Hi,
how you a train a vec2word model, i.e. something like a reverse nn.Embedding, which goes from a vector representation, to single words/one-hot representation?
So if I understand correctly, a cluster of points in embedded space represents similar words. Thus if you sample from that cluster, and use it as the input to vec2word, the output should be a mapping to similar words?
I guess I could do something similar to an encoder-decoder, but does it have to be that complicated/use so many parameters?
I think i understand how to train word2vec, using this TensorFlow tutorial 58, but how you the reverse in PyTorch?
Thanks a lot,
Ajay