diff --git a/BotSharp.Core/Engines/BotSharp/BotSharpSVMClassifier.cs b/BotSharp.Core/Engines/BotSharp/BotSharpSVMClassifier.cs
index 87a0ab3f..d0dfcd34 100644
--- a/BotSharp.Core/Engines/BotSharp/BotSharpSVMClassifier.cs
+++ b/BotSharp.Core/Engines/BotSharp/BotSharpSVMClassifier.cs
@@ -3,7 +3,9 @@ using BotSharp.Core.Agents;
 using BotSharp.NLP.Classify;
 using DotNetToolkit;
 using Microsoft.Extensions.Configuration;
+using Newtonsoft.Json;
 using Newtonsoft.Json.Linq;
+using RestSharp;
 using System;
 using System.Collections.Generic;
 using System.Diagnostics;
@@ -31,19 +33,46 @@ namespace BotSharp.Core.Engines.BotSharp
             Args args = new Args();
             args.ModelFile = Path.Combine(Configuration.GetValue<String>("BotSharpSVMClassifier:wordvec"), "wordvec_enu.bin");
             LabeledFeatureSet featureSet = svmClassifier.FeatureSetsGenerator(new VectorGenerator(args).SingleSentence2Vec(doc.Sentences[0].Text), "");
+            /*
+            //
+            var client = new RestClient("http://10.2.21.200:5005");
+            var request = new RestRequest("doc2vec", Method.GET);
+            request.AddParameter("text", doc.Sentences[0].Text);
+            var response = client.Execute<PredResult>(request);
+            PredResult pred = JsonConvert.DeserializeObject<PredResult>(response.Content);
 
+            Vec vec = new Vec();
+            vec.VecNodes = pred.Doc2Vec;
+
+            LabeledFeatureSet featureSet = svmClassifier.FeatureSetsGenerator(vec, "");
+            //
+            */
             ClassifyOptions classifyOptions = new ClassifyOptions();
-            classifyOptions.Model = SVM.BotSharp.MachineLearning.Model.Read(Path.Combine(Settings.ModelDir, "svm_classifier_model")); 
+            classifyOptions.Model = SVM.BotSharp.MachineLearning.Model.Read(Path.Combine(Settings.ModelDir, "svm_classifier_model"));
+            classifyOptions.Transform = SVM.BotSharp.MachineLearning.RangeTransform.Read(Path.Combine(Settings.ModelDir, "transform_obj_data"));
             double[][] d = svmClassifier.Predict(featureSet, classifyOptions);
 
+            string intent = null;
+            decimal confidence = 0;
+            double max = Double.MinValue;
+            for (int i = 0; i < d[0].Count(); i++)
+            {
+                if (d[0][i] > max)
+                {
+                    max = d[0][i];
+                    intent = agent.Intents[i].Name;
+                    confidence = (decimal)d[0][i];
+                }
+            }
+
             File.Delete(predictFileName);
 
-            //doc.Sentences[0].Intent = new TextClassificationResult
-            //{
-            //    Classifier = "FasttextClassifier",
-            //    Label = output.Split(' ')[0].Split(new string[] { "__label__" }, StringSplitOptions.None)[1],
-            //    Confidence = decimal.Parse(output.Split(' ')[1])
-            //};
+            doc.Sentences[0].Intent = new TextClassificationResult
+            {
+                Classifier = "SVMClassifier",
+                Label = intent,
+                Confidence = confidence
+            };
 
             return true;
         }
@@ -71,15 +100,48 @@ namespace BotSharp.Core.Engines.BotSharp
             Args args = new Args(); 
             args.ModelFile = Path.Combine(Configuration.GetValue<String>("BotSharpSVMClassifier:wordvec"), "wordvec_enu.bin");
             List<LabeledFeatureSet> featureSetList = svmClassifier.FeatureSetsGenerator(new VectorGenerator(args).Sentence2Vec(sentences), labels);
+
+            /*
+            // try using spacy doc2vec
+            var client = new RestClient("http://10.2.21.200:5005");
+            var request = new RestRequest("batchdoc2vec", Method.POST);
+            request.RequestFormat = DataFormat.Json;
+
+            request.AddParameter("application/json", JsonConvert.SerializeObject(new {Sentences = sentences}), ParameterType.RequestBody);
+
+            var response = client.Execute<Result>(request);
+            Result res = JsonConvert.DeserializeObject<Result>(response.Content);
+
+            List<Vec> vecs = new List<Vec>();
+            foreach (List<double> cur in res.Doc2vecList)
+            {
+                Vec vec = new Vec();
+                vec.VecNodes = cur;
+                vecs.Add(vec);
+            }
+            List<LabeledFeatureSet> featureSetList = svmClassifier.FeatureSetsGenerator(vecs, labels);
+            //
+            */
+
+
+
             ClassifyOptions classifyOptions = new ClassifyOptions();
             classifyOptions.ModelFilePath = Path.Combine(Settings.ModelDir, "svm_classifier_model");
+            classifyOptions.TransformFilePath = Path.Combine(Settings.ModelDir, "transform_obj_data");
             svmClassifier.Train(featureSetList, classifyOptions);
 
