step_pretrained_text_embedding {tfhub}

Pretrained text-embeddings

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)