diff --git a/BotSharp.NLP.UnitTest/SVMClassifierTest.cs b/BotSharp.NLP.UnitTest/SVMClassifierTest.cs
index f9b1d48a..732af1f5 100644
--- a/BotSharp.NLP.UnitTest/SVMClassifierTest.cs
+++ b/BotSharp.NLP.UnitTest/SVMClassifierTest.cs
@@ -1,6 +1,5 @@
 using BotSharp.NLP.Classify;
 using BotSharp.NLP.Corpus;
-using BotSharp.NLP.Models.TF_IDF;
 using BotSharp.NLP.Tokenize;
 using Microsoft.Extensions.Configuration;
 using Microsoft.VisualStudio.TestTools.UnitTesting;
@@ -29,8 +28,8 @@ namespace BotSharp.NLP.UnitTest
                 "see you Bolo",
                 "byebye Haiping"
             };
-            TFIDFGenerator tfidfGenerator = new TFIDFGenerator();
-            List<List<double>> weights = tfidfGenerator.TFIDFWeightVectorsForSentences(documents);
+            /*TFIDFGenerator tfidfGenerator = new TFIDFGenerator();
+            List<List<double>> weights = tfidfGenerator.TFIDFWeightVectorsForSentences(documents);*/
         }
 
         [TestMethod]
diff --git a/BotSharp.NLP/Classify/NaiveBayesClassifier.cs b/BotSharp.NLP/Classify/NaiveBayesClassifier.cs
index e29a881d..3baaef1b 100644
--- a/BotSharp.NLP/Classify/NaiveBayesClassifier.cs
+++ b/BotSharp.NLP/Classify/NaiveBayesClassifier.cs
@@ -53,6 +53,10 @@ namespace BotSharp.NLP.Classify
 
         public void Train(List<Sentence> sentences, ClassifyOptions options)
         {
+            var tfidf = new TFIDF();
+            tfidf.Sentences = sentences;
+            words = tfidf.EncodeAll();
+
             var encoder = new OneHotEncoder();
             encoder.Sentences = sentences;
             words = encoder.EncodeAll();
diff --git a/BotSharp.NLP/Models/TF-IDF/TFIDF.cs b/BotSharp.NLP/Models/TF-IDF/TFIDF.cs
deleted file mode 100644
index 1fcf67f6..00000000
--- a/BotSharp.NLP/Models/TF-IDF/TFIDF.cs
+++ /dev/null
@@ -1,232 +0,0 @@
-using BotSharp.NLP.Tokenize;
-using System;
-using System.Collections.Generic;
-using System.IO;
-using System.Linq;
-using System.Runtime.Serialization.Formatters.Binary;
-using System.Text;
-using System.Text.RegularExpressions;
-
-namespace BotSharp.NLP.Models.TF_IDF
-{
-    /// <summary>
-    /// Copyright (c) 2018 Bo Peng
-    /// 
-    /// Permission is hereby granted, free of charge, to any person obtaining
-    /// a copy of this software and associated documentation files (the
-    /// "Software"), to deal in the Software without restriction, including
-    /// without limitation the rights to use, copy, modify, merge, publish,
-    /// distribute, sublicense, and/or sell copies of the Software, and to
-    /// permit persons to whom the Software is furnished to do so, subject to
-    /// the following conditions:
-    /// 
-    /// The above copyright notice and this permission notice shall be
-    /// included in all copies or substantial portions of the Software.
-    /// </summary>
-    public class TFIDF
-    {
-        List<string> vocabulary { get; set; }
-
-        public TFIDF()
-        {
-        }
-
-        /// <summary>
-        /// Document vocabulary, containing each word's IDF value.
-        /// </summary>
-        private static Dictionary<string, double> _vocabularyIDF = new Dictionary<string, double>();
-
-        public static List<List<double>> GetTFIDFWeightsVectors(string[] documents, int vocabularyThreshold = 1)
-        {
-            List<List<string>> stemmedDocs;
-            List<string> vocabulary;
-            // Get the vocabulary and stem the documents at the same time.
-            vocabulary = GetVocabulary(documents, out stemmedDocs, vocabularyThreshold);
-            if (_vocabularyIDF.Count == 0)
-            {
-                // Calculate the IDF for each vocabulary term.
-                foreach (var term in vocabulary)
-                {
-                    double numberOfDocsContainingTerm = stemmedDocs.Where(d => d.Contains(term)).Count();
-                    _vocabularyIDF[term] = Math.Log((double)stemmedDocs.Count / ((double)1 + numberOfDocsContainingTerm));
-                }
-            }
-            // Transform each document into a vector of tfidf values.
