from dataclasses import dataclass, make_dataclass from enum import Enum import pandas as pd from src.display.about import Tasks def fields(raw_class): return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"] # 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 ## Leaderboard columns 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)]) """ auto_eval_column_dict.append(["eval_name", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)]) auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", True)]) auto_eval_column_dict.append(["hf_model_id", ColumnContent, ColumnContent("Model URL", "str", False)]) auto_eval_column_dict.append(["agree_cs", ColumnContent, ColumnContent("AGREE", "number", True)]) auto_eval_column_dict.append(["anli_cs", ColumnContent, ColumnContent("ANLI", "number", True)]) auto_eval_column_dict.append(["arc_challenge_cs", ColumnContent, ColumnContent("ARC-Challenge", "number", True)]) auto_eval_column_dict.append(["arc_easy_cs", ColumnContent, ColumnContent("ARC-Easy", "number", True)]) auto_eval_column_dict.append(["belebele_cs", ColumnContent, ColumnContent("Belebele", "number", True)]) auto_eval_column_dict.append(["ctkfacts_cs", ColumnContent, ColumnContent("CTKFacts", "number", True)]) auto_eval_column_dict.append(["czechnews_cs", ColumnContent, ColumnContent("Czech News", "number", True)]) auto_eval_column_dict.append(["fb_comments_cs", ColumnContent, ColumnContent("Facebook Comments", "number", True)]) auto_eval_column_dict.append(["gsm8k_cs", ColumnContent, ColumnContent("GSM8K", "number", True)]) auto_eval_column_dict.append(["klokanek_cs", ColumnContent, ColumnContent("Klokanek", "number", True)]) auto_eval_column_dict.append(["mall_reviews_cs", ColumnContent, ColumnContent("Mall Reviews", "number", True)]) auto_eval_column_dict.append(["mmlu_cs", ColumnContent, ColumnContent("MMLU", "number", True)]) auto_eval_column_dict.append(["sqad_cs", ColumnContent, ColumnContent("SQAD", "number", True)]) auto_eval_column_dict.append(["subjectivity_cs", ColumnContent, ColumnContent("Subjectivity", "number", True)]) auto_eval_column_dict.append(["truthfulqa_cs", ColumnContent, ColumnContent("TruthfulQA", "number", True)]) # We use make dataclass to dynamically fill the scores from Tasks AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True) HEADER_MAP = { "eval_name": "Model", "precision": "Precision", "hf_model_id": "Model URL", "agree_cs": "AGREE", "anli_cs": "ANLI", "arc_challenge_cs": "ARC-Challenge", "arc_easy_cs": "ARC-Easy", "belebele_cs": "Belebele", "ctkfacts_cs": "CTKFacts", "czechnews_cs": "Czech News", "fb_comments_cs": "Facebook Comments", "gsm8k_cs": "GSM8K", "klokanek_cs": "Klokanek", "mall_reviews_cs": "Mall Reviews", "mmlu_cs": "MMLU", "sqad_cs": "SQAD", "subjectivity_cs": "Subjectivity", "truthfulqa_cs": "TruthfulQA", } ## For the queue columns in the submission tab @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) ## All the model information that we might need @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): other = ModelDetails("other") float64 = ModelDetails("float64") float32 = ModelDetails("float32") 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.float64", "torch.double" ,"float64"]: return Precision.float64 if precision in ["torch.float32", "torch.float" ,"float32"]: return Precision.tfloat32 if precision in ["torch.float16", "torch.half", "float16"]: return Precision.float16 if precision in ["torch.bfloat16", "bfloat16"]: return Precision.bfloat16 if precision in ["8bit", "int8"]: return Precision.qt_8bit if precision in ["4bit", "int4"]: return Precision.qt_4bit if precision in ["GPTQ", "None"]: return Precision.qt_GPTQ return Precision.other # 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 = [HEADER_MAP[t.value.col_name] for t in Tasks] BENCHMARK_COL_IDS = [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"), }