def from_config(cls, config: Config, tensorizers: Dict[str, Tensorizer]): 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=1) output_layer = RegressionOutputLayer.from_config(config.output_layer) return cls(embedding, representation, decoder, output_layer)
def from_config(cls, config: Config, tensorizers: Dict[str, Tensorizer]): vocab = tensorizers["tokens"].vocab encoder = create_module( config.encoder, padding_idx=vocab.get_pad_index(), vocab_size=vocab.__len__(), ) decoder = create_module( config.decoder, in_dim=encoder.representation_dim, out_dim=1 ) output_layer = RegressionOutputLayer.from_config(config.output_layer) return cls(encoder, decoder, output_layer)