Create README.md

README.md

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+---
+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-2024,
+  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.