𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗳𝗼𝗿 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀! Generative AI is one of the hottest topics, with models like GPT-5 and Gemini disrupting the industry and gaining extensive media coverage. Notably, large language models lack explainability and are often used without understanding their inner workings (black box). BertViz is an interactive tool for visualizing attention in transformer language models, hence improving explainability! BertViz can be run on Jupyter notebook via a Python API that supports Huggingface models. Check the link for more information, and follow me for regular data science content! 𝗕𝗲𝗿𝘁𝗩𝗶𝘇 𝗚𝗶𝘁𝗵𝘂𝗯 𝗣𝗮𝗴𝗲: https://lnkd.in/d7UfgND4 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: https://lnkd.in/dyByK4F #datascience #python #machinelearning #deeplearning
I wonder if this works with vlms with vision tokens as well?
Love this. Attention visualization is the "print statement" of the Transformer era. It turns a magical, mysterious output into a traceable logic path. It’s incredibly useful for understanding why a model got distracted by a specific word in the prompt. Do you find it helps non-technical stakeholders trust the model more, or is it mostly a dev tool?