diff --git a/src/transformers/models/vit_mae/modeling_tf_vit_mae.py b/src/transformers/models/vit_mae/modeling_tf_vit_mae.py
index 3cb702b4f6a4bd..6ecec70623b280 100644
--- a/src/transformers/models/vit_mae/modeling_tf_vit_mae.py
+++ b/src/transformers/models/vit_mae/modeling_tf_vit_mae.py
@@ -254,7 +254,7 @@ def random_masking(self, sequence: tf.Tensor, noise: Optional[tf.Tensor] = None)
 
         # keep the first subset
         ids_keep = ids_shuffle[:, :len_keep]
-        sequence_masked = tf.gather(
+        sequence_unmasked = tf.gather(
             sequence,
             axis=1,
             batch_dims=1,
@@ -271,7 +271,7 @@ def random_masking(self, sequence: tf.Tensor, noise: Optional[tf.Tensor] = None)
         # unshuffle to get the binary mask
         mask = tf.gather(mask, axis=1, batch_dims=1, indices=ids_restore)
 
-        return sequence_masked, mask, ids_restore
+        return sequence_unmasked, mask, ids_restore
 
     def call(self, pixel_values: tf.Tensor, noise: tf.Tensor = None) -> tf.Tensor:
         embeddings = self.patch_embeddings(pixel_values)
diff --git a/src/transformers/models/vit_mae/modeling_vit_mae.py b/src/transformers/models/vit_mae/modeling_vit_mae.py
index 0667bdd73c5545..1031b7088774f4 100755
--- a/src/transformers/models/vit_mae/modeling_vit_mae.py
+++ b/src/transformers/models/vit_mae/modeling_vit_mae.py
@@ -251,7 +251,7 @@ def random_masking(self, sequence, noise=None):
 
         # keep the first subset
         ids_keep = ids_shuffle[:, :len_keep]
-        sequence_masked = torch.gather(sequence, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, dim))
+        sequence_unmasked = torch.gather(sequence, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, dim))
 
         # generate the binary mask: 0 is keep, 1 is remove
         mask = torch.ones([batch_size, seq_length], device=sequence.device)
@@ -259,7 +259,7 @@ def random_masking(self, sequence, noise=None):
         # unshuffle to get the binary mask
         mask = torch.gather(mask, dim=1, index=ids_restore)
 
-        return sequence_masked, mask, ids_restore
+        return sequence_unmasked, mask, ids_restore
 
     def forward(self, pixel_values, noise=None):
         batch_size, num_channels, height, width = pixel_values.shape