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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-_rvl_cdip-NK1000__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-_rvl_cdip-NK1000__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: 0.6065
	- Accuracy: 0.7915
	- Brier Loss: 0.3054
	- Nll: 1.9957
	- F1 Micro: 0.7915
	- F1 Macro: 0.7910
	- Ece: 0.0453
	- Aurc: 0.0607

	## 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: 50

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\|
	\| No log \| 1.0 \| 250 \| 4.1565 \| 0.1378 \| 0.9318 \| 7.9039 \| 0.1378 \| 0.1073 \| 0.0673 \| 0.8326 \|
	\| 4.1485 \| 2.0 \| 500 \| 3.6932 \| 0.3235 \| 0.8832 \| 5.1525 \| 0.3235 \| 0.2725 \| 0.2044 \| 0.5507 \|
	\| 4.1485 \| 3.0 \| 750 \| 2.3374 \| 0.4725 \| 0.6611 \| 3.3127 \| 0.4725 \| 0.4311 \| 0.0839 \| 0.2921 \|
	\| 2.392 \| 4.0 \| 1000 \| 1.6516 \| 0.588 \| 0.5470 \| 2.8681 \| 0.588 \| 0.5789 \| 0.0620 \| 0.1929 \|
	\| 2.392 \| 5.0 \| 1250 \| 1.3260 \| 0.6488 \| 0.4782 \| 2.6378 \| 0.6488 \| 0.6444 \| 0.0486 \| 0.1458 \|
	\| 1.1422 \| 6.0 \| 1500 \| 1.0390 \| 0.702 \| 0.4156 \| 2.4086 \| 0.702 \| 0.7029 \| 0.0576 \| 0.1097 \|
	\| 1.1422 \| 7.0 \| 1750 \| 0.8420 \| 0.7288 \| 0.3738 \| 2.2222 \| 0.7288 \| 0.7300 \| 0.0553 \| 0.0888 \|
	\| 0.708 \| 8.0 \| 2000 \| 0.7753 \| 0.7398 \| 0.3586 \| 2.1518 \| 0.7398 \| 0.7396 \| 0.0587 \| 0.0826 \|
	\| 0.708 \| 9.0 \| 2250 \| 0.7797 \| 0.7462 \| 0.3580 \| 2.1095 \| 0.7462 \| 0.7457 \| 0.0581 \| 0.0820 \|
	\| 0.5195 \| 10.0 \| 2500 \| 0.7101 \| 0.7602 \| 0.3404 \| 2.0711 \| 0.7602 \| 0.7612 \| 0.0473 \| 0.0733 \|
	\| 0.5195 \| 11.0 \| 2750 \| 0.6971 \| 0.7645 \| 0.3338 \| 2.0649 \| 0.7645 \| 0.7653 \| 0.0541 \| 0.0715 \|
	\| 0.4176 \| 12.0 \| 3000 \| 0.6936 \| 0.7712 \| 0.3302 \| 2.0265 \| 0.7712 \| 0.7708 \| 0.0515 \| 0.0702 \|
	\| 0.4176 \| 13.0 \| 3250 \| 0.6991 \| 0.7662 \| 0.3346 \| 2.0582 \| 0.7663 \| 0.7657 \| 0.0581 \| 0.0723 \|
	\| 0.3573 \| 14.0 \| 3500 \| 0.6672 \| 0.7722 \| 0.3246 \| 2.0053 \| 0.7722 \| 0.7723 \| 0.0551 \| 0.0683 \|
	\| 0.3573 \| 15.0 \| 3750 \| 0.6735 \| 0.777 \| 0.3244 \| 2.0387 \| 0.777 \| 0.7782 \| 0.0488 \| 0.0671 \|
	\| 0.3193 \| 16.0 \| 4000 \| 0.6567 \| 0.776 \| 0.3216 \| 2.0256 \| 0.776 \| 0.7773 \| 0.0499 \| 0.0678 \|
	\| 0.3193 \| 17.0 \| 4250 \| 0.6498 \| 0.78 \| 0.3184 \| 1.9865 \| 0.78 \| 0.7802 \| 0.0477 \| 0.0662 \|
