def train(self, train, dev, test, buckets=32, batch_size=5000, update_steps=1, punct=False, mbr=True, tree=False, proj=False, partial=False, verbose=True, **kwargs):
    '\n        Args:\n            train/dev/test (list[list] or str):\n                Filenames of the train/dev/test datasets.\n            buckets (int):\n                The number of buckets that sentences are assigned to. Default: 32.\n            batch_size (int):\n                The number of tokens in each batch. Default: 5000.\n            update_steps (int):\n                Gradient accumulation steps. Default: 1.\n            punct (bool):\n                If ``False``, ignores the punctuation during evaluation. Default: ``False``.\n            mbr (bool):\n                If ``True``, performs MBR decoding. Default: ``True``.\n            tree (bool):\n                If ``True``, ensures to output well-formed trees. Default: ``False``.\n            proj (bool):\n                If ``True``, ensures to output projective trees. Default: ``False``.\n            partial (bool):\n                ``True`` denotes the trees are partially annotated. Default: ``False``.\n            verbose (bool):\n                If ``True``, increases the output verbosity. Default: ``True``.\n            kwargs (dict):\n                A dict holding unconsumed arguments for updating training configs.\n        '
    return super().train(**Config().update(locals()))
Args:
    train/dev/test (list[list] or str):
        Filenames of the train/dev/test datasets.
    buckets (int):
        The number of buckets that sentences are assigned to. Default: 32.
    batch_size (int):
        The number of tokens in each batch. Default: 5000.
    update_steps (int):
        Gradient accumulation steps. Default: 1.
    punct (bool):
        If ``False``, ignores the punctuation during evaluation. Default: ``False``.
    mbr (bool):
        If ``True``, performs MBR decoding. Default: ``True``.
    tree (bool):
        If ``True``, ensures to output well-formed trees. Default: ``False``.
    proj (bool):
        If ``True``, ensures to output projective trees. Default: ``False``.
    partial (bool):
        ``True`` denotes the trees are partially annotated. Default: ``False``.
    verbose (bool):
        If ``True``, increases the output verbosity. Default: ``True``.
    kwargs (dict):
        A dict holding unconsumed arguments for updating training configs.
def train(self, train, dev, test, buckets=32, batch_size=5000, update_steps=1, punct=False, mbr=True, tree=False, proj=False, partial=False, verbose=True, **kwargs):
    '\n        Args:\n            train/dev/test (list[list] or str):\n                Filenames of the train/dev/test datasets.\n            buckets (int):\n                The number of buckets that sentences are assigned to. Default: 32.\n            batch_size (int):\n                The number of tokens in each batch. Default: 5000.\n            update_steps (int):\n                Gradient accumulation steps. Default: 1.\n            punct (bool):\n                If ``False``, ignores the punctuation during evaluation. Default: ``False``.\n            mbr (bool):\n                If ``True``, performs MBR decoding. Default: ``True``.\n            tree (bool):\n                If ``True``, ensures to output well-formed trees. Default: ``False``.\n            proj (bool):\n                If ``True``, ensures to output projective trees. Default: ``False``.\n            partial (bool):\n                ``True`` denotes the trees are partially annotated. Default: ``False``.\n            verbose (bool):\n                If ``True``, increases the output verbosity. Default: ``True``.\n            kwargs (dict):\n                A dict holding unconsumed arguments for updating training configs.\n        '
    return super().train(**Config().update(locals()))