update model card README.md

eefa2a8 8 months ago

No virus

7.85 kB

	---
	license: apache-2.0
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_hint_rand
	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_hint_rand

	This model is a fine-tuned version of [bdpc/resnet101_rvl-cdip](https://huggingface.co/bdpc/resnet101_rvl-cdip) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 19.9624
	- Accuracy: 0.765
	- Brier Loss: 0.3910
	- Nll: 2.2998
	- F1 Micro: 0.765
	- F1 Macro: 0.7641
	- Ece: 0.1669
	- Aurc: 0.0775

	## 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 \| 26.8546 \| 0.2102 \| 0.8652 \| 3.4225 \| 0.2102 \| 0.1897 \| 0.0659 \| 0.6137 \|
	\| 26.8891 \| 2.0 \| 500 \| 25.9803 \| 0.2845 \| 0.8381 \| 3.4360 \| 0.2845 \| 0.2457 \| 0.0762 \| 0.5573 \|
	\| 26.8891 \| 3.0 \| 750 \| 25.8252 \| 0.337 \| 0.8092 \| 3.6483 \| 0.337 \| 0.3168 \| 0.1091 \| 0.4988 \|
	\| 25.1656 \| 4.0 \| 1000 \| 24.9957 \| 0.4088 \| 0.7588 \| 3.0528 \| 0.4088 \| 0.3859 \| 0.1283 \| 0.4163 \|
	\| 25.1656 \| 5.0 \| 1250 \| 24.2209 \| 0.5517 \| 0.5964 \| 2.6661 \| 0.5517 \| 0.5510 \| 0.0726 \| 0.2489 \|
	\| 23.8526 \| 6.0 \| 1500 \| 23.4086 \| 0.5915 \| 0.5431 \| 2.5360 \| 0.5915 \| 0.5840 \| 0.0549 \| 0.1994 \|
	\| 23.8526 \| 7.0 \| 1750 \| 23.0800 \| 0.625 \| 0.5049 \| 2.4839 \| 0.625 \| 0.6268 \| 0.0608 \| 0.1668 \|
	\| 22.7511 \| 8.0 \| 2000 \| 22.9512 \| 0.6573 \| 0.4660 \| 2.4202 \| 0.6573 \| 0.6564 \| 0.0565 \| 0.1407 \|
	\| 22.7511 \| 9.0 \| 2250 \| 22.7991 \| 0.6783 \| 0.4509 \| 2.4137 \| 0.6783 \| 0.6749 \| 0.0634 \| 0.1315 \|
	\| 21.9881 \| 10.0 \| 2500 \| 22.8533 \| 0.6352 \| 0.5056 \| 2.6549 \| 0.6352 \| 0.6231 \| 0.1085 \| 0.1554 \|
	\| 21.9881 \| 11.0 \| 2750 \| 22.7499 \| 0.669 \| 0.4673 \| 2.5291 \| 0.669 \| 0.6642 \| 0.1053 \| 0.1347 \|
	\| 21.391 \| 12.0 \| 3000 \| 22.6520 \| 0.6757 \| 0.4767 \| 2.5038 \| 0.6757 \| 0.6745 \| 0.1204 \| 0.1355 \|
	\| 21.391 \| 13.0 \| 3250 \| 22.4767 \| 0.6737 \| 0.4850 \| 2.6030 \| 0.6737 \| 0.6718 \| 0.1385 \| 0.1380 \|
