diff --git a/src/Microsoft.ML.Data/Model/ModelOperationsCatalog.cs b/src/Microsoft.ML.Data/Model/ModelOperationsCatalog.cs
index 8524a5b7bf..b449bdfa8e 100644
--- a/src/Microsoft.ML.Data/Model/ModelOperationsCatalog.cs
+++ b/src/Microsoft.ML.Data/Model/ModelOperationsCatalog.cs
@@ -17,7 +17,7 @@ namespace Microsoft.ML
     /// </summary>
     public sealed class ModelOperationsCatalog : IInternalCatalog
     {
-        private const string SchemaEntryName = "Schema";
+        internal const string SchemaEntryName = "Schema";
 
         IHostEnvironment IInternalCatalog.Environment => _env;
         private readonly IHostEnvironment _env;
diff --git a/src/Microsoft.ML.Data/Utilities/ModelFileUtils.cs b/src/Microsoft.ML.Data/Utilities/ModelFileUtils.cs
index 46daa56578..80bf00a761 100644
--- a/src/Microsoft.ML.Data/Utilities/ModelFileUtils.cs
+++ b/src/Microsoft.ML.Data/Utilities/ModelFileUtils.cs
@@ -25,6 +25,8 @@ internal static class ModelFileUtils
     {
         public const string DirPredictor = "Predictor";
         public const string DirDataLoaderModel = "DataLoaderModel";
+        public const string DirTransformerChain = TransformerChain.LoaderSignature;
+        public const string SchemaEntryName = ModelOperationsCatalog.SchemaEntryName;
         // ResultsProcessor needs access to this constant.
         public const string DirTrainingInfo = "TrainingInfo";
 
@@ -64,6 +66,15 @@ public static IDataView LoadPipeline(IHostEnvironment env, RepositoryReader rep,
             Contracts.CheckValue(env, nameof(env));
             env.CheckValue(rep, nameof(rep));
             env.CheckValue(files, nameof(files));
+
+            var entry = rep.OpenEntryOrNull(SchemaEntryName);
+            if (entry != null)
+            {
+                var loader = new BinaryLoader(env, new BinaryLoader.Arguments(), entry.Stream);
+                ModelLoadContext.LoadModel<ITransformer, SignatureLoadModel>(env, out var transformerChain, rep, DirTransformerChain);
+                return transformerChain.Transform(loader);
+            }
+
             using (var ent = rep.OpenEntry(DirDataLoaderModel, ModelLoadContext.ModelStreamName))
             {
                 ILegacyDataLoader loader;
diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs b/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs
index 06796a6cad..d5bf30ab96 100644
--- a/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs
+++ b/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs
@@ -612,6 +612,100 @@ public void EntryPointExecGraphCommand()
             cmd.Run();
         }
 
+        [Fact]
+        public void ScoreTransformerChainModel()
+        {
+            var dataPath = GetDataPath("wikipedia-detox-250-line-data.tsv");
+            var modelPath = DeleteOutputPath("model.zip");
+            var outputDataPath = DeleteOutputPath("scored.idv");
+
+            var mlContext = new MLContext();
+
+            var data = new TextLoader(mlContext,
+                    new TextLoader.Options()
+                    {
+                        AllowQuoting = true,
+                        Separator = "\t",
+                        HasHeader = true,
+                        Columns = new[]
+                        {
+                            new TextLoader.Column("Label", DataKind.Boolean, 0),
+                            new TextLoader.Column("SentimentText", DataKind.String, 1)
+                        }
+                    }).Load(dataPath);
+
+            var pipeline = mlContext.Transforms.Text.FeaturizeText("Features", "SentimentText")
+                .Append(mlContext.BinaryClassification.Trainers.AveragedPerceptron());
+
+            var model = pipeline.Fit(data);
+
+            mlContext.Model.Save(model, data.Schema, modelPath);
+
+            string inputGraph = string.Format(@"
+                {{
+                  'Inputs': {{
+                    'file': '{0}',
+                    'transform_model': '{1}'
+                  }},
+                  'Nodes': [
+                    {{
+                      'Name': 'Data.CustomTextLoader',
+                      'Inputs': {{
+                        'CustomSchema': 'col=Sentiment:I8:0 col=SentimentText:TX:1 quote+ header=+ sep=tab',
+                        'InputFile': '$file'
+                      }},
+                      'Outputs': {{
+                        'Data': '$data'
+                      }}
+                    }},
+                    {{
+                      'Name': 'Transforms.DatasetTransformScorer',
+                      'Inputs': {{
+                        'Data': '$data',
+                        'TransformModel': '$transform_model'
+                      }},
+                      'Outputs': {{
+                        'ScoredData': '$scoredVectorData'
+                      }}
+                    }},
+                    {{
+                      'Inputs': {{
+                        'Data': '$scoredVectorData'
+                      }},
+                      'Name': 'Transforms.ScoreColumnSelector',
+                      'Outputs': {{
+                        'OutputData': '$scoreColumnsOnlyData'
+                      }}
+                    }},
+                    {{
+                      'Inputs': {{
+                        'Data': '$scoreColumnsOnlyData',
+                        'PredictedLabelColumn': 'PredictedLabel'
+                      }},
+                      'Name': 'Transforms.PredictedLabelColumnOriginalValueConverter',
+                      'Outputs': {{
+                        'OutputData': '$output_data'
+                      }}
+                    }}
+                  ],
+                  'Outputs': {{
+                    'output_data': '{2}'
+                  }}
+                }}", EscapePath(dataPath), EscapePath(modelPath), EscapePath(outputDataPath));
+
+            var jsonPath = DeleteOutputPath("graph.json");
+            File.WriteAllLines(jsonPath, new[] { inputGraph });
+
+            var args = new ExecuteGraphCommand.Arguments() { GraphPath = jsonPath };
+            var cmd = new ExecuteGraphCommand(Env, args);
+            cmd.Run();
+
+            var loadedData = mlContext.Data.LoadFromBinary(outputDataPath);
+
+            Assert.NotNull(loadedData.Schema.GetColumnOrNull("PredictedLabel"));
+            Assert.NotNull(loadedData.Schema.GetColumnOrNull("Score"));
+        }
+
         //[Fact]
         //public void EntryPointArrayOfVariables()
         //{