class_docstr: Word2Vec
class_docstr_tok: ["Word2Vec"]
docstr: Automatically export generated models to this directory.
  
  Type: ``str``.
  
  :examples:
  
  >>> import tempfile
  >>> from os import listdir
  >>> 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)
  >>> checkpoints_dir = tempfile.mkdtemp()
  >>> words = job_titles.tokenize(" ")
  >>> w2v_model = H2OWord2vecEstimator(epochs=1,
  ...                                  max_runtime_secs=10,
  ...                                  export_checkpoints_dir=checkpoints_dir)
  >>> w2v_model.train(training_frame=words)
  >>> len(listdir(checkpoints_dir))
docstr_tok: ["Automatically","export","generated","models","to","this","directory",".","Type",":","``","str","``",".",":","examples",":",">",">",">","import","tempfile",">",">",">","from","os","import","listdir",">",">",">","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",")",">",">",">","checkpoints_dir","=","tempfile.mkdtemp","(",")",">",">",">","words","=","job_titles.tokenize","(","``","``",")",">",">",">","w2v_model","=","H2OWord2vecEstimator","(","epochs=1",",","...","max_runtime_secs=10",",","...","export_checkpoints_dir=checkpoints_dir",")",">",">",">","w2v_model.train","(","training_frame=words",")",">",">",">","len","(","listdir","(","checkpoints_dir",")",")"]
code: def export_checkpoints_dir(self):
      """
      Automatically export generated models to this directory.
  
      Type: ``str``.
  
      :examples:
  
      >>> import tempfile
      >>> from os import listdir
      >>> 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)
      >>> checkpoints_dir = tempfile.mkdtemp()
      >>> words = job_titles.tokenize(" ")
      >>> w2v_model = H2OWord2vecEstimator(epochs=1,
      ...                                  max_runtime_secs=10,
      ...                                  export_checkpoints_dir=checkpoints_dir)
      >>> w2v_model.train(training_frame=words)
      >>> len(listdir(checkpoints_dir))
      """
      return self._parms.get("export_checkpoints_dir")
  
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 24 for calling the H2OWord2vecEstimator obj's underlying member method code with example usage: obj.export_checkpoints_dir() and returns: self