README.Rmd

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output: github_document
---

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knitr::opts_chunk$set(
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# huggingfaceR

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The goal of `huggingfaceR` is to to bring state-of-the-art NLP models to R. `huggingfaceR` is built on top of Hugging Face's [transformer](https://huggingface.co/docs/transformers/index) library.

## Installation

Prior to installing `huggingfaceR` please be sure to have your python environment set up correctly. 

```{r eval = FALSE}
install.packages("reticulate")
library(reticulate)

install_miniconda()
```

If you are having issues, more detailed instructions on how to install and configure python can be found [here](https://support.rstudio.com/hc/en-us/articles/360023654474-Installing-and-Configuring-Python-with-RStudio).

Once you have python installed and configured you need to ensure that you have the `keras` python library installed.

```{r eval = FALSE}
py_install("keras")
```

After that you can install the development version of huggingfaceR from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("farach/huggingfaceR")
```

## Example

`huggingfaceR` makes use of the `transformer` `pipline()` function to quickly make pre-trained models available for use in R. In this example we will load the `distilbert-base-uncased-finetuned-sst-2-english` model to obtain sentiment scores.

```{r example}
library(huggingfaceR)

distilBERT <- hf_load_model("distilbert-base-uncased-finetuned-sst-2-english")
```

With the model now loaded, we can begin using the model. 

```{r}
distilBERT("I like you. I love you")
```
We can use this model in a typical tidyverse processing chunk. First we load some libraries.

```{r}
library(tidyverse)
library(janeaustenr)
library(tidytext)
```

Here we get the sentiment score assigned to the text in "Sense & Sensibility".

```{r}
austen_books() |>
  filter(
    book == "Sense & Sensibility",
    text != ""
    ) %>%
  sample_n(20) |>
  mutate(
    distilBERT_sent = distilBERT(text),
    .before = text
  ) |>
  unnest_wider(distilBERT_sent)
```