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| import re | |
| import os | |
| from typing import List | |
| 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 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) | |