test bert modules

demo/ernie-classification/sentiment_cls.py

@@ -33,6 +33,7 @@ args = parser.parse_args()
 if __name__ == '__main__':
     # Step1: load Paddlehub ERNIE pretrained model
     module = hub.Module(name="ernie")
+    # module = hub.Module(name="bert_multi_cased_L-12_H-768_A-12")
     inputs, outputs, program = module.context(
         trainable=True, max_seq_len=args.max_seq_len)
 
@@ -59,9 +60,7 @@ if __name__ == '__main__':
         ]
         # Define a classfication finetune task by PaddleHub's API
         cls_task = hub.create_text_classification_task(
-            feature=pooled_output,
-            label=label,
-            num_classes=dataset.num_labels())
+            feature=pooled_output, label=label, num_classes=dataset.num_labels)
 
         # Step4: Select finetune strategy, setup config and finetune
         strategy = hub.AdamWeightDecayStrategy(