|View source on GitHub|
Classifier model based on a BERT-style transformer-based encoder.
tfm.nlp.models.BertClassifier( network, num_classes, initializer='glorot_uniform', dropout_rate=0.1, use_encoder_pooler=True, head_name='sentence_prediction', cls_head=None, **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 BertClassifier allows a user to pass in a transformer stack, and
instantiates a classification network based on the passed
num_classes is set to 1, a regression network is instantiated.
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.|