update model card README.md

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	---
	license: apache-2.0
	tags:
	- generated_from_trainer
	datasets:
	- stereoset
	metrics:
	- accuracy
	model-index:
	- name: bert-large-uncased_stereoset_classifieronly
	results:
	- task:
	name: Text Classification
	type: text-classification
	dataset:
	name: stereoset
	type: stereoset
	config: intersentence
	split: validation
	args: intersentence
	metrics:
	- name: Accuracy
	type: accuracy
	value: 0.5478806907378336
	---

	<!-- 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. -->

	# bert-large-uncased_stereoset_classifieronly

	This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the stereoset dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.6856
	- Accuracy: 0.5479
	- Tp: 0.3579
	- Tn: 0.1900
	- Fp: 0.3242
	- Fn: 0.1279

	## 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: 5e-05
	- 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
	- num_epochs: 30

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Tp \| Tn \| Fp \| Fn \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:------:\|:------:\|:------:\|
	\| 0.6938 \| 0.43 \| 20 \| 0.6891 \| 0.5573 \| 0.2630 \| 0.2943 \| 0.2198 \| 0.2229 \|
	\| 0.703 \| 0.85 \| 40 \| 0.6942 \| 0.5455 \| 0.3901 \| 0.1554 \| 0.3587 \| 0.0958 \|
	\| 0.6929 \| 1.28 \| 60 \| 0.6882 \| 0.5510 \| 0.3069 \| 0.2441 \| 0.2700 \| 0.1790 \|
	\| 0.7044 \| 1.7 \| 80 \| 0.6895 \| 0.5471 \| 0.3603 \| 0.1868 \| 0.3273 \| 0.1256 \|
	\| 0.6947 \| 2.13 \| 100 \| 0.6874 \| 0.5463 \| 0.3006 \| 0.2457 \| 0.2684 \| 0.1852 \|
	\| 0.7115 \| 2.55 \| 120 \| 0.6910 \| 0.5479 \| 0.3768 \| 0.1711 \| 0.3430 \| 0.1091 \|
	\| 0.7016 \| 2.98 \| 140 \| 0.6879 \| 0.5518 \| 0.3430 \| 0.2088 \| 0.3053 \| 0.1429 \|
	\| 0.6952 \| 3.4 \| 160 \| 0.6918 \| 0.5361 \| 0.3980 \| 0.1381 \| 0.3760 \| 0.0879 \|
	\| 0.6959 \| 3.83 \| 180 \| 0.6910 \| 0.5463 \| 0.3878 \| 0.1586 \| 0.3556 \| 0.0981 \|
	\| 0.6924 \| 4.26 \| 200 \| 0.6906 \| 0.5471 \| 0.3830 \| 0.1641 \| 0.3501 \| 0.1028 \|
	\| 0.6943 \| 4.68 \| 220 \| 0.6877 \| 0.5487 \| 0.3195 \| 0.2292 \| 0.2849 \| 0.1664 \|
	\| 0.7052 \| 5.11 \| 240 \| 0.6879 \| 0.5644 \| 0.2473 \| 0.3171 \| 0.1970 \| 0.2386 \|
	\| 0.6881 \| 5.53 \| 260 \| 0.6889 \| 0.5479 \| 0.3791 \| 0.1688 \| 0.3454 \| 0.1068 \|
	\| 0.6971 \| 5.96 \| 280 \| 0.6882 \| 0.5463 \| 0.3752 \| 0.1711 \| 0.3430 \| 0.1107 \|
	\| 0.6781 \| 6.38 \| 300 \| 0.6930 \| 0.5330 \| 0.4035 \| 0.1295 \| 0.3846 \| 0.0824 \|