	\| 20.9347 \| 14.0 \| 3500 \| 22.3023 \| 0.6767 \| 0.4832 \| 2.5438 \| 0.6767 \| 0.6770 \| 0.1594 \| 0.1301 \|
	\| 20.9347 \| 15.0 \| 3750 \| 22.1482 \| 0.693 \| 0.4666 \| 2.5622 \| 0.693 \| 0.6913 \| 0.1581 \| 0.1209 \|
	\| 20.5776 \| 16.0 \| 4000 \| 22.1655 \| 0.6943 \| 0.4849 \| 2.5685 \| 0.6943 \| 0.6994 \| 0.1766 \| 0.1288 \|
	\| 20.5776 \| 17.0 \| 4250 \| 22.0213 \| 0.686 \| 0.4922 \| 2.6576 \| 0.686 \| 0.6925 \| 0.1749 \| 0.1250 \|
	\| 20.2836 \| 18.0 \| 4500 \| 21.5434 \| 0.7023 \| 0.4560 \| 2.5508 \| 0.7023 \| 0.7018 \| 0.1720 \| 0.1146 \|
	\| 20.2836 \| 19.0 \| 4750 \| 21.7105 \| 0.715 \| 0.4501 \| 2.5953 \| 0.715 \| 0.7128 \| 0.1738 \| 0.1083 \|
	\| 20.0339 \| 20.0 \| 5000 \| 21.6301 \| 0.7057 \| 0.4645 \| 2.6131 \| 0.7057 \| 0.7033 \| 0.1701 \| 0.1153 \|
	\| 20.0339 \| 21.0 \| 5250 \| 21.9130 \| 0.7007 \| 0.4825 \| 2.7114 \| 0.7007 \| 0.6989 \| 0.1917 \| 0.1231 \|
	\| 19.8026 \| 22.0 \| 5500 \| 21.7975 \| 0.713 \| 0.4702 \| 2.7340 \| 0.713 \| 0.7117 \| 0.1879 \| 0.1148 \|
	\| 19.8026 \| 23.0 \| 5750 \| 21.5577 \| 0.7173 \| 0.4621 \| 2.7138 \| 0.7173 \| 0.7100 \| 0.1931 \| 0.1072 \|
	\| 19.6001 \| 24.0 \| 6000 \| 21.2486 \| 0.722 \| 0.4491 \| 2.5651 \| 0.722 \| 0.7214 \| 0.1853 \| 0.1045 \|
	\| 19.6001 \| 25.0 \| 6250 \| 21.0363 \| 0.7348 \| 0.4344 \| 2.4688 \| 0.7348 \| 0.7364 \| 0.1780 \| 0.0974 \|
	\| 19.4158 \| 26.0 \| 6500 \| 21.3527 \| 0.728 \| 0.4495 \| 2.7492 \| 0.728 \| 0.7219 \| 0.1864 \| 0.1005 \|
	\| 19.4158 \| 27.0 \| 6750 \| 20.8258 \| 0.7355 \| 0.4339 \| 2.4375 \| 0.7355 \| 0.7352 \| 0.1838 \| 0.0943 \|
	\| 19.2585 \| 28.0 \| 7000 \| 21.0491 \| 0.729 \| 0.4465 \| 2.6324 \| 0.729 \| 0.7245 \| 0.1953 \| 0.1010 \|
	\| 19.2585 \| 29.0 \| 7250 \| 20.7774 \| 0.7425 \| 0.4283 \| 2.4694 \| 0.7425 \| 0.7410 \| 0.1799 \| 0.0908 \|
	\| 19.1051 \| 30.0 \| 7500 \| 20.6908 \| 0.741 \| 0.4311 \| 2.4924 \| 0.7410 \| 0.7405 \| 0.1890 \| 0.0888 \|
	\| 19.1051 \| 31.0 \| 7750 \| 20.8242 \| 0.743 \| 0.4264 \| 2.5098 \| 0.743 \| 0.7407 \| 0.1826 \| 0.0903 \|
	\| 18.9722 \| 32.0 \| 8000 \| 20.6257 \| 0.7435 \| 0.4288 \| 2.4740 \| 0.7435 \| 0.7432 \| 0.1865 \| 0.0872 \|
