Update README.md

README.md

@@ -25,12 +25,30 @@ pip install -U sentence-transformers
 Then you can use the model like this:
 
 ```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+from sentence_transformers import SentenceTransformer, util
 
-model = SentenceTransformer('abbasgolestani/ag-nli-bert-mpnet-base-uncased-sentence-similarity-v1')
-embeddings = model.encode(sentences)
-print(embeddings)
+
+model = SentenceTransformer('abbasgolestani/ag-nli-bert-mpnet-base-uncased-sentence-similarity-v1') nli-mpnet-base-v2
+
+# Two lists of sentences
+sentences1 = ['I am honored to be given the opportunity to help make our company better',
+             'I love my job and what I do here',
+             'I am excited about our company’s vision']
+
+sentences2 = ['I am hopeful about the future of our company',
+              'My work is aligning with my passion',
+              'Definitely our company vision will be the next breakthrough to change the world and I’m so happy and proud to work here']
+
+#Compute embedding for both lists
+embeddings1 = model.encode(sentences1, convert_to_tensor=True)
+embeddings2 = model.encode(sentences2, convert_to_tensor=True)
+
+#Compute cosine-similarities
+cosine_scores = util.cos_sim(embeddings1, embeddings2)
+
+#Output the pairs with their score
+for i in range(len(sentences1)):
+    print("{} \t\t {} \t\t Score: {:.4f}".format(sentences1[i], sentences2[i], cosine_scores[i][i]))
 ```