	\| 0.6992 \| 6.81 \| 320 \| 0.6875 \| 0.5495 \| 0.3579 \| 0.1915 \| 0.3226 \| 0.1279 \|
	\| 0.6954 \| 7.23 \| 340 \| 0.6868 \| 0.5557 \| 0.3195 \| 0.2363 \| 0.2779 \| 0.1664 \|
	\| 0.6949 \| 7.66 \| 360 \| 0.6877 \| 0.5479 \| 0.3556 \| 0.1923 \| 0.3218 \| 0.1303 \|
	\| 0.6946 \| 8.09 \| 380 \| 0.6899 \| 0.5471 \| 0.3878 \| 0.1593 \| 0.3548 \| 0.0981 \|
	\| 0.6877 \| 8.51 \| 400 \| 0.6862 \| 0.5542 \| 0.3218 \| 0.2323 \| 0.2818 \| 0.1641 \|
	\| 0.6994 \| 8.94 \| 420 \| 0.6890 \| 0.5479 \| 0.3823 \| 0.1656 \| 0.3485 \| 0.1036 \|
	\| 0.7061 \| 9.36 \| 440 \| 0.6867 \| 0.5620 \| 0.2347 \| 0.3273 \| 0.1868 \| 0.2512 \|
	\| 0.6945 \| 9.79 \| 460 \| 0.6893 \| 0.5479 \| 0.3878 \| 0.1601 \| 0.3540 \| 0.0981 \|
	\| 0.7078 \| 10.21 \| 480 \| 0.6908 \| 0.5353 \| 0.3972 \| 0.1381 \| 0.3760 \| 0.0887 \|
	\| 0.6911 \| 10.64 \| 500 \| 0.6858 \| 0.5502 \| 0.3108 \| 0.2394 \| 0.2747 \| 0.1750 \|
	\| 0.684 \| 11.06 \| 520 \| 0.6875 \| 0.5502 \| 0.3768 \| 0.1735 \| 0.3407 \| 0.1091 \|
	\| 0.6925 \| 11.49 \| 540 \| 0.6906 \| 0.5369 \| 0.3972 \| 0.1397 \| 0.3744 \| 0.0887 \|
	\| 0.7104 \| 11.91 \| 560 \| 0.6856 \| 0.5597 \| 0.2527 \| 0.3069 \| 0.2072 \| 0.2331 \|
	\| 0.6919 \| 12.34 \| 580 \| 0.6857 \| 0.5479 \| 0.3391 \| 0.2088 \| 0.3053 \| 0.1468 \|
	\| 0.6873 \| 12.77 \| 600 \| 0.6903 \| 0.5338 \| 0.3987 \| 0.1350 \| 0.3791 \| 0.0871 \|
	\| 0.6915 \| 13.19 \| 620 \| 0.6862 \| 0.5471 \| 0.3540 \| 0.1931 \| 0.3210 \| 0.1319 \|
	\| 0.6921 \| 13.62 \| 640 \| 0.6859 \| 0.5518 \| 0.3485 \| 0.2033 \| 0.3108 \| 0.1374 \|
	\| 0.7092 \| 14.04 \| 660 \| 0.6888 \| 0.5479 \| 0.3807 \| 0.1672 \| 0.3469 \| 0.1052 \|
	\| 0.6874 \| 14.47 \| 680 \| 0.6851 \| 0.5518 \| 0.3210 \| 0.2308 \| 0.2834 \| 0.1648 \|
	\| 0.682 \| 14.89 \| 700 \| 0.6877 \| 0.5510 \| 0.3744 \| 0.1766 \| 0.3375 \| 0.1115 \|
	\| 0.6953 \| 15.32 \| 720 \| 0.6853 \| 0.5526 \| 0.3273 \| 0.2253 \| 0.2889 \| 0.1586 \|
	\| 0.7056 \| 15.74 \| 740 \| 0.6882 \| 0.5487 \| 0.3885 \| 0.1601 \| 0.3540 \| 0.0973 \|
	\| 0.6776 \| 16.17 \| 760 \| 0.6875 \| 0.5471 \| 0.3783 \| 0.1688 \| 0.3454 \| 0.1075 \|
	\| 0.6862 \| 16.6 \| 780 \| 0.6863 \| 0.5510 \| 0.3642 \| 0.1868 \| 0.3273 \| 0.1217 \|
	\| 0.6827 \| 17.02 \| 800 \| 0.6868 \| 0.5510 \| 0.3705 \| 0.1805 \| 0.3336 \| 0.1154 \|
	\| 0.7161 \| 17.45 \| 820 \| 0.6878 \| 0.5502 \| 0.3791 \| 0.1711 \| 0.3430 \| 0.1068 \|
	\| 0.6991 \| 17.87 \| 840 \| 0.6852 \| 0.5487 \| 0.3359 \| 0.2127 \| 0.3014 \| 0.1499 \|
	\| 0.6836 \| 18.3 \| 860 \| 0.6876 \| 0.5487 \| 0.3830 \| 0.1656 \| 0.3485 \| 0.1028 \|
