cerebras.modelzoo.data.nlp.bert.BertTokenClassifierDataProcessor#
Processor for PyTorch BERT fine tuning - Token classifier.
Functions
Creates the features dict for token classifier model. |
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Postprocessing of a row in the CSV file. |
Classes
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. |