import trankit

model_path = './nlpre_trankit_ud_xlm-roberta-base_nkjp-by-type'

trankit.verify_customized_pipeline(
    category='customized-mwt', # pipeline category
    save_dir=model_path, # directory used for saving models in previous steps
    embedding_name='xlm-roberta-base' # embedding version that we use for training our customized pipeline, by default, it is `xlm-roberta-base`
)

model = trankit.Pipeline(lang='customized-mwt', cache_dir=model_path, embedding='xlm-roberta-base')