diff --git a/src/transformers/trainer.py b/src/transformers/trainer.py
index 227e92fa638d91..1cdd8623e58c98 100755
--- a/src/transformers/trainer.py
+++ b/src/transformers/trainer.py
@@ -250,6 +250,8 @@ def _get_fsdp_ckpt_kwargs():
 if TYPE_CHECKING:
     import optuna
 
+    if is_datasets_available():
+        import datasets
 
 logger = logging.get_logger(__name__)
 
@@ -287,7 +289,7 @@ class Trainer:
             The function to use to form a batch from a list of elements of `train_dataset` or `eval_dataset`. Will
             default to [`default_data_collator`] if no `tokenizer` is provided, an instance of
             [`DataCollatorWithPadding`] otherwise.
-        train_dataset (`torch.utils.data.Dataset` or `torch.utils.data.IterableDataset`, *optional*):
+        train_dataset (Union[`torch.utils.data.Dataset`, `torch.utils.data.IterableDataset`, `datasets.Dataset`], *optional*):
             The dataset to use for training. If it is a [`~datasets.Dataset`], columns not accepted by the
             `model.forward()` method are automatically removed.
 
@@ -296,7 +298,7 @@ class Trainer:
             `torch.Generator` for the randomization that must be identical on all processes (and the Trainer will
             manually set the seed of this `generator` at each epoch) or have a `set_epoch()` method that internally
             sets the seed of the RNGs used.
-        eval_dataset (Union[`torch.utils.data.Dataset`, Dict[str, `torch.utils.data.Dataset`]), *optional*):
+        eval_dataset (Union[`torch.utils.data.Dataset`, Dict[str, `torch.utils.data.Dataset`, `datasets.Dataset`]), *optional*):
              The dataset to use for evaluation. If it is a [`~datasets.Dataset`], columns not accepted by the
              `model.forward()` method are automatically removed. If it is a dictionary, it will evaluate on each
              dataset prepending the dictionary key to the metric name.
@@ -358,8 +360,8 @@ def __init__(
         model: Union[PreTrainedModel, nn.Module] = None,
         args: TrainingArguments = None,
         data_collator: Optional[DataCollator] = None,
-        train_dataset: Optional[Union[Dataset, IterableDataset]] = None,
-        eval_dataset: Optional[Union[Dataset, Dict[str, Dataset]]] = None,
+        train_dataset: Optional[Union[Dataset, IterableDataset, "datasets.Dataset"]] = None,
+        eval_dataset: Optional[Union[Dataset, Dict[str, Dataset], "datasets.Dataset"]] = None,
         tokenizer: Optional[PreTrainedTokenizerBase] = None,
         model_init: Optional[Callable[[], PreTrainedModel]] = None,
         compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None,