Add model card

README.md

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+---
+license: mit
+language:
+  - en
+  - hi
+tags:
+  - ner
+  - address-parsing
+  - indian-addresses
+  - bert
+  - crf
+datasets:
+  - custom
+metrics:
+  - f1
+  - precision
+  - recall
+model-index:
+  - name: indian-address-parser-model
+    results:
+      - task:
+          type: token-classification
+          name: Named Entity Recognition
+        metrics:
+          - type: f1
+            value: 0.80
+            name: F1 (micro)
+          - type: precision
+            value: 0.79
+            name: Precision (micro)
+          - type: recall
+            value: 0.81
+            name: Recall (micro)
+---
+
+# Indian Address Parser Model
+
+A fine-tuned **IndicBERTv2-SS + CRF** model for parsing unstructured Indian addresses into structured components.
+
+## Model Description
+
+- **Base Model**: [ai4bharat/IndicBERTv2-SS](https://huggingface.co/ai4bharat/IndicBERTv2-SS)
+- **Architecture**: BERT + Conditional Random Field (CRF) layer
+- **Languages**: English, Hindi (Latin and Devanagari scripts)
+- **Training Data**: 600+ annotated Delhi addresses
+
+## Performance
+
+| Entity Type   | Precision | Recall | F1-Score |
+|---------------|-----------|--------|----------|
+| AREA          | 0.87      | 0.87   | 0.87     |
+| CITY          | 1.00      | 1.00   | 1.00     |
+| FLOOR         | 0.85      | 0.85   | 0.85     |
+| GALI          | 0.75      | 0.67   | 0.71     |
+| HOUSE_NUMBER  | 0.79      | 0.79   | 0.79     |
+| KHASRA        | 0.75      | 0.82   | 0.78     |
+| PINCODE       | 1.00      | 1.00   | 1.00     |
+| **Overall**   | **0.79**  | **0.81**| **0.80** |
+
+## Supported Entity Types
+
+- `HOUSE_NUMBER` - House/Plot/Flat numbers
+- `FLOOR` - Floor indicators (Ground, First, etc.)
+- `BLOCK` - Block identifiers
+- `SECTOR` - Sector numbers
+- `GALI` - Gali (lane) numbers
+- `COLONY` - Colony/Society names
+- `AREA` - Area/Locality names
+- `SUBAREA` - Sub-area names
+- `KHASRA` - Khasra (land record) numbers
+- `PINCODE` - 6-digit postal codes
+- `CITY` - City names
+- `STATE` - State names
+
+## Usage
+
+```python
+from address_parser import AddressParser
+
+# Load model
+parser = AddressParser.from_pretrained("YOUR_USERNAME/indian-address-parser-model")
+
+# Parse address
+result = parser.parse("PLOT NO752 FIRST FLOOR, BLOCK H-3, NEW DELHI, 110041")
+
+# Access structured output
+print(result.house_number)  # "PLOT NO752"
+print(result.floor)         # "FIRST FLOOR"
+print(result.city)          # "NEW DELHI"
+print(result.pincode)       # "110041"
+```
+
+## Demo
+
+Try the live demo: [HuggingFace Space](https://huggingface.co/spaces/YOUR_USERNAME/indian-address-parser)
+
+## License
+
+MIT License