def predict_with_model(config_path: [Path, str]) -> List[Optional[List[str]]]:
    """Returns predictions of morphotagging model given in config :config_path:.

    Args:
        config_path: a path to config

    Returns:
        a list of morphological analyses for each sentence. Each analysis is either a list of tags
        or a list of full CONLL-U descriptions.

    """
    config = parse_config(config_path)

    reader_config = config['dataset_reader']
    reader = get_model(reader_config['class_name'])()
    data_path = expand_path(reader_config.get('data_path', ''))
    read_params = {k: v for k, v in reader_config.items() if k not in ['class_name', 'data_path']}
    data: Dict = reader.read(data_path, **read_params)

    iterator_config = config['dataset_iterator']
    iterator: MorphoTaggerDatasetIterator = from_params(iterator_config, data=data)

    model = build_model(config, load_trained=True)
    answers = [None] * len(iterator.test)
    batch_size = config['predict'].get("batch_size", -1)
    for indexes, (x, _) in iterator.gen_batches(
            batch_size=batch_size, data_type="test", shuffle=False, return_indexes=True):
        y = model(x)
        for i, elem in zip(indexes, y):
            answers[i] = elem
    outfile = config['predict'].get("outfile")
    if outfile is not None:
        outfile = Path(outfile)
        if not outfile.exists():
            outfile.parent.mkdir(parents=True, exist_ok=True)
        with open(outfile, "w", encoding="utf8") as fout:
            for elem in answers:
                fout.write(elem + "\n")
    return answers
def predict_with_model(config_path: [Path, str]) -> List[Optional[List[str]]]:
    """Returns predictions of morphotagging model given in config :config_path:.

    Args:
        config_path: a path to config

    Returns:
        a list of morphological analyses for each sentence. Each analysis is either a list of tags
        or a list of full CONLL-U descriptions.

    """
    config = parse_config(config_path)

    reader_config = config['dataset_reader']
    reader = get_model(reader_config['class_name'])()
    data_path = expand_path(reader_config.get('data_path', ''))
    read_params = {k: v for k, v in reader_config.items() if k not in ['class_name', 'data_path']}
    data: Dict = reader.read(data_path, **read_params)

    iterator_config = config['dataset_iterator']
    iterator: MorphoTaggerDatasetIterator = from_params(iterator_config, data=data)

    model = build_model(config, load_trained=True)
    answers = [None] * len(iterator.test)
    batch_size = config['predict'].get("batch_size", -1)
    for indexes, (x, _) in iterator.gen_batches(
            batch_size=batch_size, data_type="test", shuffle=False, return_indexes=True):
        y = model(x)
        for i, elem in zip(indexes, y):
            answers[i] = elem
    outfile = config['predict'].get("outfile")
    if outfile is not None:
        outfile = Path(outfile)
        if not outfile.exists():
            outfile.parent.mkdir(parents=True, exist_ok=True)
        with open(outfile, "w", encoding="utf8") as fout:
            for elem in answers:
                fout.write(elem + "\n")
    return answers
def predict_with_model ( config_path : [ Path , str ] ) -> List [ Optional [ List [ str ] ] ] : config = parse_config ( config_path ) reader_config = config [ 'dataset_reader' ] reader = get_model ( reader_config [ 'class_name' ] ) ( ) data_path = expand_path ( reader_config . get ( 'data_path' , '' ) ) read_params = { k : v for k , v in reader_config . items ( ) if k not in [ 'class_name' , 'data_path' ] } data : Dict = reader . read ( data_path , * * read_params ) iterator_config = config [ 'dataset_iterator' ] iterator : MorphoTaggerDatasetIterator = from_params ( iterator_config , data = data ) model = build_model ( config , load_trained = True ) answers = [ None ] * len ( iterator . test ) batch_size = config [ 'predict' ] . get ( "batch_size" , - 1 ) for indexes , ( x , _ ) in iterator . gen_batches ( batch_size = batch_size , data_type = "test" , shuffle = False , return_indexes = True ) : y = model ( x ) for i , elem in zip ( indexes , y ) : answers [ i ] = elem outfile = config [ 'predict' ] . get ( "outfile" ) if outfile is not None : outfile = Path ( outfile ) if not outfile . exists ( ) : outfile . parent . mkdir ( parents = True , exist_ok = True ) with open ( outfile , "w" , encoding = "utf8" ) as fout : for elem in answers : fout . write ( elem + "\n" ) return answers
Returns predictions of morphotagging model given in config :config_path:.

    Args:
        config_path: a path to config

    Returns:
        a list of morphological analyses for each sentence. Each analysis is either a list of tags
        or a list of full CONLL-U descriptions.
Returns predictions of morphotagging model given in config : config_path : .