Popular GitHub Repositories for Explain

A game theoretic approach to explain the output of any machine learning model.

Fit interpretable models. Explain blackbox machine learning.

Explain, 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).

Model explainability that works seamlessly with ? transformers. Explain your transformers model in just 2 lines of code.

DevoxxGenie is a plugin for IntelliJ IDEA that uses local LLM's (Ollama, LMStudio, GPT4All, Jan and Llama.cpp) and Cloud based LLMs to help review, test, explain your project code.

Explainable 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

GptHidra is a Ghidra plugin that uses the OpenAI Chat GPT to explain functions. With GptHidra, you can easily understand the purpose and behavior of functions in your codebase. Now with GPT4 Support!

gpttools extends gptstudio for package development to help you document code, write tests, or even explain code

Implementation of "Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents"

Explanatory Model Analysis. Explore, Explain and Examine Predictive Models

A neovim plugin that uses GPT to highlight and explain code readability issues

PyTorch Explain: Interpretable Deep Learning in Python.

Use animation to explain machine learning

JavaScript library to explain any machine learning models anywhere!

Python library to explain Tree Ensemble models (TE) like XGBoost, using a rule list.

Overview of different model interpretability libraries.

Re-implementation of the StylEx paper, training a GAN to explain a classifier in StyleSpace, paper by Lang et al. (2021).

Sample project to explain the Face detection API from Firebase MLKit

This research work focuses on comparing the existing approaches to explain the decisions of models trained using time-series data and proposing the best-fit method that generates explanations for a deep neural network. The proposed approach is used specifically for explaining LSTM networks for anomaly detection task in time-series data (satellite telemetry data).

Static Code Analysis and Visualization. Convert Code to UML and Flow Diagram and explain by AI.