add

src/about.py

@@ -60,7 +60,6 @@ TITLE = """<p align="center"><img src="https://relbench.stanford.edu/img/logo.pn
 
 # What does your leaderboard evaluate?
 INTRODUCTION_TEXT = """
-Relational Deep Learning is a new approach for end-to-end representation learning on data spread across multiple tables, such as in a relational database (see our vision paper). RelBench is the accompanying benchmark which seeks to facilitate efficient, robust and reproducible research in this direction. It comprises of a collection of realistic, large-scale, and diverse datasets structured as relational tables, along with machine learning tasks defined on them. It provides full support for data downloading, task specification and standardized evaluation in an ML-framework-agnostic manner. Additionally, there is seamless integration with PyTorch Geometric to load the data as a graph and train GNN models, and with PyTorch Frame to encode the various types of table columns. Finally, there is a leaderboard for tracking progress.
 """
 
 # Which evaluations are you running? how can people reproduce what you have?
@@ -83,7 +82,6 @@ Once you have developed your model and got results, you can submit your test res
 - **Validation performance**: Validation performance of the model that is used to report the test performance above.
 - **Paper URL Link**: The original paper describing the method (arXiv link is recommended. paper needs not be peer-reviewed). If your method has any original component (e.g., even just combining existing methods XXX and YYY), you have to write a technical report describing it (e.g., how you exactly combined XXX and YYY).
 - **GitHub URL Link**: The Github repository or directory containining all code to reproduce the result. A placeholder repository is not allowed.
-- **Number of Parameters**: The number of parameters of your model, which can be calculated by sum(p.numel() for p in model.parameters()). If you use multi-stage training (e.g., apply node2vec and then MLP), please sum up all the parameters (both node2vec and MLP parameters).
 - **Honor code**: Please acknowledge that your submission adheres to all the ethical policies and your result is reproducible.
 """
 

src/populate.py

@@ -56,7 +56,7 @@ def get_leaderboard_df(EVAL_REQUESTS_PATH, task_type) -> pd.DataFrame:
     for model in model_res:
         model["test"] = literal_eval(model["test"])
         model["valid"] = literal_eval(model["valid"])
-        model["params"] = int(model["params"])
+        #model["params"] = int(model["params"])
         model['submitted_time'] = model['submitted_time'].split('T')[0]
         #model['paper_url'] = '[Link](' + model['paper_url'] + ')'
         #model['github_url'] = '[Link](' + model['github_url'] + ')'
@@ -65,7 +65,7 @@ def get_leaderboard_df(EVAL_REQUESTS_PATH, task_type) -> pd.DataFrame:
     for model in model_res:
         model.update({name2short_name[i]: str(model['test'][i][0])[:4] + '±' + str(model['test'][i][1])[:4] if i in model['test'] else '-' for i in name2short_name})
 
-    columns_to_show = ['model', 'author', 'email', 'paper_url', 'github_url', 'submitted_time', 'params'] + list(name2short_name.values())
+    columns_to_show = ['model', 'author', 'email', 'paper_url', 'github_url', 'submitted_time'] + list(name2short_name.values())
 
     # Check if model_res is empty
     if len(model_res) > 0:
@@ -77,7 +77,7 @@ def get_leaderboard_df(EVAL_REQUESTS_PATH, task_type) -> pd.DataFrame:
     #df_res = pd.DataFrame([{col: model[col] for col in columns_to_show} for model in model_res])
     print(df_res)
     ranks = df_res[list(name2short_name.values())].rank(ascending = ascending)
-    df_res.rename(columns={'model': 'Model', 'author': 'Author', 'email': 'Email', 'paper_url': 'Paper URL', 'github_url': 'Github URL', 'submitted_time': 'Time', 'params': '# of Params'}, inplace=True)
+    df_res.rename(columns={'model': 'Model', 'author': 'Author', 'email': 'Email', 'paper_url': 'Paper URL', 'github_url': 'Github URL', 'submitted_time': 'Time'}, inplace=True)
     df_res['Average Rank⬆️'] = ranks.mean(axis=1)
     df_res.sort_values(by='Average Rank⬆️', ascending=True, inplace=True)
     return df_res