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 using these word embeddings and generate predictions on test data?