class_docstr: .. versionadded:: 1.2.0
class_docstr_tok: ["..","versionadded",":",":","1.2.0"]
docstr: Parameters
  ----------
  setfit_model_args : SetFitModelArguments
      Name of the sentence transformer model.
  num_classes : int
      Number of classes.
  classification_kwargs : dict
      Keyword arguments which will be passed to `SetFitClassification`.
docstr_tok: ["Parameters","--","--","--","--","--","setfit_model_args",":","SetFitModelArguments","Name","of","the","sentence","transformer","model",".","num_classes",":","int","Number","of","classes",".","classification_kwargs",":","dict","Keyword","arguments","which","will","be","passed","to","`","SetFitClassification","`","."]
code: def __init__(
      self, setfit_model_args, num_classes, classification_kwargs={}
  ):
      """
      Parameters
      ----------
      setfit_model_args : SetFitModelArguments
          Name of the sentence transformer model.
      num_classes : int
          Number of classes.
      classification_kwargs : dict
          Keyword arguments which will be passed to `SetFitClassification`.
      """
      self.setfit_model_args = setfit_model_args
      self.num_classes = num_classes
      self.classification_kwargs = classification_kwargs
  
file_path: small-text/small_text/integrations/transformers/classifiers/factories.py
imports: from small_text.classifiers.factories import AbstractClassifierFactory
  from small_text.integrations.transformers.classifiers.classification import TransformerBasedClassification
  from small_text.integrations.transformers.classifiers.setfit import SetFitClassification
label_desc: Use image node_id 1 to create a new SetFitClassificationFactory object from inherited base classes: AbstractClassifierFactory with example: obj = SetFitClassificationFactory(setfit_model_args, num_classes, classification_kwargs)