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	---
	license: apache-2.0
	base_model: microsoft/swinv2-base-patch4-window16-256
	tags:
	- generated_from_trainer
	datasets:
	- stanford-dogs
	metrics:
	- accuracy
	- f1
	- precision
	- recall
	model-index:
	- name: microsoft-swinv2-base-patch4-window16-256-batch32-lr0.0005-standford-dogs
	results:
	- task:
	name: Image Classification
	type: image-classification
	dataset:
	name: stanford-dogs
	type: stanford-dogs
	config: default
	split: full
	args: default
	metrics:
	- name: Accuracy
	type: accuracy
	value: 0.9429057337220602
	- name: F1
	type: f1
	value: 0.9410841953165723
	- name: Precision
	type: precision
	value: 0.9431724455914652
	- name: Recall
	type: recall
	value: 0.9417046971391595
	---

	<!-- 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. -->

	# microsoft-swinv2-base-patch4-window16-256-batch32-lr0.0005-standford-dogs

	This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window16-256](https://huggingface.co/microsoft/swinv2-base-patch4-window16-256) on the stanford-dogs dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.1810
	- Accuracy: 0.9429
	- F1: 0.9411
	- Precision: 0.9432
	- Recall: 0.9417

	## 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: 32
	- eval_batch_size: 32
	- seed: 42
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 128
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- training_steps: 1000

