Improve setup

- Checkbox to hide incomplete evaluations
- No need for precision.
- Default values for parameter size

app.py

@@ -34,6 +34,7 @@ def init_leaderboard(source: str, aggregate: bool = False) -> Leaderboard:
 
     df = pd.DataFrame.from_records(all_data_json)
     df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
+    df["Incomplete"] = ~df.isna().any(axis=1)
 
     master_columns = []
     for col in fields(AutoEvalColumn):
@@ -51,8 +52,8 @@ def init_leaderboard(source: str, aggregate: bool = False) -> Leaderboard:
         for c in fields(AutoEvalColumn)
         if not c.hidden and c.name in master_columns
     ]
+    cols.append("Incomplete")
     df = df[cols].round(decimals=2)
-    # df = df[has_no_nan_values(df, benchmark_cols)]
 
     return Leaderboard(
         value=df,
@@ -72,10 +73,11 @@ def init_leaderboard(source: str, aggregate: bool = False) -> Leaderboard:
         ),
         filter_columns=[
             # fmt: off
-            ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
+            ColumnFilter("Incomplete", type="boolean", label="Hide incomplete evaluations", default=True),
+            # ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
             ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model type"),
             ColumnFilter(AutoEvalColumn.multilingual.name, type="checkboxgroup", label="Multilinguality"),
-            ColumnFilter(AutoEvalColumn.param_size.name, type="slider", min=0.01, max=150, label="Select the number of parameters (B)"),
+            ColumnFilter(AutoEvalColumn.param_size.name, type="slider", min=0.01, max=150, label="Select the number of parameters (B)", default=[-1, 83]),
             # fmt: on
         ],
         search_columns=[AutoEvalColumn.model.name],
@@ -120,6 +122,6 @@ with demo:
 
 
 scheduler = BackgroundScheduler()
-scheduler.add_job(restart_space, "interval", seconds=1800)
+scheduler.add_job(restart_space, "interval", seconds=3600)
 scheduler.start()
 demo.queue(default_concurrency_limit=40).launch()