Attention mask is important in the case of batching... (#16222)

* Attention mask is important in the case of batching...

* Improve the fix.

* Making the sentence different enough that they exhibit different
predictions.

src/transformers/pipelines/base.py

@@ -149,7 +149,7 @@ def pad_collate_fn(tokenizer, feature_extractor):
                 _padding_value = t_padding_value
             elif key in {"input_values", "pixel_values", "input_features"}:
                 _padding_value = f_padding_value
-            elif key in {"p_mask"}:
+            elif key in {"p_mask", "special_tokens_mask"}:
                 _padding_value = 1
             elif key in {"attention_mask", "token_type_ids"}:
                 _padding_value = 0

src/transformers/pipelines/token_classification.py

@@ -192,7 +192,6 @@ class TokenClassificationPipeline(Pipeline):
         truncation = True if self.tokenizer.model_max_length and self.tokenizer.model_max_length > 0 else False
         model_inputs = self.tokenizer(
             sentence,
-            return_attention_mask=False,
             return_tensors=self.framework,
             truncation=truncation,
             return_special_tokens_mask=True,

tests/pipelines/test_pipelines_token_classification.py

@@ -649,6 +649,23 @@ class TokenClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
             ],
         )
 
+        # Batch size does not affect outputs (attention_mask are required)
+        sentences = ["This is a test !", "Another test this is with longer sentence"]
+        outputs = token_classifier(sentences)
+        outputs_batched = token_classifier(sentences, batch_size=2)
+        # Batching does not make a difference in predictions
+        self.assertEqual(nested_simplify(outputs_batched), nested_simplify(outputs))
+        self.assertEqual(
+            nested_simplify(outputs_batched),
+            [
+                [
+                    {"entity": "I-MISC", "score": 0.115, "index": 1, "word": "this", "start": 0, "end": 4},
+                    {"entity": "I-MISC", "score": 0.115, "index": 2, "word": "is", "start": 5, "end": 7},
+                ],
+                [],
+            ],
+        )
+
     @require_torch
     def test_pt_ignore_subwords_slow_tokenizer_raises(self):
         model_name = "sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"