DEPRECIATED.This function trains a GloVe wordembeddings model via fully asynchronous and parallel AdaGrad.
1 2 3  glove(tcm, vocabulary_size = nrow(tcm), word_vectors_size, x_max, num_iters,
shuffle_seed = NA_integer_, learning_rate = 0.05,
convergence_threshold = 1, grain_size = 100000L, alpha = 0.75, ...)

tcm 
an object which represents a termcooccurrence matrix, which is
used in training. At the moment only 
vocabulary_size 
number of words in in the termcooccurrence matrix 
word_vectors_size 
desired dimension for word vectors 
x_max 
maximum number of cooccurrences to use in the weighting function. See the GloVe paper for details: http://nlp.stanford.edu/pubs/glove.pdf. 
num_iters 
number of AdaGrad epochs 
shuffle_seed 

learning_rate 
learning rate for SGD. I do not recommend that you modify this parameter, since AdaGrad will quickly adjust it to optimal. 
convergence_threshold 
defines early stopping strategy. We stop fitting
when one of two following conditions will be satisfied: (a) we have used
all iterations, or (b) 
grain_size 
I do not recommend adjusting this parameter. This is the
grain_size for 
alpha 
the alpha in weighting function formula : f(x) = 1 if x > x_max; else (x/x_max)^alpha 
... 
arguments passed to other methods (not used at the moment). 
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