[mrdrozdov]

Thanks Adam! It's nice work and it seems like there's a pretty epic dataset to analyze at x.ai. My main confusion was what the visualized vectors represented, but I guess you've answered that by saying they're the first layer in your model (if I'm interpreting correctly). What I don't quite understand is how you got the word vector from the inputs. It sounds like you represent each word as a one hot encoding (similar to indexes), and then you pass this one hot encoding through the first layer giving you the word vector for each input?

[adamklec]

That's right. The weights that connect the Nth neuron in the one-hot input layer to the embedding layer can be thought of as a vector encoding of the Nth word in the vocabulary.