Remove filters and extra columns

app.py

@@ -70,20 +70,21 @@ def init_leaderboard(dataframe):
         ),
         search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
         hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
-        filter_columns=[
-            ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
-            ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
-            ColumnFilter(
-                AutoEvalColumn.params.name,
-                type="slider",
-                min=0.01,
-                max=150,
-                label="Select the number of parameters (B)",
-            ),
-            ColumnFilter(
-                AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True
-            ),
-        ],
+        # filter_columns=[
+        #     ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
+        #     ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
+        #     ColumnFilter(
+        #         AutoEvalColumn.params.name,
+        #         type="slider",
+        #         min=0.01,
+        #         max=150,
+        #         label="Select the number of parameters (B)",
+        #     ),
+        #     ColumnFilter(
+        #         AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=False
+        #     ),
+        # ],
+        filter_columns=[],
         bool_checkboxgroup_label="Hide models",
         interactive=False,
     )
@@ -101,102 +102,102 @@ with demo:
         with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
             gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
 
-        with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
-            with gr.Column():
-                with gr.Row():
-                    gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
-
-                with gr.Column():
-                    with gr.Accordion(
-                        f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
-                        open=False,
-                    ):
-                        with gr.Row():
-                            finished_eval_table = gr.components.Dataframe(
-                                value=finished_eval_queue_df,
-                                headers=EVAL_COLS,
-                                datatype=EVAL_TYPES,
-                                row_count=5,
-                            )
-                    with gr.Accordion(
-                        f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})",
-                        open=False,
-                    ):
-                        with gr.Row():
-                            running_eval_table = gr.components.Dataframe(
-                                value=running_eval_queue_df,
-                                headers=EVAL_COLS,
-                                datatype=EVAL_TYPES,
-                                row_count=5,
-                            )
-
-                    with gr.Accordion(
-                        f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
-                        open=False,
-                    ):
-                        with gr.Row():
-                            pending_eval_table = gr.components.Dataframe(
-                                value=pending_eval_queue_df,
-                                headers=EVAL_COLS,
-                                datatype=EVAL_TYPES,
-                                row_count=5,
-                            )
-            with gr.Row():
-                gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
-
-            with gr.Row():
-                with gr.Column():
-                    model_name_textbox = gr.Textbox(label="Model name")
-                    revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
-                    model_type = gr.Dropdown(
-                        choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
-                        label="Model type",
-                        multiselect=False,
-                        value=None,
-                        interactive=True,
-                    )
-
-                with gr.Column():
-                    precision = gr.Dropdown(
-                        choices=[i.value.name for i in Precision if i != Precision.Unknown],
-                        label="Precision",
-                        multiselect=False,
-                        value="float16",
-                        interactive=True,
-                    )
-                    weight_type = gr.Dropdown(
-                        choices=[i.value.name for i in WeightType],
-                        label="Weights type",
-                        multiselect=False,
-                        value="Original",
-                        interactive=True,
-                    )
-                    base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
-
-            submit_button = gr.Button("Submit Eval")
-            submission_result = gr.Markdown()
-            submit_button.click(
-                add_new_eval,
-                [
-                    model_name_textbox,
-                    base_model_name_textbox,
-                    revision_name_textbox,
-                    precision,
-                    weight_type,
-                    model_type,
-                ],
-                submission_result,
-            )
-
-    with gr.Row():
-        with gr.Accordion("📙 Citation", open=False):
-            citation_button = gr.Textbox(
-                value=CITATION_BUTTON_TEXT,
-                label=CITATION_BUTTON_LABEL,
-                lines=20,
-                elem_id="citation-button",
-                show_copy_button=True,
-            )
+    #     with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
+    #         with gr.Column():
+    #             with gr.Row():
+    #                 gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
+
+    #             with gr.Column():
+    #                 with gr.Accordion(
+    #                     f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
+    #                     open=False,
+    #                 ):
+    #                     with gr.Row():
+    #                         finished_eval_table = gr.components.Dataframe(
+    #                             value=finished_eval_queue_df,
+    #                             headers=EVAL_COLS,
+    #                             datatype=EVAL_TYPES,
+    #                             row_count=5,
+    #                         )
+    #                 with gr.Accordion(
+    #                     f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})",
+    #                     open=False,
+    #                 ):
+    #                     with gr.Row():
+    #                         running_eval_table = gr.components.Dataframe(
+    #                             value=running_eval_queue_df,
+    #                             headers=EVAL_COLS,
+    #                             datatype=EVAL_TYPES,
+    #                             row_count=5,
+    #                         )
+
+    #                 with gr.Accordion(
+    #                     f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
+    #                     open=False,
+    #                 ):
+    #                     with gr.Row():
+    #                         pending_eval_table = gr.components.Dataframe(
+    #                             value=pending_eval_queue_df,
+    #                             headers=EVAL_COLS,
+    #                             datatype=EVAL_TYPES,
+    #                             row_count=5,
+    #                         )
+    #         with gr.Row():
+    #             gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
+
+    #         with gr.Row():
+    #             with gr.Column():
+    #                 model_name_textbox = gr.Textbox(label="Model name")
+    #                 revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
+    #                 model_type = gr.Dropdown(
+    #                     choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
+    #                     label="Model type",
+    #                     multiselect=False,
+    #                     value=None,
+    #                     interactive=True,
+    #                 )
+
+    #             with gr.Column():
+    #                 precision = gr.Dropdown(
+    #                     choices=[i.value.name for i in Precision if i != Precision.Unknown],
+    #                     label="Precision",
+    #                     multiselect=False,
+    #                     value="float16",
+    #                     interactive=True,
+    #                 )
+    #                 weight_type = gr.Dropdown(
+    #                     choices=[i.value.name for i in WeightType],
+    #                     label="Weights type",
+    #                     multiselect=False,
+    #                     value="Original",
+    #                     interactive=True,
+    #                 )
+    #                 base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
+
+    #         submit_button = gr.Button("Submit Eval")
+    #         submission_result = gr.Markdown()
+    #         submit_button.click(
+    #             add_new_eval,
+    #             [
+    #                 model_name_textbox,
+    #                 base_model_name_textbox,
+    #                 revision_name_textbox,
+    #                 precision,
+    #                 weight_type,
+    #                 model_type,
+    #             ],
+    #             submission_result,
+    #         )
+
+    # with gr.Row():
+    #     with gr.Accordion("📙 Citation", open=False):
+    #         citation_button = gr.Textbox(
+    #             value=CITATION_BUTTON_TEXT,
+    #             label=CITATION_BUTTON_LABEL,
+    #             lines=20,
+    #             elem_id="citation-button",
+    #             show_copy_button=True,
+    #         )
 
 scheduler = BackgroundScheduler()
 scheduler.add_job(restart_space, "interval", seconds=1800)

src/display/utils.py

@@ -23,22 +23,22 @@ class ColumnContent:
 ## Leaderboard columns
 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_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(["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)])
-auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
+# # 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(["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)])
+# auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
 
 # We use make dataclass to dynamically fill the scores from Tasks
 AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)