Word Clusters

Gives each word a class ID number.

word2clusters(train, output = NULL, classes = 0L, size = 100L,
  window = 5L, sample = 1e-05, hs = 0L, negative = 5L,
  threads = 1L, iter = 5L, min_count = 5L, alpha = 0.025,
  debug = 2L, binary = 0L, cbow = 1L, verbose = FALSE)

Arguments

train

Use text data from file to train the model.
output

Use file to save the resulting word vectors / word clusters.
classes

Number of word classes; if 0L, output word classes rather than word vectors (default 0L).
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.

Examples

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

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

# train model
model_path <- word2clusters(macbeth, classes = 50L)
# }