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Running
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CPU Upgrade
Running
on
CPU Upgrade
orionweller
commited on
Commit
•
404b92c
1
Parent(s):
3219cef
fixed update
Browse files- 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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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
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@@ -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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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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# 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)
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