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	---
	license: apache-2.0
	base_model: microsoft/resnet-50
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5

	This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.5837
	- Accuracy: 0.7867
	- Brier Loss: 0.3013
	- Nll: 1.9882
	- F1 Micro: 0.7868
	- F1 Macro: 0.7860
	- Ece: 0.0529
	- Aurc: 0.0581

	## Model description

	More information needed

	## 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: 0.0001
	- train_batch_size: 64
	- eval_batch_size: 64
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 100

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\|
	\| No log \| 1.0 \| 250 \| 4.1958 \| 0.1035 \| 0.9350 \| 9.1004 \| 0.1035 \| 0.0792 \| 0.0472 \| 0.9013 \|
	\| 4.2322 \| 2.0 \| 500 \| 4.0778 \| 0.173 \| 0.9251 \| 6.5742 \| 0.173 \| 0.1393 \| 0.0993 \| 0.7501 \|
	\| 4.2322 \| 3.0 \| 750 \| 3.6484 \| 0.339 \| 0.8778 \| 4.9108 \| 0.339 \| 0.2957 \| 0.2172 \| 0.5305 \|
	\| 3.5256 \| 4.0 \| 1000 \| 2.5967 \| 0.4592 \| 0.6991 \| 3.3640 \| 0.4592 \| 0.4220 \| 0.1274 \| 0.3285 \|
	\| 3.5256 \| 5.0 \| 1250 \| 2.0345 \| 0.5417 \| 0.6078 \| 3.0118 \| 0.5417 \| 0.5180 \| 0.0976 \| 0.2447 \|
	\| 1.9172 \| 6.0 \| 1500 \| 1.4417 \| 0.625 \| 0.5029 \| 2.7890 \| 0.625 \| 0.6123 \| 0.0549 \| 0.1623 \|
	\| 1.9172 \| 7.0 \| 1750 \| 1.3298 \| 0.639 \| 0.4852 \| 2.6110 \| 0.639 \| 0.6320 \| 0.0558 \| 0.1501 \|
	\| 1.1801 \| 8.0 \| 2000 \| 1.1697 \| 0.674 \| 0.4473 \| 2.4787 \| 0.674 \| 0.6712 \| 0.0466 \| 0.1283 \|
	\| 1.1801 \| 9.0 \| 2250 \| 0.9625 \| 0.7093 \| 0.4020 \| 2.3242 \| 0.7093 \| 0.7085 \| 0.0526 \| 0.1017 \|
	\| 0.8029 \| 10.0 \| 2500 \| 0.9477 \| 0.7215 \| 0.3893 \| 2.3193 \| 0.7215 \| 0.7228 \| 0.0515 \| 0.0971 \|
	\| 0.8029 \| 11.0 \| 2750 \| 0.8527 \| 0.7375 \| 0.3692 \| 2.2785 \| 0.7375 \| 0.7377 \| 0.0490 \| 0.0870 \|
	\| 0.5717 \| 12.0 \| 3000 \| 0.7377 \| 0.7515 \| 0.3470 \| 2.1475 \| 0.7515 \| 0.7529 \| 0.0552 \| 0.0757 \|
	\| 0.5717 \| 13.0 \| 3250 \| 0.7309 \| 0.7498 \| 0.3469 \| 2.1250 \| 0.7498 \| 0.7494 \| 0.0589 \| 0.0758 \|
	\| 0.4414 \| 14.0 \| 3500 \| 0.7165 \| 0.7558 \| 0.3427 \| 2.1045 \| 0.7558 \| 0.7576 \| 0.0582 \| 0.0721 \|
	\| 0.4414 \| 15.0 \| 3750 \| 0.6865 \| 0.7678 \| 0.3319 \| 2.0457 \| 0.7678 \| 0.7688 \| 0.0551 \| 0.0697 \|
	\| 0.3691 \| 16.0 \| 4000 \| 0.7002 \| 0.7662 \| 0.3348 \| 2.1280 \| 0.7663 \| 0.7664 \| 0.0567 \| 0.0698 \|
	\| 0.3691 \| 17.0 \| 4250 \| 0.6896 \| 0.7628 \| 0.3326 \| 2.0750 \| 0.7628 \| 0.7631 \| 0.0608 \| 0.0691 \|
	\| 0.3214 \| 18.0 \| 4500 \| 0.6666 \| 0.7715 \| 0.3258 \| 2.0468 \| 0.7715 \| 0.7707 \| 0.0544 \| 0.0680 \|
	\| 0.3214 \| 19.0 \| 4750 \| 0.6735 \| 0.7702 \| 0.3277 \| 2.0544 \| 0.7702 \| 0.7700 \| 0.0571 \| 0.0681 \|
	\| 0.2914 \| 20.0 \| 5000 \| 0.6607 \| 0.772 \| 0.3241 \| 2.0364 \| 0.772 \| 0.7729 \| 0.0525 \| 0.0659 \|
	\| 0.2914 \| 21.0 \| 5250 \| 0.6625 \| 0.7688 \| 0.3217 \| 2.0387 \| 0.7688 \| 0.7703 \| 0.0455 \| 0.0664 \|
