ryan03302024 / README.md

🍻 cheers

e9867c0 verified 8 months ago

9.64 kB

	---
	license: apache-2.0
	base_model: google/vit-base-patch16-224-in21k
	tags:
	- image-classification
	- generated_from_trainer
	model-index:
	- name: ryan03302024
	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. -->

	# ryan03302024

	This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the properties dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.2044
	- Ordinal Mae: 0.4324
	- Ordinal Accuracy: 0.6648
	- Na Accuracy: 0.8333

	## 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: 16
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 3
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Ordinal Mae \| Ordinal Accuracy \| Na Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:-----------:\|:----------------:\|:-----------:\|
	\| 0.3617 \| 0.04 \| 100 \| 0.3133 \| 0.8466 \| 0.4174 \| 0.8763 \|
	\| 0.2891 \| 0.07 \| 200 \| 0.3022 \| 0.7739 \| 0.5045 \| 0.6993 \|
	\| 0.3163 \| 0.11 \| 300 \| 0.2615 \| 0.6167 \| 0.5597 \| 0.7904 \|
	\| 0.2781 \| 0.14 \| 400 \| 0.2598 \| 0.5433 \| 0.5936 \| 0.8299 \|
	\| 0.2731 \| 0.18 \| 500 \| 0.2613 \| 0.5651 \| 0.5566 \| 0.8454 \|
	\| 0.2926 \| 0.22 \| 600 \| 0.2734 \| 0.5305 \| 0.5494 \| 0.9089 \|
	\| 0.2686 \| 0.25 \| 700 \| 0.2362 \| 0.4853 \| 0.6250 \| 0.7887 \|
	\| 0.2715 \| 0.29 \| 800 \| 0.2454 \| 0.4914 \| 0.6255 \| 0.7715 \|
	\| 0.2459 \| 0.32 \| 900 \| 0.2452 \| 0.4763 \| 0.6072 \| 0.7801 \|
	\| 0.2033 \| 0.36 \| 1000 \| 0.2365 \| 0.4967 \| 0.6106 \| 0.7663 \|
	\| 0.2234 \| 0.4 \| 1100 \| 0.2299 \| 0.4947 \| 0.6180 \| 0.8677 \|
	\| 0.2035 \| 0.43 \| 1200 \| 0.2314 \| 0.4744 \| 0.6309 \| 0.7835 \|
	\| 0.2277 \| 0.47 \| 1300 \| 0.2389 \| 0.4649 \| 0.6435 \| 0.7302 \|
	\| 0.2535 \| 0.5 \| 1400 \| 0.2259 \| 0.4509 \| 0.6021 \| 0.8247 \|
	\| 0.2209 \| 0.54 \| 1500 \| 0.2369 \| 0.4507 \| 0.6363 \| 0.7577 \|
	\| 0.2007 \| 0.58 \| 1600 \| 0.2161 \| 0.4272 \| 0.6540 \| 0.8316 \|
	\| 0.2013 \| 0.61 \| 1700 \| 0.2433 \| 0.4326 \| 0.6129 \| 0.7732 \|
	\| 0.1999 \| 0.65 \| 1800 \| 0.2227 \| 0.4460 \| 0.6553 \| 0.8247 \|
	\| 0.2157 \| 0.69 \| 1900 \| 0.2134 \| 0.4728 \| 0.6363 \| 0.8162 \|
	\| 0.2154 \| 0.72 \| 2000 \| 0.2239 \| 0.4734 \| 0.5787 \| 0.8574 \|
