Transform words (or sequences of words) to vectors using a word2vec model.

# S4 method for H2OWordEmbeddingModel
h2o.transform(model, words, aggregate_method = c("NONE", "AVERAGE"))

Arguments

model	A word2vec model.
words	An H2OFrame made of a single column containing source words.
aggregate_method	Specifies how to aggregate sequences of words. If method is `NONE` then no aggregation is performed and each input word is mapped to a single word-vector. If method is 'AVERAGE' then input is treated as sequences of words delimited by NA. Each word of a sequences is internally mapped to a vector and vectors belonging to the same sentence are averaged and returned in the result.

Examples

if (FALSE) {
h2o.init()

# Build a simple word2vec model
data <- as.character(as.h2o(c("a", "b", "a")))
w2v_model <- h2o.word2vec(data, sent_sample_rate = 0, min_word_freq = 0, epochs = 1, vec_size = 2)

# Transform words to vectors without aggregation
sentences <- as.character(as.h2o(c("b", "c", "a", NA, "b")))
h2o.transform(w2v_model, sentences) # -> 5 rows total, 2 rows NA ("c" is not in the vocabulary)

# Transform words to vectors and return average vector for each sentence
h2o.transform(w2v_model, sentences, aggregate_method = "AVERAGE") # -> 2 rows
}