Continue training args and tqdm in notebooks (#3939)

* Continue training args

* Continue training args

* added explaination

* added explaination

* added explaination

* Fixed tqdm auto

* Update src/transformers/training_args.py
Co-Authored-By: Julien Chaumond <chaumond@gmail.com>

* Update src/transformers/training_args.py

* Update src/transformers/training_args.py
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

src/transformers/trainer.py

@@ -15,7 +15,7 @@ from torch.utils.data.dataloader import DataLoader
 from torch.utils.data.dataset import Dataset
 from torch.utils.data.distributed import DistributedSampler
 from torch.utils.data.sampler import RandomSampler
-from tqdm import tqdm, trange
+from tqdm.auto import tqdm, trange
 
 from .data.data_collator import DataCollator, DefaultDataCollator
 from .modeling_utils import PreTrainedModel

src/transformers/training_args.py

@@ -29,20 +29,27 @@ class TrainingArguments:
         metadata={"help": "The output directory where the model predictions and checkpoints will be written."}
     )
     overwrite_output_dir: bool = field(
-        default=False, metadata={"help": "Overwrite the content of the output directory"}
+        default=False,
+        metadata={
+            "help": (
+                "Overwrite the content of the output directory."
+                "Use this to continue training if output_dir points to a checkpoint directory."
+            )
+        },
     )
 
     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_predict: bool = field(default=False, metadata={"help": "Whether to run predictions on the test set."})
     evaluate_during_training: bool = field(
-        default=False, metadata={"help": "Run evaluation during training at each logging step."}
+        default=False, metadata={"help": "Run evaluation during training at each logging step."},
     )
 
     per_gpu_train_batch_size: int = field(default=8, metadata={"help": "Batch size per GPU/CPU for training."})
     per_gpu_eval_batch_size: int = field(default=8, metadata={"help": "Batch size per GPU/CPU for evaluation."})
     gradient_accumulation_steps: int = field(
-        default=1, metadata={"help": "Number of updates steps to accumulate before performing a backward/update pass."}
+        default=1,
+        metadata={"help": "Number of updates steps to accumulate before performing a backward/update pass."},
     )
 
     learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for Adam."})
@@ -64,7 +71,10 @@ class TrainingArguments:
     save_total_limit: Optional[int] = field(
         default=None,
         metadata={
-            "help": "Limit the total amount of checkpoints, delete the older checkpoints in the output_dir, does not delete by default"
+            "help": (
+                "Limit the total amount of checkpoints."
+                "Deletes the older checkpoints in the output_dir. Default is unlimited checkpoints"
+            )
         },
     )
     no_cuda: bool = field(default=False, metadata={"help": "Avoid using CUDA even if it is available"})
@@ -77,8 +87,10 @@ class TrainingArguments:
     fp16_opt_level: str = field(
         default="O1",
         metadata={
-            "help": "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
+            "help": (
+                "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
                 "See details at https://nvidia.github.io/apex/amp.html"
+            )
         },
     )
     local_rank: int = field(default=-1, metadata={"help": "For distributed training: local_rank"})