class_docstr: Word2Vec
class_docstr_tok: ["Word2Vec"]
docstr: :param model_id: Destination id for this model; auto-generated if not specified.
         Defaults to ``None``.
  :type model_id: Union[None, str, H2OEstimator], optional
  :param training_frame: Id of the training data frame.
         Defaults to ``None``.
  :type training_frame: Union[None, str, H2OFrame], optional
  :param min_word_freq: This will discard words that appear less than <int> times
         Defaults to ``5``.
  :type min_word_freq: int
  :param word_model: The word model to use (SkipGram or CBOW)
         Defaults to ``"skip_gram"``.
  :type word_model: Literal["skip_gram", "cbow"]
  :param norm_model: Use Hierarchical Softmax
         Defaults to ``"hsm"``.
  :type norm_model: Literal["hsm"]
  :param vec_size: Set size of word vectors
         Defaults to ``100``.
  :type vec_size: int
  :param window_size: Set max skip length between words
         Defaults to ``5``.
  :type window_size: int
  :param sent_sample_rate: Set threshold for occurrence of words. Those that appear with higher frequency in the
         training data
                         will be randomly down-sampled; useful range is (0, 1e-5)
         Defaults to ``0.001``.
  :type sent_sample_rate: float
  :param init_learning_rate: Set the starting learning rate
         Defaults to ``0.025``.
  :type init_learning_rate: float
  :param epochs: Number of training iterations to run
         Defaults to ``5``.
  :type epochs: int
  :param pre_trained: Id of a data frame that contains a pre-trained (external) word2vec model
         Defaults to ``None``.
  :type pre_trained: Union[None, str, H2OFrame], optional
  :param max_runtime_secs: Maximum allowed runtime in seconds for model training. Use 0 to disable.
         Defaults to ``0.0``.
  :type max_runtime_secs: float
  :param export_checkpoints_dir: Automatically export generated models to this directory.
         Defaults to ``None``.
  :type export_checkpoints_dir: str, optional
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code: def __init__(
      self,
      model_id=None,  # type: Optional[Union[None, str, H2OEstimator]]
      training_frame=None,  # type: Optional[Union[None, str, H2OFrame]]
      min_word_freq=5,  # type: int
      word_model="skip_gram",  # type: Literal["skip_gram", "cbow"]
      norm_model="hsm",  # type: Literal["hsm"]
      vec_size=100,  # type: int
      window_size=5,  # type: int
      sent_sample_rate=0.001,  # type: float
      init_learning_rate=0.025,  # type: float
      epochs=5,  # type: int
      pre_trained=None,  # type: Optional[Union[None, str, H2OFrame]]
      max_runtime_secs=0.0,  # type: float
      export_checkpoints_dir=None,  # type: Optional[str]
  ):
      """
      :param model_id: Destination id for this model; auto-generated if not specified.
             Defaults to ``None``.
      :type model_id: Union[None, str, H2OEstimator], optional
      :param training_frame: Id of the training data frame.
             Defaults to ``None``.
      :type training_frame: Union[None, str, H2OFrame], optional
      :param min_word_freq: This will discard words that appear less than <int> times
             Defaults to ``5``.
      :type min_word_freq: int
      :param word_model: The word model to use (SkipGram or CBOW)
             Defaults to ``"skip_gram"``.
      :type word_model: Literal["skip_gram", "cbow"]
      :param norm_model: Use Hierarchical Softmax
             Defaults to ``"hsm"``.
      :type norm_model: Literal["hsm"]
      :param vec_size: Set size of word vectors
             Defaults to ``100``.
      :type vec_size: int
      :param window_size: Set max skip length between words
             Defaults to ``5``.
      :type window_size: int
      :param sent_sample_rate: Set threshold for occurrence of words. Those that appear with higher frequency in the
             training data
                             will be randomly down-sampled; useful range is (0, 1e-5)
             Defaults to ``0.001``.
      :type sent_sample_rate: float
      :param init_learning_rate: Set the starting learning rate
             Defaults to ``0.025``.
      :type init_learning_rate: float
      :param epochs: Number of training iterations to run
             Defaults to ``5``.
      :type epochs: int
      :param pre_trained: Id of a data frame that contains a pre-trained (external) word2vec model
             Defaults to ``None``.
      :type pre_trained: Union[None, str, H2OFrame], optional
      :param max_runtime_secs: Maximum allowed runtime in seconds for model training. Use 0 to disable.
             Defaults to ``0.0``.
      :type max_runtime_secs: float
      :param export_checkpoints_dir: Automatically export generated models to this directory.
             Defaults to ``None``.
      :type export_checkpoints_dir: str, optional
      """
      super(H2OWord2vecEstimator, self).__init__()
      self._parms = {}
      self._id = self._parms["model_id"] = model_id
      self.training_frame = training_frame
      self.min_word_freq = min_word_freq
      self.word_model = word_model
      self.norm_model = norm_model
      self.vec_size = vec_size
      self.window_size = window_size
      self.sent_sample_rate = sent_sample_rate
      self.init_learning_rate = init_learning_rate
      self.epochs = epochs
      self.pre_trained = pre_trained
      self.max_runtime_secs = max_runtime_secs
      self.export_checkpoints_dir = 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 1 to create a new H2OWord2vecEstimator object from inherited base classes: H2OEstimator with example: obj = H2OWord2vecEstimator(model_id, training_frame, min_word_freq, word_model, norm_model, vec_size, window_size, sent_sample_rate, init_learning_rate, epochs, pre_trained, max_runtime_secs, export_checkpoints_dir)