pangoling1 is an R package for estimating the predictability of
words in a given context using transformer models. The package provides
an interface for utilizing pre-trained transformer models (such as GPT-2
or BERT) to obtain word probabilities. These word probabilities are
often utilized as predictors in psycholinguistic studies. This package
can be useful for researchers in the field of psycholinguistics who want
to leverage the power of transformer models in their work.
The package is mostly a wrapper of the python package
transformers to process data
in a convenient format.
The training data of the most popular models (such as GPT-2) haven’t been released, so one cannot inspect it. It’s clear that the data contain a lot of unfiltered content from the internet, which is far from neutral. See for example the scope in the openAI team’s model card for GPT-2, but it should be the same for many other models, and the limitations and bias section of GPT-2 in Hugging Face website.
To install the latest CRAN version of pangoling use:
install.packages("pangoling")To install the latest version from github use:
install.packages("pangoling", repos = "https://ropensci.r-universe.dev")install_py_pangoling function facilitates the installation of Python
packages needed for using pangoling within an R environment, using the
reticulate package for managing Python environments. This needs to be
done once.
install_py_pangoling()This is a basic example which shows you how to get log-probabilities of words in a dataset:
library(pangoling)
library(tidytable) #fast alternative to dplyrGiven a (toy) dataset where sentences are organized with one word or short phrase in each row:
sentences <- c("The apple doesn't fall far from the tree.",
"Don't judge a book by its cover.")
(df_sent <- strsplit(x = sentences, split = " ") |>
map_dfr(.f = ~ data.frame(word = .x), .id = "sent_n"))
#> # A tidytable: 15 × 2
#> sent_n word
#> <int> <chr>
#> 1 1 The
#> 2 1 apple
#> 3 1 doesn't
#> 4 1 fall
#> 5 1 far
#> 6 1 from
#> 7 1 the
#> 8 1 tree.
#> 9 2 Don't
#> 10 2 judge
#> 11 2 a
#> 12 2 book
#> 13 2 by
#> 14 2 its
#> 15 2 cover.One can get the log-transformed probability of each word based on GPT-2 as follows:
df_sent <- df_sent |>
mutate(lp = causal_words_pred(word, by = sent_n))
#> Processing using causal model 'gpt2/' ...
#> Processing a batch of size 1 with 10 tokens.
#> Processing a batch of size 1 with 9 tokens.
#> Text id: 1
#> `The apple doesn't fall far from the tree.`
#> Text id: 2
#> `Don't judge a book by its cover.`
#> ***
df_sent
#> # A tidytable: 15 × 3
#> sent_n word lp
#> <int> <chr> <dbl>
#> 1 1 The NA
#> 2 1 apple -10.9
#> 3 1 doesn't -5.50
#> 4 1 fall -3.60
#> 5 1 far -2.91
#> 6 1 from -0.745
#> 7 1 the -0.207
#> 8 1 tree. -1.58
#> 9 2 Don't NA
#> 10 2 judge -6.27
#> 11 2 a -2.33
#> 12 2 book -1.97
#> 13 2 by -0.409
#> 14 2 its -0.257
#> 15 2 cover. -1.38citation("pangoling")
Users are encouraged to not only cite pangoling, but also the python
package `transformers` (and the specific LLM they are using):
Nicenboim B (2025-04-07 17:00:02 UTC). _pangoling: Access to large
language model predictions in R_. doi:10.5281/zenodo.7637526
<https://doi.org/10.5281/zenodo.7637526>, R package version 1.0.3,
<https://github.com/ropensci/pangoling>.
Wolf T, Debut L, Sanh V, Chaumond J, Delangue C, Moi A, Cistac P,
Rault T, Louf R, Funtowicz M, Davison J, Shleifer S, von Platen P, Ma
C, Jernite Y, Plu J, Xu C, Le Scao T, Gugger S, Drame M, Lhoest Q,
Rush AM (2020). "HuggingFace's Transformers: State-of-the-art Natural
Language Processing." 1910.03771, <https://arxiv.org/abs/1
8296
910.03771>.
To see these entries in BibTeX format, use 'print(<citation>,
bibtex=TRUE)', 'toBibtex(.)', or set
'options(citation.bibtex.max=999)'.See the Contributing guidelines.
Please note that this package is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
Another R package that act as a wrapper for
transformers is
text However, text is more general, and its
focus is on Natural Language Processing and Machine Learning.
Footnotes
-
The logo of the package was created with stable diffusion and the R package hexSticker. ↩