Make DataCollator a callable (#5015)

* Make DataCollator a callable

* Update src/transformers/data/data_collator.py

Co-authored-by: Julien Chaumond <chaumond@gmail.com>

examples/adversarial/test_hans.py

@@ -38,7 +38,6 @@ from transformers import (
     BertConfig,
     BertForSequenceClassification,
     BertTokenizer,
-    DefaultDataCollator,
     DistilBertConfig,
     DistilBertForSequenceClassification,
     DistilBertTokenizer,
@@ -51,6 +50,7 @@ from transformers import (
     XLNetConfig,
     XLNetForSequenceClassification,
     XLNetTokenizer,
+    default_data_collator,
     get_linear_schedule_with_warmup,
 )
 from utils_hans import HansDataset, hans_output_modes, hans_processors
@@ -91,10 +91,7 @@ def train(args, train_dataset, model, tokenizer):
     args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
     train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
     train_dataloader = DataLoader(
-        train_dataset,
-        sampler=train_sampler,
-        batch_size=args.train_batch_size,
-        collate_fn=DefaultDataCollator().collate_batch,
+        train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, collate_fn=default_data_collator,
     )
 
     if args.max_steps > 0:
@@ -252,10 +249,7 @@ def evaluate(args, model, tokenizer, label_list, prefix=""):
         # Note that DistributedSampler samples randomly
         eval_sampler = SequentialSampler(eval_dataset)
         eval_dataloader = DataLoader(
-            eval_dataset,
-            sampler=eval_sampler,
-            batch_size=args.eval_batch_size,
-            collate_fn=DefaultDataCollator().collate_batch,
+            eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=default_data_collator,
         )
 
         # multi-gpu eval