It can be extremely useful to make a model which had as advantageous starting point.

To do this, we can set the values of the embedding matrix.

# we give an example of this function in the day 1, word vector notebook

# now, we want to iterate over our vocabulary items
for word, emb_index in vectorizer.word_vocab.items():
# if the word is in the loaded glove vectors
if word.lower() in word_to_index:
# get the index into the glove vectors
glove_index = word_to_index[word.lower()]
# get the glove vector itself and convert to pytorch structure
glove_vec = torch.FloatTensor(word_vectors[glove_index])

# this only matters if using cuda :)
if settings.CUDA:
glove_vec = glove_vec.cuda()

# finally, if net is our network, and emb is the embedding layer:
net.emb.weight.data[emb_index, :].set_(glove_vec)