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|
	\| 4.7518 \| 0.0777 \| 10 \| 4.6391 \| 0.0741 \| 0.0533 \| 0.0667 \| 0.0705 \|
	\| 4.5585 \| 0.1553 \| 20 \| 4.3463 \| 0.1919 \| 0.1445 \| 0.1900 \| 0.1794 \|
	\| 4.2377 \| 0.2330 \| 30 \| 3.8243 \| 0.3525 \| 0.3100 \| 0.4154 \| 0.3382 \|
	\| 3.6654 \| 0.3107 \| 40 \| 2.9276 \| 0.6409 \| 0.6111 \| 0.6994 \| 0.6300 \|
	\| 2.7617 \| 0.3883 \| 50 \| 1.7703 \| 0.8248 \| 0.8042 \| 0.8361 \| 0.8182 \|
	\| 1.9475 \| 0.4660 \| 60 \| 1.0440 \| 0.8863 \| 0.8781 \| 0.8924 \| 0.8821 \|
	\| 1.3629 \| 0.5437 \| 70 \| 0.6490 \| 0.9099 \| 0.9031 \| 0.9191 \| 0.9062 \|
	\| 1.0488 \| 0.6214 \| 80 \| 0.4485 \| 0.9150 \| 0.9075 \| 0.9147 \| 0.9118 \|
	\| 0.8477 \| 0.6990 \| 90 \| 0.3744 \| 0.9206 \| 0.9169 \| 0.9294 \| 0.9190 \|
	\| 0.7184 \| 0.7767 \| 100 \| 0.3301 \| 0.9259 \| 0.9215 \| 0.9283 \| 0.9227 \|
	\| 0.7149 \| 0.8544 \| 110 \| 0.2970 \| 0.9186 \| 0.9152 \| 0.9227 \| 0.9156 \|
	\| 0.6429 \| 0.9320 \| 120 \| 0.2675 \| 0.9286 \| 0.9238 \| 0.9301 \| 0.9256 \|
	\| 0.5864 \| 1.0097 \| 130 \| 0.2609 \| 0.9291 \| 0.9258 \| 0.9338 \| 0.9272 \|
	\| 0.5414 \| 1.0874 \| 140 \| 0.2644 \| 0.9162 \| 0.9122 \| 0.9212 \| 0.9156 \|
	\| 0.5323 \| 1.1650 \| 150 \| 0.2454 \| 0.9281 \| 0.9225 \| 0.9362 \| 0.9256 \|
	\| 0.5061 \| 1.2427 \| 160 \| 0.2481 \| 0.9269 \| 0.9235 \| 0.9308 \| 0.9251 \|
	\| 0.5898 \| 1.3204 \| 170 \| 0.2306 \| 0.9346 \| 0.9324 \| 0.9389 \| 0.9331 \|
	\| 0.5277 \| 1.3981 \| 180 \| 0.2192 \| 0.9368 \| 0.9327 \| 0.9384 \| 0.9350 \|
	\| 0.4824 \| 1.4757 \| 190 \| 0.2171 \| 0.9337 \| 0.9297 \| 0.9375 \| 0.9311 \|
	\| 0.4632 \| 1.5534 \| 200 \| 0.2244 \| 0.9346 \| 0.9315 \| 0.9379 \| 0.9326 \|
	\| 0.4882 \| 1.6311 \| 210 \| 0.2237 \| 0.9361 \| 0.9323 \| 0.9404 \| 0.9345 \|
	\| 0.4583 \| 1.7087 \| 220 \| 0.2228 \| 0.9327 \| 0.9289 \| 0.9373 \| 0.9304 \|
	\| 0.4692 \| 1.7864 \| 230 \| 0.2098 \| 0.9354 \| 0.9316 \| 0.9370 \| 0.9332 \|
	\| 0.5407 \| 1.8641 \| 240 \| 0.2102 \| 0.9356 \| 0.9342 \| 0.9375 \| 0.9351 \|
	\| 0.4629 \| 1.9417 \| 250 \| 0.2045 \| 0.9378 \| 0.9349 \| 0.9396 \| 0.9367 \|
	\| 0.4363 \| 2.0194 \| 260 \| 0.2023 \| 0.9373 \| 0.9346 \| 0.9398 \| 0.9355 \|
	\| 0.4328 \| 2.0971 \| 270 \| 0.2063 \| 0.9354 \| 0.9320 \| 0.9360 \| 0.9343 \|
	\| 0.3554 \| 2.1748 \| 280 \| 0.1948 \| 0.9439 \| 0.9398 \| 0.9475 \| 0.9418 \|
	\| 0.4024 \| 2.2524 \| 290 \| 0.1985 \| 0.9388 \| 0.9372 \| 0.9397 \| 0.9377 \|
	\| 0.4006 \| 2.3301 \| 300 \| 0.2153 \| 0.9334 \| 0.9275 \| 0.9420 \| 0.9311 \|
	\| 0.3935 \| 2.4078 \| 310 \| 0.2021 \| 0.9393 \| 0.9346 \| 0.9416 \| 0.9368 \|
	\| 0.3591 \| 2.4854 \| 320 \| 0.2126 \| 0.9346 \| 0.9311 \| 0.9403 \| 0.9333 \|
	\| 0.4058 \| 2.5631 \| 330 \| 0.2020 \| 0.9378 \| 0.9357 \| 0.9393 \| 0.9358 \|
