diff --git a/src/transformers/pipelines/text_classification.py b/src/transformers/pipelines/text_classification.py
index 0c54fe1706c..6521da098d4 100644
--- a/src/transformers/pipelines/text_classification.py
+++ b/src/transformers/pipelines/text_classification.py
@@ -118,12 +118,12 @@ class TextClassificationPipeline(Pipeline):
             postprocess_params["function_to_apply"] = function_to_apply
         return preprocess_params, {}, postprocess_params
 
-    def __call__(self, *args, **kwargs):
+    def __call__(self, inputs, **kwargs):
         """
         Classify the text(s) given as inputs.
 
         Args:
-            args (`str` or `List[str]` or `Dict[str]`, or `List[Dict[str]]`):
+            inputs (`str` or `List[str]` or `Dict[str]`, or `List[Dict[str]]`):
                 One or several texts to classify. In order to use text pairs for your classification, you can send a
                 dictionary containing `{"text", "text_pair"}` keys, or a list of those.
             top_k (`int`, *optional*, defaults to `1`):
@@ -152,10 +152,11 @@ class TextClassificationPipeline(Pipeline):
 
             If `top_k` is used, one such dictionary is returned per label.
         """
-        result = super().__call__(*args, **kwargs)
+        inputs = (inputs,)
+        result = super().__call__(*inputs, **kwargs)
         # TODO try and retrieve it in a nicer way from _sanitize_parameters.
         _legacy = "top_k" not in kwargs
-        if isinstance(args[0], str) and _legacy:
+        if isinstance(inputs[0], str) and _legacy:
             # This pipeline is odd, and return a list when single item is run
             return [result]
         else: