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Cross-entropy loss with masking and label smoothing in NumPy
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Implement cross-entropy loss over a batch of logits in NumPy, stable for large values. Add a mask so padded positions do not count, then add label smoothing. Be ready to explain how the loss relates to KL divergence and what smoothing does to the gradients.
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