T5DST/T5.py [153:169]:
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    for batch in tqdm(test_loader):
        dst_outputs = model.generate(input_ids=batch["encoder_input"].to(device),
                                attention_mask=batch["attention_mask"].to(device),
                                eos_token_id=tokenizer.eos_token_id,
                                max_length=200,
                                )

        value_batch = tokenizer.batch_decode(dst_outputs, skip_special_tokens=True)

        for idx, value in enumerate(value_batch):
            dial_id = batch["ID"][idx]
            if dial_id not in predictions:
                predictions[dial_id] = {}
                predictions[dial_id]["domain"] = batch["domains"][idx][0]
                predictions[dial_id]["turns"] = {}
            if batch["turn_id"][idx] not in predictions[dial_id]["turns"]:
                predictions[dial_id]["turns"][batch["turn_id"][idx]] = {"turn_belief":batch["turn_belief"][idx], "pred_belief":[]}
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TransferQA/T5.py [137:154]:
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    for batch in tqdm(test_loader):
        dst_outputs = model.generate(input_ids=batch["encoder_input"].to(device),
                                attention_mask=batch["attention_mask"].to(device),
                                eos_token_id=tokenizer.eos_token_id,
                                max_length=200,
                                )

        value_batch = tokenizer.batch_decode(dst_outputs, skip_special_tokens=True)

        for idx, value in enumerate(value_batch):
            dial_id = batch["ID"][idx]
            if dial_id not in predictions:
                predictions[dial_id] = {}
                predictions[dial_id]["domain"] = batch["domains"][idx][0]
                predictions[dial_id]["turns"] = {}

            if batch["turn_id"][idx] not in predictions[dial_id]["turns"]:
                predictions[dial_id]["turns"][batch["turn_id"][idx]] = {"turn_belief":batch["turn_belief"][idx], "pred_belief":[]}
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