knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
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 transformers library; and has support for navigating the Hugging Face Hub The Hub.
Prior to installing huggingfaceR
please be sure to have your python environment set up correctly.
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.
After that you can install the development version of huggingfaceR from GitHub with:
# install.packages("devtools") devtools::install_github("farach/huggingfaceR")
huggingfaceR
makes use of the transformers
pipline()
abstraction to quickly make pre-trained language models available for use in R. In this example we will load the distilbert-base-uncased-finetuned-sst-2-english
model and its tokenizer into a pipeline object to obtain sentiment scores.
library(huggingfaceR) distilBERT <- hf_load_pipeline( model_id = "distilbert-base-uncased-finetuned-sst-2-english", task = "text-classification" ) distilBERT
With the pipeline now loaded, we can begin using the model.
distilBERT("I like you. I love you")
We can use this pipeline in a typical tidyverse processing chunk. First we load the tidyverse
.
library(tidyverse)
We can use the huggingfaceR
hf_load_dataset()
function to pull in the emotion Hugging Face dataset. This dataset contains English Twitter messages with six basic emotions: anger, fear, love, sadness, and surprise. We are interested in how well the Distilbert model classifies these emotions as either a positive or a negative sentiment.
emo <- hf_load_dataset( dataset = "emo", split = "train", as_tibble = TRUE, label_name = "int2str" ) emo_model <- emo %>% sample_n(100) %>% transmute( text, emotion_id = label, emotion_name = label_name, distilBERT_sent = distilBERT(text) ) %>% unnest_wider(distilBERT_sent) glimpse(emo_model)
We can use ggplot2
to visualize the results.
emo_model |> mutate( label = paste0("Distilbert class:\n", label), emotion_name = str_to_title(emotion_name) ) |> ggplot(aes(x = emotion_name, y = score, color = label)) + geom_boxplot(show.legend = FALSE, outlier.alpha = 0.4, ) + scale_color_manual(values = c("#D55E00", "#6699CC")) + facet_wrap(~ label) + labs( title = "Reviewing Distilbert classification predictions", x = "Original label", y = "Model score", caption = "source:\nhttps://huggingface.co/datasets/emo" ) + theme_minimal() + theme( plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 45), axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0)), axis.title.x = element_text(margin = margin(t = 10, r = 0, b = 0, l = 0)) )
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