update model card README.md

e36e58a over 3 years ago

7.51 kB

	---
	license: apache-2.0
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: bert-base-uncased_winobias_classifieronly
	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. -->

	# bert-base-uncased_winobias_classifieronly

	This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.6924
	- Accuracy: 0.5290
	- Tp: 0.2614
	- Tn: 0.2677
	- Fp: 0.2323
	- Fn: 0.2386

	## 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
	- num_epochs: 50

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Tp \| Tn \| Fp \| Fn \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:------:\|:------:\|:------:\|
	\| 0.6962 \| 0.8 \| 20 \| 0.6948 \| 0.5019 \| 0.2203 \| 0.2816 \| 0.2184 \| 0.2797 \|
	\| 0.6962 \| 1.6 \| 40 \| 0.6944 \| 0.4981 \| 0.2696 \| 0.2285 \| 0.2715 \| 0.2304 \|
	\| 0.6967 \| 2.4 \| 60 \| 0.6954 \| 0.5019 \| 0.4078 \| 0.0941 \| 0.4059 \| 0.0922 \|
	\| 0.6963 \| 3.2 \| 80 \| 0.6944 \| 0.5088 \| 0.0581 \| 0.4508 \| 0.0492 \| 0.4419 \|
	\| 0.6949 \| 4.0 \| 100 \| 0.6936 \| 0.4987 \| 0.2948 \| 0.2039 \| 0.2961 \| 0.2052 \|
	\| 0.7003 \| 4.8 \| 120 \| 0.6944 \| 0.5013 \| 0.0322 \| 0.4691 \| 0.0309 \| 0.4678 \|
	\| 0.6948 \| 5.6 \| 140 \| 0.6938 \| 0.4987 \| 0.3630 \| 0.1357 \| 0.3643 \| 0.1370 \|
	\| 0.695 \| 6.4 \| 160 \| 0.6945 \| 0.5032 \| 0.0284 \| 0.4747 \| 0.0253 \| 0.4716 \|
	\| 0.6954 \| 7.2 \| 180 \| 0.6939 \| 0.5025 \| 0.0739 \| 0.4287 \| 0.0713 \| 0.4261 \|
	\| 0.6914 \| 8.0 \| 200 \| 0.6936 \| 0.5019 \| 0.3491 \| 0.1528 \| 0.3472 \| 0.1509 \|
	\| 0.6968 \| 8.8 \| 220 \| 0.6945 \| 0.4981 \| 0.0234 \| 0.4747 \| 0.0253 \| 0.4766 \|
	\| 0.6961 \| 9.6 \| 240 \| 0.6934 \| 0.5069 \| 0.2020 \| 0.3049 \| 0.1951 \| 0.2980 \|
	\| 0.6957 \| 10.4 \| 260 \| 0.6932 \| 0.5038 \| 0.2917 \| 0.2121 \| 0.2879 \| 0.2083 \|
	\| 0.6929 \| 11.2 \| 280 \| 0.6939 \| 0.5032 \| 0.0271 \| 0.4760 \| 0.0240 \| 0.4729 \|
	\| 0.7065 \| 12.0 \| 300 \| 0.6934 \| 0.4987 \| 0.4009 \| 0.0979 \| 0.4021 \| 0.0991 \|
	\| 0.6986 \| 12.8 \| 320 \| 0.6934 \| 0.5025 \| 0.0833 \| 0.4192 \| 0.0808 \| 0.4167 \|
