Update leaderboard

src/about.py

@@ -14,7 +14,7 @@ class Tasks(Enum):
     # task_key in the json file, metric_key in the json file, name to display in the leaderboard 
     # task0 = Task("anli_r1", "acc", "ANLI")
     # task1 = Task("logiqa", "acc_norm", "LogiQA")
-    task_0 = Task("multi-table", "AggregationDetection-LogisticRegression", "AggregationDetection-LogisticRegression ⬇️")
+    # task_0 = Task("multi-table", "AggregationDetection-LogisticRegression", "AggregationDetection-LogisticRegression ⬇️")
     task_1 = Task("multi-table", "AggregationDetection-XGBClassifier", "AggregationDetection-XGBClassifier ⬇️")
     task_2 = Task("multi-table", "CardinalityShapeSimilarity", "CardinalityShapeSimilarity ⬆️")
 
@@ -23,7 +23,7 @@ class SingleTableTasks(Enum):
     # PairwiseCorrelationDifference
     task_1 = Task("single-table", "PairwiseCorrelationDifference", "PairwiseCorrelationDifference ⬇️")
     # SingleTableDetection-LogisticRegression
-    task_2 = Task("single-table", "SingleTableDetection-LogisticRegression", "SingleTableDetection-LogisticRegression ⬇️")
+    # task_2 = Task("single-table", "SingleTableDetection-LogisticRegression", "SingleTableDetection-LogisticRegression ⬇️")
     # SingleTableDetection-XGBClassifier
     task_3 = Task("single-table", "SingleTableDetection-XGBClassifier", "SingleTableDetection-XGBClassifier ⬇️")
 
@@ -37,7 +37,7 @@ class SingleColumnTasks(Enum):
     # KolmogorovSmirnovTest
     task_3 = Task("single-column", "KolmogorovSmirnovTest", "KolmogorovSmirnovTest ⬇️")
     # SingleColumnDetection-LogisticRegression
-    task_4 = Task("single-column", "SingleColumnDetection-LogisticRegression", "SingleColumnDetection-LogisticRegression ⬇️")
+    # task_4 = Task("single-column", "SingleColumnDetection-LogisticRegression", "SingleColumnDetection-LogisticRegression ⬇️")
     # SingleColumnDetection-XGBClassifier
     task_5 = Task("single-column", "SingleColumnDetection-XGBClassifier", "SingleColumnDetection-XGBClassifier ⬇️")
     # TotalVariationDistance
@@ -99,13 +99,12 @@ If everything is done, check you can launch the EleutherAIHarness on your model
 
 CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
 CITATION_BUTTON_TEXT = r"""
-@misc{hudovernik2024benchmarkingsyntheticrelationaldata,
-      title={Benchmarking the Fidelity and Utility of Synthetic Relational Data}, 
-      author={Valter Hudovernik and Martin Jurkovič and Erik Štrumbelj},
-      year={2024},
-      eprint={2410.03411},
-      archivePrefix={arXiv},
-      primaryClass={cs.DB},
-      url={https://arxiv.org/abs/2410.03411}, 
+@inproceedings{
+    jurkovic2025syntherela,
+    title={SyntheRela: A Benchmark For Synthetic Relational Database Generation},
+    author={Martin Jurkovic and Valter Hudovernik and Erik {\v{S}}trumbelj},
+    booktitle={Will Synthetic Data Finally Solve the Data Access Problem?},
+    year={2025},
+    url={https://openreview.net/forum?id=ZfQofWYn6n}
 }
 """

src/populate.py

@@ -53,8 +53,8 @@ def get_leaderboard_df(results_path: str, cols: list, benchmark_cols: list) -> p
 
     # iterate through all json files and add the data to the dataframe
     for data in all_data_json:
-        model = data["model"]
-        dataset = data["dataset"]
+        model = data["method_name"]
+        dataset = data["dataset_name"]
         row = {"Dataset": dataset, "Model": model}
         for metric in multi_table_metrics:
             stripped_metric = strip_emoji(metric)