Add.Training Chinese classification of BERT model

NLP通用框架BERT项目实战/第三章——基于BERT的中文情感分析实战.md

@@ -43,9 +43,47 @@ class MyDataProcessor(object):
 
 
 
-参照
+**读取数据的类get_train_examples**
 
 ~~~python
+class MyDataProcessor(DataProcessor):
+  """Base class for data converters for sequence classification data sets."""
+
+  def get_train_examples(self, data_dir):
+    """Gets a collection of `InputExample`s for the train set."""
+    file_path = os.path.join(data_dir, 'train_sentiment.txt')
+    f = open(file_path, 'r', encoding='utf-8')  # 读取数据，并指定中文常用的utf-8
+    train_data = []
+    index = 0  # ID值
+    for line in f.readline():  # 参考XnliProcessor
+        guid = "train-%d" % index
+        line = line.replace('\n', '').split('\t')  # 处理换行符，原数据是以tab分割
+        text_a = tokenization.convert_to_unicode(str(line[1]))  # 第0位置是索引，第1位置才是数据，可以查看train_sentiment.txt
+        label = str(line[2])  # 我们的label里没有什么东西，只有数值，所以转字符串即可
+        train_data.append(
+            InputExample(guid=guid, text_a=text_a, text_b=None, label=label))  # 这里我们没text_b，所以传入None
+        index += 1  # index每次不一样，所以加等于1
+    return train_data  # 这样数据就读取完成
+~~~
+
+> 参照XnliProcessor
+
+~~~python
+class XnliProcessor(DataProcessor):
+  """Processor for the XNLI data set."""
+
+  def __init__(self):
+    self.language = "zh"
+
+  def get_train_examples(self, data_dir):
+    """See base class."""
+    lines = self._read_tsv(
+        os.path.join(data_dir, "multinli",
+                     "multinli.train.%s.tsv" % self.language))
+    examples = []
+    for (i, line) in enumerate(lines):
+      if i == 0:
+        continue
       guid = "train-%d" % (i)  # 获取样本ID
       text_a = tokenization.convert_to_unicode(line[0])
       text_b = tokenization.convert_to_unicode(line[1])  # 获取text_a和b，我们只有a所以把b去掉
@@ -54,5 +92,63 @@ class MyDataProcessor(object):
         label = tokenization.convert_to_unicode("contradiction")
       examples.append(
           InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))  # 把读进来的东西传到InputExample，这个类可以点进去，里面什么都没做，只不过是模板，我们也照着做
+    return examples
+~~~
+
+
+
+**获取label**
+
+~~~
+# 也是参考XnliProcessor，把return改成0，1，2即可
+  def get_labels(self):
+    """Gets the list of labels for this data set."""
+    return ["0", "1", "2"]
+~~~
+
+
+
+**以下是完整的**
+
+~~~python
+
+~~~
+
+
+
+#### 训练BERT中文分类模型
+
+main函数增加运行内容
+
+~~~python
+def main(_):
+  tf.logging.set_verbosity(tf.logging.INFO)
+
+  processors = {
+      "cola": ColaProcessor,
+      "mnli": MnliProcessor,
+      "mrpc": MrpcProcessor,
+      "xnli": XnliProcessor,
+      'my':MyDataProcessor,  # 这是增加的部分，这样运行参数task_name才能对应上
+  }
+~~~
+
+
+
+参数
+
+~~~
+-task_name=my
+-do_train=true
+-do_eval=true
+-data_dir=data
+-vocab_file=../GLUE/BERT_BASE_DIR/chinese_L-12_H-768_A-12/vocab.txt
+-bert_config_file=../GLUE/BERT_BASE_DIR/chinese_L-12_H-768_A-12/bert_config.json
+-init_checkpoint=../GLUE/BERT_BASE_DIR/chinese_L-12_H-768_A-12/bert_model.ckpt
+-max_seq_length=70
+-train_batch_size=32
+-learning_rate=5e-5
+--num_train_epochs=3.0
+-output_dir=my_model
 ~~~