--- license: mit base_model: hyunwoongko/kobart tags: - generated_from_trainer model-index: - name: qa_kor_market results: [] --- # qa_kor_market This model is a fine-tuned version of [hyunwoongko/kobart](https://huggingface.co/hyunwoongko/kobart) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7618 ## Model description 슈퍼 마켓에서 있을 법한한 문의 내용을 입력하면, 문의 의도, 문의 항목, 답변을 리턴해주는 모델입니다. ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | No log | 0.03 | 100 | 3.4839 | | No log | 0.05 | 200 | 1.4909 | | No log | 0.08 | 300 | 1.2606 | | No log | 0.1 | 400 | 1.1675 | | 3.0259 | 0.13 | 500 | 1.1008 | | 3.0259 | 0.15 | 600 | 1.0580 | | 3.0259 | 0.18 | 700 | 1.0222 | | 3.0259 | 0.2 | 800 | 0.9938 | | 3.0259 | 0.23 | 900 | 0.9707 | | 1.0853 | 0.25 | 1000 | 0.9571 | | 1.0853 | 0.28 | 1100 | 0.9370 | | 1.0853 | 0.3 | 1200 | 0.9293 | | 1.0853 | 0.33 | 1300 | 0.9146 | | 1.0853 | 0.35 | 1400 | 0.9065 | | 0.992 | 0.38 | 1500 | 0.8997 | | 0.992 | 0.41 | 1600 | 0.8930 | | 0.992 | 0.43 | 1700 | 0.8834 | | 0.992 | 0.46 | 1800 | 0.8788 | | 0.992 | 0.48 | 1900 | 0.8714 | | 0.9418 | 0.51 | 2000 | 0.8706 | | 0.9418 | 0.53 | 2100 | 0.8676 | | 0.9418 | 0.56 | 2200 | 0.8619 | | 0.9418 | 0.58 | 2300 | 0.8548 | | 0.9418 | 0.61 | 2400 | 0.8514 | | 0.9222 | 0.63 | 2500 | 0.8511 | | 0.9222 | 0.66 | 2600 | 0.8483 | | 0.9222 | 0.68 | 2700 | 0.8425 | | 0.9222 | 0.71 | 2800 | 0.8396 | | 0.9222 | 0.74 | 2900 | 0.8384 | | 0.8981 | 0.76 | 3000 | 0.8360 | | 0.8981 | 0.79 | 3100 | 0.8295 | | 0.8981 | 0.81 | 3200 | 0.8290 | | 0.8981 | 0.84 | 3300 | 0.8273 | | 0.8981 | 0.86 | 3400 | 0.8221 | | 0.874 | 0.89 | 3500 | 0.8228 | | 0.874 | 0.91 | 3600 | 0.8213 | | 0.874 | 0.94 | 3700 | 0.8183 | | 0.874 | 0.96 | 3800 | 0.8163 | | 0.874 | 0.99 | 3900 | 0.8178 | | 0.8575 | 1.01 | 4000 | 0.8143 | | 0.8575 | 1.04 | 4100 | 0.8118 | | 0.8575 | 1.06 | 4200 | 0.8094 | | 0.8575 | 1.09 | 4300 | 0.8092 | | 0.8575 | 1.12 | 4400 | 0.8085 | | 0.8374 | 1.14 | 4500 | 0.8048 | | 0.8374 | 1.17 | 4600 | 0.8041 | | 0.8374 | 1.19 | 4700 | 0.8018 | | 0.8374 | 1.22 | 4800 | 0.8007 | | 0.8374 | 1.24 | 4900 | 0.7988 | | 0.8282 | 1.27 | 5000 | 0.7980 | | 0.8282 | 1.29 | 5100 | 0.7968 | | 0.8282 | 1.32 | 5200 | 0.7974 | | 0.8282 | 1.34 | 5300 | 0.7949 | | 0.8282 | 1.37 | 5400 | 