diff --git a/sample.lua b/sample.lua
index e6d0f0c8..3ddd0e5e 100644
--- a/sample.lua
+++ b/sample.lua
@@ -28,6 +28,7 @@ cmd:argument('-model','model checkpoint to use for sampling')
 cmd:option('-seed',123,'random number generator\'s seed')
 cmd:option('-sample',1,' 0 to use max at each timestep, 1 to sample at each timestep')
 cmd:option('-primetext',"",'used as a prompt to "seed" the state of the LSTM using a given sequence, before we sample.')
+cmd:option('-ignore_bad_primetext',false,'ignore characters in primetext that are not in the trained vocabulary')
 cmd:option('-length',2000,'number of characters to sample')
 cmd:option('-temperature',1,'temperature of sampling')
 cmd:option('-gpuid',0,'which gpu to use. -1 = use CPU')
@@ -92,6 +93,9 @@ if string.len(seed_text) > 0 then
     gprint('seeding with ' .. seed_text)
     gprint('--------------------------')
     for c in seed_text:gmatch'.' do
+        if vocab[c] == nil and opt.ignore_bad_primetext then
+            goto skip_seed_character
+        end
         prev_char = torch.Tensor{vocab[c]}
         io.write(ivocab[prev_char[1]])
         if opt.gpuid >= 0 then prev_char = prev_char:cuda() end
@@ -100,6 +104,7 @@ if string.len(seed_text) > 0 then
         current_state = {}
         for i=1,state_size do table.insert(current_state, lst[i]) end
         prediction = lst[#lst] -- last element holds the log probabilities
+        ::skip_seed_character::
     end
 else
     -- fill with uniform probabilities over characters (? hmm)