🔍 Arabic AI Text Detection Model

📖 Model Description

Arabic AI vs Human Text Detection Model - Fine-tuned AraBERT

This model is specifically designed to detect AI-generated text in Arabic language. It's fine-tuned from aubmindlab/bert-base-arabertv2 and can distinguish between:

• 🧑 Human-written Arabic text (label: 0, "HUMAN")
• 🤖 AI-generated Arabic text (label: 1, "AI")

The model was trained using advanced validation techniques with early stopping to ensure optimal performance and prevent overfitting.

🎯 Intended Use

Primary Use Cases

• Content Verification: Verify authenticity of Arabic articles and posts
• Academic Integrity: Detect AI-generated essays and assignments
• Social Media Monitoring: Identify automated Arabic content
• Research: Benchmark for Arabic AI detection studies
• Content Moderation: Flag potentially AI-generated Arabic text

Supported Text Types

• 📰 News Articles (Modern Standard Arabic)
• 📝 Essays and Academic Writing
• 💬 Social Media Posts
• 📚 Blog Posts and Articles
• 🗞️ Formal and Semi-formal Arabic Text

📊 Performance Metrics

Evaluated on a balanced validation set with equal human and AI-generated Arabic texts.

🚀 Quick Start

Installation

pip install transformers torch

Basic Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import torch

# Method 1: Using pipeline (Recommended)
classifier = pipeline(
    "text-classification",
    model="sabaridsnfuji/arabic-ai-text-detector",
    tokenizer="sabaridsnfuji/arabic-ai-text-detector"
)

# Test with Arabic text
arabic_text = "هذا مثال على نص باللغة العربية"
result = classifier(arabic_text)

print(f"Prediction: {result[0]['label']}")
print(f"Confidence: {result[0]['score']:.2%}")

Advanced Usage

# Method 2: Manual prediction with probabilities
model_name = "sabaridsnfuji/arabic-ai-text-detector"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

def predict_arabic_text(text):
    # Tokenize
    inputs = tokenizer(
        text, 
        return_tensors="pt", 
        truncation=True, 
        max_length=512,
        padding=True
    )
    
    # Predict
    with torch.no_grad():
        outputs = model(**inputs)
        probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
    
    # Get results
    predicted_class = torch.argmax(probabilities, dim=1).item()
    confidence = torch.max(probabilities, dim=1)[0].item()
    
    labels = {0: "HUMAN", 1: "AI"}
    
    return {
        "prediction": labels[predicted_class],
        "confidence": confidence,
        "probabilities": {
            "human": probabilities[0][0].item(),
            "ai": probabilities[0][1].item()
        }
    }

# Example usage
text = "النص العربي المراد تصنيفه هنا"
result = predict_arabic_text(text)
print(result)

Batch Processing

# Process multiple texts efficiently
texts = [
    "النص الأول باللغة العربية",
    "النص الثاني للتصنيف", 
    "المزيد من النصوص العربية"
]

results = classifier(texts)
for text, result in zip(texts, results):
    print(f"Text: {text[:50]}...")
    print(f"Prediction: {result['label']} ({result['score']:.2%})")
    print("-" * 50)

🏗️ Model Architecture

AraBERT-v2 Base Architecture
├── 📥 Input: Arabic text (max 512 tokens)
├── 🔤 Tokenizer: AraBERT Arabic tokenizer  
├── 🧠 Encoder: 12-layer Transformer (110M parameters)
├── 🎯 Classifier: Linear layer (768 → 2 classes)
└── 📤 Output: [Human, AI] classification + probabilities

🎓 Training Details

Dataset

• Size: Custom Arabic AI/Human dataset (4,798 samples)
• Language: Arabic (Modern Standard Arabic + dialectal variations)
• Balance: 50% human-written, 50% AI-generated
• Sources: News articles, essays, social media, academic texts
• Split: 80% training, 20% validation

