---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- text-classification
- transformers
- distilbert
- generated_from_trainer
- cmu-course
datasets:
- ecopus/pgh_restaurants
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: Cuisine Classification (Fine-Tuned DistilBERT)
  results:
  - task:
      type: text-classification
      name: Multi-class Text Classification
    dataset:
      name: ecopus/pgh_restaurants
      type: classification
      split: augmented
    metrics:
    - type: accuracy
      value: 0.969
    - type: f1
      value: 0.957
    - type: precision
      value: 0.948
    - type: recall
      value: 0.969
  - task:
      type: text-classification
      name: Multi-class Text Classification
    dataset:
      name: ecopus/pgh_restaurants
      type: classification
      split: original
    metrics:
    - type: accuracy
      value: 0.94
    - type: f1
      value: 0.92
---

# Model Card for Cuisine Classification (Fine-Tuned DistilBERT)

This model predicts the **cuisine type** of Pittsburgh restaurants based on review text.  
It was fine-tuned from [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the dataset [ecopus/pgh_restaurants](https://huggingface.co/datasets/ecopus/pgh_restaurants).  

It achieves the following results:  
- **Evaluation (Augmented split):** Accuracy 0.969, F1 0.957, Precision 0.948, Recall 0.969  
- **External Validation (Original split):** Accuracy 0.94, F1 0.92  

---

## Model Details

- **Developed by:** Xinxuan Tang (CMU)  
- **Dataset curated by:** Emily Copus (CMU)  
- **Base model:** DistilBERT (`distilbert-base-uncased`)  
- **Library:** Transformers  
- **Language(s):** English  
- **License:** apache-2.0 (dataset + model card)  

---

## Uses

### Direct Use
- Educational practice in **text classification**.  
- Experimenting with **fine-tuning compact transformers**.  

### Downstream Use
- Could be adapted for **restaurant recommendation demos**.  
- Teaching **NLP pipelines** for classification tasks.  

### Out-of-Scope Use
- Not suitable for **production deployment**.  
- Not intended for **sentiment analysis** or tasks outside cuisine prediction.  

---

## Training Procedure

### Training Hyperparameters
- **learning_rate:** 2e-05  
- **train_batch_size:** 8  
- **eval_batch_size:** 8  
- **seed:** 42  
- **optimizer:** AdamW (betas=(0.9,0.999), eps=1e-08)  
- **lr_scheduler_type:** linear  
- **num_epochs:** 5  

### Training Results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 2.6677        | 1.0   | 80   | 2.4746          | 0.3563   | 0.2142 | 0.1662    | 0.3563 |
| 1.7201        | 2.0   | 160  | 1.5893          | 0.7750   | 0.6895 | 0.6644    | 0.7750 |
| 1.1994        | 3.0   | 240  | 1.1417          | 0.8938   | 0.8503 | 0.8180    | 0.8938 |
| 1.0890        | 4.0   | 320  | 0.9315          | 0.9250   | 0.8959 | 0.8784    | 0.9250 |
| 0.7052        | 5.0   | 400  | 0.8675          | 0.9688   | 0.9570 | 0.9480    | 0.9688 |

---

## Evaluation

### Testing Data
- **Augmented split:** 1000 reviews (synthetic augmentation)  
- **Original split:** 100 reviews (external validation)  

### Metrics
- Accuracy, weighted F1, Precision, Recall  
- Confusion matrix used for external validation  

---

## Framework Versions
- **Transformers:** 4.56.1  
- **PyTorch:** 2.8.0+cu126  
- **Datasets:** 4.0.0  
- **Tokenizers:** 0.22.0  

---

## Bias, Risks, and Limitations

- **Small dataset**: only 100 original reviews.  
- **Synthetic augmentation**: may introduce artifacts.  
- **Geographic bias**: limited to Pittsburgh restaurants.  

### Recommendations
Treat results as **proof-of-concept**, not production-ready.  

---

## How to Get Started with the Model

You can load this fine-tuned DistilBERT model directly from the Hugging Face Hub and run inference:

```python
from transformers import pipeline

# Load the fine-tuned model
classifier = pipeline("text-classification", model="YOUR_USERNAME/finetuned_model")

# Example input
sample = "This cozy little spot serves delicious tacos with great service!"
print(classifier(sample))