Update README.md

README.md

@@ -18,9 +18,9 @@
 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, ... 
-   * **pretrained models** such as **BERT**, **GPT2**, **XLNet**, ...
+   * **pre-trained models** such as **BERT**, **GPT2**, **XLNet**, ...
 
-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.
+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 quickly experimented and evolved by, e.g., plugging in and swapping out different modules.
 
 <div align="center">
    <img src="https://zhitinghu.github.io/texar_web/images/texar_stack.png"><br><br>
@@ -29,8 +29,8 @@ With Texar, cutting-edge complex models can be easily constructed, freely enrich
 
 ### 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-ins 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.
+* **Modularity**. Texar decomposes diverse, complex machine learning models and 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 modules, and configure rich options for 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 PyTorch interfaces and can take advantage of the rich PyTorch 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; rich configuration options for each module, all with default values.
@@ -85,10 +85,13 @@ for batch in iterator:
     loss = model(batch)
     # ...
 ```
-Many more examples are available [here](./examples)
+Many more examples are available [here](./examples).
 
 
 ### Installation
+Texar-PyTorch requires PyTorch 1.0 and higher. Please follow the [official instructions](https://pytorch.org/get-started/locally/#start-locally) to install the appropriate version.
+
+After PyTorch is installed, please run the following commands to install Texar-PyTorch:
 ```
 git clone https://github.com/asyml/texar-pytorch.git 
 cd texar-pytorch