Update README.md

README.md

@@ -139,8 +139,8 @@ passage_embeddings = embeddings[2:]
 # Compute the (cosine) similarity between the query and document embeddings
 scores = cosine_similarity(query_embeddings, passage_embeddings)
 print(scores)
-# tensor([[0.8168, 0.1236],
-#         [0.1204, 0.5525]], grad_fn=<MmBackward0>)
+# tensor([[0.7647, 0.1115],
+#         [0.0930, 0.6606]], grad_fn=<MmBackward0>)
 ```
 </details>
 
@@ -180,8 +180,8 @@ document_embeddings = model.encode(documents, prompt_name="nl2code_document")
 # Compute the (cosine) similarity between the query and document embeddings
 similarity = model.similarity(query_embeddings, document_embeddings)
 print(similarity)
-# tensor([[0.8169, 0.1214],
-#         [0.1190, 0.5500]])
+# tensor([[0.7670, 0.1117],
+#         [0.0938, 0.6607]])
 ```
 </details>
 
@@ -260,8 +260,8 @@ passage_embeddings = embeddings[n_q:]
 # Cosine similarity matrix (queries x documents)
 scores = cosine_similarity(query_embeddings, passage_embeddings)
 print(scores)
-# tensor([[0.8171, 0.1230],
-#         [0.1207, 0.5513]])
+# tensor([[0.7650, 0.1118],
+#         [0.0937, 0.6613]])
 ```
 
 </details>