How to use the ML models

[Sreevatsava1]

I can see that I can use the ML classifier to identify the definitions from the code.
File - lexnlp.extract.en.definitions

def get_definitions(text: str,
                    return_sources=False,
                    decode_unicode=True,
                    return_coords=False,
                    locator_type: AnnotationLocatorType = AnnotationLocatorType.RegexpBased) -> Generator:
    """
    Find possible definitions in natural language in text.
    The text will be split to sentences first.
    :param return_coords: returns a (x, y) tuple in each record. x - definition's text start, y - definition's text end
    :param decode_unicode:
    :param return_sources: returns a tuple with the extracted term and the source sentence
    :param text: the input text
    :param locator_type: use default (Regexp-based) or ML-based locator
    :return: Generator[name] or Generator[name, text] or Generator[name, text, coords]
    """

So I've tried giving the 'locator_type' as AnnotationLocatorType.MlWordVectorBased for the get_definitions() function, then I'm getting this error.

"parser_ml_classifier" object should be initialized (call load_compressed method)

I've gone through the definitions file and I can see this in line 43
parser_ml_classifier = LayeredDefinitionDetector()

I tried to run the load_compressed method inside the LayeredDefinitionDetector() but it is asking for a file_path and I don't understand which file path should be given. Am I missing something, could anyone guide me on how to use the ML models for definitions? Thanks!!