	\| 0.396 \| 2.6408 \| 340 \| 0.2038 \| 0.9371 \| 0.9339 \| 0.9410 \| 0.9357 \|
	\| 0.4157 \| 2.7184 \| 350 \| 0.2091 \| 0.9332 \| 0.9288 \| 0.9352 \| 0.9308 \|
	\| 0.4222 \| 2.7961 \| 360 \| 0.1933 \| 0.9393 \| 0.9372 \| 0.9399 \| 0.9378 \|
	\| 0.3521 \| 2.8738 \| 370 \| 0.1984 \| 0.9397 \| 0.9381 \| 0.9430 \| 0.9388 \|
	\| 0.3925 \| 2.9515 \| 380 \| 0.1874 \| 0.9383 \| 0.9347 \| 0.9390 \| 0.9358 \|
	\| 0.3475 \| 3.0291 \| 390 \| 0.1994 \| 0.9383 \| 0.9364 \| 0.9410 \| 0.9376 \|
	\| 0.3526 \| 3.1068 \| 400 \| 0.1941 \| 0.9390 \| 0.9352 \| 0.9402 \| 0.9373 \|
	\| 0.351 \| 3.1845 \| 410 \| 0.1893 \| 0.9417 \| 0.9403 \| 0.9438 \| 0.9410 \|
	\| 0.3549 \| 3.2621 \| 420 \| 0.1960 \| 0.9390 \| 0.9370 \| 0.9410 \| 0.9381 \|
	\| 0.3291 \| 3.3398 \| 430 \| 0.1948 \| 0.9397 \| 0.9358 \| 0.9387 \| 0.9374 \|
	\| 0.3153 \| 3.4175 \| 440 \| 0.1992 \| 0.9441 \| 0.9415 \| 0.9453 \| 0.9427 \|
	\| 0.3116 \| 3.4951 \| 450 \| 0.2005 \| 0.9417 \| 0.9389 \| 0.9432 \| 0.9404 \|
	\| 0.3053 \| 3.5728 \| 460 \| 0.1974 \| 0.9412 \| 0.9372 \| 0.9424 \| 0.9394 \|
	\| 0.3141 \| 3.6505 \| 470 \| 0.1941 \| 0.9405 \| 0.9386 \| 0.9420 \| 0.9395 \|
	\| 0.3275 \| 3.7282 \| 480 \| 0.2182 \| 0.9334 \| 0.9301 \| 0.9374 \| 0.9321 \|
	\| 0.2997 \| 3.8058 \| 490 \| 0.2029 \| 0.9376 \| 0.9343 \| 0.9392 \| 0.9360 \|
	\| 0.3242 \| 3.8835 \| 500 \| 0.1996 \| 0.9380 \| 0.9344 \| 0.9399 \| 0.9361 \|
	\| 0.3585 \| 3.9612 \| 510 \| 0.1935 \| 0.9405 \| 0.9378 \| 0.9421 \| 0.9389 \|
	\| 0.2942 \| 4.0388 \| 520 \| 0.2028 \| 0.9368 \| 0.9341 \| 0.9428 \| 0.9367 \|
	\| 0.3233 \| 4.1165 \| 530 \| 0.2029 \| 0.9378 \| 0.9353 \| 0.9406 \| 0.9364 \|
	\| 0.2942 \| 4.1942 \| 540 \| 0.1959 \| 0.9385 \| 0.9368 \| 0.9395 \| 0.9372 \|
	\| 0.3079 \| 4.2718 \| 550 \| 0.1941 \| 0.9371 \| 0.9349 \| 0.9373 \| 0.9354 \|
	\| 0.2931 \| 4.3495 \| 560 \| 0.1871 \| 0.9414 \| 0.9388 \| 0.9410 \| 0.9394 \|
	\| 0.3058 \| 4.4272 \| 570 \| 0.1879 \| 0.9419 \| 0.9403 \| 0.9430 \| 0.9407 \|
	\| 0.3402 \| 4.5049 \| 580 \| 0.1833 \| 0.9434 \| 0.9409 \| 0.9435 \| 0.9420 \|
	\| 0.3169 \| 4.5825 \| 590 \| 0.1882 \| 0.9412 \| 0.9391 \| 0.9425 \| 0.9402 \|
	\| 0.3071 \| 4.6602 \| 600 \| 0.1821 \| 0.9448 \| 0.9425 \| 0.9460 \| 0.9431 \|
	\| 0.313 \| 4.7379 \| 610 \| 0.1879 \| 0.9429 \| 0.9401 \| 0.9441 \| 0.9413 \|
	\| 0.3338 \| 4.8155 \| 620 \| 0.1843 \| 0.9456 \| 0.9424 \| 0.9469 \| 0.9439 \|
	\| 0.2468 \| 4.8932 \| 630 \| 0.1866 \| 0.9436 \| 0.9412 \| 0.9441 \| 0.9426 \|
	\| 0.2567 \| 4.9709 \| 640 \| 0.1882 \| 0.9405 \| 0.9387 \| 0.9417 \| 0.9393 \|
	\| 0.2792 \| 5.0485 \| 650 \| 0.1914 \| 0.9429 \| 0.9407 \| 0.9442 \| 0.9418 \|
	\| 0.2985 \| 5.1262 \| 660 \| 0.1880 \| 0.9429 \| 0.9393 \| 0.9442 \| 0.9411 \|
