def METHOD_NAME(self):
    def cfg_modify_fn(cfg):
        neg_sample = 4
        cfg.task = 'text-ranking'
        cfg['preprocessor'] = {'type': 'text-ranking'}
        cfg.train.optimizer.lr = 2e-5
        cfg['dataset'] = {
            'train': {
                'type': 'bert',
                'query_sequence': 'query',
                'pos_sequence': 'positive_passages',
                'neg_sequence': 'negative_passages',
                'text_fileds': ['title', 'text'],
                'qid_field': 'query_id',
                'neg_sample': neg_sample
            },
            'val': {
                'type': 'bert',
                'query_sequence': 'query',
                'pos_sequence': 'positive_passages',
                'neg_sequence': 'negative_passages',
                'text_fileds': ['title', 'text'],
                'qid_field': 'query_id'
            },
        }
        cfg['evaluation']['dataloader']['batch_size_per_gpu'] = 30
        cfg.train.max_epochs = 1
        cfg.train.train_batch_size = 4
        cfg.train.lr_scheduler = {
            'type': 'LinearLR',
            'start_factor': 1.0,
            'end_factor': 0.0,
            'options': {
                'by_epoch': False
            }
        }
        cfg.model['neg_sample'] = 4
        cfg.train.hooks = [{
            'type': 'CheckpointHook',
            'interval': 1
        }, {
            'type': 'TextLoggerHook',
            'interval': 1
        }, {
            'type': 'IterTimerHook'
        }, {
            'type': 'EvaluationHook',
            'by_epoch': False,
            'interval': 15
        }]
        return cfg
    # load dataset
    ds = MsDataset.load('passage-ranking-demo', 'zyznull')
    train_ds = ds['train'].to_hf_dataset()
    dev_ds = ds['dev'].to_hf_dataset()
    model_id = 'damo/nlp_corom_passage-ranking_english-base'
    self.finetune(
        model_id=model_id,
        train_dataset=train_ds,
        eval_dataset=dev_ds,
        cfg_modify_fn=cfg_modify_fn)
    output_dir = os.path.join(self.tmp_dir, ModelFile.TRAIN_OUTPUT_DIR)
    self.pipeline_text_ranking(output_dir)