Clémentine
Refactor 2 - added plotting back
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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: 31.0,
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: "",
}
# Average ⬆️ human baseline is 0.897 (source: averaging human baselines below)
# ARC human baseline is 0.80 (source: https://lab42.global/arc/)
# HellaSwag human baseline is 0.95 (source: https://deepgram.com/learn/hellaswag-llm-benchmark-guide)
# MMLU human baseline is 0.898 (source: https://openreview.net/forum?id=d7KBjmI3GmQ)
# TruthfulQA human baseline is 0.94(source: https://arxiv.org/pdf/2109.07958.pdf)
# Drop: https://leaderboard.allenai.org/drop/submissions/public
# Winogrande: https://leaderboard.allenai.org/winogrande/submissions/public
# GSM8K: paper
# Define the human baselines
human_baseline_row = {
AutoEvalColumn.model.name: "<p>Human performance</p>",
AutoEvalColumn.revision.name: "N/A",
AutoEvalColumn.precision.name: None,
AutoEvalColumn.average.name: 92.75,
AutoEvalColumn.arc.name: 80.0,
AutoEvalColumn.hellaswag.name: 95.0,
AutoEvalColumn.mmlu.name: 89.8,
AutoEvalColumn.truthfulqa.name: 94.0,
AutoEvalColumn.winogrande.name: 94.0,
AutoEvalColumn.gsm8k.name: 100,
AutoEvalColumn.drop.name: 96.42,
AutoEvalColumn.dummy.name: "human_baseline",
AutoEvalColumn.model_type.name: "",
}
@dataclass
class ModelTypeDetails:
name: str
symbol: str # emoji
class ModelType(Enum):
PT = ModelTypeDetails(name="pretrained", symbol="🟢")
FT = ModelTypeDetails(name="fine-tuned", symbol="🔶")
IFT = ModelTypeDetails(name="instruction-tuned", symbol="⭕")
RL = ModelTypeDetails(name="RL-tuned", symbol="🟦")
Unknown = ModelTypeDetails(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]
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"),
}