update run_classifer.sh
@@ -22,6 +22,7 @@ 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 model checkpoint", choices=["chnsenticorp", "nlpcc_dbqa", "lcqmc"])
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.")
@@ -71,7 +72,7 @@ if __name__ == '__main__':
inputs["segment_ids"].name, inputs["input_mask"].name, label.name
]
# Define a classfication finetune task by PaddleHub's API
- cls_task = hub.create_text_classification_task(
+ cls_task = hub.create_text_cls_task(
feature=pooled_output, label=label, num_classes=dataset.num_labels)
# Step4: Select finetune strategy, setup config and finetune
