Small QOL improvements to TrainingArguments (#7475)

* Small QOL improvements to TrainingArguments

* With the self.

src/transformers/training_args.py

@@ -49,8 +49,9 @@ class TrainingArguments:
             :obj:`output_dir` points to a checkpoint directory.
         do_train (:obj:`bool`, `optional`, defaults to :obj:`False`):
             Whether to run training or not.
-        do_eval (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            Whether to run evaluation on the dev set or not.
+        do_eval (:obj:`bool`, `optional`):
+            Whether to run evaluation on the dev set or not. Will default to :obj:`evaluation_strategy` different from
+            :obj:`"no"`.
         do_predict (:obj:`bool`, `optional`, defaults to :obj:`False`):
             Whether to run predictions on the test set or not.
         evaluation_strategy(:obj:`str` or :class:`~transformers.trainer_utils.EvaluationStrategy`, `optional`, defaults to :obj:`"no"`):
@@ -183,7 +184,7 @@ class TrainingArguments:
     )
 
     do_train: bool = field(default=False, metadata={"help": "Whether to run training."})
-    do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."})
+    do_eval: bool = field(default=None, metadata={"help": "Whether to run eval on the dev set."})
     do_predict: bool = field(default=False, metadata={"help": "Whether to run predictions on the test set."})
     evaluate_during_training: bool = field(
         default=None,
@@ -333,7 +334,8 @@ class TrainingArguments:
             )
         else:
             self.evaluation_strategy = EvaluationStrategy(self.evaluation_strategy)
-
+        if self.do_eval is None:
+            self.do_eval = self.evaluation_strategy != EvaluationStrategy.NO
         if self.eval_steps is None:
             self.eval_steps = self.logging_steps
 
@@ -341,6 +343,8 @@ class TrainingArguments:
             self.metric_for_best_model = "loss"
         if self.greater_is_better is None and self.metric_for_best_model is not None:
             self.greater_is_better = self.metric_for_best_model not in ["loss", "eval_loss"]
+        if self.run_name is None:
+            self.run_name = self.output_dir
 
     @property
     def train_batch_size(self) -> int: