class_docstr: Classifies a set of documents into 1 of N classes.
  
  :param config: A :py:class:`finetune.config.Settings` object or None (for default config).
  :param \**kwargs: key-value pairs of config items to override.
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docstr: :param \*Xs: lists of text inputs, shape [batch, n_fields]
  :param Y: integer or string-valued class labels. It is necessary for the items of Y to be sortable.
  :param batch_size: integer number of examples per batch. When N_GPUS > 1, this number
                     corresponds to the number of training examples provided to each GPU.
docstr_tok: [":","param","\\","*","Xs",":","lists","of","text","inputs",",","shape","[","batch",",","n_fields","]",":","param","Y",":","integer","or","string-valued","class","labels",".","It","is","necessary","for","the","items","of","Y","to","be","sortable",".",":","param","batch_size",":","integer","number","of","examples","per","batch",".","When","N_GPUS",">","1",",","this","number","corresponds","to","the","number","of","training","examples","provided","to","each","GPU","."]
code: def finetune(self, Xs, Y=None, context=None, **kwargs):
      """
      :param \*Xs: lists of text inputs, shape [batch, n_fields]
      :param Y: integer or string-valued class labels. It is necessary for the items of Y to be sortable.
      :param batch_size: integer number of examples per batch. When N_GPUS > 1, this number
                         corresponds to the number of training examples provided to each GPU.
      """
      return BaseModel.finetune(
          self, Xs, Y=Y, context=context, **kwargs
      )
  
imports: import copy
  from finetune.errors import FinetuneError
  from finetune.target_models.classifier import Classifier, ClassificationPipeline
  from finetune.target_models.regressor import Regressor, RegressionPipeline
  from finetune.base import BaseModel
label_desc: Use image node_id 3 for calling the MultiFieldClassifier obj's underlying member method code with example usage: obj.finetune(Xs, Y, context) and returns: BaseModel