	\| 0.2744 \| 5.2039 \| 670 \| 0.1865 \| 0.9410 \| 0.9378 \| 0.9420 \| 0.9390 \|
	\| 0.2662 \| 5.2816 \| 680 \| 0.1877 \| 0.9419 \| 0.9400 \| 0.9423 \| 0.9407 \|
	\| 0.2613 \| 5.3592 \| 690 \| 0.1890 \| 0.9393 \| 0.9369 \| 0.9401 \| 0.9378 \|
	\| 0.2698 \| 5.4369 \| 700 \| 0.1849 \| 0.9429 \| 0.9409 \| 0.9441 \| 0.9417 \|
	\| 0.2592 \| 5.5146 \| 710 \| 0.1854 \| 0.9429 \| 0.9414 \| 0.9439 \| 0.9425 \|
	\| 0.2819 \| 5.5922 \| 720 \| 0.1868 \| 0.9429 \| 0.9414 \| 0.9443 \| 0.9418 \|
	\| 0.2625 \| 5.6699 \| 730 \| 0.1832 \| 0.9434 \| 0.9417 \| 0.9438 \| 0.9422 \|
	\| 0.273 \| 5.7476 \| 740 \| 0.1862 \| 0.9439 \| 0.9408 \| 0.9445 \| 0.9424 \|
	\| 0.2718 \| 5.8252 \| 750 \| 0.1838 \| 0.9441 \| 0.9417 \| 0.9443 \| 0.9428 \|
	\| 0.3055 \| 5.9029 \| 760 \| 0.1852 \| 0.9422 \| 0.9396 \| 0.9426 \| 0.9407 \|
	\| 0.276 \| 5.9806 \| 770 \| 0.1843 \| 0.9424 \| 0.9409 \| 0.9434 \| 0.9415 \|
	\| 0.2614 \| 6.0583 \| 780 \| 0.1839 \| 0.9429 \| 0.9403 \| 0.9431 \| 0.9411 \|
	\| 0.2452 \| 6.1359 \| 790 \| 0.1858 \| 0.9407 \| 0.9384 \| 0.9414 \| 0.9390 \|
	\| 0.2608 \| 6.2136 \| 800 \| 0.1851 \| 0.9429 \| 0.9411 \| 0.9437 \| 0.9417 \|
	\| 0.2639 \| 6.2913 \| 810 \| 0.1842 \| 0.9453 \| 0.9432 \| 0.9463 \| 0.9438 \|
	\| 0.2696 \| 6.3689 \| 820 \| 0.1812 \| 0.9424 \| 0.9406 \| 0.9425 \| 0.9412 \|
	\| 0.2524 \| 6.4466 \| 830 \| 0.1830 \| 0.9427 \| 0.9411 \| 0.9433 \| 0.9417 \|
	\| 0.2673 \| 6.5243 \| 840 \| 0.1823 \| 0.9451 \| 0.9436 \| 0.9464 \| 0.9442 \|
	\| 0.2991 \| 6.6019 \| 850 \| 0.1837 \| 0.9429 \| 0.9408 \| 0.9431 \| 0.9419 \|
	\| 0.2704 \| 6.6796 \| 860 \| 0.1833 \| 0.9439 \| 0.9424 \| 0.9446 \| 0.9431 \|
	\| 0.2437 \| 6.7573 \| 870 \| 0.1857 \| 0.9424 \| 0.9410 \| 0.9434 \| 0.9416 \|
	\| 0.2266 \| 6.8350 \| 880 \| 0.1846 \| 0.9431 \| 0.9416 \| 0.9436 \| 0.9423 \|
	\| 0.2276 \| 6.9126 \| 890 \| 0.1825 \| 0.9441 \| 0.9426 \| 0.9448 \| 0.9433 \|
	\| 0.2249 \| 6.9903 \| 900 \| 0.1813 \| 0.9436 \| 0.9419 \| 0.9441 \| 0.9425 \|
	\| 0.2559 \| 7.0680 \| 910 \| 0.1813 \| 0.9444 \| 0.9425 \| 0.9448 \| 0.9431 \|
	\| 0.2616 \| 7.1456 \| 920 \| 0.1813 \| 0.9441 \| 0.9421 \| 0.9443 \| 0.9428 \|
	\| 0.2247 \| 7.2233 \| 930 \| 0.1813 \| 0.9439 \| 0.9421 \| 0.9442 \| 0.9426 \|
	\| 0.2471 \| 7.3010 \| 940 \| 0.1813 \| 0.9448 \| 0.9430 \| 0.9453 \| 0.9436 \|
	\| 0.2446 \| 7.3786 \| 950 \| 0.1817 \| 0.9444 \| 0.9427 \| 0.9450 \| 0.9432 \|
	\| 0.2262 \| 7.4563 \| 960 \| 0.1819 \| 0.9434 \| 0.9417 \| 0.9439 \| 0.9423 \|
	\| 0.2632 \| 7.5340 \| 970 \| 0.1818 \| 0.9439 \| 0.9422 \| 0.9444 \| 0.9427 \|
	\| 0.2258 \| 7.6117 \| 980 \| 0.1815 \| 0.9434 \| 0.9416 \| 0.9439 \| 0.9422 \|
	\| 0.2404 \| 7.6893 \| 990 \| 0.1811 \| 0.9429 \| 0.9410 \| 0.9432 \| 0.9416 \|
	\| 0.2379 \| 7.7670 \| 1000 \| 0.1810 \| 0.9429 \| 0.9411 \| 0.9432 \| 0.9417 \|


