Apply the trained model to new sentences.
Average word embeddings and search most similar label vector.
# S3 method for Rcpp_fastrtext predict(object, sentences, k = 1, ...)
| object | trained fastText model |
|---|---|
| sentences | character containing the sentences |
| k | will return the |
| ... | not used |
list containing for each sentence the probability to be associated with k labels.
library(fastrtext) data("test_sentences") model_test_path <- system.file("extdata", "model_classification_test.bin", package = "fastrtext") model <- load_model(model_test_path) sentence <- test_sentences[1, "text"] print(predict(model, sentence))#> [[1]] #> __label__OWNX #> 0.9980469 #>