	\| 0.7023 \| 18.72 \| 880 \| 0.6862 \| 0.5487 \| 0.3595 \| 0.1892 \| 0.3250 \| 0.1264 \|
	\| 0.6939 \| 19.15 \| 900 \| 0.6854 \| 0.5495 \| 0.3485 \| 0.2009 \| 0.3132 \| 0.1374 \|
	\| 0.6883 \| 19.57 \| 920 \| 0.6860 \| 0.5479 \| 0.3587 \| 0.1892 \| 0.3250 \| 0.1272 \|
	\| 0.6872 \| 20.0 \| 940 \| 0.6866 \| 0.5518 \| 0.3697 \| 0.1821 \| 0.3320 \| 0.1162 \|
	\| 0.685 \| 20.43 \| 960 \| 0.6861 \| 0.5487 \| 0.3595 \| 0.1892 \| 0.3250 \| 0.1264 \|
	\| 0.6771 \| 20.85 \| 980 \| 0.6853 \| 0.5510 \| 0.3477 \| 0.2033 \| 0.3108 \| 0.1381 \|
	\| 0.6904 \| 21.28 \| 1000 \| 0.6859 \| 0.5487 \| 0.3564 \| 0.1923 \| 0.3218 \| 0.1295 \|
	\| 0.6925 \| 21.7 \| 1020 \| 0.6848 \| 0.5518 \| 0.3132 \| 0.2386 \| 0.2755 \| 0.1727 \|
	\| 0.6982 \| 22.13 \| 1040 \| 0.6856 \| 0.5463 \| 0.3532 \| 0.1931 \| 0.3210 \| 0.1327 \|
	\| 0.7015 \| 22.55 \| 1060 \| 0.6859 \| 0.5479 \| 0.3587 \| 0.1892 \| 0.3250 \| 0.1272 \|
	\| 0.6851 \| 22.98 \| 1080 \| 0.6860 \| 0.5518 \| 0.3650 \| 0.1868 \| 0.3273 \| 0.1209 \|
	\| 0.6875 \| 23.4 \| 1100 \| 0.6856 \| 0.5463 \| 0.3532 \| 0.1931 \| 0.3210 \| 0.1327 \|
	\| 0.7035 \| 23.83 \| 1120 \| 0.6851 \| 0.5510 \| 0.3454 \| 0.2057 \| 0.3085 \| 0.1405 \|
	\| 0.699 \| 24.26 \| 1140 \| 0.6846 \| 0.5534 \| 0.3281 \| 0.2253 \| 0.2889 \| 0.1578 \|
	\| 0.6954 \| 24.68 \| 1160 \| 0.6851 \| 0.5495 \| 0.3485 \| 0.2009 \| 0.3132 \| 0.1374 \|
	\| 0.6881 \| 25.11 \| 1180 \| 0.6851 \| 0.5510 \| 0.3485 \| 0.2025 \| 0.3116 \| 0.1374 \|
	\| 0.6931 \| 25.53 \| 1200 \| 0.6862 \| 0.5487 \| 0.3666 \| 0.1821 \| 0.3320 \| 0.1193 \|
	\| 0.6967 \| 25.96 \| 1220 \| 0.6868 \| 0.5487 \| 0.3752 \| 0.1735 \| 0.3407 \| 0.1107 \|
	\| 0.6826 \| 26.38 \| 1240 \| 0.6863 \| 0.5502 \| 0.3689 \| 0.1813 \| 0.3328 \| 0.1170 \|
	\| 0.6927 \| 26.81 \| 1260 \| 0.6857 \| 0.5487 \| 0.3587 \| 0.1900 \| 0.3242 \| 0.1272 \|
	\| 0.692 \| 27.23 \| 1280 \| 0.6853 \| 0.5471 \| 0.3524 \| 0.1947 \| 0.3195 \| 0.1334 \|
	\| 0.6936 \| 27.66 \| 1300 \| 0.6856 \| 0.5479 \| 0.3579 \| 0.1900 \| 0.3242 \| 0.1279 \|
	\| 0.6871 \| 28.09 \| 1320 \| 0.6856 \| 0.5487 \| 0.3579 \| 0.1907 \| 0.3234 \| 0.1279 \|
	\| 0.6956 \| 28.51 \| 1340 \| 0.6857 \| 0.5487 \| 0.3595 \| 0.1892 \| 0.3250 \| 0.1264 \|
	\| 0.6788 \| 28.94 \| 1360 \| 0.6859 \| 0.5479 \| 0.3611 \| 0.1868 \| 0.3273 \| 0.1248 \|
	\| 0.6933 \| 29.36 \| 1380 \| 0.6856 \| 0.5479 \| 0.3579 \| 0.1900 \| 0.3242 \| 0.1279 \|
	\| 0.6909 \| 29.79 \| 1400 \| 0.6856 \| 0.5479 \| 0.3579 \| 0.1900 \| 0.3242 \| 0.1279 \|


