def create_model(cls, en_config, de_config):
from blocks.encoders import TransformerEncoder
encoder = nn.Embedding(en_config['vocab_size'], en_config['d_model'])
decoder = TransformerEncoder(de_config)
model = cls(encoder, decoder, de_config['vocab_size'])
model.encoder.config = en_config
return model
def create_model(cls, sp_config, en_config, vocab_size):
from blocks.sp_layers import SPLayer
from blocks.encoders import TransformerEncoder
splayer = SPLayer(sp_config)
encoder = TransformerEncoder(en_config)
model = cls(splayer, encoder, vocab_size)
return model
def create_model(cls, sp_config, en_config, as_cofig, vocab_size):
from blocks.sp_layers import SPLayer
from blocks.encoders import TransformerEncoder
from blocks.attention_assigner import Attention_Assigner
splayer = SPLayer(sp_config)
encoder = TransformerEncoder(en_config)
assigner = Attention_Assigner(as_cofig)
model = cls(splayer, encoder, assigner, vocab_size)
return model
def create_model(cls, sp_config, en_config, de_config):
from blocks.sp_layers import SPLayer
from blocks.encoders import TransformerEncoder
from blocks.decoders import TransformerDecoder
splayer = SPLayer(sp_config)
encoder = TransformerEncoder(en_config)
decoder = TransformerDecoder(de_config)
model = cls(splayer, encoder, decoder)
return model
def create_model(cls, sp_config, en_config, as_cofig, de_config):
from blocks.sp_layers import SPLayer
from blocks.encoders import TransformerEncoder
from blocks.attention_assigner import Attention_Assigner
from blocks.decoders import CIF_Decoder
splayer = SPLayer(sp_config)
encoder = TransformerEncoder(en_config)
assigner = Attention_Assigner(as_cofig)
decoder = CIF_Decoder(de_config)
model = cls(splayer, encoder, assigner, decoder)
return model