diff --git a/ClickThroughRate/WideDeepLearning/how_to_make_hf_dataset.md b/ClickThroughRate/WideDeepLearning/how_to_make_hf_dataset.md
index a153a59..60926d1 100644
--- a/ClickThroughRate/WideDeepLearning/how_to_make_hf_dataset.md
+++ b/ClickThroughRate/WideDeepLearning/how_to_make_hf_dataset.md
@@ -1,6 +1,6 @@
 # How to Make High Frequency Dataset for OneFlow-WDL
 
-[**how_to_make_ofrecord_for_wdl**](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/master/ClickThroughRate/WideDeepLearning/how_to_make_ofrecord_for_wdl.md)一文中介绍了如何利用spark制作OneFlow-WDL使用的ofrecord数据集，GPU&CPU混合embedding的实践中，这个数据集就不好用了，主要原因是没有按照词频排序，所以需要制作新的数据集。本文将持续上文中的套路，介绍一下如何制作按照词频排序的数据集。
+[**how_to_make_ofrecord_for_wdl**](https://github.com/Oneflow-Inc/OneFlow-Benchmark/blob/master/ClickThroughRate/WideDeepLearning/how_to_make_ofrecord_for_wdl.md)一文中介绍了如何利用spark制作OneFlow-WDL使用的ofrecord数据集，GPU&CPU混合embedding的实践中需要把特征根据词频从大到小排序，本文将持续上文中的套路，介绍一下如何制作按照词频排序的数据集。
 
 ## 数据集及预处理
 
@@ -53,14 +53,12 @@ Files.createDirectories(Paths.get(tmp_dir))
 ```scala
 // load input file
 var input = spark.read.options(Map("delimiter"->"\t")).csv("file:///DATA/disk1/xuan/train.shuf.bak")
-// var input = spark.read.options(Map("delimiter"->"\t")).csv("file:///DATA/disk1/xuan/train.shuf.txt")
 
 // rename columns [label, I1,...,I13, C1,...,C26]
 val NUM_INTEGER_COLUMNS = 13
 val NUM_CATEGORICAL_COLUMNS = 26
 
-// val integer_cols = (1 to NUM_INTEGER_COLUMNS).map{id=>s"I$id"}
-val integer_cols = (1 to NUM_INTEGER_COLUMNS).map{id=>$"I$id"} // note
+val integer_cols = (1 to NUM_INTEGER_COLUMNS).map{id=>$"I$id"} 
 val categorical_cols = (1 to NUM_CATEGORICAL_COLUMNS).map{id=>s"C$id"}
 val feature_cols = integer_cols.map{c=>c.toString} ++ categorical_cols
 val all_cols = (Seq(s"labels") ++ feature_cols)
@@ -133,7 +131,6 @@ for(column_name <- integer_cols) {
   } else {
       scaledDf = indexedDf.withColumn(col_name, col(col_index) + lit(1)) // trick: reuse col_name
                           .select("id", col_name)
-                          //.withColumn(col_name, col(col_index).cast(IntegerType))
   }
   val col_dir = tmp_dir ++ "/" ++ col_name
   scaledDf = scaledDf.withColumn(col_name, getItem(column_name, lit(0)))
@@ -233,8 +230,6 @@ for(cross_pair <- cross_pairs) {
   val df_col = spark.read.parquet(tmp_dir ++ "/" ++ cross_pair)
   df = df.join(df_col, Seq("id"))
 }
-// df.select("C1_C2", "C3_C4").createOrReplaceTempView("f")
-// df.select(cross_pairs.map{id=>col(id)}:_*).createOrReplaceTempView("f")
 df.select(cross_pairs map col: _*).createOrReplaceTempView("f")
 
 val orderedValues = spark.sql("select cid, count(*) as cnt from (select explode( array(" + cross_pairs.mkString(",") + ") ) as cid  from f) group by cid ").filter("cnt>=6").orderBy($"cnt".desc)