	\| 0.2653 \| 22.0 \| 5500 \| 0.6543 \| 0.775 \| 0.3200 \| 2.0560 \| 0.775 \| 0.7752 \| 0.0507 \| 0.0647 \|
	\| 0.2653 \| 23.0 \| 5750 \| 0.6409 \| 0.7725 \| 0.3188 \| 2.0091 \| 0.7725 \| 0.7733 \| 0.0554 \| 0.0647 \|
	\| 0.2482 \| 24.0 \| 6000 \| 0.6452 \| 0.7758 \| 0.3191 \| 2.0256 \| 0.7758 \| 0.7756 \| 0.0502 \| 0.0655 \|
	\| 0.2482 \| 25.0 \| 6250 \| 0.6401 \| 0.7742 \| 0.3196 \| 2.0668 \| 0.7742 \| 0.7745 \| 0.0528 \| 0.0648 \|
	\| 0.2354 \| 26.0 \| 6500 \| 0.6316 \| 0.775 \| 0.3171 \| 2.0150 \| 0.775 \| 0.7755 \| 0.0555 \| 0.0634 \|
	\| 0.2354 \| 27.0 \| 6750 \| 0.6257 \| 0.7808 \| 0.3147 \| 2.0129 \| 0.7808 \| 0.7808 \| 0.0503 \| 0.0624 \|
	\| 0.2229 \| 28.0 \| 7000 \| 0.6343 \| 0.7778 \| 0.3144 \| 2.0910 \| 0.7778 \| 0.7776 \| 0.0510 \| 0.0624 \|
	\| 0.2229 \| 29.0 \| 7250 \| 0.6206 \| 0.781 \| 0.3115 \| 2.0399 \| 0.7810 \| 0.7798 \| 0.0555 \| 0.0606 \|
	\| 0.2147 \| 30.0 \| 7500 \| 0.6262 \| 0.777 \| 0.3124 \| 2.0603 \| 0.777 \| 0.7772 \| 0.0539 \| 0.0616 \|
	\| 0.2147 \| 31.0 \| 7750 \| 0.6265 \| 0.7788 \| 0.3137 \| 2.0833 \| 0.7788 \| 0.7777 \| 0.0532 \| 0.0614 \|
	\| 0.2058 \| 32.0 \| 8000 \| 0.6134 \| 0.7815 \| 0.3119 \| 2.0369 \| 0.7815 \| 0.7815 \| 0.0514 \| 0.0615 \|
	\| 0.2058 \| 33.0 \| 8250 \| 0.6153 \| 0.7772 \| 0.3133 \| 2.0513 \| 0.7773 \| 0.7772 \| 0.0534 \| 0.0623 \|
	\| 0.1994 \| 34.0 \| 8500 \| 0.6143 \| 0.7853 \| 0.3098 \| 2.0188 \| 0.7853 \| 0.7857 \| 0.0508 \| 0.0611 \|
	\| 0.1994 \| 35.0 \| 8750 \| 0.6096 \| 0.7827 \| 0.3086 \| 2.0134 \| 0.7828 \| 0.7828 \| 0.0512 \| 0.0606 \|
	\| 0.1932 \| 36.0 \| 9000 \| 0.6094 \| 0.784 \| 0.3067 \| 2.0151 \| 0.7840 \| 0.7847 \| 0.0471 \| 0.0602 \|
	\| 0.1932 \| 37.0 \| 9250 \| 0.6142 \| 0.7833 \| 0.3111 \| 2.0213 \| 0.7833 \| 0.7829 \| 0.0542 \| 0.0608 \|
	\| 0.1895 \| 38.0 \| 9500 \| 0.6103 \| 0.7812 \| 0.3094 \| 2.0594 \| 0.7812 \| 0.7799 \| 0.0529 \| 0.0603 \|
	\| 0.1895 \| 39.0 \| 9750 \| 0.6059 \| 0.781 \| 0.3078 \| 2.0386 \| 0.7810 \| 0.7806 \| 0.0545 \| 0.0607 \|
	\| 0.1848 \| 40.0 \| 10000 \| 0.6042 \| 0.782 \| 0.3072 \| 2.0133 \| 0.782 \| 0.7824 \| 0.0527 \| 0.0603 \|
	\| 0.1848 \| 41.0 \| 10250 \| 0.5991 \| 0.785 \| 0.3043 \| 2.0124 \| 0.785 \| 0.7853 \| 0.0496 \| 0.0594 \|
	\| 0.1793 \| 42.0 \| 10500 \| 0.6034 \| 0.784 \| 0.3058 \| 2.0607 \| 0.7840 \| 0.7838 \| 0.0490 \| 0.0599 \|
	\| 0.1793 \| 43.0 \| 10750 \| 0.6047 \| 0.7827 \| 0.3068 \| 2.0139 \| 0.7828 \| 0.7819 \| 0.0492 \| 0.0595 \|
	\| 0.1768 \| 44.0 \| 11000 \| 0.5982 \| 0.785 \| 0.3057 \| 2.0303 \| 0.785 \| 0.7843 \| 0.0473 \| 0.0596 \|
	\| 0.1768 \| 45.0 \| 11250 \| 0.6036 \| 0.7795 \| 0.3087 \| 2.0173 \| 0.7795 \| 0.7788 \| 0.0549 \| 0.0607 \|
	\| 0.1743 \| 46.0 \| 11500 \| 0.5974 \| 0.785 \| 0.3060 \| 2.0026 \| 0.785 \| 0.7839 \| 0.0478 \| 0.0596 \|
	\| 0.1743 \| 47.0 \| 11750 \| 0.5996 \| 0.782 \| 0.3068 \| 2.0144 \| 0.782 \| 0.7825 \| 0.0480 \| 0.0598 \|
