tgoop.com/jdata_blog/338
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💥 Список библиотек для XAI
Хочу зафиналить год useful постом, который лежит у меня дольше, чем ..... ладно, просто долго!
💫Основные библиотеки для моделей ML (и DL)
— LIME https://github.com/marcotcr/lime
— ELI5 https://eli5.readthedocs.io/en/latest/
— SHAP https://github.com/slundberg/shap
— DICE ML https://interpret.ml/DiCE/dice_ml.html#
— Pdpbox https://pdpbox.readthedocs.io/en/latest/
☄️Для моделей Deep Learning и LLM
* Captum (pytorch) https://github.com/pytorch/captum
* Anchors https://github.com/marcotcr/anchor
* DALEX https://github.com/ModelOriented/DALEX
* AIX360 https://github.com/IBM/AIX360
* ALIBI https://github.com/SeldonIO/alibi
* Quantus https://github.com/understandable-machine-intelligence-lab/Quantus
* OmniXAI https://github.com/salesforce/OmniXAI
* pytorch_explain https://github.com/pietrobarbiero/pytorch_explain
* DeepExplain https://github.com/marcoancona/DeepExplain
* Grad-CAM https://github.com/ramprs/grad-cam
* Integrated Gradients https://github.com/ankurtaly/Integrated-Gradients
* DeepLIFT https://github.com/kundajelab/deeplift
* interpret-text https://github.com/interpretml/interpret-text?ysclid=ldebiwr4jr824513795
* ExBERT https://github.com/bhoov/exbert
* ExplainaBoard (NLP) https://github.com/neulab/ExplainaBoard
* CARLA https://github.com/carla-recourse/CARLA
Никому не верьте, и всегда сомневайтесь в моделях!
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