diff --git a/src/transformers/trainer.py b/src/transformers/trainer.py
index 5b8ffeafc7c8ea..45b45992bf425a 100755
--- a/src/transformers/trainer.py
+++ b/src/transformers/trainer.py
@@ -3048,7 +3048,7 @@ def log(self, logs: Dict[str, float]) -> None:
                 The values to log.
         """
         if self.state.epoch is not None:
-            logs["epoch"] = round(self.state.epoch, 2)
+            logs["epoch"] = self.state.epoch
         if self.args.include_num_input_tokens_seen:
             logs["num_input_tokens_seen"] = self.state.num_input_tokens_seen
 
diff --git a/src/transformers/trainer_callback.py b/src/transformers/trainer_callback.py
index f5bbcdbd4218d5..225f645d631e41 100644
--- a/src/transformers/trainer_callback.py
+++ b/src/transformers/trainer_callback.py
@@ -15,6 +15,7 @@
 """
 Callbacks to use with the Trainer class and customize the training loop.
 """
+import copy
 import dataclasses
 import json
 from dataclasses import dataclass
@@ -520,7 +521,12 @@ def on_predict(self, args, state, control, **kwargs):
 
     def on_log(self, args, state, control, logs=None, **kwargs):
         if state.is_world_process_zero and self.training_bar is not None:
+            # avoid modifying the logs object as it is shared between callbacks
+            logs = copy.deepcopy(logs)
             _ = logs.pop("total_flos", None)
+            # round numbers so that it looks better in console
+            if "epoch" in logs:
+                logs["epoch"] = round(logs["epoch"], 2)
             self.training_bar.write(str(logs))
 
     def on_train_end(self, args, state, control, **kwargs):