the ten so we need approximately ten layers of this before we are actually arriving at a thousand classes but it also means that we only have to weigh classifications each time so in the hierarchical softmax we build trees like this and then we so we have a word we look up its vector sorry its vector and then we classify it for each of these nodes so your output isn't going to be a thousand a thousand log probabilities your output is going to be a log probability a binary log probability for each of the nodes right here so you want to know