diff --git a/demo/senta/predict.py b/demo/senta/predict.py
index 093967ed21bc8d52283ee3884a89558f4ff30b9a..1ccb28205aebc5ebf48f4e6d0e4dba21c888e889 100644
--- a/demo/senta/predict.py
+++ b/demo/senta/predict.py
@@ -26,7 +26,7 @@ if __name__ == '__main__':
 
     # Sentence classification  dataset reader
     dataset = hub.dataset.ChnSentiCorp()
-    reader = hub.reader.TextClassificationReader(
+    reader = hub.reader.LACClassifyReader(
         dataset=dataset, vocab_path=module.get_vocab_path())
 
     place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
diff --git a/demo/senta/run_classifier.sh b/demo/senta/run_classifier.sh
index 6d50cd187d20f26eadb77d3d989565a8575d44e6..5fd33cf014d4d4a230a9f6c46fc0a4bb18dfdd9c 100644
--- a/demo/senta/run_classifier.sh
+++ b/demo/senta/run_classifier.sh
@@ -1,4 +1,4 @@
-export CUDA_VISIBLE_DEVICES=2
+export CUDA_VISIBLE_DEVICES=0
 
 # User can select chnsenticorp, nlpcc_dbqa, lcqmc for different task
 DATASET="chnsenticorp"
@@ -6,6 +6,6 @@ CKPT_DIR="./ckpt_${DATASET}"
 
 python -u text_classifier.py \
                    --batch_size=24 \
-                   --use_gpu=True \
+                   --use_gpu=False \
                    --checkpoint_dir=${CKPT_DIR} \
                    --num_epoch=10
diff --git a/demo/senta/run_predict.sh b/demo/senta/run_predict.sh
index ac436eaba08d39054d4fd4644fadcbbbe7941c97..1a82ae551ce1b81aadd9e69e11e413ed2b2ae42f 100644
--- a/demo/senta/run_predict.sh
+++ b/demo/senta/run_predict.sh
@@ -1,4 +1,4 @@
 export CUDA_VISIBLE_DEVICES=0
 
 CKPT_DIR="./ckpt_chnsenticorp/best_model"
-python -u predict.py --checkpoint_dir $CKPT_DIR --use_gpu True
+python -u predict.py --checkpoint_dir $CKPT_DIR --use_gpu False
diff --git a/demo/senta/text_classifier.py b/demo/senta/text_classifier.py
index be4bada6b1b43bd621093afba9a247cb2a827ec3..ee8ed97af15de4cc0b666877ed931c0ed1a8c8b5 100644
--- a/demo/senta/text_classifier.py
+++ b/demo/senta/text_classifier.py
@@ -21,7 +21,7 @@ if __name__ == '__main__':
     # Step2: Download dataset and use TextClassificationReader to read dataset
     dataset = hub.dataset.ChnSentiCorp()
 
-    reader = hub.reader.LACTokenizeReader(
+    reader = hub.reader.LACClassifyReader(
         dataset=dataset, vocab_path=module.get_vocab_path())
 
     sent_feature = outputs["sequence_output"]
diff --git a/paddlehub/reader/__init__.py b/paddlehub/reader/__init__.py
index bc7f63b5c9abb3d6878aa875e47fa6ad166bf952..a0e119df90869fe3c46b8cec70cf3c089d1ebccc 100644
--- a/paddlehub/reader/__init__.py
+++ b/paddlehub/reader/__init__.py
@@ -14,5 +14,5 @@
 
 from .nlp_reader import ClassifyReader
 from .nlp_reader import SequenceLabelReader
-from .nlp_reader import LACTokenizeReader
+from .nlp_reader import LACClassifyReader
 from .cv_reader import ImageClassificationReader
diff --git a/paddlehub/reader/nlp_reader.py b/paddlehub/reader/nlp_reader.py
index 9204cc93791b347f1398d85e17589eb15a4a345a..1751d4329d03b092197a8d380f540d6625f0a435 100644
--- a/paddlehub/reader/nlp_reader.py
+++ b/paddlehub/reader/nlp_reader.py
@@ -382,7 +382,7 @@ class ExtractEmbeddingReader(BaseReader):
         return return_list
 
 
-class LACTokenizeReader(object):
+class LACClassifyReader(object):
     def __init__(self, dataset, vocab_path):
         self.dataset = dataset
         self.lac = hub.Module(name="lac")