def load_default_model_sentencizer(lang):
loading_start = time.time()<EOL>lang_class = get_lang_class(lang)<EOL>nlp = lang_class()<EOL>nlp.add_pipe(nlp.create_pipe('<STR_LIT>'))<EOL>loading_end = time.time()<EOL>loading_time = loading_end - loading_start<EOL>return nlp, loading_time, lang + "<STR_LIT>" + '<STR_LIT>'<EOL>
Load a generic spaCy model and add the sentencizer for sentence tokenization