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	---
	library_name: transformers
	license: apache-2.0
	base_model: google/vit-base-patch16-224-in21k
	tags:
	- generated_from_trainer
	datasets:
	- imagefolder
	metrics:
	- accuracy
	model-index:
	- name: sashes_model
	results:
	- task:
	name: Image Classification
	type: image-classification
	dataset:
	name: imagefolder
	type: imagefolder
	config: default
	split: train
	args: default
	metrics:
	- name: Accuracy
	type: accuracy
	value: 0.875968992248062
	---

	<!-- 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. -->

	# sashes_model

	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 imagefolder dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.3784
	- Accuracy: 0.8760

	## 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: 16
	- eval_batch_size: 16
	- seed: 42
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 64
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 112

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:--------:\|:----:\|:---------------:\|:--------:\|
	\| No log \| 0.9697 \| 8 \| 2.2973 \| 0.1434 \|
	\| 2.2994 \| 1.9394 \| 16 \| 2.2717 \| 0.1957 \|
	\| 2.2791 \| 2.9091 \| 24 \| 2.2377 \| 0.2287 \|
	\| 2.2378 \| 4.0 \| 33 \| 2.1866 \| 0.3178 \|
	\| 2.1604 \| 4.9697 \| 41 \| 2.1096 \| 0.3934 \|
	\| 2.1604 \| 5.9394 \| 49 \| 2.0257 \| 0.4322 \|
	\| 2.0801 \| 6.9091 \| 57 \| 1.9312 \| 0.4264 \|
	\| 1.9587 \| 8.0 \| 66 \| 1.7939 \| 0.4942 \|
	\| 1.821 \| 8.9697 \| 74 \| 1.6869 \| 0.5465 \|
	\| 1.6903 \| 9.9394 \| 82 \| 1.6025 \| 0.5736 \|
	\| 1.5687 \| 10.9091 \| 90 \| 1.4849 \| 0.6202 \|
	\| 1.5687 \| 12.0 \| 99 \| 1.4674 \| 0.5407 \|
	\| 1.4183 \| 12.9697 \| 107 \| 1.3539 \| 0.6163 \|
	\| 1.3907 \| 13.9394 \| 115 \| 1.2365 \| 0.6938 \|
	\| 1.3058 \| 14.9091 \| 123 \| 1.2258 \| 0.6938 \|
	\| 1.2181 \| 16.0 \| 132 \| 1.1759 \| 0.6822 \|
	\| 1.1537 \| 16.9697 \| 140 \| 1.1413 \| 0.7074 \|
	\| 1.1537 \| 17.9394 \| 148 \| 1.0586 \| 0.7248 \|
	\| 1.0819 \| 18.9091 \| 156 \| 1.0059 \| 0.7558 \|
	\| 0.9905 \| 20.0 \| 165 \| 0.9575 \| 0.7578 \|
	\| 1.0055 \| 20.9697 \| 173 \| 0.9807 \| 0.7442 \|
	\| 0.9484 \| 21.9394 \| 181 \| 0.9553 \| 0.7539 \|
	\| 0.9484 \| 22.9091 \| 189 \| 0.8213 \| 0.8004 \|
