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README.Rmd
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README.Rmd
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---
output: github_document
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# pangoling <a href="http://bruno.nicenboim.me/pangoling/"><img src="man/figures/logo.png" align="right" height="139" /></a>
<!-- badges: start -->
[](https://app.codecov.io/gh/bnicenboim/pangoling?branch=main)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[](https://github.com/bnicenboim/pangoling/actions/workflows/R-CMD-check.yaml)
[](https://www.repostatus.org/#wip)
[](https://zenodo.org/badge/latestdoi/497831295)
[](https://github.com/ropensci/software-review/issues/575)
<!-- badges: end -->
`pangoling`^[The logo of the package was created with [stable diffusion ](https://huggingface.co/spaces/stabilityai/stable-diffusion) and the R package [hexSticker](https://github.com/GuangchuangYu/hexSticker).] 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`](https://pypi.org/project/transformers/) to process data in a convenient format.
## Important! Limitations and bias
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](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases), but it should be the same for many other models, and the [limitations and bias section of GPT-2 in Hugging Face website](https://huggingface.co/gpt2).
## Installation
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:
```{r, eval = FALSE}
# install.packages("remotes") # if needed
remotes::install_github("bnicenboim/pangoling")
```
`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.
```{r, eval = FALSE}
install_py_pangoling()
```
## Example
This is a basic example which shows you how to get log-probabilities of words in a dataset:
```{r, message = FALSE}
library(pangoling)
library(tidytable) #fast alternative to dplyr
```
Given a (toy) dataset where sentences are organized with one word or short phrase in each row:
```{r, cache = TRUE}
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"))
```
One can get the log-transformed probability of each word based on GPT-2 as follows:
```{r, cache = TRUE}
df_sent <- df_sent |>
mutate(lp = causal_lp(word, by = sent_n))
df_sent
```
## How to cite
> Nicenboim B (2023). _pangoling: Access to
> language model predictions in R_. R package
> version `r packageVersion("pangoling")`,
> DOI: [10.5281/zenodo.7637526](https://zenodo.org/badge/latestdoi/497831295),
> <https://github.com/bnicenboim/pangoling>.
## How to contribute
See the [Contributing guidelines](.github/CONTRIBUTING.md).
## Code of conduct
Please note that this package is released with a [Contributor
Code of Conduct](https://ropensci.org/code-of-conduct/).
By contributing to this project, you agree to abide by its terms.
## See also
Another R package that act as a wrapper for [`transformers`](https://pypi.org/project/transformers/) is [`text`](https://r-text.org//) However, `text` is more general, and its focus
is on Natural Language Processing and Machine Learning.