---
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)