Merge remote-tracking branch 'origin/master'

README.md

@@ -1,8 +1,11 @@
 <div align="center">
-   <img src="https://asyml.github.io/texar_web/images/logo_h_035.png"><br><br>
+   <img src="https://zhitinghu.github.io/texar_web/images/logo_h_035.png"><br><br>
 </div>
 
 -----------------
+
+[![Documentation Status](https://readthedocs.org/projects/texar/badge/?version=latest)](https://texar.readthedocs.io/en/latest/?badge=latest)
+
 
 **Texar** is an open-source toolkit based on Tensorflow, aiming to support a broad set of machine learning especially **text generation tasks**, such as machine translation, dialog, summarization, content manipulation, language modeling, and so on. Texar is designed for both researchers and practitioners for fast prototyping and experimentation.
 
@@ -13,7 +16,7 @@ With the design goals of **modularity, versatility, and extensibility** in mind,
 With Texar, cutting-edge complex models can be easily constructed, freely enriched with best modeling/training practices, readily fitted into standard training/evaluation pipelines, and fastly experimented and evolved by, e.g., plugging-in and swapping-out different modules.
 
 <div align="center">
-   <img src="https://asyml.github.io/texar_web/images/texar_stack.png"><br><br>
+   <img src="https://zhitinghu.github.io/texar_web/images/texar_stack.png"><br><br>
 </div> 
 
 ### Key Features
@@ -24,7 +27,7 @@ Users can construct their own models at a high conceptual level just like assemb
 * Interfaces with different functionality levels. Users can customize a model through 1) simple **Python/YAML configuration files** of provided model templates/examples; 2) programming with **Python Library APIs** for maximal customizability.
 * Easy-to-use APIs: 1) Convenient automatic variable re-use---no worry about the complicated TF variable scopes; 2) Pytorch-like callable modules; 3) Rich configuration options for each module, all with default values; ...
 * Well-structured high-quality code of uniform design patterns and consistent styles. 
-* Clean, detailed [documentation](.) and rich [examples](./examples).
+* Clean, detailed [documentation](https://texar.readthedocs.io) and rich [examples](./examples).
 
 ### Library API Example
 Builds a (self-)attentional sequence encoder-decoder model, with different learning algorithms:
@@ -77,7 +80,7 @@ pip install texar
 
 ### Getting Started
 * [Examples](./examples)
-* [Documentations](.)
+* [Documentation](https://texar.readthedocs.io)
 
 ### Reference
 If you use Texar, please cite the [report](.) with the following BibTex entry:

docs/get_started.md

@@ -1,26 +1,26 @@
 # Overview #
 
-**Texar** is an open-source toolkit based on Tensorflow, aiming to support a broad set of machine learning especially **text generation tasks**, such as machine translation, dialog, summarization, content manipulation, language modeling, and so on. Texar is design for both researchers and practitoners for fast prototyping and experimentation.
+**Texar** is an open-source toolkit based on Tensorflow, aiming to support a broad set of machine learning especially **text generation tasks**, such as machine translation, dialog, summarization, content manipulation, language modeling, and so on. Texar is designed for both researchers and practitioners for fast prototyping and experimentation.
 
-With the design goals of **modularity, versatility, and extensibility** in mind, Texar extacts the common patterns underlying the diverse tasks and methodologies, creates a library of highly reusable modules and functionalities, and facilitates **arbitrary model architectures and algorithmic paradigms**, e.g., 
+With the design goals of **modularity, versatility, and extensibility** in mind, Texar extracts the common patterns underlying the diverse tasks and methodologies, creates a library of highly reusable modules and functionalities, and facilitates **arbitrary model architectures and algorithmic paradigms**, e.g., 
    * encoder(s) to decoder(s), sequential- and self-attentions, memory, hierarchical models, classifiers... 
    * maximum likelihood learning, reinforcement learning, adversarial learning, probabilistic modeling, ... 
 
