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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.
Attributes | |
---|---|
checkpoint_items
|
Methods
call
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).
Args | |
---|---|
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. |
Returns | |
---|---|
A tensor if there is a single output, or a list of tensors if there are more than one outputs. |