removed dummy column

app.py

@@ -154,7 +154,7 @@ def load_query(request: gr.Request):  # triggered only once at startup => read q
 
 
 def search_model(df: pd.DataFrame, query: str) -> pd.DataFrame:
-    return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False, na=False))]
+    return df[(df[AutoEvalColumn.model.name].str.contains(query, case=False, na=False))]
 
 
 def search_license(df: pd.DataFrame, query: str) -> pd.DataFrame:
@@ -163,14 +163,10 @@ def search_license(df: pd.DataFrame, query: str) -> pd.DataFrame:
 
 def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
     always_here_cols = [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
-    dummy_col = [AutoEvalColumn.dummy.name]
-    # AutoEvalColumn.model_type_symbol.name,
-    # AutoEvalColumn.model.name,
-    # We use COLS to maintain sorting
-    filtered_df = df[always_here_cols + [c for c in COLS if c in df.columns and c in columns] + dummy_col]
+    # Use AutoEvalColumn.model.name directly if needed
+    filtered_df = df[always_here_cols + [c for c in COLS if c in df.columns and c in columns]]
     return filtered_df
 
-
 def filter_queries(query: str, df: pd.DataFrame):
     tmp_result_df = []
 
@@ -327,16 +323,13 @@ with demo:
 
             leaderboard_table = gr.components.Dataframe(
                 value=leaderboard_df[
-                    [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
-                    + shown_columns.value
-                    + [AutoEvalColumn.dummy.name]
+                    [c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value
                 ],
                 headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
                 datatype=TYPES,
                 elem_id="leaderboard-table",
                 interactive=False,
                 visible=True,
-                # column_widths=["2%", "33%"]
             )
 
             # Dummy leaderboard for handling the case when the user uses backspace key

src/display/css_html_js.py

@@ -1,9 +1,4 @@
 custom_css = """
-/* Hides the final AutoEvalColumn */
-#llm-benchmark-tab-table table td:last-child,
-#llm-benchmark-tab-table table th:last-child {
-    display: none;
-}
 
 /* Limit the width of the first AutoEvalColumn so that names don't expand too much */
 table td:first-child,
@@ -44,7 +39,7 @@ table th:first-child {
     background: none;
     border: none;
 }
- 
+
 #search-bar {
     padding: 0px;
 }
@@ -94,4 +89,4 @@ get_window_url_params = """
         url_params = Object.fromEntries(params);
         return url_params;
     }
-    """
+    """

src/display/utils.py

@@ -47,31 +47,29 @@ class ColumnContent:
     dummy: bool = False
 
 
-auto_eval_column_dict = []
-# Init
-auto_eval_column_dict.append(["model_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)])
-auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
-# Scores
-auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Average ⬆️", "number", True)])
-for task in Tasks:
-    auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
-# Model information
-auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False)])
-auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
-auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
-auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)])
-auto_eval_column_dict.append(["merged", ColumnContent, ColumnContent("Merged", "bool", False)])
-auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
-auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
-auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)])
-auto_eval_column_dict.append(
-    ["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False, hidden=True)]
-)
-auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
-auto_eval_column_dict.append(["flagged", ColumnContent, ColumnContent("Flagged", "bool", False, hidden=True)])
-auto_eval_column_dict.append(["moe", ColumnContent, ColumnContent("MoE", "bool", False, hidden=True)])
-# Dummy column for the search bar (hidden by the custom CSS)
-auto_eval_column_dict.append(["dummy", ColumnContent, ColumnContent("model_name_for_query", "str", False, dummy=True)])
+static_columns = [
+    ["model_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)],
+    ["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)],
+    ["average", ColumnContent, ColumnContent("Average ⬆️", "number", True)],
+    ["model_type", ColumnContent, ColumnContent("Type", "str", False)],
+    ["architecture", ColumnContent, ColumnContent("Architecture", "str", False)],
+    ["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)],
+    ["precision", ColumnContent, ColumnContent("Precision", "str", False)],
+    ["merged", ColumnContent, ColumnContent("Merged", "bool", False)],
+    ["license", ColumnContent, ColumnContent("Hub License", "str", False)],
+    ["params", ColumnContent, ColumnContent("#Params (B)", "number", False)],
+    ["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)],
+    ["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False, hidden=True)],
+    ["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)],
+    ["flagged", ColumnContent, ColumnContent("Flagged", "bool", False, hidden=True)],
+    ["moe", ColumnContent, ColumnContent("MoE", "bool", False, hidden=True)],
+]
+
+# Append task specific columns using a comprehension
+task_columns = [[task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)] for task in Tasks]
+
+# Finally, combine them into one list
+auto_eval_column_dict = static_columns + task_columns
 
