Class for GloVe word-embeddings model.
It can be trained via fully can asynchronous and parallel
AdaGrad with `$fit_transform()`

method.

`GloVe`

`R6Class`

object.

`components`

represents context word vectors

`n_dump_every`

`integer = 0L`

by default. Defines frequency of dumping word vectors. For example user
can ask to dump word vectors each 5 iteration.

`shuffle`

`logical = 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_size`

`integer = 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_history`

get history of SGD costs and word vectors (if

`n_dump_every > 0)`

- glove
A

`GloVe`

object- x
An input term co-occurence matrix. Preferably in

`dgTMatrix`

format- n_iter
`integer`

number of SGD iterations- word_vectors_size
desired dimension for word vectors

- vocabulary
`character`

vector or instance of`text2vec_vocabulary`

class. Each word should correspond to dimension of co-occurence matrix.- x_max
`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- learning_rate
`numeric`

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

, 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- initial
`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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