	\| 0.8974 \| 24.0 \| 198 \| 0.8305 \| 0.8043 \|
	\| 0.8545 \| 24.9697 \| 206 \| 0.8273 \| 0.7849 \|
	\| 0.8724 \| 25.9394 \| 214 \| 0.8177 \| 0.7519 \|
	\| 0.8642 \| 26.9091 \| 222 \| 0.7692 \| 0.7926 \|
	\| 0.7609 \| 28.0 \| 231 \| 0.7293 \| 0.8062 \|
	\| 0.7609 \| 28.9697 \| 239 \| 0.7001 \| 0.8198 \|
	\| 0.7418 \| 29.9394 \| 247 \| 0.7899 \| 0.7636 \|
	\| 0.7552 \| 30.9091 \| 255 \| 0.6595 \| 0.8101 \|
	\| 0.7291 \| 32.0 \| 264 \| 0.6971 \| 0.7907 \|
	\| 0.693 \| 32.9697 \| 272 \| 0.7215 \| 0.7946 \|
	\| 0.6891 \| 33.9394 \| 280 \| 0.6980 \| 0.8004 \|
	\| 0.6891 \| 34.9091 \| 288 \| 0.6200 \| 0.8372 \|
	\| 0.6936 \| 36.0 \| 297 \| 0.7245 \| 0.7733 \|
	\| 0.6698 \| 36.9697 \| 305 \| 0.6724 \| 0.7984 \|
	\| 0.6502 \| 37.9394 \| 313 \| 0.6701 \| 0.8023 \|
	\| 0.6988 \| 38.9091 \| 321 \| 0.6049 \| 0.8236 \|
	\| 0.6709 \| 40.0 \| 330 \| 0.6397 \| 0.7965 \|
	\| 0.6709 \| 40.9697 \| 338 \| 0.5654 \| 0.8391 \|
	\| 0.652 \| 41.9394 \| 346 \| 0.6371 \| 0.8101 \|
	\| 0.64 \| 42.9091 \| 354 \| 0.6341 \| 0.8062 \|
	\| 0.6368 \| 44.0 \| 363 \| 0.5662 \| 0.8527 \|
	\| 0.595 \| 44.9697 \| 371 \| 0.5744 \| 0.8411 \|
	\| 0.595 \| 45.9394 \| 379 \| 0.5465 \| 0.8430 \|
	\| 0.5823 \| 46.9091 \| 387 \| 0.6254 \| 0.7984 \|
	\| 0.5514 \| 48.0 \| 396 \| 0.5368 \| 0.8333 \|
	\| 0.5693 \| 48.9697 \| 404 \| 0.5705 \| 0.8043 \|
	\| 0.5244 \| 49.9394 \| 412 \| 0.5685 \| 0.8314 \|
	\| 0.5495 \| 50.9091 \| 420 \| 0.5811 \| 0.8120 \|
	\| 0.5495 \| 52.0 \| 429 \| 0.5037 \| 0.8469 \|
	\| 0.5501 \| 52.9697 \| 437 \| 0.5423 \| 0.8372 \|
	\| 0.5405 \| 53.9394 \| 445 \| 0.5487 \| 0.8178 \|
	\| 0.534 \| 54.9091 \| 453 \| 0.5607 \| 0.8217 \|
	\| 0.5502 \| 56.0 \| 462 \| 0.5141 \| 0.8198 \|
	\| 0.4772 \| 56.9697 \| 470 \| 0.4813 \| 0.8605 \|
	\| 0.4772 \| 57.9394 \| 478 \| 0.5007 \| 0.8566 \|
	\| 0.4823 \| 58.9091 \| 486 \| 0.4847 \| 0.8624 \|
	\| 0.5107 \| 60.0 \| 495 \| 0.5273 \| 0.8333 \|
	\| 0.5205 \| 60.9697 \| 503 \| 0.4981 \| 0.8430 \|
	\| 0.5171 \| 61.9394 \| 511 \| 0.4819 \| 0.8430 \|
	\| 0.5171 \| 62.9091 \| 519 \| 0.4415 \| 0.8682 \|
	\| 0.5498 \| 64.0 \| 528 \| 0.4578 \| 0.8566 \|
	\| 0.4732 \| 64.9697 \| 536 \| 0.4614 \| 0.8450 \|
	\| 0.4623 \| 65.9394 \| 544 \| 0.4923 \| 0.8488 \|
