Change in line with review points on other PRs

src/huggingface_hub/inference/_client.py

@@ -677,8 +677,8 @@ def object_detection(
         return output
 
     def question_answering(
-        self, question: str, context: str, model: str, *, parameters: Optional[Dict[str, Any]] = None
-    ) -> List[QuestionAnsweringOutput]:
+        self, question: str, context: str, *, model: Optional[str] = None
+    ) -> QuestionAnsweringOutput:
         """
         Retrieve the answer to a question from a given text.
 
@@ -690,17 +690,9 @@ def question_answering(
             model (`str`):
                 The model to use for the question answering task. Can be a model ID hosted on the Hugging Face Hub or a URL to
                 a deployed Inference Endpoint.
-            parameters (`Dict[str, Any]`, *optional*):
-                Additional parameters for the question answering task. Defaults to None. For more details about the available
-                parameters, please refer to [this page](https://huggingface.co/docs/api-inference/detailed_parameters#question-answering-task)
-
 
         Returns:
-            `Dict`: a dictionary containing:
-            - answer:	A string that’s the answer within the text.
-            - score:	A float that represents how likely that the answer is correct
-            - start:	The index (string wise) of the start of the answer within context.
-            - stop:	    The index (string wise) of the stop of the answer within context.
+            `Dict`: a dictionary of question answering output containing the score, start index, end index, and answer.
 
         Raises:
             [`InferenceTimeoutError`]:
@@ -717,18 +709,14 @@ def question_answering(
         {'score': 0.9326562285423279, 'start': 11, 'end': 16, 'answer': 'Clara'}
         ```
         """
-        if model is None:
-            raise ValueError("You must specify a model. Task question-answering has no recommended standard model.")
 
         payload: Dict[str, Any] = {"question": question, "context": context}
-        if parameters is not None:
-            payload["parameters"] = parameters
         response = self.post(
             json=payload,
             model=model,
             task="question-answering",
         )
-        return _bytes_to_dict(response)
+        return _bytes_to_dict(response)  # type: ignore
 
     def sentence_similarity(
         self, sentence: str, other_sentences: List[str], *, model: Optional[str] = None

src/huggingface_hub/inference/_generated/_async_client.py

@@ -682,8 +682,8 @@ async def object_detection(
         return output
 
     async def question_answering(
-        self, question: str, context: str, model: str, *, parameters: Optional[Dict[str, Any]] = None
-    ) -> List[QuestionAnsweringOutput]:
+        self, question: str, context: str, *, model: Optional[str] = None
+    ) -> QuestionAnsweringOutput:
         """
         Retrieve the answer to a question from a given text.
 
@@ -695,17 +695,9 @@ async def question_answering(
             model (`str`):
                 The model to use for the question answering task. Can be a model ID hosted on the Hugging Face Hub or a URL to
                 a deployed Inference Endpoint.
-            parameters (`Dict[str, Any]`, *optional*):
-                Additional parameters for the question answering task. Defaults to None. For more details about the available
-                parameters, please refer to [this page](https://huggingface.co/docs/api-inference/detailed_parameters#question-answering-task)
-
 
         Returns:
-            `Dict`: a dictionary containing:
-            - answer:	A string that’s the answer within the text.
-            - score:	A float that represents how likely that the answer is correct
-            - start:	The index (string wise) of the start of the answer within context.
-            - stop:	    The index (string wise) of the stop of the answer within context.
+            `Dict`: a dictionary of question answering output containing the score, start index, end index, and answer.
 
         Raises:
             [`InferenceTimeoutError`]:
@@ -723,18 +715,14 @@ async def question_answering(
         {'score': 0.9326562285423279, 'start': 11, 'end': 16, 'answer': 'Clara'}
         ```
         """
-        if model is None:
-            raise ValueError("You must specify a model. Task question-answering has no recommended standard model.")
 
         payload: Dict[str, Any] = {"question": question, "context": context}
-        if parameters is not None:
-            payload["parameters"] = parameters
         response = await self.post(
             json=payload,
             model=model,
             task="question-answering",
         )
-        return _bytes_to_dict(response)
+        return _bytes_to_dict(response)  # type: ignore
 
     async def sentence_similarity(
         self, sentence: str, other_sentences: List[str], *, model: Optional[str] = None

src/huggingface_hub/inference/_types.py

@@ -104,13 +104,14 @@ class QuestionAnsweringOutput(TypedDict):
     """Dictionary containing information about a [`~InferenceClient.question_answering`] task.
 
     Args:
-        label (`str`):
-            The label corresponding to the detected object.
-        box (`dict`):
-            A dict response of bounding box coordinates of
-            the detected object: xmin, ymin, xmax, ymax
         score (`float`):
-            The score corresponding to the detected object.
+            A float that represents how likely that the answer is correct.
+        start (`int`):
+            The index (string wise) of the start of the answer within context.
+        end (`int`):
+            The index (string wise) of the end of the answer within context.
+        answer (`str`):
+            A string that is the answer within the text.
     """
 
     score: float