orionweller commited on
Commit
404b92c
1 Parent(s): 3219cef

fixed update

Browse files
Files changed (1) hide show
  1. refresh.py +10 -7
refresh.py CHANGED
@@ -323,11 +323,17 @@ def get_mteb_data(tasks=["Clustering"], langs=[], datasets=[], fillna=True, add_
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  df['MLSUMClusteringS2S (fr)'] = df['MLSUMClusteringS2S (fr)'].fillna(df['MLSUMClusteringS2S'])
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  datasets.remove('MLSUMClusteringS2S')
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  if ('PawsXPairClassification (fr)' in datasets) and ('PawsX (fr)' in cols):
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- if 'PawsXPairClassification (fr)' in cols:
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- df['PawsXPairClassification (fr)'] = df['PawsXPairClassification (fr)'].fillna(df['PawsX (fr)'])
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- else:
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  df['PawsXPairClassification (fr)'] = df['PawsX (fr)']
 
 
 
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  datasets.remove('PawsX (fr)')
 
 
 
 
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  # Filter invalid columns
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  cols = [col for col in cols if col in base_columns + datasets]
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  i = 0
@@ -356,10 +362,7 @@ def get_mteb_average(task_dict: dict):
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  )
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  # Debugging:
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  # DATA_OVERALL.to_csv("overall.csv")
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- try:
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- DATA_OVERALL.insert(1, f"Average ({len(all_tasks)} datasets)", DATA_OVERALL[all_tasks].mean(axis=1, skipna=False))
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- except Exception as e:
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- breakpoint()
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  for i, (task_category, task_category_list) in enumerate(task_dict.items()):
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  DATA_OVERALL.insert(i+2, f"{task_category} Average ({len(task_category_list)} datasets)", DATA_OVERALL[task_category_list].mean(axis=1, skipna=False))
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  DATA_OVERALL.sort_values(f"Average ({len(all_tasks)} datasets)", ascending=False, inplace=True)
 
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  df['MLSUMClusteringS2S (fr)'] = df['MLSUMClusteringS2S (fr)'].fillna(df['MLSUMClusteringS2S'])
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  datasets.remove('MLSUMClusteringS2S')
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  if ('PawsXPairClassification (fr)' in datasets) and ('PawsX (fr)' in cols):
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+ # for the first bit no model has it, hence no column for it. We can remove this in a month or so
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+ if "PawsXPairClassification (fr)" not in cols:
 
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  df['PawsXPairClassification (fr)'] = df['PawsX (fr)']
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+ else:
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+ df['PawsXPairClassification (fr)'] = df['PawsXPairClassification (fr)'].fillna(df['PawsX (fr)'])
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+ # make all the columns the same
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  datasets.remove('PawsX (fr)')
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+ cols.remove('PawsX (fr)')
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+ df.drop(columns=['PawsX (fr)'], inplace=True)
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+ cols.append('PawsXPairClassification (fr)')
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+
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  # Filter invalid columns
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  cols = [col for col in cols if col in base_columns + datasets]
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  i = 0
 
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  )
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  # Debugging:
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  # DATA_OVERALL.to_csv("overall.csv")
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+ DATA_OVERALL.insert(1, f"Average ({len(all_tasks)} datasets)", DATA_OVERALL[all_tasks].mean(axis=1, skipna=False))
 
 
 
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  for i, (task_category, task_category_list) in enumerate(task_dict.items()):
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  DATA_OVERALL.insert(i+2, f"{task_category} Average ({len(task_category_list)} datasets)", DATA_OVERALL[task_category_list].mean(axis=1, skipna=False))
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  DATA_OVERALL.sort_values(f"Average ({len(all_tasks)} datasets)", ascending=False, inplace=True)