vit-base-lcdoctypev1_session3 / README.md

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	---
	license: apache-2.0
	base_model: nsugianto/vit-base-lcdoctypev1_session2
	tags:
	- image-classification
	- generated_from_trainer
	datasets:
	- imagefolder
	metrics:
	- accuracy
	model-index:
	- name: vit-base-lcdoctypev1_session3
	results:
	- task:
	name: Image Classification
	type: image-classification
	dataset:
	name: doctype_v1
	type: imagefolder
	config: default
	split: validation
	args: default
	metrics:
	- name: Accuracy
	type: accuracy
	value: 0.9669421487603306
	---

	<!-- 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. -->

	# vit-base-lcdoctypev1_session3

	This model is a fine-tuned version of [nsugianto/vit-base-lcdoctypev1_session2](https://huggingface.co/nsugianto/vit-base-lcdoctypev1_session2) on the doctype_v1 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.1050
	- Accuracy: 0.9669

	## 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: 0.0002
	- 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: 10
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|
	\| No log \| 0.08 \| 5 \| 0.3159 \| 0.9091 \|
	\| 0.1798 \| 0.17 \| 10 \| 0.2262 \| 0.9339 \|
	\| 0.1798 \| 0.25 \| 15 \| 0.9910 \| 0.7769 \|
	\| 0.3815 \| 0.33 \| 20 \| 0.3035 \| 0.9008 \|
	\| 0.3815 \| 0.42 \| 25 \| 0.2177 \| 0.9339 \|
	\| 0.1429 \| 0.5 \| 30 \| 0.4909 \| 0.8843 \|
	\| 0.1429 \| 0.58 \| 35 \| 0.3096 \| 0.9256 \|
	\| 0.2424 \| 0.67 \| 40 \| 0.3270 \| 0.9174 \|
	\| 0.2424 \| 0.75 \| 45 \| 0.2555 \| 0.9174 \|
	\| 0.1172 \| 0.83 \| 50 \| 0.2309 \| 0.9174 \|
	\| 0.1172 \| 0.92 \| 55 \| 0.2952 \| 0.9174 \|
	\| 0.1185 \| 1.0 \| 60 \| 0.2957 \| 0.9174 \|
	\| 0.1185 \| 1.08 \| 65 \| 0.3724 \| 0.8926 \|
	\| 0.1594 \| 1.17 \| 70 \| 0.4216 \| 0.8843 \|
	\| 0.1594 \| 1.25 \| 75 \| 0.3475 \| 0.9174 \|
	\| 0.1231 \| 1.33 \| 80 \| 0.3234 \| 0.8926 \|
	\| 0.1231 \| 1.42 \| 85 \| 0.4310 \| 0.8843 \|
	\| 0.0875 \| 1.5 \| 90 \| 0.3598 \| 0.9256 \|
	\| 0.0875 \| 1.58 \| 95 \| 0.3038 \| 0.9256 \|
	\| 0.0897 \| 1.67 \| 100 \| 0.2599 \| 0.9339 \|
	\| 0.0897 \| 1.75 \| 105 \| 0.1684 \| 0.9587 \|
	\| 0.1797 \| 1.83 \| 110 \| 0.1412 \| 0.9504 \|
	\| 0.1797 \| 1.92 \| 115 \| 0.1453 \| 0.9587 \|
