diff --git a/README.md b/README.md
index 407b52a..474749e 100644
--- a/README.md
+++ b/README.md
@@ -71,12 +71,14 @@ Download or [clone](https://www.mathworks.com/help/matlab/matlab_prog/use-source
 ## Example: Classify Text Data Using BERT
 The simplest use of a pretrained BERT model is to use it as a feature extractor. In particular, you can use the BERT model to convert documents to feature vectors which you can then use as inputs to train a deep learning classification network.
 
-The example [`ClassifyTextDataUsingBERT.m`](./ClassifyTextDataUsingBERT.m) shows how to use a pretrained BERT model to classify failure events given a data set of factory reports.
+The example [`ClassifyTextDataUsingBERT.m`](./ClassifyTextDataUsingBERT.m) shows how to use a pretrained BERT model to classify failure events given a data set of factory reports. This example requires the `factoryReports.csv` data set from the Text Analytics Toolbox example [Prepare Text Data for Analysis](https://www.mathworks.com/help/textanalytics/ug/prepare-text-data-for-analysis.html).
 
 ## Example: Fine-Tune Pretrained BERT Model
 To get the most out of a pretrained BERT model, you can retrain and fine tune the BERT parameters weights for your task.
 
-The example [`FineTuneBERT.m`](./FineTuneBERT.m) shows how to fine-tune a pretrained BERT model to classify failure events given a data set of factory reports.
+The example [`FineTuneBERT.m`](./FineTuneBERT.m) shows how to fine-tune a pretrained BERT model to classify failure events given a data set of factory reports. This example requires the `factoryReports.csv` data set from the Text Analytics Toolbox example [Prepare Text Data for Analysis](https://www.mathworks.com/help/textanalytics/ug/prepare-text-data-for-analysis.html).
+
+The example [`FineTuneBERTJapanese.m`](./FineTuneBERTJapanese.m) shows the same workflow using a pretrained Japanese-BERT model. This example requires the `factoryReportsJP.csv` data set from the Text Analytics Toolbox example [Analyze Japanese Text Data](https://www.mathworks.com/help/textanalytics/ug/analyze-japanese-text.html), available in R2023a or later.
 
 ## Example: Analyze Sentiment with FinBERT
 FinBERT is a sentiment analysis model trained on financial text data and fine-tuned for sentiment analysis.
diff --git a/predictMaskedToken.m b/predictMaskedToken.m
index 7aeeb6b..45a8452 100644
--- a/predictMaskedToken.m
+++ b/predictMaskedToken.m
@@ -6,7 +6,7 @@
 %   replaces instances of mdl.Tokenizer.MaskToken in the string text with
 %   the most likely token according to the BERT model mdl.
 
-% Copyright 2021 The MathWorks, Inc.
+% Copyright 2021-2023 The MathWorks, Inc.
 arguments
     mdl {mustBeA(mdl,'struct')}
     str {mustBeText}
@@ -44,7 +44,7 @@
     tokens = fulltok.tokenize(pieces(i));
     if ~isempty(tokens)
         % "" tokenizes to empty - awkward
-        x = cat(2,x,fulltok.encode(tokens));
+        x = cat(2,x,fulltok.encode(tokens{1}));
     end
     if i<numel(pieces)
         x = cat(2,x,maskCode);
diff --git a/test/tpredictMaskedToken.m b/test/tpredictMaskedToken.m
new file mode 100644
index 0000000..b21ceac
--- /dev/null
+++ b/test/tpredictMaskedToken.m
@@ -0,0 +1,47 @@
+classdef(SharedTestFixtures={
+        DownloadBERTFixture, DownloadJPBERTFixture}) tpredictMaskedToken < matlab.unittest.TestCase
+    % tpredictMaskedToken   Unit test for predictMaskedToken
+    
+    % Copyright 2023 The MathWorks, Inc.
+
+    properties(TestParameter)
+        Models = {"tiny","japanese-base-wwm"}
+        ValidText = iGetValidText;
+    end
+    
+    methods(Test)       
+        function verifyOutputDimSizes(test, Models, ValidText)
+            inSize = size(ValidText);
+            mdl = bert("Model", Models);
+            outputText = predictMaskedToken(mdl,ValidText);
+            test.verifyEqual(size(outputText), inSize);
+        end
+        
+        function maskTokenIsRemoved(test, Models)
+            text = "This has a [MASK] token.";
+            mdl = bert("Model", Models);
+            outputText = predictMaskedToken(mdl,text);
+            test.verifyFalse(contains(outputText, "[MASK]"));
+        end
+
+        function inputWithoutMASKRemainsTheSame(test, Models)
+            text = "This has a no mask token.";
+            mdl = bert("Model", Models);
+            outputText = predictMaskedToken(mdl,text);
+            test.verifyEqual(text, outputText);
+        end
+    end
+end
+
+function validText = iGetValidText
+manyStrs = ["Accelerating the pace of [MASK] and science";
+            "The cat [MASK] soundly.";
+            "The [MASK] set beautifully."];
+singleStr = "Artificial intelligence continues to shape the future of industries," + ...
+    " as innovative applications emerge in fields such as healthcare, transportation," + ...
+    " entertainment, and finance, driving productivity and enhancing human capabilities.";
+validText = struct('StringsAsColumns',manyStrs,...
+             'StringsAsRows',manyStrs',...
+             'ManyStrings',repmat(singleStr,3),...
+             'SingleString',singleStr);
+end
\ No newline at end of file