Create 源码解读.md

NLP通用框架BERT项目实战/第二章——BERT源码解读与应用实例/源码解读.md

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+### 源码解读
+
+#### 数据读取模块
+
+处理MRPC数据的类
+
+~~~python
+class MrpcProcessor(DataProcessor):
+  """Processor for the MRPC data set (GLUE version)."""
+
+  def get_train_examples(self, data_dir):
+    """See base class."""
+    return self._create_examples(
+        self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+  def get_dev_examples(self, data_dir):
+    """See base class."""
+    return self._create_examples(
+        self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
+
+  def get_test_examples(self, data_dir):
+    """See base class."""
+    return self._create_examples(
+        self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
+
+  def get_labels(self):
+    """See base class."""
+    return ["0", "1"]  # 是否是二分类
+
+  def _create_examples(self, lines, set_type):
+    """Creates examples for the training and dev sets."""
+    examples = []
+    for (i, line) in enumerate(lines):
+      if i == 0:
+        continue
+      guid = "%s-%s" % (set_type, i)
+      text_a = tokenization.convert_to_unicode(line[3])  # 相关的test_a和b怎么切分
+      text_b = tokenization.convert_to_unicode(line[4])
+      if set_type == "test":
+        label = "0"
+      else:
+        label = tokenization.convert_to_unicode(line[0])
+      examples.append(
+          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
+    return examples
+~~~
+
+
+
+读取训练数据代码：
+
+~~~python
+  if FLAGS.do_train:
+    train_examples = processor.get_train_examples(FLAGS.data_dir)
+    num_train_steps = int(
+        len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)  # 得到需要迭代的次数，len(train_examples)计算出多少数据量 除以 我们设置的train_batch_size，再乘上epochs次数。
+    num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)  # 在刚开始时，让学习率偏小，经过warmup的百分比后，再还原回原始的学习率
+~~~
+