def load_model_from_path(model_path, meta=False, **overrides):
if not meta:<EOL><INDENT>meta = get_model_meta(model_path)<EOL><DEDENT>cls = get_lang_class(meta["<STR_LIT>"])<EOL>nlp = cls(meta=meta, **overrides)<EOL>pipeline = meta.get("<STR_LIT>", [])<EOL>disable = overrides.get("<STR_LIT>", [])<EOL>if pipeline is True:<EOL><INDENT>pipeline = nlp.Defaults.pipe_names<EOL><DEDENT>elif pipeline in (False, None):<EOL><INDENT>pipeline = []<EOL><DEDENT>for name in pipeline:<EOL><INDENT>if name not in disable:<EOL><INDENT>config = meta.get("<STR_LIT>", {}).get(name, {})<EOL>component = nlp.create_pipe(name, config=config)<EOL>nlp.add_pipe(component, name=name)<EOL><DEDENT><DEDENT>return nlp.from_disk(model_path)<EOL>
Load a model from a data directory path. Creates Language class with
    pipeline from meta.json and then calls from_disk() with path.