             meta.Meta = new JObject();
             meta.Meta["compiled at"] = "Aug 31, 2018";
-
-
             return true;
         }
     }
+    public class Result
+    {
+        public List<List<double>> Doc2vecList { get; set; }
+    }
+
+    public class PredResult
+    {
+        public List<double> Doc2Vec{ get; set; }
+    }
 }
diff --git a/BotSharp.NLP/BotSharp.NLP.csproj b/BotSharp.NLP/BotSharp.NLP.csproj
index 98f53e11..4c6b55c3 100644
--- a/BotSharp.NLP/BotSharp.NLP.csproj
+++ b/BotSharp.NLP/BotSharp.NLP.csproj
@@ -39,6 +39,10 @@ Mark milestone 0.2.3. Tokenizer, POS Tagger, NER and Text Classifier modules can
     <DefineConstants>DEBUG;TRACE</DefineConstants>
   </PropertyGroup>
 
+  <ItemGroup>
+    <PackageReference Include="Newtonsoft.Json" Version="11.0.2" />
+  </ItemGroup>
+
   <ItemGroup>
     <ProjectReference Include="..\BotSharp.Algorithm\BotSharp.Algorithm.csproj" />
   </ItemGroup>
diff --git a/BotSharp.NLP/Classify/ClassifyOptions.cs b/BotSharp.NLP/Classify/ClassifyOptions.cs
index 8ac0d1a3..fce017fd 100644
--- a/BotSharp.NLP/Classify/ClassifyOptions.cs
+++ b/BotSharp.NLP/Classify/ClassifyOptions.cs
@@ -11,5 +11,7 @@ namespace BotSharp.NLP.Classify
         public string ModelFilePath { get; set; }
         public Model Model { get; set; }
         public string PrediceOutputFile { get; set; }
+        public string TransformFilePath { get; set; }
+        public RangeTransform Transform { get; set; }
     }
 }
diff --git a/BotSharp.NLP/Classify/SVMClassifier.cs b/BotSharp.NLP/Classify/SVMClassifier.cs
index 81651088..433b1c83 100644
--- a/BotSharp.NLP/Classify/SVMClassifier.cs
+++ b/BotSharp.NLP/Classify/SVMClassifier.cs
@@ -44,9 +44,9 @@ namespace BotSharp.NLP.Classify
             predict.X = GetData(featureSets).ToArray();
             predict.Y = new double[1];
             predict.Count = predict.X.Count();
-            predict.MaxIndex = 200;
+            predict.MaxIndex = 300;
 
-            RangeTransform transform = RangeTransform.Compute(predict);
+            RangeTransform transform = options.Transform;
             Problem scaled = transform.Scale(predict);
 
             return Prediction.PredictLabelsProbability(options.Model, scaled);
@@ -58,13 +58,13 @@ namespace BotSharp.NLP.Classify
         }
 
         public void SVMClassifierTrain(List<LabeledFeatureSet> featureSets, ClassifyOptions options, SvmType svm = SvmType.C_SVC, KernelType kernel = KernelType.RBF, bool probability = true, string outputFile = null)
-        {
+        {   
             // copy test multiclass Model
             Problem train = new Problem();
             train.X = GetData(featureSets).ToArray();
             train.Y = GetLabels(featureSets).ToArray();
             train.Count = train.X.Count();
-            train.MaxIndex = 200;//int.MaxValue;
+            train.MaxIndex = 300;//int.MaxValue;
 
             Parameter param = new Parameter();
             RangeTransform transform = RangeTransform.Compute(train);
@@ -85,6 +85,7 @@ namespace BotSharp.NLP.Classify
                     param.Weights[i] = 1;
             }
             var model = Training.Train(scaled, param);
+            RangeTransform.Write(options.TransformFilePath, transform);
             SVM.BotSharp.MachineLearning.Model.Write(options.ModelFilePath, model);
             Console.Write("Training finished!");
         }