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official/README.md

 # TensorFlow Official Models
 
-The TensorFlow official models 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 TensorFlow API. They should also be reasonably optimized for fast performance while still being easy to read.
+The TensorFlow official models 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 TensorFlow API. They should also be
+reasonably optimized for fast performance while still being easy to read.
 
-These models are used as end-to-end tests, ensuring that the models run with the same speed and performance with each new TensorFlow build.
+These models are used as end-to-end tests, ensuring that the models run with the
+same speed and performance with each new TensorFlow build.
 
 ## Tensorflow releases
-The master branch of the models are **in development**, and they target the [nightly binaries](https://github.com/tensorflow/tensorflow#installation) built from the [master branch of TensorFlow](https://github.com/tensorflow/tensorflow/tree/master). We aim to keep them backwards compatible with the latest release when possible (currently TensorFlow 1.5), but we cannot always guarantee compatibility.
 
-**Stable versions** of the official models targeting releases of TensorFlow are available as tagged branches or [downloadable releases](https://github.com/tensorflow/models/releases). Model repository version numbers match the target TensorFlow release, such that [branch r1.4.0](https://github.com/tensorflow/models/tree/r1.4.0) and [release v1.4.0](https://github.com/tensorflow/models/releases/tag/v1.4.0) are compatible with [TensorFlow v1.4.0](https://github.com/tensorflow/tensorflow/releases/tag/v1.4.0).
-
-If you are on a version of TensorFlow earlier than 1.4, please [update your installation](https://www.tensorflow.org/install/).
+The master branch of the models are **in development**, and they target the
+[nightly binaries](https://github.com/tensorflow/tensorflow#installation) built
+from the
+[master branch of TensorFlow](https://github.com/tensorflow/tensorflow/tree/master).
+We aim to keep them backwards compatible with the latest release when possible
+(currently TensorFlow 1.5), but we cannot always guarantee compatibility.
+
+**Stable versions** of the official models targeting releases of TensorFlow are
+available as tagged branches or
+[downloadable releases](https://github.com/tensorflow/models/releases). Model
+repository version numbers match the target TensorFlow release, such that
+[branch r1.4.0](https://github.com/tensorflow/models/tree/r1.4.0) and
+[release v1.4.0](https://github.com/tensorflow/models/releases/tag/v1.4.0) are
+compatible with
+[TensorFlow v1.4.0](https://github.com/tensorflow/tensorflow/releases/tag/v1.4.0).
+
+If you are on a version of TensorFlow earlier than 1.4, please
+[update your installation](https://www.tensorflow.org/install/).
 
 ## Requirements
-Please follow the below steps before running models in this repo:
-
-
-1. TensorFlow [nightly binaries](https://github.com/tensorflow/tensorflow#installation)
 
-2. Add the top-level ***/models*** folder to the Python path with the command:
-   ```
-   export PYTHONPATH="$PYTHONPATH:/path/to/models"
-   ```
+Please follow the below steps before running models in this repo:
 
-   Using Colab:
-   ```
-   import os
-   os.environ['PYTHONPATH'] += ":/path/to/models"
-   ```
+1.  TensorFlow
+    [nightly binaries](https://github.com/tensorflow/tensorflow#installation)
 
-3. Install dependencies:
-   ```
-   pip3 install --user -r official/requirements.txt
-   ```
-   or
-   ```
-   pip install --user -r official/requirements.txt
-   ```
+2.  Add the top-level ***/models*** folder to the Python path with the command:
+    `export PYTHONPATH="$PYTHONPATH:/path/to/models"`
 
+    Using Colab: `import os os.environ['PYTHONPATH'] += ":/path/to/models"`
 
-To make Official Models easier to use, we are planning to create a pip installable Official Models package. This is being tracked in [#917](https://github.com/tensorflow/models/issues/917).
+3.  Install dependencies: `pip3 install --user -r official/requirements.txt` or
+    `pip install --user -r official/requirements.txt`
 
+To make Official Models easier to use, we are planning to create a pip
+installable Official Models package. This is being tracked in
+[#917](https://github.com/tensorflow/models/issues/917).
 
