update model card README.md

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	---
	license: other
	tags:
	- vision
	- image-segmentation
	- generated_from_trainer
	model-index:
	- name: safety-utcustom-train-SF-RGB-b5
	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. -->

	# safety-utcustom-train-SF-RGB-b5

	This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.4046
	- Mean Iou: 0.4437
	- Mean Accuracy: 0.8171
	- Overall Accuracy: 0.9614
	- Accuracy Unlabeled: nan
	- Accuracy Safe: 0.6638
	- Accuracy Unsafe: 0.9704
	- Iou Unlabeled: 0.0
	- Iou Safe: 0.3704
	- Iou Unsafe: 0.9606

	## 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: 3e-06
	- train_batch_size: 15
	- eval_batch_size: 15
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.05
	- num_epochs: 50

	### Training results

	\| Training Loss \| Epoch \| Step \| Accuracy Safe \| Accuracy Unlabeled \| Accuracy Unsafe \| Iou Safe \| Iou Unlabeled \| Iou Unsafe \| Validation Loss \| Mean Accuracy \| Mean Iou \| Overall Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:-------------:\|:------------------:\|:---------------:\|:--------:\|:-------------:\|:----------:\|:---------------:\|:-------------:\|:--------:\|:----------------:\|
	\| 1.2239 \| 0.91 \| 10 \| 0.3992 \| nan \| 0.2951 \| 0.0314 \| 0.0 \| 0.2939 \| 1.1103 \| 0.3472 \| 0.1084 \| 0.2982 \|
	\| 1.1948 \| 1.82 \| 20 \| 0.5219 \| nan \| 0.3705 \| 0.0440 \| 0.0 \| 0.3689 \| 1.0963 \| 0.4462 \| 0.1376 \| 0.3750 \|
	\| 1.1661 \| 2.73 \| 30 \| 0.5863 \| nan \| 0.4988 \| 0.0647 \| 0.0 \| 0.4961 \| 1.0516 \| 0.5426 \| 0.1870 \| 0.5014 \|
	\| 1.1112 \| 3.64 \| 40 \| 0.5459 \| nan \| 0.5794 \| 0.0900 \| 0.0 \| 0.5754 \| 1.0048 \| 0.5626 \| 0.2218 \| 0.5784 \|
	\| 1.0907 \| 4.55 \| 50 \| 0.5993 \| nan \| 0.6367 \| 0.1094 \| 0.0 \| 0.6321 \| 0.9690 \| 0.6180 \| 0.2472 \| 0.6356 \|
	\| 1.047 \| 5.45 \| 60 \| 0.6692 \| nan \| 0.6699 \| 0.1159 \| 0.0 \| 0.6656 \| 0.9437 \| 0.6695 \| 0.2605 \| 0.6699 \|
	\| 1.0112 \| 6.36 \| 70 \| 0.6673 \| nan \| 0.7189 \| 0.1349 \| 0.0 \| 0.7137 \| 0.9084 \| 0.6931 \| 0.2829 \| 0.7173 \|
	\| 0.9925 \| 7.27 \| 80 \| 0.6842 \| nan \| 0.7665 \| 0.1452 \| 0.0 \| 0.7605 \| 0.8647 \| 0.7254 \| 0.3019 \| 0.7641 \|
	\| 0.9395 \| 8.18 \| 90 \| 0.6818 \| nan \| 0.7921 \| 0.1620 \| 0.0 \| 0.7856 \| 0.8319 \| 0.7369 \| 0.3159 \| 0.7888 \|
	\| 0.8902 \| 9.09 \| 100 \| 0.6806 \| nan \| 0.8142 \| 0.1770 \| 0.0 \| 0.8072 \| 0.8014 \| 0.7474 \| 0.3281 \| 0.8102 \|
	\| 0.9057 \| 10.0 \| 110 \| 0.6984 \| nan \| 0.8179 \| 0.1733 \| 0.0 \| 0.8109 \| 0.7867 \| 0.7581 \| 0.3281 \| 0.8143 \|
	\| 0.8321 \| 10.91 \| 120 \| 0.6744 \| nan \| 0.8494 \| 0.1862 \| 0.0 \| 0.8413 \| 0.7440 \| 0.7619 \| 0.3425 \| 0.8442 \|
