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9be823f 881357e 9be823f 881357e 9be823f 881357e 9be823f 952f09b 9be823f c35b4eb 9be823f 881357e 9be823f 881357e 9be823f 881357e 9be823f 881357e 9be823f 881357e 9be823f 881357e 9be823f 881357e 9be823f 881357e 9be823f 881357e c35b4eb 9be823f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | 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:] != "__"]
@dataclass
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))
@dataclass(frozen=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)
@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]
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]
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