diff --git a/examples/pytorch/language-modeling/run_clm.py b/examples/pytorch/language-modeling/run_clm.py
index ae50bd2ce90a..5534e6901fb6 100755
--- a/examples/pytorch/language-modeling/run_clm.py
+++ b/examples/pytorch/language-modeling/run_clm.py
@@ -43,6 +43,7 @@
     Trainer,
     TrainingArguments,
     default_data_collator,
+    is_torch_tpu_available,
     set_seed,
 )
 from transformers.testing_utils import CaptureLogger
@@ -479,8 +480,10 @@ def compute_metrics(eval_preds):
         tokenizer=tokenizer,
         # Data collator will default to DataCollatorWithPadding, so we change it.
         data_collator=default_data_collator,
-        compute_metrics=compute_metrics if training_args.do_eval else None,
-        preprocess_logits_for_metrics=preprocess_logits_for_metrics if training_args.do_eval else None,
+        compute_metrics=compute_metrics if training_args.do_eval and not is_torch_tpu_available() else None,
+        preprocess_logits_for_metrics=preprocess_logits_for_metrics
+        if training_args.do_eval and not is_torch_tpu_available()
+        else None,
     )
 
     # Training
diff --git a/examples/pytorch/language-modeling/run_mlm.py b/examples/pytorch/language-modeling/run_mlm.py
index 9926cccfae3a..7ceae8b17a8c 100755
--- a/examples/pytorch/language-modeling/run_mlm.py
+++ b/examples/pytorch/language-modeling/run_mlm.py
@@ -43,6 +43,7 @@
     HfArgumentParser,
     Trainer,
     TrainingArguments,
+    is_torch_tpu_available,
     set_seed,
 )
 from transformers.trainer_utils import get_last_checkpoint
@@ -513,8 +514,10 @@ def compute_metrics(eval_preds):
         eval_dataset=eval_dataset if training_args.do_eval else None,
         tokenizer=tokenizer,
         data_collator=data_collator,
-        compute_metrics=compute_metrics if training_args.do_eval else None,
-        preprocess_logits_for_metrics=preprocess_logits_for_metrics if training_args.do_eval else None,
+        compute_metrics=compute_metrics if training_args.do_eval and not is_torch_tpu_available() else None,
+        preprocess_logits_for_metrics=preprocess_logits_for_metrics
+        if training_args.do_eval and not is_torch_tpu_available()
+        else None,
     )
 
     # Training