The Best Cornac Alternatives in 2026

Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.

AIF360 provides fairness metrics and explanations for datasets and models, along with algorithms to mitigate bias.

Captum explains model behavior and supports interpretability analysis for PyTorch systems.

An open source Python library for drift detection in machine learning systems.

Adala automates data labeling through an autonomous agent framework for organizing labeled datasets.

pytorch tabular provides a unified framework for deep learning models on tabular data.

modAL provides a modular active learning framework for Python built on top of scikit-learn.

Spektral provides a Python framework for creating graph neural networks with Keras and TensorFlow.

Giskard provides open source evaluation and testing tools for LLM agents.

NALP covers Natural Adversarial Language Processing through a dedicated library.

neuropredict evaluates predictive performance using neuroimaging features.

Open-RAG-Eval evaluates retrieval-augmented generation without requiring golden answers.

nilearn provides Python tools for machine learning and neuroimaging analysis.

This repository implements YOLOv3 variants in PyTorch for real time object detection, training, validation, inference, and export.

AutoViz automatically creates visualizations for datasets of different sizes from a single line of code.

PyTorch Frame provides a library for applying deep learning models to tabular data.

A Python package to assess and improve fairness of machine learning models.

WhichLLM tests local language models on a user’s hardware and ranks them using recent benchmarks.

The Featuretools Python library supports automated feature engineering and is published as open source.

tsfresh automatically extracts relevant features from time series data.

Snorkel helps generate training data programmatically through weak supervision.

PyTorch Geometric provides graph neural network tools and libraries for PyTorch projects.

PySS3 supports interpretable text classification with SS3 and visualization tools for explainable AI.

Feature Engine provides Python tools for feature engineering and selection that are compatible with scikit-learn.