bandaid solution by setting macro/micro avg to follow val metric in case of single training task
@@ -571,7 +571,7 @@ class SamplingMultiTaskTrainer():
for task in tasks + ['micro', 'macro']:
if task in ['micro', 'macro']:
metric = "%s_avg" % task
- metric_decreases = False
+ metric_decreases = tasks[0].val_metric_decreases if len(tasks) == 1 else False
else:
metric = task.val_metric
metric_decreases = task.val_metric_decreases
