Description Public fields Methods References Examples

Creates Global Vectors matrix factorization model

`components`

represents context embeddings

`bias_i`

bias term i as per paper

`bias_j`

bias term j as per paper

`shuffle`

`logical = FALSE`

by default. Whether to perform shuffling before each SGD iteration. Generally shuffling is a good practice for SGD.

`new()`

Creates GloVe model object

GloVe$new( rank, x_max, learning_rate = 0.15, alpha = 0.75, lambda = 0, shuffle = FALSE, init = list(w_i = NULL, b_i = NULL, w_j = NULL, b_j = NULL) )

`rank`

desired dimension for the latent vectors

`x_max`

`integer`

maximum number of co-occurrences to use in the weighting function`learning_rate`

`numeric`

learning rate for SGD. I do not recommend that you modify this parameter, since AdaGrad will quickly adjust it to optimal`alpha`

`numeric = 0.75`

the alpha in weighting function formula :*f(x) = 1 if x > x_max; else (x/x_max)^alpha*`lambda`

`numeric = 0.0`

regularization parameter`shuffle`

see

`shuffle`

field`init`

`list(w_i = NULL, b_i = NULL, w_j = NULL, b_j = NULL)`

initialization for embeddings (w_i, w_j) and biases (b_i, b_j).`w_i, w_j`

- numeric matrices, should have #rows = rank, #columns = expected number of rows (w_i) / columns(w_j) in the input matrix.`b_i, b_j`

= numeric vectors, should have length of #expected number of rows(b_i) / columns(b_j) in input matrix

`fit_transform()`

fits model and returns embeddings

GloVe$fit_transform( x, n_iter = 10L, convergence_tol = -1, n_threads = getOption("rsparse_omp_threads", 1L), ... )

`x`

An input term co-occurence matrix. Preferably in

`dgTMatrix`

format`n_iter`

`integer`

number of SGD iterations`convergence_tol`

`numeric = -1`

defines early stopping strategy. Stop fitting when one of two following conditions will be satisfied: (a) passed all iterations (b)`cost_previous_iter / cost_current_iter - 1 < convergence_tol`

.`n_threads`

number of threads to use

`...`

not used at the moment

`get_history()`

returns value of the loss function for each epoch

GloVe$get_history()

`clone()`

The objects of this class are cloneable with this method.

GloVe$clone(deep = FALSE)

`deep`

Whether to make a deep clone.

http://nlp.stanford.edu/projects/glove/

1 2 3 4 5 6 | ```
data('movielens100k')
co_occurence = crossprod(movielens100k)
glove_model = GloVe$new(rank = 4, x_max = 10, learning_rate = .25)
embeddings = glove_model$fit_transform(co_occurence, n_iter = 2, n_threads = 1)
embeddings = embeddings + t(glove_model$components) # embeddings + context embedings
identical(dim(embeddings), c(ncol(movielens100k), 10L))
``` |

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