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.
class_docstr_tok: ["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","."]
docstr: Embeds inputs in learned feature space. Can be called before or after calling :meth:`finetune`.
  
  :param \*Xs: lists of text inputs, shape [batch, n_fields]
  :returns: np.array of features of shape (n_examples, embedding_size).
docstr_tok: ["Embeds","inputs","in","learned","feature","space",".","Can","be","called","before","or","after","calling",":","meth",":","`","finetune","`",".",":","param","\\","*","Xs",":","lists","of","text","inputs",",","shape","[","batch",",","n_fields","]",":","returns",":","np.array","of","features","of","shape","(","n_examples",",","embedding_size",")","."]
code: def featurize(self, Xs, **kwargs):
      """
      Embeds inputs in learned feature space. Can be called before or after calling :meth:`finetune`.
  
      :param \*Xs: lists of text inputs, shape [batch, n_fields]
      :returns: np.array of features of shape (n_examples, embedding_size).
      """
      return BaseModel.featurize(self, Xs, **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 6 for calling the MultiFieldClassifier obj's underlying member method code with example usage: obj.featurize(Xs) and returns: BaseModel