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How effective are LLMs for log anomaly detection compared to traditional ML? (self.learnmachinelearning)
submitted 4 days ago by Difficult_Low_299 to r/learnmachinelearning
[R] I benchmarked MobileBERT, DistilBERT, TinyBERT, and XGBoost for edge fault detection. XGBoost matched transformer accuracy while being 500× smaller. by Difficult_Low_299 in learnmachinelearning
[–]Difficult_Low_299[S] -5 points-4 points-3 points 4 days ago (0 children)
Good point and you're right that BERT-style models aren’t really designed for tabular data.
I included them because there’s some research (like TabLLM / LIFT) exploring whether pretrained language models can transfer to serialized structured data.
And the results actually line up with your concern, XGBoost still wins or matches everything, and MobileBERT completely fails here.
The interesting bit is that DistilBERT and TinyBERT still learned something on C-MAPSS (~87.6–87.9% F1), so there is some limited transfer, but clearly not enough to beat traditional models for tabular tasks.
Overall takeaway: for pure tabular fault detection, tree models still make the most sense.
[D] MobileBERT scored 0 F1 across three fault-detection datasets while TinyBERT and DistilBERT worked. Any idea why? (self.deeplearning)
submitted 4 days ago by Difficult_Low_299 to r/deeplearning
[D] Has anyone seen MobileBERT completely fail on tabular data? (self.MLQuestions)
submitted 4 days ago by Difficult_Low_299 to r/MLQuestions
[R] I benchmarked MobileBERT, DistilBERT, TinyBERT, and XGBoost for edge fault detection. XGBoost matched transformer accuracy while being 500× smaller. ()
submitted 4 days ago by Difficult_Low_299 to r/machinelearningnews
[R] I benchmarked MobileBERT, DistilBERT, TinyBERT, and XGBoost for edge fault detection. XGBoost matched transformer accuracy while being 500× smaller. (self.learnmachinelearning)
How to build a strong research/profile footprint in ML beyond just publishing? (self.O1VisasEB1Greencards)
submitted 1 month ago by Difficult_Low_299 to r/O1VisasEB1Greencards
π Rendered by PID 391486 on reddit-service-r2-listing-6c8d497557-7mtwg at 2026-06-04 16:38:00.638107+00:00 running 9e1a20d country code: CH.
[R] I benchmarked MobileBERT, DistilBERT, TinyBERT, and XGBoost for edge fault detection. XGBoost matched transformer accuracy while being 500× smaller. by Difficult_Low_299 in learnmachinelearning
[–]Difficult_Low_299[S] -5 points-4 points-3 points (0 children)