Update README.md

README.md

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----
-license: apache-2.0
----
+---
+tags:
+  - token-classification
+  - distilbert
+  - named-entity-recognition
+license: apache-2.0
+datasets: restaurant-data
+language:
+  - en
+model-index:
+  - name: DistilBERT for Token Classification
+    results:
+      - task:
+          type: token-classification
+          name: Token Classification
+        metrics:
+          - type: f1
+            value: 0.92
+            name: F1 Score
+---
+
+# DistilBERT for Token Classification
+
+## 📝 Overview
+
+This model is a fine-tuned version of **DistilBERT** for **token classification** on restaurant-related data. It is capable of recognizing various entities such as:
+
+- Dishes
+- Amenities
+- Ratings
+- Restaurant names
+- Opening hours
+- Locations
+- Prices
+- Cuisine types
+
+## 🧠 Model Details
+
+- **Architecture**: DistilBertForTokenClassification
+- **Hidden Size**: 768
+- **FFN Inner Hidden Size**: 3072
+- **Attention Heads**: 12
+- **Layers**: 6
+- **Max Position Embeddings**: 512
+- **Dropout**: 0.1
+- **Tokenizer**: DistilBERT Tokenizer
+- **Transformers Version**: 4.51.3
+
+## 🏷️ Labels
+
+The model supports the following 17 token classification labels:
+
+| ID | Label               |
+|----|---------------------|
+| 0  | O                   |
+| 1  | B-Amenity           |
+| 2  | I-Amenity           |
+| 3  | B-Dish              |
+| 4  | I-Dish              |
+| 5  | B-Rating            |
+| 6  | I-Rating            |
+| 7  | B-Restaurant_Name   |
+| 8  | I-Restaurant_Name   |
+| 9  | B-Hours             |
+| 10 | I-Hours             |
+| 11 | B-Location          |
+| 12 | I-Location          |
+| 13 | B-Price             |
+| 14 | I-Price             |
+| 15 | B-Cuisine           |
+| 16 | I-Cuisine           |
+
+## 🚀 Usage Example
+
+```python
+from transformers import DistilBertTokenizer, DistilBertForTokenClassification
+
+# Load tokenizer and model
+tokenizer = DistilBertTokenizer.from_pretrained("AbhishekBhavnani/Restaurant-Token-Classifier")
+model = DistilBertForTokenClassification.from_pretrained("AbhishekBhavnani/Restaurant-Token-Classifier")
+
+# Example input
+inputs = tokenizer("Find The Best Place To Eat Pizza in Ahmedabad", return_tensors="pt")
+outputs = model(**inputs)
+
+# Get predictions
+predictions = outputs.logits.argmax(dim=-1)
+print(predictions)