display ViDoRe V3 tab

app.py

@@ -80,107 +80,21 @@ def main():
 
     with gr.Blocks(css=css) as block:
         with gr.Tabs():
-            with gr.TabItem("ViDoRe V1"):
-                gr.Markdown("# ViDoRe: The Visual Document Retrieval Benchmark 1 📚🔍")
-                gr.Markdown("### From the paper - ColPali: Efficient Document Retrieval with Vision Language Models 👀")
-
-                gr.Markdown(
-                    """
-                Visual Document Retrieval Benchmark 1 leaderboard. To submit results, refer to the corresponding tab.  
-
-                Refer to the [ColPali paper](https://arxiv.org/abs/2407.01449) for details on metrics, tasks and models.
-                """
-                )
-                datasets_columns_1 = list(data_benchmark_1.columns[4:])
-
-                with gr.Row():
-                    metric_dropdown_1 = gr.Dropdown(choices=METRICS, value=initial_metric, label="Select Metric")
-                    research_textbox_1 = gr.Textbox(
-                        placeholder="🔍 Search Models... [press enter]",
-                        label="Filter Models by Name",
-                    )
-                    column_checkboxes_1 = gr.CheckboxGroup(
-                        choices=datasets_columns_1, value=datasets_columns_1, label="Select Columns to Display"
-                    )
-
-                with gr.Row():
-                    datatype_1 = ["number", "markdown"] + ["number"] * (num_datasets_1 + 1)
-                    dataframe_1 = gr.Dataframe(data_benchmark_1, datatype=datatype_1, type="pandas")
-
-                def update_data_1(metric, search_term, selected_columns):
-                    model_handler.get_vidore_data(metric)
-                    data = model_handler.render_df(metric, benchmark_version=1)
-                    data = add_rank_and_format(data, benchmark_version=1, selected_columns=selected_columns)
-                    data = filter_models(data, search_term)
-                    if selected_columns:
-                        data = data[["Rank", "Model", "Model Size (Million Parameters)", "Average"] + selected_columns]
-                    return data
-
-                with gr.Row():
-                    refresh_button_1 = gr.Button("Refresh")
-                    refresh_button_1.click(
-                        get_refresh_function(model_handler, benchmark_version=1),
-                        inputs=[metric_dropdown_1],
-                        outputs=dataframe_1,
-                        concurrency_limit=20,
-                    )
-
-                # Automatically refresh the dataframe when the dropdown value changes
-                metric_dropdown_1.change(
-                    get_refresh_function(model_handler, benchmark_version=1),
-                    inputs=[metric_dropdown_1],
-                    outputs=dataframe_1,
-                )
-                research_textbox_1.submit(
-                    lambda metric, search_term, selected_columns: update_data_1(metric, search_term, selected_columns),
-                    inputs=[metric_dropdown_1, research_textbox_1, column_checkboxes_1],
-                    outputs=dataframe_1,
-                )
-                column_checkboxes_1.change(
-                    lambda metric, search_term, selected_columns: update_data_1(metric, search_term, selected_columns),
-                    inputs=[metric_dropdown_1, research_textbox_1, column_checkboxes_1],
-                    outputs=dataframe_1,
-                )
-
-                gr.Markdown(
-                    f"""
-                - **Total Datasets**: {num_datasets_1}
-                - **Total Scores**: {num_scores_1}
-                - **Total Models**: {num_models_1}
-                """
-                    + r"""
-                Please consider citing:
-
-                ```bibtex
-                @misc{faysse2024colpaliefficientdocumentretrieval,
-                  title={ColPali: Efficient Document Retrieval with Vision Language Models}, 
-                  author={Manuel Faysse and Hugues Sibille and Tony Wu and Bilel Omrani and Gautier Viaud and Céline Hudelot and Pierre Colombo},
-                  year={2024},
-                  eprint={2407.01449},
-                  archivePrefix={arXiv},
-                  primaryClass={cs.IR},
-                  url={https://arxiv.org/abs/2407.01449}, 
-                }
-                @misc{macé2025vidorebenchmarkv2raising,
-                  title={ViDoRe Benchmark V2: Raising the Bar for Visual Retrieval}, 
-                  author={Quentin Macé and António Loison and Manuel Faysse},
-                  year={2025},
-                  eprint={2505.17166},
-                  archivePrefix={arXiv},
-                  primaryClass={cs.IR},
-                  url={https://arxiv.org/abs/2505.17166}, 
-                }
-
-                ```
-                """
-                )
-            with gr.TabItem("ViDoRe V2"):
+            with gr.TabItem("3️⃣ ViDoRe V3"):
+                gr.Markdown("# ViDoRe V3: a comprehensive evaluation of retrieval for enterprise use-cases 📚🔍")
+                with gr.Row(variant="panel"):
+                    gr.Markdown("""
+                                ### ⚠️ To access the ViDoRe V3 results, please refer directly to the [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
+                                **ViDoRe V3 is fully integrated into MTEB, which provides a unified platform for evaluating embedding models across various tasks, including document retrieval.**
+                                **We decided to display ViDoRe V3 results directly on MTEB to leverage its extensive features and community.**
+                                """)
+            with gr.TabItem("2️⃣ ViDoRe V2"):
                 gr.Markdown("# ViDoRe V2: A new visual Document Retrieval Benchmark 📚🔍")
                 gr.Markdown("### A harder dataset benchmark for visual document retrieval 👀")
 