	\| 0.6974 \| 13.6 \| 340 \| 0.6945 \| 0.4949 \| 0.0221 \| 0.4729 \| 0.0271 \| 0.4779 \|
	\| 0.6991 \| 14.4 \| 360 \| 0.6944 \| 0.5044 \| 0.4956 \| 0.0088 \| 0.4912 \| 0.0044 \|
	\| 0.6961 \| 15.2 \| 380 \| 0.6932 \| 0.5038 \| 0.0751 \| 0.4287 \| 0.0713 \| 0.4249 \|
	\| 0.6982 \| 16.0 \| 400 \| 0.6928 \| 0.5057 \| 0.1641 \| 0.3415 \| 0.1585 \| 0.3359 \|
	\| 0.6986 \| 16.8 \| 420 \| 0.6928 \| 0.5101 \| 0.1351 \| 0.375 \| 0.125 \| 0.3649 \|
	\| 0.695 \| 17.6 \| 440 \| 0.6927 \| 0.5107 \| 0.2702 \| 0.2405 \| 0.2595 \| 0.2298 \|
	\| 0.6954 \| 18.4 \| 460 \| 0.6928 \| 0.5095 \| 0.3441 \| 0.1654 \| 0.3346 \| 0.1559 \|
	\| 0.6954 \| 19.2 \| 480 \| 0.6926 \| 0.5133 \| 0.2626 \| 0.2506 \| 0.2494 \| 0.2374 \|
	\| 0.7003 \| 20.0 \| 500 \| 0.6926 \| 0.5183 \| 0.2708 \| 0.2475 \| 0.2525 \| 0.2292 \|
	\| 0.6982 \| 20.8 \| 520 \| 0.6926 \| 0.5082 \| 0.2652 \| 0.2431 \| 0.2569 \| 0.2348 \|
	\| 0.696 \| 21.6 \| 540 \| 0.6927 \| 0.5170 \| 0.2456 \| 0.2715 \| 0.2285 \| 0.2544 \|
	\| 0.6928 \| 22.4 \| 560 \| 0.6928 \| 0.5038 \| 0.3958 \| 0.1080 \| 0.3920 \| 0.1042 \|
	\| 0.6968 \| 23.2 \| 580 \| 0.6927 \| 0.5164 \| 0.3062 \| 0.2102 \| 0.2898 \| 0.1938 \|
	\| 0.693 \| 24.0 \| 600 \| 0.6926 \| 0.5196 \| 0.2702 \| 0.2494 \| 0.2506 \| 0.2298 \|
	\| 0.7 \| 24.8 \| 620 \| 0.6927 \| 0.5088 \| 0.4148 \| 0.0941 \| 0.4059 \| 0.0852 \|
	\| 0.6993 \| 25.6 \| 640 \| 0.6932 \| 0.5044 \| 0.0335 \| 0.4710 \| 0.0290 \| 0.4665 \|
	\| 0.7022 \| 26.4 \| 660 \| 0.6926 \| 0.5152 \| 0.1806 \| 0.3346 \| 0.1654 \| 0.3194 \|
	\| 0.6954 \| 27.2 \| 680 \| 0.6925 \| 0.5170 \| 0.2241 \| 0.2929 \| 0.2071 \| 0.2759 \|
	\| 0.6978 \| 28.0 \| 700 \| 0.6925 \| 0.5158 \| 0.3289 \| 0.1869 \| 0.3131 \| 0.1711 \|
	\| 0.7013 \| 28.8 \| 720 \| 0.6925 \| 0.5145 \| 0.2601 \| 0.2544 \| 0.2456 \| 0.2399 \|
	\| 0.7004 \| 29.6 \| 740 \| 0.6931 \| 0.5063 \| 0.4729 \| 0.0335 \| 0.4665 \| 0.0271 \|
	\| 0.6913 \| 30.4 \| 760 \| 0.6926 \| 0.5164 \| 0.1982 \| 0.3182 \| 0.1818 \| 0.3018 \|
	\| 0.6974 \| 31.2 \| 780 \| 0.6926 \| 0.5164 \| 0.1900 \| 0.3264 \| 0.1736 \| 0.3100 \|
