---
language:
- vi
tags:
- hate-speech-detection
- vietnamese-nlp
- text-classification
- offensive-language-detection
license: mit
datasets:
- vihsd
base_model: vinai/bartpho-syllable-base
---

# BARTpho

BARTpho fine-tuned cho bài toán phân loại Hate Speech tiếng Việt

## Model Details

### Model Type
BARTpho (Bidirectional and Auto-Regressive Transformer cho tiếng Việt)

### Base Model
This model is fine-tuned from [vinai/bartpho-syllable-base](https://huggingface.co/vinai/bartpho-syllable-base)

### Training Info
- **Task**: Hate Speech Classification
- **Language**: Vietnamese
- **Labels**: 
  - `0`: CLEAN (Normal content)
  - `1`: OFFENSIVE (Mildly offensive content)
  - `2`: HATE (Hate speech)

## 📊 Model Performance

| Metric | Score |
|--------|-------|
| Accuracy | 0.8985 |
| F1 Macro | 0.6791 |
| F1 Weighted | 0.8886 |

## Model Description

This model has been fine-tuned on the ViHSD (Vietnamese Hate Speech Dataset) to classify Vietnamese text into three categories: CLEAN, OFFENSIVE, and HATE.

### Architecture
BARTpho (Bidirectional and Auto-Regressive Transformer cho tiếng Việt)

The model combines the powerful pretrained representations with task-specific fine-tuning for effective hate speech detection in Vietnamese social media content.

## How to Use

### 1. Using Transformers Pipeline

```python
from transformers import pipeline

# Initialize the hate speech classifier
classifier = pipeline(
    "text-classification",
    model="visolex/hate-speech-bartpho",
    tokenizer="visolex/hate-speech-bartpho",
    return_all_scores=True
)

# Classify text
results = classifier("Văn bản tiếng Việt cần kiểm tra")
print(results)
```

### 2. Using AutoModel

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

# Load model and tokenizer
model_name = "visolex/hate-speech-bartpho"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Prepare text
text = "Văn bản tiếng Việt cần kiểm tra"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=256)

# Get predictions
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    
    # Get probabilities
    probabilities = torch.nn.functional.softmax(logits, dim=-1)
    
    # Get predicted label
    predicted_label = torch.argmax(probabilities, dim=-1).item()
    confidence = probabilities[0][predicted_label].item()

# Label mapping
label_mapping = {
    0: "CLEAN",
    1: "OFFENSIVE",
    2: "HATE"
}

print(f"Predicted: {label_mapping[predicted_label]} (Confidence: {confidence:.2%})")
```

### 3. Batch Processing

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

model_name = "visolex/hate-speech-bartpho"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# List of texts to classify
texts = [
    "Bài viết rất hay và bổ ích",
    "Đồ ngu người ta nói đúng mà",
    "Cút đi đồ chó"
]

# Tokenize and predict
inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True, max_length=256)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.argmax(outputs.logits, dim=-1)

for text, pred in zip(texts, predictions):
    label = ["CLEAN", "OFFENSIVE", "HATE"][pred.item()]
    print(f"{text[:50]} -> {label}")
```

## Training Details

### Training Data
- **Dataset**: ViHSD (Vietnamese Hate Speech Detection Dataset)
- **Total samples**: ~10,000 Vietnamese comments from social media
- **Training split**: ~70%
- **Validation split**: ~15%
- **Test split**: ~15%

### Training Configuration
- **Framework**: PyTorch + HuggingFace Transformers
- **Optimizer**: AdamW
- **Learning Rate**: 2e-5
- **Batch Size**: 32
- **Max Length**: 256 tokens
- **Epochs**: Optimized via early stopping

### Preprocessing
- Text normalization for Vietnamese
- Special character handling
- Emoji and slang processing

## Evaluation Results

Model evaluation metrics on the ViHSD test set: See Model Performance section above for details.

### Label Distribution
- **CLEAN (0)**: Normal content without offensive language
- **OFFENSIVE (1)**: Mildly offensive or inappropriate content
- **HATE (2)**: Hate speech, extremist language, severe threats

## Use Cases

- **Social Media Moderation**: Automatic detection of hate speech in Vietnamese social media platforms
- **Content Filtering**: Filtering offensive content in Vietnamese text
- **Research**: Studying hate speech patterns in Vietnamese online communities

## Limitations and Considerations

⚠️ **Important Limitations**:
- Model trained primarily on social media data, may not generalize to formal text
- Performance may vary with slang, code-switching, or regional dialects
- Model reflects biases present in training data
- Should be used as part of a larger moderation system, not sole decision-maker

## Citation

If you use this model in your research, please cite:

```bibtex
@software{vihsd_bartpho,
  title = {BARTpho for Vietnamese Hate Speech Detection},
  author = {ViSoLex Team},
  year = {2024},
  url = {https://huggingface.co/visolex/hate-speech-bartpho},
  base_model = {vinai/bartpho-syllable-base}
}
```

## Contact & Support

- **GitHub**: [ViSoLex Hate Speech Detection](https://github.com/visolex/hate-speech-detection)
- **Issues**: [Report Issues](https://github.com/visolex/hate-speech-detection/issues)
- **Questions**: Open a discussion on the model's Hugging Face page

## License

This model is distributed under the MIT License.

## Acknowledgments

- Base model trained by vinai
- Dataset: ViHSD (Vietnamese Hate Speech Detection Dataset)
- Framework: [Hugging Face Transformers](https://huggingface.co/transformers)