edit readme

model_zoo/official/nlp/bert/README.md

@@ -411,20 +411,22 @@ epoch: 0.0, current epoch percent: 0.002, step: 200, outpus are (Tensor(shape=[1
 Before running the command below, please check the load pretrain checkpoint path has been set. Please set the checkpoint path to be the absolute full path, e.g:"/username/pretrain/checkpoint_100_300.ckpt".
 ```
 bash scripts/run_classifier.sh
-
+```
 The command above will run in the background, you can view training logs in classfier_log.txt.
 
 If you choose accuracy as assessment method, the result will be as follows:
+```
 acc_num XXX, total_num XXX, accuracy 0.588986
 ```
 
 #### evaluation on cluener dataset when running on Ascend    
 ```
 bash scripts/ner.sh
-
+```
 The command above will run in the background, you can view training logs in ner_log.txt.
 
 If you choose F1 as assessment method, the result will be as follows:
+```
 Precision 0.920507
 Recall 0.948683
 F1 0.920507
@@ -433,9 +435,10 @@ F1 0.920507
 #### evaluation on squad v1.1 dataset when running on Ascend   
 ```
 bash scripts/squad.sh
-
+```
 The command above will run in the background, you can view training logs in squad_log.txt.
 The result will be as follows:
+```
 {"exact_match": 80.3878923040233284, "f1": 87.6902384023850329}
 ```
 

model_zoo/official/nlp/bert/scripts/ascend_distributed_launcher/README.md

@@ -1,11 +1,11 @@
 # Run distribute pretrain
 
 ## description
-The number of D chips can be automatically allocated based on the device_num set in hccl config file, You don not need to specify that.
+The number of Ascend accelerators can be automatically allocated based on the device_num set in hccl config file, You don not need to specify that.
 
 
 ## how to use
-For example, if we want to generate the launch command of the distributed training of Bert model on D chip, we can run the following command in `/bert/` dir:
+For example, if we want to generate the launch command of the distributed training of Bert model on Ascend accelerators, we can run the following command in `/bert/` dir:
 ```
 python ./scripts/ascend_distributed_launcher/get_distribute_pretrain_cmd.py --run_script_dir ./run_pretrain.py --hyper_parameter_config_dir ./scripts/ascend_distributed_launcher/hyper_parameter_config.ini --data_dir /path/dataset/ --hccl_config_dir model_zoo/utils/hccl_tools/hccl_2p_56_x.x.x.x.json
 ```

model_zoo/official/nlp/bert/scripts/ascend_distributed_launcher/get_distribute_pretrain_cmd.py

@@ -59,7 +59,7 @@ def append_cmd_env(cmd, key, value):
 
 def distribute_pretrain():
     """
-    distribute pretrain scripts. The number of D chips can be automatically allocated
+    distribute pretrain scripts. The number of Ascend accelerators can be automatically allocated
     based on the device_num set in hccl config file, You don not need to specify that.
     """
     cmd = ""

model_zoo/utils/ascend_distributed_launcher/README.md

@@ -1,11 +1,11 @@
 # Run distribute pretrain
 
 ## description
-The number of D chips can be automatically allocated based on the device_num set in hccl config file, You don not need to specify that.
+The number of Ascend accelerators can be automatically allocated based on the device_num set in hccl config file, You don not need to specify that.
 
 
 ## how to use
-For example, if we want to generate the launch command of the distributed training of Bert model on D chip, we can run the following command in `/bert/` dir:
+For example, if we want to generate the launch command of the distributed training of Bert model on Ascend accelerators, we can run the following command in `/bert/` dir:
 ```
 python ./scripts/ascend_distributed_launcher/get_distribute_pretrain_cmd.py --run_script_dir ./run_pretrain.py --hyper_parameter_config_dir ./scripts/ascend_distributed_launcher/hyper_parameter_config.ini --data_dir /path/dataset/ --hccl_config_dir model_zoo/utils/hccl_tools/hccl_2p_56_x.x.x.x.json
 ```

model_zoo/utils/ascend_distributed_launcher/get_distribute_pretrain_cmd.py

@@ -59,7 +59,7 @@ def append_cmd_env(cmd, key, value):
 
 def distribute_pretrain():
     """
-    distribute pretrain scripts. The number of D chips can be automatically allocated
+    distribute pretrain scripts. The number of Ascend accelerators can be automatically allocated
     based on the device_num set in hccl config file, You don not need to specify that.
     """
     cmd = ""

model_zoo/utils/hccl_tools/README.md

@@ -1,6 +1,6 @@
 # description
 
-mindspore distributed training launch helper utilty that will generate hccl config file.
+MindSpore distributed training launch helper utilty that will generate hccl config file.
 
 # use
 
@@ -14,4 +14,4 @@ hccl_[device_num]p_[which device]_[server_ip].json
 ```
 
 # Note
-Please note that the D chips used must be continuous, such [0,4) means to use four chips 0，1，2，3; [0,1) means to use chip 0; The first four chips are a group, and the last four chips are a group. In addition to the [0,8) chips are allowed, other cross-group such as [3,6) are prohibited.
+Please note that the Ascend accelerators used must be continuous, such [0,4) means to use four chips 0，1，2，3; [0,1) means to use chip 0; The first four chips are a group, and the last four chips are a group. In addition to the [0,8) chips are allowed, other cross-group such as [3,6) are prohibited.

model_zoo/utils/hccl_tools/hccl_tools.py

@@ -37,7 +37,7 @@ def parse_args():
                                         "helper utilty that will generate hccl"
                                         " config file")
     parser.add_argument("--device_num", type=str, default="[0,8)",
-                        help="The number of the D chip used. please note that the D chips"
+                        help="The number of the Ascend accelerators used. please note that the Ascend accelerators"
                              "used must be continuous, such [0,4) means to use four chips "
                              "0，1，2，3; [0,1) means to use chip 0; The first four chips are"
                              "a group, and the last four chips are a group. In addition to"