diff --git a/test/Microsoft.ML.Tests/Transformers/TextFeaturizerTests.cs b/test/Microsoft.ML.Tests/Transformers/TextFeaturizerTests.cs
index e0f32bcaa1..6604961c57 100644
--- a/test/Microsoft.ML.Tests/Transformers/TextFeaturizerTests.cs
+++ b/test/Microsoft.ML.Tests/Transformers/TextFeaturizerTests.cs
@@ -31,6 +31,7 @@ private class TestClass
         {
             public string A;
             public string[] OutputTokens;
+            public float[] Features = null;
         }
 
         [Fact]
@@ -41,7 +42,7 @@ public void TextFeaturizerWithPredefinedStopWordRemoverTest()
             var dataView = ML.Data.LoadFromEnumerable(data);
 
             var options = new TextFeaturizingEstimator.Options() { StopWordsRemoverOptions = new StopWordsRemovingEstimator.Options(), OutputTokensColumnName = "OutputTokens" };
-            var pipeline = ML.Transforms.Text.FeaturizeText("OutputText", options, "A");
+            var pipeline = ML.Transforms.Text.FeaturizeText("Features", options, "A");
             var model = pipeline.Fit(dataView);
             var engine = model.CreatePredictionEngine<TestClass, TestClass>(ML);
             var prediction = engine.Predict(data[0]);
@@ -51,6 +52,95 @@ public void TextFeaturizerWithPredefinedStopWordRemoverTest()
             Assert.Equal("stop words", string.Join(" ", prediction.OutputTokens));
         }
 