	\| 0.8152 \| 11.82 \| 130 \| 0.6688 \| nan \| 0.8590 \| 0.2006 \| 0.0 \| 0.8507 \| 0.7270 \| 0.7639 \| 0.3504 \| 0.8534 \|
	\| 0.7929 \| 12.73 \| 140 \| 0.6660 \| nan \| 0.8657 \| 0.2085 \| 0.0 \| 0.8572 \| 0.7045 \| 0.7658 \| 0.3553 \| 0.8598 \|
	\| 0.7568 \| 13.64 \| 150 \| 0.6571 \| nan \| 0.8838 \| 0.2185 \| 0.0 \| 0.8748 \| 0.6744 \| 0.7704 \| 0.3644 \| 0.8771 \|
	\| 0.7085 \| 14.55 \| 160 \| 0.6519 \| nan \| 0.8934 \| 0.2260 \| 0.0 \| 0.8842 \| 0.6556 \| 0.7727 \| 0.3701 \| 0.8863 \|
	\| 0.7147 \| 15.45 \| 170 \| 0.6561 \| nan \| 0.8964 \| 0.2283 \| 0.0 \| 0.8872 \| 0.6509 \| 0.7762 \| 0.3718 \| 0.8893 \|
	\| 0.6991 \| 16.36 \| 180 \| 0.6620 \| nan \| 0.8964 \| 0.2267 \| 0.0 \| 0.8874 \| 0.6502 \| 0.7792 \| 0.3714 \| 0.8895 \|
	\| 0.6357 \| 17.27 \| 190 \| 0.6612 \| nan \| 0.9051 \| 0.2411 \| 0.0 \| 0.8960 \| 0.6230 \| 0.7831 \| 0.3790 \| 0.8979 \|
	\| 0.6815 \| 18.18 \| 200 \| 0.6484 \| nan \| 0.9178 \| 0.2594 \| 0.0 \| 0.9082 \| 0.5993 \| 0.7831 \| 0.3892 \| 0.9098 \|
	\| 0.6398 \| 19.09 \| 210 \| 0.6414 \| nan \| 0.9258 \| 0.2682 \| 0.0 \| 0.9159 \| 0.5785 \| 0.7836 \| 0.3947 \| 0.9174 \|
	\| 0.5845 \| 20.0 \| 220 \| 0.6426 \| nan \| 0.9286 \| 0.2698 \| 0.0 \| 0.9187 \| 0.5641 \| 0.7856 \| 0.3962 \| 0.9202 \|
	\| 0.6062 \| 20.91 \| 230 \| 0.6520 \| nan \| 0.9252 \| 0.2641 \| 0.0 \| 0.9156 \| 0.5693 \| 0.7886 \| 0.3932 \| 0.9171 \|
	\| 0.6071 \| 21.82 \| 240 \| 0.6592 \| nan \| 0.9283 \| 0.2675 \| 0.0 \| 0.9188 \| 0.5627 \| 0.7937 \| 0.3955 \| 0.9203 \|
	\| 0.6209 \| 22.73 \| 250 \| 0.6619 \| nan \| 0.9300 \| 0.2724 \| 0.0 \| 0.9205 \| 0.5632 \| 0.7959 \| 0.3977 \| 0.9220 \|
	\| 0.5609 \| 23.64 \| 260 \| 0.6505 \| nan \| 0.9379 \| 0.2868 \| 0.0 \| 0.9281 \| 0.5416 \| 0.7942 \| 0.4050 \| 0.9294 \|
	\| 0.5752 \| 24.55 \| 270 \| 0.6412 \| nan \| 0.9451 \| 0.2983 \| 0.0 \| 0.9350 \| 0.5141 \| 0.7932 \| 0.4111 \| 0.9362 \|
	\| 0.6004 \| 25.45 \| 280 \| 0.6492 \| nan \| 0.9412 \| 0.2907 \| 0.0 \| 0.9313 \| 0.5255 \| 0.7952 \| 0.4073 \| 0.9326 \|
	\| 0.5524 \| 26.36 \| 290 \| 0.6588 \| nan \| 0.9387 \| 0.2868 \| 0.0 \| 0.9291 \| 0.5314 \| 0.7987 \| 0.4053 \| 0.9304 \|
	\| 0.5758 \| 27.27 \| 300 \| 0.6544 \| nan \| 0.9423 \| 0.2913 \| 0.0 \| 0.9326 \| 0.5268 \| 0.7984 \| 0.4080 \| 0.9338 \|
	\| 0.5598 \| 28.18 \| 310 \| 0.6605 \| nan \| 0.9408 \| 0.2897 \| 0.0 \| 0.9312 \| 0.5240 \| 0.8006 \| 0.4070 \| 0.9325 \|
	\| 0.5505 \| 29.09 \| 320 \| 0.6582 \| nan \| 0.9421 \| 0.2959 \| 0.0 \| 0.9324 \| 0.5165 \| 0.8002 \| 0.4094 \| 0.9337 \|
	\| 0.5754 \| 30.0 \| 330 \| 0.5145 \| 0.4098 \| 0.8005 \| 0.9348 \| nan \| 0.6578 \| 0.9433 \| 0.0 \| 0.2959 \| 0.9336 \|
