prior sets off by default

app.py

@@ -239,6 +239,8 @@ def regex_table(dataframe, regex, filter_button, style=True):
     if isinstance(filter_button, list) or isinstance(filter_button, str):
         if "Prior Sets" not in filter_button and 'Prior Sets (0.5 weight)' in dataframe.columns:
             update_scores = True
+            # remove the column "Prior Sets (0.5 weight)" from the outputted table
+            dataframe = dataframe.drop(columns=['Prior Sets (0.5 weight)'])
         if "Seq. Classifiers" not in filter_button:
             dataframe = dataframe[~dataframe["Model Type"].str.contains("Seq. Classifier", case=False, na=False)]
         if "DPO" not in filter_button:
@@ -253,7 +255,8 @@ def regex_table(dataframe, regex, filter_button, style=True):
     # if update the score to not use prior sets, do so
     if update_scores:
         data["Score"] = (data["Chat"] + data["Chat Hard"] + data["Safety"] + data["Reasoning"]) / 4
-        data["Prior Sets (0.5 weight)"] = np.NaN
+        # if "Prior Sets (0.5 weight)" in data.columns:
+        # data["Prior Sets (0.5 weight)"] = np.NaN
         # sort array by Score column
         data = data.sort_values(by='Score', ascending=False)
 
@@ -302,7 +305,7 @@ with gr.Blocks(css=custom_css) as app:
                                       placeholder="Model Search (delimit with , )",
                                       show_label=False)
                 model_types_1 = gr.CheckboxGroup(["Seq. Classifiers", "DPO", "Custom Classifiers", "Generative", "Prior Sets"], 
-                                                 value=["Seq. Classifiers", "DPO", "Custom Classifiers", "Generative", "Prior Sets"], 
+                                                 value=["Seq. Classifiers", "DPO", "Custom Classifiers", "Generative"], 
                                                  label="Model Types", 
                                                  show_label=False,
                                                 #  info="Which model types to include.",
@@ -316,7 +319,7 @@ with gr.Blocks(css=custom_css) as app:
                     visible=False,
                 )
                 rewardbench_table = gr.Dataframe(
-                    regex_table(rewardbench_data_avg.copy(), "", ["Seq. Classifiers", "DPO", "Custom Classifiers",  "Generative", "Prior Sets"]),
+                    regex_table(rewardbench_data_avg.copy(), "", ["Seq. Classifiers", "DPO", "Custom Classifiers",  "Generative"]),
                     datatype=col_types_rewardbench_avg,
                     headers=rewardbench_data_avg.columns.tolist(),
                     elem_id="rewardbench_dataframe_avg",