nn.Embedding holds a Tensor of dimension
(vocab_size, vector_size), i.e. of the size of the vocabulary x the dimension of each vector embedding, and a method that does the lookup.
When you create an embedding layer, the Tensor is initialised randomly. It is only when you train it when this similarity between similar words should appear. Unless you have overwritten the values of the embedding with a previously trained model, like GloVe or Word2Vec, but that’s another story.
So, once you have the embedding layer defined, and the vocabulary defined and encoded (i.e. assign a unique number to each word in the vocabulary) you can use the instance of the nn.Embedding class to get the corresponding embedding.
import torch from torch import nn embedding = nn.Embedding(1000,128) embedding(torch.LongTensor([3,4]))
will return the embedding vectors corresponding to the word 3 and 4 in your vocabulary. As no model has been trained, they will be random.