self.model = MT5ForConditionalGeneration.from_pretrained(model_dir)
results = conditional_generation_summarizer.batch_summarize(
            documents=temp,
            # max_length=MAX_LENGTH,
            # bad_words=bad_words,
            max_new_tokens=MAX_LENGTH,
            num_beams=6,
            # num_beam_groups=3,
            # temperature=4.0,
            # diversity_penalty=2.0,
            # max_new_tokens=30,
            no_repeat_ngram_size=2,
            do_sample=True,
            top_k=20,
            # top_k=1,
            top_p=0.9,
            repetition_penalty=4.0,
            length_penalty=20.0,
            early_stopping=True,
            # num_return_sequences=6
        )

result = self.model.generate(
            input_ids,
            # attention_mask=attention_mask,
            decoder_start_token_id=self.tokenizer.cls_token_id,
            eos_token_id=self.tokenizer.sep_token_id,
            # max_length=max_length,
            # early_stopping=True,
            # num_beams=num_beams,
            **kwargs
        )