 # We use make dataclass to dynamically fill the scores from Tasks
 AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
@@ -99,7 +97,6 @@ baseline_row = {
     AutoEvalColumn.truthfulqa.name: 25.0,
     AutoEvalColumn.winogrande.name: 50.0,
     AutoEvalColumn.gsm8k.name: 0.21,
-    AutoEvalColumn.dummy.name: "baseline",
     AutoEvalColumn.model_type.name: "",
     AutoEvalColumn.flagged.name: False,
 }
@@ -124,7 +121,6 @@ human_baseline_row = {
     AutoEvalColumn.truthfulqa.name: 94.0,
     AutoEvalColumn.winogrande.name: 94.0,
     AutoEvalColumn.gsm8k.name: 100,
-    AutoEvalColumn.dummy.name: "human_baseline",
     AutoEvalColumn.model_type.name: "",
     AutoEvalColumn.flagged.name: False,
 }

src/leaderboard/filter_models.py

@@ -128,14 +128,11 @@ DO_NOT_SUBMIT_MODELS = [
     "TigerResearch/tigerbot-70b-chat-v4-4k",  # per authors request
 ]
 
-
 def flag_models(leaderboard_data: list[dict]):
+    """Flags models based on external criteria or flagged status."""
     for model_data in leaderboard_data:
-        # Merges and moes are flagged automatically
-        if model_data[AutoEvalColumn.flagged.name]:
-            flag_key = "merged"
-        else:
-            flag_key = model_data["model_name_for_query"]
+        # Use the primary model name for checking flags
+        flag_key = model_data[AutoEvalColumn.model.name]  # Use the direct model name
 
         if flag_key in FLAGGED_MODELS:
             issue_num = FLAGGED_MODELS[flag_key].split("/")[-1]
@@ -152,16 +149,18 @@ def flag_models(leaderboard_data: list[dict]):
 
 
 def remove_forbidden_models(leaderboard_data: list[dict]):
+    """Removes models from the leaderboard based on the DO_NOT_SUBMIT list."""
     indices_to_remove = []
     for ix, model in enumerate(leaderboard_data):
-        if model["model_name_for_query"] in DO_NOT_SUBMIT_MODELS:
+        # Use the correct field that now holds the model name
+        if model[AutoEvalColumn.model.name] in DO_NOT_SUBMIT_MODELS:
             indices_to_remove.append(ix)
 
+    # Remove the models from the list
     for ix in reversed(indices_to_remove):
         leaderboard_data.pop(ix)
     return leaderboard_data
 
-
 def filter_models_flags(leaderboard_data: list[dict]):
     leaderboard_data = remove_forbidden_models(leaderboard_data)
     flag_models(leaderboard_data)

src/leaderboard/read_evals.py

@@ -133,7 +133,6 @@ class EvalResult:
             AutoEvalColumn.weight_type.name: self.weight_type.value.name,
             AutoEvalColumn.architecture.name: self.architecture,
             AutoEvalColumn.model.name: make_clickable_model(self.full_model),
-            AutoEvalColumn.dummy.name: self.full_model,
             AutoEvalColumn.revision.name: self.revision,
             AutoEvalColumn.average.name: average,
             AutoEvalColumn.license.name: self.license,

src/tools/collections.py

@@ -60,7 +60,7 @@ def update_collections(df: DataFrame):
         for size, interval in intervals.items():
             filtered_df = _filter_by_type_and_size(df, model_type, interval)
             best_models = list(
-                filtered_df.sort_values(AutoEvalColumn.average.name, ascending=False)[AutoEvalColumn.dummy.name][:10]
+                filtered_df.sort_values(AutoEvalColumn.average.name, ascending=False)[AutoEvalColumn.model.name][:10]
             )
             print(model_type.value.symbol, size, best_models)
             _add_models_to_collection(collection, best_models, model_type, size)
@@ -73,4 +73,4 @@ def update_collections(df: DataFrame):
         try:
             delete_collection_item(collection_slug=PATH_TO_COLLECTION, item_object_id=item_id, token=H4_TOKEN)
         except HfHubHTTPError:
-            continue
+            continue