cerebras.modelzoo.data.nlp.t5.T5HDF5DataProcessor.T5HDF5DataProcessorConfig#

class cerebras.modelzoo.data.nlp.t5.T5HDF5DataProcessor.T5HDF5DataProcessorConfig(*args, **kwargs)[source]#

Bases: cerebras.modelzoo.data.common.HDF5IterableDataProcessor.HDF5IterableDataProcessorConfig

Methods

`check_for_deprecated_fields`
`check_literal_discriminator_field`
`copy`
`get_orig_class`
`get_orig_class_args`
`model_copy`
`model_post_init`
`post_init`

Attributes

`batch_size`	Batch size.
`data_dir`	Path to dataset HDF5 files
`discriminator`
`discriminator_value`
`drop_last`	similar to the PyTorch drop_last setting except that samples that when set to True, samples that would have been dropped at the end of one epoch are yielded at the start of the next epoch so that there is no data loss.
`features_list`	List of features to include in the batch
`model_config`
`num_workers`	How many subprocesses to use for data loading.
`persistent_workers`	If True, the data loader will not shutdown the worker processes after a dataset has been consumed once.
`prefetch_factor`	Number of batches loaded in advance by each worker.
`shuffle`	Flag to enable data shuffling.
`shuffle_buffer`	Size of shuffle buffer in samples.
`shuffle_seed`	Shuffle seed.
`use_vsl`	Flag to enable variable sequence length training.
`vocab_size`
`data_processor`

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