README.md

---
title: Multilingual Hate Speech Detector
emoji: 🛡️
colorFrom: red
colorTo: blue
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: mit
short_description: Hate speech detector
models:
  - xlm-roberta-base
datasets:
  - hate-speech
---

# 🛡️ Multilingual Hate Speech Detector

**Advanced AI system for detecting hate speech in English and Serbian text with innovative contextual analysis**

## 🔬 Key Innovations

### 1. **Contextual Analysis** 🌈
- **Word-level importance highlighting** using transformer attention weights
- Visual explanation showing which words most influenced the classification decision
- Color-coded highlighting: 🔴 Red (high influence) → 🟠 Orange → 🟡 Yellow → ⚪ Gray (low influence)

### 2. **Confidence Visualization** 📊
- Interactive Plotly charts showing model confidence across **all 8 categories**
- Real-time confidence distribution analysis
- Color-coded bars distinguishing hate speech categories from appropriate content

### 3. **Interactive Feedback System** 💬
- User rating system (1-5 stars) for continuous model improvement
- Feedback collection for enhancing accuracy
- Community-driven model refinement

## 📋 Hate Speech Categories

The system detects 8 categories:
- **Race**: Racial discrimination and slurs
- **Sexual Orientation**: Homophobic content, LGBTQ+ discrimination  
- **Gender**: Sexist content, misogyny, gender-based harassment
- **Physical Appearance**: Body shaming, lookism, appearance-based harassment
- **Religion**: Religious discrimination, islamophobia, antisemitism
- **Class**: Classist content, economic discrimination
- **Disability**: Ableist content, discrimination against disabled people
- **Appropriate**: Non-hateful, normal conversation

## 🌍 Multilingual Support

- **English**: Comprehensive hate speech detection
- **Serbian**: Native Serbian language support with Cyrillic and Latin scripts
- **Cross-lingual**: XLM-RoBERTa architecture enables robust multilingual understanding

## 🔧 Technical Architecture

- **Base Model**: XLM-RoBERTa (Cross-lingual Language Model)
- **Training**: Fine-tuned on multilingual hate speech datasets
- **Attention Mechanism**: Transformer attention weights for explainable AI
- **Real-time Processing**: Optimized for instant classification
- **GPU Acceleration**: CUDA support for faster inference

## 🚀 How to Use

1. **Input Text**: Enter any text in English or Serbian
2. **Analyze**: Click "Analyze Text" for instant classification
3. **Review Results**: See category prediction with confidence score
4. **Examine Context**: Check word-level highlighting to understand the decision
5. **View Confidence**: Analyze the confidence distribution chart
6. **Provide Feedback**: Rate the analysis to help improve the model

## 🎯 Example Analyses

### Appropriate Content
```
"I really enjoyed that movie last night! Great acting and storyline."
→ ✅ Appropriate (95% confidence)
```

### Hate Speech Detection
```
"You people are all the same, always causing problems everywhere."
→ ⚠️ Race (87% confidence)
```

### Serbian Language
```
"Ovaj film je bio odličan, preporučujem svima!"
→ ✅ Appropriate (92% confidence)
```

## ⚡ Performance

- **Accuracy**: High-confidence predictions with detailed explanations
- **Speed**: Real-time processing (< 2 seconds per analysis)
- **Languages**: English and Serbian with cross-lingual capabilities
- **Explainability**: Visual attention analysis for transparent decisions

## 🛠️ Local Development

```bash
# Clone the repository
git clone <repository-url>
cd hate-speech-detector

# Install dependencies
pip install -r requirements.txt

# Run the application
python app.py
```

## 📝 Research & Education

This AI system is designed for:
- **Research purposes**: Understanding hate speech patterns
- **Educational use**: Learning about AI explainability
- **Content moderation**: Assisting human moderators
- **Linguistic analysis**: Cross-lingual hate speech research

## ⚠️ Important Notes

- Results should be interpreted carefully
- Human judgment should always be applied for critical decisions
- The system is designed to assist, not replace, human moderation
- Continuous improvement through user feedback

## 🤝 Contributing

We welcome feedback and contributions! Please use the interactive feedback system within the application to help improve model accuracy.

## 📄 License

MIT License - See LICENSE file for details

---

**⚡ Powered by**: Transformer Neural Networks | **🌍 Languages**: English, Serbian | **🎯 Focus**: Explainable AI