step_texthash creates a specification of a recipe step that will convert a tokenlist into multiple variables using the hashing trick.

  role = "predictor",
  trained = FALSE,
  columns = NULL,
  signed = TRUE,
  num_terms = 1024,
  prefix = "hash",
  skip = FALSE,
  id = rand_id("texthash")

# S3 method for step_texthash
tidy(x, ...)



A recipe object. The step will be added to the sequence of operations for this recipe.


One or more selector functions to choose variables. For step_texthash, this indicates the variables to be encoded into a tokenlist. See recipes::selections() for more details. For the tidy method, these are not currently used.


For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new columns created by the original variables will be used as predictors in a model.


A logical to indicate if the recipe has been baked.


A list of tibble results that define the encoding. This is NULL until the step is trained by recipes::prep.recipe().


A logical, indicating whether to use a signed hash-function to reduce collisions when hashing. Defaults to TRUE.


An integer, the number of variables to output. Defaults to 1024.


A character string that will be the prefix to the resulting new variables. See notes below.


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.


A character string that is unique to this step to identify it.


A step_texthash object.


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


Feature hashing, or the hashing trick, is a transformation of a text variable into a new set of numerical variables. This is done by applying a hashing function over the tokens and using the hash values as feature indices. This allows for a low memory representation of the text. This implementation is done using the MurmurHash3 method.

The argument num_terms controls the number of indices that the hashing function will map to. This is the tuning parameter for this transformation. Since the hashing function can map two different tokens to the same index, will a higher value of num_terms result in a lower chance of collision.

The new components will have names that begin with prefix, then the name of the variable, followed by the tokens all separated by -. The variable names are padded with zeros. For example, if num_terms < 10, their names will be hash1 - hash9. If num_terms = 101, their names will be hash001 - hash101.


Kilian Weinberger; Anirban Dasgupta; John Langford; Alex Smola; Josh Attenberg (2009).

See also

step_tokenize() to turn character into tokenlist.

Other tokenlist to numeric steps: step_tfidf(), step_tf(), step_word_embeddings()


if (requireNamespace("text2vec", quietly = TRUE)) { library(recipes) library(modeldata) data(okc_text) okc_rec <- recipe(~ ., data = okc_text) %>% step_tokenize(essay0) %>% step_tokenfilter(essay0, max_tokens = 10) %>% step_texthash(essay0) okc_obj <- okc_rec %>% prep() bake(okc_obj, okc_text) tidy(okc_rec, number = 2) tidy(okc_obj, number = 2) }
#> # A tibble: 1 x 3 #> terms value id #> <quos> <list> <chr> #> 1 essay0 <int [1]> tokenfilter_dDBpr