	\| 0.5284 \| 30.91 \| 340 \| 0.5175 \| 0.4086 \| 0.8065 \| 0.9331 \| nan \| 0.6719 \| 0.9411 \| 0.0 \| 0.2941 \| 0.9318 \|
	\| 0.5463 \| 31.82 \| 350 \| 0.5016 \| 0.4125 \| 0.8066 \| 0.9367 \| nan \| 0.6684 \| 0.9448 \| 0.0 \| 0.3020 \| 0.9354 \|
	\| 0.4923 \| 32.73 \| 360 \| 0.4947 \| 0.4145 \| 0.8075 \| 0.9381 \| nan \| 0.6688 \| 0.9463 \| 0.0 \| 0.3066 \| 0.9369 \|
	\| 0.4922 \| 33.64 \| 370 \| 0.4738 \| 0.4191 \| 0.8094 \| 0.9420 \| nan \| 0.6685 \| 0.9504 \| 0.0 \| 0.3165 \| 0.9409 \|
	\| 0.4976 \| 34.55 \| 380 \| 0.4663 \| 0.4225 \| 0.8142 \| 0.9453 \| nan \| 0.6748 \| 0.9535 \| 0.0 \| 0.3233 \| 0.9443 \|
	\| 0.4922 \| 35.45 \| 390 \| 0.4295 \| 0.4345 \| 0.8081 \| 0.9560 \| nan \| 0.6509 \| 0.9653 \| 0.0 \| 0.3484 \| 0.9552 \|
	\| 0.4608 \| 36.36 \| 400 \| 0.4434 \| 0.4348 \| 0.8109 \| 0.9547 \| nan \| 0.6580 \| 0.9637 \| 0.0 \| 0.3507 \| 0.9538 \|
	\| 0.4836 \| 37.27 \| 410 \| 0.4328 \| 0.4383 \| 0.8092 \| 0.9569 \| nan \| 0.6522 \| 0.9662 \| 0.0 \| 0.3588 \| 0.9561 \|
	\| 0.459 \| 38.18 \| 420 \| 0.4211 \| 0.4407 \| 0.8084 \| 0.9596 \| nan \| 0.6477 \| 0.9691 \| 0.0 \| 0.3632 \| 0.9588 \|
	\| 0.4528 \| 39.09 \| 430 \| 0.4239 \| 0.4381 \| 0.8131 \| 0.9577 \| nan \| 0.6593 \| 0.9668 \| 0.0 \| 0.3574 \| 0.9569 \|
	\| 0.4202 \| 40.0 \| 440 \| 0.4141 \| 0.4413 \| 0.8130 \| 0.9597 \| nan \| 0.6572 \| 0.9689 \| 0.0 \| 0.3650 \| 0.9590 \|
	\| 0.4805 \| 40.91 \| 450 \| 0.4012 \| 0.4458 \| 0.8097 \| 0.9628 \| nan \| 0.6470 \| 0.9724 \| 0.0 \| 0.3754 \| 0.9621 \|
	\| 0.4611 \| 41.82 \| 460 \| 0.4025 \| 0.4444 \| 0.8122 \| 0.9624 \| nan \| 0.6525 \| 0.9718 \| 0.0 \| 0.3716 \| 0.9617 \|
	\| 0.4339 \| 42.73 \| 470 \| 0.3951 \| 0.4456 \| 0.8107 \| 0.9631 \| nan \| 0.6487 \| 0.9726 \| 0.0 \| 0.3744 \| 0.9624 \|
	\| 0.4361 \| 43.64 \| 480 \| 0.3946 \| 0.4468 \| 0.8094 \| 0.9643 \| nan \| 0.6448 \| 0.9740 \| 0.0 \| 0.3769 \| 0.9636 \|
	\| 0.4416 \| 44.55 \| 490 \| 0.3871 \| 0.4475 \| 0.8097 \| 0.9649 \| nan \| 0.6447 \| 0.9746 \| 0.0 \| 0.3783 \| 0.9642 \|
	\| 0.4524 \| 45.45 \| 500 \| 0.4025 \| 0.4438 \| 0.8151 \| 0.9620 \| nan \| 0.6589 \| 0.9712 \| 0.0 \| 0.3701 \| 0.9612 \|
	\| 0.4319 \| 46.36 \| 510 \| 0.4169 \| 0.4391 \| 0.8202 \| 0.9586 \| nan \| 0.6730 \| 0.9673 \| 0.0 \| 0.3594 \| 0.9578 \|
	\| 0.4224 \| 47.27 \| 520 \| 0.3986 \| 0.4443 \| 0.8158 \| 0.9620 \| nan \| 0.6603 \| 0.9712 \| 0.0 \| 0.3716 \| 0.9613 \|
	\| 0.4409 \| 48.18 \| 530 \| 0.4073 \| 0.4419 \| 0.8179 \| 0.9601 \| nan \| 0.6667 \| 0.9691 \| 0.0 \| 0.3664 \| 0.9594 \|
	\| 0.4228 \| 49.09 \| 540 \| 0.4031 \| 0.4431 \| 0.8168 \| 0.9614 \| nan \| 0.6631 \| 0.9705 \| 0.0 \| 0.3685 \| 0.9607 \|
	\| 0.4605 \| 50.0 \| 550 \| 0.4046 \| 0.4437 \| 0.8171 \| 0.9614 \| nan \| 0.6638 \| 0.9704 \| 0.0 \| 0.3704 \| 0.9606 \|


