alozowski's picture
alozowski HF staff
maintainers-highlight-submission (#877)
49a5f27 verified
raw
history blame
10.8 kB
from dataclasses import dataclass, make_dataclass
from datasets import load_dataset
from enum import Enum
import json
import logging
from datetime import datetime
import pandas as pd
from src.envs import MAINTAINERS_HIGHLIGHT_REPO
# Configure logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
dataset = load_dataset(MAINTAINERS_HIGHLIGHT_REPO)
curated_authors = dataset["train"][0]["CURATED_SET"]
# Convert ISO 8601 dates to datetime objects for comparison
def parse_iso8601_datetime(date_str):
if date_str.endswith('Z'):
date_str = date_str[:-1] + '+00:00'
return datetime.fromisoformat(date_str)
def parse_datetime(datetime_str):
formats = [
"%Y-%m-%dT%H-%M-%S.%f", # Format with dashes
"%Y-%m-%dT%H:%M:%S.%f", # Standard format with colons
"%Y-%m-%dT%H %M %S.%f", # Spaces as separator
]
for fmt in formats:
try:
return datetime.strptime(datetime_str, fmt)
except ValueError:
continue
# in rare cases set unix start time for files with incorrect time (legacy files)
logging.error(f"No valid date format found for: {datetime_str}")
return datetime(1970, 1, 1)
def load_json_data(file_path):
"""Safely load JSON data from a file."""
try:
with open(file_path, "r") as file:
return json.load(file)
except json.JSONDecodeError:
print(f"Error reading JSON from {file_path}")
return None # Or raise an exception
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):
ifeval = Task("leaderboard_ifeval", "strict_acc,none", "IFEval")
ifeval_raw = Task("leaderboard_ifeval", "strict_acc,none", "IFEval Raw")
bbh = Task("leaderboard_bbh", "acc_norm,none", "BBH")
bbh_raw = Task("leaderboard_bbh", "acc_norm,none", "BBH Raw")
math = Task("leaderboard_math_hard", "exact_match,none", "MATH Lvl 5")
math_raw = Task("leaderboard_math_hard", "exact_match,none", "MATH Lvl 5 Raw")
gpqa = Task("leaderboard_gpqa", "acc_norm,none", "GPQA")
gpqa_raw = Task("leaderboard_gpqa", "acc_norm,none", "GPQA Raw")
musr = Task("leaderboard_musr", "acc_norm,none", "MUSR")
musr_raw = Task("leaderboard_musr", "acc_norm,none", "MUSR Raw")
mmlu_pro = Task("leaderboard_mmlu_pro", "acc,none", "MMLU-PRO")
mmlu_pro_raw = Task("leaderboard_mmlu_pro", "acc,none", "MMLU-PRO Raw")
# These classes are for user facing column names,
# to avoid having to change them all around the code
# when a modif is needed
@dataclass(frozen=True)
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:
displayed_by_default = not task.name.endswith("_raw")
auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", displayed_by_default=displayed_by_default)])
# 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("Not_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, hidden=True)]
)
auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
auto_eval_column_dict.append(["not_flagged", ColumnContent, ColumnContent("Flagged", "bool", False, hidden=True)])
auto_eval_column_dict.append(["moe", ColumnContent, ColumnContent("MoE", "bool", False, hidden=True)])
auto_eval_column_dict.append(["submission_date", ColumnContent, ColumnContent("Submission Date", "bool", False, hidden=False)])
auto_eval_column_dict.append(["upload_to_hub", ColumnContent, ColumnContent("Upload To Hub Date", "bool", False, hidden=False)])
auto_eval_column_dict.append(["use_chat_template", ColumnContent, ColumnContent("Chat Template", "bool", False)])
auto_eval_column_dict.append(["maintainers_highlight", ColumnContent, ColumnContent("Maintainer's Highlight", "bool", False, hidden=True)])
