bert text classification-single label and multi-label comparison

Single label:

def create_model_original(bert_config, is_training, input_ids, input_mask, segment_ids, labels, num_labels, use_one_hot_embeddings):
  """Creates a classification model."""
  model = modeling.BertModel(
      config=bert_config,
      is_training=is_training,
      input_ids=input_ids,
      input_mask=input_mask,
      token_type_ids=segment_ids,
      use_one_hot_embeddings=use_one_hot_embeddings)

  output_layer = model.get_pooled_output() # 从主干模型获得模型的输出

  hidden_size = output_layer.shape[-1].value

  output_weights = tf.get_variable( # 分类模型特有的分类层的参数
      "output_weights", [num_labels, hidden_size],
      initializer=tf.truncated_normal_initializer(stddev=0.02))

  output_bias = tf.get_variable( # 分类模型特有的bias
      "output_bias", [num_labels], initializer=tf.zeros_initializer())

  with tf.variable_scope("loss"):
    if is_training:
      # I.e., 0.1 dropout
      output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)

    logits = tf.matmul(output_layer, output_weights, transpose_b=True) # 分类模型特有的分类层
    logits = tf.nn.bias_add(logits, output_bias)
	
    probabilities = tf.nn.softmax(logits, axis=-1)
	
    log_probs = tf.nn.log_softmax(logits, axis=-1)
    one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
    per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1) # 利用交叉熵就和
    loss = tf.reduce_mean(per_example_loss)

    return (loss, per_example_loss, logits, probabilities)

Multiple tags:

def create_model(bert_config, is_training, input_ids, input_mask, segment_ids, labels, num_labels, use_one_hot_embeddings):
  """Creates a classification model."""
  model = modeling.BertModel(
      config=bert_config,
      is_training=is_training,
      input_ids=input_ids,
      input_mask=input_mask,
      token_type_ids=segment_ids,
      use_one_hot_embeddings=use_one_hot_embeddings)

  output_layer = model.get_pooled_output() # 从主干模型获得模型的输出

  hidden_size = output_layer.shape[-1].value

  output_weights = tf.get_variable( # 分类模型特有的分类层的参数
      "output_weights", [num_labels, hidden_size],
      initializer=tf.truncated_normal_initializer(stddev=0.02))

  output_bias = tf.get_variable( # 分类模型特有的bias
      "output_bias", [num_labels], initializer=tf.zeros_initializer())

  with tf.variable_scope("loss"):
    if is_training:
      # I.e., 0.1 dropout
      output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)

    logits = tf.matmul(output_layer, output_weights, transpose_b=True) 
	# 分类模型特有的分类层
    logits = tf.nn.bias_add(logits, output_bias)
    
    probabilities=tf.nn.sigmoid(logits)
	
    labels=tf.cast(labels,tf.float32)
    
    per_example_loss=tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits)
    loss = tf.reduce_mean(per_example_loss)

    return (loss, per_example_loss, logits, probabilities)