	### Framework versions

	- Transformers 4.30.2
	- Pytorch 2.0.1+cu117
	- Datasets 2.13.1
	- Tokenizers 0.13.3

	---
	license: other
	tags:
	- vision
	- image-segmentation
	- generated_from_trainer
	model-index:
	- name: safety-utcustom-train-SF-RGB-b5
	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. -->

	# safety-utcustom-train-SF-RGB-b5

	This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.4046
	- Mean Iou: 0.4437
	- Mean Accuracy: 0.8171
	- Overall Accuracy: 0.9614
	- Accuracy Unlabeled: nan
	- Accuracy Safe: 0.6638
	- Accuracy Unsafe: 0.9704
	- Iou Unlabeled: 0.0
	- Iou Safe: 0.3704
	- Iou Unsafe: 0.9606

	## 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: 3e-06
	- train_batch_size: 15
	- eval_batch_size: 15
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.05
	- num_epochs: 50

	### Training results

	\| Training Loss \| Epoch \| Step \| Accuracy Safe \| Accuracy Unlabeled \| Accuracy Unsafe \| Iou Safe \| Iou Unlabeled \| Iou Unsafe \| Validation Loss \| Mean Accuracy \| Mean Iou \| Overall Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:-------------:\|:------------------:\|:---------------:\|:--------:\|:-------------:\|:----------:\|:---------------:\|:-------------:\|:--------:\|:----------------:\|
	\| 1.2239 \| 0.91 \| 10 \| 0.3992 \| nan \| 0.2951 \| 0.0314 \| 0.0 \| 0.2939 \| 1.1103 \| 0.3472 \| 0.1084 \| 0.2982 \|
	\| 1.1948 \| 1.82 \| 20 \| 0.5219 \| nan \| 0.3705 \| 0.0440 \| 0.0 \| 0.3689 \| 1.0963 \| 0.4462 \| 0.1376 \| 0.3750 \|
	\| 1.1661 \| 2.73 \| 30 \| 0.5863 \| nan \| 0.4988 \| 0.0647 \| 0.0 \| 0.4961 \| 1.0516 \| 0.5426 \| 0.1870 \| 0.5014 \|
	\| 1.1112 \| 3.64 \| 40 \| 0.5459 \| nan \| 0.5794 \| 0.0900 \| 0.0 \| 0.5754 \| 1.0048 \| 0.5626 \| 0.2218 \| 0.5784 \|
	\| 1.0907 \| 4.55 \| 50 \| 0.5993 \| nan \| 0.6367 \| 0.1094 \| 0.0 \| 0.6321 \| 0.9690 \| 0.6180 \| 0.2472 \| 0.6356 \|
	\| 1.047 \| 5.45 \| 60 \| 0.6692 \| nan \| 0.6699 \| 0.1159 \| 0.0 \| 0.6656 \| 0.9437 \| 0.6695 \| 0.2605 \| 0.6699 \|
