docstr: Parameters
  ----------
  transformer_model_args : TransformerModelArguments
      Name of the sentence transformer model.
  num_classes : int
      Number of classes.
  kwargs : dict
      Keyword arguments which will be passed to `TransformerBasedClassification`.
docstr_tok: ["Parameters","--","--","--","--","--","transformer_model_args",":","TransformerModelArguments","Name","of","the","sentence","transformer","model",".","num_classes",":","int","Number","of","classes",".","kwargs",":","dict","Keyword","arguments","which","will","be","passed","to","`","TransformerBasedClassification","`","."]
code: def __init__(self, transformer_model_args, num_classes, kwargs={}):
      """
      Parameters
      ----------
      transformer_model_args : TransformerModelArguments
          Name of the sentence transformer model.
      num_classes : int
          Number of classes.
      kwargs : dict
          Keyword arguments which will be passed to `TransformerBasedClassification`.
      """
      self.transformer_model_args = transformer_model_args
      self.num_classes = num_classes
      self.kwargs = 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 TransformerBasedClassificationFactory object from inherited base classes: AbstractClassifierFactory with example: obj = TransformerBasedClassificationFactory(transformer_model_args, num_classes, kwargs)