	### Framework versions

	- Transformers 4.40.2
	- Pytorch 2.3.0
	- Datasets 2.19.1
	- Tokenizers 0.19.1

	---
	license: apache-2.0
	base_model: microsoft/swinv2-base-patch4-window16-256
	tags:
	- generated_from_trainer
	datasets:
	- stanford-dogs
	metrics:
	- accuracy
	- f1
	- precision
	- recall
	model-index:
	- name: microsoft-swinv2-base-patch4-window16-256-batch32-lr0.0005-standford-dogs
	results:
	- task:
	name: Image Classification
	type: image-classification
	dataset:
	name: stanford-dogs
	type: stanford-dogs
	config: default
	split: full
	args: default
	metrics:
	- name: Accuracy
	type: accuracy
	value: 0.9429057337220602
	- name: F1
	type: f1
	value: 0.9410841953165723
	- name: Precision
	type: precision
	value: 0.9431724455914652
	- name: Recall
	type: recall
	value: 0.9417046971391595
	---

	<!-- 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. -->

	# microsoft-swinv2-base-patch4-window16-256-batch32-lr0.0005-standford-dogs

	This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window16-256](https://huggingface.co/microsoft/swinv2-base-patch4-window16-256) on the stanford-dogs dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.1810
	- Accuracy: 0.9429
	- F1: 0.9411
	- Precision: 0.9432
	- Recall: 0.9417

	## 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: 32
	- eval_batch_size: 32
	- seed: 42
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 128
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- training_steps: 1000