	\| 0.1707 \| 48.0 \| 12000 \| 0.5958 \| 0.7833 \| 0.3079 \| 2.0344 \| 0.7833 \| 0.7827 \| 0.0500 \| 0.0598 \|
	\| 0.1707 \| 49.0 \| 12250 \| 0.5969 \| 0.782 \| 0.3060 \| 2.0162 \| 0.782 \| 0.7820 \| 0.0482 \| 0.0597 \|
	\| 0.1683 \| 50.0 \| 12500 \| 0.5933 \| 0.784 \| 0.3043 \| 1.9897 \| 0.7840 \| 0.7836 \| 0.0496 \| 0.0589 \|
	\| 0.1683 \| 51.0 \| 12750 \| 0.5935 \| 0.7833 \| 0.3042 \| 2.0142 \| 0.7833 \| 0.7829 \| 0.0501 \| 0.0586 \|
	\| 0.1649 \| 52.0 \| 13000 \| 0.5950 \| 0.7847 \| 0.3050 \| 2.0125 \| 0.7847 \| 0.7851 \| 0.0475 \| 0.0591 \|
	\| 0.1649 \| 53.0 \| 13250 \| 0.5904 \| 0.7837 \| 0.3020 \| 1.9830 \| 0.7837 \| 0.7837 \| 0.0504 \| 0.0584 \|
	\| 0.1636 \| 54.0 \| 13500 \| 0.5926 \| 0.785 \| 0.3042 \| 2.0006 \| 0.785 \| 0.7845 \| 0.0493 \| 0.0588 \|
	\| 0.1636 \| 55.0 \| 13750 \| 0.5885 \| 0.7847 \| 0.3029 \| 2.0025 \| 0.7847 \| 0.7843 \| 0.0505 \| 0.0585 \|
	\| 0.1616 \| 56.0 \| 14000 \| 0.5920 \| 0.788 \| 0.3041 \| 2.0174 \| 0.788 \| 0.7878 \| 0.0520 \| 0.0591 \|
	\| 0.1616 \| 57.0 \| 14250 \| 0.5927 \| 0.7863 \| 0.3033 \| 2.0321 \| 0.7863 \| 0.7858 \| 0.0521 \| 0.0588 \|
	\| 0.1592 \| 58.0 \| 14500 \| 0.5878 \| 0.787 \| 0.3017 \| 1.9751 \| 0.787 \| 0.7874 \| 0.0461 \| 0.0584 \|
	\| 0.1592 \| 59.0 \| 14750 \| 0.5888 \| 0.7867 \| 0.3030 \| 1.9996 \| 0.7868 \| 0.7864 \| 0.0494 \| 0.0582 \|
	\| 0.1585 \| 60.0 \| 15000 \| 0.5929 \| 0.786 \| 0.3052 \| 2.0237 \| 0.786 \| 0.7857 \| 0.0512 \| 0.0584 \|
	\| 0.1585 \| 61.0 \| 15250 \| 0.5894 \| 0.7865 \| 0.3026 \| 1.9895 \| 0.7865 \| 0.7864 \| 0.0548 \| 0.0585 \|
	\| 0.1562 \| 62.0 \| 15500 \| 0.5903 \| 0.7873 \| 0.3033 \| 1.9670 \| 0.7873 \| 0.7870 \| 0.0481 \| 0.0584 \|
	\| 0.1562 \| 63.0 \| 15750 \| 0.5896 \| 0.7853 \| 0.3023 \| 1.9681 \| 0.7853 \| 0.7850 \| 0.0520 \| 0.0587 \|
	\| 0.1548 \| 64.0 \| 16000 \| 0.5903 \| 0.7847 \| 0.3027 \| 1.9865 \| 0.7847 \| 0.7846 \| 0.0506 \| 0.0587 \|
	\| 0.1548 \| 65.0 \| 16250 \| 0.5910 \| 0.7853 \| 0.3039 \| 2.0009 \| 0.7853 \| 0.7849 \| 0.0515 \| 0.0593 \|
	\| 0.1537 \| 66.0 \| 16500 \| 0.5866 \| 0.7883 \| 0.3012 \| 1.9561 \| 0.7883 \| 0.7881 \| 0.0447 \| 0.0581 \|
	\| 0.1537 \| 67.0 \| 16750 \| 0.5858 \| 0.7867 \| 0.3009 \| 1.9868 \| 0.7868 \| 0.7861 \| 0.0486 \| 0.0577 \|
	\| 0.1526 \| 68.0 \| 17000 \| 0.5886 \| 0.7867 \| 0.3024 \| 2.0009 \| 0.7868 \| 0.7862 \| 0.0530 \| 0.0587 \|
	\| 0.1526 \| 69.0 \| 17250 \| 0.5850 \| 0.7863 \| 0.3010 \| 2.0095 \| 0.7863 \| 0.7860 \| 0.0510 \| 0.0581 \|
	\| 0.1508 \| 70.0 \| 17500 \| 0.5867 \| 0.7865 \| 0.3019 \| 2.0304 \| 0.7865 \| 0.7861 \| 0.0525 \| 0.0583 \|
	\| 0.1508 \| 71.0 \| 17750 \| 0.5895 \| 0.7857 \| 0.3038 \| 2.0013 \| 0.7857 \| 0.7853 \| 0.0478 \| 0.0586 \|
	\| 0.15 \| 72.0 \| 18000 \| 0.5894 \| 0.7847 \| 0.3025 \| 2.0051 \| 0.7847 \| 0.7845 \| 0.0500 \| 0.0586 \|
	\| 0.15 \| 73.0 \| 18250 \| 0.5867 \| 0.7865 \| 0.3022 \| 1.9634 \| 0.7865 \| 0.7860 \| 0.0489 \| 0.0582 \|
	\| 0.149 \| 74.0 \| 18500 \| 0.5888 \| 0.7857 \| 0.3026 \| 1.9817 \| 0.7857 \| 0.7851 \| 0.0497 \| 0.0584 \|
