class_docstr: Word2Vec
class_docstr_tok: ["Word2Vec"]
docstr: The word model to use (SkipGram or CBOW)
  
  Type: ``Literal["skip_gram", "cbow"]``, defaults to ``"skip_gram"``.
  
  :examples:
  
  >>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"), 
  ...                               col_names = ["category", "jobtitle"], 
  ...                               col_types = ["string", "string"], 
  ...                               header = 1)
  >>> words = job_titles.tokenize(" ")
  >>> w2v_model = H2OWord2vecEstimator(epochs=3, word_model="skip_gram")
  >>> w2v_model.train(training_frame=words)
  >>> synonyms = w2v_model.find_synonyms("assistant", 3)
  >>> print(synonyms)
docstr_tok: ["The","word","model","to","use","(","SkipGram","or","CBOW",")","Type",":","``","Literal","[","``","skip_gram","''",",","``","cbow","''","]","``",",","defaults","to","``","``","skip_gram","''","``",".",":","examples",":",">",">",">","job_titles","=","h2o.import_file","(","(","``","https",":","\/\/s3.amazonaws.com\/h2o-public-test-data\/smalldata\/craigslistJobTitles.csv","''",")",",","...","col_names","=","[","``","category","''",",","``","jobtitle","''","]",",","...","col_types","=","[","``","string","''",",","``","string","''","]",",","...","header","=","1",")",">",">",">","words","=","job_titles.tokenize","(","``","``",")",">",">",">","w2v_model","=","H2OWord2vecEstimator","(","epochs=3",",","word_model=","''","skip_gram","''",")",">",">",">","w2v_model.train","(","training_frame=words",")",">",">",">","synonyms","=","w2v_model.find_synonyms","(","``","assistant","''",",","3",")",">",">",">","print","(","synonyms",")"]
code: def word_model(self):
      """
      The word model to use (SkipGram or CBOW)
  
      Type: ``Literal["skip_gram", "cbow"]``, defaults to ``"skip_gram"``.
  
      :examples:
  
      >>> job_titles = h2o.import_file(("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv"),
      ...                               col_names = ["category", "jobtitle"],
      ...                               col_types = ["string", "string"],
      ...                               header = 1)
      >>> words = job_titles.tokenize(" ")
      >>> w2v_model = H2OWord2vecEstimator(epochs=3, word_model="skip_gram")
      >>> w2v_model.train(training_frame=words)
      >>> synonyms = w2v_model.find_synonyms("assistant", 3)
      >>> print(synonyms)
      """
      return self._parms.get("word_model")
  
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 6 for calling the H2OWord2vecEstimator obj's underlying member method code with example usage: obj.word_model() and returns: self