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)

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

to

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