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Span labeler model based on a BERT-style transformer-based encoder.
tfm.nlp.models.BertSpanLabeler( network, initializer='glorot_uniform', output='logits', **kwargs )
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 BertSpanLabeler allows a user to pass in a transformer encoder, and instantiates a span labeling network based on a single dense layer.
A transformer network. This network should output a sequence output
and a classification output. Furthermore, it should expose its embedding
table via a
||The initializer (if any) to use in the span labeling network. Defaults to a Glorot uniform initializer.|
The output style for this network. Can be either
call( inputs, training=None, mask=None )
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).
||Input tensor, or dict/list/tuple of input tensors.|
Boolean or boolean scalar tensor, indicating whether to run
||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.|