	\| 18.9722 \| 33.0 \| 8250 \| 20.6265 \| 0.745 \| 0.4289 \| 2.4552 \| 0.745 \| 0.7435 \| 0.1862 \| 0.0929 \|
	\| 18.854 \| 34.0 \| 8500 \| 20.4251 \| 0.7505 \| 0.4124 \| 2.4631 \| 0.7505 \| 0.7513 \| 0.1790 \| 0.0845 \|
	\| 18.854 \| 35.0 \| 8750 \| 20.4164 \| 0.741 \| 0.4278 \| 2.3888 \| 0.7410 \| 0.7402 \| 0.1859 \| 0.0889 \|
	\| 18.7477 \| 36.0 \| 9000 \| 20.3432 \| 0.751 \| 0.4184 \| 2.4020 \| 0.751 \| 0.7485 \| 0.1800 \| 0.0850 \|
	\| 18.7477 \| 37.0 \| 9250 \| 20.4310 \| 0.7555 \| 0.4154 \| 2.4639 \| 0.7555 \| 0.7528 \| 0.1759 \| 0.0842 \|
	\| 18.6548 \| 38.0 \| 9500 \| 20.1987 \| 0.7542 \| 0.4111 \| 2.2921 \| 0.7542 \| 0.7542 \| 0.1792 \| 0.0815 \|
	\| 18.6548 \| 39.0 \| 9750 \| 20.2326 \| 0.7562 \| 0.4017 \| 2.3536 \| 0.7562 \| 0.7537 \| 0.1767 \| 0.0829 \|
	\| 18.5776 \| 40.0 \| 10000 \| 20.1571 \| 0.7575 \| 0.3985 \| 2.3405 \| 0.7575 \| 0.7568 \| 0.1703 \| 0.0811 \|
	\| 18.5776 \| 41.0 \| 10250 \| 20.1580 \| 0.7625 \| 0.3962 \| 2.3855 \| 0.7625 \| 0.7621 \| 0.1713 \| 0.0814 \|
	\| 18.5133 \| 42.0 \| 10500 \| 20.0952 \| 0.7572 \| 0.4038 \| 2.3600 \| 0.7572 \| 0.7563 \| 0.1768 \| 0.0794 \|
	\| 18.5133 \| 43.0 \| 10750 \| 20.1483 \| 0.7575 \| 0.4008 \| 2.3713 \| 0.7575 \| 0.7564 \| 0.1755 \| 0.0820 \|
	\| 18.4613 \| 44.0 \| 11000 \| 20.0749 \| 0.762 \| 0.3992 \| 2.3372 \| 0.762 \| 0.7618 \| 0.1720 \| 0.0795 \|
	\| 18.4613 \| 45.0 \| 11250 \| 20.0664 \| 0.7578 \| 0.4035 \| 2.3570 \| 0.7577 \| 0.7566 \| 0.1769 \| 0.0795 \|
	\| 18.4218 \| 46.0 \| 11500 \| 19.9611 \| 0.7622 \| 0.3946 \| 2.3399 \| 0.7622 \| 0.7617 \| 0.1674 \| 0.0784 \|
	\| 18.4218 \| 47.0 \| 11750 \| 19.9678 \| 0.7632 \| 0.3907 \| 2.3011 \| 0.7632 \| 0.7635 \| 0.1692 \| 0.0772 \|
	\| 18.3945 \| 48.0 \| 12000 \| 19.9950 \| 0.763 \| 0.3910 \| 2.2773 \| 0.763 \| 0.7616 \| 0.1695 \| 0.0775 \|
	\| 18.3945 \| 49.0 \| 12250 \| 20.0013 \| 0.7625 \| 0.3911 \| 2.2875 \| 0.7625 \| 0.7618 \| 0.1705 \| 0.0777 \|
	\| 18.3792 \| 50.0 \| 12500 \| 19.9624 \| 0.765 \| 0.3910 \| 2.2998 \| 0.765 \| 0.7641 \| 0.1669 \| 0.0775 \|


