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from dataclasses import dataclass, make_dataclass |
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from src.about import EvalDimensions |
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def fields(raw_class): |
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return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"] |
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@dataclass |
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class ColumnContent: |
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name: str |
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type: str |
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displayed_by_default: bool |
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hidden: bool = False |
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never_hidden: bool = False |
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auto_eval_column_dict = [] |
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auto_eval_column_dict.append(["rank", ColumnContent, ColumnContent("Rank", "str", True, False)]) |
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auto_eval_column_dict.append(["model_source", ColumnContent, ColumnContent("Source", "str", True, False)]) |
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auto_eval_column_dict.append(["model_category", ColumnContent, ColumnContent("Size", "str", True, False)]) |
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auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model Name", "markdown", True, never_hidden=True)]) |
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auto_eval_column_dict.append(["average_score", ColumnContent, ColumnContent("Benchmark Score (0-10)", "number", True)]) |
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for eval_dim in EvalDimensions: |
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if eval_dim.value.metric in ["speed", "contamination_score"]: |
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auto_eval_column_dict.append([eval_dim.name, ColumnContent, ColumnContent(eval_dim.value.col_name, "number", True)]) |
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else: |
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auto_eval_column_dict.append([eval_dim.name, ColumnContent, ColumnContent(eval_dim.value.col_name, "number", False)]) |
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AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True) |
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@dataclass(frozen=True) |
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class EvalQueueColumn: |
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model = ColumnContent("model", "markdown", True) |
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revision = ColumnContent("revision", "str", True) |
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status = ColumnContent("status", "str", True) |
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COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden] |
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EVAL_COLS = [c.name for c in fields(EvalQueueColumn)] |
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EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)] |
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BENCHMARK_COLS = [t.value.col_name for t in EvalDimensions] |
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