Make sure padding, cls and another non-context tokens cannot appear in the answer.

transformers/pipelines.py

@@ -188,14 +188,18 @@ class QuestionAnsweringPipeline(Pipeline):
                 start, end = start.cpu().numpy(), end.cpu().numpy()
 
         answers = []
-        for i, (example, feature, start_, end_) in enumerate(zip(texts, features, start, end)):
-            start_, end_ = start_ * np.abs(np.array(feature.p_mask) - 1), end_ * np.abs(np.array(feature.p_mask) - 1)
-
+        for (example, feature, start_, end_) in zip(texts, features, start, end):
             # Normalize logits and spans to retrieve the answer
             start_ = np.exp(start_) / np.sum(np.exp(start_))
             end_ = np.exp(end_) / np.sum(np.exp(end_))
-            starts, ends, scores = self.decode(start_, end_, kwargs['topk'], kwargs['max_answer_len'])
 
+            # Mask padding and question
+            start_, end_ = start_ * np.abs(np.array(feature.p_mask) - 1), end_ * np.abs(np.array(feature.p_mask) - 1)
+
+            # Mask CLS
+            start_[0] = end_[0] = 0
+
+            starts, ends, scores = self.decode(start_, end_, kwargs['topk'], kwargs['max_answer_len'])
             char_to_word = np.array(example.char_to_word_offset)
 
             # Convert the answer (tokens) back to the original text