 With Texar, cutting-edge complex models can be easily constructed, freely enriched with best modeling/training practices, readily fitted into standard training/evaluation pipelines, and fastly experimented and evolved by, e.g., plugging-in and swapping-out different modules.
 
 <div align="center">
-   <img src="https://asyml.github.io/texar_web/images/texar_stack.png"><br><br>
+   <img src="https://zhitinghu.github.io/texar_web/images/texar_stack.png"><br><br>
 </div> 
 
 ### Key Features
 * **Versatility**. Texar contains a wide range of modules and functionalities for composing arbitrary model architectures and implementing various learning algorithms, as well as for data processing, evaluation, prediction, etc.
 * **Modularity**. Texar decomposes diverse complex machine learning models/algorithms into a set of highly-reusable modules. In particular, model **architecture, losses, and learning processes** are fully decomposed.  
-Users can construct their own models at a high conceptual level just like assembling building blocks. It is convenient to plug-in or swap-out moduels, and configure rich options of each module. For example, switching between maximum likelihood learning and reinforcement learning involves only changing several lines of code.
-* **Extensibility**. It is straightforward to integrate any user-customized, external modules. Also, Texar is fully compatible with the native Tensorflow interfaces, and can take advantage of the rich Tensorflow features, and resources from the vibrant open-source community.
+Users can construct their own models at a high conceptual level just like assembling building blocks. It is convenient to plug in or swap out modules, and configure rich options of each module. For example, switching between maximum likelihood learning and reinforcement learning involves only changing several lines of code.
+* **Extensibility**. It is straightforward to integrate any user-customized, external modules. Also, Texar is fully compatible with the native Tensorflow interfaces and can take advantage of the rich Tensorflow features, and resources from the vibrant open-source community.
 * Interfaces with different functionality levels. Users can customize a model through 1) simple **Python/YAML configuration files** of provided model templates/examples; 2) programming with **Python Library APIs** for maximal customizability.
 * Easy-to-use APIs: 1) Convenient automatic variable re-use---no worry about the complicated TF variable scopes; 2) Pytorch-like callable modules; 3) Rich configuration options for each module, all with default values; ...
 * Well-structured high-quality code of uniform design patterns and consistent styles. 
-* Clean, detailed [documentations](.) and rich [examples](.).
+* Clean, detailed [documentation](https://texar.readthedocs.io) and rich [examples](https://github.com/asyml/texar/tree/master/examples).
 
 ### Library API Example
 Builds a (self-)attentional sequence encoder-decoder model, with different learning algorithms:
@@ -64,16 +64,16 @@ agent = tx.agents.SeqPGAgent(samples=outputs.sample_id,
                              sequence_length=batch['target_length']-1,
                              hparams=config_model.agent)
 ```
-Many more examples are available [here](.)
+Many more examples are available [here](https://github.com/asyml/texar/tree/master/examples)
 
 ### Installtion
 ```
 pip install texar
 ```
 
 ### Getting Started
-* [Examples](.)
-* [Documentations](.)
+* [Examples](https://github.com/asyml/texar/tree/master/examples)
+* [Documentations](https://texar.readthedocs.io)
 
 ### Reference
 If you use Texar, please cite the [report](.) with the following BibTex entry:
@@ -90,4 +90,4 @@ Zhiting Hu, Haoran Shi, Zichao Yang, Bowen Tan, Tiancheng Zhao, Junxian He, Went
 ```
 
 ### License
-[Apache License 2.0](.)
+[Apache License 2.0](https://github.com/asyml/texar/blob/master/LICENSE)

docs/index.rst

@@ -6,7 +6,7 @@
 Welcome to Texar's documentation!
 *********************************
 
-Texar is a flexible TensorFlow library to generate text.
+Texar is a modularized, versatile, and extensible toolkit for text generation tasks and beyond. 
 
 
 .. toctree::