diff --git a/examples/roberta/README.md b/examples/roberta/README.md
index 1b8d637ccb..68dc6701ea 100644
--- a/examples/roberta/README.md
+++ b/examples/roberta/README.md
@@ -146,11 +146,26 @@ logprobs = roberta.predict('new_task', tokens)  # tensor([[-1.1050, -1.0672, -1.
 
 ##### Batched prediction:
 ```python
+import torch
 from fairseq.data.data_utils import collate_tokens
-sentences = ['Hello world.', 'Another unrelated sentence.']
-batch = collate_tokens([roberta.encode(sent) for sent in sentences], pad_idx=1)
-logprobs = roberta.predict('new_task', batch)
-assert logprobs.size() == torch.Size([2, 3])
+
+roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli')
+roberta.eval()
+
+batch_of_pairs = [
+    ['Roberta is a heavily optimized version of BERT.', 'Roberta is not very optimized.'],
+    ['Roberta is a heavily optimized version of BERT.', 'Roberta is based on BERT.'],
+    ['potatoes are awesome.', 'I like to run.'],
+    ['Mars is very far from earth.', 'Mars is very close.'],
+]
+
+batch = collate_tokens(
+    [roberta.encode(pair[0], pair[1]) for pair in batch_of_pairs], pad_idx=1
+)
+
+logprobs = roberta.predict('mnli', batch)
+print(logprobs.argmax(dim=1))
+# tensor([0, 2, 1, 0])
 ```
 
 ##### Using the GPU: