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Top 13 Jupyter Notebook explainable-ai Projects
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imodels
Interpretable ML package ๐ for concise, transparent, and accurate predictive modeling (sklearn-compatible).
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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transformers-interpret
Model explainability that works seamlessly with ๐ค transformers. Explain your transformers model in just 2 lines of code.
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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.
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Quantus
[JMLR 2023] Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
When the updated flow proved reliable across datasets and explainers, we contributed the implementation back to Quantus as an open-source PR. The link is here Fix NonSensitivity metric
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diffusers-interpret
Diffusers-Interpret ๐ค๐งจ๐ต๏ธโโ๏ธ: Model explainability for ๐ค Diffusers. Get explanations for your generated images.
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SegGradCAM
SEG-GRAD-CAM: Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping
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quoptuna
Fairness-aware, explainable AutoML for quantum + classical ML โ 21 models, one Optuna search, SHAP & AI reports. uvx quoptuna
Project mention: I built AutoML for quantum machine learning โ here's the architecture | dev.to | 2026-07-19TL;DR โ uvx quoptuna boots a full AutoML web app with no install. It runs one hyperparameter search across 21 quantum and classical classifiers, prunes weak configs early, audits every model for fairness, explains the winner with SHAP, and can draft the report for you. Apache-2.0. Repo: github.com/Qentora/quoptuna. Stars and feedback very welcome. โญ
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tinyshap
Python package providing a minimal implementation of the SHAP algorithm using the Kernel method
Jupyter Notebook explainable-ai discussion
Jupyter Notebook explainable-ai related posts
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[D] Have researchers given up on traditional machine learning methods?
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Stable Diffusion links from around September 29, 2022 that I collected for further processing
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[P] XAI Recipes for the HuggingFace ๐ค Image Classification Models
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Diffusers-Interpret ๐ค๐งจ๐ต๏ธโโ๏ธ - Model explainability for ๐ค Diffusers
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Diffusers-Interpret v0.4.0 is out! Explainability for Stable Diffusion
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Diffusers-Interpret v0.4.0 is out! Explainability for Stable Diffusion
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Commas, How do they work?!
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A note from our sponsor - SaaSHub
www.saashub.com | 14 Aug 2026
Index
What are some of the best open-source explainable-ai projects in Jupyter Notebook? This list will help you:
| # | Project | Stars |
|---|---|---|
| 1 | imodels | 1,612 |
| 2 | transformers-interpret | 1,412 |
| 3 | OmniXAI | 971 |
| 4 | Transformer-MM-Explainability | 908 |
| 5 | daam | 798 |
| 6 | Quantus | 673 |
| 7 | facet | 533 |
| 8 | diffusers-interpret | 278 |
| 9 | Awesome-Data-Science | 164 |
| 10 | SegGradCAM | 107 |
| 11 | quoptuna | 19 |
| 12 | CWOX | 10 |
| 13 | tinyshap | 8 |