Merge branch 'master' into absl

README.md

@@ -2,12 +2,10 @@
 
 This repository contains a number of different models implemented in [TensorFlow](https://www.tensorflow.org):
 
-The [official models](official) are a collection of example models that use TensorFlow's high-level APIs. They are intended to be well-maintained, tested, and kept up to date with the latest stable TensorFlow API. They should also be reasonably optimized for fast performance while still being easy to read. We especially recommend newer TensorFlow users to start here.
+The [official models](official) are a collection of example models that use TensorFlow 2's high-level APIs. They are intended to be well-maintained, tested, and kept up to date with the latest stable TensorFlow API. They should also be reasonably optimized for fast performance while still being easy to read. We especially recommend newer TensorFlow users to start here.
 
 The [research models](https://github.com/tensorflow/models/tree/master/research) are a large collection of models implemented in TensorFlow by researchers. They are not officially supported or available in release branches; it is up to the individual researchers to maintain the models and/or provide support on issues and pull requests.
 
-The [tutorials folder](tutorials) is a collection of models described in the [TensorFlow tutorials](https://www.tensorflow.org/tutorials/).
-
 ## Contribution guidelines
 
 If you want to contribute to models, be sure to review the [contribution guidelines](CONTRIBUTING.md).

official/README.md

@@ -1,6 +1,6 @@
 # TensorFlow Official Models
 
-The TensorFlow official models are a collection of example models that use
+The TensorFlow official models are a collection of models that use
 TensorFlow's high-level APIs. They are intended to be well-maintained, tested,
 and kept up to date with the latest TensorFlow API. They should also be
 reasonably optimized for fast performance while still being easy to read.
@@ -83,7 +83,7 @@ installable Official Models package. This is being tracked in
 *   [bert](nlp/bert): A powerful pre-trained language representation model:
     BERT, which stands for Bidirectional Encoder Representations from
     Transformers.
-*   [transformer](transformer): A transformer model to translate the WMT English
+*   [transformer](nlp/transformer): A transformer model to translate the WMT English
     to German dataset.
 *   [xlnet](nlp/xlnet): XLNet: Generalized Autoregressive Pretraining for
     Language Understanding.