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README.md

@@ -117,30 +117,26 @@ embeddings = model.encode(sentences, convert_to_tensor=True)
 def find_closest_sentence(query_embedding, sentence_embeddings, sentences):
     # Compute cosine similarities
     cosine_scores = util.pytorch_cos_sim(query_embedding, sentence_embeddings)[0]
-
     # Find the position of the highest score
     best_match_index = torch.argmax(cosine_scores).item()
-
     return sentences[best_match_index], cosine_scores[best_match_index].item()
 
 query = "Kini olu ilu England"
 query_embedding = model.encode(query, convert_to_tensor=True)
-
 closest_sentence, similarity_score = find_closest_sentence(query_embedding, embeddings, sentences)
 
-print(f"Query: {query}")
-print(f"Closest matching sentence: {closest_sentence}")
-print(f"Similarity score: {similarity_score:.4f}")
+print(f"Ibeere: {query}")
+print(f"Gbolohun ti o jọ mọ julọ: {closest_sentence}")
+print(f"Iwọn ijọra: {similarity_score:.4f}")
 
 # You can also try with a new sentence not in the original list
 new_query = "Kini oruko oba to wa ni ilu Oyo?"
 new_query_embedding = model.encode(new_query, convert_to_tensor=True)
-
 closest_sentence, similarity_score = find_closest_sentence(new_query_embedding, embeddings, sentences)
 
-print(f"\nNew Query: {new_query}")
-print(f"Closest matching sentence: {closest_sentence}")
-print(f"Similarity score: {similarity_score:.4f}")
+print(f"\nIbeere Tuntun: {new_query}")
+print(f"Gbolohun ti o jọ mọ julọ: {closest_sentence}")
+print(f"Iwọn ijọra: {similarity_score:.4f}")
 ```
 
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