	### Framework versions

	- Transformers 4.26.1
	- Pytorch 1.13.1
	- Datasets 2.10.1
	- Tokenizers 0.13.2

	---
	license: apache-2.0
	tags:
	- generated_from_trainer
	datasets:
	- stereoset
	metrics:
	- accuracy
	model-index:
	- name: bert-large-uncased_stereoset_classifieronly
	results:
	- task:
	name: Text Classification
	type: text-classification
	dataset:
	name: stereoset
	type: stereoset
	config: intersentence
	split: validation
	args: intersentence
	metrics:
	- name: Accuracy
	type: accuracy
	value: 0.5478806907378336
	---

	<!-- 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. -->

	# bert-large-uncased_stereoset_classifieronly

	This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the stereoset dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.6856
	- Accuracy: 0.5479
	- Tp: 0.3579
	- Tn: 0.1900
	- Fp: 0.3242
	- Fn: 0.1279

	## 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: 5e-05
	- 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
	- num_epochs: 30

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Tp \| Tn \| Fp \| Fn \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:------:\|:------:\|:------:\|
	\| 0.6938 \| 0.43 \| 20 \| 0.6891 \| 0.5573 \| 0.2630 \| 0.2943 \| 0.2198 \| 0.2229 \|
	\| 0.703 \| 0.85 \| 40 \| 0.6942 \| 0.5455 \| 0.3901 \| 0.1554 \| 0.3587 \| 0.0958 \|
	\| 0.6929 \| 1.28 \| 60 \| 0.6882 \| 0.5510 \| 0.3069 \| 0.2441 \| 0.2700 \| 0.1790 \|
	\| 0.7044 \| 1.7 \| 80 \| 0.6895 \| 0.5471 \| 0.3603 \| 0.1868 \| 0.3273 \| 0.1256 \|
	\| 0.6947 \| 2.13 \| 100 \| 0.6874 \| 0.5463 \| 0.3006 \| 0.2457 \| 0.2684 \| 0.1852 \|
	\| 0.7115 \| 2.55 \| 120 \| 0.6910 \| 0.5479 \| 0.3768 \| 0.1711 \| 0.3430 \| 0.1091 \|
	\| 0.7016 \| 2.98 \| 140 \| 0.6879 \| 0.5518 \| 0.3430 \| 0.2088 \| 0.3053 \| 0.1429 \|
	\| 0.6952 \| 3.4 \| 160 \| 0.6918 \| 0.5361 \| 0.3980 \| 0.1381 \| 0.3760 \| 0.0879 \|
