Hi cooganb, when your model was done training, and you had it generate some text for you, what did you do when the predicted embedding wasn’t an actual word? It seems unreasonable to expect all 100 dimensions of the prediction to perfectly match a real word, so did you find the euclidean-closest embedding to the predicted one? Or maybe find the most cosine-similar embedding to the predicted one? Neither of those is getting good results for me, so I would really love to know how far you got with this.