	\| 0.6904 \| 32.0 \| 800 \| 0.6926 \| 0.5107 \| 0.3933 \| 0.1174 \| 0.3826 \| 0.1067 \|
	\| 0.6917 \| 32.8 \| 820 \| 0.6925 \| 0.5271 \| 0.2961 \| 0.2311 \| 0.2689 \| 0.2039 \|
	\| 0.6956 \| 33.6 \| 840 \| 0.6927 \| 0.5114 \| 0.1098 \| 0.4015 \| 0.0985 \| 0.3902 \|
	\| 0.6941 \| 34.4 \| 860 \| 0.6927 \| 0.5133 \| 0.4350 \| 0.0783 \| 0.4217 \| 0.0650 \|
	\| 0.6935 \| 35.2 \| 880 \| 0.6928 \| 0.5114 \| 0.4571 \| 0.0543 \| 0.4457 \| 0.0429 \|
	\| 0.6935 \| 36.0 \| 900 \| 0.6925 \| 0.5114 \| 0.3643 \| 0.1471 \| 0.3529 \| 0.1357 \|
	\| 0.6935 \| 36.8 \| 920 \| 0.6924 \| 0.5177 \| 0.2020 \| 0.3157 \| 0.1843 \| 0.2980 \|
	\| 0.6916 \| 37.6 \| 940 \| 0.6925 \| 0.5177 \| 0.3314 \| 0.1862 \| 0.3138 \| 0.1686 \|
	\| 0.703 \| 38.4 \| 960 \| 0.6925 \| 0.5183 \| 0.3220 \| 0.1963 \| 0.3037 \| 0.1780 \|
	\| 0.698 \| 39.2 \| 980 \| 0.6925 \| 0.5234 \| 0.3005 \| 0.2229 \| 0.2771 \| 0.1995 \|
	\| 0.6966 \| 40.0 \| 1000 \| 0.6925 \| 0.5240 \| 0.3150 \| 0.2090 \| 0.2910 \| 0.1850 \|
	\| 0.6942 \| 40.8 \| 1020 \| 0.6925 \| 0.5290 \| 0.3049 \| 0.2241 \| 0.2759 \| 0.1951 \|
	\| 0.6916 \| 41.6 \| 1040 \| 0.6924 \| 0.5240 \| 0.2999 \| 0.2241 \| 0.2759 \| 0.2001 \|
	\| 0.6946 \| 42.4 \| 1060 \| 0.6925 \| 0.5221 \| 0.1862 \| 0.3359 \| 0.1641 \| 0.3138 \|
	\| 0.6925 \| 43.2 \| 1080 \| 0.6926 \| 0.5025 \| 0.1155 \| 0.3870 \| 0.1130 \| 0.3845 \|
	\| 0.6977 \| 44.0 \| 1100 \| 0.6925 \| 0.5170 \| 0.1559 \| 0.3611 \| 0.1389 \| 0.3441 \|
	\| 0.6938 \| 44.8 \| 1120 \| 0.6924 \| 0.5221 \| 0.2008 \| 0.3213 \| 0.1787 \| 0.2992 \|
	\| 0.6972 \| 45.6 \| 1140 \| 0.6924 \| 0.5208 \| 0.2449 \| 0.2759 \| 0.2241 \| 0.2551 \|
	\| 0.6938 \| 46.4 \| 1160 \| 0.6924 \| 0.5227 \| 0.3213 \| 0.2014 \| 0.2986 \| 0.1787 \|
	\| 0.6924 \| 47.2 \| 1180 \| 0.6924 \| 0.5107 \| 0.3396 \| 0.1711 \| 0.3289 \| 0.1604 \|
	\| 0.7034 \| 48.0 \| 1200 \| 0.6924 \| 0.5322 \| 0.2980 \| 0.2342 \| 0.2658 \| 0.2020 \|
	\| 0.6935 \| 48.8 \| 1220 \| 0.6924 \| 0.5316 \| 0.2809 \| 0.2506 \| 0.2494 \| 0.2191 \|
	\| 0.6971 \| 49.6 \| 1240 \| 0.6924 \| 0.5290 \| 0.2614 \| 0.2677 \| 0.2323 \| 0.2386 \|


