Actually, that’s not possible, unless you compute cosine similarity between the mean of the last hidden state and the embedding vectors of each token in BERT’s vocabulary. You can do that easily using sklearn.
The embedding matrix of BERT can be obtained as follows:
from transformers import BertModel
model = BertModel.from_pretrained("bert-base-uncased")
embedding_matrix = model.embeddings.word_embeddings.weight
However, I’m not sure it is useful to compare the vector of an entire sentence with each of the rows of the embedding matrix, as the sentence vector is a “summary” of the entire sentence.