Update the usage snippet

README.md

@@ -16,13 +16,28 @@ pip install -U sentence-transformers
 The usage is as simple as:
 ```python
 from sentence_transformers import SentenceTransformer
-model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
 
-# Sentences we want to encode. Example:
-sentence = ['This framework generates embeddings for each input sentence']
+# 1. Load a pretrained Sentence Transformer model
+model = SentenceTransformer("all-MiniLM-L6-v2")
 
-# Sentences are encoded by calling model.encode()
-embedding = model.encode(sentence)
+# The sentences to encode
+sentences = [
+    "The weather is lovely today.",
+    "It's so sunny outside!",
+    "He drove to the stadium.",
+]
+
+# 2. Calculate embeddings by calling model.encode()
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# 3. Calculate the embedding similarities
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.6660, 0.1046],
+#         [0.6660, 1.0000, 0.1411],
+#         [0.1046, 0.1411, 1.0000]])
 ```
 
 Hugging Face makes it easy to collaboratively build and showcase your [Sentence Transformers](https://www.sbert.net/) models! You can collaborate with your organization, upload and showcase your own models in your profile ❤️