	### Framework versions

	- Transformers 4.26.1
	- Pytorch 1.13.1.post200
	- Datasets 2.9.0
	- Tokenizers 0.13.2

	---
	license: apache-2.0
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_hint_rand
	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_hint_rand

	This model is a fine-tuned version of [bdpc/resnet101_rvl-cdip](https://huggingface.co/bdpc/resnet101_rvl-cdip) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 19.9624
	- Accuracy: 0.765
	- Brier Loss: 0.3910
	- Nll: 2.2998
	- F1 Micro: 0.765
	- F1 Macro: 0.7641
	- Ece: 0.1669
	- Aurc: 0.0775

	## 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 \| 26.8546 \| 0.2102 \| 0.8652 \| 3.4225 \| 0.2102 \| 0.1897 \| 0.0659 \| 0.6137 \|
	\| 26.8891 \| 2.0 \| 500 \| 25.9803 \| 0.2845 \| 0.8381 \| 3.4360 \| 0.2845 \| 0.2457 \| 0.0762 \| 0.5573 \|
	\| 26.8891 \| 3.0 \| 750 \| 25.8252 \| 0.337 \| 0.8092 \| 3.6483 \| 0.337 \| 0.3168 \| 0.1091 \| 0.4988 \|
	\| 25.1656 \| 4.0 \| 1000 \| 24.9957 \| 0.4088 \| 0.7588 \| 3.0528 \| 0.4088 \| 0.3859 \| 0.1283 \| 0.4163 \|
	\| 25.1656 \| 5.0 \| 1250 \| 24.2209 \| 0.5517 \| 0.5964 \| 2.6661 \| 0.5517 \| 0.5510 \| 0.0726 \| 0.2489 \|
	\| 23.8526 \| 6.0 \| 1500 \| 23.4086 \| 0.5915 \| 0.5431 \| 2.5360 \| 0.5915 \| 0.5840 \| 0.0549 \| 0.1994 \|
	\| 23.8526 \| 7.0 \| 1750 \| 23.0800 \| 0.625 \| 0.5049 \| 2.4839 \| 0.625 \| 0.6268 \| 0.0608 \| 0.1668 \|
	\| 22.7511 \| 8.0 \| 2000 \| 22.9512 \| 0.6573 \| 0.4660 \| 2.4202 \| 0.6573 \| 0.6564 \| 0.0565 \| 0.1407 \|
	\| 22.7511 \| 9.0 \| 2250 \| 22.7991 \| 0.6783 \| 0.4509 \| 2.4137 \| 0.6783 \| 0.6749 \| 0.0634 \| 0.1315 \|
	\| 21.9881 \| 10.0 \| 2500 \| 22.8533 \| 0.6352 \| 0.5056 \| 2.6549 \| 0.6352 \| 0.6231 \| 0.1085 \| 0.1554 \|
	\| 21.9881 \| 11.0 \| 2750 \| 22.7499 \| 0.669 \| 0.4673 \| 2.5291 \| 0.669 \| 0.6642 \| 0.1053 \| 0.1347 \|
	\| 21.391 \| 12.0 \| 3000 \| 22.6520 \| 0.6757 \| 0.4767 \| 2.5038 \| 0.6757 \| 0.6745 \| 0.1204 \| 0.1355 \|
	\| 21.391 \| 13.0 \| 3250 \| 22.4767 \| 0.6737 \| 0.4850 \| 2.6030 \| 0.6737 \| 0.6718 \| 0.1385 \| 0.1380 \|
