Upgrade examples to pl=0.8.1(#5146)

examples/text-classification/run_pl_glue.py

@@ -108,7 +108,7 @@ class GLUETransformer(BaseTransformer):
 
         return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
 
-    def _eval_end(self, outputs):
+    def _eval_end(self, outputs) -> tuple:
         val_loss_mean = torch.stack([x["val_loss"] for x in outputs]).mean().detach().cpu().item()
         preds = np.concatenate([x["pred"] for x in outputs], axis=0)
 
@@ -132,20 +132,14 @@ class GLUETransformer(BaseTransformer):
         logs = ret["log"]
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
 
-    def test_epoch_end(self, outputs):
-        # updating to test_epoch_end instead of deprecated test_end
+    def test_epoch_end(self, outputs) -> dict:
         ret, predictions, targets = self._eval_end(outputs)
-
-        # Converting to the dic required by pl
-        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
-        # pytorch_lightning/trainer/logging.py#L139
         logs = ret["log"]
         # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
         return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
 
     @staticmethod
     def add_model_specific_args(parser, root_dir):
-        # Add NER specific options
         BaseTransformer.add_model_specific_args(parser, root_dir)
         parser.add_argument(
             "--max_seq_length",