discard use_taskid

demo/multi-label-classification/multi_label_classifier.py

@@ -30,36 +30,21 @@ parser.add_argument("--warmup_proportion", type=float, default=0.1, help="Warmup
 parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
 parser.add_argument("--max_seq_len", type=int, default=128, help="Number of words of the longest seqence.")
 parser.add_argument("--batch_size", type=int, default=1, help="Total examples' number in batch for training.")
-parser.add_argument("--use_taskid", type=ast.literal_eval, default=False, help="Whether to user ernie v2 , if not to use bert.")
 args = parser.parse_args()
 # yapf: enable.
 
 if __name__ == '__main__':
     # Load Paddlehub BERT pretrained model
-    if args.use_taskid:
-        module = hub.Module(name="ernie_v2_eng_base")
+    module = hub.Module(name="ernie_v2_eng_base")
 
-        inputs, outputs, program = module.context(
-            trainable=True, max_seq_len=args.max_seq_len)
+    inputs, outputs, program = module.context(
+        trainable=True, max_seq_len=args.max_seq_len)
 
-        # Setup feed list for data feeder
-        feed_list = [
-            inputs["input_ids"].name, inputs["position_ids"].name,
-            inputs["segment_ids"].name, inputs["input_mask"].name
-        ]
-    else:
-        module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
-
-        inputs, outputs, program = module.context(
-            trainable=True, max_seq_len=args.max_seq_len)
-
-        # Setup feed list for data feeder
-        feed_list = [
-            inputs["input_ids"].name,
-            inputs["position_ids"].name,
-            inputs["segment_ids"].name,
-            inputs["input_mask"].name,
-        ]
+    # Setup feed list for data feeder
+    feed_list = [
+        inputs["input_ids"].name, inputs["position_ids"].name,
+        inputs["segment_ids"].name, inputs["input_mask"].name
+    ]
 
     # Download dataset and use MultiLabelReader to read dataset
     dataset = hub.dataset.Toxic()

demo/multi-label-classification/predict.py

@@ -36,38 +36,23 @@ parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory
 parser.add_argument("--batch_size",     type=int,   default=1, help="Total examples' number in batch for training.")
 parser.add_argument("--max_seq_len", type=int, default=128, help="Number of words of the longest seqence.")
 parser.add_argument("--use_gpu", type=ast.literal_eval, default=True, help="Whether use GPU for finetuning, input should be True or False")
-parser.add_argument("--use_taskid", type=ast.literal_eval, default=False, help="Whether to user ernie v2 , if not to use bert.")
 args = parser.parse_args()
 # yapf: enable.
 
 if __name__ == '__main__':
     # Load Paddlehub BERT pretrained model
-    if args.use_taskid:
-        module = hub.Module(name="ernie_eng_base.hub_module")
+    module = hub.Module(name="ernie_eng_base.hub_module")
 
-        inputs, outputs, program = module.context(
-            trainable=True, max_seq_len=args.max_seq_len)
+    inputs, outputs, program = module.context(
+        trainable=True, max_seq_len=args.max_seq_len)
 
-        # Setup feed list for data feeder
-        feed_list = [
-            inputs["input_ids"].name,
-            inputs["position_ids"].name,
-            inputs["segment_ids"].name,
-            inputs["input_mask"].name,
-        ]
-    else:
-        module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
-
-        inputs, outputs, program = module.context(
-            trainable=True, max_seq_len=args.max_seq_len)
-
-        # Setup feed list for data feeder
-        feed_list = [
-            inputs["input_ids"].name,
-            inputs["position_ids"].name,
-            inputs["segment_ids"].name,
-            inputs["input_mask"].name,
-        ]
+    # Setup feed list for data feeder
+    feed_list = [
+        inputs["input_ids"].name,
+        inputs["position_ids"].name,
+        inputs["segment_ids"].name,
+        inputs["input_mask"].name,
+    ]
 
     # Download dataset and use MultiLabelReader to read dataset
     dataset = hub.dataset.Toxic()

demo/multi-label-classification/run_classifier.sh

@@ -16,5 +16,4 @@ python -u multi_label_classifier.py \
                    --learning_rate=5e-5 \
                    --weight_decay=0.01 \
                    --max_seq_len=128 \
-                   --num_epoch=3 \
-                   --use_taskid=False
+                   --num_epoch=3

demo/multi-label-classification/run_predict.sh

@@ -2,4 +2,4 @@ export FLAGS_eager_delete_tensor_gb=0.0
 export CUDA_VISIBLE_DEVICES=0
 
 CKPT_DIR="./ckpt_toxic"
-python -u predict.py --checkpoint_dir $CKPT_DIR --max_seq_len 128 --use_gpu True --use_taskid False
+python -u predict.py --checkpoint_dir $CKPT_DIR --max_seq_len 128 --use_gpu True

demo/regression/regression.py

@@ -34,7 +34,6 @@ parser.add_argument("--max_seq_len", type=int, default=512, help="Number of word
 parser.add_argument("--batch_size", type=int, default=32, help="Total examples' number in batch for training.")
 parser.add_argument("--use_pyreader", type=ast.literal_eval, default=False, help="Whether use pyreader to feed data.")
 parser.add_argument("--use_data_parallel", type=ast.literal_eval, default=False, help="Whether use data parallel.")
-parser.add_argument("--use_taskid", type=ast.literal_eval, default=False, help="Whether to use taskid ,if yes to use ernie v2.")
 args = parser.parse_args()
 # yapf: enable.
 