	\| 0.149 \| 75.0 \| 18750 \| 0.5823 \| 0.7885 \| 0.2994 \| 1.9873 \| 0.7885 \| 0.7880 \| 0.0476 \| 0.0577 \|
	\| 0.1483 \| 76.0 \| 19000 \| 0.5866 \| 0.7853 \| 0.3025 \| 1.9870 \| 0.7853 \| 0.7849 \| 0.0531 \| 0.0583 \|
	\| 0.1483 \| 77.0 \| 19250 \| 0.5866 \| 0.7867 \| 0.3013 \| 1.9933 \| 0.7868 \| 0.7862 \| 0.0498 \| 0.0577 \|
	\| 0.1478 \| 78.0 \| 19500 \| 0.5844 \| 0.787 \| 0.3010 \| 1.9793 \| 0.787 \| 0.7868 \| 0.0465 \| 0.0579 \|
	\| 0.1478 \| 79.0 \| 19750 \| 0.5850 \| 0.7857 \| 0.3005 \| 1.9856 \| 0.7857 \| 0.7855 \| 0.0489 \| 0.0580 \|
	\| 0.1463 \| 80.0 \| 20000 \| 0.5829 \| 0.7893 \| 0.2999 \| 2.0003 \| 0.7893 \| 0.7890 \| 0.0543 \| 0.0578 \|
	\| 0.1463 \| 81.0 \| 20250 \| 0.5845 \| 0.7867 \| 0.3011 \| 2.0178 \| 0.7868 \| 0.7864 \| 0.0494 \| 0.0580 \|
	\| 0.1457 \| 82.0 \| 20500 \| 0.5878 \| 0.7865 \| 0.3022 \| 2.0108 \| 0.7865 \| 0.7861 \| 0.0507 \| 0.0583 \|
	\| 0.1457 \| 83.0 \| 20750 \| 0.5862 \| 0.7865 \| 0.3016 \| 1.9996 \| 0.7865 \| 0.7865 \| 0.0505 \| 0.0585 \|
	\| 0.1452 \| 84.0 \| 21000 \| 0.5851 \| 0.7863 \| 0.3011 \| 2.0002 \| 0.7863 \| 0.7859 \| 0.0481 \| 0.0582 \|
	\| 0.1452 \| 85.0 \| 21250 \| 0.5850 \| 0.787 \| 0.3013 \| 1.9659 \| 0.787 \| 0.7867 \| 0.0524 \| 0.0582 \|
	\| 0.1449 \| 86.0 \| 21500 \| 0.5878 \| 0.7867 \| 0.3023 \| 1.9837 \| 0.7868 \| 0.7866 \| 0.0526 \| 0.0581 \|
	\| 0.1449 \| 87.0 \| 21750 \| 0.5844 \| 0.7873 \| 0.3010 \| 1.9807 \| 0.7873 \| 0.7865 \| 0.0522 \| 0.0577 \|
	\| 0.1437 \| 88.0 \| 22000 \| 0.5846 \| 0.7877 \| 0.3012 \| 1.9947 \| 0.7877 \| 0.7869 \| 0.0464 \| 0.0580 \|
	\| 0.1437 \| 89.0 \| 22250 \| 0.5859 \| 0.787 \| 0.3016 \| 2.0002 \| 0.787 \| 0.7867 \| 0.0503 \| 0.0581 \|
	\| 0.143 \| 90.0 \| 22500 \| 0.5838 \| 0.7865 \| 0.3010 \| 1.9996 \| 0.7865 \| 0.7859 \| 0.0496 \| 0.0576 \|
	\| 0.143 \| 91.0 \| 22750 \| 0.5843 \| 0.7837 \| 0.3011 \| 1.9683 \| 0.7837 \| 0.7834 \| 0.0501 \| 0.0583 \|
	\| 0.1426 \| 92.0 \| 23000 \| 0.5843 \| 0.7873 \| 0.3010 \| 1.9960 \| 0.7873 \| 0.7870 \| 0.0524 \| 0.0578 \|
	\| 0.1426 \| 93.0 \| 23250 \| 0.5827 \| 0.7847 \| 0.3005 \| 1.9719 \| 0.7847 \| 0.7844 \| 0.0506 \| 0.0579 \|
	\| 0.1428 \| 94.0 \| 23500 \| 0.5831 \| 0.7865 \| 0.3009 \| 1.9781 \| 0.7865 \| 0.7862 \| 0.0517 \| 0.0579 \|
	\| 0.1428 \| 95.0 \| 23750 \| 0.5821 \| 0.784 \| 0.3001 \| 1.9641 \| 0.7840 \| 0.7838 \| 0.0505 \| 0.0579 \|
	\| 0.1424 \| 96.0 \| 24000 \| 0.5850 \| 0.7845 \| 0.3020 \| 1.9667 \| 0.7845 \| 0.7842 \| 0.0526 \| 0.0584 \|
	\| 0.1424 \| 97.0 \| 24250 \| 0.5850 \| 0.7847 \| 0.3012 \| 1.9776 \| 0.7847 \| 0.7844 \| 0.0508 \| 0.0579 \|
	\| 0.142 \| 98.0 \| 24500 \| 0.5845 \| 0.7877 \| 0.3011 \| 1.9745 \| 0.7877 \| 0.7870 \| 0.0491 \| 0.0579 \|
	\| 0.142 \| 99.0 \| 24750 \| 0.5834 \| 0.7853 \| 0.3010 \| 1.9679 \| 0.7853 \| 0.7852 \| 0.0506 \| 0.0581 \|
	\| 0.1416 \| 100.0 \| 25000 \| 0.5837 \| 0.7867 \| 0.3013 \| 1.9882 \| 0.7868 \| 0.7860 \| 0.0529 \| 0.0581 \|


