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import re
import os
from typing import List
from tqdm import tqdm
from src.utils_display import AutoEvalColumn
from src.auto_leaderboard.model_metadata_type import get_model_type
from huggingface_hub import HfApi
import huggingface_hub
api = HfApi(token=os.environ.get("H4_TOKEN", None))
def get_model_infos_from_hub(leaderboard_data: List[dict]):
for model_data in tqdm(leaderboard_data):
model_name = model_data["model_name_for_query"]
try:
model_info = api.model_info(model_name)
except huggingface_hub.utils._errors.RepositoryNotFoundError:
print("Repo not found!", model_name)
model_data[AutoEvalColumn.license.name] = None
model_data[AutoEvalColumn.likes.name] = None
model_data[AutoEvalColumn.params.name] = get_model_size(model_name, None)
continue
model_data[AutoEvalColumn.license.name] = get_model_license(model_info)
model_data[AutoEvalColumn.likes.name] = get_model_likes(model_info)
model_data[AutoEvalColumn.params.name] = get_model_size(model_name, model_info)
def get_model_license(model_info):
try:
return model_info.cardData["license"]
except Exception:
return None
def get_model_likes(model_info):
return model_info.likes
size_pattern = re.compile(r"(\d\.)?\d+(b|m)")
def get_model_size(model_name, model_info):
# In billions
try:
return round(model_info.safetensors["total"] / 1e9, 3)
except AttributeError:
try:
size_match = re.search(size_pattern, model_name.lower())
size = size_match.group(0)
return round(float(size[:-1]) if size[-1] == "b" else float(size[:-1]) / 1e3, 3)
except AttributeError:
return None
def apply_metadata(leaderboard_data: List[dict]):
get_model_type(leaderboard_data)
get_model_infos_from_hub(leaderboard_data)
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