@@ -43,10 +42,7 @@ if __name__ == '__main__':
     # Download dataset and use ClassifyReader to read dataset
     if args.dataset.lower() == "sts-b":
         dataset = hub.dataset.GLUE("STS-B")
-        if args.use_taskid:
-            module = hub.Module(name="ernie_v2_eng_base")
-        else:
-            module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        module = hub.Module(name="ernie_v2_eng_base")
     else:
         raise ValueError("%s dataset is not defined" % args.dataset)
 

demo/regression/run_regssion.sh

@@ -16,5 +16,4 @@ python -u regression.py \
                    --max_seq_len=128 \
                    --num_epoch=3 \
                    --use_pyreader=True \
-                   --use_data_parallel=True \
-                   --use_taskid=False \
+                   --use_data_parallel=True

demo/sequence-labeling/sequence_label.py

@@ -40,10 +40,6 @@ if __name__ == '__main__':
     module = hub.Module(name="ernie_tiny")
     inputs, outputs, program = module.context(
         trainable=True, max_seq_len=args.max_seq_len)
-    if module.name.startswith("ernie_v2"):
-        use_taskid = True
-    else:
-        use_taskid = False
 
     # Download dataset and use SequenceLabelReader to read dataset
     dataset = hub.dataset.MSRA_NER()

demo/text-classification/predict.py

@@ -36,7 +36,6 @@ parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whe
 parser.add_argument("--use_pyreader", type=ast.literal_eval, default=False, help="Whether use pyreader to feed data.")
 parser.add_argument("--dataset", type=str, default="chnsenticorp", help="The choice of dataset")
 parser.add_argument("--use_data_parallel", type=ast.literal_eval, default=False, help="Whether use data parallel.")
-parser.add_argument("--use_taskid", type=ast.literal_eval, default=False, help="Whether to use taskid ,if yes to use ernie v2.")
 args = parser.parse_args()
 # yapf: enable.
 
@@ -58,60 +57,36 @@ if __name__ == '__main__':
         metrics_choices = ["acc"]
     elif args.dataset.lower() == "mrpc":
         dataset = hub.dataset.GLUE("MRPC")
-        if args.use_taskid:
-            module = hub.Module(name="ernie_v2_eng_base")
-        else:
-            module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        module = hub.Module(name="ernie_v2_eng_base")
         metrics_choices = ["f1", "acc"]
     # The first metric will be choose to eval. Ref: task.py:799
     elif args.dataset.lower() == "qqp":
         dataset = hub.dataset.GLUE("QQP")
-        if args.use_taskid:
-            module = hub.Module(name="ernie_v2_eng_base")
-        else:
-            module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        module = hub.Module(name="ernie_v2_eng_base")
         metrics_choices = ["f1", "acc"]
     elif args.dataset.lower() == "sst-2":
         dataset = hub.dataset.GLUE("SST-2")
-        if args.use_taskid:
-            module = hub.Module(name="ernie_v2_eng_base")
-        else:
-            module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        module = hub.Module(name="ernie_v2_eng_base")
         metrics_choices = ["acc"]
     elif args.dataset.lower() == "cola":
         dataset = hub.dataset.GLUE("CoLA")
-        if args.use_taskid:
-            module = hub.Module(name="ernie_v2_eng_base")
-        else:
-            module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        module = hub.Module(name="ernie_v2_eng_base")
         metrics_choices = ["matthews", "acc"]
     elif args.dataset.lower() == "qnli":
         dataset = hub.dataset.GLUE("QNLI")
-        if args.use_taskid:
-            module = hub.Module(name="ernie_v2_eng_base")
-        else:
-            module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        module = hub.Module(name="ernie_v2_eng_base")
         metrics_choices = ["acc"]
     elif args.dataset.lower() == "rte":
         dataset = hub.dataset.GLUE("RTE")
-        if args.use_taskid:
-            module = hub.Module(name="ernie_v2_eng_base")
-        else:
-            module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        module = hub.Module(name="ernie_v2_eng_base")
         metrics_choices = ["acc"]
     elif args.dataset.lower() == "mnli" or args.dataset.lower() == "mnli_m":
         dataset = hub.dataset.GLUE("MNLI_m")
-        if args.use_taskid:
-            module = hub.Module(name="ernie_v2_eng_base")
-        else:
-            module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        module = hub.Module(name="ernie_v2_eng_base")
         metrics_choices = ["acc"]
     elif args.dataset.lower() == "mnli_mm":
         dataset = hub.dataset.GLUE("MNLI_mm")
-        if args.use_taskid:
-            module = hub.Module(name="ernie_v2_eng_base")
-        else:
-            module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        module = hub.Module(name="ernie_v2_eng_base")
         metrics_choices = ["acc"]
     elif args.dataset.lower().startswith("xnli"):
         dataset = hub.dataset.XNLI(language=args.dataset.lower()[-2:])

demo/text-classification/run_predict.sh

@@ -18,4 +18,3 @@ python -u predict.py --checkpoint_dir=$CKPT_DIR \
                             --use_gpu=True \
                             --dataset=${DATASET} \
                             --batch_size=150 \
-                            --use_taskid=False \