step_hf_embed: Embedding Recipe Step

In huggingfaceR: Access 'Hugging Face' Models and Datasets

Embedding Recipe Step

Description

Create text embeddings using a Hugging Face model as part of a tidymodels recipe. This step converts text columns into embedding features for downstream modeling.

Usage

step_hf_embed(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  model = hf_default_model("embed"),
  token = NULL,
  embeddings = NULL,
  skip = FALSE,
  id = recipes::rand_id("hf_embed")
)

## S3 method for class 'step_hf_embed'
tidy(x, ...)

## S3 method for class 'step_hf_embed'
tunable(x, ...)

Arguments

recipe	A recipe object.
...	One or more text column selectors (see recipes::selections()).
role	Character string. Role for the new embedding variables. Default: "predictor".
trained	Logical. Internal use only.
model	Character string. Hugging Face model ID for embeddings. Default: "BAAI/bge-small-en-v1.5".
token	Character string or NULL. API token for authentication.
embeddings	List. Internal use only (stores embeddings during training).
skip	Logical. Should step be skipped when baking? Default: FALSE.
id	Character string. Unique ID for this step.
x	A step_hf_embed object

Value

An updated recipe object.

Examples

## Not run: 
library(tidymodels)
library(dplyr)

# Create a recipe with embeddings
rec <- recipe(sentiment ~ text, data = train_data) |>
  step_hf_embed(text, model = "BAAI/bge-small-en-v1.5")

# Use in a workflow
wf <- workflow() |>
  add_recipe(rec) |>
  add_model(logistic_reg()) |>
  fit(data = train_data)

## End(Not run)

huggingfaceR documentation built on Aug. 30, 2026, 1:06 a.m.