                 gr.Markdown(
                     """
-                Visual Document Retrieval Benchmark 2 leaderboard. To submit results, refer to the corresponding tab.  
+                Visual Document Retrieval Benchmark 2 leaderboard. To submit results, refer to the corresponding tab.
 
                 Refer to the [ColPali paper](https://arxiv.org/abs/2407.01449) for details on metrics and models.
                 """
@@ -223,7 +137,7 @@ def main():
                 with gr.Row():
                     gr.Markdown(
                         """
-                    **Note**: For now, all models were evaluated using the vidore-benchmark package and custom retrievers on our side. 
+                    **Note**: For now, all models were evaluated using the vidore-benchmark package and custom retrievers on our side.
                     Those numbers are not numbers obtained from the organisations that released those models.
                     """
                     )
@@ -256,150 +170,146 @@ def main():
 
                 ```bibtex
                 @misc{faysse2024colpaliefficientdocumentretrieval,
-                  title={ColPali: Efficient Document Retrieval with Vision Language Models}, 
+                  title={ColPali: Efficient Document Retrieval with Vision Language Models},
                   author={Manuel Faysse and Hugues Sibille and Tony Wu and Bilel Omrani and Gautier Viaud and Céline Hudelot and Pierre Colombo},
                   year={2024},
                   eprint={2407.01449},
                   archivePrefix={arXiv},
                   primaryClass={cs.IR},
-                  url={https://arxiv.org/abs/2407.01449}, 
+                  url={https://arxiv.org/abs/2407.01449},
                 }
 
                 @misc{macé2025vidorebenchmarkv2raising,
-                      title={ViDoRe Benchmark V2: Raising the Bar for Visual Retrieval}, 
+                      title={ViDoRe Benchmark V2: Raising the Bar for Visual Retrieval},
                       author={Quentin Macé and António Loison and Manuel Faysse},
                       year={2025},
                       eprint={2505.17166},
                       archivePrefix={arXiv},
                       primaryClass={cs.IR},
-                      url={https://arxiv.org/abs/2505.17166}, 
+                      url={https://arxiv.org/abs/2505.17166},
                 }
                 ```
                 """
                 )
-            with gr.TabItem("📚 Submit your model"):
-                gr.Markdown("# How to Submit a New Model to the Leaderboard")
-                gr.Markdown(
-                    """
-                    To submit a new model to the ViDoRe leaderboard, follow these steps:
-
-                    1. **Evaluate your model**:
-                       - Follow the evaluation procedure provided in the [ViDoRe GitHub repository](https://github.com/illuin-tech/vidore-benchmark/) that uses MTEB.
-
-                    2. **Format your submission file**:
-                        - Add the generated files to [MTEB results](https://github.com/embeddings-benchmark/results) project. Check the [Colpali results](https://github.com/embeddings-benchmark/results/tree/main/results/vidore__colpali-v1.3/1b5c8929330df1a66de441a9b5409a878f0de5b0) for an example.
-
-                    And you're done! Your model will appear on the leaderboard when you click refresh! Once the space
-                    gets rebooted, it will appear on startup.
 
