class_docstr: .. versionadded:: 1.2.0
docstr: Parameters
  ----------
  sentence_transformer_model : str
      Name of a sentence transformer model.
  model_loading_strategy: ModelLoadingStrategy, default=ModelLoadingStrategy.DEFAULT
      Specifies if there should be attempts to download the model or if only local
      files should be used.
docstr_tok: ["Parameters","--","--","--","--","--","sentence_transformer_model",":","str","Name","of","a","sentence","transformer","model",".","model_loading_strategy",":","ModelLoadingStrategy",",","default=ModelLoadingStrategy.DEFAULT","Specifies","if","there","should","be","attempts","to","download","the","model","or","if","only","local","files","should","be","used","."]
code: def __init__(
      self,
      sentence_transformer_model: str,
      model_loading_strategy: ModelLoadingStrategy = ModelLoadingStrategy.DEFAULT,
  ):
      """
      Parameters
      ----------
      sentence_transformer_model : str
          Name of a sentence transformer model.
      model_loading_strategy: ModelLoadingStrategy, default=ModelLoadingStrategy.DEFAULT
          Specifies if there should be attempts to download the model or if only local
          files should be used.
      """
      self.sentence_transformer_model = sentence_transformer_model
      self.model_loading_strategy = model_loading_strategy
  
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 1 to create a new SetFitModelArguments object from inherited base classes: object with example: obj = SetFitModelArguments(sentence_transformer_model, model_loading_strategy)