Handle long answer needs to be updated. (#14279)

`start_` and `end_` tensors now contain a batch_size at this point.

src/transformers/pipelines/question_answering.py

@@ -412,7 +412,7 @@ class QuestionAnsweringPipeline(Pipeline):
             end_ = np.exp(end_ - np.log(np.sum(np.exp(end_), axis=-1, keepdims=True)))
 
             if handle_impossible_answer:
-                min_null_score = min(min_null_score, (start_[0] * end_[0]).item())
+                min_null_score = min(min_null_score, (start_[0, 0] * end_[0, 0]).item())
 
             # Mask CLS
             start_[0, 0] = end_[0, 0] = 0.0

tests/test_pipelines_question_answering.py

@@ -50,6 +50,12 @@ class QAPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
             question="Where was HuggingFace founded ?", context="HuggingFace was founded in Paris."
         )
         self.assertEqual(outputs, {"answer": ANY(str), "start": ANY(int), "end": ANY(int), "score": ANY(float)})
+        outputs = question_answerer(
+            question="Where was HuggingFace founded ?",
+            context="HuggingFace was founded in Paris.",
+            handle_impossible_answer=True,
+        )
+        self.assertEqual(outputs, {"answer": ANY(str), "start": ANY(int), "end": ANY(int), "score": ANY(float)})
 
         outputs = question_answerer(
             question=["In what field is HuggingFace working ?", "In what field is HuggingFace working ?"],