model_id: Destination id for this model; auto-generated if not specified.
min_word_freq: This will discard words that appear less than times Defaults to 5.
word_model: The word model to use (SkipGram or CBOW) Must be one of: "SkipGram", "CBOW". 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.
export_checkpoints_dir: Automatically export generated models to this directory.
Examples
## Not run:library(h2o)h2o.init()# Import the CraigslistJobTitles datasetjob_titles <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv", col.names = c("category","jobtitle"), col.types = c("String","String"), header =TRUE)# Build and train the Word2Vec modelwords <- h2o.tokenize(job_titles," ")vec <- h2o.word2vec(training_frame = words)h2o.findSynonyms(vec,"teacher", count =20)## End(Not run)