	\| 0.6959 \| 3.83 \| 180 \| 0.6910 \| 0.5463 \| 0.3878 \| 0.1586 \| 0.3556 \| 0.0981 \|
	\| 0.6924 \| 4.26 \| 200 \| 0.6906 \| 0.5471 \| 0.3830 \| 0.1641 \| 0.3501 \| 0.1028 \|
	\| 0.6943 \| 4.68 \| 220 \| 0.6877 \| 0.5487 \| 0.3195 \| 0.2292 \| 0.2849 \| 0.1664 \|
	\| 0.7052 \| 5.11 \| 240 \| 0.6879 \| 0.5644 \| 0.2473 \| 0.3171 \| 0.1970 \| 0.2386 \|
	\| 0.6881 \| 5.53 \| 260 \| 0.6889 \| 0.5479 \| 0.3791 \| 0.1688 \| 0.3454 \| 0.1068 \|
	\| 0.6971 \| 5.96 \| 280 \| 0.6882 \| 0.5463 \| 0.3752 \| 0.1711 \| 0.3430 \| 0.1107 \|
	\| 0.6781 \| 6.38 \| 300 \| 0.6930 \| 0.5330 \| 0.4035 \| 0.1295 \| 0.3846 \| 0.0824 \|
	\| 0.6992 \| 6.81 \| 320 \| 0.6875 \| 0.5495 \| 0.3579 \| 0.1915 \| 0.3226 \| 0.1279 \|
	\| 0.6954 \| 7.23 \| 340 \| 0.6868 \| 0.5557 \| 0.3195 \| 0.2363 \| 0.2779 \| 0.1664 \|
	\| 0.6949 \| 7.66 \| 360 \| 0.6877 \| 0.5479 \| 0.3556 \| 0.1923 \| 0.3218 \| 0.1303 \|
	\| 0.6946 \| 8.09 \| 380 \| 0.6899 \| 0.5471 \| 0.3878 \| 0.1593 \| 0.3548 \| 0.0981 \|
	\| 0.6877 \| 8.51 \| 400 \| 0.6862 \| 0.5542 \| 0.3218 \| 0.2323 \| 0.2818 \| 0.1641 \|
	\| 0.6994 \| 8.94 \| 420 \| 0.6890 \| 0.5479 \| 0.3823 \| 0.1656 \| 0.3485 \| 0.1036 \|
	\| 0.7061 \| 9.36 \| 440 \| 0.6867 \| 0.5620 \| 0.2347 \| 0.3273 \| 0.1868 \| 0.2512 \|
	\| 0.6945 \| 9.79 \| 460 \| 0.6893 \| 0.5479 \| 0.3878 \| 0.1601 \| 0.3540 \| 0.0981 \|
	\| 0.7078 \| 10.21 \| 480 \| 0.6908 \| 0.5353 \| 0.3972 \| 0.1381 \| 0.3760 \| 0.0887 \|
	\| 0.6911 \| 10.64 \| 500 \| 0.6858 \| 0.5502 \| 0.3108 \| 0.2394 \| 0.2747 \| 0.1750 \|
	\| 0.684 \| 11.06 \| 520 \| 0.6875 \| 0.5502 \| 0.3768 \| 0.1735 \| 0.3407 \| 0.1091 \|
	\| 0.6925 \| 11.49 \| 540 \| 0.6906 \| 0.5369 \| 0.3972 \| 0.1397 \| 0.3744 \| 0.0887 \|
	\| 0.7104 \| 11.91 \| 560 \| 0.6856 \| 0.5597 \| 0.2527 \| 0.3069 \| 0.2072 \| 0.2331 \|
	\| 0.6919 \| 12.34 \| 580 \| 0.6857 \| 0.5479 \| 0.3391 \| 0.2088 \| 0.3053 \| 0.1468 \|