	### 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
	metrics:
	- accuracy
	model-index:
	- name: bert-base-uncased_winobias_classifieronly
	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. -->

	# bert-base-uncased_winobias_classifieronly

	This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.6924
	- Accuracy: 0.5290
	- Tp: 0.2614
	- Tn: 0.2677
	- Fp: 0.2323
	- Fn: 0.2386

	## 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
	- num_epochs: 50

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Tp \| Tn \| Fp \| Fn \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:------:\|:------:\|:------:\|
	\| 0.6962 \| 0.8 \| 20 \| 0.6948 \| 0.5019 \| 0.2203 \| 0.2816 \| 0.2184 \| 0.2797 \|
	\| 0.6962 \| 1.6 \| 40 \| 0.6944 \| 0.4981 \| 0.2696 \| 0.2285 \| 0.2715 \| 0.2304 \|
	\| 0.6967 \| 2.4 \| 60 \| 0.6954 \| 0.5019 \| 0.4078 \| 0.0941 \| 0.4059 \| 0.0922 \|
	\| 0.6963 \| 3.2 \| 80 \| 0.6944 \| 0.5088 \| 0.0581 \| 0.4508 \| 0.0492 \| 0.4419 \|
	\| 0.6949 \| 4.0 \| 100 \| 0.6936 \| 0.4987 \| 0.2948 \| 0.2039 \| 0.2961 \| 0.2052 \|
	\| 0.7003 \| 4.8 \| 120 \| 0.6944 \| 0.5013 \| 0.0322 \| 0.4691 \| 0.0309 \| 0.4678 \|
	\| 0.6948 \| 5.6 \| 140 \| 0.6938 \| 0.4987 \| 0.3630 \| 0.1357 \| 0.3643 \| 0.1370 \|
	\| 0.695 \| 6.4 \| 160 \| 0.6945 \| 0.5032 \| 0.0284 \| 0.4747 \| 0.0253 \| 0.4716 \|
	\| 0.6954 \| 7.2 \| 180 \| 0.6939 \| 0.5025 \| 0.0739 \| 0.4287 \| 0.0713 \| 0.4261 \|
	\| 0.6914 \| 8.0 \| 200 \| 0.6936 \| 0.5019 \| 0.3491 \| 0.1528 \| 0.3472 \| 0.1509 \|
	\| 0.6968 \| 8.8 \| 220 \| 0.6945 \| 0.4981 \| 0.0234 \| 0.4747 \| 0.0253 \| 0.4766 \|
	\| 0.6961 \| 9.6 \| 240 \| 0.6934 \| 0.5069 \| 0.2020 \| 0.3049 \| 0.1951 \| 0.2980 \|
	\| 0.6957 \| 10.4 \| 260 \| 0.6932 \| 0.5038 \| 0.2917 \| 0.2121 \| 0.2879 \| 0.2083 \|
	\| 0.6929 \| 11.2 \| 280 \| 0.6939 \| 0.5032 \| 0.0271 \| 0.4760 \| 0.0240 \| 0.4729 \|
	\| 0.7065 \| 12.0 \| 300 \| 0.6934 \| 0.4987 \| 0.4009 \| 0.0979 \| 0.4021 \| 0.0991 \|
	\| 0.6986 \| 12.8 \| 320 \| 0.6934 \| 0.5025 \| 0.0833 \| 0.4192 \| 0.0808 \| 0.4167 \|
