quantiq 0.2.0

Confidence-aware AI toolkit with uncertainty estimation for Transformers and Deep Ensembles

QuantIQ 🔍

QuantIQ is a unified Python library for building robust, uncertainty-aware deep learning systems. It brings together lightweight, modular tools to help researchers and practitioners gain insight into model reliability and risk through principled uncertainty estimation.

📦 What's Inside

Quantiq currently includes two powerful uncertainty quantification tools:

1. DropWise 🔁

A plug-and-play PyTorch/HuggingFace wrapper for Monte Carlo Dropout–based uncertainty estimation in Transformers.

• Supports classification, regression, QA, and token tagging
• Computes entropy, confidence, and class-wise variances
• Enables dropout during inference for Bayesian-style sampling

📖 Full DropWise Documentation

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2. SmartEnsemble 🧠

A deep ensemble wrapper for PyTorch models with support for adversarial training and dual-mode (epistemic + aleatoric) uncertainty estimation.

• Works with any PyTorch model
• Enables risk scoring and calibration
• Includes built-in visualization and prediction APIs

📖 Full SmartEnsemble Documentation

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🔧 Installation

pip install quantiq

Or install from source:

git clone https://github.com/aryanator/QuantIQ.git
cd quantiq
pip install -e .

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🧪 Use Cases

• Safety-critical predictions (medical AI, self-driving, finance)
• Uncertainty-aware active learning
• Robust ML pipelines with explainable confidence
• Research experiments involving confidence, entropy, risk

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📚 Documentation & Examples

Explore examples, API usage, and task-specific walkthroughs in the GitHub repository:
🔗 https://github.com/aryanator/QuantIQ

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📝 License

MIT License

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Built by Aryan Patil to make uncertainty estimation simpler, smarter, and production-ready.