Training Configuration

• Base Model: aubmindlab/bert-base-arabertv2 (AraBERT-v2)
• Strategy: Step-by-step training with validation loss tracking
• Epochs: 3 with early stopping
• Batch Size: 8
• Learning Rate: 2e-05
• Max Sequence Length: 512 tokens
• Optimizer: AdamW with weight decay (0.01)
• Hardware: GPU training with mixed precision (FP16)

Training Process

1. Step-by-step training: Model trained in small chunks (0.2 epochs each)
2. Frequent validation: Evaluation after each training chunk
3. Best model selection: Saved only when validation loss improved
4. Early stopping: Prevented overfitting with patience mechanism

📈 Evaluation & Benchmarks

Test Performance

• Validation Accuracy: 95.0%
• Cross-domain Testing: Tested on various Arabic text sources
• Robustness: Evaluated on different writing styles and topics

Comparison with Baselines

⚠️ Limitations & Considerations

Known Limitations

• Text Length: Optimized for texts up to 512 tokens
• Domain: Best performance on formal/semi-formal Arabic
• Dialects: Primarily trained on Modern Standard Arabic
• Temporal: Training data has a specific time cutoff

Potential Biases

• Source Bias: Training data may reflect specific domains
• Dialectal Bias: May perform differently on regional Arabic varieties
• AI Model Bias: Trained primarily on specific AI models' outputs

Recommendations

• Best for: News articles, essays, formal Arabic text
• Consider carefully for: Informal chat, poetry, technical jargon
• Combine with: Human review for critical applications

🛠️ Technical Specifications

Model Details

• Architecture: BERT-based binary classifier
• Parameters: ~110M total parameters
• Model Size: ~440MB
• Precision: FP16 optimized for inference
• Inference Speed: ~50ms per text (GPU), ~200ms (CPU)

Input/Output Specification

# Input
{
    "text": "النص العربي المراد تصنيفه",
    "max_length": 512
}

# Output  
{
    "label": "HUMAN" | "AI",
    "score": 0.95,  # Confidence score
    "probabilities": {
        "HUMAN": 0.95,
        "AI": 0.05
    }
}

🔬 Usage Examples

Example 1: News Article Detection

news_text = '''
أعلنت وزارة التعليم عن إطلاق برنامج جديد لتطوير المناهج الدراسية 
في المرحلة الثانوية، والذي يهدف إلى تعزيز مهارات الطلاب في التفكير 
النقدي والإبداع. ويأتي هذا البرنامج ضمن رؤية 2030 لتطوير التعليم.
'''

result = classifier(news_text)
# Expected: HUMAN (news articles are typically human-written)

Example 2: AI-Generated Text Detection

ai_text = '''
في هذا المقال، سنناقش موضوع التكنولوجيا. التكنولوجيا مهمة جداً في 
حياتنا. يجب أن نفهم التكنولوجيا بشكل صحيح. التكنولوجيا تساعدنا كثيراً.
'''

result = classifier(ai_text)
# Expected: AI (repetitive patterns typical of AI generation)

📚 Citation

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

@misc{sabaridsnfuji-arabic-ai-detector-20250730,
  title={Arabic AI Text Detection Model},
  author={sabaridsnfuji},
  year={2025},
  publisher={Hugging Face},
  journal={Hugging Face Model Hub},
  howpublished={\url{https://huggingface.co/sabaridsnfuji/arabic-ai-text-detector}}
}

🤝 Contributing & Feedback

• Model Issues: Please report issues in the discussions tab
• Improvements: Suggestions for model improvements are welcome
• Collaborations: Open to research collaborations in Arabic NLP

📄 License

This model is released under the Apache 2.0 license. You are free to:

• ✅ Use commercially
• ✅ Modify and distribute
• ✅ Use in research
• ✅ Include in applications

🙏 Acknowledgments

• Base Model: aubmindlab/bert-base-arabertv2
• Framework: Hugging Face Transformers
• Infrastructure: Google Colab for training
• Community: Arabic NLP research community

📞 Contact

• 🤗 Hugging Face: sabaridsnfuji
• 📧 Issues: Use the repository discussions for questions
• 🔗 Model Page: https://huggingface.co/sabaridsnfuji/arabic-ai-text-detector

Last updated: 2025-07-30