添加readme

README.md

@@ -1,16 +1,53 @@
-# bert_feature
+# bert-utils
 
-how to use Bert generate the sentence vector
+本文对BERT进行了进一步的封装，方便生成句向量与做文本分类
 
-1、download the model 
+1、下载BERT中文模型 
 
-model path: https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip
+下载地址: https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip
 
-2、Move the model in the same directory
+2、把下载好的模型添加到当前目录下
 
-3、init BertVector object and invokes the encode method, the param must be list
+3、句向量生成
+
+生成句向量不需要做fine tune，使用预先训练好的模型即可，可参考`extract_feature.py`的`main`方法，注意参数必须是一个list
 ```
 from bert.extrac_feature import BertVector
 bv = BertVector()
 bv.encode(['你好'])
 ```
+
+4、文本分类
+
+文本分类需要做fine tune，首先把数据准备好存放在`data`目录下，训练集的名字必须为`train.csv`，验证集的名字必须为`dev.csv`，测试集的名字必须为`test.csv`，
+必须先调用`set_mode`方法，可参考`similarity.py`的`main`方法，
+
+训练：
+```
+from similarity import BertSim
+import tensorflow as tf
+
+bs = BertSim()
+bs.set_mode(tf.estimator.ModeKeys.TRAIN)
+bs.train()
+```
+
+验证：
+```
+from similarity import BertSim
+import tensorflow as tf
+
+bs = BertSim()
+bs.set_mode(tf.estimator.ModeKeys.EVAL)
+bs.eval()
+```
+
+测试：
+```
+from similarity import BertSim
+import tensorflow as tf
+
+bs = BertSim()
+bs.set_mode(tf.estimator.ModeKeys.PREDICT)
+bs.test
+```

similarity.py

@@ -79,7 +79,7 @@ class SimProcessor(DataProcessor):
         return train_data
 
     def get_dev_examples(self, data_dir):
-        file_path = os.path.join(data_dir, 'test.csv')
+        file_path = os.path.join(data_dir, 'dev.csv')
         dev_df = pd.read_csv(file_path, encoding='utf-8')
         dev_data = []
         for index, dev in enumerate(dev_df.values):