Feedback #2

app.py

@@ -71,30 +71,39 @@ def hide_skill_columns(dataframe, exceptions=[]):
 
 
 def perform_cell_formatting(dataframe):
-    return dataframe.style.format({'Contamination Score': "{:.2f}",'Benchmark Score': "{:.2f}",'Speed (words/sec)': "{:.2f}"}).apply(
+    
+    return dataframe.style.format({'Contamination Score': "{:.2f}",'Speed (words/sec)': "{:.2f}"}).apply(
         lambda rows: [
-            "background-color: red;" if (value >0) else "background-color: green;" for value in rows
+            "background-color: red;color: white !important" if (value >0) else "color: green !important;" for value in rows
         ],
         subset=["Contamination Score"],
     )
 
+def make_column_bold(df_col):
+    return df_col.apply(lambda x: "<b>"+str(x)+"</b>")
+
 def init_leaderboard(dataframe):
 
     dataframe = hide_skill_columns(dataframe)
 
+    
+    dataframe["Benchmark Score (0-10)"] = make_column_bold(dataframe["Benchmark Score (0-10)"])
      
     styler = perform_cell_formatting(dataframe)
 
+
+
     return gr.Dataframe(
             value=styler,
             datatype="markdown",
-            wrap=True,
+            wrap=False,
             show_fullscreen_button=False,
             interactive=False,
-            column_widths=[30,50,50,150,60,60,60],
+            column_widths=[30,50,50,150,90,60,60],
             max_height=420,
             elem_classes="leaderboard_col_style",
-            show_search="search"
+            show_search="search",
+            max_chars=None
         )
 
 
@@ -109,6 +118,11 @@ def init_skill_leaderboard(dataframe):
     def filter_dataframe(skill):
         filtered_df = dataframe.sort_values(by=[skill], ascending=False).reset_index(drop=True)
         filtered_df = hide_skill_columns(filtered_df, exceptions=[skill])
+        new_skill_name = skill+" Score"
+        filtered_df.rename(columns={skill: new_skill_name}, inplace=True)
+        filtered_df[new_skill_name] = make_column_bold(filtered_df[new_skill_name])
+        ## reorder columns of filtered_df and insert skill in the middle
+        filtered_df = filtered_df[list(filtered_df.columns[:4]) + [new_skill_name] + list(filtered_df.columns[4:-1])]
         filtered_df["Rank"] = range(1, len(filtered_df) + 1)
         styler = perform_cell_formatting(filtered_df)
         return gr.Dataframe(
@@ -132,6 +146,8 @@ def init_size_leaderboard(dataframe):
 
     dataframe = hide_skill_columns(dataframe)
 
+    dataframe["Benchmark Score (0-10)"] = make_column_bold(dataframe["Benchmark Score (0-10)"])
+
     size_keys = ["Large","Medium","Small","Nano"]
 
     size_names = ["Large (More than 30B Parameter)","Medium (~30B)","Small (~10B)","Nano (~3B)"]
@@ -142,7 +158,7 @@ def init_size_leaderboard(dataframe):
         size_name_mapped_to_key = size_keys[size_names.index(size_name)]
         ##slice array from 0 to index of size
         size_list = size_keys[size_keys.index(size_name_mapped_to_key):]
-        filtered_df = dataframe[dataframe["Category"].isin(size_list)].reset_index(drop=True)
+        filtered_df = dataframe[dataframe["Size"].isin(size_list)].reset_index(drop=True)
         filtered_df["Rank"] = range(1, len(filtered_df) + 1)
         styler = perform_cell_formatting(filtered_df)
         return gr.Dataframe(
@@ -174,10 +190,10 @@ def get_model_info_blocks(chosen_model_name):
     filtered_df = LEADERBOARD_DF[LEADERBOARD_DF["Model Name"]==model_name_full].reset_index(drop=True)
     skills_bar_df = pd.DataFrame({
         'Skills': skills,
-        'Benchmark Score': filtered_df[skills].values[0]
+        'Benchmark Score (0-10)': filtered_df[skills].values[0]
     })
 
-    skills_bar_df = skills_bar_df.sort_values(by=['Benchmark Score'], ascending=False).reset_index(drop=True)
+    skills_bar_df = skills_bar_df.sort_values(by=['Benchmark Score (0-10)'], ascending=False).reset_index(drop=True)
 
     def get_metric_html(metric_title):
         return f"<div class='deep-dive-metric'><b>{metric_title}</b><span class='ddm-value'>{{}}</div>"
@@ -187,17 +203,17 @@ def get_model_info_blocks(chosen_model_name):
         with gr.Row():
             model_name = gr.HTML(get_metric_html("Model Name").format(chosen_model_name))
         with gr.Row():
-            benchmark_score = gr.HTML(get_metric_html("Benchmark Score").format(str(filtered_df["Benchmark Score"][0])+"/10"))
+            benchmark_score = gr.HTML(get_metric_html("Benchmark Score (0-10)").format(str(filtered_df["Benchmark Score (0-10)"][0])))
             rank = gr.HTML(get_metric_html("Benchmark Rank").format(filtered_df["Rank"][0]))
             speed = gr.HTML(get_metric_html("Speed <br/>(words per second)").format(filtered_df["Speed (words/sec)"][0]))
             contamination =  gr.HTML(get_metric_html("Contamination Score").format(filtered_df["Contamination Score"][0]))
-            size =  gr.HTML(get_metric_html("Size Category").format(filtered_df["Category"][0]))
+            size =  gr.HTML(get_metric_html("Size Category").format(filtered_df["Size"][0]))
 
     with gr.Row():
         skills_bar = gr.BarPlot(
                         value=skills_bar_df,
                         x="Skills",
-                        y="Benchmark Score",
+                        y="Benchmark Score (0-10)",
                         width=500,
                         height=500,
                         x_label_angle=45,

src/display/css_html_js.py

@@ -100,11 +100,11 @@ custom_css = """
 
 }
 .leaderboard_col_style th button  {
-    font-size:14px !important
+    font-size:15px !important
 }
 
-.leaderboard_col_style td:nth-child(7) p{
-    color: white !important;
+.leaderboard_col_style th button span{
+       white-space: break-spaces !important;
    
 }
 .abl_header{
@@ -149,6 +149,10 @@ border-radius: 10px;
     display: flex;
     flex-direction: column !important;
 }
+
+.prose *{
+color:unset;
+}
 """
 
 get_window_url_params = """

src/display/utils.py

@@ -26,13 +26,13 @@ auto_eval_column_dict = []
 auto_eval_column_dict.append(["rank", ColumnContent, ColumnContent("Rank", "str", True, False)])
 
 auto_eval_column_dict.append(["model_source", ColumnContent, ColumnContent("Source", "str", True, False)])
-auto_eval_column_dict.append(["model_category", ColumnContent, ColumnContent("Category", "str", True, False)])
+auto_eval_column_dict.append(["model_category", ColumnContent, ColumnContent("Size", "str", True, False)])
 
 
 #auto_eval_column_dict.append(["model_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)])
 auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model Name", "markdown", True, never_hidden=True)])
 #Scores
-auto_eval_column_dict.append(["average_score", ColumnContent, ColumnContent("Benchmark Score", "number", True)])
+auto_eval_column_dict.append(["average_score", ColumnContent, ColumnContent("Benchmark Score (0-10)", "number", True)])
 for eval_dim in EvalDimensions:
     if eval_dim.value.metric in ["speed", "contamination_score"]:
          auto_eval_column_dict.append([eval_dim.name, ColumnContent, ColumnContent(eval_dim.value.col_name, "number", True)])