	\| 0.2169 \| 0.76 \| 2100 \| 0.2394 \| 0.4392 \| 0.6255 \| 0.8849 \|
	\| 0.2719 \| 0.79 \| 2200 \| 0.2283 \| 0.4324 \| 0.6229 \| 0.8780 \|
	\| 0.2244 \| 0.83 \| 2300 \| 0.2140 \| 0.4483 \| 0.6314 \| 0.8729 \|
	\| 0.2072 \| 0.87 \| 2400 \| 0.2198 \| 0.4330 \| 0.6440 \| 0.8213 \|
	\| 0.1754 \| 0.9 \| 2500 \| 0.2099 \| 0.4198 \| 0.6712 \| 0.8419 \|
	\| 0.1773 \| 0.94 \| 2600 \| 0.2053 \| 0.4105 \| 0.6586 \| 0.8643 \|
	\| 0.2378 \| 0.97 \| 2700 \| 0.2044 \| 0.4324 \| 0.6648 \| 0.8333 \|
	\| 0.1295 \| 1.01 \| 2800 \| 0.2044 \| 0.4016 \| 0.6843 \| 0.8247 \|
	\| 0.1126 \| 1.05 \| 2900 \| 0.2302 \| 0.4025 \| 0.6805 \| 0.7577 \|
	\| 0.1262 \| 1.08 \| 3000 \| 0.2205 \| 0.4017 \| 0.6517 \| 0.8093 \|
	\| 0.1104 \| 1.12 \| 3100 \| 0.2117 \| 0.3931 \| 0.6779 \| 0.8454 \|
	\| 0.1657 \| 1.15 \| 3200 \| 0.2174 \| 0.3890 \| 0.6666 \| 0.8591 \|
	\| 0.1186 \| 1.19 \| 3300 \| 0.2299 \| 0.4013 \| 0.6622 \| 0.8058 \|
	\| 0.1304 \| 1.23 \| 3400 \| 0.2176 \| 0.3801 \| 0.6902 \| 0.8110 \|
	\| 0.1081 \| 1.26 \| 3500 \| 0.2330 \| 0.3867 \| 0.6643 \| 0.8316 \|
	\| 0.1281 \| 1.3 \| 3600 \| 0.2320 \| 0.3954 \| 0.6902 \| 0.7680 \|
	\| 0.1192 \| 1.33 \| 3700 \| 0.2312 \| 0.4109 \| 0.6769 \| 0.7990 \|
	\| 0.1029 \| 1.37 \| 3800 \| 0.2195 \| 0.3870 \| 0.6820 \| 0.8024 \|
	\| 0.1159 \| 1.41 \| 3900 \| 0.2200 \| 0.3860 \| 0.6812 \| 0.7904 \|
	\| 0.1159 \| 1.44 \| 4000 \| 0.2159 \| 0.3712 \| 0.6982 \| 0.7990 \|
	\| 0.107 \| 1.48 \| 4100 \| 0.2262 \| 0.3757 \| 0.6905 \| 0.8213 \|
	\| 0.1262 \| 1.51 \| 4200 \| 0.2291 \| 0.3841 \| 0.6835 \| 0.8247 \|
	\| 0.1437 \| 1.55 \| 4300 \| 0.2311 \| 0.3751 \| 0.6923 \| 0.8007 \|
	\| 0.0916 \| 1.59 \| 4400 \| 0.2343 \| 0.3743 \| 0.6792 \| 0.8660 \|
	\| 0.1266 \| 1.62 \| 4500 \| 0.2251 \| 0.3724 \| 0.6861 \| 0.8505 \|
	\| 0.1185 \| 1.66 \| 4600 \| 0.2242 \| 0.3666 \| 0.6902 \| 0.8265 \|
	\| 0.1037 \| 1.69 \| 4700 \| 0.2219 \| 0.3700 \| 0.6846 \| 0.8522 \|
	\| 0.1264 \| 1.73 \| 4800 \| 0.2211 \| 0.3677 \| 0.6892 \| 0.8351 \|
	\| 0.1404 \| 1.77 \| 4900 \| 0.2206 \| 0.3718 \| 0.6946 \| 0.7938 \|
	\| 0.1238 \| 1.8 \| 5000 \| 0.2098 \| 0.3723 \| 0.6948 \| 0.8265 \|
	\| 0.0868 \| 1.84 \| 5100 \| 0.2089 \| 0.3574 \| 0.7025 \| 0.8144 \|
