Extract Model Description from a mable - r

Question

I have a mable object that is like so:
models
# A mable: 1 x 3
ets arima nnetar
<model> <model> <model>
1 <ETS(M,Ad,M)> <ARIMA(2,1,2)(0,0,2)[12]> <NNAR(14,1,10)[12]>
I just want the models descriptions so I can place them in a plot. So I ran the following code:
model_desc <- models %>%
gather() %>%
select(key, value) %>%
set_names("model","model_desc") %>%
mutate(model_desc_char = model_desc %>% as.character())
as_tibble() %>%
select(model, model_desc)
This still gives me back a tibble where model_desc is still a list object. I think this is because of how a mable is constructed and how its structure is supposed to be.
** UPDATE **
I solved the problem by doing the following:
model_desc <- models %>%
as_tibble() %>%
gather() %>%
mutate(model_desc = print(value)) %>%
select(key, model_desc) %>%
set_names("model", "model_desc")

Answer 1

For anybody else who will encounter this going forward, I have pasted a solution that works for me with the latest versions of fable/fabletools.
library(fable)
#> Loading required package: fabletools
library(tsibble)
library(tsibbledata)
library(lubridate)
#>
#> Attaching package: 'lubridate'
#> The following object is masked from 'package:tsibble':
#>
#> interval
#> The following objects are masked from 'package:base':
#>
#> date, intersect, setdiff, union
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(tidyr)
aus_retail %>%
filter(
State %in% c("New South Wales", "Victoria"),
Industry == "Department stores"
) %>%
model(
ets = ETS(box_cox(Turnover, 0.3)),
arima = ARIMA(log(Turnover)),
snaive = SNAIVE(Turnover)
) %>%
pivot_longer(cols = -c(State, Industry),
names_to = "model_type",
values_to = "model_specifics_mdl") %>%
mutate(model_specifics = format(model_specifics_mdl)) %>%
select(-model_specifics_mdl)
#> # A tibble: 6 x 4
#> State Industry model_type model_specifics
#> <chr> <chr> <chr> <chr>
#> 1 New South Wales Department stores ets <ETS(A,Ad,A)>
#> 2 New South Wales Department stores arima <ARIMA(2,1,1)(2,1,1)[12]>
#> 3 New South Wales Department stores snaive <SNAIVE>
#> 4 Victoria Department stores ets <ETS(A,A,A)>
#> 5 Victoria Department stores arima <ARIMA(2,1,1)(1,1,2)[12]>
#> 6 Victoria Department stores snaive <SNAIVE>
Created on 2020-09-07 by the reprex package (v0.3.0)

Answer 2

This ended up solving my issue:
model_desc <- models %>%
as_tibble() %>%
gather() %>%
mutate(model_desc = print(value)) %>%
select(key, model_desc) %>%
set_names("model", "model_desc")