	\| 0.2893 \| 18.0 \| 4500 \| 0.6763 \| 0.7755 \| 0.3264 \| 2.0844 \| 0.7755 \| 0.7755 \| 0.0531 \| 0.0697 \|
	\| 0.2893 \| 19.0 \| 4750 \| 0.6519 \| 0.7815 \| 0.3183 \| 2.0458 \| 0.7815 \| 0.7817 \| 0.0513 \| 0.0658 \|
	\| 0.271 \| 20.0 \| 5000 \| 0.6432 \| 0.7823 \| 0.3147 \| 2.0291 \| 0.7823 \| 0.7827 \| 0.0440 \| 0.0645 \|
	\| 0.271 \| 21.0 \| 5250 \| 0.6456 \| 0.781 \| 0.3156 \| 2.0493 \| 0.7810 \| 0.7813 \| 0.0487 \| 0.0652 \|
	\| 0.2516 \| 22.0 \| 5500 \| 0.6336 \| 0.7823 \| 0.3144 \| 1.9829 \| 0.7823 \| 0.7822 \| 0.0522 \| 0.0642 \|
	\| 0.2516 \| 23.0 \| 5750 \| 0.6333 \| 0.7837 \| 0.3128 \| 2.0196 \| 0.7837 \| 0.7836 \| 0.0492 \| 0.0641 \|
	\| 0.2397 \| 24.0 \| 6000 \| 0.6337 \| 0.7817 \| 0.3147 \| 2.0180 \| 0.7817 \| 0.7815 \| 0.0494 \| 0.0644 \|
	\| 0.2397 \| 25.0 \| 6250 \| 0.6347 \| 0.7857 \| 0.3145 \| 2.0187 \| 0.7857 \| 0.7856 \| 0.0510 \| 0.0641 \|
	\| 0.23 \| 26.0 \| 6500 \| 0.6311 \| 0.7815 \| 0.3129 \| 2.0132 \| 0.7815 \| 0.7819 \| 0.0495 \| 0.0637 \|
	\| 0.23 \| 27.0 \| 6750 \| 0.6329 \| 0.7853 \| 0.3125 \| 2.0708 \| 0.7853 \| 0.7852 \| 0.0502 \| 0.0635 \|
	\| 0.2191 \| 28.0 \| 7000 \| 0.6222 \| 0.786 \| 0.3109 \| 2.0022 \| 0.786 \| 0.7856 \| 0.0483 \| 0.0638 \|
	\| 0.2191 \| 29.0 \| 7250 \| 0.6195 \| 0.7863 \| 0.3096 \| 2.0028 \| 0.7863 \| 0.7859 \| 0.0550 \| 0.0620 \|
	\| 0.2155 \| 30.0 \| 7500 \| 0.6196 \| 0.7883 \| 0.3090 \| 1.9972 \| 0.7883 \| 0.7883 \| 0.0486 \| 0.0624 \|
	\| 0.2155 \| 31.0 \| 7750 \| 0.6167 \| 0.787 \| 0.3080 \| 2.0173 \| 0.787 \| 0.7871 \| 0.0443 \| 0.0623 \|
	\| 0.2074 \| 32.0 \| 8000 \| 0.6143 \| 0.7897 \| 0.3073 \| 2.0223 \| 0.7897 \| 0.7893 \| 0.0443 \| 0.0614 \|
	\| 0.2074 \| 33.0 \| 8250 \| 0.6123 \| 0.787 \| 0.3078 \| 1.9869 \| 0.787 \| 0.7866 \| 0.0458 \| 0.0619 \|
	\| 0.2028 \| 34.0 \| 8500 \| 0.6137 \| 0.7873 \| 0.3070 \| 1.9883 \| 0.7873 \| 0.7868 \| 0.0457 \| 0.0623 \|
	\| 0.2028 \| 35.0 \| 8750 \| 0.6152 \| 0.786 \| 0.3085 \| 2.0108 \| 0.786 \| 0.7863 \| 0.0497 \| 0.0626 \|
	\| 0.1982 \| 36.0 \| 9000 \| 0.6133 \| 0.7863 \| 0.3077 \| 2.0205 \| 0.7863 \| 0.7862 \| 0.0515 \| 0.0615 \|
	\| 0.1982 \| 37.0 \| 9250 \| 0.6145 \| 0.7877 \| 0.3081 \| 1.9930 \| 0.7877 \| 0.7879 \| 0.0444 \| 0.0621 \|
	\| 0.1948 \| 38.0 \| 9500 \| 0.6116 \| 0.7857 \| 0.3078 \| 2.0072 \| 0.7857 \| 0.7854 \| 0.0508 \| 0.0619 \|
	\| 0.1948 \| 39.0 \| 9750 \| 0.6090 \| 0.788 \| 0.3059 \| 1.9954 \| 0.788 \| 0.7882 \| 0.0430 \| 0.0614 \|
