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@ -257,10 +257,7 @@ class Trainer:
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logger.info(
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f"The following columns {dset_description}don't have a corresponding argument in `{self.model.__class__.__name__}.forward` and have been ignored: {', '.join(ignored_columns)}."
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)
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ds_type = dataset.format["type"]
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if ds_type == "python":
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ds_type = None
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dataset.set_format(type=ds_type, columns=columns)
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dataset.set_format(type=dataset.format["type"], columns=columns)
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def _get_train_sampler(self) -> Optional[torch.utils.data.sampler.Sampler]:
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if isinstance(self.train_dataset, torch.utils.data.IterableDataset):
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