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 class Tasks(Enum): # XXX include me back at some point # nqopen = Task("nq8", "em", "NQ Open/EM") # triviaqa = Task("tqa8", "em", "TriviaQA/EM") # truthfulqa_mc1 = Task("truthfulqa_mc1", "acc", "TruthQA MC1/Acc") # truthfulqa_mc2 = Task("truthfulqa_mc2", "acc", "TruthQA MC2/Acc") # truthfulqa_gen = Task("truthfulqa_gen", "rougeL_acc", "TruthQA Gen/ROUGE") # xsum_r = Task("xsum_v2", "rougeL", "XSum/ROUGE") # xsum_f = Task("xsum_v2", "factKB", "XSum/factKB") # xsum_b = Task("xsum_v2", "bertscore_precision", "XSum/BERT-P") # cnndm_r = Task("cnndm_v2", "rougeL", "CNN-DM/ROUGE") # cnndm_f = Task("cnndm_v2", "factKB", "CNN-DM/factKB") # cnndm_b = Task("cnndm_v2", "bertscore_precision", "CNN-DM/BERT-P") # race = Task("race", "acc", "RACE/Acc") # squadv2 = Task("squadv2", "exact", "SQUaDv2/EM") # memotrap = Task("memo-trap_v2", "acc", "MemoTrap/Acc") # ifeval = Task("ifeval", "prompt_level_strict_acc", "IFEval/Acc") # faithdial = Task("faithdial_hallu_v2", "acc", "FaithDial/Acc") # halueval_qa = Task("halueval_qa", "acc", "HaluQA/Acc") # halueval_summ = Task("halueval_summarization", "acc", "HaluSumm/Acc") # halueval_dial = Task("halueval_dialogue", "acc", "HaluDial/Acc") # # XXX include me back at some point selfcheck = Task("selfcheckgpt", "max-selfcheckgpt", "SelfCheckGPT") mmlu = Task("mmlu", "acc", "MMLU/Acc") # 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("Avg", "number", True)]) # Inference framework auto_eval_column_dict.append(["inference_framework", ColumnContent, ColumnContent("Inference framework", "str", 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) @dataclass class ModelDetails: name: str symbol: str = "" # emoji, only for the model type class ModelType(Enum): PT = ModelDetails(name="pretrained", symbol="🟢") FT = ModelDetails(name="fine-tuned on domain-specific datasets", symbol="🔶") chat = ModelDetails(name="chat models (RLHF, DPO, IFT, ...)", symbol="💬") merges = ModelDetails(name="base merges and moerges", 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 any([k in type for k in ["instruction-tuned", "RL-tuned", "chat", "🟦", "⭕", "💬"]]): return ModelType.chat if "merge" in type or "🤝" in type: return ModelType.merges return ModelType.Unknown class InferenceFramework(Enum): # "moe-infinity", hf-chat MoE_Infinity = ModelDetails("moe-infinity") HF_Chat = ModelDetails("hf-chat") Unknown = ModelDetails("?") def to_str(self): return self.value.name @staticmethod def from_str(inference_framework: str): if inference_framework in ["moe-infinity"]: return InferenceFramework.MoE_Infinity if inference_framework in ["hf-chat"]: return InferenceFramework.HF_Chat return InferenceFramework.Unknown class WeightType(Enum): Adapter = ModelDetails("Adapter") Original = ModelDetails("Original") Delta = ModelDetails("Delta") class Precision(Enum): float32 = ModelDetails("float32") float16 = ModelDetails("float16") bfloat16 = ModelDetails("bfloat16") qt_8bit = ModelDetails("8bit") qt_4bit = ModelDetails("4bit") qt_GPTQ = ModelDetails("GPTQ") Unknown = ModelDetails("?") @staticmethod def from_str(precision: str): if precision in ["torch.float32", "float32"]: return Precision.float32 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"), # }