table_bert/vanilla_table_bert.py [279:305]:
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        logging_info_list = []
        with torch.no_grad():
            with tqdm(total=len(data_loader), desc=f"Evaluation", file=sys.stdout) as pbar:
                for step, batch in enumerate(data_loader):
                    loss_sum, logging_info = self(**batch)
                    logging_info = {k: logging_info[k] for k in keys}
                    logging_info_list.append(logging_info)

                    pbar.update(1)

        if was_training:
            self.train()

        stats = {
            k: sum(x[k] for x in logging_info_list)
            for k in keys
        }

        # handel distributed evaluation
        if args.multi_gpu:
            stats = distributed_utils.all_gather_list(stats)
            stats = {
                k: sum(x[k] for x in stats)
                for k in keys
            }

        valid_result = {
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table_bert/vertical/vertical_attention_table_bert.py [474:500]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        logging_info_list = []
        with torch.no_grad():
            with tqdm(total=len(data_loader), desc=f"Evaluation", file=sys.stdout) as pbar:
                for step, batch in enumerate(data_loader):
                    loss_sum, logging_info = self(**batch)
                    logging_info = {k: logging_info[k] for k in keys}
                    logging_info_list.append(logging_info)

                    pbar.update(1)

        if was_training:
            self.train()

        stats = {
            k: sum(x[k] for x in logging_info_list)
            for k in keys
        }

        # handel distributed evaluation
        if args.multi_gpu:
            stats = distributed_utils.all_gather_list(stats)
            stats = {
                k: sum(x[k] for x in stats)
                for k in keys
            }

        valid_result = {
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