diff --git a/README.md b/README.md
index 912c3ecd..afcac62a 100644
--- a/README.md
+++ b/README.md
@@ -31,24 +31,31 @@ python setup.py develop
 Examples
 ---------
 Users can keep their structure files in POSCAR, .cif, or .xyz files in a directory. In the examples below we will use POSCAR format files. In the same directory, there should be id_prop.csv file.
-In this id_prop.csv, the filenames, and correponding target values are kept in comma separated values (csv) format.
-Here is an example of training OptB88vdw bandgaps of 50 materials from JARVIS-DFT. The example is created using the script provided in the script folder.
-Users can modify the script more than 50 data, or make their own dataset in this format. The dataset in split in 80:10:10 as training-validation-test set. To change the split proportion and other parameters, change the config_example.json file. Sometimes, we want to train on certain sets and val/test on another dataset. For such cases, set n_train, n_val, n_test manually in the config_example.json and also set keep_data_order as True there.  With the configuration parameters given in config_example.json, the model is trained.
+
+In this directory, `id_prop.csv`, the filenames, and correponding target values are kept in comma separated values (csv) format.
+
+Here is an example of training OptB88vdw bandgaps of 50 materials from JARVIS-DFT database. The example is created using the examples/sample_data/scripts/generate_sample_data_reg.py script. Users can modify the script more than 50 data, or make their own dataset in this format. 
+
+The dataset in split in 80:10:10 as training-validation-test set (controlled by `train_ratio, val_ratio, test_ratio`) . To change the split proportion and other parameters, change the `config_example.json` file. If, users want to train on certain sets and val/test on another dataset, set `n_train`, `n_val`, `n_test` manually in the `config_example.json` and also set `keep_data_order` as True there so that random shuffle is disabled.  
+
+Now, the model is trained.
 
 ```
 python alignn/scripts/train_folder.py --root_dir "alignn/examples/sample_data" --config "alignn/examples/sample_data/config_example.json"
 ```
+
 While the above example is for regression, the follwoing example shows a classification task for metal/non-metal based on the above bandgap values. We transform the dataset
-into 1 or 0 based on a threshold of 0.01 eV (controlled by the parameter, 'classification_threshold') and train a similar classification model.
+into 1 or 0 based on a threshold of 0.01 eV (controlled by the parameter, `classification_threshold`) and train a similar classification model. Currently, the script allows binary classification tasks only.
 ```
-python alignn/scripts/train_folder.py --root_dir "alignn/examples/sample_data" --config "alignn/examples/sample_data/config_example.json"
+python alignn/scripts/train_folder.py --root_dir "alignn/examples/sample_data" --classification_threshold 0.01 --config "alignn/examples/sample_data/config_example.json"
 ```
+
+
 While the above example regression was for single-output values, we can train multi-output regression models as well.
-An example is given below for training formation energy per atom, bandgap and total energy per atom simulataneously. The script to generate the example data is provided in the script folder of the sample_data_multi_prop. 
-Another example of training electron and phonon density of states is provided also.
+An example is given below for training formation energy per atom, bandgap and total energy per atom simulataneously. The script to generate the example data is provided in the script folder of the sample_data_multi_prop. Another example of training electron and phonon density of states is provided also.
 ```
 python alignn/scripts/train_folder.py --root_dir "alignn/examples/sample_data_multi_prop" --config "alignn/examples/sample_data/config_example.json"
 ```
 
-Users can also try multiple example scripts to run multiple dataset training. Look into the 'alignn/scripts' folder. 
-These scripts automatically download datasets from jarvis.db.fighshare module in JARVIS-Tools and train several models. Make sure you specify your specific queuing system details in the scripts. 
+Users can try training using multiple example scripts to run multiple dataset (such as JARVIS-DFT, Materials project, QM9_JCTC etc.). Look into the 'alignn/scripts' folder. 
+These scripts automatically download datasets from `jarvis.db.fighshare` module in `jarvis-tools` package and train several models. Make sure you specify your specific queuing system details in the scripts.