Thread

1. nico · 1790006937 · focus · HN ↗

   If you only need classification, and you can provide some training data, you can ask Codex/Claude to build an embeddings + logistic classifier model for you

   For emails, I get 95% accuracy with this method, with only 50-100 examples for training

   Training the model takes less than 5 minutes on a CPU

   The resulting model is <1MB, and inference is sub 100ms

   Some other cool things about this approach:

   * the model doesn’t train on some “ideal” or general classification, instead it learns your preferences

   * the model runs on pretty much any mobile device and can be retrained online on the device

   * privacy, the whole training and inference is 100% local, no data goes anywhere (except whatever you feed codex/claude while building the model)

   Note: to do a more general test, I made a classifier for the Banking77 dataset. The model is <10MB, trains in <30s on CPU and gets 94.5% accuracy, which puts it in the top 5?models by accuracy for that set (the best one is at 94.86%, but it’s 350MB in size and takes hours to train on a GPU).

   1. roseway4 · 1790047932 · focus · HN ↗

      If you don’t see good performance with LRs, you may want to try RBF SVMs. We’ve found they work super well for our use cases with the embeddinggemma model as they can better separate classes in the non-linear embedding space.

      Our resulting RBF models are tiny and fit in L1 cache, with microsecond inference latency.

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