Allow example to use a revision and work with private models (#9407)

* Allow example to use a revision and work with private models

* Copy to other examples and template

* Styling

examples/text-classification/run_glue.py

@@ -131,6 +131,17 @@ class ModelArguments:
         default=True,
         metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
     )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
+            "with private models)."
+        },
+    )
 
 
 def main():
@@ -236,17 +247,23 @@ def main():
         num_labels=num_labels,
         finetuning_task=data_args.task_name,
         cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
     )
     model = AutoModelForSequenceClassification.from_pretrained(
         model_args.model_name_or_path,
         from_tf=bool(".ckpt" in model_args.model_name_or_path),
         config=config,
         cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
     )
 
     # Preprocessing the datasets