It shows the input output pairs you would use to train the network. Projection is simply your fully connected layer of dimension the embedding size you want (e.

It shows the input output pairs you would use to train the network. Projection is simply your fully connected layer of dimension the embedding size you want (e.g., something like 300). The output column is what is being predicted by the model, for which you have the true data and you'll calculate a loss and backprop as usual. In the BOW case you take multiple context words and predict the middle word (as shown in your diagram) and skip gram is the opposite approach.