	\| 0.0828 \| 1.88 \| 5200 \| 0.2204 \| 0.3680 \| 0.7031 \| 0.7818 \|
	\| 0.0986 \| 1.91 \| 5300 \| 0.2126 \| 0.3543 \| 0.6982 \| 0.8127 \|
	\| 0.0869 \| 1.95 \| 5400 \| 0.2247 \| 0.3532 \| 0.7108 \| 0.8076 \|
	\| 0.1006 \| 1.98 \| 5500 \| 0.2268 \| 0.3637 \| 0.7028 \| 0.8162 \|
	\| 0.0639 \| 2.02 \| 5600 \| 0.2252 \| 0.3479 \| 0.7069 \| 0.8110 \|
	\| 0.0569 \| 2.06 \| 5700 \| 0.2315 \| 0.3399 \| 0.7167 \| 0.8076 \|
	\| 0.0626 \| 2.09 \| 5800 \| 0.2304 \| 0.3481 \| 0.7028 \| 0.8127 \|
	\| 0.0502 \| 2.13 \| 5900 \| 0.2381 \| 0.3624 \| 0.6954 \| 0.8093 \|
	\| 0.0541 \| 2.16 \| 6000 \| 0.2298 \| 0.3405 \| 0.7159 \| 0.8110 \|
	\| 0.0671 \| 2.2 \| 6100 \| 0.2432 \| 0.3529 \| 0.7031 \| 0.7990 \|
	\| 0.0672 \| 2.24 \| 6200 \| 0.2431 \| 0.3361 \| 0.7195 \| 0.7715 \|
	\| 0.0446 \| 2.27 \| 6300 \| 0.2447 \| 0.3401 \| 0.7141 \| 0.7938 \|
	\| 0.0424 \| 2.31 \| 6400 \| 0.2426 \| 0.3485 \| 0.7018 \| 0.8162 \|
	\| 0.0386 \| 2.34 \| 6500 \| 0.2488 \| 0.3387 \| 0.7123 \| 0.8127 \|
	\| 0.0736 \| 2.38 \| 6600 \| 0.2454 \| 0.3382 \| 0.7054 \| 0.8316 \|
	\| 0.0421 \| 2.42 \| 6700 \| 0.2513 \| 0.3394 \| 0.7120 \| 0.8316 \|
	\| 0.0607 \| 2.45 \| 6800 \| 0.2546 \| 0.3370 \| 0.7092 \| 0.8265 \|
	\| 0.0517 \| 2.49 \| 6900 \| 0.2594 \| 0.3376 \| 0.7082 \| 0.8299 \|
	\| 0.062 \| 2.52 \| 7000 \| 0.2533 \| 0.3369 \| 0.7105 \| 0.8110 \|
	\| 0.0664 \| 2.56 \| 7100 \| 0.2534 \| 0.3329 \| 0.7185 \| 0.8024 \|
	\| 0.0389 \| 2.6 \| 7200 \| 0.2470 \| 0.3288 \| 0.7259 \| 0.8093 \|
	\| 0.0671 \| 2.63 \| 7300 \| 0.2516 \| 0.3294 \| 0.7159 \| 0.8041 \|
	\| 0.0416 \| 2.67 \| 7400 \| 0.2507 \| 0.3307 \| 0.7133 \| 0.8058 \|
	\| 0.0541 \| 2.7 \| 7500 \| 0.2529 \| 0.3355 \| 0.7110 \| 0.8058 \|
	\| 0.0374 \| 2.74 \| 7600 \| 0.2530 \| 0.3315 \| 0.7149 \| 0.8110 \|
	\| 0.04 \| 2.78 \| 7700 \| 0.2520 \| 0.3290 \| 0.7167 \| 0.8076 \|
	\| 0.0507 \| 2.81 \| 7800 \| 0.2555 \| 0.3297 \| 0.7105 \| 0.8127 \|
	\| 0.0379 \| 2.85 \| 7900 \| 0.2531 \| 0.3274 \| 0.7162 \| 0.8127 \|
	\| 0.0736 \| 2.88 \| 8000 \| 0.2526 \| 0.3279 \| 0.7164 \| 0.8196 \|
	\| 0.0589 \| 2.92 \| 8100 \| 0.2522 \| 0.3267 \| 0.7144 \| 0.8162 \|
	\| 0.0449 \| 2.96 \| 8200 \| 0.2521 \| 0.3272 \| 0.7149 \| 0.8162 \|
	\| 0.0498 \| 2.99 \| 8300 \| 0.2520 \| 0.3265 \| 0.7167 \| 0.8144 \|


