Black box. It does seem to put messages with an URL in a certain category though, but also that's not always correct. It's trained on 3000 normal dR messages, and 3000 spam dR messages. 6000 dR messages in total. Many epochs but not good for use yet. The idea that the system could classify without discriminating new users is from the table. That discrimination is needed as a safe margin. Original spam system is a bit simple, but it doesn't do false positive and works great. Still, I want to make smth advanced out of it for the sake of education. Tomorrow I'll have my neural networks book. Probably over two weeks I have some good insights how to improve this all. New hobby :)

(pretrained 3b models are fine for recognizing spam btw. But it costs resource. 8 CPU's 100%. A self trained model pure on spam doesn't and is fast. With a pretrained model you can't do mass classification.)