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