	### Framework versions

	- Transformers 4.39.1
	- Pytorch 2.2.1+cu121
	- Datasets 2.18.0
	- Tokenizers 0.15.2

	---
	license: apache-2.0
	base_model: google/vit-base-patch16-224-in21k
	tags:
	- image-classification
	- generated_from_trainer
	model-index:
	- name: ryan03302024
	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. -->

	# ryan03302024

	This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the properties dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.2044
	- Ordinal Mae: 0.4324
	- Ordinal Accuracy: 0.6648
	- Na Accuracy: 0.8333

	## 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: 16
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 3
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Ordinal Mae \| Ordinal Accuracy \| Na Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:-----------:\|:----------------:\|:-----------:\|
	\| 0.3617 \| 0.04 \| 100 \| 0.3133 \| 0.8466 \| 0.4174 \| 0.8763 \|
	\| 0.2891 \| 0.07 \| 200 \| 0.3022 \| 0.7739 \| 0.5045 \| 0.6993 \|
	\| 0.3163 \| 0.11 \| 300 \| 0.2615 \| 0.6167 \| 0.5597 \| 0.7904 \|
	\| 0.2781 \| 0.14 \| 400 \| 0.2598 \| 0.5433 \| 0.5936 \| 0.8299 \|
	\| 0.2731 \| 0.18 \| 500 \| 0.2613 \| 0.5651 \| 0.5566 \| 0.8454 \|
	\| 0.2926 \| 0.22 \| 600 \| 0.2734 \| 0.5305 \| 0.5494 \| 0.9089 \|
	\| 0.2686 \| 0.25 \| 700 \| 0.2362 \| 0.4853 \| 0.6250 \| 0.7887 \|
	\| 0.2715 \| 0.29 \| 800 \| 0.2454 \| 0.4914 \| 0.6255 \| 0.7715 \|
	\| 0.2459 \| 0.32 \| 900 \| 0.2452 \| 0.4763 \| 0.6072 \| 0.7801 \|
	\| 0.2033 \| 0.36 \| 1000 \| 0.2365 \| 0.4967 \| 0.6106 \| 0.7663 \|
	\| 0.2234 \| 0.4 \| 1100 \| 0.2299 \| 0.4947 \| 0.6180 \| 0.8677 \|
	\| 0.2035 \| 0.43 \| 1200 \| 0.2314 \| 0.4744 \| 0.6309 \| 0.7835 \|
	\| 0.2277 \| 0.47 \| 1300 \| 0.2389 \| 0.4649 \| 0.6435 \| 0.7302 \|
	\| 0.2535 \| 0.5 \| 1400 \| 0.2259 \| 0.4509 \| 0.6021 \| 0.8247 \|
	\| 0.2209 \| 0.54 \| 1500 \| 0.2369 \| 0.4507 \| 0.6363 \| 0.7577 \|
	\| 0.2007 \| 0.58 \| 1600 \| 0.2161 \| 0.4272 \| 0.6540 \| 0.8316 \|
	\| 0.2013 \| 0.61 \| 1700 \| 0.2433 \| 0.4326 \| 0.6129 \| 0.7732 \|
	\| 0.1999 \| 0.65 \| 1800 \| 0.2227 \| 0.4460 \| 0.6553 \| 0.8247 \|
	\| 0.2157 \| 0.69 \| 1900 \| 0.2134 \| 0.4728 \| 0.6363 \| 0.8162 \|
	\| 0.2154 \| 0.72 \| 2000 \| 0.2239 \| 0.4734 \| 0.5787 \| 0.8574 \|
