Doing Multi-Label classification with BERT

Question

I want to use BERT model to do multi-label classification with Tensorflow.

To do so, I want to adapt the example run_classifier.py from BERT github repository , which is an example on how to use BERT to do simple classification, using the pre-trained weights given by Google Research . (For example with BERT-Base, Cased )

I have X different labels which have value of either 0 or 1, so I want to add to the original BERT model a new Dense layer of size X and using the sigmoid_cross_entropy_with_logits activation function.

So, for the theorical part I think I am OK.

The problem is that I don't know how I can append a new output layer and retrain only this new layer with my dataset, using the existing BertModel class.

Here is the original create_model() function from run_classifier.py where I guess I have to do my modifications. But I am a bit lost on what to do.

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()

  hidden_size = output_layer.shape[-1].value

  output_weights = tf.get_variable(
      "output_weights", [num_labels, hidden_size],
      initializer=tf.truncated_normal_initializer(stddev=0.02))

  output_bias = tf.get_variable(
      "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)
    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)
    loss = tf.reduce_mean(per_example_loss)

    return (loss, per_example_loss, logits, probabilities)

And here is the same function, with some of my modifications, but where there is things missing (and wrong things too? )

def create_model(bert_config, is_training, input_ids, input_mask, segment_ids, labels, num_labels):
  """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)

  output_layer = model.get_pooled_output()

  hidden_size = output_layer.shape[-1].value

  output_weights = tf.get_variable("output_weights", [num_labels, hidden_size],initializer=tf.truncated_normal_initializer(stddev=0.02))

  output_bias = tf.get_variable("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)
    logits = tf.nn.bias_add(logits, output_bias)
    probabilities = tf.nn.softmax(logits, axis=-1)
    log_probs = tf.nn.log_softmax(logits, axis=-1)

    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)

The other things I have adapted in the code and for which I had no problem :

• DataProcessor to load and parse my custom dataset
• Changing the type of labels variable from numerical values to arrays everywhere it is used

So, if anyone knows what I should do to resolve my problem, or even point out some obvious mistake I may have done, I would be glad to hear it.

Notes :

• I found this article that correspond pretty well to what I am trying to do, but it use PyTorch, and I can not translate it into Tensorflow.

Answer 1

You want to replace the softmax that models a single distribution over possible outputs (all scores sum up to one) with sigmoid which models an independent distribution for each class (there is yes/no distribution for each output).

So, you correctly change the loss function, but you also need to change how you compute the probabilities. It should be:

probabilities = tf.sigmoid(logits)

In this case, you don't need the log_probs .