from dataclasses import dataclass import streamlit as st from huggingface_hub import DatasetFilter, HfApi from huggingface_hub.hf_api import DatasetInfo @dataclass(frozen=True, eq=True) class EvaluationInfo: task: str model: str dataset_name: str dataset_config: str dataset_split: str def compute_evaluation_id(dataset_info: DatasetInfo) -> int: if dataset_info.cardData is not None: metadata = dataset_info.cardData["eval_info"] metadata.pop("col_mapping", None) evaluation_info = EvaluationInfo(**metadata) return hash(evaluation_info) else: return None def get_evaluation_ids(): filt = DatasetFilter(author="autoevaluate") evaluation_datasets = HfApi().list_datasets(filter=filt, full=True) return [compute_evaluation_id(dset) for dset in evaluation_datasets] def filter_evaluated_models(models, task, dataset_name, dataset_config, dataset_split): evaluation_ids = get_evaluation_ids() for idx, model in enumerate(models): evaluation_info = EvaluationInfo( task=task, model=model, dataset_name=dataset_name, dataset_config=dataset_config, dataset_split=dataset_split, ) candidate_id = hash(evaluation_info) if candidate_id in evaluation_ids: st.info(f"Model {model} has already been evaluated on this configuration. Skipping evaluation...") models.pop(idx) return models