diff --git a/src/transformers/trainer_tf.py b/src/transformers/trainer_tf.py
index e34b16c27de..7907f030773 100644
--- a/src/transformers/trainer_tf.py
+++ b/src/transformers/trainer_tf.py
@@ -141,7 +141,7 @@ class TFTrainer:
                 self.optimizer = tf.keras.optimizers.get(
                     {"class_name": self.args.optimizer_name, "config": {"learning_rate": self.args.learning_rate}}
                 )
-        logger.info("Created an/a {} optimizer".format(self.optimizer))
+        logger.info("Created an/a {} optimizer".format(self.args.optimizer_name))
 
     def _create_checkpoint_manager(self, max_to_keep: int = 5, load_model: bool = True) -> None:
         """
@@ -335,12 +335,8 @@ class TFTrainer:
             gradient / tf.cast(gradient_scale, gradient.dtype) for gradient in self.gradient_accumulator.gradients
         ]
         gradients = [(tf.clip_by_value(grad, -self.args.max_grad_norm, self.args.max_grad_norm)) for grad in gradients]
-        vars = self.model.trainable_variables
 
-        if self.args.mode in ["token-classification", "question-answering"]:
-            vars = [var for var in self.model.trainable_variables if "pooler" not in var.name]
-
-        self.optimizer.apply_gradients(list(zip(gradients, vars)))
+        self.optimizer.apply_gradients(list(zip(gradients, self.model.trainable_variables)))
         self.gradient_accumulator.reset()
 
     def _accumulate_next_gradients(self):
@@ -375,12 +371,10 @@ class TFTrainer:
     def _forward(self, features, labels):
         """Forwards a training example and accumulates the gradients."""
         per_example_loss, _ = self._run_model(features, labels, True)
-        vars = self.model.trainable_variables
-
-        if self.args.mode in ["token-classification", "question-answering"]:
-            vars = [var for var in self.model.trainable_variables if "pooler" not in var.name]
-
-        gradients = self.optimizer.get_gradients(per_example_loss, vars)
+        gradients = tf.gradients(per_example_loss, self.model.trainable_variables)
+        gradients = [
+            g if g is not None else tf.zeros_like(v) for g, v in zip(gradients, self.model.trainable_variables)
+        ]
 
         self.gradient_accumulator(gradients)