SBert1_loadmodel.py

from sentence_transformers import SentenceTransformer
sbert_model = SentenceTransformer('bert-base-nli-mean-tokens')

Sbert2_encode.py

sentence_embeddings = model.encode(sentences)

#print('Sample BERT embedding vector - length', len(sentence_embeddings[0]))
#print('Sample BERT embedding vector - note includes negative values', sentence_embeddings[0])

Sbert3_test.py

query = "I had pizza and pasta"
query_vec = model.encode([query])[0]

Sbert4_compute.py

for sent in sentences:
  sim = cosine(query_vec, model.encode([sent])[0])
  print("Sentence = ", sent, "; similarity = ", sim)

Hi dear. .encode function is not working.
Error is : ----> 'Doc2Vec' object has no attribute 'encode'

NameError: name 'cosine' is not defined

Correct one is:

from scipy.spatial import distance

for sent in sentences:
sim = distance.cosine(query_vec, model.encode([sent])[0])
print("Sentence = ", sent, "; similarity (close is good) = ", sim)

from sentence_transformers import SentenceTransformer
sbert_model = SentenceTransformer('bert-base-nli-mean-tokens')

to

model = SentenceTransformer('bert-base-nli-mean-tokens')