Man, I’ve been there. Tried throwing BERT at enzyme data once—looked fine in eval, totally flopped in the wild. Classic overfit-on-vibes scenario.
Honestly, for straight-up classification? I’d pick SVM or logistic any day. Transformers are cool, but unless your data’s super clean, they just hallucinate confidently. Like giving GPT a multiple-choice test on gibberish—it will pick something, and say it with its chest.
Lately, I just steal embeddings from big models and slap a dumb classifier on top. Works better, runs faster, less drama.
Appreciate this post. Needed that reality check before I fine-tune something stupid again.
Honestly, for straight-up classification? I’d pick SVM or logistic any day. Transformers are cool, but unless your data’s super clean, they just hallucinate confidently. Like giving GPT a multiple-choice test on gibberish—it will pick something, and say it with its chest.
Lately, I just steal embeddings from big models and slap a dumb classifier on top. Works better, runs faster, less drama.
Appreciate this post. Needed that reality check before I fine-tune something stupid again.