	\| 0.1933 \| 40.0 \| 10000 \| 0.6143 \| 0.7897 \| 0.3072 \| 1.9943 \| 0.7897 \| 0.7899 \| 0.0462 \| 0.0618 \|
	\| 0.1933 \| 41.0 \| 10250 \| 0.6061 \| 0.7887 \| 0.3041 \| 1.9900 \| 0.7887 \| 0.7889 \| 0.0439 \| 0.0606 \|
	\| 0.1882 \| 42.0 \| 10500 \| 0.6070 \| 0.7865 \| 0.3058 \| 1.9907 \| 0.7865 \| 0.7868 \| 0.0438 \| 0.0607 \|
	\| 0.1882 \| 43.0 \| 10750 \| 0.6083 \| 0.788 \| 0.3054 \| 2.0095 \| 0.788 \| 0.7877 \| 0.0489 \| 0.0608 \|
	\| 0.1871 \| 44.0 \| 11000 \| 0.6083 \| 0.787 \| 0.3054 \| 1.9828 \| 0.787 \| 0.7872 \| 0.0469 \| 0.0607 \|
	\| 0.1871 \| 45.0 \| 11250 \| 0.6092 \| 0.7893 \| 0.3057 \| 2.0140 \| 0.7893 \| 0.7891 \| 0.0483 \| 0.0608 \|
	\| 0.1862 \| 46.0 \| 11500 \| 0.6057 \| 0.7893 \| 0.3053 \| 2.0064 \| 0.7893 \| 0.7890 \| 0.0450 \| 0.0609 \|
	\| 0.1862 \| 47.0 \| 11750 \| 0.6042 \| 0.79 \| 0.3044 \| 1.9691 \| 0.79 \| 0.7899 \| 0.0435 \| 0.0607 \|
	\| 0.1845 \| 48.0 \| 12000 \| 0.6068 \| 0.79 \| 0.3053 \| 2.0052 \| 0.79 \| 0.7899 \| 0.0438 \| 0.0608 \|
	\| 0.1845 \| 49.0 \| 12250 \| 0.6081 \| 0.7893 \| 0.3062 \| 2.0117 \| 0.7893 \| 0.7890 \| 0.0485 \| 0.0612 \|
	\| 0.1836 \| 50.0 \| 12500 \| 0.6065 \| 0.7915 \| 0.3054 \| 1.9957 \| 0.7915 \| 0.7910 \| 0.0453 \| 0.0607 \|


	### 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-_rvl_cdip-NK1000__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-_rvl_cdip-NK1000__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: 0.6065
	- Accuracy: 0.7915
	- Brier Loss: 0.3054
	- Nll: 1.9957
	- F1 Micro: 0.7915
	- F1 Macro: 0.7910
	- Ece: 0.0453
	- Aurc: 0.0607

	## 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: 50

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\|
	\| No log \| 1.0 \| 250 \| 4.1565 \| 0.1378 \| 0.9318 \| 7.9039 \| 0.1378 \| 0.1073 \| 0.0673 \| 0.8326 \|
	\| 4.1485 \| 2.0 \| 500 \| 3.6932 \| 0.3235 \| 0.8832 \| 5.1525 \| 0.3235 \| 0.2725 \| 0.2044 \| 0.5507 \|
	\| 4.1485 \| 3.0 \| 750 \| 2.3374 \| 0.4725 \| 0.6611 \| 3.3127 \| 0.4725 \| 0.4311 \| 0.0839 \| 0.2921 \|
	\| 2.392 \| 4.0 \| 1000 \| 1.6516 \| 0.588 \| 0.5470 \| 2.8681 \| 0.588 \| 0.5789 \| 0.0620 \| 0.1929 \|
	\| 2.392 \| 5.0 \| 1250 \| 1.3260 \| 0.6488 \| 0.4782 \| 2.6378 \| 0.6488 \| 0.6444 \| 0.0486 \| 0.1458 \|
	\| 1.1422 \| 6.0 \| 1500 \| 1.0390 \| 0.702 \| 0.4156 \| 2.4086 \| 0.702 \| 0.7029 \| 0.0576 \| 0.1097 \|
	\| 1.1422 \| 7.0 \| 1750 \| 0.8420 \| 0.7288 \| 0.3738 \| 2.2222 \| 0.7288 \| 0.7300 \| 0.0553 \| 0.0888 \|
