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@onusozI have a GPU now, so I can do ML experiments on @janitr_ai crypto/scam detection dataset - I trained a tiny student BERT (transformer for the nonfamiliar), 3.6 MB ONNX model, still lightweight for a browser extension - Still fully local on your device (no cloud inference) - On frozen unseen holdout data (n=1,069), exact prediction accuracy improved from 77% -> 82% - Scam detection improved: precision 91% -> 94%, recall 55% -> 61% - Scam false alarm rate improved from 1.58% -> 1.21% And models are on huggingface org now, handle is janitr
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Training all these models of different sizes, on changing datasets and running experiments have also revealed some challenges that I feel profs would never teach at a uni ML program Like how to cleanly keep track of the gazillion runs Yeah I can name them after layer dims and other stuff, but that's to me like trying to remember UUIDs So I ended up choosing iso datestamp + petname, like 2026-02-15-flying-narwhal If anyone has a convention that is easier on the brain and the eyes, I am all ears