	\| 0.2169 \| 0.76 \| 2100 \| 0.2394 \| 0.4392 \| 0.6255 \| 0.8849 \|
	\| 0.2719 \| 0.79 \| 2200 \| 0.2283 \| 0.4324 \| 0.6229 \| 0.8780 \|
	\| 0.2244 \| 0.83 \| 2300 \| 0.2140 \| 0.4483 \| 0.6314 \| 0.8729 \|
	\| 0.2072 \| 0.87 \| 2400 \| 0.2198 \| 0.4330 \| 0.6440 \| 0.8213 \|
	\| 0.1754 \| 0.9 \| 2500 \| 0.2099 \| 0.4198 \| 0.6712 \| 0.8419 \|
	\| 0.1773 \| 0.94 \| 2600 \| 0.2053 \| 0.4105 \| 0.6586 \| 0.8643 \|
	\| 0.2378 \| 0.97 \| 2700 \| 0.2044 \| 0.4324 \| 0.6648 \| 0.8333 \|
	\| 0.1295 \| 1.01 \| 2800 \| 0.2044 \| 0.4016 \| 0.6843 \| 0.8247 \|
	\| 0.1126 \| 1.05 \| 2900 \| 0.2302 \| 0.4025 \| 0.6805 \| 0.7577 \|
	\| 0.1262 \| 1.08 \| 3000 \| 0.2205 \| 0.4017 \| 0.6517 \| 0.8093 \|
	\| 0.1104 \| 1.12 \| 3100 \| 0.2117 \| 0.3931 \| 0.6779 \| 0.8454 \|
	\| 0.1657 \| 1.15 \| 3200 \| 0.2174 \| 0.3890 \| 0.6666 \| 0.8591 \|
	\| 0.1186 \| 1.19 \| 3300 \| 0.2299 \| 0.4013 \| 0.6622 \| 0.8058 \|
	\| 0.1304 \| 1.23 \| 3400 \| 0.2176 \| 0.3801 \| 0.6902 \| 0.8110 \|
	\| 0.1081 \| 1.26 \| 3500 \| 0.2330 \| 0.3867 \| 0.6643 \| 0.8316 \|
	\| 0.1281 \| 1.3 \| 3600 \| 0.2320 \| 0.3954 \| 0.6902 \| 0.7680 \|
	\| 0.1192 \| 1.33 \| 3700 \| 0.2312 \| 0.4109 \| 0.6769 \| 0.7990 \|
	\| 0.1029 \| 1.37 \| 3800 \| 0.2195 \| 0.3870 \| 0.6820 \| 0.8024 \|
	\| 0.1159 \| 1.41 \| 3900 \| 0.2200 \| 0.3860 \| 0.6812 \| 0.7904 \|
	\| 0.1159 \| 1.44 \| 4000 \| 0.2159 \| 0.3712 \| 0.6982 \| 0.7990 \|
	\| 0.107 \| 1.48 \| 4100 \| 0.2262 \| 0.3757 \| 0.6905 \| 0.8213 \|
	\| 0.1262 \| 1.51 \| 4200 \| 0.2291 \| 0.3841 \| 0.6835 \| 0.8247 \|
	\| 0.1437 \| 1.55 \| 4300 \| 0.2311 \| 0.3751 \| 0.6923 \| 0.8007 \|
	\| 0.0916 \| 1.59 \| 4400 \| 0.2343 \| 0.3743 \| 0.6792 \| 0.8660 \|
	\| 0.1266 \| 1.62 \| 4500 \| 0.2251 \| 0.3724 \| 0.6861 \| 0.8505 \|
	\| 0.1185 \| 1.66 \| 4600 \| 0.2242 \| 0.3666 \| 0.6902 \| 0.8265 \|
	\| 0.1037 \| 1.69 \| 4700 \| 0.2219 \| 0.3700 \| 0.6846 \| 0.8522 \|
	\| 0.1264 \| 1.73 \| 4800 \| 0.2211 \| 0.3677 \| 0.6892 \| 0.8351 \|
	\| 0.1404 \| 1.77 \| 4900 \| 0.2206 \| 0.3718 \| 0.6946 \| 0.7938 \|
	\| 0.1238 \| 1.8 \| 5000 \| 0.2098 \| 0.3723 \| 0.6948 \| 0.8265 \|
	\| 0.0868 \| 1.84 \| 5100 \| 0.2089 \| 0.3574 \| 0.7025 \| 0.8144 \|
	\| 0.0828 \| 1.88 \| 5200 \| 0.2204 \| 0.3680 \| 0.7031 \| 0.7818 \|
