add simple demo

demo/text-classification/simple_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
+dataset = hub.dataset.ChnSentiCorp()
+reader = hub.reader.ClassifyReader(
+    dataset=dataset, 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"]
+
+    cls_task = hub.create_text_classification_task(
+        feature=pooled_output, label=label, num_classes=dataset.num_labels)
+
+# Step4
+strategy = hub.AdamWeightDecayStrategy(learning_rate=5e-5, weight_decay=0.01)
+
+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)