from sentence_transformers import SentenceTransformer, util

def main():
    # Load the fine-tuned model
    model = SentenceTransformer('fine_tuned_sbert_triplet')

    # Example sentences
    sentences = [
        "A man is playing a guitar",
        "A person is playing a guitar",
        "A woman is reading a book"
    ]

    # Compute embeddings
    embeddings = model.encode(sentences, convert_to_tensor=True)

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

    # Display similarity matrix
    print("Cosine Similarity Matrix:")
    print(cosine_sim)

if __name__ == "__main__":
    main()