word2vec simply is a neural network with a hidden layer of n perceptrons. The number they use in the paper is 300 I believe. Thus, this is the dimensionality of

word2vec simply is a neural network with a hidden layer of n perceptrons. The number they use in the paper is 300 I believe. Thus, this is the dimensionality of each word. To train the model, we generate feature-label pairs by taking a word and randomly choosing a word next to it. We feed this into our model. After training, we are left with a 300 long vector for each word (input words x hidden layer since it’s fully connected).