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: "

Baseline

", 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: "

Human performance

", 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"), }