Update README in the QM9 example

examples/qm9/README.md

@@ -1,44 +1,46 @@
-# QM9
+# QM9 Regression Example
 
-QM9 dataset regression
+This example performs regression on the QM9 dataset.
 
-## Dependency
+## Dependencies
 
-This example depends on the following package as well as 
-Chainer Chemistry and its dependent packages:
+To run the example, the following packages also need to be installed:
 
+- [`matplotlib`](https://matplotlib.org/)
+- [`seaborn`](https://seaborn.pydata.org/)
 - [`scikit-learn`](http://scikit-learn.org/stable/)
 
 ## How to run the code
 
-### Train the model with qm9 dataset
+### Training a model
 
-With CPU:
+To train a model, run the following:
+
+On the CPU:
 ```angular2html
 python train_qm9.py
 ```
 
-With GPU:
+On the GPU:
 ```angular2html
 python train_qm9.py -g 0
 ```
 
-### Inference with the trained model with qm9 dataset using Regressor
-
-As of v0.3.0, `Regressor` class is introduced which supports `predict`
-method for easier inference.
+### Inference using a pretrained model
 
-`Regressor` also supports `load_pickle` method, user may load
-the instance of pretrained-model using `pickle` file.
+As of v0.3.0, the `Regressor` class has been introduced, which provides the
+`predict` method for easier inference. `Regressor` also supports the
+`load_pickle` method, which allows for loading of a pretrained model, using the
+`pickle` library.
 
-The example implemented in `predict_qm9.py`.
+The perform inference using a pretrained model, run the following:
 
-With CPU:
+On the CPU:
 ```
 python predict_qm9.py [-i /path/to/training/result/directory]
 ```
 
-With GPU:
+On the GPU:
 ```
 python predict_qm9.py -g 0 [-i /path/to/training/result/directory]
 ```