Expand the README into a usage guide#6
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The README now documents the full BinaryClassifier and BinaryClassifierSet API behavior, the model requirements, and the metadata schema. It also walks through training a model with the example script, running the bundled example and tests, and configuring SnortML in Snort 3. This gives new users the getting started material requested in issue snort3#1.
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This expands the README into a proper usage guide to help with the documentation requests in #1. It documents the BinaryClassifier and BinaryClassifierSet API in detail, including the zero left padding of short inputs, truncation of long inputs, the lowercase metadata flag, and how a classifier set picks a model by input size. The model requirements that build verifies are spelled out, so it is clear that any TFLite model with a single float32 input tensor and a single element float32 output tensor will work. There is a walkthrough of the train.py example showing how to go from labeled data to a model file, plus instructions for running the bundled classifier example against the test models. The Snort 3 integration section shows the snort_ml_engine and snort_ml Lua configuration along with the builtin alert GID and SID. A further reading section links the Talos announcement, the training video, and the official Snort docs. All build, test, and example commands in the doc were run and verified on a fresh build.