Classifier model based on XLNet.

This is an implementation of the network structure surrounding a Transformer-XL encoder as described in "XLNet: Generalized Autoregressive Pretraining for Language Understanding" (

network An XLNet/Transformer-XL based network. This network should output a sequence output and list of state tensors.
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 RandomNormal initializer.
summary_type Method used to summarize a sequence into a compact vector.
dropout_rate The dropout probability of the cls head.
head_name Name of the classification head.




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Calls the model on new inputs and returns the outputs as tensors.

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 Input tensor, or dict/list/tuple of input 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 boolean tensor or None (no mask). For more details, check the guide here.

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