	\| 0.6974 \| 13.6 \| 340 \| 0.6945 \| 0.4949 \| 0.0221 \| 0.4729 \| 0.0271 \| 0.4779 \|
	\| 0.6991 \| 14.4 \| 360 \| 0.6944 \| 0.5044 \| 0.4956 \| 0.0088 \| 0.4912 \| 0.0044 \|
	\| 0.6961 \| 15.2 \| 380 \| 0.6932 \| 0.5038 \| 0.0751 \| 0.4287 \| 0.0713 \| 0.4249 \|
	\| 0.6982 \| 16.0 \| 400 \| 0.6928 \| 0.5057 \| 0.1641 \| 0.3415 \| 0.1585 \| 0.3359 \|
	\| 0.6986 \| 16.8 \| 420 \| 0.6928 \| 0.5101 \| 0.1351 \| 0.375 \| 0.125 \| 0.3649 \|
	\| 0.695 \| 17.6 \| 440 \| 0.6927 \| 0.5107 \| 0.2702 \| 0.2405 \| 0.2595 \| 0.2298 \|
	\| 0.6954 \| 18.4 \| 460 \| 0.6928 \| 0.5095 \| 0.3441 \| 0.1654 \| 0.3346 \| 0.1559 \|
	\| 0.6954 \| 19.2 \| 480 \| 0.6926 \| 0.5133 \| 0.2626 \| 0.2506 \| 0.2494 \| 0.2374 \|
	\| 0.7003 \| 20.0 \| 500 \| 0.6926 \| 0.5183 \| 0.2708 \| 0.2475 \| 0.2525 \| 0.2292 \|
	\| 0.6982 \| 20.8 \| 520 \| 0.6926 \| 0.5082 \| 0.2652 \| 0.2431 \| 0.2569 \| 0.2348 \|
	\| 0.696 \| 21.6 \| 540 \| 0.6927 \| 0.5170 \| 0.2456 \| 0.2715 \| 0.2285 \| 0.2544 \|
	\| 0.6928 \| 22.4 \| 560 \| 0.6928 \| 0.5038 \| 0.3958 \| 0.1080 \| 0.3920 \| 0.1042 \|
	\| 0.6968 \| 23.2 \| 580 \| 0.6927 \| 0.5164 \| 0.3062 \| 0.2102 \| 0.2898 \| 0.1938 \|
	\| 0.693 \| 24.0 \| 600 \| 0.6926 \| 0.5196 \| 0.2702 \| 0.2494 \| 0.2506 \| 0.2298 \|
	\| 0.7 \| 24.8 \| 620 \| 0.6927 \| 0.5088 \| 0.4148 \| 0.0941 \| 0.4059 \| 0.0852 \|
	\| 0.6993 \| 25.6 \| 640 \| 0.6932 \| 0.5044 \| 0.0335 \| 0.4710 \| 0.0290 \| 0.4665 \|
	\| 0.7022 \| 26.4 \| 660 \| 0.6926 \| 0.5152 \| 0.1806 \| 0.3346 \| 0.1654 \| 0.3194 \|
	\| 0.6954 \| 27.2 \| 680 \| 0.6925 \| 0.5170 \| 0.2241 \| 0.2929 \| 0.2071 \| 0.2759 \|
	\| 0.6978 \| 28.0 \| 700 \| 0.6925 \| 0.5158 \| 0.3289 \| 0.1869 \| 0.3131 \| 0.1711 \|
	\| 0.7013 \| 28.8 \| 720 \| 0.6925 \| 0.5145 \| 0.2601 \| 0.2544 \| 0.2456 \| 0.2399 \|
	\| 0.7004 \| 29.6 \| 740 \| 0.6931 \| 0.5063 \| 0.4729 \| 0.0335 \| 0.4665 \| 0.0271 \|
	\| 0.6913 \| 30.4 \| 760 \| 0.6926 \| 0.5164 \| 0.1982 \| 0.3182 \| 0.1818 \| 0.3018 \|
	\| 0.6974 \| 31.2 \| 780 \| 0.6926 \| 0.5164 \| 0.1900 \| 0.3264 \| 0.1736 \| 0.3100 \|
	\| 0.6904 \| 32.0 \| 800 \| 0.6926 \| 0.5107 \| 0.3933 \| 0.1174 \| 0.3826 \| 0.1067 \|
