def METHOD_NAME(self, model, sentence_list):
    results = {}
    types = {}
    logits = {}
    for id, sentence in enumerate(sentence_list):
        current_id_predictions = []
        tokenized_sub_sentence = self.config['tokenizer'].tokenize(sentence)
        if len(tokenized_sub_sentence) == 0:
            # If the tokenized sentence are empty
            sub_sentence_prediction_kword_list, sub_type_list = [], []
        elif len(tokenized_sub_sentence) <= 512:  # TODO:ignore sentences that are too long for now
            # If the tokenized sentence are less than 512
            sub_sentence_prediction_kword_list, sub_type_list = self.prediction_fn(model, tokenized_sub_sentence)
        if len(sub_sentence_prediction_kword_list) != 0:  # found some keywords
            current_id_predictions = current_id_predictions + sub_sentence_prediction_kword_list
        results[id] = [self.remove_stwords(x) for x in
                       current_id_predictions]  # [x.upper() for x in list(set(current_id_predictions))]
        types[id] = sub_type_list
    return results, types