class Config(DocModel.Config):
class ByteModelInput(DocModel.Config.ModelInput):
token_bytes: ByteTokenTensorizer.Config = ByteTokenTensorizer.Config(
)
inputs: ByteModelInput = ByteModelInput()
byte_embedding: CharacterEmbedding.Config = CharacterEmbedding.Config()
class Config(WordTaggingModel.Config):
class ByteModelInput(WordTaggingModel.Config.ModelInput):
# We should support characters as well, but CharacterTokenTensorizer
# does not support adding characters to vocab yet.
tokens: ByteTokenTensorizer.Config = ByteTokenTensorizer.Config()
inputs: ByteModelInput = ByteModelInput()
embedding: CharacterEmbedding.Config = CharacterEmbedding.Config()
def create_embedding(cls, config, tensorizers):
return CharacterEmbedding(
tensorizers["token_bytes"].NUM_BYTES,
config.embedding.embed_dim,
config.embedding.cnn.kernel_num,
config.embedding.cnn.kernel_sizes,
config.embedding.highway_layers,
config.embedding.projection_dim,
)
def create_embedding(cls, config, tensorizers: Dict[str, Tensorizer]):
word_tensorizer = config.inputs.tokens
byte_tensorizer = config.inputs.token_bytes
assert word_tensorizer.column == byte_tensorizer.column
word_embedding = create_module(config.embedding,
tensorizer=tensorizers["tokens"])
byte_embedding = CharacterEmbedding(
ByteTokenTensorizer.NUM_BYTES,
config.byte_embedding.embed_dim,
config.byte_embedding.cnn.kernel_num,
config.byte_embedding.cnn.kernel_sizes,
config.byte_embedding.highway_layers,
config.byte_embedding.projection_dim,
)
return EmbeddingList([word_embedding, byte_embedding], concat=True)