0.7919 | | 0.8149 | 1.39 | 5500 | 0.7931 | | 0.8149 | 1.42 | 5600 | 0.7900 | | 0.8149 | 1.44 | 5700 | 0.7887 | | 0.8149 | 1.47 | 5800 | 0.7875 | | 0.8149 | 1.5 | 5900 | 0.7883 | | 0.8098 | 1.52 | 6000 | 0.7886 | | 0.8098 | 1.55 | 6100 | 0.7860 | | 0.8098 | 1.57 | 6200 | 0.7873 | | 0.8098 | 1.6 | 6300 | 0.7822 | | 0.8098 | 1.62 | 6400 | 0.7841 | | 0.8306 | 1.65 | 6500 | 0.7828 | | 0.8306 | 1.67 | 6600 | 0.7817 | | 0.8306 | 1.7 | 6700 | 0.7812 | | 0.8306 | 1.72 | 6800 | 0.7814 | | 0.8306 | 1.75 | 6900 | 0.7799 | | 0.7974 | 1.77 | 7000 | 0.7774 | | 0.7974 | 1.8 | 7100 | 0.7795 | | 0.7974 | 1.83 | 7200 | 0.7782 | | 0.7974 | 1.85 | 7300 | 0.7786 | | 0.7974 | 1.88 | 7400 | 0.7773 | | 0.7945 | 1.9 | 7500 | 0.7749 | | 0.7945 | 1.93 | 7600 | 0.7737 | | 0.7945 | 1.95 | 7700 | 0.7743 | | 0.7945 | 1.98 | 7800 | 0.7742 | | 0.7945 | 2.0 | 7900 | 0.7732 | | 0.8005 | 2.03 | 8000 | 0.7758 | | 0.8005 | 2.05 | 8100 | 0.7726 | | 0.8005 | 2.08 | 8200 | 0.7716 | | 0.8005 | 2.1 | 8300 | 0.7742 | | 0.8005 | 2.13 | 8400 | 0.7720 | | 0.7788 | 2.15 | 8500 | 0.7706 | | 0.7788 | 2.18 | 8600 | 0.7701 | | 0.7788 | 2.21 | 8700 | 0.7702 | | 0.7788 | 2.23 | 8800 | 0.7676 | | 0.7788 | 2.26 | 8900 | 0.7699 | | 0.7685 | 2.28 | 9000 | 0.7689 | | 0.7685 | 2.31 | 9100 | 0.7677 | | 0.7685 | 2.33 | 9200 | 0.7686 | | 0.7685 | 2.36 | 9300 | 0.7671 | | 0.7685 | 2.38 | 9400 | 0.7668 | | 0.7814 | 2.41 | 9500 | 0.7670 | | 0.7814 | 2.43 | 9600 | 0.7669 | | 0.7814 | 2.46 | 9700 | 0.7661 | | 0.7814 | 2.48 | 9800 | 0.7653 | | 0.7814 | 2.51 | 9900 | 0.7663 | | 0.7824 | 2.53 | 10000 | 0.7655 | | 0.7824 | 2.56 | 10100 | 0.7654 | | 0.7824 | 2.59 | 10200 | 0.7653 | | 0.7824 | 2.61 | 10300 | 0.7652 | | 0.7824 | 2.64 | 10400 | 0.7640 | | 0.7798 | 2.66 | 10500 | 0.7647 | | 0.7798 | 2.69 | 10600 | 0.7637 | | 0.7798 | 2.71 | 10700 | 0.7636 | | 0.7798 | 2.74 | 10800 | 0.7629 | | 0.7798 | 2.76 | 10900 | 0.7629 | | 0.7619 | 2.79 | 11000 | 0.7629 | | 0.7619 | 2.81 | 11100 | 0.7624 | | 0.7619 | 2.84 | 11200 | 0.7621 | | 0.7619 | 2.86 | 11300 | 0.7621 | | 0.7619 | 2.89 | 11400 | 0.7623 | | 0.7723 | 2.92 | 11500 | 0.7621 | | 0.7723 | 2.94 | 11600 | 0.7619 | | 0.7723 | 2.97 | 11700 | 0.7619 | | 0.7723 | 2.99 | 11800 | 0.7618 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2