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Really looking forward to testing and benchmarking this on my spam filtering benchmark. gemma-3-27b was a really strong model, surpassed later by gpt-oss:20b (which was also much faster). qwen models always had more variance.


If you wouldn't mind chatting about your usage, my email is in my profile, and I'd love to share experiences with other HNers using self-hosted models.


Does spam filtering really need a better model? My impression is that the whole game is based on having the best and freshest user-contributed labels.


He said it’s a benchmark.


[flagged]


In my experience the contents of the message are all but totally irrelevant to the classification, and it is the behavior of the mailing peer that gives all the relevant features.


Based on how much blatant gmail->gmail spam I receive, the gmail team agrees with this strategy.




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