Update README.md

README.md

@@ -281,7 +281,7 @@ configs:
 Introducing Distributional MIPLIB (D-MIPLIB), the first comprehensive standardized dataset for evaluating ML-guided Mixed Integer Linear Programming (MILP) solving. Details of D-MIPLIB can be found on our [website](https://sites.google.com/usc.edu/distributional-miplib).
 
 ### Composition
-Distributional MIPLIB currently contains 48 MILP distributions from 13 application domains. A list of all the domains and all the distributions is provided at [website](https://sites.google.com/usc.edu/distributional-miplib). Each distribution contains a set of similar MILP optimization problem instances that correspond to the same model (a class of combinatorial optimization with specific parameters).
+Distributional MIPLIB contains MILP distributions from 13 application domains and of different hardness levels. A list of all the domains and all the distributions is provided at [website](https://sites.google.com/usc.edu/distributional-miplib). Each distribution contains a set of similar MILP optimization problem instances that correspond to the same model (a class of combinatorial optimization with specific parameters).
 
 For each distribution, there are 1000 instances in total, except for cases where more instances have been made publicly available previously or when limited instances are available. For Neural Network Verification, there are 3692 instances in total. Maritime Inventory Routing Problem contains 118 instances. Seismic-Resilient Pipe Network Planning domain  contains 219 and 206 instances in the Easy and Hard distribution, respectively.
 
@@ -290,11 +290,10 @@ For each distribution, there are 1000 instances in total, except for cases where
 MILP instances in this library are stored as text strings. You can download the MILP as *.lp or *.mps files by
 ```python
 from datasets import load_dataset
-
+# load the first test instance
 dataset = load_dataset("weiminhu/D-MIPLIB","CA-easy", split='test')
 test_id = 0
 test_instance = dataset[test_id]['MILP']
-
 # check file format
 file_format='lp'
 if 'format' in list(dataset[0].keys()):