Execute command on fastText model (including training)

Source: R/API.R

Use the same commands than the one to use for the command line.

execute(commands)

Arguments

commands

character of commands

Examples

# NOT RUN {
# Supervised learning example
library(fastrtext)

data("train_sentences")
data("test_sentences")

# prepare data
tmp_file_model <- tempfile()

train_labels <- paste0("__label__", train_sentences[,"class.text"])
train_texts <- tolower(train_sentences[,"text"])
train_to_write <- paste(train_labels, train_texts)
train_tmp_file_txt <- tempfile()
writeLines(text = train_to_write, con = train_tmp_file_txt)

test_labels <- paste0("__label__", test_sentences[,"class.text"])
test_texts <- tolower(test_sentences[,"text"])
test_to_write <- paste(test_labels, test_texts)

# learn model
execute(commands = c("supervised", "-input", train_tmp_file_txt,
                     "-output", tmp_file_model, "-dim", 20, "-lr", 1,
                     "-epoch", 20, "-wordNgrams", 2, "-verbose", 1))

model <- load_model(tmp_file_model)
predict(model, sentences = test_sentences[1, "text"])

# Unsupervised learning example
library(fastrtext)

data("train_sentences")
data("test_sentences")
texts <- tolower(train_sentences[,"text"])
tmp_file_txt <- tempfile()
tmp_file_model <- tempfile()
writeLines(text = texts, con = tmp_file_txt)
execute(commands = c("skipgram", "-input", tmp_file_txt, "-output", tmp_file_model, "-verbose", 1))

model <- load_model(tmp_file_model)
dict <- get_dictionary(model)
get_word_vectors(model, head(dict, 5))
# }