diff --git a/examples/run_glue.py b/examples/run_glue.py
index 445a9a59..c0f70e08 100644
--- a/examples/run_glue.py
+++ b/examples/run_glue.py
@@ -279,7 +279,7 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False):
             sep_token=tokenizer.sep_token,
             sep_token_extra=bool(args.model_type in ['roberta']),           # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
             pad_on_left=bool(args.model_type in ['xlnet']),                 # pad on the left for xlnet
-            pad_token=1 if args.model_type in ['roberta'] else 0, # TODO(Lysandre: replace with tokenizer.pad_token when implemented)
+            pad_token=tokenizer.encoder[tokenizer.pad_token] if args.model_type in ['roberta'] else tokenizer.vocab[tokenizer.pad_token],
             pad_token_segment_id=4 if args.model_type in ['xlnet'] else 0,
         )
         if args.local_rank in [-1, 0]:
diff --git a/pytorch_transformers/tokenization_roberta.py b/pytorch_transformers/tokenization_roberta.py
index 8f5cecee..1db80131 100644
--- a/pytorch_transformers/tokenization_roberta.py
+++ b/pytorch_transformers/tokenization_roberta.py
@@ -73,9 +73,10 @@ class RobertaTokenizer(PreTrainedTokenizer):
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 
     def __init__(self, vocab_file, merges_file, errors='replace', bos_token="<s>", eos_token="</s>", sep_token="</s>",
-                 cls_token="<s>", unk_token="<unk>", **kwargs):
+                 cls_token="<s>", unk_token="<unk>", pad_token='<pad>', mask_token='<mask>', **kwargs):
         super(RobertaTokenizer, self).__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token,
-                                               sep_token=sep_token, cls_token=cls_token, **kwargs)
+                                               sep_token=sep_token, cls_token=cls_token, pad_token=pad_token,
+                                               mask_token=mask_token, **kwargs)
 
         self.encoder = json.load(open(vocab_file, encoding="utf-8"))
         self.decoder = {v: k for k, v in self.encoder.items()}