n_steps=n_steps, n_features=n_features, n_classes=n_classes, n_samples_each_class=100, missing_rate=0.1 ...
Uses embedding-based similarity to find the most relevant classes from your dataset. This step handles the core classification by computing cosine similarity between the input text and all class ...
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