def __init__(<EOL>self, vocab=True, make_doc=True, max_length=<NUM_LIT:10> ** <NUM_LIT:6>, meta={}, **kwargs<EOL>):
user_factories = util.get_entry_points("<STR_LIT>")<EOL>self.factories.update(user_factories)<EOL>self._meta = dict(meta)<EOL>self._path = None<EOL>if vocab is True:<EOL><INDENT>factory = self.Defaults.create_vocab<EOL>vocab = factory(self, **meta.get("<STR_LIT>", {}))<EOL>if vocab.vectors.name is None:<EOL><INDENT>vocab.vectors.name = meta.get("<STR_LIT>", {}).get("<STR_LIT:name>")<EOL><DEDENT><DEDENT>self.vocab = vocab<EOL>if make_doc is True:<EOL><INDENT>factory = self.Defaults.create_tokenizer<EOL>make_doc = factory(self, **meta.get("<STR_LIT>", {}))<EOL><DEDENT>self.tokenizer = make_doc<EOL>self.pipeline = []<EOL>self.max_length = max_length<EOL>self._optimizer = None<EOL>
Initialise a Language object.

        vocab (Vocab): A `Vocab` object. If `True`, a vocab is created via
            `Language.Defaults.create_vocab`.
        make_doc (callable): A function that takes text and returns a `Doc`
            object. Usually a `Tokenizer`.
        meta (dict): Custom meta data for the Language class. Is written to by
            models to add model meta data.
        max_length (int) :
            Maximum number of characters in a single text. The current v2 models
            may run out memory on extremely long texts, due to large internal
            allocations. You should segment these texts into meaningful units,
            e.g. paragraphs, subsections etc, before passing them to spaCy.
            Default maximum length is 1,000,000 characters (1mb). As a rule of
            thumb, if all pipeline components are enabled, spaCy's default
            models currently requires roughly 1GB of temporary memory per
            100,000 characters in one text.
        RETURNS (Language): The newly constructed object.