update readme

README.md

@@ -63,7 +63,7 @@ train.dist.pipeline_parallel_size = 1
 
 预训练命令：
 
-    cd bert-large_oneflow
+    cd home/bert-large_oneflow
     bash tools/train.sh tools/train_net.py configs/bert_large_pretrain.py 4
 
 ### 精度

libai/data/datasets/bert_dataset.py

@@ -233,7 +233,7 @@ def pad_and_convert_to_numpy(
     tokentypes_np = np.array(tokentypes + filler, dtype=np.int64)
 
     # Padding mask.
-    padding_mask_np = np.array([1] * num_tokens + [0] * padding_length, dtype=np.bool)
+    padding_mask_np = np.array([1] * num_tokens + [0] * padding_length, dtype=np.bool_)
 
     # Lables and loss mask.
     labels = [-1] * max_seq_length
@@ -243,7 +243,7 @@ def pad_and_convert_to_numpy(
         labels[masked_positions[i]] = masked_labels[i]
         loss_mask[masked_positions[i]] = 1
     labels_np = np.array(labels, dtype=np.int64)
-    loss_mask_np = np.array(loss_mask, dtype=np.bool)
+    loss_mask_np = np.array(loss_mask, dtype=np.bool_)
 
     return tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np
 

model.properties

 # 模型唯一标识
 modelCode=65
 # 模型名称
-modelName=Bert-Large_oneflow
+modelName=bert-large_oneflow
 # 模型描述
 modelDescription=基于OneFlow框架的Bert-Large模型
 # 应用场景
-appScenario=训练,推理,train,inference,nlp,智能聊天助手
+appScenario=训练,推理,nlp,智能聊天助手,科研
 # 框架类型
 frameType=OneFlow,Libai

requirements.txt

@@ -22,7 +22,7 @@ portalocker
 dill
 flake8==3.8.1 
 isort==5.10.1
-black==21.4b2
+black==21.4b
 autoflake
 tensorboardX
 pytest