Update README: Added pygeometric use

README.md

@@ -9,6 +9,8 @@ license: mit
 - [Dataset Description](#dataset-description)
   - [Dataset Summary](#dataset-summary)
   - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+- [External Use](#external-use)
+  - [PyGeometric](#pygeometric)
 - [Dataset Structure](#dataset-structure)
   - [Data Properties](#data-properties)
   - [Data Fields](#data-fields)
@@ -34,6 +36,24 @@ The `ogbg-molhiv` dataset is a small molecular property prediction dataset, adap
 `ogbg-molhiv` should be used for molecular property prediction (aiming to predict whether molecules inhibit HIV or not), a binary classification task.
 The associated leaderboards are here: [OGB leaderboard](https://ogb.stanford.edu/docs/leader_graphprop/#ogbg-molhiv) and [Papers with code leaderboard](https://paperswithcode.com/sota/graph-property-prediction-on-ogbg-molhiv).
 
+## External Use
+### PyGeometric
+To load in PyGeometric, do the following:
+
+```python
+from datasets import load_dataset
+
+from torch_geometric.data import Data
+from torch_geometric.loader import DataLoader
+
+ogbg_molhiv = load_dataset("graphs-datasets/ogbg-molhiv")
+# For the train set (replace by valid or test as needed)
+ogbg_molhiv_pg_list = [Data(graph) for graph in ogbg_molhiv["train"]]
+ogbg_molhiv_pg = DataLoader(ogbg_molhiv_pg_list)
+
+```
+
+
 ## Dataset Structure
 
 ### Data Properties