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|
	\| 4.7518 \| 0.0777 \| 10 \| 4.6391 \| 0.0741 \| 0.0533 \| 0.0667 \| 0.0705 \|
	\| 4.5585 \| 0.1553 \| 20 \| 4.3463 \| 0.1919 \| 0.1445 \| 0.1900 \| 0.1794 \|
	\| 4.2377 \| 0.2330 \| 30 \| 3.8243 \| 0.3525 \| 0.3100 \| 0.4154 \| 0.3382 \|
	\| 3.6654 \| 0.3107 \| 40 \| 2.9276 \| 0.6409 \| 0.6111 \| 0.6994 \| 0.6300 \|
	\| 2.7617 \| 0.3883 \| 50 \| 1.7703 \| 0.8248 \| 0.8042 \| 0.8361 \| 0.8182 \|
	\| 1.9475 \| 0.4660 \| 60 \| 1.0440 \| 0.8863 \| 0.8781 \| 0.8924 \| 0.8821 \|
	\| 1.3629 \| 0.5437 \| 70 \| 0.6490 \| 0.9099 \| 0.9031 \| 0.9191 \| 0.9062 \|
	\| 1.0488 \| 0.6214 \| 80 \| 0.4485 \| 0.9150 \| 0.9075 \| 0.9147 \| 0.9118 \|
	\| 0.8477 \| 0.6990 \| 90 \| 0.3744 \| 0.9206 \| 0.9169 \| 0.9294 \| 0.9190 \|
	\| 0.7184 \| 0.7767 \| 100 \| 0.3301 \| 0.9259 \| 0.9215 \| 0.9283 \| 0.9227 \|
	\| 0.7149 \| 0.8544 \| 110 \| 0.2970 \| 0.9186 \| 0.9152 \| 0.9227 \| 0.9156 \|
	\| 0.6429 \| 0.9320 \| 120 \| 0.2675 \| 0.9286 \| 0.9238 \| 0.9301 \| 0.9256 \|
	\| 0.5864 \| 1.0097 \| 130 \| 0.2609 \| 0.9291 \| 0.9258 \| 0.9338 \| 0.9272 \|
	\| 0.5414 \| 1.0874 \| 140 \| 0.2644 \| 0.9162 \| 0.9122 \| 0.9212 \| 0.9156 \|
	\| 0.5323 \| 1.1650 \| 150 \| 0.2454 \| 0.9281 \| 0.9225 \| 0.9362 \| 0.9256 \|
	\| 0.5061 \| 1.2427 \| 160 \| 0.2481 \| 0.9269 \| 0.9235 \| 0.9308 \| 0.9251 \|
	\| 0.5898 \| 1.3204 \| 170 \| 0.2306 \| 0.9346 \| 0.9324 \| 0.9389 \| 0.9331 \|
	\| 0.5277 \| 1.3981 \| 180 \| 0.2192 \| 0.9368 \| 0.9327 \| 0.9384 \| 0.9350 \|
	\| 0.4824 \| 1.4757 \| 190 \| 0.2171 \| 0.9337 \| 0.9297 \| 0.9375 \| 0.9311 \|
	\| 0.4632 \| 1.5534 \| 200 \| 0.2244 \| 0.9346 \| 0.9315 \| 0.9379 \| 0.9326 \|
	\| 0.4882 \| 1.6311 \| 210 \| 0.2237 \| 0.9361 \| 0.9323 \| 0.9404 \| 0.9345 \|
	\| 0.4583 \| 1.7087 \| 220 \| 0.2228 \| 0.9327 \| 0.9289 \| 0.9373 \| 0.9304 \|
	\| 0.4692 \| 1.7864 \| 230 \| 0.2098 \| 0.9354 \| 0.9316 \| 0.9370 \| 0.9332 \|
	\| 0.5407 \| 1.8641 \| 240 \| 0.2102 \| 0.9356 \| 0.9342 \| 0.9375 \| 0.9351 \|
	\| 0.4629 \| 1.9417 \| 250 \| 0.2045 \| 0.9378 \| 0.9349 \| 0.9396 \| 0.9367 \|
	\| 0.4363 \| 2.0194 \| 260 \| 0.2023 \| 0.9373 \| 0.9346 \| 0.9398 \| 0.9355 \|
	\| 0.4328 \| 2.0971 \| 270 \| 0.2063 \| 0.9354 \| 0.9320 \| 0.9360 \| 0.9343 \|
	\| 0.3554 \| 2.1748 \| 280 \| 0.1948 \| 0.9439 \| 0.9398 \| 0.9475 \| 0.9418 \|
	\| 0.4024 \| 2.2524 \| 290 \| 0.1985 \| 0.9388 \| 0.9372 \| 0.9397 \| 0.9377 \|
	\| 0.4006 \| 2.3301 \| 300 \| 0.2153 \| 0.9334 \| 0.9275 \| 0.9420 \| 0.9311 \|
	\| 0.3935 \| 2.4078 \| 310 \| 0.2021 \| 0.9393 \| 0.9346 \| 0.9416 \| 0.9368 \|
	\| 0.3591 \| 2.4854 \| 320 \| 0.2126 \| 0.9346 \| 0.9311 \| 0.9403 \| 0.9333 \|
	\| 0.4058 \| 2.5631 \| 330 \| 0.2020 \| 0.9378 \| 0.9357 \| 0.9393 \| 0.9358 \|
