batch_encode_plus generates the encoder_attention_mask to avoid attending over padded values.

transformers/pipelines.py

@@ -132,7 +132,7 @@ class QuestionAnsweringPipeline(Pipeline):
 
         # Tabular input
         if 'question' in kwargs and 'context' in kwargs:
-            texts = QuestionAnsweringPipeline.create_sample(kwargs['questions'], kwargs['contexts'])
+            texts = QuestionAnsweringPipeline.create_sample(kwargs['question'], kwargs['context'])
         elif 'data' in kwargs:
             texts = kwargs['data']
         # Generic compatibility with sklearn and Keras
@@ -156,7 +156,10 @@ class QuestionAnsweringPipeline(Pipeline):
         )
 
         # Remove special_tokens_mask to avoid KeyError
-        _ = inputs.pop('special_tokens_mask')
+        special_tokens_mask, input_len = inputs.pop('special_tokens_mask'), inputs.pop('input_len')
+
+        # TODO : Harmonize model arguments across all model
+        inputs['attention_mask'] = inputs.pop('encoder_attention_mask')
 
         # if is_tf_available():
         if False: