code/run_mrqa.py [631:677]:
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        logger.info("***** Eval results *****")
        for key in sorted(result.keys()):
            logger.info("  %s = %s", key, str(result[key]))
    return result, preds, nbest_preds


def main(args):
    device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
    n_gpu = torch.cuda.device_count()
    logger.info("device: {}, n_gpu: {}, 16-bits training: {}".format(
        device, n_gpu, args.fp16))

    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    if n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed)

    if args.gradient_accumulation_steps < 1:
        raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
                            args.gradient_accumulation_steps))
    args.train_batch_size = \
        args.train_batch_size // args.gradient_accumulation_steps

    if not args.do_train and not args.do_eval:
        raise ValueError("At least one of `do_train` or `do_eval` must be True.")

    if args.do_train:
        assert (args.train_file is not None) and (args.dev_file is not None)

    if args.eval_test:
        assert args.test_file is not None
    else:
        assert args.dev_file is not None

    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)
    if args.do_train:
        logger.addHandler(logging.FileHandler(os.path.join(args.output_dir, "train.log"), 'w'))
    else:
        logger.addHandler(logging.FileHandler(os.path.join(args.output_dir, "eval.log"), 'w'))
    logger.info(args)

    tokenizer = BertTokenizer.from_pretrained(
        args.model, do_lower_case=args.do_lower_case)

    if args.do_train or (not args.eval_test):
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code/run_squad.py [809:855]:
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    logger.info("***** Eval results *****")
    for key in sorted(result.keys()):
        logger.info("  %s = %s", key, str(result[key]))
    return result, preds, nbest_preds


def main(args):
    device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
    n_gpu = torch.cuda.device_count()
    logger.info("device: {}, n_gpu: {}, 16-bits training: {}".format(
        device, n_gpu, args.fp16))

    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    if n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed)

    if args.gradient_accumulation_steps < 1:
        raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
                            args.gradient_accumulation_steps))
    args.train_batch_size = \
        args.train_batch_size // args.gradient_accumulation_steps

    if not args.do_train and not args.do_eval:
        raise ValueError("At least one of `do_train` or `do_eval` must be True.")

    if args.do_train:
        assert (args.train_file is not None) and (args.dev_file is not None)

    if args.eval_test:
        assert args.test_file is not None
    else:
        assert args.dev_file is not None

    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)
    if args.do_train:
        logger.addHandler(logging.FileHandler(os.path.join(args.output_dir, "train.log"), 'w'))
    else:
        logger.addHandler(logging.FileHandler(os.path.join(args.output_dir, "eval.log"), 'w'))
    logger.info(args)

    tokenizer = BertTokenizer.from_pretrained(
        args.model, do_lower_case=args.do_lower_case)

    if args.do_train or (not args.eval_test):
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