Full softmax is the standard softmax computation over all candidate classes or tokens. It contrasts with sampled methods such as candidate sampling, which approximate training by considering only a subset of negative classes.
Synonym for softmax. Contrast with candidate sampling. See Neural networks: Multi-class classification in Machine Learning Crash Course for more information.