README.md

---
language: vi
tags:
- intent-classification
- smart-home
- vietnamese
- phobert
license: mit
datasets:
- custom-vn-slu-augmented
metrics:
- accuracy
- f1
model-index:
- name: PhoBERT Intent Classifier for Vietnamese Smart Home
  results:
  - task:
      type: text-classification
      name: Intent Classification
    dataset:
      name: VN-SLU Augmented Dataset
      type: custom
    metrics:
    - type: accuracy
      value: 98.3
      name: Accuracy
    - type: f1
      value: 97.72
      name: F1 Score (Weighted)
    - type: f1
      value: 71.90
      name: F1 Score (Macro)
widget:
- text: "bật đèn phòng khách"
- text: "tắt quạt phòng ngủ lúc 10 giờ tối"
- text: "kiểm tra tình trạng điều hòa"
- text: "tăng độ sáng đèn bàn"
- text: "mở cửa chính"
---

# PhoBERT Fine-tuned for Vietnamese Smart Home Intent Classification

This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) specifically trained for intent classification in Vietnamese smart home commands.

## Model Description

- **Base Model**: vinai/phobert-base
- **Task**: Intent Classification for Smart Home Commands
- **Language**: Vietnamese
- **Training Data**: VN-SLU Augmented Dataset (4,000 training samples)
- **Number of Intent Classes**: 13

## Intended Uses & Limitations

### Intended Uses
- Classifying user intents in Vietnamese smart home voice commands
- Integration with voice assistants for home automation
- Research in Vietnamese NLP for IoT applications

### Limitations
- Optimized specifically for smart home domain
- May not generalize well to other domains
- Trained on Vietnamese language only

## Performance

Based on evaluation with 1,000 test samples:

| Metric | Value |
|--------|-------|
| Accuracy | 98.3% |
| F1 Score (Weighted) | 97.72% |
| F1 Score (Macro) | 71.90% |
| Eval Loss | 0.0834 |

## Training Details

### Training Configuration
- Learning Rate: 2e-5
- Batch Size: 16
- Number of Epochs: 3
- Warmup Ratio: 0.1
- Weight Decay: 0.01
- Max Length: 128

### Hardware
- Trained on: NVIDIA GPU
- Training Time: ~79 seconds
- Optimization: Designed for deployment on Raspberry Pi 5

## Intent Classes

The model can classify the following 13 intents:
1. `bật thiết bị` (turn on device)
2. `tắt thiết bị` (turn off device)
3. `mở thiết bị` (open device)
4. `đóng thiết bị` (close device)
5. `tăng độ sáng của thiết bị` (increase device brightness)
6. `giảm độ sáng của thiết bị` (decrease device brightness)
7. `kiểm tra tình trạng thiết bị` (check device status)
8. `điều chỉnh nhiệt độ` (adjust temperature)
9. `hẹn giờ` (set timer)
10. `kích hoạt cảnh` (activate scene)
11. `tắt tất cả thiết bị` (turn off all devices)
12. `mở khóa` (unlock)
13. `khóa` (lock)

## How to Use

### Using Transformers Library

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import pickle

# Load model and tokenizer
model_name = "ntgiaky/phobert-intent-classifier-smart-home"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Load label encoder
with open('intent_encoder.pkl', 'rb') as f:
    label_encoder = pickle.load(f)

# Predict intent
def predict_intent(text):
    # Tokenize
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
    
    # Predict
    with torch.no_grad():
        outputs = model(**inputs)
        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
        predicted_class = torch.argmax(predictions, dim=-1)
    
    # Decode label
    intent = label_encoder.inverse_transform(predicted_class.cpu().numpy())[0]
    confidence = predictions[0][predicted_class].item()
    
    return intent, confidence

# Example usage
text = "bật đèn phòng khách"
intent, confidence = predict_intent(text)
print(f"Intent: {intent}, Confidence: {confidence:.2f}")
```

### Using Pipeline

```python
from transformers import pipeline

# Load pipeline
classifier = pipeline(
    "text-classification",
    model="ntgiaky/phobert-intent-classifier-smart-home",
    device=0  # Use -1 for CPU
)

# Predict
result = classifier("tắt quạt phòng ngủ")
print(result)
```

## Integration Example

```python
# For Raspberry Pi deployment
import onnxruntime as ort
import numpy as np

# Convert to ONNX first (one-time)
from transformers import AutoModel
model = AutoModel.from_pretrained("ntgiaky/phobert-intent-classifier-smart-home")
# ... ONNX conversion code ...

# Then use ONNX Runtime for inference
session = ort.InferenceSession("model.onnx")
# ... inference code ...
```

## Dataset

This model was trained on an augmented version of the VN-SLU dataset, which includes:
- Original recordings from 240 Vietnamese speakers
- Augmented samples using various techniques
- Smart home specific vocabulary and commands

## Citation

If you use this model, please cite:

```bibtex
@misc{phobert-smart-home-2025,
  author = {Trần Quang Huy and Nguyễn Trần Gia Kỳ},
  title = {PhoBERT Fine-tuned for Vietnamese Smart Home Intent Classification},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub},
  howpublished = {\url{https://huggingface.co/ntgiaky/phobert-intent-classifier-smart-home}}
}
```

## Authors

- **Trần Quang Huy** 
- **Nguyễn Trần Gia Kỳ** 
- **Advisor**: TS. Đoàn Duy

## License

This model is released under the MIT License.

## Contact

For questions or issues, please open an issue on the [model repository](https://huggingface.co/ntgiaky/phobert-intent-classifier-smart-home) or contact the authors through the university.