def begin_training(self, _=tuple(), pipeline=None, sgd=None, **kwargs):
        """Allocate model, using width from tensorizer in pipeline.

        gold_tuples (iterable): Gold-standard training data.
        pipeline (list): The pipeline the model is part of.
        """
        if self.model is True:
            self.model = self.Model(pipeline[0].model.nO)
            link_vectors_to_models(self.vocab)
        if sgd is None:
            sgd = self.create_optimizer()
        return sgd