File size: 5,140 Bytes
2a5f9fb df66f6e 2a5f9fb |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 |
from dataclasses import dataclass
from enum import Enum
import pandas as pd
# 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
dummy: bool = False
def fields(raw_class):
return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
@dataclass(frozen=True)
class AutoEvalColumn: # Auto evals column
model_type_symbol = ColumnContent("T", "str", True, never_hidden=True)
model = ColumnContent("Model", "markdown", True, never_hidden=True)
average = ColumnContent("Average ⬆️", "number", True)
arc = ColumnContent("ARC", "number", True)
hellaswag = ColumnContent("HellaSwag", "number", True)
mmlu = ColumnContent("MMLU", "number", True)
truthfulqa = ColumnContent("TruthfulQA", "number", True)
winogrande = ColumnContent("Winogrande", "number", True)
gsm8k = ColumnContent("GSM8K", "number", True)
drop = ColumnContent("DROP", "number", True)
model_type = ColumnContent("Type", "str", False)
weight_type = ColumnContent("Weight type", "str", False, True)
precision = ColumnContent("Precision", "str", False) # , True)
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)
dummy = ColumnContent(
"model_name_for_query", "str", False, dummy=True
) # dummy col to implement search bar (hidden by custom CSS)
@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)
baseline_row = {
AutoEvalColumn.model.name: "<p>Baseline</p>",
AutoEvalColumn.revision.name: "N/A",
AutoEvalColumn.precision.name: None,
AutoEvalColumn.average.name: 25.0,
AutoEvalColumn.arc.name: 25.0,
AutoEvalColumn.hellaswag.name: 25.0,
AutoEvalColumn.mmlu.name: 25.0,
AutoEvalColumn.truthfulqa.name: 25.0,
AutoEvalColumn.winogrande.name: 50.0,
AutoEvalColumn.gsm8k.name: 0.21,
AutoEvalColumn.drop.name: 0.47,
AutoEvalColumn.dummy.name: "baseline",
AutoEvalColumn.model_type.name: "",
}
@dataclass
class ModelInfo:
name: str
symbol: str # emoji
class ModelType(Enum):
PT = ModelInfo(name="pretrained", symbol="🟢")
FT = ModelInfo(name="fine-tuned", symbol="🔶")
IFT = ModelInfo(name="instruction-tuned", symbol="⭕")
RL = ModelInfo(name="RL-tuned", symbol="🟦")
Unknown = ModelInfo(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
@dataclass
class Task:
benchmark: str
metric: str
col_name: str
class Tasks(Enum):
arc = Task("arc:challenge", "acc_norm", AutoEvalColumn.arc.name)
hellaswag = Task("hellaswag", "acc_norm", AutoEvalColumn.hellaswag.name)
mmlu = Task("hendrycksTest", "acc", AutoEvalColumn.mmlu.name)
truthfulqa = Task("truthfulqa:mc", "mc2", AutoEvalColumn.truthfulqa.name)
winogrande = Task("winogrande", "acc", AutoEvalColumn.winogrande.name)
gsm8k = Task("gsm8k", "acc", AutoEvalColumn.gsm8k.name)
drop = Task("drop", "f1", AutoEvalColumn.drop.name)
# Column selection
COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]
COLS_LITE = [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
TYPES_LITE = [c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and 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 if t.value.col_name in fields(AutoEvalColumn)]
NUMERIC_INTERVALS = {
"?": pd.Interval(-1, 0, closed="right"),
"~1.5": pd.Interval(0, 2, closed="right"),
"~3": pd.Interval(2, 4, closed="right"),
"~7": pd.Interval(4, 9, closed="right"),
"~13": pd.Interval(9, 20, closed="right"),
"~35": pd.Interval(20, 45, closed="right"),
"~60": pd.Interval(45, 70, closed="right"),
"70+": pd.Interval(70, 10000, closed="right"),
}
|