Problem type if statement seems to be wrong in text_classification.py?
I was reading the source code in transformers\pipelines\text_classification.pygithub.comhuggingface/transformers/blob/main/src/transformers/pipelines/text_classification.pyimport warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING


def sigmoid(_outputs):
    return 1.0 / (1.0 + np.exp(-_outputs))This file has been truncated.show originalLine  175-178if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                function_to_apply = ClassificationFunction.SIGMOID
            elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                function_to_apply = ClassificationFunction.SOFTMAXAltough when I was debugging the code, it seems problem_type is empty and it is the num_lables to decide which function to apply, but should it be SIGMOID for single label classification and  SOFTMAX for multi-label?