diff --git a/examples/tutorials/sst2_classification_non_distributed.py b/examples/tutorials/sst2_classification_non_distributed.py
index 509b51a36b..fbd602db9c 100644
--- a/examples/tutorials/sst2_classification_non_distributed.py
+++ b/examples/tutorials/sst2_classification_non_distributed.py
@@ -40,7 +40,7 @@
 # 2. Convert tokens into (integer) IDs
 # 3. Add any special tokens IDs
 #
-# XLM-R uses sentencepiece model for text tokenization. Below, we use pre-trained sentencepiepce
+# XLM-R uses sentencepiece model for text tokenization. Below, we use pre-trained sentencepiece
 # model along with corresponding vocabulary to build text pre-processing pipeline using torchtext's transforms.
 # The transforms are pipelined using :py:func:`torchtext.transforms.Sequential` which is similar to :py:func:`torch.nn.Sequential`
 # but is torchscriptable. Note that the transforms support both batched and non-batched text inputs i.e, one