	### Framework versions

	- Transformers 4.33.3
	- Pytorch 2.2.0.dev20231002
	- Datasets 2.7.1
	- Tokenizers 0.13.3

	---
	license: apache-2.0
	base_model: microsoft/resnet-50
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5

	This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.5837
	- Accuracy: 0.7867
	- Brier Loss: 0.3013
	- Nll: 1.9882
	- F1 Micro: 0.7868
	- F1 Macro: 0.7860
	- Ece: 0.0529
	- Aurc: 0.0581

	## Model description

	More information needed

	## 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: 0.0001
	- train_batch_size: 64
	- eval_batch_size: 64
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 100

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\|
	\| No log \| 1.0 \| 250 \| 4.1958 \| 0.1035 \| 0.9350 \| 9.1004 \| 0.1035 \| 0.0792 \| 0.0472 \| 0.9013 \|
	\| 4.2322 \| 2.0 \| 500 \| 4.0778 \| 0.173 \| 0.9251 \| 6.5742 \| 0.173 \| 0.1393 \| 0.0993 \| 0.7501 \|
	\| 4.2322 \| 3.0 \| 750 \| 3.6484 \| 0.339 \| 0.8778 \| 4.9108 \| 0.339 \| 0.2957 \| 0.2172 \| 0.5305 \|
	\| 3.5256 \| 4.0 \| 1000 \| 2.5967 \| 0.4592 \| 0.6991 \| 3.3640 \| 0.4592 \| 0.4220 \| 0.1274 \| 0.3285 \|
	\| 3.5256 \| 5.0 \| 1250 \| 2.0345 \| 0.5417 \| 0.6078 \| 3.0118 \| 0.5417 \| 0.5180 \| 0.0976 \| 0.2447 \|
	\| 1.9172 \| 6.0 \| 1500 \| 1.4417 \| 0.625 \| 0.5029 \| 2.7890 \| 0.625 \| 0.6123 \| 0.0549 \| 0.1623 \|
	\| 1.9172 \| 7.0 \| 1750 \| 1.3298 \| 0.639 \| 0.4852 \| 2.6110 \| 0.639 \| 0.6320 \| 0.0558 \| 0.1501 \|
	\| 1.1801 \| 8.0 \| 2000 \| 1.1697 \| 0.674 \| 0.4473 \| 2.4787 \| 0.674 \| 0.6712 \| 0.0466 \| 0.1283 \|
	\| 1.1801 \| 9.0 \| 2250 \| 0.9625 \| 0.7093 \| 0.4020 \| 2.3242 \| 0.7093 \| 0.7085 \| 0.0526 \| 0.1017 \|
	\| 0.8029 \| 10.0 \| 2500 \| 0.9477 \| 0.7215 \| 0.3893 \| 2.3193 \| 0.7215 \| 0.7228 \| 0.0515 \| 0.0971 \|
	\| 0.8029 \| 11.0 \| 2750 \| 0.8527 \| 0.7375 \| 0.3692 \| 2.2785 \| 0.7375 \| 0.7377 \| 0.0490 \| 0.0870 \|
	\| 0.5717 \| 12.0 \| 3000 \| 0.7377 \| 0.7515 \| 0.3470 \| 2.1475 \| 0.7515 \| 0.7529 \| 0.0552 \| 0.0757 \|
	\| 0.5717 \| 13.0 \| 3250 \| 0.7309 \| 0.7498 \| 0.3469 \| 2.1250 \| 0.7498 \| 0.7494 \| 0.0589 \| 0.0758 \|
	\| 0.4414 \| 14.0 \| 3500 \| 0.7165 \| 0.7558 \| 0.3427 \| 2.1045 \| 0.7558 \| 0.7576 \| 0.0582 \| 0.0721 \|
	\| 0.4414 \| 15.0 \| 3750 \| 0.6865 \| 0.7678 \| 0.3319 \| 2.0457 \| 0.7678 \| 0.7688 \| 0.0551 \| 0.0697 \|
	\| 0.3691 \| 16.0 \| 4000 \| 0.7002 \| 0.7662 \| 0.3348 \| 2.1280 \| 0.7663 \| 0.7664 \| 0.0567 \| 0.0698 \|
	\| 0.3691 \| 17.0 \| 4250 \| 0.6896 \| 0.7628 \| 0.3326 \| 2.0750 \| 0.7628 \| 0.7631 \| 0.0608 \| 0.0691 \|
	\| 0.3214 \| 18.0 \| 4500 \| 0.6666 \| 0.7715 \| 0.3258 \| 2.0468 \| 0.7715 \| 0.7707 \| 0.0544 \| 0.0680 \|
	\| 0.3214 \| 19.0 \| 4750 \| 0.6735 \| 0.7702 \| 0.3277 \| 2.0544 \| 0.7702 \| 0.7700 \| 0.0571 \| 0.0681 \|
	\| 0.2914 \| 20.0 \| 5000 \| 0.6607 \| 0.772 \| 0.3241 \| 2.0364 \| 0.772 \| 0.7729 \| 0.0525 \| 0.0659 \|
	\| 0.2914 \| 21.0 \| 5250 \| 0.6625 \| 0.7688 \| 0.3217 \| 2.0387 \| 0.7688 \| 0.7703 \| 0.0455 \| 0.0664 \|
