ai-text-detector 1.0.0

A multilingual AI-generated text detection library

AI Text Detector

An English AI-generated text detection library using statistical features and machine learning.

Overview

ai-text-detector is a Python library that detects whether English text is likely AI-generated or human-written. It uses 60 statistical features and an XGBoost classifier trained on the RAID benchmark dataset.

Key Features:

• English text detection
• Fast and lightweight detection
• Pre-trained model included
• Simple, intuitive API
• Batch processing support

Installation

pip install ai-text-detector

Requirements

• Python >= 3.7
• numpy
• scipy
• xgboost
• scikit-learn

Quick Start

from ai_detector import AITextDetector

# Initialize detector
detector = AITextDetector()

# Detect AI-generated text
text = """
Artificial intelligence is transforming the world in unprecedented ways.
Machine learning algorithms are becoming increasingly sophisticated,
enabling computers to perform tasks that were once thought to be
exclusively human.
"""

result = detector.detect(text)

print(f"AI Probability: {result['ai_probability']:.2%}")
print(f"Label: {result['label']}")
print(f"Confidence: {result['confidence']}")

Output:

AI Probability: 85.32%
Label: AI
Confidence: high

Usage

Basic Detection

from ai_detector import AITextDetector

detector = AITextDetector()
result = detector.detect("Your English text here...")

if result['is_ai']:
    print(f"This text is likely AI-generated ({result['ai_probability']:.2%})")
else:
    print(f"This text is likely human-written ({result['ai_probability']:.2%})")

Getting AI Score Only

detector = AITextDetector()
ai_score = detector.get_ai_score("Your English text here...")
print(f"AI probability: {ai_score:.2%}")

Boolean Classification

detector = AITextDetector()
if detector.is_ai_generated("Your English text here..."):
    print("This is AI-generated text")

Batch Processing

detector = AITextDetector()
texts = ["Text 1...", "Text 2...", "Text 3..."]
results = detector.detect_batch(texts)

for text, result in zip(texts, results):
    print(f"{text[:50]}... -> {result['label']} ({result['ai_probability']:.2%})")

Custom Parameters

# Adjust decision threshold (default: 0.5)
detector = AITextDetector(threshold=0.7)

# Set minimum character length (default: 100)
detector = AITextDetector(min_chars=50)

# Both parameters
detector = AITextDetector(threshold=0.7, min_chars=50)

Result Format

The detect() method returns a dictionary with the following fields:

Field	Type	Description
ai_probability	float	Probability (0-1) that text is AI-generated
is_ai	bool	True if classified as AI (based on threshold)
confidence	str	'high', 'medium', or 'low' confidence
label	str	'AI' or 'Human' classification
warning	str (optional)	Warning if text is too short

Confidence Levels

• high: Probability differs from threshold by > 0.3
• medium: Probability differs from threshold by 0.15-0.3
• low: Probability differs from threshold by < 0.15

Text Length Recommendations

For best results, use English text with at least 100 characters. Shorter texts will return a warning and low confidence.

Model Information

Get information about the loaded model:

detector = AITextDetector()
info = detector.get_model_info()
print(f"Model version: {info['version']}")
print(f"Model AUC: {info['auc']:.4f}")
print(f"Model accuracy: {info['accuracy']:.4f}")

Model Details

• Features: 60 statistical features (compression ratio, entropy, burstiness, etc.)
• Training Data: RAID benchmark (English text from multiple domains)
• Algorithm: XGBoost classifier
• Language Support: English text only

Limitations

1. English Only: This model is trained on English text and works best with English content
2. Text Length: Short texts (< 100 characters) have unreliable results
3. Domain Specific: Model trained on general text; specialized domains may vary
4. Evolving AI: As AI models improve, detection accuracy may decrease

Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

License

This project is licensed under the MIT License.

Citation

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

@software{ai_text_detector,
  title={AI Text Detector: English AI-Generated Text Detection},
  author={Your Name},
  year={2025},
  url={https://github.com/yourusername/ai-text-detector}
}

References

This model is based on research using the RAID benchmark:

• RAID: A Benchmark for AI-Generated Text Detection (ACL 2024)

Support

For issues, questions, or contributions, please visit the GitHub repository.