diff --git a/.github/hub/push_evaluations_to_hub.py b/.github/hub/push_evaluations_to_hub.py
index 73bde44ce..866afb3ce 100644
--- a/.github/hub/push_evaluations_to_hub.py
+++ b/.github/hub/push_evaluations_to_hub.py
@@ -75,7 +75,7 @@ def push_module_to_hub(module_path, type, token, commit_hash, tag=None):
         # make sure we don't accidentally expose token
         raise OSError(f"Could not clone from '{clean_repo_url}'")
 
-    repo = Repository(local_dir=repo_path / module_name, use_auth_token=token)
+    repo = Repository(local_dir=repo_path / module_name, token=token)
     
     copy_recursive(module_path, repo_path / module_name)
     update_evaluate_dependency(repo_path / module_name / "requirements.txt", commit_hash)
diff --git a/docs/source/base_evaluator.mdx b/docs/source/base_evaluator.mdx
index 043c82518..921fb9366 100644
--- a/docs/source/base_evaluator.mdx
+++ b/docs/source/base_evaluator.mdx
@@ -275,7 +275,7 @@ Let's have a look at how can evaluate image classification models on large datas
 The evaluator can be used on large datasets! Below, an example shows how to use it on ImageNet-1k for image classification. Beware that this example will require to download ~150 GB.
 
 ```python
-data = load_dataset("imagenet-1k", split="validation", use_auth_token=True)
+data = load_dataset("imagenet-1k", split="validation", token=True)
 
 pipe = pipeline(
     task="image-classification",
diff --git a/src/evaluate/utils/file_utils.py b/src/evaluate/utils/file_utils.py
index c52d44a95..f021ef4fa 100644
--- a/src/evaluate/utils/file_utils.py
+++ b/src/evaluate/utils/file_utils.py
@@ -182,7 +182,7 @@ def cached_path(
             local_files_only=download_config.local_files_only,
             use_etag=download_config.use_etag,
             max_retries=download_config.max_retries,
-            use_auth_token=download_config.use_auth_token,
+            token=download_config.token,
             ignore_url_params=download_config.ignore_url_params,
             download_desc=download_config.download_desc,
         )
@@ -223,14 +223,15 @@ def get_datasets_user_agent(user_agent: Optional[Union[str, dict]] = None) -> st
     return ua
 
 
-def get_authentication_headers_for_url(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> dict:
+def get_authentication_headers_for_url(url: str, token: Optional[Union[str, bool]] = None) -> dict:
     """Handle the HF authentication"""
     headers = {}
     if url.startswith(config.HF_ENDPOINT):
-        token = None
-        if isinstance(use_auth_token, str):
-            token = use_auth_token
-        elif bool(use_auth_token):
+        if token is False:
+            token = None
+        elif isinstance(token, str):
+            token = token
+        else:
             from huggingface_hub import hf_api
 
             token = hf_api.HfFolder.get_token()
@@ -388,8 +389,8 @@ def http_head(
     return response
 
 
-def request_etag(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> Optional[str]:
-    headers = get_authentication_headers_for_url(url, use_auth_token=use_auth_token)
+def request_etag(url: str, token: Optional[Union[str, bool]] = None) -> Optional[str]:
+    headers = get_authentication_headers_for_url(url, token=token)
     response = http_head(url, headers=headers, max_retries=3)
     response.raise_for_status()
     etag = response.headers.get("ETag") if response.ok else None
@@ -407,7 +408,7 @@ def get_from_cache(
     local_files_only=False,
     use_etag=True,
     max_retries=0,
-    use_auth_token=None,
+    token=None,
     ignore_url_params=False,
     download_desc=None,
 ) -> str:
@@ -452,7 +453,7 @@ def get_from_cache(
         return cache_path
 
     # Prepare headers for authentication
-    headers = get_authentication_headers_for_url(url, use_auth_token=use_auth_token)
+    headers = get_authentication_headers_for_url(url, token=token)
     if user_agent is not None:
         headers["user-agent"] = user_agent
 
@@ -495,9 +496,9 @@ def get_from_cache(
             ):
                 connected = True
                 logger.info(f"Couldn't get ETag version for url {url}")
-            elif response.status_code == 401 and config.HF_ENDPOINT in url and use_auth_token is None:
+            elif response.status_code == 401 and config.HF_ENDPOINT in url and token is None:
                 raise ConnectionError(
-                    f"Unauthorized for URL {url}. Please use the parameter ``use_auth_token=True`` after logging in with ``huggingface-cli login``"
+                    f"Unauthorized for URL {url}. Please use the parameter ``token=True`` after logging in with ``huggingface-cli login``"
                 )
         except (OSError, requests.exceptions.Timeout) as e:
             # not connected