Update pagination

app.py

@@ -9,7 +9,7 @@ from backend.utils import load_gender_data
 st.title('Demo using Flax-Sentence-Tranformers')
 
 st.sidebar.title('Tasks')
-menu = st.sidebar.radio("", options=["Sentence Similarity", "Asymmetric QA", "Search / Cluster",
+menu = st.sidebar.radio("", options=["Contributions & Evaluation", "Sentence Similarity", "Asymmetric QA", "Search / Cluster",
                                      "Gender Bias Evaluation"], index=0)
 
 st.markdown('''
@@ -23,23 +23,28 @@ In addition, we trained [20 models](https://huggingface.co/flax-sentence-embeddi
 We also uploaded [8 datasets](https://huggingface.co/flax-sentence-embeddings) specialized for Question Answering, Sentence-Similiarity and Gender Evaluation.
 You can view our models and datasets [here](https://huggingface.co/flax-sentence-embeddings).
 
-| Model     | [FullEvaluation](https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=1809754143) Average     | 20Newsgroups Clustering | StackOverflow DupQuestions | Twitter SemEval2015    |
-|-----------|---------------------------------------|-------|-------|-------|
-| paraphrase-mpnet-base-v2 (previous SOTA)  | 67.97 | 47.79 | 49.03 | 72.36 |
-| all_datasets_v3_roberta-large (400k steps) | **70.22** | 50.12 | 52.18 | 75.28 |
+''')
 
+if menu == "Contributions & Evaluation":
+    st.markdown('''    
 ## Contributions
-
-- **20 performant Sentence Embedding models** that can be utilized for Sentence Simliarity / Asymmetric QA / Search & Clustering.
+    
+- **20 Sentence Embedding models** that can be utilized for Sentence Simliarity / Asymmetric QA / Search & Clustering.
 - **8 Datasets** from Stackexchange and StackOverflow, PAWS, Gender Evaluation uploaded to HuggingFace Hub.
 - **Achieve SOTA** on multiple general purpose Sentence Similarity evaluation tasks by utilizing large TPU memory to maximize
  customized Contrastive Loss. [Full Evaluation here](https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=1809754143).
 - **Gender Bias demonstration** that explores inherent bias in general purpose datasets.
 - **Search / Clustering demonstration** that showcases real-world use-cases for Sentence Embeddings.
 
-''')
+## Model Evaluations
 
-if menu == "Sentence Similarity":
+| Model     | [FullEvaluation](https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=1809754143) Average     | 20Newsgroups Clustering | StackOverflow DupQuestions | Twitter SemEval2015    |
+|-----------|---------------------------------------|-------|-------|-------|
+| paraphrase-mpnet-base-v2 (previous SOTA)  | 67.97 | 47.79 | 49.03 | 72.36 |
+| all_datasets_v3_roberta-large (400k steps) | **70.22** | 50.12 | 52.18 | 75.28 |
+| all_datasets_v3_mpnet-base (440k steps) | **70.01**   | 50.22 | 52.24 | 76.27 |
+''')
+elif menu == "Sentence Similarity":
     st.header('Sentence Similarity')
     st.markdown('''
 **Instructions**: You can compare the similarity of the main text with other texts of your choice. In the background,