simplify text-classification

demo/text-classification/run_classifier.sh

 export CUDA_VISIBLE_DEVICES=5
 
-# User can select senticorp, nlpcc_dbqa, lcqmc for different task
-DATASET="senticorp"
+# User can select chnsenticorp, nlpcc_dbqa, lcqmc for different task
+DATASET="chnsenticorp"
 CKPT_DIR="./ckpt_${DATASET}"
 # Recommending hyper parameters for difference task
 # ChnSentiCorp: batch_size=24, weight_decay=0.01, num_epoch=3, max_seq_len=128, lr=5e-5

demo/text-classification/run_predict.sh

 export CUDA_VISIBLE_DEVICES=5
 
-CKPT_DIR="./ckpt_sentiment_cls/best_model"
-python -u cls_predict.py --checkpoint_dir $CKPT_DIR --max_seq_len 128
+CKPT_DIR="./ckpt_chnsenticorp/best_model"
+python -u predict.py --checkpoint_dir $CKPT_DIR --max_seq_len 128

demo/text-classification/text_classifier.py

@@ -43,7 +43,7 @@ if __name__ == '__main__':
 
     # Step2: Download dataset and use ClassifyReader to read dataset
     dataset = None
-    if args.dataset.lower() == "senticorp":
+    if args.dataset.lower() == "chnsenticorp":
         dataset = hub.dataset.ChnSentiCorp()
     elif args.dataset.lower() == "nlpcc_dbqa":
         dataset = hub.dataset.NLPCC_DBQA()