	\| 0.396 \| 2.6408 \| 340 \| 0.2038 \| 0.9371 \| 0.9339 \| 0.9410 \| 0.9357 \|
	\| 0.4157 \| 2.7184 \| 350 \| 0.2091 \| 0.9332 \| 0.9288 \| 0.9352 \| 0.9308 \|
	\| 0.4222 \| 2.7961 \| 360 \| 0.1933 \| 0.9393 \| 0.9372 \| 0.9399 \| 0.9378 \|
	\| 0.3521 \| 2.8738 \| 370 \| 0.1984 \| 0.9397 \| 0.9381 \| 0.9430 \| 0.9388 \|
	\| 0.3925 \| 2.9515 \| 380 \| 0.1874 \| 0.9383 \| 0.9347 \| 0.9390 \| 0.9358 \|
	\| 0.3475 \| 3.0291 \| 390 \| 0.1994 \| 0.9383 \| 0.9364 \| 0.9410 \| 0.9376 \|
	\| 0.3526 \| 3.1068 \| 400 \| 0.1941 \| 0.9390 \| 0.9352 \| 0.9402 \| 0.9373 \|
	\| 0.351 \| 3.1845 \| 410 \| 0.1893 \| 0.9417 \| 0.9403 \| 0.9438 \| 0.9410 \|
	\| 0.3549 \| 3.2621 \| 420 \| 0.1960 \| 0.9390 \| 0.9370 \| 0.9410 \| 0.9381 \|
	\| 0.3291 \| 3.3398 \| 430 \| 0.1948 \| 0.9397 \| 0.9358 \| 0.9387 \| 0.9374 \|
	\| 0.3153 \| 3.4175 \| 440 \| 0.1992 \| 0.9441 \| 0.9415 \| 0.9453 \| 0.9427 \|
	\| 0.3116 \| 3.4951 \| 450 \| 0.2005 \| 0.9417 \| 0.9389 \| 0.9432 \| 0.9404 \|
	\| 0.3053 \| 3.5728 \| 460 \| 0.1974 \| 0.9412 \| 0.9372 \| 0.9424 \| 0.9394 \|
	\| 0.3141 \| 3.6505 \| 470 \| 0.1941 \| 0.9405 \| 0.9386 \| 0.9420 \| 0.9395 \|
	\| 0.3275 \| 3.7282 \| 480 \| 0.2182 \| 0.9334 \| 0.9301 \| 0.9374 \| 0.9321 \|
	\| 0.2997 \| 3.8058 \| 490 \| 0.2029 \| 0.9376 \| 0.9343 \| 0.9392 \| 0.9360 \|
	\| 0.3242 \| 3.8835 \| 500 \| 0.1996 \| 0.9380 \| 0.9344 \| 0.9399 \| 0.9361 \|
	\| 0.3585 \| 3.9612 \| 510 \| 0.1935 \| 0.9405 \| 0.9378 \| 0.9421 \| 0.9389 \|
	\| 0.2942 \| 4.0388 \| 520 \| 0.2028 \| 0.9368 \| 0.9341 \| 0.9428 \| 0.9367 \|
	\| 0.3233 \| 4.1165 \| 530 \| 0.2029 \| 0.9378 \| 0.9353 \| 0.9406 \| 0.9364 \|
	\| 0.2942 \| 4.1942 \| 540 \| 0.1959 \| 0.9385 \| 0.9368 \| 0.9395 \| 0.9372 \|
	\| 0.3079 \| 4.2718 \| 550 \| 0.1941 \| 0.9371 \| 0.9349 \| 0.9373 \| 0.9354 \|
	\| 0.2931 \| 4.3495 \| 560 \| 0.1871 \| 0.9414 \| 0.9388 \| 0.9410 \| 0.9394 \|
	\| 0.3058 \| 4.4272 \| 570 \| 0.1879 \| 0.9419 \| 0.9403 \| 0.9430 \| 0.9407 \|
	\| 0.3402 \| 4.5049 \| 580 \| 0.1833 \| 0.9434 \| 0.9409 \| 0.9435 \| 0.9420 \|
	\| 0.3169 \| 4.5825 \| 590 \| 0.1882 \| 0.9412 \| 0.9391 \| 0.9425 \| 0.9402 \|
	\| 0.3071 \| 4.6602 \| 600 \| 0.1821 \| 0.9448 \| 0.9425 \| 0.9460 \| 0.9431 \|
	\| 0.313 \| 4.7379 \| 610 \| 0.1879 \| 0.9429 \| 0.9401 \| 0.9441 \| 0.9413 \|
	\| 0.3338 \| 4.8155 \| 620 \| 0.1843 \| 0.9456 \| 0.9424 \| 0.9469 \| 0.9439 \|
	\| 0.2468 \| 4.8932 \| 630 \| 0.1866 \| 0.9436 \| 0.9412 \| 0.9441 \| 0.9426 \|
	\| 0.2567 \| 4.9709 \| 640 \| 0.1882 \| 0.9405 \| 0.9387 \| 0.9417 \| 0.9393 \|
	\| 0.2792 \| 5.0485 \| 650 \| 0.1914 \| 0.9429 \| 0.9407 \| 0.9442 \| 0.9418 \|
	\| 0.2985 \| 5.1262 \| 660 \| 0.1880 \| 0.9429 \| 0.9393 \| 0.9442 \| 0.9411 \|
	\| 0.2744 \| 5.2039 \| 670 \| 0.1865 \| 0.9410 \| 0.9378 \| 0.9420 \| 0.9390 \|
