diff --git a/README.md b/README.md
index 72420e3f..24c5fd11 100644
--- a/README.md
+++ b/README.md
@@ -103,11 +103,11 @@ To train to model we simply run:
 
 > ./bin/training/train --logtostderr -num_threads 16 --input training_data
 
-where training_data is the file produced in previous step and num_threads specifies how many threads should the training algorithm used. To get full options available for training (such as learning rate, regularization and margin), use:
+where `training_data` is the file produced in previous step and `num_threads` specifies how many threads should the training algorithm use. To get full options available for training (such as learning rate, regularization and margin), use:
 
 > ./bin/training/train --help
 
-As a result, it creates two files with the trained model: `model_strings` and `model_features`.
+After the training finishes, two files are created which contains the trained model: `model_strings` and `model_features`.
 
 Predicting Properties (Nice2Predict)
 -------
@@ -117,4 +117,5 @@ To predict properties for new programs, start a server after a model was trained
 > ./bin/server/nice2server --logtostderr
 
 Then, the server will predict properties for programs given in JsonRPC format. One can debug and observe deobfuscation from the viewer available in the [viewer/viewer.html](https://github.com/eth-srl/Nice2Predict/blob/master/viewer/viewer.html) (online demo available at http://www.nice2predict.org).
+The server takes as an input same JSON format as described above and returns best assigment to the unknown properties (labelled as `inf`).