-                    Note: For proper hyperlink redirection, please ensure that your model repository name is in
-                    kebab-case, e.g. `my-model-name`.
-                    """
-                )
-            with gr.TabItem("Deprecated ViDoRe V1"):
-                gr.Markdown(
-                    "## <span style='color:red'>Deprecation notice: This leaderboard contains the results computed with the "
-                    "[vidore-benchmark](https://github.com/illuin-tech/vidore-benchmark) package, "
-                    "which is no longer maintained. Results should be computed using the "
-                    "[mteb](https://github.com/embeddings-benchmark/mteb) package as described "
-                    "[here](https://github.com/illuin-tech/vidore-benchmark/blob/main/README.md).</span>"
-                )
-                gr.Markdown("## <span style='color:red'>Missing results in the new leaderboard are being added as they are re-computed.</span>")
-                gr.Markdown("# <span style='color:red'>[Deprecated]</span> ViDoRe: The Visual Document Retrieval Benchmark 1 📚🔍")
+            with gr.TabItem("1️⃣ ViDoRe V1"):
+                gr.Markdown("# ViDoRe: The Visual Document Retrieval Benchmark 1 📚🔍")
                 gr.Markdown("### From the paper - ColPali: Efficient Document Retrieval with Vision Language Models 👀")
 
                 gr.Markdown(
                     """
-                Visual Document Retrieval Benchmark 1 leaderboard. To submit results, refer to the corresponding tab.  
+                Visual Document Retrieval Benchmark 1 leaderboard. To submit results, refer to the corresponding tab.
 
                 Refer to the [ColPali paper](https://arxiv.org/abs/2407.01449) for details on metrics, tasks and models.
                 """
                 )
-                deprecated_datasets_columns_1 = list(deprecated_data_benchmark_1.columns[3:])
+                datasets_columns_1 = list(data_benchmark_1.columns[4:])
 
                 with gr.Row():
-                    deprecated_metric_dropdown_1 = gr.Dropdown(choices=METRICS, value=initial_metric, label="Select Metric")
-                    deprecated_research_textbox_1 = gr.Textbox(
+                    metric_dropdown_1 = gr.Dropdown(choices=METRICS, value=initial_metric, label="Select Metric")
+                    research_textbox_1 = gr.Textbox(
                         placeholder="🔍 Search Models... [press enter]",
                         label="Filter Models by Name",
                     )
-                    deprecated_column_checkboxes_1 = gr.CheckboxGroup(
-                        choices=deprecated_datasets_columns_1, value=deprecated_datasets_columns_1, label="Select Columns to Display"
+                    column_checkboxes_1 = gr.CheckboxGroup(
+                        choices=datasets_columns_1, value=datasets_columns_1, label="Select Columns to Display"
                     )
 
                 with gr.Row():
-                    deprecated_datatype_1 = ["number", "markdown"] + ["number"] * (deprecated_num_datasets_1 + 1)
-                    deprecated_dataframe_1 = gr.Dataframe(deprecated_data_benchmark_1, datatype=deprecated_datatype_1, type="pandas")
+                    datatype_1 = ["number", "markdown"] + ["number"] * (num_datasets_1 + 1)
+                    dataframe_1 = gr.Dataframe(data_benchmark_1, datatype=datatype_1, type="pandas")
 
-                def deprecated_update_data_1(metric, search_term, selected_columns):
-                    deprecated_model_handler.get_vidore_data(metric)
-                    data = deprecated_model_handler.render_df(metric, benchmark_version=1)
+                def update_data_1(metric, search_term, selected_columns):
+                    model_handler.get_vidore_data(metric)
+                    data = model_handler.render_df(metric, benchmark_version=1)
                     data = add_rank_and_format(data, benchmark_version=1, selected_columns=selected_columns)
                     data = filter_models(data, search_term)
-                    # data = remove_duplicates(data)  # Add this line
                     if selected_columns:
-                        data = data[["Rank", "Model", "Average"] + selected_columns]
+                        data = data[["Rank", "Model", "Model Size (Million Parameters)", "Average"] + selected_columns]
                     return data
 
                 with gr.Row():
-                    deprecated_refresh_button_1 = gr.Button("Refresh")
-                    deprecated_refresh_button_1.click(
-                        deprecated_get_refresh_function(deprecated_model_handler, benchmark_version=1),
-                        inputs=[deprecated_metric_dropdown_1],
-                        outputs=deprecated_dataframe_1,
+                    refresh_button_1 = gr.Button("Refresh")
+                    refresh_button_1.click(
+                        get_refresh_function(model_handler, benchmark_version=1),
+                        inputs=[metric_dropdown_1],
+                        outputs=dataframe_1,
                         concurrency_limit=20,
                     )
 
