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1596349 8e5aec2 1596349 8e5aec2 1596349 a1cf030 986e6dd a1cf030 8e5aec2 1596349 f0bd283 986e6dd f0bd283 1596349 8e5aec2 1596349 8e5aec2 1596349 8e5aec2 1596349 8e5aec2 1596349 8e5aec2 1596349 8e5aec2 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | from dataclasses import 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
class ColumnContent:
name: str
type: str
displayed_by_default: bool
hidden: bool = False
never_hidden: bool = False
BENCHMARK_DISPLAY_NAME_OVERRIDES = {
"Scientific Figure": "Sci. Fig",
}
def benchmark_display_name(name: str) -> str:
return BENCHMARK_DISPLAY_NAME_OVERRIDES.get(name, name)
def benchmark_internal_name(name: str) -> str:
for internal_name, display_name in BENCHMARK_DISPLAY_NAME_OVERRIDES.items():
if name == display_name:
return internal_name
return name
## Leaderboard columns
auto_eval_column_dict = []
# Core columns
auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "str", True, never_hidden=True)])
# Scores
auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Average", "str", True)])
auto_eval_column_dict.append(["dom_webpage", ColumnContent, ColumnContent("Webpage", "str", True)])
auto_eval_column_dict.append(["dom_poster", ColumnContent, ColumnContent("Poster", "str", True)])
auto_eval_column_dict.append(["dom_chart", ColumnContent, ColumnContent("Chart", "str", True)])
auto_eval_column_dict.append(
["dom_scientific_figure", ColumnContent, ColumnContent("Sci. Fig", "str", True)]
)
auto_eval_column_dict.append(["dim_layout", ColumnContent, ColumnContent("Layout", "str", True)])
auto_eval_column_dict.append(["dim_attribute", ColumnContent, ColumnContent("Attribute", "str", True)])
auto_eval_column_dict.append(["dim_text", ColumnContent, ColumnContent("Text", "str", True)])
auto_eval_column_dict.append(["dim_knowledge", ColumnContent, ColumnContent("Knowledge", "str", True)])
auto_eval_column_dict.append(["dom_slides", ColumnContent, ColumnContent("Slides", "str", True)])
# Build a dynamic class instead of dataclass defaults to keep compatibility
# with newer Python versions that reject mutable dataclass defaults.
AutoEvalColumn = type("AutoEvalColumn", (), {name: value for name, _, value in auto_eval_column_dict})
## 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)
private = ColumnContent("private", "bool", True)
precision = ColumnContent("precision", "str", True)
weight_type = ColumnContent("weight_type", "str", "Original")
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="🟢")
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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