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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 | |
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:] != "__"] | |
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) | |
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: "", | |
} | |
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}" | |
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 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"), | |
} | |