                 # Automatically refresh the dataframe when the dropdown value changes
-                deprecated_metric_dropdown_1.change(
-                    deprecated_get_refresh_function(deprecated_model_handler, benchmark_version=1),
-                    inputs=[deprecated_metric_dropdown_1],
-                    outputs=deprecated_dataframe_1,
+                metric_dropdown_1.change(
+                    get_refresh_function(model_handler, benchmark_version=1),
+                    inputs=[metric_dropdown_1],
+                    outputs=dataframe_1,
                 )
-                deprecated_research_textbox_1.submit(
-                    lambda metric, search_term, selected_columns: deprecated_update_data_1(metric, search_term, selected_columns),
-                    inputs=[deprecated_metric_dropdown_1, deprecated_research_textbox_1, deprecated_column_checkboxes_1],
-                    outputs=deprecated_dataframe_1,
+                research_textbox_1.submit(
+                    lambda metric, search_term, selected_columns: update_data_1(metric, search_term, selected_columns),
+                    inputs=[metric_dropdown_1, research_textbox_1, column_checkboxes_1],
+                    outputs=dataframe_1,
                 )
-                deprecated_column_checkboxes_1.change(
-                    lambda metric, search_term, selected_columns: deprecated_update_data_1(metric, search_term, selected_columns),
-                    inputs=[deprecated_metric_dropdown_1, deprecated_research_textbox_1, deprecated_column_checkboxes_1],
-                    outputs=deprecated_dataframe_1,
+                column_checkboxes_1.change(
+                    lambda metric, search_term, selected_columns: update_data_1(metric, search_term, selected_columns),
+                    inputs=[metric_dropdown_1, research_textbox_1, column_checkboxes_1],
+                    outputs=dataframe_1,
                 )
 
                 gr.Markdown(
                     f"""
-                - **Total Datasets**: {deprecated_num_datasets_1}
-                - **Total Scores**: {deprecated_num_scores_1}
-                - **Total Models**: {deprecated_num_models_1}
+                - **Total Datasets**: {num_datasets_1}
+                - **Total Scores**: {num_scores_1}
+                - **Total Models**: {num_models_1}
                 """
                     + r"""
                 Please consider citing:
 
