cerebras.modelzoo.data.nlp.bert.BertTokenClassifierDataProcessor.BertTokenClassifierDataProcessor#

class cerebras.modelzoo.data.nlp.bert.BertTokenClassifierDataProcessor.BertTokenClassifierDataProcessor[source]#

Bases: torch.utils.data.IterableDataset

Reads csv file containing the input token ids, and label_ids. Creates attention_masks and sedment_ids on the fly :param <dict> params: dict containing input parameters for creating dataset.

Expects the following fields:

  • “vocab_file” (str): Path to the vocab file.

  • “label_vocab_file” (str): Path to json file with class name to class index.

  • “data_dir” (str): Path to directory containing the CSV files.

  • “batch_size” (int): Batch size.

  • “max_sequence_length” (int): Maximum length of the sequence.

  • “do_lower” (bool): Flag to lower case the texts.

  • “shuffle” (bool): Flag to enable data shuffling.

  • “shuffle_seed” (int): Shuffle seed.

  • “shuffle_buffer” (int): Shuffle buffer size.

  • “num_workers” (int): How many subprocesses to use for data loading.

  • “drop_last” (bool): If True and the dataset size is not divisible

    by the batch size, the last incomplete batch will be dropped.

  • “prefetch_factor” (int): Number of samples loaded in advance by each worker.

  • “persistent_workers” (bool): If True, the data loader will not shutdown

    the worker processes after a dataset has been consumed once.

Pram model_params (dict)

Model parameters for creating the dataset. Expects the following to be defined: - “include_padding_in_loss” (bool): If set to true then a loss mask will be

generated such that padding tokens will be included in the loss calculation.

Methods

create_dataloader

Classmethod to create the dataloader object.

load_buffer

Generator to read the data in chunks of size of data_buffer.
__init__(params, model_params)[source]#
create_dataloader()[source]#

Classmethod to create the dataloader object.

load_buffer()[source]#

Generator to read the data in chunks of size of data_buffer.

Returns

Yields the data stored in the data_buffer.

__call__(*args: Any, **kwargs: Any) → Any#

Call self as a function.

static __new__(cls, *args: Any, **kwargs: Any) → Any#

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