	\| 20.9347 \| 14.0 \| 3500 \| 22.3023 \| 0.6767 \| 0.4832 \| 2.5438 \| 0.6767 \| 0.6770 \| 0.1594 \| 0.1301 \|
	\| 20.9347 \| 15.0 \| 3750 \| 22.1482 \| 0.693 \| 0.4666 \| 2.5622 \| 0.693 \| 0.6913 \| 0.1581 \| 0.1209 \|
	\| 20.5776 \| 16.0 \| 4000 \| 22.1655 \| 0.6943 \| 0.4849 \| 2.5685 \| 0.6943 \| 0.6994 \| 0.1766 \| 0.1288 \|
	\| 20.5776 \| 17.0 \| 4250 \| 22.0213 \| 0.686 \| 0.4922 \| 2.6576 \| 0.686 \| 0.6925 \| 0.1749 \| 0.1250 \|
	\| 20.2836 \| 18.0 \| 4500 \| 21.5434 \| 0.7023 \| 0.4560 \| 2.5508 \| 0.7023 \| 0.7018 \| 0.1720 \| 0.1146 \|
	\| 20.2836 \| 19.0 \| 4750 \| 21.7105 \| 0.715 \| 0.4501 \| 2.5953 \| 0.715 \| 0.7128 \| 0.1738 \| 0.1083 \|
	\| 20.0339 \| 20.0 \| 5000 \| 21.6301 \| 0.7057 \| 0.4645 \| 2.6131 \| 0.7057 \| 0.7033 \| 0.1701 \| 0.1153 \|
	\| 20.0339 \| 21.0 \| 5250 \| 21.9130 \| 0.7007 \| 0.4825 \| 2.7114 \| 0.7007 \| 0.6989 \| 0.1917 \| 0.1231 \|
	\| 19.8026 \| 22.0 \| 5500 \| 21.7975 \| 0.713 \| 0.4702 \| 2.7340 \| 0.713 \| 0.7117 \| 0.1879 \| 0.1148 \|
	\| 19.8026 \| 23.0 \| 5750 \| 21.5577 \| 0.7173 \| 0.4621 \| 2.7138 \| 0.7173 \| 0.7100 \| 0.1931 \| 0.1072 \|
	\| 19.6001 \| 24.0 \| 6000 \| 21.2486 \| 0.722 \| 0.4491 \| 2.5651 \| 0.722 \| 0.7214 \| 0.1853 \| 0.1045 \|
	\| 19.6001 \| 25.0 \| 6250 \| 21.0363 \| 0.7348 \| 0.4344 \| 2.4688 \| 0.7348 \| 0.7364 \| 0.1780 \| 0.0974 \|
	\| 19.4158 \| 26.0 \| 6500 \| 21.3527 \| 0.728 \| 0.4495 \| 2.7492 \| 0.728 \| 0.7219 \| 0.1864 \| 0.1005 \|
	\| 19.4158 \| 27.0 \| 6750 \| 20.8258 \| 0.7355 \| 0.4339 \| 2.4375 \| 0.7355 \| 0.7352 \| 0.1838 \| 0.0943 \|
	\| 19.2585 \| 28.0 \| 7000 \| 21.0491 \| 0.729 \| 0.4465 \| 2.6324 \| 0.729 \| 0.7245 \| 0.1953 \| 0.1010 \|
	\| 19.2585 \| 29.0 \| 7250 \| 20.7774 \| 0.7425 \| 0.4283 \| 2.4694 \| 0.7425 \| 0.7410 \| 0.1799 \| 0.0908 \|
	\| 19.1051 \| 30.0 \| 7500 \| 20.6908 \| 0.741 \| 0.4311 \| 2.4924 \| 0.7410 \| 0.7405 \| 0.1890 \| 0.0888 \|
	\| 19.1051 \| 31.0 \| 7750 \| 20.8242 \| 0.743 \| 0.4264 \| 2.5098 \| 0.743 \| 0.7407 \| 0.1826 \| 0.0903 \|
	\| 18.9722 \| 32.0 \| 8000 \| 20.6257 \| 0.7435 \| 0.4288 \| 2.4740 \| 0.7435 \| 0.7432 \| 0.1865 \| 0.0872 \|