	\| 0.1178 \| 2.0 \| 120 \| 0.3831 \| 0.8926 \|
	\| 0.1178 \| 2.08 \| 125 \| 0.3321 \| 0.9091 \|
	\| 0.1969 \| 2.17 \| 130 \| 0.2546 \| 0.9091 \|
	\| 0.1969 \| 2.25 \| 135 \| 0.1839 \| 0.9504 \|
	\| 0.0362 \| 2.33 \| 140 \| 0.2027 \| 0.9587 \|
	\| 0.0362 \| 2.42 \| 145 \| 0.2877 \| 0.9091 \|
	\| 0.1047 \| 2.5 \| 150 \| 0.4504 \| 0.8926 \|
	\| 0.1047 \| 2.58 \| 155 \| 0.1811 \| 0.9504 \|
	\| 0.1232 \| 2.67 \| 160 \| 0.2107 \| 0.9421 \|
	\| 0.1232 \| 2.75 \| 165 \| 0.2086 \| 0.9504 \|
	\| 0.0611 \| 2.83 \| 170 \| 0.2971 \| 0.9339 \|
	\| 0.0611 \| 2.92 \| 175 \| 0.2732 \| 0.9339 \|
	\| 0.0815 \| 3.0 \| 180 \| 0.1679 \| 0.9587 \|
	\| 0.0815 \| 3.08 \| 185 \| 0.2416 \| 0.9339 \|
	\| 0.0469 \| 3.17 \| 190 \| 0.2927 \| 0.9256 \|
	\| 0.0469 \| 3.25 \| 195 \| 0.2831 \| 0.9339 \|
	\| 0.0443 \| 3.33 \| 200 \| 0.2745 \| 0.9421 \|
	\| 0.0443 \| 3.42 \| 205 \| 0.4193 \| 0.8926 \|
	\| 0.0823 \| 3.5 \| 210 \| 0.3746 \| 0.9174 \|
	\| 0.0823 \| 3.58 \| 215 \| 0.3030 \| 0.9421 \|
	\| 0.0101 \| 3.67 \| 220 \| 0.2146 \| 0.9504 \|
	\| 0.0101 \| 3.75 \| 225 \| 0.2514 \| 0.9421 \|
	\| 0.16 \| 3.83 \| 230 \| 0.2552 \| 0.9421 \|
	\| 0.16 \| 3.92 \| 235 \| 0.2239 \| 0.9421 \|
	\| 0.1687 \| 4.0 \| 240 \| 0.2571 \| 0.9256 \|
	\| 0.1687 \| 4.08 \| 245 \| 0.1357 \| 0.9752 \|
	\| 0.0758 \| 4.17 \| 250 \| 0.1734 \| 0.9504 \|
	\| 0.0758 \| 4.25 \| 255 \| 0.1197 \| 0.9752 \|
	\| 0.042 \| 4.33 \| 260 \| 0.2339 \| 0.9421 \|
	\| 0.042 \| 4.42 \| 265 \| 0.2924 \| 0.9174 \|
	\| 0.0114 \| 4.5 \| 270 \| 0.2318 \| 0.9504 \|
	\| 0.0114 \| 4.58 \| 275 \| 0.1765 \| 0.9587 \|
	\| 0.0197 \| 4.67 \| 280 \| 0.1263 \| 0.9669 \|
	\| 0.0197 \| 4.75 \| 285 \| 0.1253 \| 0.9669 \|
	\| 0.0283 \| 4.83 \| 290 \| 0.1239 \| 0.9669 \|
	\| 0.0283 \| 4.92 \| 295 \| 0.1278 \| 0.9669 \|
	\| 0.1115 \| 5.0 \| 300 \| 0.2528 \| 0.9339 \|
	\| 0.1115 \| 5.08 \| 305 \| 0.3164 \| 0.9339 \|
	\| 0.0404 \| 5.17 \| 310 \| 0.2842 \| 0.9339 \|
	\| 0.0404 \| 5.25 \| 315 \| 0.1713 \| 0.9504 \|
	\| 0.0719 \| 5.33 \| 320 \| 0.1896 \| 0.9339 \|
	\| 0.0719 \| 5.42 \| 325 \| 0.1855 \| 0.9256 \|
	\| 0.0435 \| 5.5 \| 330 \| 0.1541 \| 0.9669 \|
	\| 0.0435 \| 5.58 \| 335 \| 0.1050 \| 0.9669 \|
	\| 0.0129 \| 5.67 \| 340 \| 0.1063 \| 0.9587 \|
