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e148f6b f45905d e148f6b 7d3bb98 77ec02d e148f6b 0e8f5d6 e148f6b f45905d e148f6b 0e8f5d6 e148f6b 0e8f5d6 e148f6b 4ae29ac e148f6b | 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 | from dataclasses import dataclass, make_dataclass
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:] != "__"]
# These classes are for user facing column names,
# to avoid having to change them all around the code
# when a modif is needed
@dataclass(frozen=True)
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", ColumnContent, ColumnContent("Type", "str", True)])
auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
#Scores
for task in Tasks:
auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
# Model information
auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False, hidden=True)])
# We use make dataclass to dynamically fill the scores from Tasks
AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
## All the model information that we might need
@dataclass(frozen=True)
class ModelDetails:
name: str
display_name: str = ""
symbol: str = "" # emoji
class ModelType(Enum):
Frontier = ModelDetails(name="Frontier", symbol="🚀")
OpenSource = ModelDetails(name="Open Source", symbol="🟢")
Specialized = ModelDetails(name="RTL Specialized", 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 "Frontier" in type or "🚀" in type:
return ModelType.Frontier
if "Open Source" in type or "🟢" in type:
return ModelType.OpenSource
if "Specialized" in type or "🔶" in type:
return ModelType.Specialized
return ModelType.Unknown
# Column selection
COLS = [c.name for c in fields(AutoEvalColumn)]
BENCHMARK_COLS = [t.value.col_name for t in Tasks]
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