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

GloVe
GloVe embeddings are not contextual, as you already know, but they can
also be easily retrieved using WordEmbeddings from flair:
from flair.embeddings import WordEmbeddings
glove_embedding = WordEmbeddings('glove')
Now, let’s retrieve the word embeddings for our sentences, but first, and this
is very important, we need to create new Sentence objects for them:
new_flair_sentences = [Sentence(s) for s in sentences]
glove_embedding.embed(new_flair_sentences)
Output
[Sentence: "The Hatter was the first to break the silence . `
What day of the month is it ? ' he said , turning to Alice :
he had taken his watch out of his pocket , and was looking at
it uneasily , shaking it every now and then , and holding it
to his ear ." [ Tokens: 58],
Sentence: "Alice thought this a very curious thing , and she
went nearer to watch them , and just as she came up to them
she heard one of them say , ` Look out now , Five ! Do n't go
splashing paint over me like that !" [ Tokens: 48]]
Never reuse a Sentence object to retrieve different word
embeddings! The embedding attribute may be partially
overwritten (depending on the number of dimensions), and
you may end up with mixed embeddings (e.g., 3,072
dimensions from ELMo, but the first 100 values are
overwritten by GloVe embeddings).
Since GloVe is not contextual, the word "watch" will have the same
embedding regardless of which sentence you retrieve it from:
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