def METHOD_NAME():
    model_dump_path = "./hf_text_test"
    base_model_name = "nlpaueb/legal-bert-small-uncased"
    dataset = ALL_DATASETS["ae"]
    predictor_1 = MultiModalPredictor(
        label=dataset.label_columns[0], problem_type=dataset.problem_type, eval_metric=dataset.metric
    )
    hyperparameters = {
        "optimization.max_epochs": 1,
        "model.hf_text.checkpoint_name": base_model_name,
    }
    predictor_1.fit(
        train_data=dataset.train_df,
        hyperparameters=hyperparameters,
        time_limit=5,
        seed=42,
    )
    predictor_1.dump_model(save_path=model_dump_path)
    model = transformers.AutoModel.from_pretrained(f"{model_dump_path}/hf_text")
    assert isinstance(model, transformers.models.bert.modeling_bert.BertModel)
    predictor_2 = MultiModalPredictor(
        label=dataset.label_columns[0], problem_type=dataset.problem_type, eval_metric=dataset.metric
    )
    hyperparameters = {
        "optimization.max_epochs": 1,
        "model.hf_text.checkpoint_name": f"{model_dump_path}/hf_text",
    }
    predictor_2.fit(
        train_data=dataset.train_df,
        hyperparameters=hyperparameters,
        time_limit=5,
        seed=42,
    )