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Token classifier model based on a BERT-style transformer-based encoder.

This is an implementation of the network structure surrounding a transformer encoder as described in "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" (https://arxiv.org/abs/1810.04805).

The BertTokenClassifier allows a user to pass in a transformer stack, and instantiates a token classification network based on the passed num_classes argument.

network A transformer network. This network should output a sequence output and a classification output. Furthermore, it should expose its embedding table via a "get_embedding_table" method.
num_classes Number of classes to predict from the classification network.
initializer The initializer (if any) to use in the classification networks. Defaults to a Glorot uniform initializer.
output The output style for this network. Can be either 'logits' or 'predictions'.




Calls the model on new inputs.

In this case call just reapplies all ops in the graph to the new inputs (e.g. build a new computational graph from the provided inputs).

inputs A tensor or list of tensors.
training Boolean or boolean scalar tensor, indicating whether to run the Network in training mode or inference mode.
mask A mask or list of masks. A mask can be either a tensor or None (no mask).

A tensor if there is a single output, or a list of tensors if there are more than one outputs.