Update README.md

README.md

@@ -91,11 +91,12 @@ with torch.no_grad():
   # Perform pooling. In this case, cls pooling.
   sentence_embeddings = model_output[0][:, 0]
 
-
 # normalize embeddings
-sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:", sentence_embeddings)
-# [[0.5566 0.3013]
-# [0.1703 0.7124]]
+sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
+
+# [[0.5567, 0.3014],
+#  [0.1701, 0.7122]]
+scores = (sentence_embeddings[:2] @ sentence_embeddings[2:].T)
 ```
 
 Also, you can use native [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding) library for evaluation. Usage is described in [`bge-m3` model card](https://huggingface.co/BAAI/bge-m3).