def build_dataloader(self,
data,
transform: TransformList = None,
training=False,
device=None,
logger: logging.Logger = None,
gradient_accumulation=1,
**kwargs) -> DataLoader:
if isinstance(data, list):
data = BiaffineSemanticDependencyParser.build_samples(
self, data, self.config.use_pos)
dataset = BiaffineSemanticDependencyParser.build_dataset(
self, data, transform)
if isinstance(data, str):
dataset.purge_cache()
if self.vocabs.mutable:
BiaffineSemanticDependencyParser.build_vocabs(self,
dataset,
logger,
transformer=True)
if dataset.cache:
timer = CountdownTimer(len(dataset))
BiaffineSemanticDependencyParser.cache_dataset(
self, dataset, timer, training, logger)
return PadSequenceDataLoader(batch_sampler=self.sampler_builder.build(
self.compute_lens(data, dataset),
shuffle=training,
gradient_accumulation=gradient_accumulation),
device=device,
dataset=dataset,
pad=self.get_pad_dict())
def build_samples(self, inputs, cls_is_bos=False, sep_is_eos=False):
return BiaffineSemanticDependencyParser.build_samples(self, inputs, self.config.use_pos)