 ## Available models
 
-**NOTE:** Please make sure to follow the steps in the [Requirements](#requirements) section.
+**NOTE:** Please make sure to follow the steps in the
+[Requirements](#requirements) section.
 
-* [bert](bert): A powerful pre-trained language representation model: BERT, which
-  stands for Bidirectional Encoder Representations from Transformers.
-* [mnist](mnist): A basic model to classify digits from the MNIST dataset.
-* [resnet](resnet): A deep residual network that can be used to classify both CIFAR-10 and ImageNet's dataset of 1000 classes.
-* [transformer](transformer): A transformer model to translate the WMT English to German dataset.
-* More models to come!
+*   [bert](bert): A powerful pre-trained language representation model: BERT,
+    which stands for Bidirectional Encoder Representations from Transformers.
+*   [mnist](mnist): A basic model to classify digits from the MNIST dataset.
+*   [resnet](vision/image_classification): A deep residual network that can be
+    used to classify both CIFAR-10 and ImageNet's dataset of 1000 classes.
+*   [transformer](transformer): A transformer model to translate the WMT English
+    to German dataset.
+*   [ncf](recommendation): Neural Collaborative Filtering model for
+    recommendation tasks.
 
 Models that will not update to TensorFlow 2.x stay inside R1 directory:
 
-* [boosted_trees](r1/boosted_trees): A Gradient Boosted Trees model to classify
-  higgs boson process from HIGGS Data Set.
-* [wide_deep](r1/wide_deep): A model that combines a wide model and deep network
-  to classify census income data.
+*   [boosted_trees](r1/boosted_trees): A Gradient Boosted Trees model to
+    classify higgs boson process from HIGGS Data Set.
+*   [wide_deep](r1/wide_deep): A model that combines a wide model and deep
+    network to classify census income data.
+
+## More models to come!
+
+We are in the progress to revamp official model garden with TensorFlow 2.0 and
+Keras. In the near future, we will bring:
 
+*   State-of-the-art language understanding models: XLNet, GPT2, and more
+    members in Transformer family.
+*   Start-of-the-art image classification models: EfficientNet, MnasNet and
+    variants.
+*   A set of excellent objection detection models.
 
-If you would like to make any fixes or improvements to the models, please [submit a pull request](https://github.com/tensorflow/models/compare).
+If you would like to make any fixes or improvements to the models, please
+[submit a pull request](https://github.com/tensorflow/models/compare).
 
 ## New Models
 
-The team is actively working to add new models to the repository. Every model should follow the following guidelines, to uphold the
-our objectives of readable, usable, and maintainable code.
+The team is actively working to add new models to the repository. Every model
+should follow the following guidelines, to uphold the our objectives of
+readable, usable, and maintainable code.
 
-**General guidelines**
-* Code should be well documented and tested.
-* Runnable from a blank environment with relative ease.
-* Trainable on: single GPU/CPU (baseline), multiple GPUs, TPU
-* Compatible with Python 2 and 3 (using [six](https://pythonhosted.org/six/) when necessary)
-* Conform to [Google Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)
+**General guidelines** * Code should be well documented and tested. * Runnable
+from a blank environment with relative ease. * Trainable on: single GPU/CPU
+(baseline), multiple GPUs, TPU * Compatible with Python 2 and 3 (using
+[six](https://pythonhosted.org/six/) when necessary) * Conform to
+[Google Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)
 
 **Implementation guidelines**
 
-These guidelines exist so the model implementations are consistent for better readability and maintainability.
+These guidelines exist so the model implementations are consistent for better
+readability and maintainability.
 
-* Use [common utility functions](utils)
-* Export SavedModel at the end of training.
-* Consistent flags and flag-parsing library ([read more here](utils/flags/guidelines.md))
-* Produce benchmarks and logs ([read more here](utils/logs/guidelines.md))
+*   Use [common utility functions](utils)
+*   Export SavedModel at the end of training.
+*   Consistent flags and flag-parsing library
+    ([read more here](utils/flags/guidelines.md))
+*   Produce benchmarks and logs ([read more here](utils/logs/guidelines.md))