diff --git a/examples/openvino/image-classification/run_image_classification.py b/examples/openvino/image-classification/run_image_classification.py
index 8a7c009e46..5f98d95cb5 100644
--- a/examples/openvino/image-classification/run_image_classification.py
+++ b/examples/openvino/image-classification/run_image_classification.py
@@ -151,12 +151,12 @@ class ModelArguments:
         metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
     )
     feature_extractor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."})
-    use_auth_token: bool = field(
-        default=False,
+    token: str = field(
+        default=None,
         metadata={
             "help": (
-                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
-                "with private models)."
+                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
+                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
             )
         },
     )
@@ -239,8 +239,7 @@ def main():
             data_args.dataset_name,
             data_args.dataset_config_name,
             cache_dir=model_args.cache_dir,
-            task="image-classification",
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=model_args.token,
         )
     else:
         data_files = {}
@@ -252,7 +251,6 @@ def main():
             "imagefolder",
             data_files=data_files,
             cache_dir=model_args.cache_dir,
-            task="image-classification",
         )
 
     # If we don't have a validation split, split off a percentage of train as validation.
@@ -287,7 +285,7 @@ def compute_metrics(p):
         finetuning_task="image-classification",
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=model_args.token,
     )
     model = AutoModelForImageClassification.from_pretrained(
         model_args.model_name_or_path,
@@ -295,7 +293,7 @@ def compute_metrics(p):
         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,
+        token=model_args.token,
         ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
     )
 
@@ -311,7 +309,7 @@ def compute_metrics(p):
         model_args.feature_extractor_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=model_args.token,
     )
 
     # Define torchvision transforms to be applied to each image.