How do i build a model using Glove word embeddings and predict on Test data using text2vec in R

Question

I am building a classification model on text data into two categories(i.e. classifying each comment into 2 categories) using GloVe word embeddings. I have two columns, one with textual data(comments) and the other one is a binary Target variable(whether a comment is actionable or not). I was able to generate Glove word embeddings for textual data using the following code from text2vec documentation.

glove_model <- GlobalVectors$new(word_vectors_size = 50,vocabulary = 
glove_pruned_vocab,x_max = 20L)
#fit model and get word vectors
word_vectors_main <- glove_model$fit_transform(glove_tcm,n_iter = 20,convergence_tol=-1)
word_vectors_context <- glove_model$components
word_vectors <- word_vectors_main+t(word_vectors_context)

How do i build a model and generate predictions on test data?

Answer 1

text2vec has a standard predict method (like most of the R libraries anyway) that you can use in a straightforward fashion: have a look at the documentation.

To make a long story short, just use

predictions <- predict(fitted_model, data)