The L-BAM library comprises information about pre-trained models. The
models can be called with textPredict(),
textAssess() or textClassify() like this:
library(text)
# Example calling a model using the URL
textPredict(
model_info = "facebook_valence",
texts = "what is the valence of this text?"
)
# Example calling a model having an abbreviation
textClassify(
model_info = "implicit_power_fine_tuned_roberta",
texts = "It looks like they have problems collaborating."
)The text prediction functions can be given a model and a text, and automatically transform the text to word embeddings and produce estimated scores or probabilities.
If you want to add a pre-trained model to the L-BAM library, please fill out the details in this Google sheet and email us (oscar [ d_o t] kjell [a _ t] psy [DOT] lu [d_o_t]se) so that we can update the table online.
Note that you can adjust the width of the columns when scrolling the table.
Gu, Kjell, Schwartz & Kjell. (2024). Natural Language Response Formats for Assessing Depression and Worry with Large Language Models: A Sequential Evaluation with Model Pre-registration.
Kjell, O. N., Sikström, S., Kjell, K., & Schwartz, H. A. (2022). Natural language analyzed with AI-based transformers predict traditional subjective well-being measures approaching the theoretical upper limits in accuracy. Scientific reports, 12(1), 3918.
Nilsson, Runge, Ganesan, Lövenstierne, Soni & Kjell (2024) Automatic Implicit Motives Codings are at Least as Accurate as Humans’ and 99% Faster