616 skills found · Page 1 of 21
shap / ShapA game theoretic approach to explain the output of any machine learning model.
poloclub / Transformer ExplainerTransformer Explained Visually: Learn How LLM Transformer Models Work with Interactive Visualization
interpretml / InterpretFit interpretable models. Explain blackbox machine learning.
MAIF / Shapash🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
SeldonIO / AlibiAlgorithms for explaining machine learning models
oegedijk / ExplainerdashboardQuickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
jalammar / EccoExplain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0).
Trusted-AI / AIX360Interpretability and explainability of data and machine learning models
cdpierse / Transformers InterpretModel explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
hila-chefer / Transformer MM Explainability[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
youneslaaroussi / Css DocsCSS Selectors, Flexbox, Grid, Box Model, visually explained.
ModelOriented / DrWhyDrWhy is the collection of tools for eXplainable AI (XAI). It's based on shared principles and simple grammar for exploration, explanation and visualisation of predictive models.
jphall663 / Interpretable Machine Learning With PythonExamples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
zhipeixu / FakeShield🔥 [ICLR 2025] FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models
explainX / ExplainxExplainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms8909@nyu.edu
interpretml / Interpret TextA library that incorporates state-of-the-art explainers for text-based machine learning models and visualizes the result with a built-in dashboard.
apcj / ArrowsJavaScript library for drawing diagrams of small graphs, using D3 to generate SVG. Useful for explaining Neo4j graph modelling concepts in presentations and blogs.
sergioburdisso / Pyss3A Python library for Interpretable Machine Learning in Text Classification using the SS3 model, with easy-to-use visualization tools for Explainable AI :octocat:
ur-whitelab / ExmolExplainer for black box models that predict molecule properties
CraftJarvis / MC PlannerImplementation of "Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents"
