Intepretation of von mises regression

Modeling
1

hi!
I’m fitting the following with the von mises as likelihood function:

form_ri_int <- bf(diff_theta ~ 0 + Intercept + emotion * mask + (1|id), 
                  kappa ~ 0 + Intercept + emotion * mask + (1|id))

prior_von_mises <- c(
  prior(normal(0, 2), class = "b", dpar = ""), # betas prior
  prior(normal(0, 1.5), class = "b", dpar = "kappa") # kappa prior
)

fit_ri_int <- brm(form_ri_int,
                 data = dat_fit,
                 prior = prior_von_mises,
                 family = von_mises(link = "tan_half", link_kappa = "log"),
                 chains = 15,
                 cores = 15,
                 iter = 10000,
                 file = "theta/fit_ri_int",
                 seed = seed)

Where diff_theta is a variable measured in radians of which I’m predicting the circular mean and the spread (kappa).
I have some doubt about interpreting kappa given the link function. I’ve extracted my posterior draws for each parameters that I need and the doing exp(kappa) otherwise some parameters can have negative values. Now the interpretation is that values around 0 means that the distribution is close to uniform and large values means less spread right?