-            List<List<double>> vectors = new List<List<double>>();
-            foreach (var doc in stemmedDocs)
-            {
-                List<double> vector = new List<double>();
-                foreach (string word in doc)
-                {
-                    double tf = doc.Where(d => d == word).Count();
-                    double tfidf = tf * _vocabularyIDF[word];
-                    vector.Add(tfidf);
-                }
-                vectors.Add(vector);
-            }
-            return vectors;
-        }
-        
-
-        /// <summary>
-        /// Normalizes a TF*IDF array of vectors using L2-Norm.
-        /// Xi = Xi / Sqrt(X0^2 + X1^2 + .. + Xn^2)
-        /// </summary>
-        /// <param name="vectors">List<List<double>></param>
-        /// <returns>List<List<double>></returns>
-        public static List<List<double>> Normalize(List<List<double>> vectors)
-        {
-            // Normalize the vectors using L2-Norm.
-            List<List<double>> normalizedVectors = new List<List<double>>();
-            foreach (var vector in vectors)
-            {
-                var normalized = Normalize(vector);
-                normalizedVectors.Add(normalized);
-            }
-
-            return normalizedVectors;
-        }
-
-        /// <summary>
-        /// Normalizes a TF*IDF vector using L2-Norm.
-        /// Xi = Xi / Sqrt(X0^2 + X1^2 + .. + Xn^2)
-        /// </summary>
-        /// <param name="vectors"> List<double> </param>
-        /// <returns> List<double> </returns>
-        public static List<double> Normalize(List<double> vector)
-        {
-            List<double> result = new List<double>();
-
-            double sumSquared = 0;
-            foreach (var value in vector)
-            {
-                sumSquared += value * value;
-            }
-
-            double SqrtSumSquared = Math.Sqrt(sumSquared);
-
-            foreach (var value in vector)
-            {
-                // L2-norm: Xi = Xi / Sqrt(X0^2 + X1^2 + .. + Xn^2)
-                result.Add(value / SqrtSumSquared);
-            }
-            return result;
-        }
-
-        /// <summary>
-        /// Saves the TFIDF vocabulary to disk.
-        /// </summary>
-        /// <param name="filePath">File path</param>
-        public static void Save(string filePath = "vocabulary.dat")
-        {
-            // Save result to disk.
-            using (FileStream fs = new FileStream(filePath, FileMode.Create))
-            {
-                BinaryFormatter formatter = new BinaryFormatter();
-                formatter.Serialize(fs, _vocabularyIDF);
-            }
-        }
-
-        /// <summary>
-        /// Loads the TFIDF vocabulary from disk.
-        /// </summary>
-        /// <param name="filePath">File path</param>
-        public static void Load(string filePath = "vocabulary.dat")
-        {
-            // Load from disk.
-            using (FileStream fs = new FileStream(filePath, FileMode.Open))
-            {
-                BinaryFormatter formatter = new BinaryFormatter();
-                _vocabularyIDF = (Dictionary<string, double>)formatter.Deserialize(fs);
-            }
-        }
-        
-        /// <summary>
-        /// Parses and tokenizes a list of documents, returning a vocabulary of words.
-        /// </summary>
-        /// <param name="docs">string[]</param>
-        /// <param name="stemmedDocs">List of List of string</param>
-        /// <returns>Vocabulary (list of strings)</returns>
-        private static List<string> GetVocabulary(string[] docs, out List<List<string>> stemmedDocs, int vocabularyThreshold)
-        {
-            List<string> vocabulary = new List<string>();
-            Dictionary<string, int> wordCountList = new Dictionary<string, int>();
-            stemmedDocs = new List<List<string>>();
-            int docIndex = 0;
-            var tokenizer = new TokenizerFactory<RegexTokenizer>(new TokenizationOptions
-            {
-                Pattern = RegexTokenizer.WHITE_SPACE
-            }, SupportedLanguage.English);
-
-            foreach (var doc in docs)
-            {
-                List<string> stemmedDoc = new List<string>();
-                docIndex++;
-                if (docIndex % 100 == 0)
-                {
-                    Console.WriteLine("Processing " + docIndex + "/" + docs.Length);
-                }
-
-                List<Token> tokens = tokenizer.Tokenize(doc);
-                List<string> list = new List<string>(); 
-                tokenizer.Tokenize(doc).ForEach( token => {
-                    list.Add(token.Text.ToLower());
-                });
-                string[] parts2 = list.ToArray();
-                //string[] parts2 = Tokenize(doc);
-                List<string> words = new List<string>();
-                foreach (string part in parts2)
-                {
-                    // Strip non-alphanumeric characters.
