inst/doc/LBAM.R

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

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----textPredict_examples, eval = FALSE, echo=TRUE----------------------------
#  library(text)
#  
#  # Example calling a model using the URL
#  textPredict(
#    model_info = "valence_facebook_mxbai23_eijsbroek2024",
#    texts = "what is the valence of this text?"
#  )
#  
#  
#  # Example calling a model having an abbreviation
#  textClassify(
#    model_info = "implicitpower_roberta23_nilsson2024",
#    texts = "It looks like they have problems collaborating."
#  )

## ----models_table, eval = TRUE, echo=FALSE------------------------------------
library("reactable")
# see vignette: https://glin.github.io/reactable/articles/examples.html#custom-rendering

model_data <- read.csv(system.file("extdata",
                                   "The_L-BAM_Library.csv",
                                   package = "text"),
                       header = TRUE, 
                       skip = 3)

reactable::reactable(
  data = model_data,
  filterable = TRUE,
  defaultPageSize = 20,
  highlight = TRUE, 
  resizable = TRUE,
  theme = reactableTheme(
    borderColor = "#1f7a1f",
  #  stripedColor = "#e6ffe6",
    highlightColor = "#ebfaeb",
    cellPadding = "8px 12px",
    style = list(fontFamily = "-apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial, sans-serif")
  ),
  columns = list(
    Construct_Concept_Behaviours = colDef(minWidth = 280),
    Outcome = colDef(minWidth = 280),
    Language = colDef(minWidth = 280),
    Language_type = colDef(minWidth = 280),
    Level = colDef(minWidth = 280),
    N_training = colDef(minWidth = 280),
    N_evaluation = colDef(minWidth = 280),
    Source = colDef(minWidth = 280),
    Participants_training = colDef(minWidth = 280),
    Participants_evaluation = colDef(minWidth = 280),
    Label_types = colDef(minWidth = 280),
    Language_domain_distribution = colDef(minWidth = 280),
    Open_data = colDef(minWidth = 280),
    Model_type = colDef(minWidth = 280),
    Features = colDef(minWidth = 280),
    Validation_metric1 = colDef(minWidth = 280),
    N_fold_cv_accuracy.1 = colDef(minWidth = 280),
    Held_out_accuracy.1 = colDef(minWidth = 280),
    SEMP_accuracy.1 = colDef(minWidth = 280),
    Other_metrics_n_fold_cv = colDef(minWidth = 280),
    Other_metrics_held_out = colDef(minWidth = 280),
    Other_metrics_SEMP = colDef(minWidth = 280),
    Ethical_approval = colDef(minWidth = 280),
    Ethical_statement = colDef(minWidth = 280),
    Reference = colDef(minWidth = 280),
    Date = colDef(minWidth = 280),
    Contact_details = colDef(minWidth = 280),
    License = colDef(minWidth = 280),
    Study_type = colDef(minWidth = 280),
    Original = colDef(minWidth = 280),
    Miscellaneous = colDef(minWidth = 280),
    Command_info = colDef(minWidth = 800),
    Name = colDef(minWidth = 280),
    Path = colDef(minWidth = 280),
    Model_Type = colDef(minWidth = 280)
  ), 
  showPageSizeOptions = TRUE,
  groupBy = "Construct_Concept_Behaviours"
)