	\| 1.0112 \| 6.36 \| 70 \| 0.6673 \| nan \| 0.7189 \| 0.1349 \| 0.0 \| 0.7137 \| 0.9084 \| 0.6931 \| 0.2829 \| 0.7173 \|
	\| 0.9925 \| 7.27 \| 80 \| 0.6842 \| nan \| 0.7665 \| 0.1452 \| 0.0 \| 0.7605 \| 0.8647 \| 0.7254 \| 0.3019 \| 0.7641 \|
	\| 0.9395 \| 8.18 \| 90 \| 0.6818 \| nan \| 0.7921 \| 0.1620 \| 0.0 \| 0.7856 \| 0.8319 \| 0.7369 \| 0.3159 \| 0.7888 \|
	\| 0.8902 \| 9.09 \| 100 \| 0.6806 \| nan \| 0.8142 \| 0.1770 \| 0.0 \| 0.8072 \| 0.8014 \| 0.7474 \| 0.3281 \| 0.8102 \|
	\| 0.9057 \| 10.0 \| 110 \| 0.6984 \| nan \| 0.8179 \| 0.1733 \| 0.0 \| 0.8109 \| 0.7867 \| 0.7581 \| 0.3281 \| 0.8143 \|
	\| 0.8321 \| 10.91 \| 120 \| 0.6744 \| nan \| 0.8494 \| 0.1862 \| 0.0 \| 0.8413 \| 0.7440 \| 0.7619 \| 0.3425 \| 0.8442 \|
	\| 0.8152 \| 11.82 \| 130 \| 0.6688 \| nan \| 0.8590 \| 0.2006 \| 0.0 \| 0.8507 \| 0.7270 \| 0.7639 \| 0.3504 \| 0.8534 \|
	\| 0.7929 \| 12.73 \| 140 \| 0.6660 \| nan \| 0.8657 \| 0.2085 \| 0.0 \| 0.8572 \| 0.7045 \| 0.7658 \| 0.3553 \| 0.8598 \|
	\| 0.7568 \| 13.64 \| 150 \| 0.6571 \| nan \| 0.8838 \| 0.2185 \| 0.0 \| 0.8748 \| 0.6744 \| 0.7704 \| 0.3644 \| 0.8771 \|
	\| 0.7085 \| 14.55 \| 160 \| 0.6519 \| nan \| 0.8934 \| 0.2260 \| 0.0 \| 0.8842 \| 0.6556 \| 0.7727 \| 0.3701 \| 0.8863 \|
	\| 0.7147 \| 15.45 \| 170 \| 0.6561 \| nan \| 0.8964 \| 0.2283 \| 0.0 \| 0.8872 \| 0.6509 \| 0.7762 \| 0.3718 \| 0.8893 \|
	\| 0.6991 \| 16.36 \| 180 \| 0.6620 \| nan \| 0.8964 \| 0.2267 \| 0.0 \| 0.8874 \| 0.6502 \| 0.7792 \| 0.3714 \| 0.8895 \|
	\| 0.6357 \| 17.27 \| 190 \| 0.6612 \| nan \| 0.9051 \| 0.2411 \| 0.0 \| 0.8960 \| 0.6230 \| 0.7831 \| 0.3790 \| 0.8979 \|
	\| 0.6815 \| 18.18 \| 200 \| 0.6484 \| nan \| 0.9178 \| 0.2594 \| 0.0 \| 0.9082 \| 0.5993 \| 0.7831 \| 0.3892 \| 0.9098 \|
	\| 0.6398 \| 19.09 \| 210 \| 0.6414 \| nan \| 0.9258 \| 0.2682 \| 0.0 \| 0.9159 \| 0.5785 \| 0.7836 \| 0.3947 \| 0.9174 \|
	\| 0.5845 \| 20.0 \| 220 \| 0.6426 \| nan \| 0.9286 \| 0.2698 \| 0.0 \| 0.9187 \| 0.5641 \| 0.7856 \| 0.3962 \| 0.9202 \|
	\| 0.6062 \| 20.91 \| 230 \| 0.6520 \| nan \| 0.9252 \| 0.2641 \| 0.0 \| 0.9156 \| 0.5693 \| 0.7886 \| 0.3932 \| 0.9171 \|
