Interactive architecture diagrams for codebases
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Updated
Oct 20, 2026 - Python
Interactive architecture diagrams for codebases
Make your agents talk human. ADHD-friendly output styles for Claude Code, Codex, and others. So you can pay attention, not tokens.
XAI - An eXplainability toolbox for machine learning
Shapley Values and Shapley Interactions for Machine Learning
Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet
Explain & debug any blackbox machine learning model with a single line of code.
Official implementation of Score-CAM in PyTorch
Neural network visualization toolkit for tf.keras
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
For calculating global feature importance using Shapley values.
Training & evaluation library for text-based neural re-ranking and dense retrieval models built with PyTorch
Can we use explanations to improve hate speech models? Our paper accepted at AAAI 2021 tries to explore that question.
Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
GraphXAI: Resource to support the development and evaluation of GNN explainers
🗺️ Data Cleaning and Textual Data Visualization 🗺️
[Not Actively Maintained] Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes
Interpret text data with LLMs (sklearn compatible).
Evaluating ChatGPT’s Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness
Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!
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