	\| 0.0986 \| 1.91 \| 5300 \| 0.2126 \| 0.3543 \| 0.6982 \| 0.8127 \|
	\| 0.0869 \| 1.95 \| 5400 \| 0.2247 \| 0.3532 \| 0.7108 \| 0.8076 \|
	\| 0.1006 \| 1.98 \| 5500 \| 0.2268 \| 0.3637 \| 0.7028 \| 0.8162 \|
	\| 0.0639 \| 2.02 \| 5600 \| 0.2252 \| 0.3479 \| 0.7069 \| 0.8110 \|
	\| 0.0569 \| 2.06 \| 5700 \| 0.2315 \| 0.3399 \| 0.7167 \| 0.8076 \|
	\| 0.0626 \| 2.09 \| 5800 \| 0.2304 \| 0.3481 \| 0.7028 \| 0.8127 \|
	\| 0.0502 \| 2.13 \| 5900 \| 0.2381 \| 0.3624 \| 0.6954 \| 0.8093 \|
	\| 0.0541 \| 2.16 \| 6000 \| 0.2298 \| 0.3405 \| 0.7159 \| 0.8110 \|
	\| 0.0671 \| 2.2 \| 6100 \| 0.2432 \| 0.3529 \| 0.7031 \| 0.7990 \|
	\| 0.0672 \| 2.24 \| 6200 \| 0.2431 \| 0.3361 \| 0.7195 \| 0.7715 \|
	\| 0.0446 \| 2.27 \| 6300 \| 0.2447 \| 0.3401 \| 0.7141 \| 0.7938 \|
	\| 0.0424 \| 2.31 \| 6400 \| 0.2426 \| 0.3485 \| 0.7018 \| 0.8162 \|
	\| 0.0386 \| 2.34 \| 6500 \| 0.2488 \| 0.3387 \| 0.7123 \| 0.8127 \|
	\| 0.0736 \| 2.38 \| 6600 \| 0.2454 \| 0.3382 \| 0.7054 \| 0.8316 \|
	\| 0.0421 \| 2.42 \| 6700 \| 0.2513 \| 0.3394 \| 0.7120 \| 0.8316 \|
	\| 0.0607 \| 2.45 \| 6800 \| 0.2546 \| 0.3370 \| 0.7092 \| 0.8265 \|
	\| 0.0517 \| 2.49 \| 6900 \| 0.2594 \| 0.3376 \| 0.7082 \| 0.8299 \|
	\| 0.062 \| 2.52 \| 7000 \| 0.2533 \| 0.3369 \| 0.7105 \| 0.8110 \|
	\| 0.0664 \| 2.56 \| 7100 \| 0.2534 \| 0.3329 \| 0.7185 \| 0.8024 \|
	\| 0.0389 \| 2.6 \| 7200 \| 0.2470 \| 0.3288 \| 0.7259 \| 0.8093 \|
	\| 0.0671 \| 2.63 \| 7300 \| 0.2516 \| 0.3294 \| 0.7159 \| 0.8041 \|
	\| 0.0416 \| 2.67 \| 7400 \| 0.2507 \| 0.3307 \| 0.7133 \| 0.8058 \|
	\| 0.0541 \| 2.7 \| 7500 \| 0.2529 \| 0.3355 \| 0.7110 \| 0.8058 \|
	\| 0.0374 \| 2.74 \| 7600 \| 0.2530 \| 0.3315 \| 0.7149 \| 0.8110 \|
	\| 0.04 \| 2.78 \| 7700 \| 0.2520 \| 0.3290 \| 0.7167 \| 0.8076 \|
	\| 0.0507 \| 2.81 \| 7800 \| 0.2555 \| 0.3297 \| 0.7105 \| 0.8127 \|
	\| 0.0379 \| 2.85 \| 7900 \| 0.2531 \| 0.3274 \| 0.7162 \| 0.8127 \|
	\| 0.0736 \| 2.88 \| 8000 \| 0.2526 \| 0.3279 \| 0.7164 \| 0.8196 \|
	\| 0.0589 \| 2.92 \| 8100 \| 0.2522 \| 0.3267 \| 0.7144 \| 0.8162 \|
	\| 0.0449 \| 2.96 \| 8200 \| 0.2521 \| 0.3272 \| 0.7149 \| 0.8162 \|
	\| 0.0498 \| 2.99 \| 8300 \| 0.2520 \| 0.3265 \| 0.7167 \| 0.8144 \|


	### Framework versions

	- Transformers 4.39.1
	- Pytorch 2.2.1+cu121
	- Datasets 2.18.0
	- Tokenizers 0.15.2