	\| 0.6071 \| 21.82 \| 240 \| 0.6592 \| nan \| 0.9283 \| 0.2675 \| 0.0 \| 0.9188 \| 0.5627 \| 0.7937 \| 0.3955 \| 0.9203 \|
	\| 0.6209 \| 22.73 \| 250 \| 0.6619 \| nan \| 0.9300 \| 0.2724 \| 0.0 \| 0.9205 \| 0.5632 \| 0.7959 \| 0.3977 \| 0.9220 \|
	\| 0.5609 \| 23.64 \| 260 \| 0.6505 \| nan \| 0.9379 \| 0.2868 \| 0.0 \| 0.9281 \| 0.5416 \| 0.7942 \| 0.4050 \| 0.9294 \|
	\| 0.5752 \| 24.55 \| 270 \| 0.6412 \| nan \| 0.9451 \| 0.2983 \| 0.0 \| 0.9350 \| 0.5141 \| 0.7932 \| 0.4111 \| 0.9362 \|
	\| 0.6004 \| 25.45 \| 280 \| 0.6492 \| nan \| 0.9412 \| 0.2907 \| 0.0 \| 0.9313 \| 0.5255 \| 0.7952 \| 0.4073 \| 0.9326 \|
	\| 0.5524 \| 26.36 \| 290 \| 0.6588 \| nan \| 0.9387 \| 0.2868 \| 0.0 \| 0.9291 \| 0.5314 \| 0.7987 \| 0.4053 \| 0.9304 \|
	\| 0.5758 \| 27.27 \| 300 \| 0.6544 \| nan \| 0.9423 \| 0.2913 \| 0.0 \| 0.9326 \| 0.5268 \| 0.7984 \| 0.4080 \| 0.9338 \|
	\| 0.5598 \| 28.18 \| 310 \| 0.6605 \| nan \| 0.9408 \| 0.2897 \| 0.0 \| 0.9312 \| 0.5240 \| 0.8006 \| 0.4070 \| 0.9325 \|
	\| 0.5505 \| 29.09 \| 320 \| 0.6582 \| nan \| 0.9421 \| 0.2959 \| 0.0 \| 0.9324 \| 0.5165 \| 0.8002 \| 0.4094 \| 0.9337 \|
	\| 0.5754 \| 30.0 \| 330 \| 0.5145 \| 0.4098 \| 0.8005 \| 0.9348 \| nan \| 0.6578 \| 0.9433 \| 0.0 \| 0.2959 \| 0.9336 \|
	\| 0.5284 \| 30.91 \| 340 \| 0.5175 \| 0.4086 \| 0.8065 \| 0.9331 \| nan \| 0.6719 \| 0.9411 \| 0.0 \| 0.2941 \| 0.9318 \|
	\| 0.5463 \| 31.82 \| 350 \| 0.5016 \| 0.4125 \| 0.8066 \| 0.9367 \| nan \| 0.6684 \| 0.9448 \| 0.0 \| 0.3020 \| 0.9354 \|
	\| 0.4923 \| 32.73 \| 360 \| 0.4947 \| 0.4145 \| 0.8075 \| 0.9381 \| nan \| 0.6688 \| 0.9463 \| 0.0 \| 0.3066 \| 0.9369 \|
	\| 0.4922 \| 33.64 \| 370 \| 0.4738 \| 0.4191 \| 0.8094 \| 0.9420 \| nan \| 0.6685 \| 0.9504 \| 0.0 \| 0.3165 \| 0.9409 \|
	\| 0.4976 \| 34.55 \| 380 \| 0.4663 \| 0.4225 \| 0.8142 \| 0.9453 \| nan \| 0.6748 \| 0.9535 \| 0.0 \| 0.3233 \| 0.9443 \|
	\| 0.4922 \| 35.45 \| 390 \| 0.4295 \| 0.4345 \| 0.8081 \| 0.9560 \| nan \| 0.6509 \| 0.9653 \| 0.0 \| 0.3484 \| 0.9552 \|
