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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_labels_without_prefix <- 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)) # load model model <- load_model(tmp_file_model) # prediction are returned as a list with words and probabilities predictions <- predict(model, sentences = test_to_write) print(head(predictions, 5)) # Compute accuracy mean(names(unlist(predictions)) == test_labels_without_prefix) # because there is only one category by observation, hamming loss will be the same get_hamming_loss(as.list(test_labels_without_prefix), predictions) # test predictions predictions <- predict(model, sentences = test_to_write) print(head(predictions, 5)) # you can get flat list of results when you are retrieving only one label per observation print(head(predict(model, sentences = test_to_write, simplify = TRUE))) # free memory unlink(train_tmp_file_txt) unlink(tmp_file_model) rm(model) gc()
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