	\| 0.6873 \| 12.77 \| 600 \| 0.6903 \| 0.5338 \| 0.3987 \| 0.1350 \| 0.3791 \| 0.0871 \|
	\| 0.6915 \| 13.19 \| 620 \| 0.6862 \| 0.5471 \| 0.3540 \| 0.1931 \| 0.3210 \| 0.1319 \|
	\| 0.6921 \| 13.62 \| 640 \| 0.6859 \| 0.5518 \| 0.3485 \| 0.2033 \| 0.3108 \| 0.1374 \|
	\| 0.7092 \| 14.04 \| 660 \| 0.6888 \| 0.5479 \| 0.3807 \| 0.1672 \| 0.3469 \| 0.1052 \|
	\| 0.6874 \| 14.47 \| 680 \| 0.6851 \| 0.5518 \| 0.3210 \| 0.2308 \| 0.2834 \| 0.1648 \|
	\| 0.682 \| 14.89 \| 700 \| 0.6877 \| 0.5510 \| 0.3744 \| 0.1766 \| 0.3375 \| 0.1115 \|
	\| 0.6953 \| 15.32 \| 720 \| 0.6853 \| 0.5526 \| 0.3273 \| 0.2253 \| 0.2889 \| 0.1586 \|
	\| 0.7056 \| 15.74 \| 740 \| 0.6882 \| 0.5487 \| 0.3885 \| 0.1601 \| 0.3540 \| 0.0973 \|
	\| 0.6776 \| 16.17 \| 760 \| 0.6875 \| 0.5471 \| 0.3783 \| 0.1688 \| 0.3454 \| 0.1075 \|
	\| 0.6862 \| 16.6 \| 780 \| 0.6863 \| 0.5510 \| 0.3642 \| 0.1868 \| 0.3273 \| 0.1217 \|
	\| 0.6827 \| 17.02 \| 800 \| 0.6868 \| 0.5510 \| 0.3705 \| 0.1805 \| 0.3336 \| 0.1154 \|
	\| 0.7161 \| 17.45 \| 820 \| 0.6878 \| 0.5502 \| 0.3791 \| 0.1711 \| 0.3430 \| 0.1068 \|
	\| 0.6991 \| 17.87 \| 840 \| 0.6852 \| 0.5487 \| 0.3359 \| 0.2127 \| 0.3014 \| 0.1499 \|
	\| 0.6836 \| 18.3 \| 860 \| 0.6876 \| 0.5487 \| 0.3830 \| 0.1656 \| 0.3485 \| 0.1028 \|
	\| 0.7023 \| 18.72 \| 880 \| 0.6862 \| 0.5487 \| 0.3595 \| 0.1892 \| 0.3250 \| 0.1264 \|
	\| 0.6939 \| 19.15 \| 900 \| 0.6854 \| 0.5495 \| 0.3485 \| 0.2009 \| 0.3132 \| 0.1374 \|
	\| 0.6883 \| 19.57 \| 920 \| 0.6860 \| 0.5479 \| 0.3587 \| 0.1892 \| 0.3250 \| 0.1272 \|
	\| 0.6872 \| 20.0 \| 940 \| 0.6866 \| 0.5518 \| 0.3697 \| 0.1821 \| 0.3320 \| 0.1162 \|
	\| 0.685 \| 20.43 \| 960 \| 0.6861 \| 0.5487 \| 0.3595 \| 0.1892 \| 0.3250 \| 0.1264 \|
	\| 0.6771 \| 20.85 \| 980 \| 0.6853 \| 0.5510 \| 0.3477 \| 0.2033 \| 0.3108 \| 0.1381 \|
	\| 0.6904 \| 21.28 \| 1000 \| 0.6859 \| 0.5487 \| 0.3564 \| 0.1923 \| 0.3218 \| 0.1295 \|
