Update the sentiment_classification demo and use the PaddleHub to get the ERNIE model (#2388)

PaddleNLP/sentiment_classification/README.md

@@ -82,6 +82,18 @@ senta_config.json中需要修改如下：
 --model_type "ernie_bilstm"
 ```
 
+我们也提供了使用PaddleHub加载ERNIE模型的选项，PaddleHub是PaddlePaddle的预训练模型管理工具，可以一行代码完成预训练模型的加载，简化预训练模型的使用和迁移学习。更多相关的介绍，可以查看[PaddleHub](https://github.com/PaddlePaddle/PaddleHub)
+
+如果想使用该功能，需要修改run_ernie.sh中的配置如下：
+```shell
+# 在eval()函数中，修改如下参数：
+--use_paddle_hub true
+```
+注意：使用该选项需要先安装PaddleHub，安装命令如下
+```shell
+$ pip install paddlehub
+```
+
 #### 模型训练
 
 基于示例的数据集，可以运行下面的命令，在训练集（train.tsv）上进行模型训练，并在开发集（dev.tsv）验证

PaddleNLP/sentiment_classification/run_ernie.sh

@@ -16,6 +16,7 @@ train() {
         --do_train true \
         --do_val true \
         --do_infer false \
+        --use_paddle_hub false \
         --batch_size 24 \
         --init_checkpoint $ERNIE_PRETRAIN/params \
         --train_set $DATA_PATH/train.tsv \
@@ -43,6 +44,7 @@ evaluate() {
         --do_train false \
         --do_val true \
         --do_infer false \
+        --use_paddle_hub false \
         --batch_size 24 \
         --init_checkpoint ./save_models/step_5000/ \
         --dev_set $DATA_PATH/dev.tsv \
@@ -58,6 +60,7 @@ evaluate() {
         --do_train false \
         --do_val true \
         --do_infer false \
+        --use_paddle_hub false \
         --batch_size 24 \
         --init_checkpoint ./save_models/step_5000/ \
         --dev_set $DATA_PATH/test.tsv \
@@ -76,6 +79,7 @@ infer() {
         --do_train false \
         --do_val false \
         --do_infer true \
+        --use_paddle_hub false \
         --batch_size 24 \
         --init_checkpoint ./save_models/step_5000 \
         --test_set $DATA_PATH/test.tsv \

PaddleNLP/sentiment_classification/run_ernie_classifier.py

@@ -29,7 +29,7 @@ from nets import ernie_base_net
 from nets import ernie_bilstm_net
 from preprocess.ernie import task_reader
 from models.representation.ernie import ErnieConfig
-from models.representation.ernie import ernie_encoder
+from models.representation.ernie import ernie_encoder, ernie_encoder_with_paddle_hub
 from models.representation.ernie import ernie_pyreader
 from utils import ArgumentGroup
 from utils import print_arguments
@@ -43,6 +43,7 @@ model_g.add_arg("senta_config_path", str, None, "Path to the json file for senta
 model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.")
 model_g.add_arg("checkpoints", str, "checkpoints", "Path to save checkpoints")
 model_g.add_arg("model_type", str, "ernie_base", "Type of current ernie model")
+model_g.add_arg("use_paddle_hub", bool, False, "Whether to load ERNIE using PaddleHub")
 
 train_g = ArgumentGroup(parser, "training", "training options.")
 train_g.add_arg("epoch", int, 10, "Number of epoches for training.")
@@ -212,6 +213,9 @@ def main(args):
                     pyreader_name='train_reader')
 
                 # get ernie_embeddings
+                if args.use_paddle_hub:
+                    embeddings = ernie_encoder_with_paddle_hub(ernie_inputs, args.max_seq_len)
+                else:
                     embeddings = ernie_encoder(ernie_inputs, ernie_config=ernie_config)
 
                 # user defined model based on ernie embeddings
@@ -244,6 +248,9 @@ def main(args):
                     pyreader_name='eval_reader')
 
                 # get ernie_embeddings
+                if args.use_paddle_hub:
+                    embeddings = ernie_encoder_with_paddle_hub(ernie_inputs, args.max_seq_len)
+                else:
                     embeddings = ernie_encoder(ernie_inputs, ernie_config=ernie_config)
 
                 # user defined model based on ernie embeddings
@@ -264,6 +271,9 @@ def main(args):
                     pyreader_name="infer_reader")
 
                 # get ernie_embeddings
+                if args.use_paddle_hub:
+                    embeddings = ernie_encoder_with_paddle_hub(ernie_inputs, args.max_seq_len)
+                else:
                     embeddings = ernie_encoder(ernie_inputs, ernie_config=ernie_config)
 
                 probs = create_model(args,
@@ -280,15 +290,23 @@ def main(args):
             init_checkpoint(
                 exe,
                 args.init_checkpoint,
-                main_program=startup_prog)
-    elif args.do_val or args.do_infer:
+                main_program=train_program)
+    elif args.do_val:
+        if not args.init_checkpoint:
+            raise ValueError("args 'init_checkpoint' should be set if"
+                             "only doing validation or testing!")
+        init_checkpoint(
+            exe,
+            args.init_checkpoint,
+            main_program=test_prog)
+    elif args.do_infer:
         if not args.init_checkpoint:
             raise ValueError("args 'init_checkpoint' should be set if"
                              "only doing validation or testing!")
         init_checkpoint(
             exe,
             args.init_checkpoint,
-            main_program=startup_prog)
+            main_program=infer_prog)
 
     if args.do_train:
         exec_strategy = fluid.ExecutionStrategy()