Update text cls demo to adapted to ernie v2

demo/text-classification/predict.py

@@ -29,12 +29,14 @@ import paddlehub as hub
 
 # yapf: disable
 parser = argparse.ArgumentParser(__doc__)
-parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
+parser.add_argument("--checkpoint_dir", type=str, default="ckpt_20190802182531", help="Directory to model checkpoint")
 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=512, help="Number of words of the longest seqence.")
 parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for finetuning, input should be True or False")
 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="Directory to dataset")
+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.
 
@@ -52,25 +54,46 @@ if __name__ == '__main__':
         module = hub.Module(name="ernie")
     elif args.dataset.lower() == "mrpc":
         dataset = hub.dataset.GLUE("MRPC")
-        module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        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")
     elif args.dataset.lower() == "qqp":
         dataset = hub.dataset.GLUE("QQP")
-        module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        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")
     elif args.dataset.lower() == "sst-2":
         dataset = hub.dataset.GLUE("SST-2")
-        module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        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")
     elif args.dataset.lower() == "cola":
         dataset = hub.dataset.GLUE("CoLA")
-        module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        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")
     elif args.dataset.lower() == "qnli":
         dataset = hub.dataset.GLUE("QNLI")
-        module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        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")
     elif args.dataset.lower() == "rte":
         dataset = hub.dataset.GLUE("RTE")
-        module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        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")
     elif args.dataset.lower() == "mnli":
         dataset = hub.dataset.GLUE("MNLI")
-        module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        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")
     elif args.dataset.lower().startswith("xnli"):
         dataset = hub.dataset.XNLI(language=args.dataset.lower()[-2:])
         module = hub.Module(name="bert_multi_cased_L-12_H-768_A-12")
@@ -82,7 +105,8 @@ if __name__ == '__main__':
     reader = hub.reader.ClassifyReader(
         dataset=dataset,
         vocab_path=module.get_vocab_path(),
-        max_seq_len=args.max_seq_len)
+        max_seq_len=args.max_seq_len,
+        use_task_id=args.use_taskid)
 
     # Construct transfer learning network
     # Use "pooled_output" for classification tasks on an entire sentence.
@@ -98,6 +122,15 @@ if __name__ == '__main__':
         inputs["input_mask"].name,
     ]
 
+    if args.use_taskid:
+        feed_list = [
+            inputs["input_ids"].name,
+            inputs["position_ids"].name,
+            inputs["segment_ids"].name,
+            inputs["input_mask"].name,
+            inputs["task_ids"].name,
+        ]
+
     # Setup runing config for PaddleHub Finetune API
     config = hub.RunConfig(
         use_data_parallel=False,

demo/text-classification/run_classifier.sh

@@ -26,3 +26,4 @@ python -u text_classifier.py \
                    --num_epoch=3 \
                    --use_pyreader=True \
                    --use_data_parallel=True \
+                   --use_taskid=False \

demo/text-classification/run_predict.sh

@@ -15,4 +15,4 @@ CKPT_DIR="./ckpt_${DATASET}"
 #       sw: Swahili     th: Thai           tr: Turkish
 #       ur: Urdu        vi: Vietnamese     zh: Chinese (Simplified)
 
-python -u predict.py --checkpoint_dir $CKPT_DIR --max_seq_len 128 --use_gpu False --dataset=${DATASET}
+python -u predict.py --checkpoint_dir $CKPT_DIR --max_seq_len 128 --use_gpu False --dataset=${DATASET} ----use_taskid False

demo/text-classification/text_classifier.py

@@ -22,17 +22,18 @@ import paddlehub as hub
 # yapf: disable
 parser = argparse.ArgumentParser(__doc__)
 parser.add_argument("--num_epoch", type=int, default=3, help="Number of epoches for fine-tuning.")
-parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for finetuning, input should be True or False")
-parser.add_argument("--dataset", type=str, default="chnsenticorp", help="Directory to dataset")
+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("--dataset", type=str, default="chnsenticorp", help="The choice of dataset")
 parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
 parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
-parser.add_argument("--warmup_proportion", type=float, default=0.0, help="Warmup proportion params for warmup strategy")
+parser.add_argument("--warmup_proportion", type=float, default=0.1, help="Warmup proportion params for warmup strategy")
 parser.add_argument("--data_dir", type=str, default=None, help="Path to training data.")
 parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
 parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")
 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.
 
@@ -50,25 +51,46 @@ if __name__ == '__main__':
         module = hub.Module(name="ernie")
     elif args.dataset.lower() == "mrpc":
         dataset = hub.dataset.GLUE("MRPC")
-        module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        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")
     elif args.dataset.lower() == "qqp":
         dataset = hub.dataset.GLUE("QQP")
-        module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        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")
     elif args.dataset.lower() == "sst-2":
         dataset = hub.dataset.GLUE("SST-2")
-        module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        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")
     elif args.dataset.lower() == "cola":
         dataset = hub.dataset.GLUE("CoLA")
-        module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        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")
     elif args.dataset.lower() == "qnli":
         dataset = hub.dataset.GLUE("QNLI")
-        module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        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")
     elif args.dataset.lower() == "rte":
         dataset = hub.dataset.GLUE("RTE")
-        module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        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")
     elif args.dataset.lower() == "mnli":
         dataset = hub.dataset.GLUE("MNLI")
-        module = hub.Module(name="bert_uncased_L-12_H-768_A-12")
+        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")
     elif args.dataset.lower().startswith("xnli"):
         dataset = hub.dataset.XNLI(language=args.dataset.lower()[-2:])
         module = hub.Module(name="bert_multi_cased_L-12_H-768_A-12")
@@ -80,7 +102,8 @@ if __name__ == '__main__':
     reader = hub.reader.ClassifyReader(
         dataset=dataset,
         vocab_path=module.get_vocab_path(),
-        max_seq_len=args.max_seq_len)
+        max_seq_len=args.max_seq_len,
+        use_task_id=args.use_taskid)
 
     # Construct transfer learning network
     # Use "pooled_output" for classification tasks on an entire sentence.
@@ -96,6 +119,14 @@ if __name__ == '__main__':
         inputs["input_mask"].name,
     ]
 
+    if args.use_taskid:
+        feed_list = [
+            inputs["input_ids"].name,
+            inputs["position_ids"].name,
+            inputs["segment_ids"].name,
+            inputs["input_mask"].name,
+            inputs["task_ids"].name,
+        ]
     # Select finetune strategy, setup config and finetune
     strategy = hub.AdamWeightDecayStrategy(
         weight_decay=args.weight_decay,

paddlehub/reader/nlp_reader.py

@@ -332,6 +332,8 @@ class ClassifyReader(BaseReader):
             ]
 
             if self.use_task_id:
+                padded_task_ids = np.ones_like(
+                    padded_token_ids, dtype="int64") * self.task_id
                 return_list = [
                     padded_token_ids, padded_position_ids, padded_text_type_ids,
                     input_mask, padded_task_ids