class Config(Model.Config):
representation: Union[
BiLSTMSlotAttention.Config, BSeqCNNRepresentation.Config,
PassThroughRepresentation.Config, ] = BiLSTMSlotAttention.Config()
output_layer: Union[
WordTaggingOutputLayer.Config,
CRFOutputLayer.Config] = WordTaggingOutputLayer.Config()
decoder: MLPDecoder.Config = MLPDecoder.Config()
class Config(BaseModel.Config):
class WordTaggingInputConfig(ConfigBase):
tokens: RoBERTaTokenLevelTensorizer.Config = (
RoBERTaTokenLevelTensorizer.Config())
inputs: WordTaggingInputConfig = WordTaggingInputConfig()
encoder: RoBERTaEncoderBase.Config = RoBERTaEncoderJit.Config()
decoder: MLPDecoder.Config = MLPDecoder.Config()
output_layer: WordTaggingOutputLayer.Config = WordTaggingOutputLayer.Config(
)
def from_config(cls, config, tensorizers):
labels = tensorizers["labels"].vocab
embedding = cls.create_embedding(config, tensorizers)
representation = create_module(config.representation,
embed_dim=embedding.embedding_dim)
decoder = create_module(
config.decoder,
in_dim=representation.representation_dim,
out_dim=len(labels),
)
# TODO after migration: create_module(config.output_layer, tensorizers=tensorizers)
output_layer = WordTaggingOutputLayer(labels, CrossEntropyLoss(None))
return cls(embedding, representation, decoder, output_layer)
class Config(Model.Config):
class ModelInput(Model.Config.ModelInput):
tokens: TokenTensorizer.Config = TokenTensorizer.Config()
labels: SlotLabelTensorizer.Config = SlotLabelTensorizer.Config()
inputs: ModelInput = ModelInput()
embedding: WordEmbedding.Config = WordEmbedding.Config()
representation: Union[BiLSTMSlotAttention.
Config, # TODO: make default when sorting solved
BSeqCNNRepresentation.Config,
PassThroughRepresentation.
Config, ] = PassThroughRepresentation.Config()
output_layer: Union[
WordTaggingOutputLayer.Config,
CRFOutputLayer.Config] = WordTaggingOutputLayer.Config()
decoder: MLPDecoder.Config = MLPDecoder.Config()