	\| 0.2662 \| 5.2816 \| 680 \| 0.1877 \| 0.9419 \| 0.9400 \| 0.9423 \| 0.9407 \|
	\| 0.2613 \| 5.3592 \| 690 \| 0.1890 \| 0.9393 \| 0.9369 \| 0.9401 \| 0.9378 \|
	\| 0.2698 \| 5.4369 \| 700 \| 0.1849 \| 0.9429 \| 0.9409 \| 0.9441 \| 0.9417 \|
	\| 0.2592 \| 5.5146 \| 710 \| 0.1854 \| 0.9429 \| 0.9414 \| 0.9439 \| 0.9425 \|
	\| 0.2819 \| 5.5922 \| 720 \| 0.1868 \| 0.9429 \| 0.9414 \| 0.9443 \| 0.9418 \|
	\| 0.2625 \| 5.6699 \| 730 \| 0.1832 \| 0.9434 \| 0.9417 \| 0.9438 \| 0.9422 \|
	\| 0.273 \| 5.7476 \| 740 \| 0.1862 \| 0.9439 \| 0.9408 \| 0.9445 \| 0.9424 \|
	\| 0.2718 \| 5.8252 \| 750 \| 0.1838 \| 0.9441 \| 0.9417 \| 0.9443 \| 0.9428 \|
	\| 0.3055 \| 5.9029 \| 760 \| 0.1852 \| 0.9422 \| 0.9396 \| 0.9426 \| 0.9407 \|
	\| 0.276 \| 5.9806 \| 770 \| 0.1843 \| 0.9424 \| 0.9409 \| 0.9434 \| 0.9415 \|
	\| 0.2614 \| 6.0583 \| 780 \| 0.1839 \| 0.9429 \| 0.9403 \| 0.9431 \| 0.9411 \|
	\| 0.2452 \| 6.1359 \| 790 \| 0.1858 \| 0.9407 \| 0.9384 \| 0.9414 \| 0.9390 \|
	\| 0.2608 \| 6.2136 \| 800 \| 0.1851 \| 0.9429 \| 0.9411 \| 0.9437 \| 0.9417 \|
	\| 0.2639 \| 6.2913 \| 810 \| 0.1842 \| 0.9453 \| 0.9432 \| 0.9463 \| 0.9438 \|
	\| 0.2696 \| 6.3689 \| 820 \| 0.1812 \| 0.9424 \| 0.9406 \| 0.9425 \| 0.9412 \|
	\| 0.2524 \| 6.4466 \| 830 \| 0.1830 \| 0.9427 \| 0.9411 \| 0.9433 \| 0.9417 \|
	\| 0.2673 \| 6.5243 \| 840 \| 0.1823 \| 0.9451 \| 0.9436 \| 0.9464 \| 0.9442 \|
	\| 0.2991 \| 6.6019 \| 850 \| 0.1837 \| 0.9429 \| 0.9408 \| 0.9431 \| 0.9419 \|
	\| 0.2704 \| 6.6796 \| 860 \| 0.1833 \| 0.9439 \| 0.9424 \| 0.9446 \| 0.9431 \|
	\| 0.2437 \| 6.7573 \| 870 \| 0.1857 \| 0.9424 \| 0.9410 \| 0.9434 \| 0.9416 \|
	\| 0.2266 \| 6.8350 \| 880 \| 0.1846 \| 0.9431 \| 0.9416 \| 0.9436 \| 0.9423 \|
	\| 0.2276 \| 6.9126 \| 890 \| 0.1825 \| 0.9441 \| 0.9426 \| 0.9448 \| 0.9433 \|
	\| 0.2249 \| 6.9903 \| 900 \| 0.1813 \| 0.9436 \| 0.9419 \| 0.9441 \| 0.9425 \|
	\| 0.2559 \| 7.0680 \| 910 \| 0.1813 \| 0.9444 \| 0.9425 \| 0.9448 \| 0.9431 \|
	\| 0.2616 \| 7.1456 \| 920 \| 0.1813 \| 0.9441 \| 0.9421 \| 0.9443 \| 0.9428 \|
	\| 0.2247 \| 7.2233 \| 930 \| 0.1813 \| 0.9439 \| 0.9421 \| 0.9442 \| 0.9426 \|
	\| 0.2471 \| 7.3010 \| 940 \| 0.1813 \| 0.9448 \| 0.9430 \| 0.9453 \| 0.9436 \|
	\| 0.2446 \| 7.3786 \| 950 \| 0.1817 \| 0.9444 \| 0.9427 \| 0.9450 \| 0.9432 \|
	\| 0.2262 \| 7.4563 \| 960 \| 0.1819 \| 0.9434 \| 0.9417 \| 0.9439 \| 0.9423 \|
	\| 0.2632 \| 7.5340 \| 970 \| 0.1818 \| 0.9439 \| 0.9422 \| 0.9444 \| 0.9427 \|
	\| 0.2258 \| 7.6117 \| 980 \| 0.1815 \| 0.9434 \| 0.9416 \| 0.9439 \| 0.9422 \|
	\| 0.2404 \| 7.6893 \| 990 \| 0.1811 \| 0.9429 \| 0.9410 \| 0.9432 \| 0.9416 \|
	\| 0.2379 \| 7.7670 \| 1000 \| 0.1810 \| 0.9429 \| 0.9411 \| 0.9432 \| 0.9417 \|


	### Framework versions

	- Transformers 4.40.2
	- Pytorch 2.3.0
	- Datasets 2.19.1
	- Tokenizers 0.19.1