使用Bert模型的run_classifier进行Fine-Tuning

1. 废话不多说直接说怎么做

首先我们需要下载中文模型文件，直接给出链接下载即可https://pan.baidu.com/s/1-c068UOgfhrMyIIhR5fHXg，提取码是: 2z2r，解压完成后会出现五个文件，其中一个词汇表文件vocab.txt，还有三个Bert tensorflow的模型文件，这里就不一一列举了，还有一个参数设置文件bert_config.json。接下来再去github上down下来模型就可以开始搞了！

开搞！

首先在main()下面的processors里面创建一个自己的项目，例如我自己的创建成叫做my_bert，在后面接着定义一个类的名称。

def main(_):
  tf.logging.set_verbosity(tf.logging.INFO)  #设计日志级别
  '''在这里创建一个项目'''
  processors = {
      "cola": ColaProcessor,
      "mnli": MnliProcessor,
      "mrpc": MrpcProcessor,
      "xnli": XnliProcessor,
      "my_bert":my_bertProcessor
  } 

接下来我们可以按照processors里的其它项目进行改动，比如说MrpcProcessor()，我们可以把class MrpcProcessor(DataProcessor):整个都复制过来，然后在下面重新粘贴一下即可，然后我们把下面的代码段进行略微的改动即可进行分类，具体怎么改看下面介绍

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"]

  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])
      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

我们需要将这个类的名字改成你自己定义的那个，然后把def get_labels(self):里面的分类类别改动一下，然后在def _create_examples(self, lines, set_type):进行细微改动即可，具体改动我把我的改动给大家，看一下可以对比一下，就很明显知道为什么要这么改了

class my_bertProcessor(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 ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35']


  def _create_examples(self, lines, set_type):
    """Creates examples for the training and dev sets."""
    examples = []
  
    for (i, line) in enumerate(lines):
      guid = "%s-%s" % (set_type, i)
      text_a = tokenization.convert_to_unicode(line[1])  
      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=None, label=label))
    return examples

在这里解释一下为什么要这么改动，首先我们要做多分类，所以必须要将label改成我们所需要的，其次我们在进行文本处理的时候，和我们输入数据的格式有关系，这里的line的格式类似[label , context]，所以我们在提取的时候需要设置一下文本和对应提取的内容，再就是我们进行文本分类的时候，我们只有一句话，所以不需要text_b，我们在下面直接设置一下None就可以了

下面就介绍一下参数的给定，我没有按照github上面那样给定，而是直接在文件里面进行设置了一下，下面代码给出的是我的设置 

flags.DEFINE_string(
    "data_dir", "data", #这里面需要添加你自己的分类数据文件夹的名字
    "The input data dir. Should contain the .tsv files (or other data files) "
    "for the task.")

flags.DEFINE_string(
    "bert_config_file", "BERT/bert_config.json", #这里面加入参数文件 
    "The config json file corresponding to the pre-trained BERT model. "
    "This specifies the model architecture.")

flags.DEFINE_string("task_name", "my_bert", "The name of the task to train.") #这个task里添加的是你自己的项目名字

flags.DEFINE_string("vocab_file", "BERT/vocab.txt",  #解压完后的五个文件之一
                    "The vocabulary file that the BERT model was trained on.")

flags.DEFINE_string(
    "output_dir", "model/", #这个里将会存储你接下来训练的模型文件和验证的结果
    "The output directory where the model checkpoints will be written.")

## Other parameters

flags.DEFINE_string(
    "init_checkpoint", 'BERT/bert_model.ckpt', #这里面添加上你下载的bert训练好的模型文件
    "Initial checkpoint (usually from a pre-trained BERT model).")

flags.DEFINE_bool(
    "do_lower_case", True,
    "Whether to lower case the input text. Should be True for uncased "
    "models and False for cased models.")

flags.DEFINE_integer(
    "max_seq_length", 128, 
    "The maximum total input sequence length after WordPiece tokenization. "
    "Sequences longer than this will be truncated, and sequences shorter "
    "than this will be padded.")

flags.DEFINE_bool("do_train", True, "Whether to run training.") #训练的时候训练成True

flags.DEFINE_bool("do_eval", True, "Whether to run eval on the dev set.")

flags.DEFINE_bool(
    "do_predict", True, #想要验证就tr
    "Whether to run the model in inference mode on the test set.")

flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
#这个batchsize越大模型效果越好，当然这取决你的机器内存多大
flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")

flags.DEFINE_integer("predict_batch_size", 8, "Total batch size for predict.")

flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")

flags.DEFINE_float("num_train_epochs", 3.0,
                   "Total number of training epochs to perform.")

flags.DEFINE_float(
    "warmup_proportion", 0.1,
    "Proportion of training to perform linear learning rate warmup for. "
    "E.g., 0.1 = 10% of training.")

flags.DEFINE_integer("save_checkpoints_steps", 100,
                     "How often to save the model checkpoint.")

flags.DEFINE_integer("iterations_per_loop", 50,
                     "How many steps to make in each estimator call.")

flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")

tf.flags.DEFINE_string(
    "tpu_name", None,
    "The Cloud TPU to use for training. This should be either the name "
    "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
    "url.")

tf.flags.DEFINE_string(
    "tpu_zone", None,
    "[Optional] GCE zone where the Cloud TPU is located in. If not "
    "specified, we will attempt to automatically detect the GCE project from "
    "metadata.")

tf.flags.DEFINE_string(
    "gcp_project", None,
    "[Optional] Project name for the Cloud TPU-enabled project. If not "
    "specified, we will attempt to automatically detect the GCE project from "
    "metadata.")

tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")

flags.DEFINE_integer(
    "num_tpu_cores", 8,
    "Only used if `use_tpu` is True. Total number of TPU cores to use.")

接下来说一下输入数据的格式，bert模型默认读取.tsv文件，所以我们直接把txt文档的后缀改一下就可以，文件里的内容可以是这个样子，比如说：label + \t + sentence，然后运行就可以了，文件会自动打印，把你的训练数据文件改成如下图所示：

给出一部分我自己的训练的截图 

至此就这些，有需要补充的想起来再说