fontSize

app.py

@@ -123,6 +123,11 @@ with st.sidebar:
 st.markdown("""
     <style>
     /* 应用到所有的Markdown渲染文本 */
+    div[data-testid="stMarkdownContainer"] * {
+        font-size: 24px;
+        line-height: 1.6;
+        color: #4CAF50;
+    }
     .main-text {
         font-size: 24px;
         line-height: 1.6;
@@ -206,21 +211,29 @@ elif page == "Important Dates":
 elif page == "Evaluation Measures":
     st.header("Evaluation Measures")
     st.markdown("""
-<script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
-<script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
-<ul class='main-text'>
-  <li class='main-text'><strong>Acc(Accuracy): </strong>The proportion of identical preference results between the model and human annotations. Specifically, we first convert individual scores (ranks) into pairwise preferences and then calculate consistency with human annotations.</li>
-  <li class='main-text'><strong>Kendall's tau: </strong>Measures the ordinal association between two ranked variables. $$\tau = \frac{C-D}{\frac{1}{2}n(n-1)}$$
-    where:
-    C is the number of concordant pairs,
-    D is the number of discordant pairs,
-    n is the number of pairs.</li>
-  <li class='main-text'><strong>Spearman's Rank Correlation Coefficient: </strong>Measures the strength and direction of the association between two ranked variables. $$\rho = 1 - \frac{6 \sum d_i^2}{n(n^2 - 1)}$$
-    where:
-    \(d_i\) is the difference between the ranks of corresponding elements in the two lists,
-    n is the number of elements.</li>
-</ul>
-    """,unsafe_allow_html=True)
+- **Acc(Accuracy):** The proportion of identical preference results between the model and human annotations. Specifically, we first convert individual scores (ranks) into pairwise preferences and then calculate consistency with human annotations.
+
+- **Kendall's tau:** Measures the ordinal association between two ranked variables.
+  
+  $$
+      \tau = \frac{C-D}{\frac{1}{2}n(n-1)}
+  $$
+  
+  where:
+  - $C$ is the number of concordant pairs,
+  - $D$ is the number of discordant pairs,
+  - $n$ is the number of pairs.
+
+- **Spearman's Rank Correlation Coefficient:** Measures the strength and direction of the association between two ranked variables.
+  
+  $$
+      \rho = 1 - \frac{6 \sum d_i^2}{n(n^2 - 1)}
+  $$
+  
+  where:
+  - $d_i$ is the difference between the ranks of corresponding elements in the two lists,
+  - $n$ is the number of elements.
+""",unsafe_allow_html=True)
 elif page == "Data and File format":
     st.header("Data and File format")
     st.markdown("""