refine doc

dssm/README.cn.md

@@ -263,7 +263,7 @@ Pairwise Rank复用上面的DNN结构，同一个source对两个target求相似
 
 ## 执行训练
 
-可以直接执行 `python train.py -y 0 --model_arch 0` 使用 `./data/classification` 目录里的实例数据来测试能否直接运行训练分类FC模型。
+可以直接执行 `python train.py -y 0 --model_arch 0 --class_num 2` 使用 `./data/classification` 目录里的实例数据来测试能否直接运行训练分类FC模型。
 
 其他模型结构也可以通过命令行实现定制，详细命令行参数请执行 `python train.py --help`进行查阅。
 

dssm/README.md

@@ -240,12 +240,12 @@ The example of this format is as follows.
 
 ## Training
 
-We use `python train.py -y 0 --model_arch 0` with the data in  `./data/classification` to train a DSSM model for classification. The paremeters to execute the script `train.py` can be found by execution `python infer.py --help`. Some important parameters are：
+We use `python train.py -y 0 --model_arch 0 --class_num 2` with the data in  `./data/classification` to train a DSSM model for classification. The paremeters to execute the script `train.py` can be found by execution `python infer.py --help`. Some important parameters are：
 
 - `train_data_path` Training data path
 - `test_data_path`  Test data path, optional
 - `source_dic_path`  Source dictionary path
-- `target_dic_path` 目Target dictionary path
+- `target_dic_path` Target dictionary path
 - `model_type`  The type of loss function of the model: classification 0, sort 1, regression 2
 - `model_arch` Model structure: FC 0，CNN 1, RNN 2
 - `dnn_dims` The dimension of each layer of the model is set, the default is `256,128,64,32`，with 4 layers.