knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) evaluate = FALSE
# Install the text package (only needed the first time) # install.packages("text") library(text) # textrpp_install() # textrpp_initialize() # Get the LBAM as a data frame and filter for models starting with “Dep” lbam <- text::textLBAM() subset( lbam, substr(Construct_Concept_Behaviours, 1, 3) == "dep", select = c(Construct_Concept_Behaviours, Name) ) # Example text to access text_to_assess = c( "I feel down and blue all the time.", "I feel great and have no worries that bothers me.") # Produce depression severity scores using a text-trained model # This command downloads the model, creates word embeddings, and applies the model to the embeddings. depression_scores <- text::textPredict( model_info = "depression_text_phq9_roberta23_gu2024", texts = text_to_assess, dim_name = FALSE) # You can find information about a text-trained model in R. model_Gu2024 <- readRDS("depressiontext_robertaL23_phq9_Gu2024.rds") model_Gu2024 # Assess the harmony in life of the same text as above # The function now uses the same word embeddings as above (i.e., it does not produce new ones). harmony_in_life_scores <- textAssess( model_info = "harmony_text_roberta23_kjell2022", texts = text_to_assess, dim_name = FALSE) # Assign implicit motives labels using fine-tuned models implicit_motive <- text::textClassify( model_info = "implicitpower_roberta_ft_nilsson2024", texts = text_to_assess)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.