step_word_embeddings creates a specification of a recipe step that will
convert a tokenlist into word-embedding dimensions by aggregating the
vectors of each token from a pre-trained embedding.
step_word_embeddings( recipe, ..., role = "predictor", trained = FALSE, columns = NULL, embeddings, aggregation = c("sum", "mean", "min", "max"), aggregation_default = 0, prefix = "w_embed", skip = FALSE, id = rand_id("word_embeddings") ) # S3 method for step_word_embeddings 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
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
A tibble of pre-trained word embeddings, such as those returned by the embedding_glove function function from the textdata package The first column should contain tokens, and additional columns should contain embeddings vectors.
A character giving the name of the aggregation function to use. Must be one of "sum", "mean", "min", and "max". Defaults to "sum".
A numeric denoting the default value for case with no words are matched in embedding. Defaults to 0.
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
A character string that is unique to this step to identify it.
An updated version of
recipe with the new step added to the
sequence of existing steps (if any).
Word embeddings map words (or other tokens) into a high-dimensional feature space. This function maps pre-trained word embeddings onto the tokens in your data.
embeddings provides the pre-trained vectors. Each dimension
present in this tibble becomes a new feature column, with each column
aggregated across each row of your text using the function supplied in the
The new components will have names that begin with
prefix, then the name
of the aggregation function, then the name of the variable from the
embeddings tibble (usually something like "d7"). For example, using the
default "word_embeddings" prefix, the "sum" aggregation, and the GloVe
embeddings from the textdata package (where the column names are
d2, etc), new columns would be
step_tokenize() to turn character into tokenlist.
library(recipes) embeddings <- tibble( tokens = c("the", "cat", "ran"), d1 = c(1, 0, 0), d2 = c(0, 1, 0), d3 = c(0, 0, 1) ) sample_data <- tibble( text = c( "The.", "The cat.", "The cat ran." ), text_label = c("fragment", "fragment", "sentence") ) rec <- recipe(text_label ~ ., data = sample_data) %>% step_tokenize(text) %>% step_word_embeddings(text, embeddings = embeddings) obj <- rec %>% prep() bake(obj, sample_data)#> # A tibble: 3 x 4 #> text_label w_embed_sum_d1 w_embed_sum_d2 w_embed_sum_d3 #> <fct> <dbl> <dbl> <dbl> #> 1 fragment 1 0 0 #> 2 fragment 1 1 0 #> 3 sentence 1 1 1tidy(rec, number = 2)#> # A tibble: 1 x 4 #> terms embeddings_rows aggregation id #> <chr> <int> <chr> <chr> #> 1 text 3 sum word_embeddings_LxmtTtidy(obj, number = 2)#> # A tibble: 1 x 4 #> terms embeddings_rows aggregation id #> <quos> <int> <chr> <chr> #> 1 text 3 sum word_embeddings_LxmtT