Trains a word2vec model on a String column of an H2O data frame

h2o.word2vec(training_frame = NULL, model_id = NULL, min_word_freq = 5,
  word_model = c("SkipGram"), norm_model = c("HSM"), vec_size = 100,
  window_size = 5, sent_sample_rate = 0.001, init_learning_rate = 0.025,
  epochs = 5, pre_trained = NULL, max_runtime_secs = 0)

Arguments

training_frame

Id of the training data frame.

model_id

Destination id for this model; auto-generated if not specified.

min_word_freq

This will discard words that appear less than <int> times Defaults to 5.

word_model

Use the Skip-Gram model Must be one of: "SkipGram". Defaults to SkipGram.

norm_model

Use Hierarchical Softmax Must be one of: "HSM". Defaults to HSM.

vec_size

Set size of word vectors Defaults to 100.

window_size

Set max skip length between words Defaults to 5.

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.

init_learning_rate

Set the starting learning rate Defaults to 0.025.

epochs

Number of training iterations to run Defaults to 5.

pre_trained

Id of a data frame that contains a pre-trained (external) word2vec model

max_runtime_secs

Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0.