diff --git a/tests/pipeline/test_re_text_classification.py b/tests/pipeline/test_re_text_classification.py
index 35dfb82e..8eed463a 100644
--- a/tests/pipeline/test_re_text_classification.py
+++ b/tests/pipeline/test_re_text_classification.py
@@ -20,11 +20,15 @@ class ExampleDocument(TextDocument):
 
 @pytest.mark.slow
 @pytest.mark.parametrize("use_auto", [False, True])
-def test_re_text_classification(use_auto):
+@pytest.mark.parametrize("half_precision_model", [False, True])
+@pytest.mark.parametrize("half_precision_ops", [False, True])
+def test_re_text_classification(use_auto, half_precision_model, half_precision_ops):
     model_name_or_path = "pie/example-re-textclf-tacred"
     if use_auto:
         pipeline = AutoPipeline.from_pretrained(
-            model_name_or_path, taskmodule_kwargs={"create_relation_candidates": True}
+            model_name_or_path,
+            taskmodule_kwargs={"create_relation_candidates": True},
+            half_precision_model=half_precision_model,
         )
     else:
         re_taskmodule = TransformerRETextClassificationTaskModule.from_pretrained(
@@ -32,7 +36,12 @@ def test_re_text_classification(use_auto):
             create_relation_candidates=True,
         )
         re_model = TransformerTextClassificationModel.from_pretrained(model_name_or_path)
-        pipeline = Pipeline(model=re_model, taskmodule=re_taskmodule, device=-1)
+        pipeline = Pipeline(
+            model=re_model,
+            taskmodule=re_taskmodule,
+            device=-1,
+            half_precision_model=half_precision_model,
+        )
     assert pipeline.taskmodule.is_from_pretrained
     assert pipeline.model.is_from_pretrained
 
@@ -44,7 +53,7 @@ def test_re_text_classification(use_auto):
     for start, end, label in [(65, 75, "PER"), (96, 100, "ORG"), (126, 134, "ORG")]:
         document.entities.append(LabeledSpan(start=start, end=end, label=label))
 
-    pipeline(document, batch_size=2)
+    pipeline(document, batch_size=2, half_precision_ops=half_precision_ops)
     relations: Sequence[BinaryRelation] = document["relations"].predictions
     assert len(relations) == 3