	\| 0.2653 \| 22.0 \| 5500 \| 0.6543 \| 0.775 \| 0.3200 \| 2.0560 \| 0.775 \| 0.7752 \| 0.0507 \| 0.0647 \|
	\| 0.2653 \| 23.0 \| 5750 \| 0.6409 \| 0.7725 \| 0.3188 \| 2.0091 \| 0.7725 \| 0.7733 \| 0.0554 \| 0.0647 \|
	\| 0.2482 \| 24.0 \| 6000 \| 0.6452 \| 0.7758 \| 0.3191 \| 2.0256 \| 0.7758 \| 0.7756 \| 0.0502 \| 0.0655 \|
	\| 0.2482 \| 25.0 \| 6250 \| 0.6401 \| 0.7742 \| 0.3196 \| 2.0668 \| 0.7742 \| 0.7745 \| 0.0528 \| 0.0648 \|
	\| 0.2354 \| 26.0 \| 6500 \| 0.6316 \| 0.775 \| 0.3171 \| 2.0150 \| 0.775 \| 0.7755 \| 0.0555 \| 0.0634 \|
	\| 0.2354 \| 27.0 \| 6750 \| 0.6257 \| 0.7808 \| 0.3147 \| 2.0129 \| 0.7808 \| 0.7808 \| 0.0503 \| 0.0624 \|
	\| 0.2229 \| 28.0 \| 7000 \| 0.6343 \| 0.7778 \| 0.3144 \| 2.0910 \| 0.7778 \| 0.7776 \| 0.0510 \| 0.0624 \|
	\| 0.2229 \| 29.0 \| 7250 \| 0.6206 \| 0.781 \| 0.3115 \| 2.0399 \| 0.7810 \| 0.7798 \| 0.0555 \| 0.0606 \|
	\| 0.2147 \| 30.0 \| 7500 \| 0.6262 \| 0.777 \| 0.3124 \| 2.0603 \| 0.777 \| 0.7772 \| 0.0539 \| 0.0616 \|
	\| 0.2147 \| 31.0 \| 7750 \| 0.6265 \| 0.7788 \| 0.3137 \| 2.0833 \| 0.7788 \| 0.7777 \| 0.0532 \| 0.0614 \|
	\| 0.2058 \| 32.0 \| 8000 \| 0.6134 \| 0.7815 \| 0.3119 \| 2.0369 \| 0.7815 \| 0.7815 \| 0.0514 \| 0.0615 \|
	\| 0.2058 \| 33.0 \| 8250 \| 0.6153 \| 0.7772 \| 0.3133 \| 2.0513 \| 0.7773 \| 0.7772 \| 0.0534 \| 0.0623 \|
	\| 0.1994 \| 34.0 \| 8500 \| 0.6143 \| 0.7853 \| 0.3098 \| 2.0188 \| 0.7853 \| 0.7857 \| 0.0508 \| 0.0611 \|
	\| 0.1994 \| 35.0 \| 8750 \| 0.6096 \| 0.7827 \| 0.3086 \| 2.0134 \| 0.7828 \| 0.7828 \| 0.0512 \| 0.0606 \|
	\| 0.1932 \| 36.0 \| 9000 \| 0.6094 \| 0.784 \| 0.3067 \| 2.0151 \| 0.7840 \| 0.7847 \| 0.0471 \| 0.0602 \|
	\| 0.1932 \| 37.0 \| 9250 \| 0.6142 \| 0.7833 \| 0.3111 \| 2.0213 \| 0.7833 \| 0.7829 \| 0.0542 \| 0.0608 \|
	\| 0.1895 \| 38.0 \| 9500 \| 0.6103 \| 0.7812 \| 0.3094 \| 2.0594 \| 0.7812 \| 0.7799 \| 0.0529 \| 0.0603 \|
	\| 0.1895 \| 39.0 \| 9750 \| 0.6059 \| 0.781 \| 0.3078 \| 2.0386 \| 0.7810 \| 0.7806 \| 0.0545 \| 0.0607 \|
	\| 0.1848 \| 40.0 \| 10000 \| 0.6042 \| 0.782 \| 0.3072 \| 2.0133 \| 0.782 \| 0.7824 \| 0.0527 \| 0.0603 \|
	\| 0.1848 \| 41.0 \| 10250 \| 0.5991 \| 0.785 \| 0.3043 \| 2.0124 \| 0.785 \| 0.7853 \| 0.0496 \| 0.0594 \|
	\| 0.1793 \| 42.0 \| 10500 \| 0.6034 \| 0.784 \| 0.3058 \| 2.0607 \| 0.7840 \| 0.7838 \| 0.0490 \| 0.0599 \|
	\| 0.1793 \| 43.0 \| 10750 \| 0.6047 \| 0.7827 \| 0.3068 \| 2.0139 \| 0.7828 \| 0.7819 \| 0.0492 \| 0.0595 \|
	\| 0.1768 \| 44.0 \| 11000 \| 0.5982 \| 0.785 \| 0.3057 \| 2.0303 \| 0.785 \| 0.7843 \| 0.0473 \| 0.0596 \|
	\| 0.1768 \| 45.0 \| 11250 \| 0.6036 \| 0.7795 \| 0.3087 \| 2.0173 \| 0.7795 \| 0.7788 \| 0.0549 \| 0.0607 \|
	\| 0.1743 \| 46.0 \| 11500 \| 0.5974 \| 0.785 \| 0.3060 \| 2.0026 \| 0.785 \| 0.7839 \| 0.0478 \| 0.0596 \|
	\| 0.1743 \| 47.0 \| 11750 \| 0.5996 \| 0.782 \| 0.3068 \| 2.0144 \| 0.782 \| 0.7825 \| 0.0480 \| 0.0598 \|
	\| 0.1707 \| 48.0 \| 12000 \| 0.5958 \| 0.7833 \| 0.3079 \| 2.0344 \| 0.7833 \| 0.7827 \| 0.0500 \| 0.0598 \|
