add model report to lb

intermediate_leaderboard.py

@@ -103,7 +103,7 @@ def make_intermediate_lb():
         return let
     
     cld_df["fixed_letter"] = cld_df["letter"].apply(lambda x: clean_up(x))
-
+    report_cols = latest_submissions_df[['user', 'model_report']].drop_duplicates(keep='first')
 
     # gather means and stds for each metric for each user
     for metric in METRICS:
@@ -116,10 +116,12 @@ def make_intermediate_lb():
 
     # re-sort by RAE mean, lowest is best
     cld_df = cld_df.sort_values(by='RAE_mean', ascending=True).reset_index(drop=True)
+    cld_df = cld_df.merge(report_cols, on='user', how='inner')
     cld_df['user'] = cld_df['user'].str.split('___').str[1]
 
-    cld_subset = cld_df[['user', 'fixed_letter'] + [f'{metric}_display' for metric in METRICS]]
-    cld_subset = cld_subset.rename(columns={'user': 'user', 'fixed_letter': 'CLD'})
+    cld_subset = cld_df[['user', 'fixed_letter'] + [f'{metric}_display' for metric in METRICS]+ ['model_report']]
+    cld_subset = cld_subset.rename(columns={'user': 'user', 'fixed_letter': 'CLD', 'model_report': 'model details'})
+    cld_subset['model details'] = cld_subset['model details'].apply(lambda x: validate_model_details(x)).astype(str)
 
     print(cld_subset.head())
     cld_subset.to_csv("leaderboard_cld_results.csv", index=False)