step_pretrained_text_embedding: Pretrained text-embeddings

In tfhub: Interface to 'TensorFlow' Hub

Description

'step_pretrained_text_embedding' creates a *specification* of a recipe step that will transform text data into its numerical transformation based on a pretrained model.

Usage

step_pretrained_text_embedding(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  handle,
  args = NULL,
  skip = FALSE,
  id = recipes::rand_id("pretrained_text_embedding")
)

Arguments

recipe	A recipe object. The step will be added to the sequence of operations for this recipe.
...	One or more selector functions to choose variables.
role	Role for the created variables
trained	A logical to indicate if the quantities for preprocessing have been estimated.
handle	the Module handle to resolve.
args	other arguments passed to [hub_load()].
skip	A logical. Should the step be skipped when the recipe is baked by [recipes::bake.recipe()]? While all operations are baked when [recipes::prep.recipe()] is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using 'skip = TRUE' as it may affect the computations for subsequent operations
id	A character string that is unique to this step to identify it.

Examples

## Not run: 
library(tibble)
library(recipes)
df <- tibble(text = c('hi', "heello", "goodbye"), y = 0)

rec <- recipe(y ~ text, df)
rec <- rec %>% step_pretrained_text_embedding(
 text,
 handle = "https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim-with-oov/1"
)


## End(Not run)

tfhub documentation built on Dec. 19, 2021, 9:07 a.m.