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

Output
tensor(0.3504, device='cuda:0')
Indeed, they have an even lower similarity now.
For more details on Transformer word embeddings, please check
"Transformer Embeddings." [197]
In the "Pre-trained PyTorch Embeddings" section we averaged (classical) word
embeddings to get a single vector for each sentence. We could do the same thing
using contextual word embeddings instead. But we don’t have to, because we can
use…
Document Embeddings
We can use pre-trained models to generate embeddings for whole documents
instead of for single words, thus eliminating the need to average word embeddings.
In our case, a document is a sentence:
documents = [Sentence(watch1), Sentence(watch2)]
To actually get the embeddings, we use TransformerDocumentEmbeddings in the
same way as in the other examples:
from flair.embeddings import TransformerDocumentEmbeddings
bert_doc = TransformerDocumentEmbeddings('bert-base-uncased')
bert_doc.embed(documents)
Contextual Word Embeddings | 959