class_docstr: Word2Vec
class_docstr_tok: ["Word2Vec"]
docstr: Id of a data frame that contains a pre-trained (external) word2vec model
  
  Type: ``Union[None, str, H2OFrame]``.
  
  :examples:
  
  >>> words = h2o.create_frame(rows=1000,cols=1,
  ...                          string_fraction=1.0,
  ...                          missing_fraction=0.0)
  >>> embeddings = h2o.create_frame(rows=1000,cols=100,
  ...                               real_fraction=1.0,
  ...                               missing_fraction=0.0)
  >>> word_embeddings = words.cbind(embeddings)
  >>> w2v_model = H2OWord2vecEstimator(pre_trained=word_embeddings)
  >>> w2v_model.train(training_frame=word_embeddings)
  >>> model_id = w2v_model.model_id
  >>> model = h2o.get_model(model_id)
docstr_tok: ["Id","of","a","data","frame","that","contains","a","pre-trained","(","external",")","word2vec","model","Type",":","``","Union","[","None",",","str",",","H2OFrame","]","``",".",":","examples",":",">",">",">","words","=","h2o.create_frame","(","rows=1000",",","cols=1",",","...","string_fraction=1.0",",","...","missing_fraction=0.0",")",">",">",">","embeddings","=","h2o.create_frame","(","rows=1000",",","cols=100",",","...","real_fraction=1.0",",","...","missing_fraction=0.0",")",">",">",">","word_embeddings","=","words.cbind","(","embeddings",")",">",">",">","w2v_model","=","H2OWord2vecEstimator","(","pre_trained=word_embeddings",")",">",">",">","w2v_model.train","(","training_frame=word_embeddings",")",">",">",">","model_id","=","w2v_model.model_id",">",">",">","model","=","h2o.get_model","(","model_id",")"]
code: def pre_trained(self):
      """
      Id of a data frame that contains a pre-trained (external) word2vec model
  
      Type: ``Union[None, str, H2OFrame]``.
  
      :examples:
  
      >>> words = h2o.create_frame(rows=1000,cols=1,
      ...                          string_fraction=1.0,
      ...                          missing_fraction=0.0)
      >>> embeddings = h2o.create_frame(rows=1000,cols=100,
      ...                               real_fraction=1.0,
      ...                               missing_fraction=0.0)
      >>> word_embeddings = words.cbind(embeddings)
      >>> w2v_model = H2OWord2vecEstimator(pre_trained=word_embeddings)
      >>> w2v_model.train(training_frame=word_embeddings)
      >>> model_id = w2v_model.model_id
      >>> model = h2o.get_model(model_id)
      """
      return self._parms.get("pre_trained")
  
imports: from h2o.estimators.estimator_base import H2OEstimator
  from h2o.exceptions import H2OValueError
  from h2o.frame import H2OFrame
  from h2o.utils.typechecks import assert_is_type, Enum, numeric
label_desc: Use image node_id 20 for calling the H2OWord2vecEstimator obj's underlying member method code with example usage: obj.pre_trained() and returns: self