diff --git a/src/transformers/pipelines/zero_shot_classification.py b/src/transformers/pipelines/zero_shot_classification.py
index 1e2b88d7be170..f308c48b16dfb 100644
--- a/src/transformers/pipelines/zero_shot_classification.py
+++ b/src/transformers/pipelines/zero_shot_classification.py
@@ -191,10 +191,7 @@ def __call__(
         else:
             raise ValueError(f"Unable to understand extra arguments {args}")
 
-        result = super().__call__(sequences, **kwargs)
-        if len(result) == 1:
-            return result[0]
-        return result
+        return super().__call__(sequences, **kwargs)
 
     def preprocess(self, inputs, candidate_labels=None, hypothesis_template="This example is {}."):
         sequence_pairs, sequences = self._args_parser(inputs, candidate_labels, hypothesis_template)
@@ -264,4 +261,6 @@ def postprocess(self, model_outputs, multi_label=False):
                     "scores": scores[iseq, top_inds].tolist(),
                 }
             )
+        if len(result) == 1:
+            return result[0]
         return result
diff --git a/tests/test_pipelines_zero_shot.py b/tests/test_pipelines_zero_shot.py
index d22ce68621b70..ae47eb626cc42 100644
--- a/tests/test_pipelines_zero_shot.py
+++ b/tests/test_pipelines_zero_shot.py
@@ -61,6 +61,24 @@ def run_pipeline_test(self, model, tokenizer, feature_extractor):
         )
         self.assertEqual(outputs, {"sequence": ANY(str), "labels": [ANY(str)], "scores": [ANY(float)]})
 
+        # https://github.com/huggingface/transformers/issues/13846
+        outputs = classifier(["I am happy"], ["positive", "negative"])
+        self.assertEqual(
+            outputs,
+            [
+                {"sequence": ANY(str), "labels": [ANY(str), ANY(str)], "scores": [ANY(float), ANY(float)]}
+                for i in range(1)
+            ],
+        )
+        outputs = classifier(["I am happy", "I am sad"], ["positive", "negative"])
+        self.assertEqual(
+            outputs,
+            [
+                {"sequence": ANY(str), "labels": [ANY(str), ANY(str)], "scores": [ANY(float), ANY(float)]}
+                for i in range(2)
+            ],
+        )
+
         with self.assertRaises(ValueError):
             classifier("", candidate_labels="politics")