def __init__(self, **kwargs):
        """
        Args:
            annotators: set that can include pos, lemma, and ner.
            model: spaCy model to use (either path, or keyword like 'en').
        """        
        model = kwargs.get("model", "en_core_web_sm")
        self.annotators = copy.deepcopy(kwargs.get('annotators', set()))
        nlp_kwargs = {'parser': False}
        if not any([p in self.annotators for p in ['lemma', 'pos', 'ner']]):
            nlp_kwargs['tagger'] = False
        if 'ner' not in self.annotators:
            nlp_kwargs['entity'] = False
        self.nlp = spacy.load(model, **nlp_kwargs)

        Args:
            annotators: set that can include pos, lemma, and ner.
            model: spaCy model to use (either path, or keyword like 'en').
        
se2/DPR/dpr/utils/tokenizers.py