Wordpiece Tokenization of Character Variables

step_tokenize_wordpiece() creates a specification of a recipe step that will convert a character predictor into a token variable using WordPiece tokenization.

Usage

step_tokenize_wordpiece(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  columns = NULL,
  vocab = wordpiece::wordpiece_vocab(),
  unk_token = "[UNK]",
  max_chars = 100,
  skip = FALSE,
  id = rand_id("tokenize_wordpiece")
)

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 which variables are affected by the step. See recipes::selections() for more details.
role: Not used by this step since no new variables are created.
trained: A logical to indicate if the quantities for preprocessing have been estimated.
columns: A character string of variable names that will be populated (eventually) by the terms argument. This is NULL until the step is trained by recipes::prep.recipe().
vocab: Character of Character vector of vocabulary tokens. Defaults to wordpiece_vocab().
unk_token: Token to represent unknown words. Defaults to "[UNK]".
max_chars: Integer, Maximum length of word recognized. Defaults to 100.
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 = FALSE.
id: A character string that is unique to this step to identify it.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any).

Tidying

When you tidy() this step, a tibble is returned with columns terms and id:

terms: character, the selectors or variables selected
id: character, id of this step

Case weights

The underlying operation does not allow for case weights.

Examples

library(recipes)
library(modeldata)
data(tate_text)

tate_rec <- recipe(~., data = tate_text) %>%
  step_tokenize_wordpiece(medium)

tate_obj <- tate_rec %>%
  prep()

bake(tate_obj, new_data = NULL, medium) %>%
  slice(1:2)
#> # A tibble: 2 × 1
#>        medium
#>     <tknlist>
#> 1 [12 tokens]
#> 2  [4 tokens]

bake(tate_obj, new_data = NULL) %>%
  slice(2) %>%
  pull(medium)
#> <textrecipes_tokenlist[1]>
#> [1] [4 tokens]
#> # Unique Tokens: 4

tidy(tate_rec, number = 1)
#> # A tibble: 1 × 2
#>   terms  id                      
#>   <chr>  <chr>                   
#> 1 medium tokenize_wordpiece_SqmX1
tidy(tate_obj, number = 1)
#> # A tibble: 1 × 2
#>   terms  id                      
#>   <chr>  <chr>                   
#> 1 medium tokenize_wordpiece_SqmX1