update Senta

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()

demo/senta/run_classifier.sh

-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

demo/senta/run_predict.sh

 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

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"]

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

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")