diff --git a/README.md b/README.md
index 0d2b70c2d0b1118dbb0ee44482ae58ba3ce15b54..6c89ecde582430d89626f8b5ea7d6a7e2ec9b739 100644
--- a/README.md
+++ b/README.md
@@ -18,7 +18,7 @@
 
 # 简介
 
-
+使用PyTorch实现中文NER模型，拟提供BERT_BiLSTM_CRF、BiLSTM_CRF、CRF和HMM四种模型，目前实现BERT_BiLSTM_CRF。
 
 
 
@@ -36,14 +36,23 @@ torchcrf==1.1.0
 # 目录结构
 
 ```python
-Word2Vec
-├── Data  # 数据集
-│    ├── en.txt
-│    ├── zh.txt
-├── log  # 训练日志
-├── model  # 保存模型
+NER_ZH
+├── ckpts
+│    ├── bert-base-chinese  # 预训练模型BERT
+│    ├── bert_bilstm_crf    # 自己训练的bert_bilstm_crf模型
+│    ├── ...
+├── data  # 数据集
+│    ├── test.txt
+│    ├── train.txt
+│    ├── ...
+├── logs  # 训练日志
+│    ├── bert_bilstm_crf.log  # bert_bilstm_crf训练测试日志
+├── output  # 预测结果、可视乎结果等
+├── config.py
 ├── dataloader.py
-├── model.py
+├── evaluator.py
+├── main.py
+├── models.py
 ├── trainer.py
 ├── utils.py
 ```
@@ -54,8 +63,6 @@ Word2Vec
 
 ## BERT_BiLSTM_CRF
 
-
-
 ```python
 """
 1. BERT:
@@ -121,27 +128,59 @@ Word2Vec
 """
 ```
 
-
+## 模型评估
+
+- **精确率P、召回率R以及F1值**
+
+  $P=\frac{TP}{TP+FP}$
+
+  $R=\frac{TP}{TP+FN}$
+
+  $F1=\frac{2PR}{P+R}$
+
+  - ```python
+    '''
+    通过'evaluator.py'计算每个标签的精确率、召回率和F1分数，输出如下格式：
+               precision    recall  f1-score   support
+            O     0.9999    0.9999    0.9999    150935
+        I-ORG     0.9984    0.9991    0.9988      5640
+        I-LOC     0.9968    0.9970    0.9969      4370
+        B-ORG     0.9955    0.9985    0.9970      1327
+        I-PER     1.0000    0.9995    0.9997      3845
+        B-LOC     0.9990    0.9962    0.9976      2871
+        B-PER     0.9995    1.0000    0.9997      1972
+    avg/total     0.9997    0.9997    0.9997    170960
+    '''
+    ```
+    
+  - 由于标签中 “O”占非常大的比例，因此在计算指标时，采用两种方式：一是直接计算所有的指标，二是去掉“O”这个类别后再计算所有指标。修改`config.py`中的`self.remove_O = False/True`实现不同计算方式。
+  
+- **混淆矩阵**
+
+  - ```python
+    '''
+    Confusion Matrix:
+                O   I-ORG   I-LOC   B-ORG   I-PER   B-LOC   B-PER 
+        O  150921       6       7       1       0       0       0 
+    I-ORG       1    5635       0       4       0       0       0 
+    I-LOC       8       2    4357       0       0       3       0 
+    B-ORG       1       1       0    1325       0       0       0 
+    I-PER       1       0       0       0    3843       0       1 
+    B-LOC       3       0       7       1       0    2860       0 
+    B-PER       0       0       0       0       0       0    1972 
+    '''
+    ```
+
+  
 
 # 运行方式
 
-`run.py`文件内设定以下参数后，运行该py文件即可。
-
-```python
-language = 'zh'
-neg_sample = True  # 是否负采样
-embed_dim = 300
-C = 3  # 窗口大小
-K = 15  # 负采样大小
-num_epochs = 100
-batch_size = 32
-learning_rate = 0.025
-```
+根据需要自行设置`config.py`文件参数，然后运行`main.py`文件即可。
 
 
 
 # 参考
 
 1. [bert_bilstm_crf_ner_pytorch](https://gitee.com/chenzhouwy/bert_bilstm_crf_ner_pytorch/tree/master)
-2. 
+2. [named_entity_recognition](https://github.com/luopeixiang/named_entity_recognition)
 
diff --git a/config.py b/config.py
index abf216f9dfa0db2d8995cb796898c4e5b4c50e65..dc78272606bc3b2416f35586644d0eb7dbdfab4c 100644
--- a/config.py
+++ b/config.py
@@ -18,8 +18,8 @@ class Config(object):
         self.label_list = []
         self.use_gpu = True
         self.device = "cuda"
-        self.checkpoints = True
-        self.model = 'bert_bilstm_crf'  # 可选['bert_bilstm_crf','hmm','bilstm_crf]
+        self.checkpoints = True  # 使用预训练模型时设置为False
+        self.model = 'bert_bilstm_crf'  # 可选['bert_bilstm_crf','bilstm_crf','bilstm','crf','hmm']
 
         # 输入数据集、日志、输出目录
         self.train_file = os.path.join(self.base_path, 'data/train.txt')
@@ -46,3 +46,4 @@ class Config(object):
         self.adam_epsilon = 1e-8
         self.warmup_steps = 0
         self.logging_steps = 50
+        self.remove_O = False
diff --git a/trainer.py b/trainer.py
index 63a97bb7b50dbc7686e6723e875935f7472d2a5f..2cae99a46f00c85ca7116d39a8b260ce3dfc1e89 100644
--- a/trainer.py
+++ b/trainer.py
@@ -1,4 +1,6 @@
+import os
 import torch
+import logging
 
 from tqdm import tqdm, trange
 from torch.utils.data import DataLoader, SequentialSampler
@@ -173,7 +175,7 @@ class Bert_Bilstm_Crf():
 
         golden_tags = [[ttl[1] for ttl in sen] for sen in eval_sens]
         predict_tags = [[ttl[2] for ttl in sen] for sen in eval_sens]
-        cal_indicators = Metrics(golden_tags, predict_tags)
+        cal_indicators = Metrics(golden_tags, predict_tags, remove_O=config.remove_O)
         avg_metrics = cal_indicators.cal_avg_metrics()  # avg_metrics['precision'], avg_metrics['recall'], avg_metrics['f1_score']
 
         return avg_metrics, cal_indicators, eval_sens
@@ -229,7 +231,7 @@ class Bert_Bilstm_Crf():
         #
         # golden_tags = [[ttl[1] for ttl in sen] for sen in eval_sens]
         # predict_tags = [[ttl[2] for ttl in sen] for sen in eval_sens]
-        # cal_indicators = Metrics(golden_tags, predict_tags)
+        # cal_indicators = Metrics(golden_tags, predict_tags, remove_O=self.config.remove_O)
         # avg_metrics = cal_indicators.cal_avg_metrics()