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
docstr: Predicts the labels for the given dataset.
  
  Parameters
  ----------
  dataset : TextDataset
      A dataset on whose instances predictions are made.
  return_proba : bool, default=False
      If True, additionally returns the confidence distribution over all classes.
  
  Returns
  -------
  predictions : np.ndarray[np.int32] or csr_matrix[np.int32]
      List of predictions if the classifier was fitted on single-label data,
      otherwise a sparse matrix of predictions.
  probas : np.ndarray[np.float32], optional
      List of probabilities (or confidence estimates) if `return_proba` is True.
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code: def predict(self, dataset, return_proba=False):
      """Predicts the labels for the given dataset.
  
      Parameters
      ----------
      dataset : TextDataset
          A dataset on whose instances predictions are made.
      return_proba : bool, default=False
          If True, additionally returns the confidence distribution over all classes.
  
      Returns
      -------
      predictions : np.ndarray[np.int32] or csr_matrix[np.int32]
          List of predictions if the classifier was fitted on single-label data,
          otherwise a sparse matrix of predictions.
      probas : np.ndarray[np.float32], optional
          List of probabilities (or confidence estimates) if `return_proba` is True.
      """
      if len(dataset) == 0:
          return empty_result(
              self.multi_label,
              self.num_classes,
              return_prediction=True,
              return_proba=return_proba,
          )
  
      proba = self.predict_proba(dataset)
      predictions = prediction_result(
          proba, self.multi_label, self.num_classes
      )
  
      if return_proba:
          return predictions, proba
  
      return predictions
  
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 6 for calling the SetFitClassification obj's underlying member method code with example usage: obj.predict(dataset, return_proba) and returns: predictions, empty_result, predictions,  proba