Update app.py

app.py

@@ -233,8 +233,8 @@ st.markdown("""There models available to choose from:""")
     
 st.markdown(
     """Model Source: 
-    -Bi-Encoders - [multi-qa-mpnet-base-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1), [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2), [multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1) and [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
-    -Cross-Encoder - [cross-encoder/ms-marco-MiniLM-L-12-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-12-v2)""")
+    - Bi-Encoders - [multi-qa-mpnet-base-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1), [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2), [multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1) and [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
+    - Cross-Encoder - [cross-encoder/ms-marco-MiniLM-L-12-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-12-v2)""")
     
 st.markdown(
     """Code and App Inspiration Source: [Sentence Transformers](https://www.sbert.net/examples/applications/retrieve_rerank/README.html)""")
@@ -244,8 +244,8 @@ 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):""")
     
 st.markdown(    
-    """-Bi-Encoder (Retrieval): The Bi-encoder is responsible for independently embedding the sentences and search queries into a vector space. The result is then passed to the cross-encoder for checking the relevance/similarity between the query and sentences.
-    -Cross-Encoder (Re-Ranker): A re-ranker based on a Cross-Encoder can substantially improve the final results for the user. The query and a possible document is passed simultaneously to transformer network, which then outputs a single score between 0 and 1 indicating how relevant the document is for the given query. The cross-encoder further boost the performance, especially when you search over a corpus for which the bi-encoder was not trained for.""")
+    """- Bi-Encoder (Retrieval): The Bi-encoder is responsible for independently embedding the sentences and search queries into a vector space. The result is then passed to the cross-encoder for checking the relevance/similarity between the query and sentences.
+    - Cross-Encoder (Re-Ranker): A re-ranker based on a Cross-Encoder can substantially improve the final results for the user. The query and a possible document is passed simultaneously to transformer network, which then outputs a single score between 0 and 1 indicating how relevant the document is for the given query. The cross-encoder further boost the performance, especially when you search over a corpus for which the bi-encoder was not trained for.""")
 
 st.image('encoder.png', caption='Retrieval and Re-Rank')