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

We can leverage this fact to slightly modify the get_embeddings() function so it
works with both word and document embeddings:
Helper Function to Retrieve Embeddings
1 def get_embeddings(embeddings, sentence):
2 sent = Sentence(sentence)
3 embeddings.embed(sent)
4 if len(sent.embedding):
5 return sent.embedding.float()
6 else:
7 return torch.stack(
8 [token.embedding for token in sent.tokens]
9 ).float()
get_embeddings(bert_doc, watch1)
Output
tensor([-6.4245e-02, 3.5365e-01, -2.4962e-01, -5.3912e-01,
-1.9917e-01, -2.7712e-01, 1.6942e-01, 1.0867e-01,
...
7.4661e-02, -3.4777e-01, 1.5740e-01, 3.4407e-01,
-5.0272e-01, 1.7432e-01, 7.9398e-01, 7.3562e-01],
device='cuda:0',
grad_fn=<CatBackward>)
For more details on document embeddings, please check
"Tutorial 5: Document Embeddings." [198]
We can revisit the Sequential model from the "Word Embeddings" section and
modify it to use contextual word embeddings instead. But, first, we need to change
the datasets a bit as well.
Contextual Word Embeddings | 961