Semantic Search fine-tune

eg. Pre-Trained BERT Result for sentence cosine similarity

======================

Query: milk with chocolate flavor

Top 10 most similar sentences in corpus:
Milka milk chocolate 100 g (Score: 0.8672)
Alpro, Chocolate soy drink 1 ltr (Score: 0.6821)
Danone, HiPRO 25g Protein chocolate flavor 330 ml (Score: 0.6692)

in the above example, I am searching for milk the result should be milk-related first but here it returns chocolate in the first place. how do I fine-tune similarity for the result?

I googled it but do not found any proper solution, please help me.

Code:

import scipy
import numpy as np
from sentence_transformers import models, SentenceTransformer
model = SentenceTransformer('distilbert-base-multilingual-cased')

corpus = [
          "Alpro, Chocolate soy drink 1 ltr",
          "Milka milk chocolate 100 g",
          "Danone, HiPRO 25g Protein chocolate flavor 330 ml"
         ]
corpus_embeddings = model.encode(corpus)

queries = [
            'milk with chocolate flavor',
          ]
query_embeddings = model.encode(queries)

# Calculate Cosine similarity of query against each sentence i
closest_n = 10
for query, query_embedding in zip(queries, query_embeddings):
    distances = scipy.spatial.distance.cdist([query_embedding], corpus_embeddings, "cosine")[0]

    results = zip(range(len(distances)), distances)
    results = sorted(results, key=lambda x: x[1])

    print("\n======================\n")
    print("Query:", query)
    print("\nTop 10 most similar sentences in corpus:")

    for idx, distance in results[0:closest_n]:
        print(corpus[idx].strip(), "(Score: %.4f)" % (1-distance))

from Semantic Search fine-tune