Class for GloVe word-embeddings model.
It can be trained via fully can asynchronous and parallel
AdaGrad with $fit_transform() method.
GloVeR6Class object.
componentsrepresents context word vectors
n_dump_everyinteger = 0L by default. Defines frequency of dumping word vectors. For example user
can ask to dump word vectors each 5 iteration.
shufflelogical = FALSE by default. Defines shuffling before each SGD iteration.
Generally shuffling is a good idea for stochastic-gradient descent, but
from my experience in this particular case it does not improve convergence.
grain_sizeinteger = 1e5L by default. This is the
grain_size for RcppParallel::parallelReduce. For details, see
http://rcppcore.github.io/RcppParallel/#grain-size.
We don't recommend to change this parameter.
For usage details see Methods, Arguments and Examples sections.
glove = GlobalVectors$new(word_vectors_size, vocabulary, x_max, learning_rate = 0.15,
alpha = 0.75, lambda = 0.0, shuffle = FALSE, initial = NULL)
glove$fit_transform(x, n_iter = 10L, convergence_tol = -1, n_check_convergence = 1L,
n_threads = RcppParallel::defaultNumThreads(), ...)
glove$components
glove$dump()
$new(word_vectors_size, vocabulary, x_max, learning_rate = 0.15,
alpha = 0.75, lambda = 0, shuffle = FALSE,
initial = NULL)Constructor for Global vectors model. For description of arguments see Arguments section.
$fit_transform(x, n_iter = 10L, convergence_tol = -1, n_check_convergence = 1L,
n_threads = RcppParallel::defaultNumThreads(), ...)fit Glove model to input matrix x
$dump()get model internals - word vectors and biases for main and context words
$get_historyget history of SGD costs and word vectors (if n_dump_every > 0)
A GloVe object
An input term co-occurence matrix. Preferably in dgTMatrix format
integer number of SGD iterations
desired dimension for word vectors
character vector or instance of
text2vec_vocabulary class. Each word should correspond to dimension
of co-occurence matrix.
integer maximum number of co-occurrences to use in the weighting function.
see the GloVe paper for details: http://nlp.stanford.edu/pubs/glove.pdf
numeric learning rate for SGD. I do not recommend that you
modify this parameter, since AdaGrad will quickly adjust it to optimal
numeric = -1 defines early stopping strategy. We stop fitting
when one of two following conditions will be satisfied: (a) we have used
all iterations, or (b) cost_previous_iter / cost_current_iter - 1 <
convergence_tol. By default perform all iterations.
numeric = 0.75 the alpha in weighting function formula : \(f(x) = 1 if x >
x_max; else (x/x_max)^alpha\)
numeric = 0.0, L1 regularization coefficient.
0 = vanilla GloVe, corresponds to original paper and implementation.
lambda >0 corresponds to text2vec new feature and different SGD algorithm. From our experience
small lambda (like lambda = 1e-5) usually produces better results that vanilla GloVe
on small corpuses
NULL - word vectors and word biases will be initialized
randomly. Or named list which contains w_i, w_j, b_i, b_j values -
initial word vectors and biases. This is useful for fine-tuning. For example one can
pretrain model on large corpus (such as wikipedia dump) and then fine tune
on smaller task-specific dataset
# NOT RUN {
temp = tempfile()
download.file('http://mattmahoney.net/dc/text8.zip', temp)
text8 = readLines(unz(temp, "text8"))
it = itoken(text8)
vocabulary = create_vocabulary(it)
vocabulary = prune_vocabulary(vocabulary, term_count_min = 5)
v_vect = vocab_vectorizer(vocabulary)
tcm = create_tcm(it, v_vect, skip_grams_window = 5L)
glove_model = GloVe$new(word_vectors_size = 50,
vocabulary = vocabulary, x_max = 10, learning_rate = .25)
# fit model and get word vectors
word_vectors_main = glove_model$fit_transform(tcm, n_iter = 10)
word_vectors_context = glove_model$components
word_vectors = word_vectors_main + t(word_vectors_context)
# }
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