diff --git a/demo/quick_start/data/proc_from_raw_data/get_data.sh b/demo/quick_start/data/proc_from_raw_data/get_data.sh
index 9c3e9db248a182ffea40cff452e5db5e4842360d..cd85e26842dfccea78e4f26bdfee938887021f03 100755
--- a/demo/quick_start/data/proc_from_raw_data/get_data.sh
+++ b/demo/quick_start/data/proc_from_raw_data/get_data.sh
@@ -25,14 +25,17 @@ cd $DIR
 # Download data
 echo "Downloading Amazon Electronics reviews data..."
 # http://jmcauley.ucsd.edu/data/amazon/
-#wget http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz
+wget http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz
 echo "Downloading mosesdecoder..."
-#https://github.com/moses-smt/mosesdecoder
-#wget https://github.com/moses-smt/mosesdecoder/archive/master.zip
-#unzip master.zip
-#rm master.zip
-echo "Done."
+# https://github.com/moses-smt/mosesdecoder
+wget https://github.com/moses-smt/mosesdecoder/archive/master.zip
 
+unzip master.zip
+rm master.zip
+
+##################
+# Preprocess data 
+echo "Preprocess data..."
 export LC_ALL=C
 UNAME_STR=`uname`
 
@@ -42,12 +45,11 @@ else
   SHUF_PROG='gshuf'
 fi
 
-# Start preprocess 
 mkdir -p tmp
 python preprocess.py -i reviews_Electronics_5.json.gz
 # uniq and shuffle
 cd tmp
-echo 'uniq and shuffle...'
+echo 'Uniq and shuffle...'
 cat pos_*|sort|uniq|${SHUF_PROG}> pos.shuffed
 cat neg_*|sort|uniq|${SHUF_PROG}> neg.shuffed
 
@@ -74,4 +76,4 @@ echo 'test.txt' > test.list
 rm -rf tmp
 mv dict.txt dict_all.txt
 cat dict_all.txt | head -n 30001 > dict.txt
-echo 'preprocess finished'
+echo 'Done.'
diff --git a/doc/demo/quick_start/index_en.md b/doc/demo/quick_start/index_en.md
index 01f6f8ef5446db3733f63a400076acac3a501703..11d568b8f94e2b8222788693622c10ad3c5e6ac4 100644
--- a/doc/demo/quick_start/index_en.md
+++ b/doc/demo/quick_start/index_en.md
@@ -59,7 +59,7 @@ To build your text classification system, your code will need to perform five st
 ## Preprocess data into standardized format
 In this example, you are going to use [Amazon electronic product review dataset](http://jmcauley.ucsd.edu/data/amazon/) to build a bunch of deep neural network models for text classification. Each text in this dataset is a product review. This dataset has two categories: “positive” and “negative”. Positive means the reviewer likes the product, while negative means the reviewer does not like the product.
 
-`demo/quick_start` in the [source code](https://github.com/baidu/Paddle) provides script for downloading the preprocessed data as shown below. (If you want to process the raw data, you can use the script `demo/quick_start/data/proc_from_raw_data/get_data.sh`).
+`demo/quick_start` in the [source code](https://github.com/PaddlePaddle/Paddle) provides script for downloading the preprocessed data as shown below. (If you want to process the raw data, you can use the script `demo/quick_start/data/proc_from_raw_data/get_data.sh`).
 
 ```bash
 cd demo/quick_start
diff --git a/doc_cn/demo/quick_start/index.md b/doc_cn/demo/quick_start/index.md
index 514b45a487907763d62f961a3ecda0f1c02e173f..4a6e07ee1ffd94cf8f781af307b53a96a78e6b93 100644
--- a/doc_cn/demo/quick_start/index.md
+++ b/doc_cn/demo/quick_start/index.md
@@ -32,7 +32,7 @@
 
 ## 数据格式准备(Data Preparation)
 在本问题中，我们使用[Amazon电子产品评论数据](http://jmcauley.ucsd.edu/data/amazon/)，
-将评论分为好评(正样本)和差评(负样本)两类。[源码](https://github.com/baidu/Paddle)的`demo/quick_start`里提供了下载已经预处理数据的脚本（如果想从最原始的数据处理，可以使用脚本 `./demo/quick_start/data/proc_from_raw_data/get_data.sh`）。
+将评论分为好评(正样本)和差评(负样本)两类。[源码](https://github.com/PaddlePaddle/Paddle)的`demo/quick_start`里提供了下载已经预处理数据的脚本（如果想从最原始的数据处理，可以使用脚本 `./demo/quick_start/data/proc_from_raw_data/get_data.sh`）。
 
 ```bash
 cd demo/quick_start
@@ -141,7 +141,7 @@ PyDataProvider2</a>。
 
 我们将以基本的逻辑回归网络作为起点，并逐渐展示更加深入的功能。更详细的网络配置
 连接请参考<a href = "../../../doc/layer.html">Layer文档</a>。
-所有配置在[源码](https://github.com/baidu/Paddle)`demo/quick_start`目录，首先列举逻辑回归网络。
+所有配置在[源码](https://github.com/PaddlePaddle/Paddle)`demo/quick_start`目录，首先列举逻辑回归网络。
 
 ### 逻辑回归模型(Logistic Regression)