[trainer] add Trainer methods for metrics logging and saving (#10266)

* make logging and saving trainer built-in

* Update src/transformers/trainer.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

src/transformers/trainer.py

@@ -19,6 +19,7 @@ The Trainer class, to easily train a 🤗 Transformers from scratch or finetune
 import collections
 import gc
 import inspect
+import json
 import math
 import os
 import re
@@ -1370,6 +1371,38 @@ class Trainer:
 
         return metrics_copy
 
+    def log_metrics(self, split, metrics):
+        """
+        Log metrics in a specially formatted way
+
+        Args:
+            split (:obj:`str`):
+                Mode/split name: one of ``train``, ``eval``, ``test``
+            metrics (:obj:`Dict[str, float]`):
+                The metrics returned from train/evaluate/predictmetrics: metrics dict
+        """
+
+        logger.info(f"***** {split} metrics *****")
+        metrics_formatted = self.metrics_format(metrics)
+        k_width = max(len(str(x)) for x in metrics_formatted.keys())
+        v_width = max(len(str(x)) for x in metrics_formatted.values())
+        for key in sorted(metrics_formatted.keys()):
+            logger.info(f"  {key: <{k_width}} = {metrics_formatted[key]:>{v_width}}")
+
+    def save_metrics(self, split, metrics):
+        """
+        Save metrics into a json file for that split, e.g. ``train_results.json``.
+
+        Args:
+            split (:obj:`str`):
+                Mode/split name: one of ``train``, ``eval``, ``test``, ``all``
+            metrics (:obj:`Dict[str, float]`):
+                The metrics returned from train/evaluate/predict
+        """
+        path = os.path.join(self.args.output_dir, f"{split}_results.json")
+        with open(path, "w") as f:
+            json.dump(metrics, f, indent=4, sort_keys=True)
+
     def _prepare_inputs(self, inputs: Dict[str, Union[torch.Tensor, Any]]) -> Dict[str, Union[torch.Tensor, Any]]:
         """
         Prepare :obj:`inputs` before feeding them to the model, converting them to tensors if they are not already and