Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Well, they’re not quite embeddings, but at least we can use cosine similarity to find
out how similar to each other two restaurants are:
ratings = torch.as_tensor([[.7, -.4, .7],
[.3, .7, -.5],
[.9, -.55, .8],
[-.3, .8, .34]]).float()
sims = torch.zeros(4, 4)
for i in range(4):
for j in range(4):
sims[i, j] = F.cosine_similarity(ratings[i],
ratings[j],
dim=0)
sims
Output
tensor([[ 1.0000, -0.4318, 0.9976, -0.2974],
[-0.4318, 1.0000, -0.4270, 0.3581],
[ 0.9976, -0.4270, 1.0000, -0.3598],
[-0.2974, 0.3581, -0.3598, 1.0000]])
As expected, restaurants #1 and #3 are remarkably similar (0.9976), and
restaurants #2 and #4 are somewhat similar (0.3581). Restaurant #1 is quite
different from restaurants #2 and #4 (-0.4318 and -0.2974, respectively), and so is
restaurant #3 (-0.4270 and -0.3598, respectively).
Although we can compute the cosine similarity between two restaurants now, the
values in the table above are not real embeddings. It was only an example that
illustrates well the concept of embedding dimensions as attributes.
Unfortunately, the dimensions of the word embeddings learned
by the Word2Vec model do not have clear-cut meanings like
that.
On the bright side, though, it is possible to do arithmetic with
word embeddings!
"Say what?"
926 | Chapter 11: Down the Yellow Brick Rabbit Hole