	\| 0.708 \| 8.0 \| 2000 \| 0.7753 \| 0.7398 \| 0.3586 \| 2.1518 \| 0.7398 \| 0.7396 \| 0.0587 \| 0.0826 \|
	\| 0.708 \| 9.0 \| 2250 \| 0.7797 \| 0.7462 \| 0.3580 \| 2.1095 \| 0.7462 \| 0.7457 \| 0.0581 \| 0.0820 \|
	\| 0.5195 \| 10.0 \| 2500 \| 0.7101 \| 0.7602 \| 0.3404 \| 2.0711 \| 0.7602 \| 0.7612 \| 0.0473 \| 0.0733 \|
	\| 0.5195 \| 11.0 \| 2750 \| 0.6971 \| 0.7645 \| 0.3338 \| 2.0649 \| 0.7645 \| 0.7653 \| 0.0541 \| 0.0715 \|
	\| 0.4176 \| 12.0 \| 3000 \| 0.6936 \| 0.7712 \| 0.3302 \| 2.0265 \| 0.7712 \| 0.7708 \| 0.0515 \| 0.0702 \|
	\| 0.4176 \| 13.0 \| 3250 \| 0.6991 \| 0.7662 \| 0.3346 \| 2.0582 \| 0.7663 \| 0.7657 \| 0.0581 \| 0.0723 \|
	\| 0.3573 \| 14.0 \| 3500 \| 0.6672 \| 0.7722 \| 0.3246 \| 2.0053 \| 0.7722 \| 0.7723 \| 0.0551 \| 0.0683 \|
	\| 0.3573 \| 15.0 \| 3750 \| 0.6735 \| 0.777 \| 0.3244 \| 2.0387 \| 0.777 \| 0.7782 \| 0.0488 \| 0.0671 \|
	\| 0.3193 \| 16.0 \| 4000 \| 0.6567 \| 0.776 \| 0.3216 \| 2.0256 \| 0.776 \| 0.7773 \| 0.0499 \| 0.0678 \|
	\| 0.3193 \| 17.0 \| 4250 \| 0.6498 \| 0.78 \| 0.3184 \| 1.9865 \| 0.78 \| 0.7802 \| 0.0477 \| 0.0662 \|
	\| 0.2893 \| 18.0 \| 4500 \| 0.6763 \| 0.7755 \| 0.3264 \| 2.0844 \| 0.7755 \| 0.7755 \| 0.0531 \| 0.0697 \|
	\| 0.2893 \| 19.0 \| 4750 \| 0.6519 \| 0.7815 \| 0.3183 \| 2.0458 \| 0.7815 \| 0.7817 \| 0.0513 \| 0.0658 \|
	\| 0.271 \| 20.0 \| 5000 \| 0.6432 \| 0.7823 \| 0.3147 \| 2.0291 \| 0.7823 \| 0.7827 \| 0.0440 \| 0.0645 \|
	\| 0.271 \| 21.0 \| 5250 \| 0.6456 \| 0.781 \| 0.3156 \| 2.0493 \| 0.7810 \| 0.7813 \| 0.0487 \| 0.0652 \|
	\| 0.2516 \| 22.0 \| 5500 \| 0.6336 \| 0.7823 \| 0.3144 \| 1.9829 \| 0.7823 \| 0.7822 \| 0.0522 \| 0.0642 \|
	\| 0.2516 \| 23.0 \| 5750 \| 0.6333 \| 0.7837 \| 0.3128 \| 2.0196 \| 0.7837 \| 0.7836 \| 0.0492 \| 0.0641 \|
	\| 0.2397 \| 24.0 \| 6000 \| 0.6337 \| 0.7817 \| 0.3147 \| 2.0180 \| 0.7817 \| 0.7815 \| 0.0494 \| 0.0644 \|
	\| 0.2397 \| 25.0 \| 6250 \| 0.6347 \| 0.7857 \| 0.3145 \| 2.0187 \| 0.7857 \| 0.7856 \| 0.0510 \| 0.0641 \|
	\| 0.23 \| 26.0 \| 6500 \| 0.6311 \| 0.7815 \| 0.3129 \| 2.0132 \| 0.7815 \| 0.7819 \| 0.0495 \| 0.0637 \|
	\| 0.23 \| 27.0 \| 6750 \| 0.6329 \| 0.7853 \| 0.3125 \| 2.0708 \| 0.7853 \| 0.7852 \| 0.0502 \| 0.0635 \|
	\| 0.2191 \| 28.0 \| 7000 \| 0.6222 \| 0.786 \| 0.3109 \| 2.0022 \| 0.786 \| 0.7856 \| 0.0483 \| 0.0638 \|
	\| 0.2191 \| 29.0 \| 7250 \| 0.6195 \| 0.7863 \| 0.3096 \| 2.0028 \| 0.7863 \| 0.7859 \| 0.0550 \| 0.0620 \|