                 ```bibtex
                 @misc{faysse2024colpaliefficientdocumentretrieval,
-                  title={ColPali: Efficient Document Retrieval with Vision Language Models}, 
+                  title={ColPali: Efficient Document Retrieval with Vision Language Models},
                   author={Manuel Faysse and Hugues Sibille and Tony Wu and Bilel Omrani and Gautier Viaud and Céline Hudelot and Pierre Colombo},
                   year={2024},
                   eprint={2407.01449},
                   archivePrefix={arXiv},
                   primaryClass={cs.IR},
-                  url={https://arxiv.org/abs/2407.01449}, 
+                  url={https://arxiv.org/abs/2407.01449},
                 }
-                
                 @misc{macé2025vidorebenchmarkv2raising,
-                  title={ViDoRe Benchmark V2: Raising the Bar for Visual Retrieval}, 
+                  title={ViDoRe Benchmark V2: Raising the Bar for Visual Retrieval},
                   author={Quentin Macé and António Loison and Manuel Faysse},
                   year={2025},
                   eprint={2505.17166},
                   archivePrefix={arXiv},
                   primaryClass={cs.IR},
-                  url={https://arxiv.org/abs/2505.17166}, 
+                  url={https://arxiv.org/abs/2505.17166},
                 }
+
                 ```
                 """
                 )
-            with gr.TabItem("Deprecated ViDoRe V2"):
+
+            with gr.TabItem("📚 Submit your model"):
+                gr.Markdown("# How to Submit a New Model to the Leaderboard")
+                gr.Markdown(
+                    """
+                    To submit a new model to the ViDoRe leaderboard, follow these steps:
+
+                    1. **Evaluate your model**:
+                       - Follow the evaluation procedure provided in the [ViDoRe GitHub repository](https://github.com/illuin-tech/vidore-benchmark/) that uses MTEB.
+
+                    2. **Format your submission file**:
+                        - Add the generated files to [MTEB results](https://github.com/embeddings-benchmark/results) project. Check the [Colpali results](https://github.com/embeddings-benchmark/results/tree/main/results/vidore__colpali-v1.3/1b5c8929330df1a66de441a9b5409a878f0de5b0) for an example.
+
+                    And you're done! Your model will appear on the leaderboard when you click refresh! Once the space
+                    gets rebooted, it will appear on startup.
+
+                    Note: For proper hyperlink redirection, please ensure that your model repository name is in
+                    kebab-case, e.g. `my-model-name`.
+                    """
+                )
+
+            ### Deprecated Tabs ###
+
+            with gr.TabItem("⚠️ Deprecated ViDoRe V2"):
                 gr.Markdown(
                     "## <span style='color:red'>Deprecation notice: This leaderboard contains the results computed with the "
                     "[vidore-benchmark](https://github.com/illuin-tech/vidore-benchmark) package, "
@@ -413,7 +323,7 @@ def main():
 
                 gr.Markdown(
                     """
-                Visual Document Retrieval Benchmark 2 leaderboard. To submit results, refer to the corresponding tab.  
+                Visual Document Retrieval Benchmark 2 leaderboard. To submit results, refer to the corresponding tab.
 
                 Refer to the [ColPali paper](https://arxiv.org/abs/2407.01449) for details on metrics and models.
                 """
@@ -456,7 +366,7 @@ def main():
                 with gr.Row():
                     gr.Markdown(
                         """
-                    **Note**: For now, all models were evaluated using the vidore-benchmark package and custom retrievers on our side. 
+                    **Note**: For now, all models were evaluated using the vidore-benchmark package and custom retrievers on our side.
                     Those numbers are not numbers obtained from the organisations that released those models.
                     """
                     )
@@ -489,30 +399,133 @@ def main():
 
                 ```bibtex
                 @misc{faysse2024colpaliefficientdocumentretrieval,
-                  title={ColPali: Efficient Document Retrieval with Vision Language Models}, 
+                  title={ColPali: Efficient Document Retrieval with Vision Language Models},
                   author={Manuel Faysse and Hugues Sibille and Tony Wu and Bilel Omrani and Gautier Viaud and Céline Hudelot and Pierre Colombo},
                   year={2024},
                   eprint={2407.01449},
                   archivePrefix={arXiv},
                   primaryClass={cs.IR},
-                  url={https://arxiv.org/abs/2407.01449}, 
+                  url={https://arxiv.org/abs/2407.01449},
                 }
 
                 @misc{macé2025vidorebenchmarkv2raising,
-                      title={ViDoRe Benchmark V2: Raising the Bar for Visual Retrieval}, 
+                      title={ViDoRe Benchmark V2: Raising the Bar for Visual Retrieval},
                       author={Quentin Macé and António Loison and Manuel Faysse},
                       year={2025},
                       eprint={2505.17166},
                       archivePrefix={arXiv},
                       primaryClass={cs.IR},
-                      url={https://arxiv.org/abs/2505.17166}, 
+                      url={https://arxiv.org/abs/2505.17166},
                 }
                 ```
                 """
                 )
 
-    block.queue(max_size=10).launch(debug=True)
+            with gr.TabItem("⚠️ Deprecated ViDoRe V1"):
+                gr.Markdown(
+                    "## <span style='color:red'>Deprecation notice: This leaderboard contains the results computed with the "