	\| 0.4406 \| 66.9091 \| 552 \| 0.4556 \| 0.8547 \|
	\| 0.4889 \| 68.0 \| 561 \| 0.4727 \| 0.8488 \|
	\| 0.4889 \| 68.9697 \| 569 \| 0.4746 \| 0.8469 \|
	\| 0.4532 \| 69.9394 \| 577 \| 0.4496 \| 0.8585 \|
	\| 0.3988 \| 70.9091 \| 585 \| 0.4260 \| 0.8702 \|
	\| 0.4608 \| 72.0 \| 594 \| 0.4464 \| 0.8547 \|
	\| 0.4429 \| 72.9697 \| 602 \| 0.3946 \| 0.8818 \|
	\| 0.4502 \| 73.9394 \| 610 \| 0.4566 \| 0.8527 \|
	\| 0.4502 \| 74.9091 \| 618 \| 0.4472 \| 0.8663 \|
	\| 0.4381 \| 76.0 \| 627 \| 0.4701 \| 0.8372 \|
	\| 0.4437 \| 76.9697 \| 635 \| 0.4351 \| 0.8488 \|
	\| 0.4223 \| 77.9394 \| 643 \| 0.4011 \| 0.8779 \|
	\| 0.4121 \| 78.9091 \| 651 \| 0.4328 \| 0.8547 \|
	\| 0.4164 \| 80.0 \| 660 \| 0.3908 \| 0.8857 \|
	\| 0.4164 \| 80.9697 \| 668 \| 0.3774 \| 0.8876 \|
	\| 0.418 \| 81.9394 \| 676 \| 0.4397 \| 0.8643 \|
	\| 0.3961 \| 82.9091 \| 684 \| 0.4500 \| 0.8585 \|
	\| 0.4035 \| 84.0 \| 693 \| 0.3968 \| 0.8624 \|
	\| 0.4269 \| 84.9697 \| 701 \| 0.4457 \| 0.8566 \|
	\| 0.4269 \| 85.9394 \| 709 \| 0.3987 \| 0.8740 \|
	\| 0.3694 \| 86.9091 \| 717 \| 0.4074 \| 0.8760 \|
	\| 0.3642 \| 88.0 \| 726 \| 0.3781 \| 0.9012 \|
	\| 0.3985 \| 88.9697 \| 734 \| 0.3575 \| 0.8934 \|
	\| 0.4237 \| 89.9394 \| 742 \| 0.4313 \| 0.8508 \|
	\| 0.4156 \| 90.9091 \| 750 \| 0.3504 \| 0.8934 \|
	\| 0.4156 \| 92.0 \| 759 \| 0.4116 \| 0.8566 \|
	\| 0.389 \| 92.9697 \| 767 \| 0.3739 \| 0.8779 \|
	\| 0.3934 \| 93.9394 \| 775 \| 0.3990 \| 0.8779 \|
	\| 0.4231 \| 94.9091 \| 783 \| 0.4164 \| 0.8624 \|
	\| 0.3792 \| 96.0 \| 792 \| 0.3808 \| 0.8721 \|
	\| 0.3928 \| 96.9697 \| 800 \| 0.3534 \| 0.8915 \|
	\| 0.3928 \| 97.9394 \| 808 \| 0.3643 \| 0.8798 \|
	\| 0.4003 \| 98.9091 \| 816 \| 0.4150 \| 0.8624 \|
	\| 0.3929 \| 100.0 \| 825 \| 0.3477 \| 0.9050 \|
	\| 0.3992 \| 100.9697 \| 833 \| 0.4037 \| 0.8682 \|
	\| 0.387 \| 101.9394 \| 841 \| 0.3453 \| 0.9050 \|
	\| 0.387 \| 102.9091 \| 849 \| 0.4012 \| 0.8682 \|
	\| 0.3942 \| 104.0 \| 858 \| 0.3843 \| 0.8915 \|
	\| 0.3794 \| 104.9697 \| 866 \| 0.3478 \| 0.8798 \|
	\| 0.3794 \| 105.9394 \| 874 \| 0.3111 \| 0.9167 \|
	\| 0.396 \| 106.9091 \| 882 \| 0.3588 \| 0.8818 \|
	\| 0.3767 \| 108.0 \| 891 \| 0.3602 \| 0.8837 \|
	\| 0.3767 \| 108.6061 \| 896 \| 0.3784 \| 0.8760 \|


