diff --git a/fluid/sequence_tagging_for_ner/README.md b/fluid/sequence_tagging_for_ner/README.md
index 761060c72000d0767e424975ea354556949dcfa1..4e4ce90c153f25323dca1a18232d3f392801441a 100644
--- a/fluid/sequence_tagging_for_ner/README.md
+++ b/fluid/sequence_tagging_for_ner/README.md
@@ -108,11 +108,23 @@ Baghdad      NNP  I-NP  I-LOC
 3. 运行命令 `python train.py` ，**需要注意：直接运行使用的是示例数据，请替换真实的标记数据。**
 
     ```text
-    commandline:  --use_gpu=False --trainer_count=1
-    Initing parameters..
-    Init parameters done.
-    Pass 0, Batch 0, Cost 41.430110, {'ner_chunk.precision': 0.01587301678955555, 'ner_chunk.F1-score': 0.028368793427944183, 'ner_chunk.recall': 0.13333334028720856, 'error': 0.939393937587738}
-    Test with Pass 0, Batch 0, {'ner_chunk.precision': 0.0, 'ner_chunk.F1-score': 0.0, 'ner_chunk.recall': 0.0, 'error': 0.16260161995887756}
+    Pass 127, Batch 9525, Cost 4.0867705, Precision 0.3954984, Recall 0.37846154, F1_score0.38679245
+    Pass 127, Batch 9530, Cost 3.137265, Precision 0.42971888, Recall 0.38351256, F1_score0.405303
+    Pass 127, Batch 9535, Cost 3.6240938, Precision 0.4272152, Recall 0.41795665, F1_score0.4225352
+    Pass 127, Batch 9540, Cost 3.5352352, Precision 0.48464164, Recall 0.4536741, F1_score0.46864685
+    Pass 127, Batch 9545, Cost 4.1130385, Precision 0.40131578, Recall 0.3836478, F1_score0.39228293
+    Pass 127, Batch 9550, Cost 3.6826708, Precision 0.43333334, Recall 0.43730888, F1_score0.43531203
+    Pass 127, Batch 9555, Cost 3.6363933, Precision 0.42424244, Recall 0.3962264, F1_score0.4097561
+    Pass 127, Batch 9560, Cost 3.6101768, Precision 0.51363635, Recall 0.353125, F1_score0.41851854
+    Pass 127, Batch 9565, Cost 3.5935276, Precision 0.5152439, Recall 0.5, F1_score0.5075075
+    Pass 127, Batch 9570, Cost 3.4987144, Precision 0.5, Recall 0.4330218, F1_score0.46410686
+    Pass 127, Batch 9575, Cost 3.4659843, Precision 0.39864865, Recall 0.38064516, F1_score0.38943896
+    Pass 127, Batch 9580, Cost 3.1702557, Precision 0.5, Recall 0.4490446, F1_score0.47315437
+    Pass 127, Batch 9585, Cost 3.1587276, Precision 0.49377593, Recall 0.4089347, F1_score0.4473684
+    Pass 127, Batch 9590, Cost 3.5043538, Precision 0.4556962, Recall 0.4600639, F1_score0.45786962
+    Pass 127, Batch 9595, Cost 2.981989, Precision 0.44981414, Recall 0.45149255, F1_score0.4506518
+    [TrainSet] pass_id:127 pass_precision:[0.46023396] pass_recall:[0.43197003] pass_f1_score:[0.44565433]
+    [TestSet] pass_id:127 pass_precision:[0.4708409] pass_recall:[0.47971722] pass_f1_score:[0.4752376]
     ```
 ### 预测
 1. 修改 [infer.py](./infer.py) 的 `main` 函数，指定：需要测试的模型的路径、测试数据、字典文件，预测标记文件的路径，默认参数如下：