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4fd373e 81fef9c 69dc570 81fef9c 4fd373e 81fef9c 4fd373e 81fef9c 4fd373e 81fef9c 4fd373e 81fef9c 69dc570 81fef9c 69dc570 81fef9c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | from dataclasses import dataclass, field, make_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:] != "__"]
# These classes are for user facing column names,
# to avoid having to change them all around the code
# when a modif is needed
@dataclass
class ColumnContent:
name: str
type: str
displayed_by_default: bool
hidden: bool = False
never_hidden: bool = False
## Leaderboard columns
auto_eval_column_dict = []
def column_field(name: str, type: str, displayed_by_default: bool, hidden: bool = False, never_hidden: bool = False):
return (
ColumnContent,
field(
default_factory=lambda: ColumnContent(
name,
type,
displayed_by_default,
hidden,
never_hidden,
)
),
)
# Init
auto_eval_column_dict.append(["model_type_symbol", *column_field("T", "str", True, never_hidden=True)])
auto_eval_column_dict.append(["model", *column_field("Model", "markdown", True, never_hidden=True)])
auto_eval_column_dict.append(["average", *column_field("Average ⬆️", "number", True)])
for task in Tasks:
auto_eval_column_dict.append([task.name, *column_field(task.value.col_name, "number", True)])
auto_eval_column_dict.append(["model_type", *column_field("Type", "str", False)])
auto_eval_column_dict.append(["architecture", *column_field("Architecture", "str", False)])
auto_eval_column_dict.append(["weight_type", *column_field("Weight type", "str", False, True)])
auto_eval_column_dict.append(["precision", *column_field("Precision", "str", False)])
auto_eval_column_dict.append(["license", *column_field("Hub License", "str", False)])
auto_eval_column_dict.append(["params", *column_field("#Params (B)", "number", False)])
auto_eval_column_dict.append(["likes", *column_field("Hub ❤️", "number", False)])
auto_eval_column_dict.append(["still_on_hub", *column_field("Available on the hub", "bool", False)])
auto_eval_column_dict.append(["revision", *column_field("Model sha", "str", False, False)])
# We use make dataclass to dynamically fill the scores from Tasks
AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
for field_name, _, field_def in auto_eval_column_dict:
setattr(AutoEvalColumn, field_name, field_def.default_factory())
## For the queue columns in the submission tab
@dataclass(frozen=True)
class EvalQueueColumn: # Queue column
model = ColumnContent("model", "markdown", True)
revision = ColumnContent("revision", "str", True)
precision = ColumnContent("precision", "str", True)
submitted_time = ColumnContent("submitted_time", "str", True)
status = ColumnContent("status", "str", True)
## All the model information that we might need
@dataclass
class ModelDetails:
name: str
display_name: str = ""
symbol: str = "" # emoji
class ModelType(Enum):
PT = ModelDetails(name="pretrained", symbol="P")
FT = ModelDetails(name="fine-tuned", symbol="F")
IFT = ModelDetails(name="instruction-tuned", symbol="I")
RL = ModelDetails(name="RL-tuned", symbol="R")
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
# Column selection
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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