	\| 0.4608 \| 36.36 \| 400 \| 0.4434 \| 0.4348 \| 0.8109 \| 0.9547 \| nan \| 0.6580 \| 0.9637 \| 0.0 \| 0.3507 \| 0.9538 \|
	\| 0.4836 \| 37.27 \| 410 \| 0.4328 \| 0.4383 \| 0.8092 \| 0.9569 \| nan \| 0.6522 \| 0.9662 \| 0.0 \| 0.3588 \| 0.9561 \|
	\| 0.459 \| 38.18 \| 420 \| 0.4211 \| 0.4407 \| 0.8084 \| 0.9596 \| nan \| 0.6477 \| 0.9691 \| 0.0 \| 0.3632 \| 0.9588 \|
	\| 0.4528 \| 39.09 \| 430 \| 0.4239 \| 0.4381 \| 0.8131 \| 0.9577 \| nan \| 0.6593 \| 0.9668 \| 0.0 \| 0.3574 \| 0.9569 \|
	\| 0.4202 \| 40.0 \| 440 \| 0.4141 \| 0.4413 \| 0.8130 \| 0.9597 \| nan \| 0.6572 \| 0.9689 \| 0.0 \| 0.3650 \| 0.9590 \|
	\| 0.4805 \| 40.91 \| 450 \| 0.4012 \| 0.4458 \| 0.8097 \| 0.9628 \| nan \| 0.6470 \| 0.9724 \| 0.0 \| 0.3754 \| 0.9621 \|
	\| 0.4611 \| 41.82 \| 460 \| 0.4025 \| 0.4444 \| 0.8122 \| 0.9624 \| nan \| 0.6525 \| 0.9718 \| 0.0 \| 0.3716 \| 0.9617 \|
	\| 0.4339 \| 42.73 \| 470 \| 0.3951 \| 0.4456 \| 0.8107 \| 0.9631 \| nan \| 0.6487 \| 0.9726 \| 0.0 \| 0.3744 \| 0.9624 \|
	\| 0.4361 \| 43.64 \| 480 \| 0.3946 \| 0.4468 \| 0.8094 \| 0.9643 \| nan \| 0.6448 \| 0.9740 \| 0.0 \| 0.3769 \| 0.9636 \|
	\| 0.4416 \| 44.55 \| 490 \| 0.3871 \| 0.4475 \| 0.8097 \| 0.9649 \| nan \| 0.6447 \| 0.9746 \| 0.0 \| 0.3783 \| 0.9642 \|
	\| 0.4524 \| 45.45 \| 500 \| 0.4025 \| 0.4438 \| 0.8151 \| 0.9620 \| nan \| 0.6589 \| 0.9712 \| 0.0 \| 0.3701 \| 0.9612 \|
	\| 0.4319 \| 46.36 \| 510 \| 0.4169 \| 0.4391 \| 0.8202 \| 0.9586 \| nan \| 0.6730 \| 0.9673 \| 0.0 \| 0.3594 \| 0.9578 \|
	\| 0.4224 \| 47.27 \| 520 \| 0.3986 \| 0.4443 \| 0.8158 \| 0.9620 \| nan \| 0.6603 \| 0.9712 \| 0.0 \| 0.3716 \| 0.9613 \|
	\| 0.4409 \| 48.18 \| 530 \| 0.4073 \| 0.4419 \| 0.8179 \| 0.9601 \| nan \| 0.6667 \| 0.9691 \| 0.0 \| 0.3664 \| 0.9594 \|
	\| 0.4228 \| 49.09 \| 540 \| 0.4031 \| 0.4431 \| 0.8168 \| 0.9614 \| nan \| 0.6631 \| 0.9705 \| 0.0 \| 0.3685 \| 0.9607 \|
	\| 0.4605 \| 50.0 \| 550 \| 0.4046 \| 0.4437 \| 0.8171 \| 0.9614 \| nan \| 0.6638 \| 0.9704 \| 0.0 \| 0.3704 \| 0.9606 \|


	### Framework versions

	- Transformers 4.30.2
	- Pytorch 2.0.1+cu117
	- Datasets 2.13.1
	- Tokenizers 0.13.3