	\| 0.6917 \| 32.8 \| 820 \| 0.6925 \| 0.5271 \| 0.2961 \| 0.2311 \| 0.2689 \| 0.2039 \|
	\| 0.6956 \| 33.6 \| 840 \| 0.6927 \| 0.5114 \| 0.1098 \| 0.4015 \| 0.0985 \| 0.3902 \|
	\| 0.6941 \| 34.4 \| 860 \| 0.6927 \| 0.5133 \| 0.4350 \| 0.0783 \| 0.4217 \| 0.0650 \|
	\| 0.6935 \| 35.2 \| 880 \| 0.6928 \| 0.5114 \| 0.4571 \| 0.0543 \| 0.4457 \| 0.0429 \|
	\| 0.6935 \| 36.0 \| 900 \| 0.6925 \| 0.5114 \| 0.3643 \| 0.1471 \| 0.3529 \| 0.1357 \|
	\| 0.6935 \| 36.8 \| 920 \| 0.6924 \| 0.5177 \| 0.2020 \| 0.3157 \| 0.1843 \| 0.2980 \|
	\| 0.6916 \| 37.6 \| 940 \| 0.6925 \| 0.5177 \| 0.3314 \| 0.1862 \| 0.3138 \| 0.1686 \|
	\| 0.703 \| 38.4 \| 960 \| 0.6925 \| 0.5183 \| 0.3220 \| 0.1963 \| 0.3037 \| 0.1780 \|
	\| 0.698 \| 39.2 \| 980 \| 0.6925 \| 0.5234 \| 0.3005 \| 0.2229 \| 0.2771 \| 0.1995 \|
	\| 0.6966 \| 40.0 \| 1000 \| 0.6925 \| 0.5240 \| 0.3150 \| 0.2090 \| 0.2910 \| 0.1850 \|
	\| 0.6942 \| 40.8 \| 1020 \| 0.6925 \| 0.5290 \| 0.3049 \| 0.2241 \| 0.2759 \| 0.1951 \|
	\| 0.6916 \| 41.6 \| 1040 \| 0.6924 \| 0.5240 \| 0.2999 \| 0.2241 \| 0.2759 \| 0.2001 \|
	\| 0.6946 \| 42.4 \| 1060 \| 0.6925 \| 0.5221 \| 0.1862 \| 0.3359 \| 0.1641 \| 0.3138 \|
	\| 0.6925 \| 43.2 \| 1080 \| 0.6926 \| 0.5025 \| 0.1155 \| 0.3870 \| 0.1130 \| 0.3845 \|
	\| 0.6977 \| 44.0 \| 1100 \| 0.6925 \| 0.5170 \| 0.1559 \| 0.3611 \| 0.1389 \| 0.3441 \|
	\| 0.6938 \| 44.8 \| 1120 \| 0.6924 \| 0.5221 \| 0.2008 \| 0.3213 \| 0.1787 \| 0.2992 \|
	\| 0.6972 \| 45.6 \| 1140 \| 0.6924 \| 0.5208 \| 0.2449 \| 0.2759 \| 0.2241 \| 0.2551 \|
	\| 0.6938 \| 46.4 \| 1160 \| 0.6924 \| 0.5227 \| 0.3213 \| 0.2014 \| 0.2986 \| 0.1787 \|
	\| 0.6924 \| 47.2 \| 1180 \| 0.6924 \| 0.5107 \| 0.3396 \| 0.1711 \| 0.3289 \| 0.1604 \|
	\| 0.7034 \| 48.0 \| 1200 \| 0.6924 \| 0.5322 \| 0.2980 \| 0.2342 \| 0.2658 \| 0.2020 \|
	\| 0.6935 \| 48.8 \| 1220 \| 0.6924 \| 0.5316 \| 0.2809 \| 0.2506 \| 0.2494 \| 0.2191 \|
	\| 0.6971 \| 49.6 \| 1240 \| 0.6924 \| 0.5290 \| 0.2614 \| 0.2677 \| 0.2323 \| 0.2386 \|


	### Framework versions

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