Word2Vec

Train Word2Vec model.

word2vec(train, output = NULL, size = 100L, window = 5L,
  sample = 0.001, hs = 0L, negative = 5L, threads = 12L,
  iter = 5L, min_count = 5L, alpha = 0.025, debug = 2L,
  binary = 0L, cbow = 1L, verbose = TRUE)

Arguments

train	Use text data from file to train the model.
output	Use file to save the resulting word vectors / word clusters.
size	Set size of word vectors; default is 100L.
window	Set max skip length between words; default is 5L.
sample	Set threshold for occurrence of words. Those that appear with higher frequency in the training data will be randomly down-sampled; default is 1e-5.
hs	Use Hierarchical Softmax; default is 1 (0L = not used)
negative	Number of negative examples; default is 0L, common values are 5 - 10 (0L = not used).
threads	Use \(n\) threads (default 12L).
iter	Run more training iterations (default 5).
min_count	This will discard words that appear less than \(n\) times; default is 5L.
alpha	Set the starting learning rate; default is .025.
debug	Set the debug mode (default = 2L = more info during training).
binary	Save the resulting vectors in binary moded; default is 0L (off).
cbow	Use the continuous back of words model; default is 1L (skip-gram model).
verbose	Whether to print output from training.

Value

Invisibly returns the output.

See also

as_word2vec to load a pre-trained model.

Examples

# NOT RUN {
# setup word2vec Julia dependency
setup_word2vec()

# sample corpus
data("macbeth", package = "word2vec.r")

# train model
model_path <- word2vec(macbeth)
# }