from mplm_sim import Loader
# loading existing results given model_name and corpus_name
loader = Loader.from_pretrained(model_name='cis-lmu/glot500-base', corpus_name='flores200')
# Or loading results given similarity file
# loader = Loader.from_tsv('your_similarity_file.tsv')
# Getting similarity given language pairs
# iso3_script
sim = loader.get_sim('eng_Latn', 'cmn_Hani')
# or language name
sim = loader.get_sim('English', 'Chinese')

from mplm_sim import Loader
# model_name: any text/speech language model support by Huggingface
# corpus_name: specific corpus name for saving
# corpus_path: path for multi-parallel corpora, see corpora_demo for file formatting
# corpus_type: text or speech
executor = Executor(model_name='cis-lmu/glot500-base', corpus_name='own',
                    corpus_path='corpora/own', corpus_type='text')
# Run
executor.run()