I am trying to train a few shot text classifier using SetFit and Optuna. When I run my code, I get the error TypeError: ‘numpy.bool_’ object is not iterable when working with SetFit and Optuna.
I don’t understand where the error comes from, as I am not using numpy at all.

Here’s my code:

transformer_model = 'sentence-transformers/all-mpnet-base-v2' 

def make_model(params = None):
    multi_target_strategy = params['multi_target_strategy'] if params else 'one-vs-rest'
    return SetFitModel.from_pretrained(transformer_model, multi_target_strategy = multi_target_strategy)

args = TrainingArguments(
    loss = CosineSimilarityLoss,
    batch_size=32,
    num_epochs=2,
    num_iterations = 5,
    end_to_end=True
)

model_training = Trainer( # issues with Trainer 
    model_init = make_model,
    args = args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    metric='accuracy',
    #sampling_strategy = 'undersampling', 
    column_mapping={'text': 'text', 'label': 'label'}
)

def hyperparameter_search_space(trial: Trial):
    learning_rate = trial.suggest_float('learning_rate', 1e-5, 1e-3, log=True)
    batch_size = trial.suggest_int('batch_size', 4, 32)
    multi_target_strategy = trial.suggest_categorical('multi_target_strategy', ['one-vs-rest', 'classifier-chain']) #'multi_output',

    print(f"Hyperparameters: learning_rate={learning_rate}, batch_size={batch_size}, multi_target_strategy={multi_target_strategy}")
    
    return {
        'learning_rate': learning_rate,
        'batch_size': batch_size,
        'multi_target_strategy': multi_target_strategy
    }

optimised_model = model_training.hyperparameter_search(hyperparameter_search_space, n_trials= 3)`