	### Framework versions

	- Transformers 4.45.0.dev0
	- Pytorch 2.4.0+cu121
	- Datasets 2.21.0
	- Tokenizers 0.19.1

	---
	library_name: transformers
	license: apache-2.0
	base_model: google/vit-base-patch16-224-in21k
	tags:
	- generated_from_trainer
	datasets:
	- imagefolder
	metrics:
	- accuracy
	model-index:
	- name: sashes_model
	results:
	- task:
	name: Image Classification
	type: image-classification
	dataset:
	name: imagefolder
	type: imagefolder
	config: default
	split: train
	args: default
	metrics:
	- name: Accuracy
	type: accuracy
	value: 0.875968992248062
	---

	<!-- 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. -->

	# sashes_model

	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 imagefolder dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.3784
	- Accuracy: 0.8760

	## 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: 16
	- eval_batch_size: 16
	- seed: 42
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 64
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 112

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:--------:\|:----:\|:---------------:\|:--------:\|
	\| No log \| 0.9697 \| 8 \| 2.2973 \| 0.1434 \|
	\| 2.2994 \| 1.9394 \| 16 \| 2.2717 \| 0.1957 \|
	\| 2.2791 \| 2.9091 \| 24 \| 2.2377 \| 0.2287 \|
	\| 2.2378 \| 4.0 \| 33 \| 2.1866 \| 0.3178 \|
	\| 2.1604 \| 4.9697 \| 41 \| 2.1096 \| 0.3934 \|
	\| 2.1604 \| 5.9394 \| 49 \| 2.0257 \| 0.4322 \|
	\| 2.0801 \| 6.9091 \| 57 \| 1.9312 \| 0.4264 \|
	\| 1.9587 \| 8.0 \| 66 \| 1.7939 \| 0.4942 \|
	\| 1.821 \| 8.9697 \| 74 \| 1.6869 \| 0.5465 \|
	\| 1.6903 \| 9.9394 \| 82 \| 1.6025 \| 0.5736 \|
	\| 1.5687 \| 10.9091 \| 90 \| 1.4849 \| 0.6202 \|
	\| 1.5687 \| 12.0 \| 99 \| 1.4674 \| 0.5407 \|
	\| 1.4183 \| 12.9697 \| 107 \| 1.3539 \| 0.6163 \|
	\| 1.3907 \| 13.9394 \| 115 \| 1.2365 \| 0.6938 \|
	\| 1.3058 \| 14.9091 \| 123 \| 1.2258 \| 0.6938 \|
	\| 1.2181 \| 16.0 \| 132 \| 1.1759 \| 0.6822 \|
	\| 1.1537 \| 16.9697 \| 140 \| 1.1413 \| 0.7074 \|
	\| 1.1537 \| 17.9394 \| 148 \| 1.0586 \| 0.7248 \|
	\| 1.0819 \| 18.9091 \| 156 \| 1.0059 \| 0.7558 \|
	\| 0.9905 \| 20.0 \| 165 \| 0.9575 \| 0.7578 \|
	\| 1.0055 \| 20.9697 \| 173 \| 0.9807 \| 0.7442 \|
	\| 0.9484 \| 21.9394 \| 181 \| 0.9553 \| 0.7539 \|
	\| 0.9484 \| 22.9091 \| 189 \| 0.8213 \| 0.8004 \|