+        [Fact]
+        public void TextFeaturizerWithWordFeatureExtractorTest()
+        {
+            var data = new[] { new TestClass() { A = "This is some text in english", OutputTokens=null},
+                               new TestClass() { A = "This is another example", OutputTokens=null } };
+            var dataView = ML.Data.LoadFromEnumerable(data);
+
+            var options = new TextFeaturizingEstimator.Options()
+            {
+                WordFeatureExtractor = new WordBagEstimator.Options() { NgramLength = 1 },
+                CharFeatureExtractor = null,
+                Norm = TextFeaturizingEstimator.NormFunction.None,
+                OutputTokensColumnName = "OutputTokens"
+            };
+            var pipeline = ML.Transforms.Text.FeaturizeText("Features", options, "A");
+            var model = pipeline.Fit(dataView);
+            var engine = model.CreatePredictionEngine<TestClass, TestClass>(ML);
+
+            var prediction = engine.Predict(data[0]);
+            Assert.Equal(data[0].A.ToLower(), string.Join(" ", prediction.OutputTokens));
+            var expected = new float[] { 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 0.0f, 0.0f };
+            Assert.Equal(expected, prediction.Features);
+
+            prediction = engine.Predict(data[1]);
+            Assert.Equal(data[1].A.ToLower(), string.Join(" ", prediction.OutputTokens));
+            expected = new float[] { 1.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 1.0f };
+            Assert.Equal(expected, prediction.Features);
+        }
+
+        [Fact]
+        public void TextFeaturizerWithCharFeatureExtractorTest()
+        {
+            var data = new[] { new TestClass() { A = "abc efg", OutputTokens=null},
+                               new TestClass() { A = "xyz", OutputTokens=null } };
+            var dataView = ML.Data.LoadFromEnumerable(data);
+
+            var options = new TextFeaturizingEstimator.Options()
+            {
+                WordFeatureExtractor = null,
+                CharFeatureExtractor = new WordBagEstimator.Options() { NgramLength = 1 },
+                Norm = TextFeaturizingEstimator.NormFunction.None,
+                OutputTokensColumnName = "OutputTokens"
+            };
+            var pipeline = ML.Transforms.Text.FeaturizeText("Features", options, "A");
+            var model = pipeline.Fit(dataView);
+            var engine = model.CreatePredictionEngine<TestClass, TestClass>(ML);
+
+            var prediction = engine.Predict(data[0]);
+            Assert.Equal(data[0].A, string.Join(" ", prediction.OutputTokens));
+            var expected = new float[] { 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 0.0f, 0.0f, 0.0f };
+            Assert.Equal(expected, prediction.Features);
+
+            prediction = engine.Predict(data[1]);
+            Assert.Equal(data[1].A, string.Join(" ", prediction.OutputTokens));
+            expected = new float[] { 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 1.0f, 1.0f, 1.0f };
+            Assert.Equal(expected, prediction.Features);
+        }
+
+        [Fact]
+        public void TextFeaturizerWithL2NormTest()
+        {
+            var data = new[] { new TestClass() { A = "abc xyz", OutputTokens=null},
+                               new TestClass() { A = "xyz", OutputTokens=null } };
+            var dataView = ML.Data.LoadFromEnumerable(data);
+
+            var options = new TextFeaturizingEstimator.Options()
+            {
+                CharFeatureExtractor = new WordBagEstimator.Options() { NgramLength = 1},
+                Norm = TextFeaturizingEstimator.NormFunction.L2,
+                OutputTokensColumnName = "OutputTokens"
+            };
+            var pipeline = ML.Transforms.Text.FeaturizeText("Features", options, "A");
+            var model = pipeline.Fit(dataView);
+            var engine = model.CreatePredictionEngine<TestClass, TestClass>(ML);
+
+            var prediction = engine.Predict(data[0]);
+            Assert.Equal(data[0].A, string.Join(" ", prediction.OutputTokens));
+            var exp1 = 0.333333343f;
+            var exp2 = 0.707106769f;
+            var expected = new float[] { exp1, exp1, exp1, exp1, exp1, exp1, exp1, exp1, exp1, exp2, exp2 };
+            Assert.Equal(expected, prediction.Features);
+
+            prediction = engine.Predict(data[1]);
+            exp1 = 0.4472136f;
+            Assert.Equal(data[1].A, string.Join(" ", prediction.OutputTokens));
+            expected = new float[] { exp1, 0.0f, 0.0f, 0.0f, 0.0f, exp1, exp1, exp1, exp1, 0.0f, 1.0f };
+            Assert.Equal(expected, prediction.Features);
+        }
+
         [Fact]
         public void TextFeaturizerWithCustomStopWordRemoverTest()
         {
@@ -67,7 +157,7 @@ public void TextFeaturizerWithCustomStopWordRemoverTest()
                 OutputTokensColumnName = "OutputTokens",
                 CaseMode = TextNormalizingEstimator.CaseMode.None
             };
-            var pipeline = ML.Transforms.Text.FeaturizeText("OutputText", options, "A");
+            var pipeline = ML.Transforms.Text.FeaturizeText("Features", options, "A");
             var model = pipeline.Fit(dataView);
             var engine = model.CreatePredictionEngine<TestClass, TestClass>(ML);
             var prediction = engine.Predict(data[0]);
@@ -84,7 +174,7 @@ private void TestCaseMode(IDataView dataView, TestClass[] data, TextNormalizingE
                 CaseMode = caseMode,
                 OutputTokensColumnName = "OutputTokens"
             };
-            var pipeline = ML.Transforms.Text.FeaturizeText("OutputText", options, "A");
+            var pipeline = ML.Transforms.Text.FeaturizeText("Features", options, "A");
             var model = pipeline.Fit(dataView);
             var engine = model.CreatePredictionEngine<TestClass, TestClass>(ML);
             var prediction1 = engine.Predict(data[0]);
@@ -133,7 +223,7 @@ private void TestKeepNumbers(IDataView dataView, TestClass[] data, bool keepNumb
                 CaseMode = TextNormalizingEstimator.CaseMode.None,
                 OutputTokensColumnName = "OutputTokens"
             };
-            var pipeline = ML.Transforms.Text.FeaturizeText("OutputText", options, "A");
+            var pipeline = ML.Transforms.Text.FeaturizeText("Features", options, "A");
             var model = pipeline.Fit(dataView);
             var engine = model.CreatePredictionEngine<TestClass, TestClass>(ML);
             var prediction1 = engine.Predict(data[0]);
@@ -170,7 +260,7 @@ private void TestKeepPunctuations(IDataView dataView, TestClass[] data, bool kee
                 CaseMode = TextNormalizingEstimator.CaseMode.None,
                 OutputTokensColumnName = "OutputTokens"
             };
-            var pipeline = ML.Transforms.Text.FeaturizeText("OutputText", options, "A");
+            var pipeline = ML.Transforms.Text.FeaturizeText("Features", options, "A");
             var model = pipeline.Fit(dataView);
             var engine = model.CreatePredictionEngine<TestClass, TestClass>(ML);
             var prediction1 = engine.Predict(data[0]);
@@ -208,7 +298,7 @@ private void TestKeepDiacritics(IDataView dataView, TestClass[] data, bool keepD
                 CaseMode = TextNormalizingEstimator.CaseMode.None,
                 OutputTokensColumnName = "OutputTokens"
             };
-            var pipeline = ML.Transforms.Text.FeaturizeText("OutputText", options, "A");
+            var pipeline = ML.Transforms.Text.FeaturizeText("Features", options, "A");
             var model = pipeline.Fit(dataView);
             var engine = model.CreatePredictionEngine<TestClass, TestClass>(ML);
             var prediction1 = engine.Predict(data[0]);