Span labeler 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 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.
start_n_top Beam size for span start.
end_n_top Beam size for span end.
dropout_rate The dropout rate for the span labeling layer.
span_labeling_activation The activation for the span labeling head.
initializer The initializer (if any) to use in the span labeling network. Defaults to a Glorot uniform initializer.




View source

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.