!7853 mass and cnnctc readme fix.

Merge pull request !7853 from linqingke/psenet

model_zoo/official/cv/cnnctc/README.md

@@ -44,6 +44,8 @@ This is an example of training CNN+CTC model for text recognition on MJSynth and
 
 # [Dataset](#contents)
 
+Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.
+
 The [MJSynth](https://www.robots.ox.ac.uk/~vgg/data/text/) and [SynthText](https://github.com/ankush-me/SynthText) dataset are used for model training. The [The IIIT 5K-word dataset](https://cvit.iiit.ac.in/research/projects/cvit-projects/the-iiit-5k-word-dataset) dataset is used for evaluation.
 
 - step 1:
@@ -247,7 +249,7 @@ The model will be evaluated on the IIIT dataset, sample results and overall accu
 
 ### Training Performance 
 
-| Parameters                 | FasterRcnn                                                   |
+| Parameters                 | CNNCTC                                                   |
 | -------------------------- | ----------------------------------------------------------- |
 | Model Version              | V1                                                |
 | Resource                   | Ascend 910 ；CPU 2.60GHz，192cores；Memory，755G             |
@@ -265,7 +267,7 @@ The model will be evaluated on the IIIT dataset, sample results and overall accu
 
 ### Evaluation Performance
 
-| Parameters          | FasterRcnn                   |
+| Parameters          | CNNCTC                   |
 | ------------------- | --------------------------- |
 | Model Version       | V1                |
 | Resource            | Ascend 910                  |

model_zoo/official/cv/faster_rcnn/README.md

@@ -33,6 +33,8 @@ FasterRcnn is a two-stage target detection network,This network uses a region pr
 
 # Dataset
 
+Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.
+
 Dataset used: [COCO2017](<https://cocodataset.org/>) 
 
 - Dataset size：19G

model_zoo/official/cv/psenet/README.md

@@ -35,6 +35,9 @@ With the development of convolutional neural network, scene text detection techn
 Progressive Scale Expansion Network (PSENet) is a text detector which is able to well detect the arbitrary-shape text in natural scene.
 
 # [Dataset](#contents)
+
+Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.
+
 Dataset used: [ICDAR2015](https://rrc.cvc.uab.es/?ch=4&com=tasks#TextLocalization)
 A training set of 1000 images containing about 4500 readable words 
 A testing set containing about 2000 readable words

model_zoo/official/nlp/mass/README.md

@@ -61,6 +61,8 @@ get the most possible prediction results.
 
 # Dataset
 
+Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.
+
 Dataset used: 
 - monolingual English data from News Crawl dataset(WMT 2019) for pre-training.
 - Gigaword Corpus(Graff et al., 2003) for Text Summarization.
@@ -590,7 +592,7 @@ The comparisons between MASS and other baseline methods in terms of PPL on Corne
 | Model Version              | v1                                                                        |
 | Resource                   | Ascend 910, cpu 2.60GHz, 192cores；memory, 755G                            |
 | uploaded Date              | 05/24/2020                                                                |
-| MindSpore Version          | 0.2.0                                                                     |
+| MindSpore Version          | 1.0.0                                                                     |
 | Dataset                    | News Crawl 2007-2017 English monolingual corpus, Gigaword corpus, Cornell Movie Dialog corpus |
 | Training Parameters        | Epoch=50, steps=XXX, batch_size=192, lr=1e-4                              |
 | Optimizer                  | Adam                                                                      |
@@ -613,7 +615,7 @@ The comparisons between MASS and other baseline methods in terms of PPL on Corne
 | Model Version              | V1                                                         |
 | Resource                   | Huawei 910                                                 |
 | uploaded Date              | 05/24/2020                                                 |
-| MindSpore Version          | 0.2.0                                                      |
+| MindSpore Version          | 1.0.0                                                      |
 | Dataset                    | Gigaword corpus, Cornell Movie Dialog corpus               |
 | batch_size                 | ---                                                        |
 | outputs                    | Sentence and probability                                   |