Fix PABEE division by zero error (#5233)

* Fix PABEE division by zero error

* patience=0 by default

examples/bert-loses-patience/run_glue_with_pabee.py

@@ -254,12 +254,15 @@ def train(args, train_dataset, model, tokenizer):
     return global_step, tr_loss / global_step
 
 
-def evaluate(args, model, tokenizer, prefix=""):
+def evaluate(args, model, tokenizer, prefix="", patience=0):
 
-    # PABEE STATS
     if args.model_type == "albert":
+        model.albert.set_regression_threshold(args.regression_threshold)
+        model.albert.set_patience(patience)
         model.albert.reset_stats()
     elif args.model_type == "bert":
+        model.bert.set_regression_threshold(args.regression_threshold)
+        model.bert.set_patience(patience)
         model.bert.reset_stats()
     else:
         raise NotImplementedError()
@@ -331,7 +334,7 @@ def evaluate(args, model, tokenizer, prefix=""):
                 print("  %s = %s" % (key, str(result[key])))
                 writer.write("%s = %s\n" % (key, str(result[key])))
 
-    if args.eval_all_checkpoints:
+    if args.eval_all_checkpoints and patience != 0:
         if args.model_type == "albert":
             model.albert.log_stats()
         elif args.model_type == "bert":
@@ -690,15 +693,7 @@ def main():
 
             print(f"Evaluation for checkpoint {prefix}")
             for patience in patience_list:
-                if args.model_type == "albert":
-                    model.albert.set_regression_threshold(args.regression_threshold)
-                    model.albert.set_patience(patience)
-                elif args.model_type == "bert":
-                    model.bert.set_regression_threshold(args.regression_threshold)
-                    model.bert.set_patience(patience)
-                else:
-                    raise NotImplementedError()
-                result = evaluate(args, model, tokenizer, prefix=prefix)
+                result = evaluate(args, model, tokenizer, prefix=prefix, patience=patience)
                 result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
                 results.update(result)
     return results