diff --git a/examples/roberta/README.md b/examples/roberta/README.md
index 21b04c845a..537c55f3fa 100644
--- a/examples/roberta/README.md
+++ b/examples/roberta/README.md
@@ -134,6 +134,19 @@ roberta.cuda()
 roberta.predict('new_task', tokens)  # tensor([[-1.1050, -1.0672, -1.1245]], device='cuda:0', grad_fn=<LogSoftmaxBackward>)
 ```
 
+##### Filling mask:
+Some examples from the [Natural Questions dataset](https://ai.google.com/research/NaturalQuestions/).
+```python
+>>> roberta.fill_mask("The first Star wars movie came out in <mask>", topk=3)
+[('The first Star wars movie came out in 1977', 0.9504712224006653), ('The first Star wars movie came out in 1978', 0.009986752644181252), ('The first Star wars movie came out in 1979', 0.00957468245178461)]
+
+>>> roberta.fill_mask("Vikram samvat calender is official in <mask>", topk=3)
+[('Vikram samvat calender is official in India', 0.21878768503665924), ('Vikram samvat calender is official in Delhi', 0.08547217398881912), ('Vikram samvat calender is official in Gujarat', 0.07556255906820297)]
+
+>>> roberta.fill_mask("<mask> is the common currency of the European Union", topk=3)
+[('Euro is the common currency of the European Union', 0.945650577545166), ('euro is the common currency of the European Union', 0.025747718289494514), ('€ is the common currency of the European Union', 0.011183015070855618)]
+```
+
 ##### Evaluating the `roberta.large.mnli` model
 
 Example python code snippet to evaluate accuracy on the MNLI dev_matched set.
diff --git a/fairseq/models/roberta/hub_interface.py b/fairseq/models/roberta/hub_interface.py
index f7ba231e3b..22ce96e89f 100644
--- a/fairseq/models/roberta/hub_interface.py
+++ b/fairseq/models/roberta/hub_interface.py
@@ -133,3 +133,48 @@ def extract_features_aligned_to_words(self, sentence: str, return_all_hiddens: b
         assert len(doc) == aligned_feats.size(0)
         doc.user_token_hooks['vector'] = lambda token: aligned_feats[token.i]
         return doc
+
+    def fill_mask(self, masked_input: str, topk: int = 5):
+        masked_token = '<mask>'
+        assert masked_token in masked_input and masked_input.count(masked_token) == 1, \
+            "Please add one {0} token for the input, eg: 'He is a {0} guy'".format(masked_token)
+
+        text_spans = masked_input.split(masked_token)
+        text_spans_bpe = (' {0} '.format(masked_token)).join(
+            [self.bpe.encode(text_span.rstrip()) for text_span in text_spans]
+        ).strip()
+        tokens = self.task.source_dictionary.encode_line(
+            '<s> ' + text_spans_bpe,
+            append_eos=True,
+        )
+
+        masked_index = (tokens == self.task.mask_idx).nonzero()
+        if tokens.dim() == 1:
+            tokens = tokens.unsqueeze(0)
+
+        features, extra = self.model(
+            tokens.long().to(device=self.device),
+            features_only=False,
+            return_all_hiddens=False,
+        )
+        logits = features[0, masked_index, :].squeeze()
+        prob = logits.softmax(dim=0)
+        values, index = prob.topk(k=topk, dim=0)
+        topk_predicted_token_bpe = self.task.source_dictionary.string(index)
+
+        topk_filled_outputs = []
+        for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ')):
+            predicted_token = self.bpe.decode(predicted_token_bpe)
+            if " {0}".format(masked_token) in masked_input:
+                topk_filled_outputs.append((
+                    masked_input.replace(
+                        ' {0}'.format(masked_token), predicted_token
+                    ),
+                    values[index].item(),
+                ))
+            else:
+                topk_filled_outputs.append((
+                    masked_input.replace(masked_token, predicted_token),
+                    values[index].item(),
+                ))
+        return topk_filled_outputs