# fullname structure: <user>/<model_name>
auto_eval_column_dict.append(["fullname", ColumnContent, ColumnContent("fullname", "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_link = ColumnContent("model_link", "markdown", True)
model_name = ColumnContent("model_name", "str", True)
revision = ColumnContent("revision", "str", True)
#private = ColumnContent("private", "bool", True) # Should not be displayed
precision = ColumnContent("precision", "str", True)
#weight_type = ColumnContent("weight_type", "str", "Original") # Might be confusing, to think about
status = ColumnContent("status", "str", True)
# baseline_row = {
# AutoEvalColumn.model.name: "<p>Baseline</p>",
# AutoEvalColumn.revision.name: "N/A",
# AutoEvalColumn.precision.name: None,
# AutoEvalColumn.merged.name: False,
# AutoEvalColumn.average.name: 31.0,
# AutoEvalColumn.arc.name: 25.0,
# AutoEvalColumn.hellaswag.name: 25.0,
# AutoEvalColumn.mmlu.name: 25.0,
# AutoEvalColumn.truthfulqa.name: 25.0,
# AutoEvalColumn.winogrande.name: 50.0,
# AutoEvalColumn.gsm8k.name: 0.21,
# AutoEvalColumn.fullname.name: "baseline",
# AutoEvalColumn.model_type.name: "",
# AutoEvalColumn.not_flagged.name: False,
# }
# Average ⬆️ human baseline is 0.897 (source: averaging human baselines below)
# ARC human baseline is 0.80 (source: https://lab42.global/arc/)
# HellaSwag human baseline is 0.95 (source: https://deepgram.com/learn/hellaswag-llm-benchmark-guide)
# MMLU human baseline is 0.898 (source: https://openreview.net/forum?id=d7KBjmI3GmQ)
# TruthfulQA human baseline is 0.94(source: https://arxiv.org/pdf/2109.07958.pdf)
# Winogrande: https://leaderboard.allenai.org/winogrande/submissions/public
# GSM8K: paper
# Define the human baselines
# human_baseline_row = {
# AutoEvalColumn.model.name: "<p>Human performance</p>",
# AutoEvalColumn.revision.name: "N/A",
# AutoEvalColumn.precision.name: None,
# AutoEvalColumn.average.name: 92.75,
# AutoEvalColumn.merged.name: False,
# AutoEvalColumn.arc.name: 80.0,
# AutoEvalColumn.hellaswag.name: 95.0,
# AutoEvalColumn.mmlu.name: 89.8,
# AutoEvalColumn.truthfulqa.name: 94.0,
# AutoEvalColumn.winogrande.name: 94.0,
# AutoEvalColumn.gsm8k.name: 100,
# AutoEvalColumn.fullname.name: "human_baseline",
# AutoEvalColumn.model_type.name: "",
# AutoEvalColumn.not_flagged.name: False,
# }
@dataclass
class ModelDetails:
name: str
symbol: str = "" # emoji, only for the model type
class ModelType(Enum):
PT = ModelDetails(name="🟒 pretrained", symbol="🟒")
CPT = ModelDetails(name="🟩 continuously pretrained", symbol="🟩")
FT = ModelDetails(name="πŸ”Ά fine-tuned on domain-specific datasets", symbol="πŸ”Ά")
chat = ModelDetails(name="πŸ’¬ chat models (RLHF, DPO, IFT, ...)", symbol="πŸ’¬")
merges = ModelDetails(name="🀝 base merges and moerges", symbol="🀝")
Unknown = ModelDetails(name="❓ other", symbol="❓")
def to_str(self, separator=" "):
return f"{self.value.symbol}{separator}{self.value.name}"
@staticmethod
def from_str(m_type):
if any([k for k in m_type if k in ["fine-tuned","πŸ”Ά", "finetuned"]]):
return ModelType.FT
if "continuously pretrained" in m_type or "🟩" in m_type:
return ModelType.CPT
if "pretrained" in m_type or "🟒" in m_type:
return ModelType.PT
if any([k in m_type for k in ["instruction-tuned", "RL-tuned", "chat", "🟦", "β­•", "πŸ’¬"]]):
return ModelType.chat
if "merge" in m_type or "🀝" in m_type:
return ModelType.merges
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("?")
@staticmethod
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)]
TYPES = [c.type for c in fields(AutoEvalColumn)]
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"),
}