!11303 fix links in model zoo

From: @zhao_ting_v
Reviewed-by: @wuxuejian,@oacjiewen
Signed-off-by: @wuxuejian

model_zoo/official/cv/centerface/README.md

@@ -84,7 +84,7 @@ other datasets need to use the same format as WiderFace.
 - Hardware（Ascend）
     - Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
 - Framework
-    - [MindSpore](https://cmc-szv.clouddragon.huawei.com/cmcversion/index/search?searchKey=Do-MindSpore%20V100R001C00B622)
+    - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
     - [MindSpore tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
     - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)

model_zoo/official/cv/deeptext/README.md

@@ -187,7 +187,7 @@ class 1 precision is 88.01%, recall is 82.77%
 | Loss Function              | SoftmaxCrossEntropyWithLogits for classification, SmoothL2Loss for bbox regression|
 | Loss                       | ~0.008                                                       |
 | Total time (8p)            | 4h                                                           |
-| Scripts                    | [deeptext script](https://gitee.com/mindspore/mindspore/tree/r1.1/mindspore/official/cv/deeptext) |
+| Scripts                    | [deeptext script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/deeptext) |
 
 #### Inference Performance
 

model_zoo/official/cv/psenet/README.md

@@ -197,7 +197,7 @@ Calculated!{"precision": 0.814796668299853, "recall": 0.8006740491092923, "hmean
 | Total time                 | 1pc: 75.48 h;  8pcs: 10.01 h                                |
 | Parameters (M)             | 27.36                                                       |
 | Checkpoint for Fine tuning | 109.44M (.ckpt file)                                        |
-| Scripts                    | <https://gitee.com/mindspore/mindspore/tree/master/model_zoo/psenet> |
+| Scripts                    | <https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/psenet> |
 
 ### Inference Performance
 

model_zoo/official/cv/psenet/README_CN.md

@@ -195,7 +195,7 @@ Calculated!{"precision": 0.8147966668299853，"recall"：0.8006740491092923，"h
 | 总时间 | 1卡：75.48小时；4卡：18.87小时|
 | 参数(M) | 27.36 |
 | 微调检查点 | 109.44M （.ckpt file） |
-| 脚本 | <https://gitee.com/mindspore/mindspore/tree/master/model_zoo/psenet> |
+| 脚本 | <https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/psenet> |
 
 ### 推理性能
 

model_zoo/official/nlp/fasttext/README.md

@@ -1,4 +1,4 @@
-![](https://www.mindspore.cn/static/img/logo.a3e472c9.png)
+![](https://www.mindspore.cn/static/img/logo_black.6a5c850d.png)
 
 <!-- TOC -->
 

model_zoo/official/nlp/gnmt_v2/README.md

@@ -1,4 +1,4 @@
-![](https://www.mindspore.cn/static/img/logo.a3e472c9.png)
+![](https://www.mindspore.cn/static/img/logo_black.6a5c850d.png)
 
 <!-- TOC -->
 

model_zoo/official/nlp/mass/README_CN.md

@@ -47,7 +47,7 @@ BERT（Devlin等人，2018年）采用有屏蔽的语料丰富文本预训练Tra
 
 受BERT、GPT及其他语言模型的启发，微软致力于在此基础上研究[掩式序列到序列（MASS）预训练语言生成](https://www.microsoft.com/en-us/research/uploads/prod/2019/06/MASS-paper-updated-002.pdf)。MASS的参数k很重要，用来控制屏蔽后的分片长度。BERT和GPT属于特例，k等于1或者句长。
 
-[MASS介绍 — 序列对序列语言生成任务中性能优于BERT和GPT的预训练方法](https://www.microsoft.com/en-us/research/blog/introduction-mass-a-pre-training-method-thing-forts-bert-and-gpt-in-sequence-to-sequence-language-generate-tasks/)
+[MASS介绍 — 序列对序列语言生成任务中性能优于BERT和GPT的预训练方法](https://www.microsoft.com/en-us/research/blog/introducing-mass-a-pre-training-method-that-outperforms-bert-and-gpt-in-sequence-to-sequence-language-generation-tasks/)
 
 [论文](https://www.microsoft.com/en-us/research/uploads/prod/2019/06/MASS-paper-updated-002.pdf): Song, Kaitao, Xu Tan, Tao Qin, Jianfeng Lu and Tie-Yan Liu.“MASS: Masked Sequence to Sequence Pre-training for Language Generation.”ICML (2019).
 

model_zoo/official/nlp/prophetnet/README.md

@@ -655,4 +655,4 @@ The model has been validated on Ascend environment, not validated on CPU and GPU
 
 # ModelZoo Homepage  
 
- [Link](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo)
+ [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)

model_zoo/research/audio/fcn-4/README.md

@@ -192,7 +192,7 @@ Parameters for both training and evaluation can be set in config.py
 | Speed                      | 1pc: 160 samples/sec;                                       |
 | Total time                 | 1pc: 20 mins;                                               |
 | Checkpoint for Fine tuning | 198.73M(.ckpt file)                                         |
-| Scripts                    | [music_auto_tagging script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/audio/fcn-4) |
+| Scripts                    | [music_auto_tagging script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/audio/fcn-4) |
 
 ## [ModelZoo Homepage](#contents)  
 

model_zoo/research/cv/centernet/README.md

@@ -79,7 +79,7 @@ Dataset used: [COCO2017](https://cocodataset.org/)
 - Hardware（Ascend）
     - Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
 - Framework
-    - [MindSpore](https://cmc-szv.clouddragon.huawei.com/cmcversion/index/search?searchKey=Do-MindSpore%20V100R001C00B622)
+    - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
     - [MindSpore tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
     - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)