	\| 0.1707 \| 49.0 \| 12250 \| 0.5969 \| 0.782 \| 0.3060 \| 2.0162 \| 0.782 \| 0.7820 \| 0.0482 \| 0.0597 \|
	\| 0.1683 \| 50.0 \| 12500 \| 0.5933 \| 0.784 \| 0.3043 \| 1.9897 \| 0.7840 \| 0.7836 \| 0.0496 \| 0.0589 \|
	\| 0.1683 \| 51.0 \| 12750 \| 0.5935 \| 0.7833 \| 0.3042 \| 2.0142 \| 0.7833 \| 0.7829 \| 0.0501 \| 0.0586 \|
	\| 0.1649 \| 52.0 \| 13000 \| 0.5950 \| 0.7847 \| 0.3050 \| 2.0125 \| 0.7847 \| 0.7851 \| 0.0475 \| 0.0591 \|
	\| 0.1649 \| 53.0 \| 13250 \| 0.5904 \| 0.7837 \| 0.3020 \| 1.9830 \| 0.7837 \| 0.7837 \| 0.0504 \| 0.0584 \|
	\| 0.1636 \| 54.0 \| 13500 \| 0.5926 \| 0.785 \| 0.3042 \| 2.0006 \| 0.785 \| 0.7845 \| 0.0493 \| 0.0588 \|
	\| 0.1636 \| 55.0 \| 13750 \| 0.5885 \| 0.7847 \| 0.3029 \| 2.0025 \| 0.7847 \| 0.7843 \| 0.0505 \| 0.0585 \|
	\| 0.1616 \| 56.0 \| 14000 \| 0.5920 \| 0.788 \| 0.3041 \| 2.0174 \| 0.788 \| 0.7878 \| 0.0520 \| 0.0591 \|
	\| 0.1616 \| 57.0 \| 14250 \| 0.5927 \| 0.7863 \| 0.3033 \| 2.0321 \| 0.7863 \| 0.7858 \| 0.0521 \| 0.0588 \|
	\| 0.1592 \| 58.0 \| 14500 \| 0.5878 \| 0.787 \| 0.3017 \| 1.9751 \| 0.787 \| 0.7874 \| 0.0461 \| 0.0584 \|
	\| 0.1592 \| 59.0 \| 14750 \| 0.5888 \| 0.7867 \| 0.3030 \| 1.9996 \| 0.7868 \| 0.7864 \| 0.0494 \| 0.0582 \|
	\| 0.1585 \| 60.0 \| 15000 \| 0.5929 \| 0.786 \| 0.3052 \| 2.0237 \| 0.786 \| 0.7857 \| 0.0512 \| 0.0584 \|
	\| 0.1585 \| 61.0 \| 15250 \| 0.5894 \| 0.7865 \| 0.3026 \| 1.9895 \| 0.7865 \| 0.7864 \| 0.0548 \| 0.0585 \|
	\| 0.1562 \| 62.0 \| 15500 \| 0.5903 \| 0.7873 \| 0.3033 \| 1.9670 \| 0.7873 \| 0.7870 \| 0.0481 \| 0.0584 \|
	\| 0.1562 \| 63.0 \| 15750 \| 0.5896 \| 0.7853 \| 0.3023 \| 1.9681 \| 0.7853 \| 0.7850 \| 0.0520 \| 0.0587 \|
	\| 0.1548 \| 64.0 \| 16000 \| 0.5903 \| 0.7847 \| 0.3027 \| 1.9865 \| 0.7847 \| 0.7846 \| 0.0506 \| 0.0587 \|
	\| 0.1548 \| 65.0 \| 16250 \| 0.5910 \| 0.7853 \| 0.3039 \| 2.0009 \| 0.7853 \| 0.7849 \| 0.0515 \| 0.0593 \|
	\| 0.1537 \| 66.0 \| 16500 \| 0.5866 \| 0.7883 \| 0.3012 \| 1.9561 \| 0.7883 \| 0.7881 \| 0.0447 \| 0.0581 \|
	\| 0.1537 \| 67.0 \| 16750 \| 0.5858 \| 0.7867 \| 0.3009 \| 1.9868 \| 0.7868 \| 0.7861 \| 0.0486 \| 0.0577 \|
	\| 0.1526 \| 68.0 \| 17000 \| 0.5886 \| 0.7867 \| 0.3024 \| 2.0009 \| 0.7868 \| 0.7862 \| 0.0530 \| 0.0587 \|
	\| 0.1526 \| 69.0 \| 17250 \| 0.5850 \| 0.7863 \| 0.3010 \| 2.0095 \| 0.7863 \| 0.7860 \| 0.0510 \| 0.0581 \|
	\| 0.1508 \| 70.0 \| 17500 \| 0.5867 \| 0.7865 \| 0.3019 \| 2.0304 \| 0.7865 \| 0.7861 \| 0.0525 \| 0.0583 \|
	\| 0.1508 \| 71.0 \| 17750 \| 0.5895 \| 0.7857 \| 0.3038 \| 2.0013 \| 0.7857 \| 0.7853 \| 0.0478 \| 0.0586 \|
	\| 0.15 \| 72.0 \| 18000 \| 0.5894 \| 0.7847 \| 0.3025 \| 2.0051 \| 0.7847 \| 0.7845 \| 0.0500 \| 0.0586 \|
	\| 0.15 \| 73.0 \| 18250 \| 0.5867 \| 0.7865 \| 0.3022 \| 1.9634 \| 0.7865 \| 0.7860 \| 0.0489 \| 0.0582 \|
	\| 0.149 \| 74.0 \| 18500 \| 0.5888 \| 0.7857 \| 0.3026 \| 1.9817 \| 0.7857 \| 0.7851 \| 0.0497 \| 0.0584 \|