+                    "[vidore-benchmark](https://github.com/illuin-tech/vidore-benchmark) package, "
+                    "which is no longer maintained. Results should be computed using the "
+                    "[mteb](https://github.com/embeddings-benchmark/mteb) package as described "
+                    "[here](https://github.com/illuin-tech/vidore-benchmark/blob/main/README.md).</span>"
+                )
+                gr.Markdown("## <span style='color:red'>Missing results in the new leaderboard are being added as they are re-computed.</span>")
+                gr.Markdown("# <span style='color:red'>[Deprecated]</span> ViDoRe: The Visual Document Retrieval Benchmark 1 📚🔍")
+                gr.Markdown("### From the paper - ColPali: Efficient Document Retrieval with Vision Language Models 👀")
+
+                gr.Markdown(
+                    """
+                Visual Document Retrieval Benchmark 1 leaderboard. To submit results, refer to the corresponding tab.
+
+                Refer to the [ColPali paper](https://arxiv.org/abs/2407.01449) for details on metrics, tasks and models.
+                """
+                )
+                deprecated_datasets_columns_1 = list(deprecated_data_benchmark_1.columns[3:])
+
+                with gr.Row():
+                    deprecated_metric_dropdown_1 = gr.Dropdown(choices=METRICS, value=initial_metric, label="Select Metric")
+                    deprecated_research_textbox_1 = gr.Textbox(
+                        placeholder="🔍 Search Models... [press enter]",
+                        label="Filter Models by Name",
+                    )
+                    deprecated_column_checkboxes_1 = gr.CheckboxGroup(
+                        choices=deprecated_datasets_columns_1, value=deprecated_datasets_columns_1, label="Select Columns to Display"
+                    )
+
+                with gr.Row():
+                    deprecated_datatype_1 = ["number", "markdown"] + ["number"] * (deprecated_num_datasets_1 + 1)
+                    deprecated_dataframe_1 = gr.Dataframe(deprecated_data_benchmark_1, datatype=deprecated_datatype_1, type="pandas")
+
+                def deprecated_update_data_1(metric, search_term, selected_columns):
+                    deprecated_model_handler.get_vidore_data(metric)
+                    data = deprecated_model_handler.render_df(metric, benchmark_version=1)
+                    data = add_rank_and_format(data, benchmark_version=1, selected_columns=selected_columns)
+                    data = filter_models(data, search_term)
+                    # data = remove_duplicates(data)  # Add this line
+                    if selected_columns:
+                        data = data[["Rank", "Model", "Average"] + selected_columns]
+                    return data
 
+                with gr.Row():
+                    deprecated_refresh_button_1 = gr.Button("Refresh")
+                    deprecated_refresh_button_1.click(
+                        deprecated_get_refresh_function(deprecated_model_handler, benchmark_version=1),
+                        inputs=[deprecated_metric_dropdown_1],
+                        outputs=deprecated_dataframe_1,
+                        concurrency_limit=20,
+                    )
+
+                # Automatically refresh the dataframe when the dropdown value changes
+                deprecated_metric_dropdown_1.change(
+                    deprecated_get_refresh_function(deprecated_model_handler, benchmark_version=1),
+                    inputs=[deprecated_metric_dropdown_1],
+                    outputs=deprecated_dataframe_1,
+                )
+                deprecated_research_textbox_1.submit(
+                    lambda metric, search_term, selected_columns: deprecated_update_data_1(metric, search_term, selected_columns),
+                    inputs=[deprecated_metric_dropdown_1, deprecated_research_textbox_1, deprecated_column_checkboxes_1],
+                    outputs=deprecated_dataframe_1,
+                )
+                deprecated_column_checkboxes_1.change(
+                    lambda metric, search_term, selected_columns: deprecated_update_data_1(metric, search_term, selected_columns),
+                    inputs=[deprecated_metric_dropdown_1, deprecated_research_textbox_1, deprecated_column_checkboxes_1],
+                    outputs=deprecated_dataframe_1,
+                )
+
+                gr.Markdown(
+                    f"""
+                - **Total Datasets**: {deprecated_num_datasets_1}
+                - **Total Scores**: {deprecated_num_scores_1}
+                - **Total Models**: {deprecated_num_models_1}
+                """
+                    + r"""
+                Please consider citing:
+
+                ```bibtex
+                @misc{faysse2024colpaliefficientdocumentretrieval,
+                  title={ColPali: Efficient Document Retrieval with Vision Language Models},
+                  author={Manuel Faysse and Hugues Sibille and Tony Wu and Bilel Omrani and Gautier Viaud and Céline Hudelot and Pierre Colombo},
+                  year={2024},
+                  eprint={2407.01449},
+                  archivePrefix={arXiv},
+                  primaryClass={cs.IR},
+                  url={https://arxiv.org/abs/2407.01449},
+                }
+
+                @misc{macé2025vidorebenchmarkv2raising,
+                  title={ViDoRe Benchmark V2: Raising the Bar for Visual Retrieval},
+                  author={Quentin Macé and António Loison and Manuel Faysse},
+                  year={2025},
+                  eprint={2505.17166},
+                  archivePrefix={arXiv},
+                  primaryClass={cs.IR},
+                  url={https://arxiv.org/abs/2505.17166},
+                }
+                ```
+                """
+                )
+
+    block.queue(max_size=10).launch(debug=True)
 
 if __name__ == "__main__":
     main()