program.py

from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer("all-MiniLM-L6-v2")

sentences = [
    "A man is eating food.",
    "A man is eating a piece of bread.",
    "The girl is carrying a baby.",
    "A man is riding a horse.",
    "A woman is playing violin.",
    "Two men pushed carts through the woods.",
    "A man is riding a white horse on an enclosed ground.",
    "A monkey is playing drums.",
    "Someone in a gorilla costume is playing a set of drums.",
    "plying music is fun",
    "I am hungry",
    "A toast for breakfast",
    "animals are amzing",
    "We are going to the zoo",
    "she is playing the piano",
]

# Encode all sentences
embeddings = model.encode(sentences)

# Compute cosine similarity between all pairs
cos_sim = util.cos_sim(embeddings, embeddings)

# Add all pairs to a list with their cosine similarity score
all_sentence_combinations = []
for i in range(len(cos_sim) - 1):
    for j in range(i + 1, len(cos_sim)):
        all_sentence_combinations.append([cos_sim[i][j], i, j])

# Sort list by the highest cosine similarity score
all_sentence_combinations = sorted(all_sentence_combinations, key=lambda x: x[0], reverse=True)

print("Top-20 most similar pairs:")
for score, i, j in all_sentence_combinations[0:20]:
    print("{} \t {} \t {:.4f}".format(sentences[i], sentences[j], cos_sim[i][j]))