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pangoling: Access to Large Language Model Predictions

Provides access to word predictability estimates using large language models (LLMs) based on 'transformer' architectures via integration with the 'Hugging Face' ecosystem <https://huggingface.co/>. The package interfaces with pre-trained neural networks and supports both causal/auto-regressive LLMs (e.g., 'GPT-2') and masked/bidirectional LLMs (e.g., 'BERT') to compute the probability of words, phrases, or tokens given their linguistic context. For details on GPT-2 and causal models, see Radford et al. (2019) <https://storage.prod.researchhub.com/uploads/papers/2020/06/01/language-models.pdf>, for details on BERT and masked models, see Devlin et al. (2019) <doi:10.48550/arXiv.1810.04805>. By enabling a straightforward estimation of word predictability, the package facilitates research in psycholinguistics, computational linguistics, and natural language processing (NLP).

Version:	1.0.3
Depends:	R (≥ 4.1.0)
Imports:	cachem,data.table,memoise,reticulate,rstudioapi, stats,tidyselect,tidytable (≥ 0.7.2), utils
Suggests:	brms,knitr, parallel,rmarkdown,spelling,testthat (≥3.0.0),tictoc,covr
Published:	2025-04-07
DOI:	10.32614/CRAN.package.pangoling
Author:	Bruno Nicenboim [aut, cre], Chris Emmerly [ctb], Giovanni Cassani [ctb], Lisa Levinson [rev], Utku Turk [rev]
Maintainer:	Bruno Nicenboim <b.nicenboim at tilburguniversity.edu>
BugReports:	https://github.com/ropensci/pangoling/issues
License:	MIT + fileLICENSE
URL:	https://docs.ropensci.org/pangoling/,https://github.com/ropensci/pangoling
NeedsCompilation:	no
Language:	en-US
Citation:	pangoling citation info
Materials:	NEWS
CRAN checks:	pangoling results

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