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| 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:] != "__"] | |
| class Task: | |
| benchmark: str | |
| metric: str | |
| col_name: str | |
| class Tasks(Enum): | |
| arc = Task("ko_arc_challenge", "acc_norm", "Ko-ARC") | |
| hellaswag = Task("ko_hellaswag", "acc_norm", "Ko-HellaSwag") | |
| mmlu = Task("ko_mmlu", "acc", "Ko-MMLU") | |
| truthfulqa = Task("ko_truthfulqa_mc", "mc2", "Ko-TruthfulQA") | |
| winogrande = Task("ko_winogrande", "acc_norm", "Ko-Winogrande") | |
| gsm8k = Task("ko_gsm8k", "acc_norm", "Ko-GSM8k") | |
| commongen_v2 = Task("ko_commongen_v2", "acc_norm", "Ko-CommonGen V2") | |
| # 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 | |
| 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(["merged", ColumnContent, ColumnContent("Merged", "bool", 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)]) | |
| auto_eval_column_dict.append(["flagged", ColumnContent, ColumnContent("Flagged", "bool", 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) | |
| 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) | |
| # Define the human baselines | |
| human_baseline_row = { | |
| AutoEvalColumn.model.name: "<p>Human performance</p>", | |
| } | |
| 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", 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}" | |
| 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): | |
| 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.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 = { | |
| "Unknown": pd.Interval(-1, 0, closed="right"), | |
| "0~3B": pd.Interval(0, 3, closed="right"), | |
| "3~7B": pd.Interval(3, 7.3, closed="right"), | |
| "7~13B": pd.Interval(7.3, 13, closed="right"), | |
| "13~35B": pd.Interval(13, 35, closed="right"), | |
| "35~60B": pd.Interval(35, 60, closed="right"), | |
| "60B+": pd.Interval(60, 10000, closed="right"), | |
| } | |