fix bert2bert test (#10063)

tests/test_trainer_seq2seq.py

@@ -24,15 +24,9 @@ if is_datasets_available():
 
 class Seq2seqTrainerTester(TestCasePlus):
     @slow
-    @require_datasets
     @require_torch
+    @require_datasets
     def test_finetune_bert2bert(self):
-        """
-        Currently fails with:
-
-        ImportError: To be able to use this metric, you need to install the following dependencies['absl', 'nltk', 'rouge_score']
-        """
-
         bert2bert = EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny", "prajjwal1/bert-tiny")
         tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
 
@@ -47,8 +41,6 @@ class Seq2seqTrainerTester(TestCasePlus):
         train_dataset = train_dataset.select(range(32))
         val_dataset = val_dataset.select(range(16))
 
-        rouge = datasets.load_metric("rouge")
-
         batch_size = 4
 
         def _map_to_encoder_decoder_inputs(batch):
@@ -78,15 +70,9 @@ class Seq2seqTrainerTester(TestCasePlus):
             pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
             label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)
 
-            rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=["rouge2"])[
+            accuracy = sum([int(pred_str[i] == label_str[i]) for i in range(len(pred_str))]) / len(pred_str)
-                "rouge2"
-            ].mid
 
-            return {
+            return {"accuracy": accuracy}
-                "rouge2_precision": round(rouge_output.precision, 4),
-                "rouge2_recall": round(rouge_output.recall, 4),
-                "rouge2_fmeasure": round(rouge_output.fmeasure, 4),
-            }
 
         # map train dataset
         train_dataset = train_dataset.map(