Fix Seq2SeqTrainingArguments docs (#16295)

* Indent Seq2Seq Train Args docs

* Add Args keyword to Seq2Seq Train Args docs

src/transformers/training_args_seq2seq.py

@@ -27,20 +27,21 @@ logger = logging.getLogger(__name__)
 @add_start_docstrings(TrainingArguments.__doc__)
 class Seq2SeqTrainingArguments(TrainingArguments):
     """
-    sortish_sampler (`bool`, *optional*, defaults to `False`):
-        Whether to use a *sortish sampler* or not. Only possible if the underlying datasets are *Seq2SeqDataset* for
-        now but will become generally available in the near future.
-
-        It sorts the inputs according to lengths in order to minimize the padding size, with a bit of randomness for
-        the training set.
-    predict_with_generate (`bool`, *optional*, defaults to `False`):
-        Whether to use generate to calculate generative metrics (ROUGE, BLEU).
-    generation_max_length (`int`, *optional*):
-        The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the
-        `max_length` value of the model configuration.
-    generation_num_beams (`int`, *optional*):
-        The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the
-        `num_beams` value of the model configuration.
+    Args:
+        sortish_sampler (`bool`, *optional*, defaults to `False`):
+            Whether to use a *sortish sampler* or not. Only possible if the underlying datasets are *Seq2SeqDataset*
+            for now but will become generally available in the near future.
+
+            It sorts the inputs according to lengths in order to minimize the padding size, with a bit of randomness
+            for the training set.
+        predict_with_generate (`bool`, *optional*, defaults to `False`):
+            Whether to use generate to calculate generative metrics (ROUGE, BLEU).
+        generation_max_length (`int`, *optional*):
+            The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the
+            `max_length` value of the model configuration.
+        generation_num_beams (`int`, *optional*):
+            The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the
+            `num_beams` value of the model configuration.
     """
 
     sortish_sampler: bool = field(default=False, metadata={"help": "Whether to use SortishSampler or not."})