Huggingface的Transformer库经验总结

本文主要是介绍Huggingface的Transformer库经验总结，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

transformers

transformers.Trainer

class Trainer:#这段代码根据训练数据集的类型和硬件环境，选择适当的采样器来处理数据集。def _get_train_sampler(self) -> Optional[torch.utils.data.sampler.Sampler]:if isinstance(self.train_dataset, torch.utils.data.IterableDataset):return Noneelif is_torch_tpu_available():return get_tpu_sampler(self.train_dataset)else:return (RandomSampler(self.train_dataset)if self.args.local_rank == -1else DistributedSampler(self.train_dataset))

class Trainer:#这段代码通过检查和设置采样器来创建一个适用于训练数据集的 DataLoader 对象，并返回它，以便在训练过程中使用。def get_train_dataloader(self) -> DataLoader:"""Returns the training :class:`~torch.utils.data.DataLoader`.Will use no sampler if :obj:`self.train_dataset` is a :obj:`torch.utils.data.IterableDataset`, a random sampler(adapted to distributed training if necessary) otherwise.Subclass and override this method if you want to inject some custom behavior."""if self.train_dataset is None:raise ValueError("Trainer: training requires a train_dataset.")train_sampler = self._get_train_sampler()return DataLoader(self.train_dataset,batch_size=self.args.train_batch_size,sampler=train_sampler,collate_fn=self.data_collator,drop_last=self.args.dataloader_drop_last,)

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