update Tutorial_zh_CN.md (#31)

* Update Tutorial.md

* Update Tutorial.md

* fix CPU train --> GPU test, GPU train --> CPU test. But CPU train --> multi-GPU test will report error.

* remove ujson

* remove glove for model_zoo/demo/conf.json

* modify glove for Tutorial.md

* add nltk.download('punkt') in problem.py

* Create README_zh_CN.md

* modify README

* Update README_zh_CN.md

* Update README.md

* Update README.md

* Update README_zh_CN.md

* add requirements_new.txt

* modify requirements_new.txt

* fix CPU train --> multi GPUs test.

* Add CPU/GPU table for README.md

* Add CPU/GPU table for README_zh_CN.md

* Update README.md

* add Chinese support

* add license for core/Stopwords.py

* modify conf_chinese_sentiment_analysis_bilstm.json

* add nltk.download('stopwords')

* Update problem.py

* Update README.md

* Update README_zh_CN.md

* add language supported check

* modify dataset of chinese sentiment analysis

* Update Tutorial.md

* Update Tutorial.md

* Update Tutorial.md

* Update Tutorial_zh_CN.md

Author: woailaosang

Tutorial_zh_CN.md

@@ -6,6 +6,7 @@
 * [快速开始](#quick-start)
 * [如何设计 NLP 模型](#design-model)
     * [定义模型配置文件](#define-conf)
+    * [中文支持](#chinese-support)
     * [模型可视化](#visualize)
 * [NLP 任务 Model Zoo](#model-zoo)
     * [任务 1: 文本分类](#task-1)
@@ -18,6 +19,7 @@
         2. [文本匹配的模型压缩](#task-6.2)
         3. [槽填充的模型压缩](#task-6.3)
         4. [机器阅读理解模型的模型压缩](#task-6.4)
+    * [任务 7: 中文情感分析](#task-7)
 * [高阶用法](#advanced-usage)
     * [额外的feature](#extra-feature)
     * [学习率衰减](#lr-decay)
@@ -70,6 +72,8 @@ python predict.py --conf_path=model_zoo/demo/conf.json
 以 *[PROJECTROOT/model_zoo/demo/conf.json](./model_zoo/demo/conf.json)* 为例 (便于说明工具包的用法，这个展示用例的网络结构并不是一个实际的结构)。 这个配置文件定义的任务是问答对匹配问题， 也就是判断一个答案是否可以回答对应的问题。 相关的样例数据保存在 *[PROJECTROOT/dataset/demo/](./dataset/demo/)*.
 
 配置文件的架构如下:
+
+- **language**. [optional, default: English] Firstly define language type here, we support English and Chinese now.
 - **inputs**. This part defines the input configuration.
     - ***use_cache***. If *use_cache* is true, the toolkit would make cache at the first time so that we can accelerate the training process at the next time.
     - ***dataset_type***. Declare the task type here. Currently, we support classification, regression and so on.
@@ -135,6 +139,7 @@ python predict.py --conf_path=model_zoo/demo/conf.json
     - ***batch_num_to_show_results***. [necessary for training] During the training process, show the results every batch_num_to_show_results batches.
     - ***max_epoch***. [necessary for training] The maximum number of epochs to train.
     - ***valid_times_per_epoch***. [optional for training, default: 1] Define how many times to conduct validation per epoch. Usually, we conduct validation after each epoch, but for a very large corpus, we'd better validate multiple times in case to miss the best state of our model. The default value is 1.
+    - ***tokenizer***. [optional] Define tokenizer here. Currently, we support 'nltk' and 'jieba'. By default, 'nltk' for English and 'jieba' for Chinese.
 - **architecture**. Define the model architecture. The node is a list of layers (blocks) in block_zoo to represent a model. The supported layers of this toolkit are given in [block_zoo overview](https://microsoft.github.io/NeuronBlocks). 
 
     - ***Embedding layer***. The first layer of this example (as shown below) defines the embedding layer, which is composed of one type of embedding: "word" (word embedding) and the dimension of "word" are 300.  You need to keep this dimension and the dimension of pre-trained embeddings consistent if you specify pre-trained embeddings in *inputs/data_paths/pre_trained_emb*.
@@ -184,9 +189,13 @@ python predict.py --conf_path=model_zoo/demo/conf.json
 
 *Tips: The [optional] and [necessary] mark means corresponding node in the configuration file is optional or necessary for training/test/prediction. If there is no mark, it means the node is necessary all the time. Actually, it would be more convenient to prepare a configuration file that contains all the configurations for training, test and prediction.*
 
+### <span id="chinese-support">中文支持</span>
+
+在使用中文数据时，JSON配置里的*language*应被设置为'Chinese'。中文默认使用jieba分词。中文任务示例参见[任务 7: 中文情感分析](#task-7)。
 
+另外，我们也支持中文预处理词向量。首先从[Chinese Word Vectors](https://github.com/Embedding/Chinese-Word-Vectors#pre-trained-chinese-word-vectors)下载中文词向量并解压，然后将其放置在某一文件夹下（例如 *dataset/chinese_word_vectors/* ），最后在JSON配置里定义 *inputs/data_paths/pre_trained_emb* 。
 
-## <span id="visualize">模型可视化</span>
+### <span id="visualize">模型可视化</span>
 
 本项目提供了一个模型可视化工具，用于模型的可视化和模型配置文件的语法正确性检查。请参考 [Model Visualizer README](./model_visualizer/README.md).
 
@@ -492,6 +501,29 @@ This task is to train a query-passage regression model to learn from a heavy tea
 #### <span id="task-6.3">6.3: 槽填充的模型压缩 (ongoing)</span>
 #### <span id="task-6.4">6.4: 机器阅读理解模型的模型压缩 (ongoing)</span>
 
+### <span id="task-7">任务 7: 中文情感分析</span>
+
+这里给出一个中文情感分析的示例。
+
+- ***数据集***
+
+    *PROJECT_ROOT/dataset/chinese_sentiment_analysis* 是中文情感分析的样例数据。
+
+- ***用法***
+
+    1. 训练中文情感分析模型。
+    ```bash
+    cd PROJECT_ROOT
+    python train.py --conf_path=model_zoo/nlp_tasks/chinese_sentiment_analysis/conf_chinese_sentiment_analysis_bilstm.json
+    ```
+    2. 测试模型。
+    ```bash
+    cd PROJECT_ROOT
+    python test.py --conf_path=model_zoo/nlp_tasks/chinese_sentiment_analysis/conf_chinese_sentiment_analysis_bilstm.json
+    ```
+     *提示：您可以通过运行不同的JSON配置文件来尝试不同的模型。当训练完成后，模型文件和训练日志文件可以在JSON配置的outputs/save_base_dir目录中找到。*
+
+
 ## <span id="advanced-usage">高阶用法</span>
 
 After building a model, the next goal is to train a model with good performance. It depends on a highly expressive model and tricks of the model training. NeuronBlocks provides some tricks of model training.