Update app.py

app.py

@@ -38,7 +38,7 @@ custom_css = """
 
 # Define the columns for the different leaderboards
 UGI_COLS = ['#P', 'Model', 'UGI 🏆', 'W/10 👍', 'Unruly', 'Internet', 'Stats', 'Writing', 'PolContro']
-WRITING_STYLE_COLS = ['#P', 'Model', 'Reg+MyScore 🏆', 'Reg 🏆', 'MyScore 🏆', 'ASSS⬇️', 'SMOG⬆️', 'Yule⬇️']
+WRITING_STYLE_COLS = ['#P', 'Model', 'Reg+MyScore 🏆', 'Reg+Int 🏆', 'MyScore 🏆', 'ASSS⬇️', 'SMOG⬆️', 'Yule⬇️']
 ANIME_RATING_COLS = ['#P', 'Model', 'Score 🏆', 'Dif', 'Cor', 'Std']
 
 # Load the leaderboard data from a CSV file
@@ -179,9 +179,11 @@ with GraInter:
             <br>
             **MyScore:** After generating the story, I give it a rating from 0 to 1 on how well written it was and how well it followed the prompt.
             <br>
-            **Reg:** Using 13 unique lexical analysis metrics as the input and my scores as the output, I trained a regression model to recognize what types of writing styles people like. To have a better seperation between model sizes, I weighted it by UGI intelligence-focused questions.
+            Using 13 unique lexical analysis metrics as the input and my scores as the output, I trained a regression model to recognize what types of writing styles people like.
             <br>
-            **Reg+MyScore:** The average between Unweighted Reg and MyScore.
+            **Reg+MyScore:** The regression weighted by MyScore.
+            <br>
+            **Reg+Int:** The regression weighted by UGI intelligence-focused questions.
             <br><br>
             Below are three of the metrics used which may be useful by themselves at detecting certain writing styles.
             <br>