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| from dataclasses import dataclass | |
| from enum import Enum | |
| from src.about import Tasks | |
| def fields(raw_class): | |
| return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"] | |
| class ColumnContent: | |
| name: str | |
| type: str | |
| displayed_by_default: bool | |
| hidden: bool = False | |
| never_hidden: bool = False | |
| ## Leaderboard columns | |
| class AutoEvalColumn: | |
| model_type_symbol = ColumnContent("T", "str", False, never_hidden=True) | |
| model = ColumnContent("Model", "str", True, never_hidden=True) | |
| team = ColumnContent("Team", "str", True, never_hidden=True) | |
| average = ColumnContent("Total Score ⬆️", "number", True) | |
| model_type = ColumnContent("Type", "str", False) | |
| architecture = ColumnContent("Architecture", "str", False) | |
| weight_type = ColumnContent("Weight type", "str", False, True) | |
| precision = ColumnContent("Precision", "str", False) | |
| license = ColumnContent("Hub License", "str", False) | |
| params = ColumnContent("#Params (B)", "number", False) | |
| likes = ColumnContent("Hub ❤️", "number", False) | |
| still_on_hub = ColumnContent("Available on the hub", "bool", False) | |
| revision = ColumnContent("Model sha", "str", False, False) | |
| for task in Tasks: | |
| setattr(AutoEvalColumn, task.name, ColumnContent(task.value.col_name, "number", True)) | |
| class EvalQueueColumn: | |
| model = ColumnContent("model", "markdown", True) | |
| team = ColumnContent("team", "str", True) | |
| revision = ColumnContent("revision", "str", True) | |
| private = ColumnContent("private", "bool", True) | |
| precision = ColumnContent("precision", "str", True) | |
| weight_type = ColumnContent("weight_type", "str", "Original") | |
| status = ColumnContent("status", "str", True) | |
| class ModelDetails: | |
| name: str | |
| display_name: str = "" | |
| symbol: str = "" | |
| class ModelType(Enum): | |
| PT = ModelDetails(name="pretrained", symbol="🟢") | |
| FT = ModelDetails(name="fine-tuned", symbol="🔶") | |
| IFT = ModelDetails(name="instruction-tuned", symbol="⭕") | |
| RL = ModelDetails(name="RL-tuned", symbol="🟦") | |
| Unknown = ModelDetails(name="", symbol="?") | |
| def to_str(self, separator=" "): | |
| return f"{self.value.symbol}{separator}{self.value.name}" | |
| def from_str(type): | |
| if "fine-tuned" in type or "🔶" in type: | |
| return ModelType.FT | |
| if "pretrained" in type or "🟢" in type: | |
| return ModelType.PT | |
| if "RL-tuned" in type or "🟦" in type: | |
| return ModelType.RL | |
| if "instruction-tuned" in type or "⭕" in type: | |
| return ModelType.IFT | |
| return ModelType.Unknown | |
| class WeightType(Enum): | |
| Adapter = ModelDetails("Adapter") | |
| Original = ModelDetails("Original") | |
| Delta = ModelDetails("Delta") | |
| class Precision(Enum): | |
| float16 = ModelDetails("float16") | |
| bfloat16 = ModelDetails("bfloat16") | |
| Unknown = ModelDetails("?") | |
| def from_str(precision): | |
| if precision in ["torch.float16", "float16"]: | |
| return Precision.float16 | |
| if precision in ["torch.bfloat16", "bfloat16"]: | |
| return Precision.bfloat16 | |
| return Precision.Unknown | |
| COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden] | |
| EVAL_COLS = [c.name for c in fields(EvalQueueColumn)] | |
| EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)] | |
| BENCHMARK_COLS = [t.value.col_name for t in Tasks] | |
| DATASET_LEADERBOARD_COLS = ["model", "team"] | |
| def get_dataset_metric_cols(task) -> list[str]: | |
| """Column names for per-dataset metric display.""" | |
| return [m.col_name for m in task.value.metrics] | |