minor updates

app.py

@@ -93,7 +93,7 @@ elif menu == "Asymmetric QA":
 **Instructions**: You can compare the Answer likeliness of a given Query with answer candidates of your choice. In the 
 background, we'll create an embedding for each answer, and then we'll use the cosine similarity function to calculate a 
 similarity metric between our query sentence and the others.
-`mpnet_asymmetric_qa` model works best for hard-negative answers or distinguishing answers that are actually questions 
+`mpnet_asymmetric_qa` model works best for hard-negative answers or distinguishing answer candidates that are actually questions 
 due to separate models applied for encoding questions and answers.
 
 For more cool information on sentence embeddings, see the [sBert project](https://www.sbert.net/examples/applications/computing-embeddings/README.html).