pangoling1 is an R package for estimating the log-probabilities 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 log-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.
There is still no released version of
pangoling. The package is in the ** early** stages of development, and it will probably be subject to changes. To install the latest version from github use:
# install.packages("remotes") # if needed remotes::install_github("bnicenboim/pangoling")
This is a basic example which shows you how to get log-probabilities of words in a dataset:
Given 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_lp(word, .by = sent_n)) #> Processing using causal model 'gpt2'... #> Processing 1 batch(es) of 10 tokens. #> Text id: 1 #> `The apple doesn't fall far from the tree.` #> Processing 1 batch(es) of 9 tokens. #> 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. -1.38
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.