	\| 0.8974 \| 24.0 \| 198 \| 0.8305 \| 0.8043 \|
	\| 0.8545 \| 24.9697 \| 206 \| 0.8273 \| 0.7849 \|
	\| 0.8724 \| 25.9394 \| 214 \| 0.8177 \| 0.7519 \|
	\| 0.8642 \| 26.9091 \| 222 \| 0.7692 \| 0.7926 \|
	\| 0.7609 \| 28.0 \| 231 \| 0.7293 \| 0.8062 \|
	\| 0.7609 \| 28.9697 \| 239 \| 0.7001 \| 0.8198 \|
	\| 0.7418 \| 29.9394 \| 247 \| 0.7899 \| 0.7636 \|
	\| 0.7552 \| 30.9091 \| 255 \| 0.6595 \| 0.8101 \|
	\| 0.7291 \| 32.0 \| 264 \| 0.6971 \| 0.7907 \|
	\| 0.693 \| 32.9697 \| 272 \| 0.7215 \| 0.7946 \|
	\| 0.6891 \| 33.9394 \| 280 \| 0.6980 \| 0.8004 \|
	\| 0.6891 \| 34.9091 \| 288 \| 0.6200 \| 0.8372 \|
	\| 0.6936 \| 36.0 \| 297 \| 0.7245 \| 0.7733 \|
	\| 0.6698 \| 36.9697 \| 305 \| 0.6724 \| 0.7984 \|
	\| 0.6502 \| 37.9394 \| 313 \| 0.6701 \| 0.8023 \|
	\| 0.6988 \| 38.9091 \| 321 \| 0.6049 \| 0.8236 \|
	\| 0.6709 \| 40.0 \| 330 \| 0.6397 \| 0.7965 \|
	\| 0.6709 \| 40.9697 \| 338 \| 0.5654 \| 0.8391 \|
	\| 0.652 \| 41.9394 \| 346 \| 0.6371 \| 0.8101 \|
	\| 0.64 \| 42.9091 \| 354 \| 0.6341 \| 0.8062 \|
	\| 0.6368 \| 44.0 \| 363 \| 0.5662 \| 0.8527 \|
	\| 0.595 \| 44.9697 \| 371 \| 0.5744 \| 0.8411 \|
	\| 0.595 \| 45.9394 \| 379 \| 0.5465 \| 0.8430 \|
	\| 0.5823 \| 46.9091 \| 387 \| 0.6254 \| 0.7984 \|
	\| 0.5514 \| 48.0 \| 396 \| 0.5368 \| 0.8333 \|
	\| 0.5693 \| 48.9697 \| 404 \| 0.5705 \| 0.8043 \|
	\| 0.5244 \| 49.9394 \| 412 \| 0.5685 \| 0.8314 \|
	\| 0.5495 \| 50.9091 \| 420 \| 0.5811 \| 0.8120 \|
	\| 0.5495 \| 52.0 \| 429 \| 0.5037 \| 0.8469 \|
	\| 0.5501 \| 52.9697 \| 437 \| 0.5423 \| 0.8372 \|
	\| 0.5405 \| 53.9394 \| 445 \| 0.5487 \| 0.8178 \|
	\| 0.534 \| 54.9091 \| 453 \| 0.5607 \| 0.8217 \|
	\| 0.5502 \| 56.0 \| 462 \| 0.5141 \| 0.8198 \|
	\| 0.4772 \| 56.9697 \| 470 \| 0.4813 \| 0.8605 \|
	\| 0.4772 \| 57.9394 \| 478 \| 0.5007 \| 0.8566 \|
	\| 0.4823 \| 58.9091 \| 486 \| 0.4847 \| 0.8624 \|
	\| 0.5107 \| 60.0 \| 495 \| 0.5273 \| 0.8333 \|
	\| 0.5205 \| 60.9697 \| 503 \| 0.4981 \| 0.8430 \|
	\| 0.5171 \| 61.9394 \| 511 \| 0.4819 \| 0.8430 \|
	\| 0.5171 \| 62.9091 \| 519 \| 0.4415 \| 0.8682 \|
	\| 0.5498 \| 64.0 \| 528 \| 0.4578 \| 0.8566 \|
	\| 0.4732 \| 64.9697 \| 536 \| 0.4614 \| 0.8450 \|
	\| 0.4623 \| 65.9394 \| 544 \| 0.4923 \| 0.8488 \|
	\| 0.4406 \| 66.9091 \| 552 \| 0.4556 \| 0.8547 \|
