docstr: ```
  make predictions on text data
  Args:
    x_test(list or np.ndarray or str): array of texts on which to make predictions or a string representing text
  ```
docstr_tok: ["``","`","make","predictions","on","text","data","Args",":","x_test","(","list","or","np.ndarray","or","str",")",":","array","of","texts","on","which","to","make","predictions","or","a","string","representing","text","``","`"]
code: def predict(self, x_test, return_proba=False):
      """
      ```
      make predictions on text data
      Args:
        x_test(list or np.ndarray or str): array of texts on which to make predictions or a string representing text
      ```
      """
      if return_proba and not hasattr(
          self.model["clf"], "predict_proba"
      ):
          raise ValueError(
              "%s does not support predict_proba"
              % (type(self.model["clf"]).__name__)
          )
      if isinstance(x_test, str):
          x_test = [x_test]
      lang = U.detect_lang(x_test)
      if U.is_chinese(lang):
          x_test = U.split_chinese(x_test)
      if self.model is None:
          raise ValueError(
              "model is None - call fit or load to set the model"
          )
      if return_proba:
          predicted = self.model.predict_proba(x_test)
      else:
          predicted = self.model.predict(x_test)
      if len(predicted) == 1:
          predicted = predicted[0]
      return predicted
  
imports: from .None import utils
  from .imports import *
label_desc: Use image node_id 6 for calling the Classifier obj's underlying member method code with example usage: obj.predict(x_test, return_proba) and returns: predicted