quick_start.py

from config import Config
from data import get_dataset, get_dataloader
from system import get_system
from pretrained_model import PretrainedModelForKBRDQwen
import sys

DEBUG = True


def run_crslab(config, save_data=False, restore_data=False, save_system=False, restore_system=False,
               interact=False, debug=False, tensorboard=False):
    """A fast running api, which includes the complete process of training and testing models on specified datasets.

    Args:
        config (Config or str): an instance of ``Config`` or path to the config file,
            which should be in ``yaml`` format. You can use default config provided in the `Github repo`_,
            or write it by yourself.
        save_data (bool): whether to save data. Defaults to False.
        restore_data (bool): whether to restore data. Defaults to False.
        save_system (bool): whether to save system. Defaults to False.
        restore_system (bool): whether to restore system. Defaults to False.
        interact (bool): whether to interact with the system. Defaults to False.
        debug (bool): whether to debug the system. Defaults to False.

    
    """
    # dataset & dataloader
    if isinstance(config['tokenize'], str):
        
        PretrainedModel = PretrainedModelForKBRDQwen(config)
        
        CRS_dataset = get_dataset(config, config['tokenize'], restore_data, save_data)
        side_data = CRS_dataset.side_data
        vocab = CRS_dataset.vocab

        train_dataloader = get_dataloader(config, CRS_dataset.train_data, vocab, side_data,PretrainedModel)
        valid_dataloader = get_dataloader(config, CRS_dataset.valid_data, vocab,side_data,PretrainedModel)
        test_dataloader = get_dataloader(config, CRS_dataset.test_data, vocab,side_data,PretrainedModel)
    # system
    CRS = get_system(PretrainedModel, config, train_dataloader, valid_dataloader, test_dataloader, vocab, side_data,  restore_system,
                     interact, debug, tensorboard)
    if interact:
        CRS.interact()
    else:
        CRS.fit()
        if save_system:
            CRS.save_model()