llm-trustworthy-leaderboard

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llm-trustworthy-leaderboard / src /display /utils.py

Clémentine

Simplified leaderboard v0

9833cdb 7 months ago

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6.25 kB

	from dataclasses import dataclass, make_dataclass
	from enum import Enum

	import pandas as pd

	def fields(raw_class):
	return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]


	@dataclass
	class Task:
	benchmark: str
	metric: str
	col_name: str

	# Init: to update with your specific keys
	class Tasks(Enum):
	task0 = Task("Key in the harness", "metric in the harness", "display name")
	task1 = Task("Key in the harness", "metric in the harness", "display name")

	# 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

	auto_eval_column_dict = []
	# Init
	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", "markdown", True, never_hidden=True)])
	#Scores
	auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Average ⬆️", "number", True)])
	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(["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("Hub License", "str", False)])
	auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
	auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "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)])
	# Dummy column for the search bar (hidden by the custom CSS)
	auto_eval_column_dict.append(["dummy", ColumnContent, ColumnContent("model_name_for_query", "str", False, dummy=True)])

	# We use make dataclass to dynamically fill the scores from Tasks
	AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)

	@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: 0,
	AutoEvalColumn.dummy.name: "baseline",
	AutoEvalColumn.model_type.name: "",
	}

	human_baseline_row = {
	AutoEvalColumn.model.name: "<p>Human performance</p>",
	AutoEvalColumn.revision.name: "N/A",
	AutoEvalColumn.precision.name: None,
	AutoEvalColumn.average.name: 0,
	AutoEvalColumn.dummy.name: "human_baseline",
	AutoEvalColumn.model_type.name: "",
	}

	@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")
	qt_8bit = ModelDetails("8bit")
	qt_4bit = ModelDetails("4bit")
	qt_GPTQ = ModelDetails("GPTQ")
	Unknown = ModelDetails("?")

	def from_str(precision):
	if precision in ["torch.float16", "float16"]:
	return Precision.float16
	if precision in ["torch.bfloat16", "bfloat16"]:
	return Precision.bfloat16
	if precision in ["8bit"]:
	return Precision.qt_8bit
	if precision in ["4bit"]:
	return Precision.qt_4bit
	if precision in ["GPTQ", "None"]:
	return Precision.qt_GPTQ
	return Precision.Unknown

	# 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]

	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"),
	}