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from dataclasses import dataclass, make_dataclass
from enum import Enum
import pandas as pd
from src.about import EvalDimensions
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(["rank", ColumnContent, ColumnContent("Rank", "str", True, False)])
auto_eval_column_dict.append(["model_source", ColumnContent, ColumnContent("Source", "str", True, False)])
auto_eval_column_dict.append(["model_category", ColumnContent, ColumnContent("Size", "str", True, False)])
#auto_eval_column_dict.append(["model_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)])
auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model Name", "markdown", True, never_hidden=True)])
#Scores
auto_eval_column_dict.append(["average_score", ColumnContent, ColumnContent("Benchmark Score (0-10)", "number", True)])
for eval_dim in EvalDimensions:
if eval_dim.value.metric in ["speed", "contamination_score"]:
auto_eval_column_dict.append([eval_dim.name, ColumnContent, ColumnContent(eval_dim.value.col_name, "number", True)])
else:
auto_eval_column_dict.append([eval_dim.name, ColumnContent, ColumnContent(eval_dim.value.col_name, "number", False)])
# Model information
#auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False)])
#auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
#auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
#auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)])
#auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("License", "str", False)])
#auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
#auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Popularity (Likes)", "number", False)])
#auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
#auto_eval_column_dict.append(["revision", ColumnContent, 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, frozen=True)
## 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 EvalDimensions]
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