update ernie tiny demo

demo/ernie-classification/ernie_tiny_demo.py

 import paddle.fluid as fluid
 import paddlehub as hub
 
+# Step1
 module = hub.Module(name="ernie")
 inputs, outputs, program = module.context(trainable=True, max_seq_len=128)
 
+# Step2
 reader = hub.reader.ClassifyReader(
     dataset=hub.dataset.ChnSentiCorp(),
     vocab_path=module.get_vocab_path(),
     max_seq_len=128)
 
+# Step3
 with fluid.program_guard(program):
     label = fluid.layers.data(name="label", shape=[1], dtype='int64')
 
     pooled_output = outputs["pooled_output"]
 
-    feed_list = [
-        inputs["input_ids"].name, inputs["position_ids"].name,
-        inputs["segment_ids"].name, inputs["input_mask"].name, label.name
-    ]
-
     cls_task = hub.create_text_classification_task(
-        pooled_output, label, num_classes=reader.get_num_labels())
+        feature=pooled_output, label=label, num_classes=reader.get_num_labels())
+
+# Step4
+strategy = hub.BERTFinetuneStrategy(
+    learning_rate=5e-5,
+    warmup_proportion=0.1,
+    warmup_strategy="linear_warmup_decay",
+    weight_decay=0.01)
 
-    strategy = hub.BERTFinetuneStrategy(
-        weight_decay=0.01,
-        learning_rate=5e-5,
-        warmup_strategy="linear_warmup_decay",
-    )
+config = hub.RunConfig(
+    use_cuda=True, num_epoch=3, batch_size=32, strategy=strategy)
 
-    config = hub.RunConfig(
-        use_cuda=True, num_epoch=3, batch_size=32, strategy=strategy)
+feed_list = [
+    inputs["input_ids"].name, inputs["position_ids"].name,
+    inputs["segment_ids"].name, inputs["input_mask"].name, label.name
+]
 
-    hub.finetune_and_eval(
-        task=cls_task, data_reader=reader, feed_list=feed_list, config=config)
+hub.finetune_and_eval(
+    task=cls_task, data_reader=reader, feed_list=feed_list, config=config)