mass and fasterrcnn path change.

model_zoo/official/nlp/mass/README.md

@@ -548,7 +548,7 @@ Run the shell script `run.sh` as followed:
 ```bash
 sh run.sh -t t -n 1 -i 1 -c /mass/config/config.json
 ```
-Get the log and output files under the path `./run_mass_*/`, and the model file under the path assigned in the `config/config.json` file.
+Get the log and output files under the path `./train_mass_*/`, and the model file under the path assigned in the `config/config.json` file.
 
 ## Fine-tuning
 For fine-tuning a model, config the options in `config.json` firstly:
@@ -562,7 +562,7 @@ Run the shell script `run.sh` as followed:
 ```bash
 sh run.sh -t t -n 1 -i 1 -c config/config.json
 ```
-Get the log and output files under the path `./run_mass_*/`, and the model file under the path assigned in the `config/config.json` file.
+Get the log and output files under the path `./train_mass_*/`, and the model file under the path assigned in the `config/config.json` file.
 
 ## Inference
 If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/network_migration.html).

model_zoo/official/nlp/mass/scripts/run.sh

@@ -149,19 +149,19 @@ do
   cd $file_path || exit
   cd ../ || exit
 
-  rm -rf ./run_mass_$DEVICE_ID
-  mkdir ./run_mass_$DEVICE_ID
+  rm -rf ./${task}_mass_$DEVICE_ID
+  mkdir ./${task}_mass_$DEVICE_ID
 
-  cp train.py ./run_mass_$DEVICE_ID
-  cp eval.py ./run_mass_$DEVICE_ID
-  cp $configurations ./run_mass_$DEVICE_ID
+  cp train.py ./${task}_mass_$DEVICE_ID
+  cp eval.py ./${task}_mass_$DEVICE_ID
+  cp $configurations ./${task}_mass_$DEVICE_ID
 
   if [ $vocab ]
   then
-    cp $vocab ./run_mass_$DEVICE_ID
+    cp $vocab ./${task}_mass_$DEVICE_ID
   fi
 
-  cd ./run_mass_$DEVICE_ID || exit
+  cd ./${task}_mass_$DEVICE_ID || exit
   env > log.log
   echo $task
   if [ "$task" == "train" ]