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 |
| Maintainer: | Bruno Nicenboim <b.nicenboim at tilburguniversity.edu> |
| BugReports: | https://github.com/ropensci/pangoling/issues |
| License: | MIT + file LICENSE |
| 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 |
| Package source: | pangoling_1.0.3.tar.gz |
| Windows binaries: | r-devel: pangoling_1.0.3.zip, r-release: pangoling_1.0.3.zip, r-oldrel: pangoling_1.0.3.zip |
| macOS binaries: | r-release (arm64): pangoling_1.0.3.tgz, r-oldrel (arm64): pangoling_1.0.3.tgz, r-release (x86_64): pangoling_1.0.3.tgz, r-oldrel (x86_64): pangoling_1.0.3.tgz |
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