-                    string stripped = Regex.Replace(part, "[^a-zA-Z0-9]", "");
-                    try
-                    {
-                        var english = new EnglishWord(stripped);
-                        string stem = english.Stem;
-                        words.Add(stem);
-
-                        if (stem.Length > 0)
-                        {
-                            // Build the word count list.
-                            if (wordCountList.ContainsKey(stem))
-                            {
-                                wordCountList[stem]++;
-                            }
-                            else
-                            {
-                                wordCountList.Add(stem, 0);
-                            }
-                            stemmedDoc.Add(stem);
-                        }
-                    }
-                    catch
-                    {
-                    }
-                }
-                stemmedDocs.Add(stemmedDoc);
-            }
-            // Get the top words.
-            var vocabList = wordCountList.Where(w => w.Value >= vocabularyThreshold);
-            foreach (var item in vocabList)
-            {
-                vocabulary.Add(item.Key);
-            }
-            return vocabulary;
-        }
-        
-        
-    }
-    public class EnglishWord
-    {
-        public EnglishWord(string input)
-        {
-            this.Original = input;
-            this.Stem = input;
-            this.Length = input.Length;
-        }
-
-        public string Stem { get; set; }
-        public string Original { get; }
-        public int Length { get; }
-    }
-}
diff --git a/BotSharp.NLP/Models/TF-IDF/TFIDFGenerator.cs b/BotSharp.NLP/Models/TF-IDF/TFIDFGenerator.cs
deleted file mode 100644
index 670f84b3..00000000
--- a/BotSharp.NLP/Models/TF-IDF/TFIDFGenerator.cs
+++ /dev/null
@@ -1,30 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-
-namespace BotSharp.NLP.Models.TF_IDF
-{
-    /// <summary>
-    /// Copyright (c) 2018 Bo Peng
-    /// 
-    /// Permission is hereby granted, free of charge, to any person obtaining
-    /// a copy of this software and associated documentation files (the
-    /// "Software"), to deal in the Software without restriction, including
-    /// without limitation the rights to use, copy, modify, merge, publish,
-    /// distribute, sublicense, and/or sell copies of the Software, and to
-    /// permit persons to whom the Software is furnished to do so, subject to
-    /// the following conditions:
-    /// 
-    /// The above copyright notice and this permission notice shall be
-    /// included in all copies or substantial portions of the Software.
-    /// </summary>
-    public class TFIDFGenerator
-    {
-        public List<List<double>> TFIDFWeightVectorsForSentences(string[]documents)
-        {
-            List<List<double>> res = TFIDF.GetTFIDFWeightsVectors(documents, 0);
-            res = TFIDF.Normalize(res);
-            return res;
-        }
-    }
-}
diff --git a/BotSharp.NLP/Txt2Vec/TFIDF.cs b/BotSharp.NLP/Txt2Vec/TFIDF.cs
new file mode 100644
index 00000000..b7e64a94
--- /dev/null
+++ b/BotSharp.NLP/Txt2Vec/TFIDF.cs
@@ -0,0 +1,148 @@
+/// <summary>
+/// Copyright (c) 2018 Bo Peng
+/// 
+/// Permission is hereby granted, free of charge, to any person obtaining
+/// a copy of this software and associated documentation files (the
+/// "Software"), to deal in the Software without restriction, including
+/// without limitation the rights to use, copy, modify, merge, publish,
+/// distribute, sublicense, and/or sell copies of the Software, and to
+/// permit persons to whom the Software is furnished to do so, subject to
+/// the following conditions:
+/// 
+/// The above copyright notice and this permission notice shall be
+/// included in all copies or substantial portions of the Software.
