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d8ffffa 1fb5aba 4e0c92e 1fb5aba 4e0c92e 1fb5aba d8ffffa 4e0c92e d8ffffa 1fb5aba d8ffffa 1fb5aba d8ffffa 1fb5aba 527f68e 1fb5aba 4e0c92e 1fb5aba 527f68e 1fb5aba d8ffffa 1fb5aba 4e0c92e 1fb5aba 4e0c92e 1fb5aba 4e0c92e 1fb5aba 4e0c92e 1fb5aba 4e0c92e 1fb5aba | 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 | 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 = []
# Init
auto_eval_column_dict.append(["model_type_symbol", ColumnContent, field(default_factory=lambda: ColumnContent("T", "str", True, never_hidden=True))])
auto_eval_column_dict.append(["model", ColumnContent, field(default_factory=lambda: ColumnContent("Model", "markdown", True, never_hidden=True))])
# Scores
auto_eval_column_dict.append(["average", ColumnContent, field(default_factory=lambda: ColumnContent("Average ⬆️", "number", True))])
for task in Tasks:
auto_eval_column_dict.append([task.name, ColumnContent, field(default_factory=lambda t=task: ColumnContent(t.value.col_name, "number", True))])
# Model information
auto_eval_column_dict.append(["model_type", ColumnContent, field(default_factory=lambda: ColumnContent("Type", "str", False))])
auto_eval_column_dict.append(["architecture", ColumnContent, field(default_factory=lambda: ColumnContent("Architecture", "str", False))])
auto_eval_column_dict.append(["weight_type", ColumnContent, field(default_factory=lambda: ColumnContent("Weight type", "str", False, True))])
auto_eval_column_dict.append(["precision", ColumnContent, field(default_factory=lambda: ColumnContent("Precision", "str", False))])
auto_eval_column_dict.append(["license", ColumnContent, field(default_factory=lambda: ColumnContent("Hub License", "str", False))])
auto_eval_column_dict.append(["params", ColumnContent, field(default_factory=lambda: ColumnContent("#Params (B)", "number", False))])
auto_eval_column_dict.append(["likes", ColumnContent, field(default_factory=lambda: ColumnContent("Hub ❤️", "number", False))])
auto_eval_column_dict.append(["still_on_hub", ColumnContent, field(default_factory=lambda: ColumnContent("Available on the hub", "bool", False))])
auto_eval_column_dict.append(["revision", ColumnContent, field(default_factory=lambda: ColumnContent("Model sha", "str", False, False))])
# We use make dataclass to dynamically fill the scores from Tasks
AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict)
## For the queue columns in the submission tab
@dataclass
class EvalQueueColumn: # Queue column
model: ColumnContent = field(default_factory=lambda: ColumnContent("model", "markdown", True))
revision: ColumnContent = field(default_factory=lambda: ColumnContent("revision", "str", True))
private: ColumnContent = field(default_factory=lambda: ColumnContent("private", "bool", True))
precision: ColumnContent = field(default_factory=lambda: ColumnContent("precision", "str", True))
weight_type: ColumnContent = field(default_factory=lambda: ColumnContent("weight_type", "str", "Original"))
status: ColumnContent = field(default_factory=lambda: 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="🟢")
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
# 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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