	\| 18.9722 \| 33.0 \| 8250 \| 20.6265 \| 0.745 \| 0.4289 \| 2.4552 \| 0.745 \| 0.7435 \| 0.1862 \| 0.0929 \|
	\| 18.854 \| 34.0 \| 8500 \| 20.4251 \| 0.7505 \| 0.4124 \| 2.4631 \| 0.7505 \| 0.7513 \| 0.1790 \| 0.0845 \|
	\| 18.854 \| 35.0 \| 8750 \| 20.4164 \| 0.741 \| 0.4278 \| 2.3888 \| 0.7410 \| 0.7402 \| 0.1859 \| 0.0889 \|
	\| 18.7477 \| 36.0 \| 9000 \| 20.3432 \| 0.751 \| 0.4184 \| 2.4020 \| 0.751 \| 0.7485 \| 0.1800 \| 0.0850 \|
	\| 18.7477 \| 37.0 \| 9250 \| 20.4310 \| 0.7555 \| 0.4154 \| 2.4639 \| 0.7555 \| 0.7528 \| 0.1759 \| 0.0842 \|
	\| 18.6548 \| 38.0 \| 9500 \| 20.1987 \| 0.7542 \| 0.4111 \| 2.2921 \| 0.7542 \| 0.7542 \| 0.1792 \| 0.0815 \|
	\| 18.6548 \| 39.0 \| 9750 \| 20.2326 \| 0.7562 \| 0.4017 \| 2.3536 \| 0.7562 \| 0.7537 \| 0.1767 \| 0.0829 \|
	\| 18.5776 \| 40.0 \| 10000 \| 20.1571 \| 0.7575 \| 0.3985 \| 2.3405 \| 0.7575 \| 0.7568 \| 0.1703 \| 0.0811 \|
	\| 18.5776 \| 41.0 \| 10250 \| 20.1580 \| 0.7625 \| 0.3962 \| 2.3855 \| 0.7625 \| 0.7621 \| 0.1713 \| 0.0814 \|
	\| 18.5133 \| 42.0 \| 10500 \| 20.0952 \| 0.7572 \| 0.4038 \| 2.3600 \| 0.7572 \| 0.7563 \| 0.1768 \| 0.0794 \|
	\| 18.5133 \| 43.0 \| 10750 \| 20.1483 \| 0.7575 \| 0.4008 \| 2.3713 \| 0.7575 \| 0.7564 \| 0.1755 \| 0.0820 \|
	\| 18.4613 \| 44.0 \| 11000 \| 20.0749 \| 0.762 \| 0.3992 \| 2.3372 \| 0.762 \| 0.7618 \| 0.1720 \| 0.0795 \|
	\| 18.4613 \| 45.0 \| 11250 \| 20.0664 \| 0.7578 \| 0.4035 \| 2.3570 \| 0.7577 \| 0.7566 \| 0.1769 \| 0.0795 \|
	\| 18.4218 \| 46.0 \| 11500 \| 19.9611 \| 0.7622 \| 0.3946 \| 2.3399 \| 0.7622 \| 0.7617 \| 0.1674 \| 0.0784 \|
	\| 18.4218 \| 47.0 \| 11750 \| 19.9678 \| 0.7632 \| 0.3907 \| 2.3011 \| 0.7632 \| 0.7635 \| 0.1692 \| 0.0772 \|
	\| 18.3945 \| 48.0 \| 12000 \| 19.9950 \| 0.763 \| 0.3910 \| 2.2773 \| 0.763 \| 0.7616 \| 0.1695 \| 0.0775 \|
	\| 18.3945 \| 49.0 \| 12250 \| 20.0013 \| 0.7625 \| 0.3911 \| 2.2875 \| 0.7625 \| 0.7618 \| 0.1705 \| 0.0777 \|
	\| 18.3792 \| 50.0 \| 12500 \| 19.9624 \| 0.765 \| 0.3910 \| 2.2998 \| 0.765 \| 0.7641 \| 0.1669 \| 0.0775 \|


	### Framework versions

	- Transformers 4.26.1
	- Pytorch 1.13.1.post200
	- Datasets 2.9.0
	- Tokenizers 0.13.2