Update notebook.py to support multi eval datasets (#25796)

* Update notebook.py

fix multi eval datasets

* Update notebook.py

* Update notebook.py

using `black` to reformat

* Update notebook.py

support Validation Loss

* Update notebook.py

reformat

* Update notebook.py

src/transformers/utils/notebook.py

@@ -235,12 +235,24 @@ class NotebookTrainingTracker(NotebookProgressBar):
             self.inner_table = [list(values.keys()), list(values.values())]
         else:
             columns = self.inner_table[0]
-            if len(self.inner_table) == 1:
-                # We give a chance to update the column names at the first iteration
             for key in values.keys():
                 if key not in columns:
                     columns.append(key)
             self.inner_table[0] = columns
+            if len(self.inner_table) > 1:
+                last_values = self.inner_table[-1]
+                first_column = self.inner_table[0][0]
+                if last_values[0] != values[first_column]:
+                    # write new line
+                    self.inner_table.append([values[c] if c in values else "No Log" for c in columns])
+                else:
+                    # update last line
+                    new_values = values
+                    for c in columns:
+                        if c not in new_values.keys():
+                            new_values[c] = last_values[columns.index(c)]
+                    self.inner_table[-1] = [new_values[c] for c in columns]
+            else:
                 self.inner_table.append([values[c] for c in columns])
 
     def add_child(self, total, prefix=None, width=300):
@@ -341,11 +353,11 @@ class NotebookProgressCallback(TrainerCallback):
             _ = metrics.pop(f"{metric_key_prefix}_steps_per_second", None)
             _ = metrics.pop(f"{metric_key_prefix}_jit_compilation_time", None)
             for k, v in metrics.items():
-                if k == f"{metric_key_prefix}_loss":
-                    values["Validation Loss"] = v
-                else:
                 splits = k.split("_")
                 name = " ".join([part.capitalize() for part in splits[1:]])
+                if name == "Loss":
+                    # Single dataset
+                    name = "Validation Loss"
                 values[name] = v
             self.training_tracker.write_line(values)
             self.training_tracker.remove_child()