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Span labeler model based on XLNet.
tfm.nlp.models.XLNetSpanLabeler(
network: Union[tf.keras.layers.Layer, tf.keras.Model],
start_n_top: int = 5,
end_n_top: int = 5,
dropout_rate: float = 0.1,
span_labeling_activation: tf.keras.initializers.Initializer = 'tanh',
initializer: tf.keras.initializers.Initializer = 'glorot_uniform',
**kwargs
)
This is an implementation of the network structure surrounding a Transformer-XL encoder as described in "XLNet: Generalized Autoregressive Pretraining for Language Understanding" (https://arxiv.org/abs/1906.08237).
Attributes | |
---|---|
checkpoint_items
|
Methods
call
call(
inputs: Mapping[str, Any]
)
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. |