h2o (version 3.40.0.4)

h2o.word2vec: Trains a word2vec model on a String column of an H2O data frame

Description

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

Usage

h2o.word2vec(
  training_frame = NULL,
  model_id = NULL,
  min_word_freq = 5,
  word_model = c("SkipGram", "CBOW"),
  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,
  export_checkpoints_dir = NULL
)

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

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

if (FALSE) {
library(h2o)
h2o.init()

# Import the CraigslistJobTitles dataset
job_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 model
words <- h2o.tokenize(job_titles, " ")
vec <- h2o.word2vec(training_frame = words)
h2o.findSynonyms(vec, "teacher", count = 20)
}