Evaluation and Tracking for LLM Experiments
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Updated
Jun 29, 2024 - Jupyter Notebook
Evaluation and Tracking for LLM Experiments
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
Automating machine learning training and save an SQL version of the model
A curated list of awesome responsible machine learning resources.
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Fit interpretable models. Explain blackbox machine learning.
OmniXAI: A Library for eXplainable AI
Explainable Artificial Intelligence through Contextual Importance and Utility
AntakIA is THE tool to explain an ML model or replace it with a collection of basic explainable models.
GAM (Global Attribution Mapping) explains the landscape of neural network predictions across subpopulations
Explaining the output of machine learning models with more accurately estimated Shapley values
Fast and explainable clustering in Python
Explainable Machine Learning in Survival Analysis
Per Class Feature Importance (PCFI): an explainability method for decision tree classifiers.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
moDel Agnostic Language for Exploration and eXplanation
A novel Inductive Logic Programming(ILP) system based on Meta Inverse Entailment in Python.
Interpretable Machine Learning via Rule Extraction
👋 Xplique is a Neural Networks Explainability Toolbox
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
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