class_docstr: A classifier that operates through Sentence Transformer Finetuning (SetFit, [TRE+22]_).
  
  This class is a wrapper which encapsulates the
  `Hugging Face SetFit implementation <https://github.com/huggingface/setfit>_` .
  
  .. note ::
     This strategy requires the optional dependency `setfit`.
  
  .. versionadded:: 1.2.0
code: def _predict_proba(self, dataset):
      proba = np.empty((0, self.num_classes), dtype=float)
  
      num_batches = int(np.ceil(len(dataset) / self.mini_batch_size))
      for batch in np.array_split(dataset.x, num_batches, axis=0):
          proba_tmp = np.zeros(
              (batch.shape[0], self.num_classes), dtype=float
          )
          proba_tmp[
              :, self.model.model_head.classes_
          ] = self.model.predict_proba(batch)
          proba = np.append(proba, proba_tmp, axis=0)
  
      return proba
  
imports: import types
  import numpy
  from sklearn.exceptions import NotFittedError
  from sklearn.utils.validation import check_is_fitted
  from small_text.base import check_optional_dependency
  from small_text.classifiers.classification import Classifier, EmbeddingMixin
  from small_text.exceptions import UnsupportedOperationException
  from small_text.utils.classification import empty_result, _multi_label_list_to_multi_hot, prediction_result
  from small_text.utils.context import build_pbar_context
  from small_text.utils.labels import csr_to_list
  from small_text.integrations.transformers.classifiers.base import ModelLoadingStrategy
label_desc: Use image node_id 8 for calling the SetFitClassification obj's underlying member method code with example usage: obj._predict_proba(dataset) and returns: proba