| from dataclasses import dataclass, make_dataclass |
| from dataclasses import dataclass, field |
| from enum import Enum |
|
|
| import pandas as pd |
|
|
| 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:] != "__"] |
|
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|
|
| |
| |
| |
| @dataclass |
| class ColumnContent: |
| name: str |
| type: str |
| displayed_by_default: bool |
| hidden: bool = False |
| never_hidden: bool = False |
| emoji: str = '' |
| color: str = '' |
|
|
| |
| attrs = {} |
|
|
| |
| attrs['model_type_symbol'] = ColumnContent( |
| name="", |
| type="str", |
| displayed_by_default=True, |
| emoji='🤖', |
| color='blue' |
| ) |
|
|
| attrs['model'] = ColumnContent("Model", "markdown", True, never_hidden=True) |
|
|
| |
| attrs['average'] = ColumnContent("Average ⬆️", "number", True) |
|
|
| for task in Tasks: |
| attrs[task.name] = ColumnContent(task.value.col_name, "number", True) |
|
|
| |
| attrs['model_type'] = ColumnContent("Type", "str", False) |
| attrs['architecture'] = ColumnContent("Architecture", "str", False) |
| attrs['weight_type'] = ColumnContent("Weight type", "str", False, True) |
| attrs['precision'] = ColumnContent("Precision", "str", False) |
| attrs['license'] = ColumnContent("Hub License", "str", False) |
| attrs['params'] = ColumnContent("#Params (B)", "number", False) |
| attrs['likes'] = ColumnContent("Hub ❤️", "number", False) |
| attrs['still_on_hub'] = ColumnContent("Available on the hub", "bool", False) |
| attrs['revision'] = ColumnContent("Model sha", "str", False, False) |
|
|
| |
| AutoEvalColumn = type('AutoEvalColumn', (), attrs) |
|
|
| |
| @dataclass(frozen=True) |
| class EvalQueueColumn: |
| model = ColumnContent("model", "markdown", 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) |
|
|
| |
| @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): |
| 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] |
|
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|
