket-ai/open-modernbert-indian-address-ner) - Modern architecture |
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- [IndicBERT](https://huggingface.co/shiprocket-ai/open-indicbert-indian-address-ner) - Indic language optimized |
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## What it does |
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This application allows you to: |
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1. **Single Model Analysis**: Choose one model and extract entities from Indian addresses |
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2. **Model Comparison**: Compare how all three models perform on the same address |
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3. **Interactive Testing**: Use sample addresses or input your own |
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The models can identify: |
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- Building names |
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- Floor numbers |
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- House details |
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- Roads |
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- Sub-localities |
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- Localities |
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- Landmarks |
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- Cities |
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- States |
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- Countries |
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- Pincodes |
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## How to use |
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### Single Model Analysis |
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1. Select a model from the dropdown (TinyBERT, ModernBERT, or IndicBERT) |
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2. Enter an Indian address in the text box |
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3. Click "Extract Entities" or press Enter |
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4. View the extracted entities with confidence scores |
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### Model Comparison |
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1. Go to the "Model Comparison" tab |
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2. Enter an address |
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3. Click "Compare All Models" |
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4. See how each model performs on the same input |
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## Example addresses |
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- Shop No 123, Sunshine Apartments, Andheri West, Mumbai, 400058 |
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- DLF Cyber City, Sector 25, Gurgaon, Haryana |
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- Flat 201, MG Road, Bangalore, Karnataka, 560001 |
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## Model Information |
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### TinyBERT |
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- **Parameters**: ~66.4M |
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- **Advantages**: Fastest inference, lowest memory |
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- **Best for**: Real-time applications, mobile deployment |
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### ModernBERT |
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- **Parameters**: ~599MB model |
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- **Advantages**: Modern architecture, balanced performance |
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- **Best for**: High accuracy with reasonable speed |
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### IndicBERT |
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- **Parameters**: ~131MB model |
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- **Advantages**: Optimized for Indian languages/contexts |
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- **Best for**: Mixed language addresses, regional contexts |
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**Framework**: PyTorch + Transformers |
