BPE Tokenization of Character Variables

step_tokenize_bpe() creates a specification of a recipe step that will convert a character predictor into a token variable using Byte Pair Encoding.

Usage

step_tokenize_bpe(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  columns = NULL,
  vocabulary_size = 1000,
  options = list(),
  res = NULL,
  skip = FALSE,
  id = rand_id("tokenize_bpe")
)

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().
vocabulary_size: Integer, indicating the number of tokens in the final vocabulary. Defaults to 1000. Highly encouraged to be tuned.
options: A list of options passed to the tokenizer.
res: The fitted tokenizers.bpe::bpe() model tokenizer will be stored here once this preprocessing step has be trained by prep.recipe().
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 with columns terms (the selectors or variables selected).

Tuning Parameters

This step has 1 tuning parameters:

vocabulary_size: # Unique Tokens in Vocabulary (type: integer, default: 1000)

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_bpe(medium)

tate_obj <- tate_rec %>%
  prep()

bake(tate_obj, new_data = NULL, medium) %>%
  slice(1:2)
#> # A tibble: 2 × 1
#>       medium
#>    <tknlist>
#> 1 [8 tokens]
#> 2 [3 tokens]

bake(tate_obj, new_data = NULL) %>%
  slice(2) %>%
  pull(medium)
#> <textrecipes_tokenlist[1]>
#> [1] [3 tokens]
#> # Unique Tokens: 3

tidy(tate_rec, number = 1)
#> # A tibble: 1 × 2
#>   terms  id                
#>   <chr>  <chr>             
#> 1 medium tokenize_bpe_1JGsH
tidy(tate_obj, number = 1)
#> # A tibble: 1 × 2
#>   terms  id                
#>   <chr>  <chr>             
#> 1 medium tokenize_bpe_1JGsH