Update app.py

app.py

@@ -226,7 +226,8 @@ bi_encoder_type = st.sidebar.selectbox(
 top_k = st.sidebar.slider("Number of Top Hits Generated",min_value=1,max_value=5,value=2)
 
 st.markdown(
-    """- The app supports asymmetric Semantic search which seeks to improve search accuracy of documents/URL by understanding the content of the search query in contrast to traditional search engines which only find documents based on lexical matches. 
+    """
+    - The app supports asymmetric Semantic search which seeks to improve search accuracy of documents/URL by understanding the content of the search query in contrast to traditional search engines which only find documents based on lexical matches. 
     - The idea behind semantic search is to embed all entries in your corpus, whether they be sentences, paragraphs, or documents, into a vector space. At search time, the query is embedded into the same vector space and the closest embeddings from your corpus are found. These entries should have a high semantic overlap with the query.
     - The all-* models where trained on all available training data (more than 1 billion training pairs) and are designed as general purpose models. The all-mpnet-base-v2 model provides the best quality, while all-MiniLM-L6-v2 is 5 times faster and still offers good quality. The models used have been trained on broad datasets, however, if your document/corpus is specialised, such as for science or economics, the results returned might be unsatisfactory.""")
      
@@ -240,7 +241,6 @@ st.markdown(
 st.markdown(
     """Code and App Inspiration Source: [Sentence Transformers](https://www.sbert.net/examples/applications/retrieve_rerank/README.html)""")
     
-    
 st.markdown(    
     """Quick summary of the purposes of a Bi and Cross-encoder below, the image and info were adapted from [www.sbert.net](https://www.sbert.net/examples/applications/semantic-search/README.html):""")
     
@@ -289,7 +289,7 @@ if search:
     if bi_encoder_type:
 
         with st.spinner(
-            text=f"Loading {bi_encoder_type} Bi-Encoder and embedding document into vector space. This might take a few seconds depending on the length of your document..."
+            text=f"Loading {bi_encoder_type} bi-encoder and embedding document into vector space. This might take a few seconds depending on the length of your document..."
         ):
             corpus_embeddings = bi_encoder(bi_encoder_type,passages)
             cross_encoder = cross_encoder()