update readme

README.md

@@ -52,10 +52,29 @@ Then you can use the model like this:
 
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+import torch.nn.functional as F
+
 model = SentenceTransformer('utrobinmv/t5_translate_en_ru_zh_base_200_sent')
-embeddings = model.encode(sentences)
-print(embeddings)
+
+sentences_1 = ["The purpose of the development is to provide users with a personal simultaneous interpreter.",
+            "Съешь ещё этих мягких французских булок.",
+            "再吃这些法国的甜蜜的面包。"]
+
+sentences_2 = ["Цель разработки — предоставить пользователям личного синхронного переводчика.",
+            "Have some more of these soft French rolls.",
+            "开发的目的就是向用户提供个性化的同步翻译。"]
+
+embeddings = model.encode(sentences_1+sentences_2)
+embeddings_1 = embeddings[:len(sentences_1)]
+embeddings_2 = embeddings[len(sentences_1):]
+
+similarity = embeddings_1 @ embeddings_2.T
+print(similarity)
+#[[ 0.8956245  -0.0390042   0.8493222 ]
+# [ 0.00778637  0.85185283 -0.010229  ]
+# [ 0.01991986  0.72560245  0.02547248]]
+​
+
 ```