| from dataclasses import dataclass, make_dataclass |
| from enum import Enum |
|
|
| try: |
| from src.about import Tasks |
| except ImportError: |
| from about import Tasks |
|
|
|
|
| @dataclass(frozen=True) |
| class ColumnContent: |
| name: str |
| type: str |
| displayed_by_default: bool |
| hidden: bool = False |
| never_hidden: bool = False |
|
|
|
|
| def make_auto_eval_column_dict(): |
| cols = [] |
| |
| cols.append(["model_type_symbol", ColumnContent, ColumnContent("T", "markdown", True, never_hidden=True)]) |
| cols.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)]) |
|
|
| for task in Tasks: |
| cols.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)]) |
|
|
| cols.append(["sample_adequate", ColumnContent, ColumnContent("Calib. Sample OK", "str", True)]) |
| cols.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)]) |
| cols.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)]) |
| cols.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", True)]) |
| cols.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)]) |
| cols.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False)]) |
| return cols |
|
|
|
|
| auto_eval_column_dict = make_auto_eval_column_dict() |
| AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True) |
|
|
|
|
| def fields(raw_class=None): |
| return [col[2] for col in auto_eval_column_dict] |
|
|
|
|
| @dataclass |
| 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}" |
|
|
| @staticmethod |
| def from_str(type_str): |
| if not type_str: |
| return ModelType.Unknown |
| if "fine-tuned" in type_str or "🔶" in type_str: |
| return ModelType.FT |
| if "pretrained" in type_str or "🟢" in type_str: |
| return ModelType.PT |
| if "RL-tuned" in type_str or "🟦" in type_str: |
| return ModelType.RL |
| if "instruction-tuned" in type_str or "⭕" in type_str: |
| return ModelType.IFT |
| return ModelType.Unknown |
|
|
|
|
| class Precision(Enum): |
| bfloat16 = ModelDetails("bfloat16") |
| float16 = ModelDetails("float16") |
| float32 = ModelDetails("float32") |
| Unknown = ModelDetails("?") |
|
|
| @staticmethod |
| def from_str(prec): |
| if not prec: |
| return Precision.Unknown |
| prec_str = str(prec).lower() |
| if "bfloat16" in prec_str: |
| return Precision.bfloat16 |
| if "float16" in prec_str: |
| return Precision.float16 |
| if "float32" in prec_str: |
| return Precision.float32 |
| return Precision.Unknown |
|
|
|
|
| COLS = [c.name for c in fields()] |
| EVAL_COLS = [c.name for c in fields()] |
| EVAL_TYPES = [c.type for c in fields()] |
| BENCHMARK_COLS = [t.value.col_name for t in Tasks] |
