def bert_24_1024_16(dataset_name=None, vocab=None, pretrained=True, ctx=mx.cpu(),<EOL>use_pooler=True, use_decoder=True, use_classifier=True,<EOL>root=os.path.join(get_home_dir(), '<STR_LIT>'), **kwargs):
return get_bert_model(model_name='<STR_LIT>', vocab=vocab,<EOL>dataset_name=dataset_name, pretrained=pretrained,<EOL>ctx=ctx, use_pooler=use_pooler,<EOL>use_decoder=use_decoder, use_classifier=use_classifier,<EOL>root=root, **kwargs)<EOL>
Generic BERT LARGE model.

    The number of layers (L) is 24, number of units (H) is 1024, and the
    number of self-attention heads (A) is 16.

    Parameters
    ----------
    dataset_name : str or None, default None
        Options include 'book_corpus_wiki_en_uncased' and 'book_corpus_wiki_en_cased'.
    vocab : gluonnlp.vocab.BERTVocab or None, default None
        Vocabulary for the dataset. Must be provided if dataset is not specified.
    pretrained : bool, default True
        Whether to load the pretrained weights for model.
    ctx : Context, default CPU
        The context in which to load the pretrained weights.
    root : str, default '$MXNET_HOME/models'
        Location for keeping the model parameters.
        MXNET_HOME defaults to '~/.mxnet'.
    use_pooler : bool, default True
        Whether to include the pooler which converts the encoded sequence tensor of shape
        (batch_size, seq_length, units) to a tensor of shape (batch_size, units)
        for for segment level classification task.
    use_decoder : bool, default True
        Whether to include the decoder for masked language model prediction.
    use_classifier : bool, default True
        Whether to include the classifier for next sentence classification.

    Returns
    -------
    BERTModel, gluonnlp.vocab.BERTVocab