	\| 0.6925 \| 21.7 \| 1020 \| 0.6848 \| 0.5518 \| 0.3132 \| 0.2386 \| 0.2755 \| 0.1727 \|
	\| 0.6982 \| 22.13 \| 1040 \| 0.6856 \| 0.5463 \| 0.3532 \| 0.1931 \| 0.3210 \| 0.1327 \|
	\| 0.7015 \| 22.55 \| 1060 \| 0.6859 \| 0.5479 \| 0.3587 \| 0.1892 \| 0.3250 \| 0.1272 \|
	\| 0.6851 \| 22.98 \| 1080 \| 0.6860 \| 0.5518 \| 0.3650 \| 0.1868 \| 0.3273 \| 0.1209 \|
	\| 0.6875 \| 23.4 \| 1100 \| 0.6856 \| 0.5463 \| 0.3532 \| 0.1931 \| 0.3210 \| 0.1327 \|
	\| 0.7035 \| 23.83 \| 1120 \| 0.6851 \| 0.5510 \| 0.3454 \| 0.2057 \| 0.3085 \| 0.1405 \|
	\| 0.699 \| 24.26 \| 1140 \| 0.6846 \| 0.5534 \| 0.3281 \| 0.2253 \| 0.2889 \| 0.1578 \|
	\| 0.6954 \| 24.68 \| 1160 \| 0.6851 \| 0.5495 \| 0.3485 \| 0.2009 \| 0.3132 \| 0.1374 \|
	\| 0.6881 \| 25.11 \| 1180 \| 0.6851 \| 0.5510 \| 0.3485 \| 0.2025 \| 0.3116 \| 0.1374 \|
	\| 0.6931 \| 25.53 \| 1200 \| 0.6862 \| 0.5487 \| 0.3666 \| 0.1821 \| 0.3320 \| 0.1193 \|
	\| 0.6967 \| 25.96 \| 1220 \| 0.6868 \| 0.5487 \| 0.3752 \| 0.1735 \| 0.3407 \| 0.1107 \|
	\| 0.6826 \| 26.38 \| 1240 \| 0.6863 \| 0.5502 \| 0.3689 \| 0.1813 \| 0.3328 \| 0.1170 \|
	\| 0.6927 \| 26.81 \| 1260 \| 0.6857 \| 0.5487 \| 0.3587 \| 0.1900 \| 0.3242 \| 0.1272 \|
	\| 0.692 \| 27.23 \| 1280 \| 0.6853 \| 0.5471 \| 0.3524 \| 0.1947 \| 0.3195 \| 0.1334 \|
	\| 0.6936 \| 27.66 \| 1300 \| 0.6856 \| 0.5479 \| 0.3579 \| 0.1900 \| 0.3242 \| 0.1279 \|
	\| 0.6871 \| 28.09 \| 1320 \| 0.6856 \| 0.5487 \| 0.3579 \| 0.1907 \| 0.3234 \| 0.1279 \|
	\| 0.6956 \| 28.51 \| 1340 \| 0.6857 \| 0.5487 \| 0.3595 \| 0.1892 \| 0.3250 \| 0.1264 \|
	\| 0.6788 \| 28.94 \| 1360 \| 0.6859 \| 0.5479 \| 0.3611 \| 0.1868 \| 0.3273 \| 0.1248 \|
	\| 0.6933 \| 29.36 \| 1380 \| 0.6856 \| 0.5479 \| 0.3579 \| 0.1900 \| 0.3242 \| 0.1279 \|
	\| 0.6909 \| 29.79 \| 1400 \| 0.6856 \| 0.5479 \| 0.3579 \| 0.1900 \| 0.3242 \| 0.1279 \|


	### Framework versions

	- Transformers 4.26.1
	- Pytorch 1.13.1
	- Datasets 2.10.1
	- Tokenizers 0.13.2