The usual way is to use a softmax with the inner products (computed as a large matrix product) of the tensor with the embeddimg vectors. That puts you in the same place as with usual classification problems with a softmax on top.
There are advanced ways (eg mixture of softmax 13 ), but this should give you a start.
Check out Stanford’s cs224n lectures on word vectors for full background 24 and OpenNMT-py to see it in action.
Best regards
Thomas