Fix #5114 (#5122)

src/transformers/data/data_collator.py

@@ -42,10 +42,10 @@ def default_data_collator(features: List[InputDataClass]) -> Dict[str, torch.Ten
     # Special handling for labels.
     # Ensure that tensor is created with the correct type
     # (it should be automatically the case, but let's make sure of it.)
-    if "label" in first:
+    if "label" in first and first["label"] is not None:
         dtype = torch.long if type(first["label"]) is int else torch.float
         batch["labels"] = torch.tensor([f["label"] for f in features], dtype=dtype)
-    elif "label_ids" in first:
+    elif "label_ids" in first and first["label_ids"] is not None:
         if isinstance(first["label_ids"], torch.Tensor):
             batch["labels"] = torch.stack([f["label_ids"] for f in features])
         else:

tests/test_trainer.py

@@ -25,7 +25,7 @@ PATH_SAMPLE_TEXT = "./tests/fixtures/sample_text.txt"
 @require_torch
 class DataCollatorIntegrationTest(unittest.TestCase):
     def test_default_with_dict(self):
-        features = [{"labels": i, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
+        features = [{"label": i, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
         batch = default_data_collator(features)
         self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8)))))
         self.assertEqual(batch["labels"].dtype, torch.long)
@@ -39,12 +39,24 @@ class DataCollatorIntegrationTest(unittest.TestCase):
         self.assertEqual(batch["inputs"].shape, torch.Size([8, 6]))
 
         # Features can already be tensors
-        features = [{"labels": i, "inputs": torch.randint(10, [10])} for i in range(8)]
+        features = [{"label": i, "inputs": torch.randint(10, [10])} for i in range(8)]
         batch = default_data_collator(features)
         self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8)))))
         self.assertEqual(batch["labels"].dtype, torch.long)
         self.assertEqual(batch["inputs"].shape, torch.Size([8, 10]))
 
+    def test_default_with_no_labels(self):
+        features = [{"label": None, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
+        batch = default_data_collator(features)
+        self.assertTrue("labels" not in batch)
+        self.assertEqual(batch["inputs"].shape, torch.Size([8, 6]))
+
+        # With label_ids
+        features = [{"label_ids": None, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
+        batch = default_data_collator(features)
+        self.assertTrue("labels" not in batch)
+        self.assertEqual(batch["inputs"].shape, torch.Size([8, 6]))
+
     def test_default_classification(self):
         MODEL_ID = "bert-base-cased-finetuned-mrpc"
         tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)