README.md

metadata

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

# 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

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