	\| 0.4889 \| 68.0 \| 561 \| 0.4727 \| 0.8488 \|
	\| 0.4889 \| 68.9697 \| 569 \| 0.4746 \| 0.8469 \|
	\| 0.4532 \| 69.9394 \| 577 \| 0.4496 \| 0.8585 \|
	\| 0.3988 \| 70.9091 \| 585 \| 0.4260 \| 0.8702 \|
	\| 0.4608 \| 72.0 \| 594 \| 0.4464 \| 0.8547 \|
	\| 0.4429 \| 72.9697 \| 602 \| 0.3946 \| 0.8818 \|
	\| 0.4502 \| 73.9394 \| 610 \| 0.4566 \| 0.8527 \|
	\| 0.4502 \| 74.9091 \| 618 \| 0.4472 \| 0.8663 \|
	\| 0.4381 \| 76.0 \| 627 \| 0.4701 \| 0.8372 \|
	\| 0.4437 \| 76.9697 \| 635 \| 0.4351 \| 0.8488 \|
	\| 0.4223 \| 77.9394 \| 643 \| 0.4011 \| 0.8779 \|
	\| 0.4121 \| 78.9091 \| 651 \| 0.4328 \| 0.8547 \|
	\| 0.4164 \| 80.0 \| 660 \| 0.3908 \| 0.8857 \|
	\| 0.4164 \| 80.9697 \| 668 \| 0.3774 \| 0.8876 \|
	\| 0.418 \| 81.9394 \| 676 \| 0.4397 \| 0.8643 \|
	\| 0.3961 \| 82.9091 \| 684 \| 0.4500 \| 0.8585 \|
	\| 0.4035 \| 84.0 \| 693 \| 0.3968 \| 0.8624 \|
	\| 0.4269 \| 84.9697 \| 701 \| 0.4457 \| 0.8566 \|
	\| 0.4269 \| 85.9394 \| 709 \| 0.3987 \| 0.8740 \|
	\| 0.3694 \| 86.9091 \| 717 \| 0.4074 \| 0.8760 \|
	\| 0.3642 \| 88.0 \| 726 \| 0.3781 \| 0.9012 \|
	\| 0.3985 \| 88.9697 \| 734 \| 0.3575 \| 0.8934 \|
	\| 0.4237 \| 89.9394 \| 742 \| 0.4313 \| 0.8508 \|
	\| 0.4156 \| 90.9091 \| 750 \| 0.3504 \| 0.8934 \|
	\| 0.4156 \| 92.0 \| 759 \| 0.4116 \| 0.8566 \|
	\| 0.389 \| 92.9697 \| 767 \| 0.3739 \| 0.8779 \|
	\| 0.3934 \| 93.9394 \| 775 \| 0.3990 \| 0.8779 \|
	\| 0.4231 \| 94.9091 \| 783 \| 0.4164 \| 0.8624 \|
	\| 0.3792 \| 96.0 \| 792 \| 0.3808 \| 0.8721 \|
	\| 0.3928 \| 96.9697 \| 800 \| 0.3534 \| 0.8915 \|
	\| 0.3928 \| 97.9394 \| 808 \| 0.3643 \| 0.8798 \|
	\| 0.4003 \| 98.9091 \| 816 \| 0.4150 \| 0.8624 \|
	\| 0.3929 \| 100.0 \| 825 \| 0.3477 \| 0.9050 \|
	\| 0.3992 \| 100.9697 \| 833 \| 0.4037 \| 0.8682 \|
	\| 0.387 \| 101.9394 \| 841 \| 0.3453 \| 0.9050 \|
	\| 0.387 \| 102.9091 \| 849 \| 0.4012 \| 0.8682 \|
	\| 0.3942 \| 104.0 \| 858 \| 0.3843 \| 0.8915 \|
	\| 0.3794 \| 104.9697 \| 866 \| 0.3478 \| 0.8798 \|
	\| 0.3794 \| 105.9394 \| 874 \| 0.3111 \| 0.9167 \|
	\| 0.396 \| 106.9091 \| 882 \| 0.3588 \| 0.8818 \|
	\| 0.3767 \| 108.0 \| 891 \| 0.3602 \| 0.8837 \|
	\| 0.3767 \| 108.6061 \| 896 \| 0.3784 \| 0.8760 \|


	### Framework versions

	- Transformers 4.45.0.dev0
	- Pytorch 2.4.0+cu121
	- Datasets 2.21.0
	- Tokenizers 0.19.1