def build_model(self, args): from fairseq import models, quantization_utils model = models.build_model(args, self) model.register_classification_head( getattr(args, "classification_head_name", "sentence_classification_head"), num_classes=self.args.num_classes, ) model = quantization_utils.quantize_model_scalar(model, args) return model
def build_model(self, args): """ Build the :class:`~fairseq.models.BaseFairseqModel` instance for this task. Args: args (argparse.Namespace): parsed command-line arguments Returns: a :class:`~fairseq.models.BaseFairseqModel` instance """ from fairseq import models, quantization_utils model = models.build_model(args, self) return quantization_utils.quantize_model_scalar(model, args)
def build_model(self, cfg: FairseqDataclass): """ Build the :class:`~fairseq.models.BaseFairseqModel` instance for this task. Args: cfg (FairseqDataclass): configuration object Returns: a :class:`~fairseq.models.BaseFairseqModel` instance """ from fairseq import models, quantization_utils model = models.build_model(cfg, self) model = quantization_utils.quantize_model_scalar(model, cfg) return model
def build_model(self, args: Namespace): """ Build the :class:`~fairseq.models.BaseFairseqModel` instance for this task. Args: args (argparse.Namespace): parsed command-line arguments Returns: a :class:`~fairseq.models.BaseFairseqModel` instance """ from fairseq import models, quantization_utils model = models.build_model(args, self) if getattr(args, "tpu", False): model.prepare_for_tpu_() model = quantization_utils.quantize_model_scalar(model, args) return model
def build_model(self, cfg: DictConfig): """ Build the :class:`~fairseq.models.BaseFairseqModel` instance for this task. Args: cfg (omegaconf.DictConfig): configuration object Returns: a :class:`~fairseq.models.BaseFairseqModel` instance """ from fairseq import models, quantization_utils model = models.build_model(cfg, self) if getattr(cfg, "tpu", False): model.prepare_for_tpu_() model = quantization_utils.quantize_model_scalar(model, cfg) return model