	\| 0.2155 \| 30.0 \| 7500 \| 0.6196 \| 0.7883 \| 0.3090 \| 1.9972 \| 0.7883 \| 0.7883 \| 0.0486 \| 0.0624 \|
	\| 0.2155 \| 31.0 \| 7750 \| 0.6167 \| 0.787 \| 0.3080 \| 2.0173 \| 0.787 \| 0.7871 \| 0.0443 \| 0.0623 \|
	\| 0.2074 \| 32.0 \| 8000 \| 0.6143 \| 0.7897 \| 0.3073 \| 2.0223 \| 0.7897 \| 0.7893 \| 0.0443 \| 0.0614 \|
	\| 0.2074 \| 33.0 \| 8250 \| 0.6123 \| 0.787 \| 0.3078 \| 1.9869 \| 0.787 \| 0.7866 \| 0.0458 \| 0.0619 \|
	\| 0.2028 \| 34.0 \| 8500 \| 0.6137 \| 0.7873 \| 0.3070 \| 1.9883 \| 0.7873 \| 0.7868 \| 0.0457 \| 0.0623 \|
	\| 0.2028 \| 35.0 \| 8750 \| 0.6152 \| 0.786 \| 0.3085 \| 2.0108 \| 0.786 \| 0.7863 \| 0.0497 \| 0.0626 \|
	\| 0.1982 \| 36.0 \| 9000 \| 0.6133 \| 0.7863 \| 0.3077 \| 2.0205 \| 0.7863 \| 0.7862 \| 0.0515 \| 0.0615 \|
	\| 0.1982 \| 37.0 \| 9250 \| 0.6145 \| 0.7877 \| 0.3081 \| 1.9930 \| 0.7877 \| 0.7879 \| 0.0444 \| 0.0621 \|
	\| 0.1948 \| 38.0 \| 9500 \| 0.6116 \| 0.7857 \| 0.3078 \| 2.0072 \| 0.7857 \| 0.7854 \| 0.0508 \| 0.0619 \|
	\| 0.1948 \| 39.0 \| 9750 \| 0.6090 \| 0.788 \| 0.3059 \| 1.9954 \| 0.788 \| 0.7882 \| 0.0430 \| 0.0614 \|
	\| 0.1933 \| 40.0 \| 10000 \| 0.6143 \| 0.7897 \| 0.3072 \| 1.9943 \| 0.7897 \| 0.7899 \| 0.0462 \| 0.0618 \|
	\| 0.1933 \| 41.0 \| 10250 \| 0.6061 \| 0.7887 \| 0.3041 \| 1.9900 \| 0.7887 \| 0.7889 \| 0.0439 \| 0.0606 \|
	\| 0.1882 \| 42.0 \| 10500 \| 0.6070 \| 0.7865 \| 0.3058 \| 1.9907 \| 0.7865 \| 0.7868 \| 0.0438 \| 0.0607 \|
	\| 0.1882 \| 43.0 \| 10750 \| 0.6083 \| 0.788 \| 0.3054 \| 2.0095 \| 0.788 \| 0.7877 \| 0.0489 \| 0.0608 \|
	\| 0.1871 \| 44.0 \| 11000 \| 0.6083 \| 0.787 \| 0.3054 \| 1.9828 \| 0.787 \| 0.7872 \| 0.0469 \| 0.0607 \|
	\| 0.1871 \| 45.0 \| 11250 \| 0.6092 \| 0.7893 \| 0.3057 \| 2.0140 \| 0.7893 \| 0.7891 \| 0.0483 \| 0.0608 \|
	\| 0.1862 \| 46.0 \| 11500 \| 0.6057 \| 0.7893 \| 0.3053 \| 2.0064 \| 0.7893 \| 0.7890 \| 0.0450 \| 0.0609 \|
	\| 0.1862 \| 47.0 \| 11750 \| 0.6042 \| 0.79 \| 0.3044 \| 1.9691 \| 0.79 \| 0.7899 \| 0.0435 \| 0.0607 \|
	\| 0.1845 \| 48.0 \| 12000 \| 0.6068 \| 0.79 \| 0.3053 \| 2.0052 \| 0.79 \| 0.7899 \| 0.0438 \| 0.0608 \|
	\| 0.1845 \| 49.0 \| 12250 \| 0.6081 \| 0.7893 \| 0.3062 \| 2.0117 \| 0.7893 \| 0.7890 \| 0.0485 \| 0.0612 \|
	\| 0.1836 \| 50.0 \| 12500 \| 0.6065 \| 0.7915 \| 0.3054 \| 1.9957 \| 0.7915 \| 0.7910 \| 0.0453 \| 0.0607 \|


	### Framework versions

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