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chore: Remove commented out code for model information in utils.py
Browse files- src/display/utils.py +0 -10
- src/populate.py +1 -0
src/display/utils.py
CHANGED
@@ -29,16 +29,6 @@ auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model Name"
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auto_eval_column_dict.append(["Overall", ColumnContent, ColumnContent("Total", "number", True)])
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for task in Tasks:
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auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
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# Model information
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# auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False)])
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# auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
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# auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
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# auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)])
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# auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
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# auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
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# auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)])
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# auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
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# auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
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# We use make dataclass to dynamically fill the scores from Tasks
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AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
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auto_eval_column_dict.append(["Overall", ColumnContent, ColumnContent("Total", "number", True)])
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for task in Tasks:
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auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
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# We use make dataclass to dynamically fill the scores from Tasks
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AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
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src/populate.py
CHANGED
@@ -27,6 +27,7 @@ from datasets import load_dataset
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def get_leaderboard_df(results_repo, results_path, dataset_version):
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hf_leaderboard = load_dataset(results_repo, dataset_version, split="test", cache_dir=results_path)
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df = hf_leaderboard.to_pandas()
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print(df)
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return df
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def get_leaderboard_df(results_repo, results_path, dataset_version):
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hf_leaderboard = load_dataset(results_repo, dataset_version, split="test", cache_dir=results_path)
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df = hf_leaderboard.to_pandas()
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df = df.sort_values(by="Total", ascending=False)
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print(df)
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return df
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