diff --git a/examples/hetero/recommender_system.py b/examples/hetero/recommender_system.py
index 8dd5d8349123..824a19fffb8a 100644
--- a/examples/hetero/recommender_system.py
+++ b/examples/hetero/recommender_system.py
@@ -23,7 +23,7 @@
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 path = osp.join(osp.dirname(osp.realpath(__file__)), '../../data/MovieLens')
-data = MovieLens(path, model_name='all-MiniLM-L6-v2')[0]
+data = MovieLens(path)[0]
 
 # Add user node features for message passing:
 data['user'].x = torch.eye(data['user'].num_nodes)
diff --git a/torch_geometric/datasets/movie_lens.py b/torch_geometric/datasets/movie_lens.py
index 1c9f435c6a74..f73ec687b42d 100644
--- a/torch_geometric/datasets/movie_lens.py
+++ b/torch_geometric/datasets/movie_lens.py
@@ -42,7 +42,7 @@ def __init__(
         root: str,
         transform: Optional[Callable] = None,
         pre_transform: Optional[Callable] = None,
-        model_name: Optional[str] = 'all-MiniLM-L6-v2',
+        model_name: Optional[str] = 'Alibaba-NLP/gte-modernbert-base',
         force_reload: bool = False,
     ) -> None:
         self.model_name = model_name
@@ -68,7 +68,8 @@ def download(self) -> None:
 
     def process(self) -> None:
         import pandas as pd
-        from sentence_transformers import SentenceTransformer
+
+        from torch_geometric.llm.models import SentenceTransformer
 
         data = HeteroData()
 
@@ -78,10 +79,11 @@ def process(self) -> None:
         genres = df['genres'].str.get_dummies('|').values
         genres = torch.from_numpy(genres).to(torch.float)
 
-        model = SentenceTransformer(self.model_name)
+        model = SentenceTransformer(model_name=self.model_name)
+        if torch.cuda.is_available():
+            model = model.cuda()
         with torch.no_grad():
-            emb = model.encode(df['title'].values, show_progress_bar=True,
-                               convert_to_tensor=True).cpu()
+            emb = model.encode(text=df['title'].values).cpu()
 
         data['movie'].x = torch.cat([emb, genres], dim=-1)