The Language-Based Model Assessment (L-BAM) Library

The text-package allows you to create pre-trained models using the text-train functions (e.g., textTrain()) or the fine tune functions (e.g., textFineTuneDomain). The models can be saved and used on new data using the text prediction functions (e.g., textPredict()). The L-BAM library below shows pre-trained models that are available to download. The models can be called with textPredict(), textAssess() or textClassify() like this:

library(text)

# Example calling a model using the URL
textPredict(
  model_info = "https://github.com/OscarKjell/text_models/raw/main/valence_models/facebook_model.rds",
  texts = "what is the valence of this text?"
)


# Example calling a model having an abbreviation
textClassify(
  model_info = "implicit_power_roberta_large_L23_v1",
  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.

References

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