1 Repo
Awesome GitHub RepositoriesLanguage Model Interpretability
Specialized techniques for explaining the outputs of large-scale natural language models.
Distinct from Large Language Models: Focuses on explaining LLM outputs rather than orchestrating or deploying them
Explore 1 awesome GitHub repository matching artificial intelligence & ml · Language Model Interpretability. Refine with filters or upvote what's useful.
Awesome Language Model Interpretability GitHub Repositories
Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
- slundberg/shap
slundberg/shap
25,535Auf GitHub ansehenSHAP is a machine learning explainer that uses a game-theoretic framework to estimate the contribution of each feature to a model prediction. It provides a set of tools for quantifying how individual input features push a specific output away from a baseline value. The project includes specialized explainers for different architectures, including high-speed implementations for decision trees and ensemble models, linearization algorithms for deep learning networks, and covariance integration for linear models. It also features a model-agnostic interpretability tool that uses a kernel method to
Uses coalitional rules to explain the outputs of large natural language models with reduced function evaluations.
Jupyter Notebook
