Introduce new intervals: 100M & 250M and 250M & 500M

app.py

@@ -1863,7 +1863,8 @@ def update_url_language(event: gr.SelectData, current_task_language: dict, langu
 
 NUMERIC_INTERVALS = {
     "<100M": pd.Interval(0, 100, closed="right"),
-    ">100M, <500M": pd.Interval(100, 500, closed="right"),
+    ">100M, <250M": pd.Interval(100, 250, closed="right"),
+    ">250M, <500M": pd.Interval(250, 500, closed="right"),
     ">500M, <1B": pd.Interval(500, 1000, closed="right"),
     ">1B": pd.Interval(1000, 1_000_000, closed="right"),
 }
@@ -1897,17 +1898,11 @@ def filter_data(search_query, model_types, model_sizes, *full_dataframes):
             df = df[reduce(lambda a, b: a | b, masks)]
 
         # Apply the model size filtering
-        if model_sizes != ["?", *NUMERIC_INTERVALS.keys()]:
-            masks = []
-            # Handle the ? only
-            if "?" in model_sizes:
-                masks.append(df["Model Size (Million Parameters)"] == "")
-                model_sizes.remove("?")
-            # Handle the numeric intervals only
+        if model_sizes != list(NUMERIC_INTERVALS.keys()):
             numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[model_size] for model_size in model_sizes]))
             sizes = df["Model Size (Million Parameters)"].replace('', 0)
-            masks.append(sizes.apply(lambda size: any(numeric_interval.contains(size))))
-            df = df[reduce(lambda a, b: a | b, masks)]
+            mask = sizes.apply(lambda size: any(numeric_interval.contains(size)))
+            df = df[mask]
 
         output_dataframes.append(df)
     return output_dataframes
@@ -1937,8 +1932,8 @@ with gr.Blocks(css=css) as block:
         )
         filter_model_sizes = gr.CheckboxGroup(
             label="Model sizes (in number of parameters)",
-            choices=["?"] + list(NUMERIC_INTERVALS.keys()),
-            value=["?"] + list(NUMERIC_INTERVALS.keys()),
+            choices=list(NUMERIC_INTERVALS.keys()),
+            value=list(NUMERIC_INTERVALS.keys()),
             interactive=True,
             elem_classes=["filter-checkbox-group"]
         )