R/5_5_textQA.R

In OscarKjell/text: Analyses of Text using Transformers Models from HuggingFace, Natural Language Processing and Machine Learning

#' Question Answering. (experimental)
#' @param question (string)  A question
#' @param context (string)  The context(s) where the model will look for the answer.
#' @param model (string)  HuggingFace name of a pre-trained language model that have been fine-tuned
#' on a question answering task.
#' @param device (string)  Device to use: 'cpu', 'gpu', or 'gpu:k' where k is a specific device number
#' @param tokenizer_parallelism (boolean)  If TRUE this will turn on tokenizer parallelism.
#' @param logging_level (string)  Set the logging level.
#' Options (ordered from less logging to more logging): critical, error, warning, info, debug
#' @param force_return_results (boolean)  Stop returning some incorrectly formatted/structured results.
#' This setting does CANOT evaluate the actual results (whether or not they make sense, exist, etc.).
#' All it does is to ensure the returned results are formatted correctly (e.g., does the question-answering
#' dictionary contain the key "answer", is sentiments from textClassify containing the labels "positive"
#'  and "negative").
#' @param top_k (integer) (int)  Indicates number of possible answer span(s) to get from the model output.
#' @param doc_stride (integer)   If the context is too long to fit with the question for the model, it will be split
#' into overlapping chunks. This setting controls the overlap size.
#' @param max_answer_len (integer)  Max answer size to be extracted from the model’s output.
#' @param max_seq_len (integer)  The max total sentence length (context + question) in tokens of each chunk
#' passed to the model. If needed, the context is split in chunks (using doc_stride as overlap).
#' @param max_question_len (integer)   The max question length after tokenization. It will be truncated if needed.
#' @param handle_impossible_answer (boolean)  Whether or not impossible is accepted as an answer.
#' @param set_seed (Integer) Set seed.
#' @return Answers.
#' @examples
#' \donttest{
#' #   qa_examples <- textQA(question = "Which colour have trees?",
#' #     context = "Trees typically have leaves, are mostly green and like water.")
#' }
#' @seealso see \code{\link{textClassify}}, \code{\link{textGeneration}}, \code{\link{textNER}},
#'  \code{\link{textSum}}, \code{\link{textQA}}, \code{\link{textTranslate}}
#' @importFrom reticulate source_python
#' @importFrom tibble as_tibble_col
#' @export
textQA <- function(question,
                   context,
                   model = "",
                   device = "cpu",
                   tokenizer_parallelism = FALSE,
                   logging_level = "warning",
                   force_return_results = FALSE,
                   top_k = 1L,
                   doc_stride = 128L,
                   max_answer_len = 15L,
                   max_seq_len = 384L,
                   max_question_len = 64L,
                   handle_impossible_answer = FALSE,
                   set_seed = 202208L) {
  T1_text_all <- Sys.time()

  # Run python file with HunggingFace interface to state-of-the-art transformers
  reticulate::source_python(system.file("python",
    "huggingface_Interface3.py",
    package = "text",
    mustWork = TRUE
  ))

  ALL_output <- list()
  # Loop over all character variables; i_variables = 1
  for (i_variables in seq_len(length(question))) {
    T1_variable <- Sys.time()

    hg_QA <- hgTransformerGetQA(
      question = question[[i_variables]],
      context = context[[i_variables]],
      model = model,
      device = device,
      tokenizer_parallelism = tokenizer_parallelism,
      logging_level = logging_level,
      force_return_results = force_return_results,
      top_k = top_k,
      doc_stride = doc_stride,
      max_answer_len = max_answer_len,
      max_seq_len = max_seq_len,
      max_question_len = max_question_len,
      handle_impossible_answer = handle_impossible_answer,
      set_seed = set_seed
    )#[[1]]

    output1 <- dplyr::bind_rows(hg_QA)

    ALL_output[[i_variables]] <- output1

    T2_variable <- Sys.time()
    variable_time <- T2_variable - T1_variable
    variable_time <- sprintf(
      "Duration: %f %s",
      variable_time,
      units(variable_time)
    )

    loop_text <- paste(question[[i_variables]], "completed:",
      variable_time,
      "\n",
      sep = " "
    )

    message(colourise(loop_text, "green"))
  }

  ALL_output <- dplyr::bind_rows(ALL_output)

  # Time to complete all variables
  T2_text_all <- Sys.time()
  all_time <- T2_text_all - T1_text_all
  all_time <- sprintf(
    "Duration to predict all variables: %f %s",
    all_time,
    units(all_time)
  )


  # Adding informative comment help(comment)
  comment(ALL_output) <- paste("Information about the textSum  ",
    "model: ", model, "; ",
    "time: ", all_time, ";",
    "text_version: ", packageVersion("text"), ".",
    sep = "",
    collapse = "\n"
  )
  return(ALL_output)
}