Formatted the app

app.py

@@ -49,27 +49,22 @@ st.image(dificult_reading, caption='Reading sessions descriptions.')
 st.markdown("---")
 # section 2: how can transformers help?
 st.markdown("### How can Transformers Help?🪄 ")
-st.markdown("* Semantic search have shown to improve the accuracy and of the seFEVER, or Fact Extraction and VERification, was introduced in 2018 as the first dataset containing {fact, evdience, entailment_label} information. They extracted altering sentences from Wikipedia and had annotators report the relationship between the setences: entailment, contradition, not enough information.")
-#st.markdown("* Since then, other researchers have expanded on this area in different domains")
+st.markdown("* Semantic search applies the user intent, context, and conceptual meanings to match a user query to the corresponding content. It uses vector search to returns results that aim to match a users query. The components work together to retrieve and rank the results based on the meaning.")
+
 st.markdown("**Sentence Similarity**")
 
-#st.markdown("* Here, we use Climate FEVER (3), a similar dataset developed and annotated by ")
+st.markdown("* In this  a similar project we used a dataset from the Nashville Analytics Summit")
+st.markdown("* Given a search phrase, the model search for an abstract that marches the search phrase using cosine similarity. ")
 
-#st.markdown("**Fact Verification / Fact-Checking**")
-#st.markdown("* This is simply an extenstion of the textual entailment task")
-#st.markdown("* Given two sentences, sent1 and sent2, determine the relationship: entail, contradict, neutral")
-#st.markdown("* With fact verification, we can think of the sentences as claim and evidence and labels as support, refute, or not enough information to refute or support.")
 st.markdown("---")
 # section 4: The process
-# this is the pipeline in my notes (u are here highlight)
 st.markdown("### The Process 🔍")
 
-#st.markdown("Imagine: A person is curious about whether a claim they heard about climate change is true. How can transformers help validate or refute the claim?")
+st.markdown("A participant inputs a search phrase of the sessions he/she would like to attend and the semantic serach would return the title of the session, the abstract of the session, the room number and the time the session will be offered.")
 
-#st.markdown("1. User inputs a climate claim")
 
-st.markdown("2. Retrieve evidence related to input claim \
-	- For each claim, collect N related documents. These documents are selected by finding the N documents with the highest similarity scores to the claim. A current area of research: How do we keep the set of curated documents up-to-date? Validate their contents?")
+st.markdown("The output of the search query \
+	- Once the user input their phrase or keywords to search sessions of interest, the appropriate sessions are selected by finding the top 3 sessions with the highest similarity scores to the phrase or keyword.")
 
 #st.markdown("3. Send (claim, evidence) pairs to a transformer model. Have the model predict whether each evidence supports, refutes, or is not relevant to the claim. (📍 YOU ARE HERE!)")
 
@@ -174,9 +169,8 @@ st.markdown("---")
 st.markdown("## Resource Links")
 
 st.markdown("### References")
-#st.markdown("0. My [huggingface model card](https://huggingface.co/amandakonet/climatebert-fact-checking), [adopted Climate FEVER dataset card](https://huggingface.co/datasets/amandakonet/climate_fever_adopted), and [project code on github](https://github.com/amandakonet/climate-change-misinformation)")
 st.markdown("1. https://www.sbert.net/examples/applications/semantic-search/README.html")
 st.markdown("2. https://www.sbert.net/docs/pretrained-models/msmarco-v3.html")
 st.markdown("3. Semantic search [colab](https://colab.research.google.com/drive/12cn5Oo0v3HfQQ8Tv6-ukgxXSmT3zl35A?usp=sharing)")
 st.markdown("4. [project code on github](https://github.com/vanderbilt-data-science/abstract-search)")
-#st.markdown("4. [ClimateBERT](https://climatebert.ai/), [paper](https://arxiv.org/abs/2110.12010)")    
+