alt Hacker NewsFor email you can use a classifier
One way: separately embed sender, recipients, subject, body - then use the embedding vectors as input to a logistic classifier
With that setup, I get 95% accuracy on email classification, training on 50-100 base examples. The model trains on CPU in under 1min, and it does inference in under 20ms (most of it is running the embeddings, so you can make it faster if you train your own embeddings model)
Here’s a gist with some sample code: https://gist.github.com/nicobrenner/056a5aaff5d0119c0032ecda...
That code applies the embeddings + classifier setup on the Banking77 dataset. It gets 93-94% accuracy depending on the embeddings you use (SOTA for this is ~95%, with much bigger and slower models)
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I wonder what numbers you'd get using another system one model - Contrastive Language Model https://contrastive-lm.notion.site/
That model scales very well with quantities of requests.
I can’t see your gist but spam classification is a textbook example of something you shouldn’t measure with accuracy. If 95% of your samples are not spam you can get 95% accuracy by always guessing not spam.
You should use precision (when your model says “spam” how often is it spam?), recall (how many of the spam emails did it catch), or f1 (balanced between those two).