Errors in plots from R package LIME when using text to predict outcome with tidymodels

Question

I am trying to use the lime package to explain the results from a lasso model fitted with tidymodels that uses text to predict the outcome. I have done my best to code this up, but there are two problems:

1. plot_features(explanation) does not produce the desired figure that lime should be producing.

2. plot_text_explanations(explanation) issues an error instead of producing the plot (see this blog post for examples of what this plot should look like).

I want to use tidymodels specifically, and not another package like caret or another model function. Any help with this would be much appreciated. Below is a reproducible example:

library("conflicted")
library("lime")
conflict_prefer("explain", "lime")
library("textrecipes")
library("tidymodels")
library("tidyverse")
conflict_prefer("slice", "dplyr")

reviews <- quanteda.textmodels::data_corpus_moviereviews |> 
  quanteda::convert(to = "data.frame") |> 
  tibble() |> 
  select(rating = sentiment, text)

set.seed(1234)
review_split <- initial_split(reviews, strata = rating)
review_train <- training(review_split)
review_test <- testing(review_split)

lasso_recipe <- recipe(rating ~ text, data = review_train) |>
  step_tokenize(text) |>
  step_stopwords(text) |>
  step_tokenfilter(text, max_tokens = 100) |>
  step_tfidf(text) |>
  step_normalize(all_predictors())

lasso_spec <- logistic_reg(penalty = 0.1, mixture = 1) |>
  set_mode("classification") |>
  set_engine("glmnet")

lasso_wf <- workflow() |>
  add_recipe(lasso_recipe) |>
  add_model(lasso_spec)

lasso_fit <- lasso_wf |>
  fit(data = review_train)

predict(lasso_fit, review_test)
#> # A tibble: 500 × 1
#>    .pred_class
#>    <fct>      
#>  1 neg        
#>  2 neg        
#>  3 neg        
#>  4 pos        
#>  5 neg        
#>  6 neg        
#>  7 pos        
#>  8 pos        
#>  9 pos        
#> 10 neg        
#> # … with 490 more rows

preprocess <- function(input) {
  baked <- recipe(rating ~ text, data = input) |>
    step_tokenize(text) |>
    step_stopwords(text) |>
    step_tokenfilter(text, max_tokens = 100) |>
    step_tfidf(text) |>
    step_normalize(all_predictors()) |>
    prep() |>
    bake(new_data = NULL) |>
    select(-rating)
  return(baked)
}

preprocess(slice(reviews, 1:3))
#> # A tibble: 3 × 100
#>   tfidf_text_10 tfidf_…¹ tfidf…² tfidf…³ tfidf…⁴ tfidf…⁵ tfidf…⁶ tfidf…⁷ tfidf…⁸
#>           <dbl>    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#> 1         1.15    -0.577   1.15    1.15   -0.813   1.15    0.283   1.15   -0.577
#> 2        -0.577   -0.577  -0.577  -0.577   1.12   -0.577  -1.11   -0.577   1.15 
#> 3        -0.577    1.15   -0.577  -0.577  -0.304  -0.577   0.828  -0.577  -0.577
#> # … with 91 more variables: tfidf_text_attempt <dbl>,
#> #   tfidf_text_audience <dbl>, tfidf_text_away <dbl>, tfidf_text_back <dbl>,
#> #   tfidf_text_bad <dbl>, tfidf_text_baldwin <dbl>, tfidf_text_based <dbl>,
#> #   tfidf_text_big <dbl>, tfidf_text_biggest <dbl>,
#> #   tfidf_text_characters <dbl>, tfidf_text_chase <dbl>,
#> #   tfidf_text_claire <dbl>, tfidf_text_clear <dbl>, tfidf_text_comes <dbl>,
#> #   tfidf_text_coming <dbl>, tfidf_text_cool <dbl>, tfidf_text_course <dbl>, …

explainer <- lime(
  slice(reviews, 1:3),
  model = extract_fit_parsnip(lasso_fit),
  preprocess = preprocess
)

explanation <- explain(
  slice(reviews, 1:3),
  explainer = explainer,
  labels = "pos",
  n_features = 10
)

# plot_features() does not produce the correct plot
plot_features(explanation)

# plot_text_explanations() issues an error
plot_text_explanations(explanation)
#> Error: original_text is not a string (a length one character vector).