def __init__(
        self,
        model_name="distilbert-base-uncased",
        to_lower=False,
        cache_dir=".",
        max_nsents=200,
        max_src_ntokens=2000,
        min_nsents=3,
        min_src_ntokens=5,
    ):
        """ Initialize the preprocessor.

        Args:
            model_name (str, optional): Transformer model name used in preprocessing.
                check MODEL_CLASS for supported models. Defaults to "bert-base-cased".
            to_lower (bool, optional): Whether to convert all letters to lower case
                during tokenization. This is determined by if a cased model is used.
                Defaults to False, which corresponds to a cased model.
            cache_dir (str, optional): Directory to cache the tokenizer.
                Defaults to ".".
            max_nsents (int, optional): Max number of sentences that can be used
                as input. Defaults to 200.
            max_src_ntokens (int, optional): Max number of tokens that be used
                as input. Defaults to 2000.
            min_nsents (int, optional): Minimum number of sentences that are required
                as input. If the input has less number of sentences than this value,
                it's skipped and cannot be used as a valid input. Defaults to 3.
            min_src_ntokens (int, optional): Minimum number of tokens that are required
                as an input sentence.If the input sentence has less number of tokens
                than this value, it's skipped and cannot be used as a valid sentence.
                Defaults to 5.

        """
        self.model_name = model_name
        self.tokenizer = AutoTokenizer.from_pretrained(
            model_name,
            do_lower_case=to_lower,
            cache_dir=cache_dir,
            output_loading_info=False,
        )
        self.sep_vid = self.tokenizer.vocab["[SEP]"]
        self.cls_vid = self.tokenizer.vocab["[CLS]"]
        self.pad_vid = self.tokenizer.vocab["[PAD]"]

        self.max_nsents = max_nsents
        self.max_src_ntokens = max_src_ntokens
        self.min_nsents = min_nsents
        self.min_src_ntokens = min_src_ntokens
 Initialize the preprocessor.

        Args:
            model_name (str, optional): Transformer model name used in preprocessing.
                check MODEL_CLASS for supported models. Defaults to "bert-base-cased".
            to_lower (bool, optional): Whether to convert all letters to lower case
                during tokenization. This is determined by if a cased model is used.
                Defaults to False, which corresponds to a cased model.
            cache_dir (str, optional): Directory to cache the tokenizer.
                Defaults to ".".
            max_nsents (int, optional): Max number of sentences that can be used
                as input. Defaults to 200.
            max_src_ntokens (int, optional): Max number of tokens that be used
                as input. Defaults to 2000.
            min_nsents (int, optional): Minimum number of sentences that are required
                as input. If the input has less number of sentences than this value,
                it's skipped and cannot be used as a valid input. Defaults to 3.
            min_src_ntokens (int, optional): Minimum number of tokens that are required
                as an input sentence.If the input sentence has less number of tokens
                than this value, it's skipped and cannot be used as a valid sentence.
                Defaults to 5.

        
utils_nlp/models/transformers/extractive_summarization.py