R/xlm_roberta_sentence_embeddings.R

In r-spark/sparknlp: R Interface to John Snow Labs Spark NLP

#' Load a pretrained Spark NLP XlmRoBertaSentenceEmbeddings model
#' 
#' Create a pretrained Spark NLP \code{XlmRoBertaSentenceEmbeddings} model.
#' See \url{https://nlp.johnsnowlabs.com/docs/en/annotators#xlmrobertasentenceembeddings}
#' 
#' Sentence-level embeddings using XLM-RoBERTa. The XLM-RoBERTa model was proposed in 
#' Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, 
#' Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, 
#' Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on 
#' Facebook's RoBERTa model released in 2019. It is a large multi-lingual language model, 
#' trained on 2.5TB of filtered CommonCrawl data.
#' 
#' @template roxlate-pretrained-params
#' @template roxlate-inputs-output-params
#' @param batch_size batch size
#' @param case_sensitive whether to lowercase tokens or not
#' @param dimension defines the output layer of BERT when calculating embeddings
#' @param max_sentence_length max sentence length to process
#' 
#' @export
nlp_xlm_roberta_sentence_embeddings_pretrained <- function(sc, input_cols, output_col, case_sensitive = NULL,
                                              batch_size = NULL, dimension = NULL, 
                                              max_sentence_length = NULL, 
                                              name = NULL, lang = NULL, remote_loc = NULL) {
  args <- list(
    input_cols = input_cols,
    output_col = output_col,
    case_sensitive = case_sensitive,
    batch_size = batch_size,
    dimension = dimension,
    max_sentence_length = max_sentence_length
  ) %>%
    validator_nlp_xlm_roberta_sentence_embeddings()
  
  model_class <- "com.johnsnowlabs.nlp.embeddings.XlmRoBertaSentenceEmbeddings"
  model <- pretrained_model(sc, model_class, name, lang, remote_loc)
  spark_jobj(model) %>%
    sparklyr::jobj_set_param("setInputCols", args[["input_cols"]]) %>% 
    sparklyr::jobj_set_param("setOutputCol", args[["output_col"]]) %>% 
    sparklyr::jobj_set_param("setCaseSensitive", args[["case_sensitive"]]) %>% 
    sparklyr::jobj_set_param("setBatchSize", args[["batch_size"]]) %>% 
    sparklyr::jobj_set_param("setDimension", args[["dimension"]]) %>% 
    sparklyr::jobj_set_param("setMaxSentenceLength", args[["max_sentence_length"]])
  
  new_nlp_xlm_roberta_sentence_embeddings(model)
}

#' @import forge
validator_nlp_xlm_roberta_sentence_embeddings <- function(args) {
  args[["input_cols"]] <- cast_string_list(args[["input_cols"]])
  args[["output_col"]] <- cast_string(args[["output_col"]])
  args[["batch_size"]] <- cast_nullable_integer(args[["batch_size"]])
  args[["case_sensitive"]] <- cast_nullable_logical(args[["case_sensitive"]])
  args[["dimension"]] <- cast_nullable_integer(args[["dimension"]])
  args[["max_sentence_length"]] <- cast_nullable_integer(args[["max_sentence_length"]])
  args
}

new_nlp_xlm_roberta_sentence_embeddings <- function(jobj) {
  sparklyr::new_ml_transformer(jobj, class = "nlp_xlm_roberta_sentence_embeddings")
}