	\| 0.149 \| 75.0 \| 18750 \| 0.5823 \| 0.7885 \| 0.2994 \| 1.9873 \| 0.7885 \| 0.7880 \| 0.0476 \| 0.0577 \|
	\| 0.1483 \| 76.0 \| 19000 \| 0.5866 \| 0.7853 \| 0.3025 \| 1.9870 \| 0.7853 \| 0.7849 \| 0.0531 \| 0.0583 \|
	\| 0.1483 \| 77.0 \| 19250 \| 0.5866 \| 0.7867 \| 0.3013 \| 1.9933 \| 0.7868 \| 0.7862 \| 0.0498 \| 0.0577 \|
	\| 0.1478 \| 78.0 \| 19500 \| 0.5844 \| 0.787 \| 0.3010 \| 1.9793 \| 0.787 \| 0.7868 \| 0.0465 \| 0.0579 \|
	\| 0.1478 \| 79.0 \| 19750 \| 0.5850 \| 0.7857 \| 0.3005 \| 1.9856 \| 0.7857 \| 0.7855 \| 0.0489 \| 0.0580 \|
	\| 0.1463 \| 80.0 \| 20000 \| 0.5829 \| 0.7893 \| 0.2999 \| 2.0003 \| 0.7893 \| 0.7890 \| 0.0543 \| 0.0578 \|
	\| 0.1463 \| 81.0 \| 20250 \| 0.5845 \| 0.7867 \| 0.3011 \| 2.0178 \| 0.7868 \| 0.7864 \| 0.0494 \| 0.0580 \|
	\| 0.1457 \| 82.0 \| 20500 \| 0.5878 \| 0.7865 \| 0.3022 \| 2.0108 \| 0.7865 \| 0.7861 \| 0.0507 \| 0.0583 \|
	\| 0.1457 \| 83.0 \| 20750 \| 0.5862 \| 0.7865 \| 0.3016 \| 1.9996 \| 0.7865 \| 0.7865 \| 0.0505 \| 0.0585 \|
	\| 0.1452 \| 84.0 \| 21000 \| 0.5851 \| 0.7863 \| 0.3011 \| 2.0002 \| 0.7863 \| 0.7859 \| 0.0481 \| 0.0582 \|
	\| 0.1452 \| 85.0 \| 21250 \| 0.5850 \| 0.787 \| 0.3013 \| 1.9659 \| 0.787 \| 0.7867 \| 0.0524 \| 0.0582 \|
	\| 0.1449 \| 86.0 \| 21500 \| 0.5878 \| 0.7867 \| 0.3023 \| 1.9837 \| 0.7868 \| 0.7866 \| 0.0526 \| 0.0581 \|
	\| 0.1449 \| 87.0 \| 21750 \| 0.5844 \| 0.7873 \| 0.3010 \| 1.9807 \| 0.7873 \| 0.7865 \| 0.0522 \| 0.0577 \|
	\| 0.1437 \| 88.0 \| 22000 \| 0.5846 \| 0.7877 \| 0.3012 \| 1.9947 \| 0.7877 \| 0.7869 \| 0.0464 \| 0.0580 \|
	\| 0.1437 \| 89.0 \| 22250 \| 0.5859 \| 0.787 \| 0.3016 \| 2.0002 \| 0.787 \| 0.7867 \| 0.0503 \| 0.0581 \|
	\| 0.143 \| 90.0 \| 22500 \| 0.5838 \| 0.7865 \| 0.3010 \| 1.9996 \| 0.7865 \| 0.7859 \| 0.0496 \| 0.0576 \|
	\| 0.143 \| 91.0 \| 22750 \| 0.5843 \| 0.7837 \| 0.3011 \| 1.9683 \| 0.7837 \| 0.7834 \| 0.0501 \| 0.0583 \|
	\| 0.1426 \| 92.0 \| 23000 \| 0.5843 \| 0.7873 \| 0.3010 \| 1.9960 \| 0.7873 \| 0.7870 \| 0.0524 \| 0.0578 \|
	\| 0.1426 \| 93.0 \| 23250 \| 0.5827 \| 0.7847 \| 0.3005 \| 1.9719 \| 0.7847 \| 0.7844 \| 0.0506 \| 0.0579 \|
	\| 0.1428 \| 94.0 \| 23500 \| 0.5831 \| 0.7865 \| 0.3009 \| 1.9781 \| 0.7865 \| 0.7862 \| 0.0517 \| 0.0579 \|
	\| 0.1428 \| 95.0 \| 23750 \| 0.5821 \| 0.784 \| 0.3001 \| 1.9641 \| 0.7840 \| 0.7838 \| 0.0505 \| 0.0579 \|
	\| 0.1424 \| 96.0 \| 24000 \| 0.5850 \| 0.7845 \| 0.3020 \| 1.9667 \| 0.7845 \| 0.7842 \| 0.0526 \| 0.0584 \|
	\| 0.1424 \| 97.0 \| 24250 \| 0.5850 \| 0.7847 \| 0.3012 \| 1.9776 \| 0.7847 \| 0.7844 \| 0.0508 \| 0.0579 \|
	\| 0.142 \| 98.0 \| 24500 \| 0.5845 \| 0.7877 \| 0.3011 \| 1.9745 \| 0.7877 \| 0.7870 \| 0.0491 \| 0.0579 \|
	\| 0.142 \| 99.0 \| 24750 \| 0.5834 \| 0.7853 \| 0.3010 \| 1.9679 \| 0.7853 \| 0.7852 \| 0.0506 \| 0.0581 \|
	\| 0.1416 \| 100.0 \| 25000 \| 0.5837 \| 0.7867 \| 0.3013 \| 1.9882 \| 0.7868 \| 0.7860 \| 0.0529 \| 0.0581 \|


	### Framework versions

	- Transformers 4.33.3
	- Pytorch 2.2.0.dev20231002
	- Datasets 2.7.1
	- Tokenizers 0.13.3