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from dataclasses import dataclass, make_dataclass | |
from enum import Enum | |
import pandas as pd | |
from src.display.about import Tasks, Languages | |
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 | |
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", False, True ,never_hidden=False)]) | |
auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)]) | |
#Scores | |
auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("πͺπΊ Average β¬οΈ", "number", True)]) | |
for lang in Languages: | |
auto_eval_column_dict.append([lang.name, ColumnContent, ColumnContent(lang.value.col_name, "number", True)]) | |
for task in Tasks: | |
auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", False)]) | |
# Model information | |
auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False, never_hidden=False, dummy=True)]) | |
auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False, never_hidden=False, dummy=True)]) | |
auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, never_hidden=False, dummy=True)]) | |
auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False, never_hidden=False, dummy=True)]) | |
auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False, never_hidden=False, dummy=True)]) | |
auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False, never_hidden=False, dummy=True)]) | |
auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub β€οΈ", "number", False, never_hidden=False, dummy=True)]) | |
auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False, never_hidden=False, dummy=True)]) | |
auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False, never_hidden=False, dummy=True)]) | |
# 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) | |
## For the queue columns in the submission tab | |
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 | |
class ModelDetails: | |
name: str | |
display_name: str = "" | |
symbol: str = "" # emoji | |
class ModelType(Enum): | |
PT = ModelDetails(name="pretrained", symbol="π’") | |
FT = ModelDetails(name="fine-tuned", symbol="πΆ") | |
IFT = ModelDetails(name="instruction-tuned", symbol="β") | |
RL = ModelDetails(name="RL-tuned", symbol="π¦") | |
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 "πΆ" in type: | |
return ModelType.FT | |
if "pretrained" in type or "π’" in type: | |
return ModelType.PT | |
if "RL-tuned" in type or "π¦" in type: | |
return ModelType.RL | |
if "instruction-tuned" in type or "β" 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 = { | |
"?": 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"), | |
} | |