+/// </summary>
+/// 
+using BotSharp.NLP.Tokenize;
+using System;
+using System.Collections.Generic;
+using System.IO;
+using System.Linq;
+using System.Runtime.Serialization.Formatters.Binary;
+using System.Text;
+using System.Text.RegularExpressions;
+
+namespace BotSharp.NLP.Txt2Vec
+{
+    public class TFIDF
+    {
+        public List<Sentence> Sentences { get; set; }
+
+        public List<string> Words { get; set; }
+
+        public void Encode(Sentence sentence)
+        {
+            InitDictionary();
+
+            // var featureSets = Sentences.Select(x => new Tuple<string, double[]>(x.Label, x.Vector)).ToList();
+
+            var labelDist = Sentences.Select(x => x.Label).Distinct().ToList();
+
+            labelDist.ForEach(label =>
+            {
+                // https://zhuanlan.zhihu.com/p/31197209
+                // calculate TF
+                // all words in the article
+                List<string> words = new List<string>();
+                Sentences.Where(x => x.Label == label).ToList().ForEach(sent =>
+                {
+                    words.AddRange(sent.Words.Select(w => w.Text));
+                });
+
+                List<Tuple<string, double>> tfs = new List<Tuple<string, double>>();
+                words.Distinct().ToList().ForEach(w =>
+                {
+                    // TF
+                    int c1 = words.Count(x => x == w);
+                    double tf = (c1 + 1.0) / words.Count();
+
+                    // IDF
+                    var sents = Sentences.Where(s => s.Words.Select(x => x.Text).Contains(w)).ToList();
+                    double idf = Math.Log(Sentences.Count / (sents.Count() + 1.0));
+
+                    tfs.Add(new Tuple<string, double>(w, tf * idf));
+                });
+
+                tfs = tfs.OrderByDescending(x => x.Item2).Take(words.Count / 10).ToList();
+            });
+
+            
+
+            sentence.Words.ForEach(w =>
+            {
+                int index = Words.IndexOf(w.Text.ToLower());
+            });
+        }
+
+        public List<string> EncodeAll()
+        {
+            InitDictionary();
+
+            Sentences.ForEach(sent => Encode(sent));
+            //Parallel.ForEach(Sentences, sent => Encode(sent));
+
+            return Words;
+        }
+
+        private List<string> InitDictionary()
+        {
+            if (Words == null)
+            {
+                Words = new List<string>();
+                Sentences.ForEach(x =>
+                {
+                    Words.AddRange(x.Words.Where(w => w.IsAlpha).Select(w => w.Text.ToLower()));
+                });
+                Words = Words.Distinct().OrderBy(x => x).ToList();
+            }
+
+            return Words;
+        }
+
+
+        /// <summary>
+        /// Normalizes a TF*IDF array of vectors using L2-Norm.
+        /// Xi = Xi / Sqrt(X0^2 + X1^2 + .. + Xn^2)
+        /// </summary>
+        /// <param name="vectors">List<List<double>></param>
+        /// <returns>List<List<double>></returns>
+        public static List<List<double>> Normalize(List<List<double>> vectors)
+        {
+            // Normalize the vectors using L2-Norm.
+            List<List<double>> normalizedVectors = new List<List<double>>();
+            foreach (var vector in vectors)
+            {
+                var normalized = Normalize(vector);
+                normalizedVectors.Add(normalized);
+            }
+
+            return normalizedVectors;
+        }
+
+        /// <summary>
+        /// Normalizes a TF*IDF vector using L2-Norm.
+        /// Xi = Xi / Sqrt(X0^2 + X1^2 + .. + Xn^2)
+        /// </summary>
+        /// <param name="vectors"> List<double> </param>
+        /// <returns> List<double> </returns>
+        public static List<double> Normalize(List<double> vector)
+        {
+            List<double> result = new List<double>();
+
+            double sumSquared = 0;
+            foreach (var value in vector)
+            {
+                sumSquared += value * value;
+            }
+
+            double SqrtSumSquared = Math.Sqrt(sumSquared);
+
+            foreach (var value in vector)
+            {
+                // L2-norm: Xi = Xi / Sqrt(X0^2 + X1^2 + .. + Xn^2)
+                result.Add(value / SqrtSumSquared);
+            }
+            return result;
+        }
+    }
+}
diff --git a/BotSharp.NLP/Txt2Vec/VectorGenerator.cs b/BotSharp.NLP/Txt2Vec/VectorGenerator.cs
index dba24342..7c848031 100644
--- a/BotSharp.NLP/Txt2Vec/VectorGenerator.cs
+++ b/BotSharp.NLP/Txt2Vec/VectorGenerator.cs
@@ -5,7 +5,6 @@ using System.Text;
 using System.Threading.Tasks;
 using System.IO;
 using System.Threading;
-using BotSharp.NLP.Models.TF_IDF;
 //using AdvUtils;
 
 namespace Txt2Vec
@@ -37,8 +36,8 @@ namespace Txt2Vec
         public List<Vec> Sentence2Vec(List<string> sentences, WeightingScheme weightingScheme = WeightingScheme.AVG)
         {
             // Inplementing TF-IDF
-            TFIDFGenerator tfidfGenerator = new TFIDFGenerator();
-            List<List<double>> weights = tfidfGenerator.TFIDFWeightVectorsForSentences(sentences.ToArray());
+            // TFIDFGenerator tfidfGenerator = new TFIDFGenerator();
+            List<List<double>> weights = null;// tfidfGenerator.TFIDFWeightVectorsForSentences(sentences.ToArray());
 
             List<List<Vec>> matixList = new List<List<Vec>>();