update README.md

demo/ernie-classification/README.md

 # ERNIE Classification
 
-本示例如果使用PaddleHub Finetune API快速的完成Transformer类模型ERNIE或BERT完成文本分类任务。
+本示例将展示如何使用PaddleHub Finetune API利用ERNIE完成分类任务。
+
+其中分类任务可以分为两大类
+
+* 单句分类
+- 中文情感分析任务 ChnSentiCorp
+
+
+* 句对分类
+- 语义相似度 LCQMC
+- 检索式问答任务 nlpcc-dbqa
+
+## 如何开始Finetune
+
+在完成安装PaddlePaddle与PaddleHub后，通过执行脚本`sh run_sentiment_cls.sh`即可开始使用ERNIE对ChnSentiCorp数据集进行Finetune。
+
+其中脚本参数说明如下：
+
+```bash
+--batch_size: 批处理大小，请结合显存情况进行调整，若出现显存不足错误，请调低这一参数值
+--weight_decay:
+--checkpoint_dir: 模型保存路径，PaddleHub会自动保存验证集上表现最好的模型
+--num_epoch: Finetune迭代的轮数
+--max_seq_len: ERNIE模型使用的最大序列长度，最大不能超过512,
+  若出现显存不足错误，请调低这一参数
+```
+
+## 代码步骤
+
+使用PaddleHub Finetune API进行Finetune可以分为一下4个步骤
+
+### Step1: 加载预训练模型
+
+```python
+    module = hub.Module(name="ernie")
+    inputs, outputs, program = module.context(
+        trainable=True, max_seq_len=128)
+```
+其中最大序列长度`max_seq_len`是可以调整的参数，建议值128，根据任务文本长度不同可以调整该值，但最大不超过512。
+
+如果想尝试BERT模型，例如BERT中文模型，只需要更换Module中的参数即可.
+PaddleHub除了ERNIE，还提供以下BERT模型:
+
+BERT模型名                         | PaddleHub Module name
+---------------------------------- | :------:
+BERT-Base, Uncased                 | bert_uncased_L-12_H-768_A-12
+BERT-Large, Uncased                | bert_uncased_L-24_H-1024_A-16
+BERT-Base, Cased                   | bert_cased_L-12_H-768_A-12
+BERT-Large, Cased                  | bert_cased_L-24_H-1024_A-16
+BERT-Base, Multilingual Cased      | bert_multi_cased_L-12_H-768_A-12
+BERT-Base, Chinese                 | bert_chinese_L-12_H-768_A-12
+
+
+```python
+    # 即可无缝切换BERT中文模型
+    module = hub.Module(name="bert_chinese_L-12_H-768_A-12")
+```

demo/ernie-classification/question_answering.py

@@ -22,7 +22,6 @@ import paddlehub as hub
 parser = argparse.ArgumentParser(__doc__)
 parser.add_argument("--num_epoch", type=int, default=3, help="Number of epoches for fine-tuning.")
 parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
-parser.add_argument("--hub_module_dir", type=str, default=None, help="PaddleHub module directory")
 parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
 parser.add_argument("--data_dir", type=str, default=None, help="Path to training data.")
 parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")

demo/ernie-classification/question_matching.py

@@ -22,7 +22,6 @@ import paddlehub as hub
 parser = argparse.ArgumentParser(__doc__)
 parser.add_argument("--num_epoch", type=int, default=3, help="Number of epoches for fine-tuning.")
 parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
-parser.add_argument("--hub_module_dir", type=str, default=None, help="PaddleHub module directory")
 parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
 parser.add_argument("--data_dir", type=str, default=None, help="Path to training data.")
 parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")

demo/ernie-classification/run_question_matching.sh

-export CUDA_VISIBLE_DEVICES=0
+export CUDA_VISIBLE_DEVICES=5
 
 CKPT_DIR="./ckpt_question_matching"
 python -u question_matching.py \

demo/ernie-classification/run_sentiment_cls.sh

-export CUDA_VISIBLE_DEVICES=3
+export CUDA_VISIBLE_DEVICES=5
 
 CKPT_DIR="./ckpt_sentiment_cls"
 python -u sentiment_cls.py \

demo/ernie-classification/sentiment_cls.py

@@ -22,7 +22,6 @@ import paddlehub as hub
 parser = argparse.ArgumentParser(__doc__)
 parser.add_argument("--num_epoch", type=int, default=3, help="Number of epoches for fine-tuning.")
 parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
-parser.add_argument("--hub_module_dir", type=str, default=None, help="PaddleHub module directory")
 parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
 parser.add_argument("--data_dir", type=str, default=None, help="Path to training data.")
 parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")

demo/ernie-seq-labeling/run_sequence_labeling.sh

-export CUDA_VISIBLE_DEVICES=0
+export CUDA_VISIBLE_DEVICES=6
 
 CKPT_DIR="./ckpt_sequence_labeling"
 

demo/ernie-seq-labeling/sequence_labeling.py

@@ -13,7 +13,8 @@
 # limitations under the License.
 """Finetuning on sequence labeling task."""
 
-import paddle
+import argparse
+
 import paddle.fluid as fluid
 import paddlehub as hub
 
@@ -21,7 +22,6 @@ import paddlehub as hub
 parser = argparse.ArgumentParser(__doc__)
 parser.add_argument("--num_epoch", type=int, default=3, help="Number of epoches for fine-tuning.")
 parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
-parser.add_argument("--hub_module_dir", type=str, default=None, help="PaddleHub module directory")
 parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
 parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
 parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")