cerebras.modelzoo.data.nlp.bert.BertTokenClassifierDataProcessor.BertTokenClassifierDataProcessor#
- class cerebras.modelzoo.data.nlp.bert.BertTokenClassifierDataProcessor.BertTokenClassifierDataProcessor(*args, **kwargs)[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.
Methods
Classmethod to create the dataloader object.
Generator to read the data in chunks of size of data_buffer.