from dataclasses import dataclass, make_dataclass from enum import Enum import pandas as pd from src.about import Tasks from src.about import ClinicalTypes 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 dataset_task_col: bool = False clinical_type_col: 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, False, dataset_task_col=True)]) for task in ClinicalTypes: auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True, False, clinical_type_col=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(["backbone", ColumnContent, ColumnContent("Base Model", "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, True)]) 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, True)]) auto_eval_column_dict.append( ["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False, True)] ) auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, True)]) # We use make dataclass to dynamically fill the scores from Tasks AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True) ## 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) architecture = ColumnContent("model_architecture", "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): ZEROSHOT = ModelDetails(name="zero-shot", symbol="⚫") FINETUNED = ModelDetails(name="fine-tuned", symbol="⚪") # 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 "zero-shot" in type or "⚫" in type: return ModelType.ZEROSHOT if "fine-tuned" in type or "⚪" in type: return ModelType.FINETUNED # 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 ModelArch(Enum): Encoder = ModelDetails("Encoder") Decoder = ModelDetails("Decoder") GLiNEREncoder = ModelDetails("GLiNER Encoder") Unknown = ModelDetails(name="Other", symbol="?") def to_str(self, separator=" "): return f"{self.value.name}" @staticmethod def from_str(type): if "Encoder" == type: return ModelArch.Encoder if "Decoder" == type: return ModelArch.Decoder if "GLiNER Encoder" == type: return ModelArch.GLiNEREncoder # if "unknown" in type: # return ModelArch.Unknown return ModelArch.Unknown class WeightType(Enum): Adapter = ModelDetails("Adapter") Original = ModelDetails("Original") Delta = ModelDetails("Delta") class Precision(Enum): float16 = ModelDetails("float16") bfloat16 = ModelDetails("bfloat16") float32 = ModelDetails("float32") # 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 ["float32"]: return Precision.float32 # 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 class PromptTemplateName(Enum): UniversalNERTemplate = "universal_ner" LLMHTMLHighlightedSpansTemplate = "llm_html_highlighted_spans" LLMHTMLHighlightedSpansTemplateV1 = "llm_html_highlighted_spans_v1" LLamaNERTemplate = "llama_70B_ner" # MixtralNERTemplate = "mixtral_ner_v0.3" class EvaluationMetrics(Enum): SpanBased = "Span Based" TokenBased = "Token Based" # Column selection DATASET_COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden and not c.clinical_type_col] Clinical_TYPES_COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden and not c.dataset_task_col] 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)] DATASET_BENCHMARK_COLS = [t.value.col_name for t in Tasks] TYPES_BENCHMARK_COLS = [t.value.col_name for t in ClinicalTypes] 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"), }