	\| 0.0129 \| 5.75 \| 345 \| 0.1138 \| 0.9587 \|
	\| 0.0222 \| 5.83 \| 350 \| 0.1144 \| 0.9587 \|
	\| 0.0222 \| 5.92 \| 355 \| 0.1238 \| 0.9669 \|
	\| 0.0431 \| 6.0 \| 360 \| 0.1343 \| 0.9752 \|
	\| 0.0431 \| 6.08 \| 365 \| 0.1441 \| 0.9669 \|
	\| 0.0064 \| 6.17 \| 370 \| 0.1471 \| 0.9669 \|
	\| 0.0064 \| 6.25 \| 375 \| 0.1361 \| 0.9752 \|
	\| 0.0576 \| 6.33 \| 380 \| 0.1316 \| 0.9752 \|
	\| 0.0576 \| 6.42 \| 385 \| 0.1232 \| 0.9669 \|
	\| 0.0298 \| 6.5 \| 390 \| 0.1255 \| 0.9669 \|
	\| 0.0298 \| 6.58 \| 395 \| 0.1359 \| 0.9669 \|
	\| 0.0097 \| 6.67 \| 400 \| 0.1435 \| 0.9669 \|
	\| 0.0097 \| 6.75 \| 405 \| 0.1451 \| 0.9669 \|
	\| 0.0153 \| 6.83 \| 410 \| 0.1439 \| 0.9669 \|
	\| 0.0153 \| 6.92 \| 415 \| 0.1353 \| 0.9752 \|
	\| 0.0406 \| 7.0 \| 420 \| 0.1316 \| 0.9752 \|
	\| 0.0406 \| 7.08 \| 425 \| 0.1309 \| 0.9752 \|
	\| 0.0154 \| 7.17 \| 430 \| 0.1305 \| 0.9752 \|
	\| 0.0154 \| 7.25 \| 435 \| 0.1310 \| 0.9752 \|
	\| 0.0209 \| 7.33 \| 440 \| 0.1301 \| 0.9752 \|
	\| 0.0209 \| 7.42 \| 445 \| 0.1459 \| 0.9587 \|
	\| 0.0298 \| 7.5 \| 450 \| 0.1663 \| 0.9587 \|
	\| 0.0298 \| 7.58 \| 455 \| 0.1559 \| 0.9587 \|
	\| 0.0052 \| 7.67 \| 460 \| 0.1516 \| 0.9587 \|
	\| 0.0052 \| 7.75 \| 465 \| 0.1396 \| 0.9587 \|
	\| 0.0172 \| 7.83 \| 470 \| 0.1330 \| 0.9587 \|
	\| 0.0172 \| 7.92 \| 475 \| 0.1236 \| 0.9752 \|
	\| 0.0348 \| 8.0 \| 480 \| 0.1210 \| 0.9752 \|
	\| 0.0348 \| 8.08 \| 485 \| 0.1175 \| 0.9752 \|
	\| 0.0068 \| 8.17 \| 490 \| 0.1185 \| 0.9752 \|
	\| 0.0068 \| 8.25 \| 495 \| 0.1229 \| 0.9752 \|
	\| 0.0305 \| 8.33 \| 500 \| 0.1230 \| 0.9752 \|
	\| 0.0305 \| 8.42 \| 505 \| 0.1205 \| 0.9752 \|
	\| 0.0154 \| 8.5 \| 510 \| 0.1197 \| 0.9752 \|
	\| 0.0154 \| 8.58 \| 515 \| 0.1217 \| 0.9752 \|
	\| 0.0177 \| 8.67 \| 520 \| 0.1239 \| 0.9752 \|
	\| 0.0177 \| 8.75 \| 525 \| 0.1244 \| 0.9752 \|
	\| 0.0123 \| 8.83 \| 530 \| 0.1271 \| 0.9669 \|
	\| 0.0123 \| 8.92 \| 535 \| 0.1300 \| 0.9669 \|
	\| 0.0154 \| 9.0 \| 540 \| 0.1314 \| 0.9669 \|
	\| 0.0154 \| 9.08 \| 545 \| 0.1296 \| 0.9669 \|
	\| 0.0331 \| 9.17 \| 550 \| 0.1251 \| 0.9752 \|
	\| 0.0331 \| 9.25 \| 555 \| 0.1269 \| 0.9752 \|
	\| 0.0196 \| 9.33 \| 560 \| 0.1284 \| 0.9752 \|
	\| 0.0196 \| 9.42 \| 565 \| 0.1298 \| 0.9669 \|
	\| 0.0058 \| 9.5 \| 570 \| 0.1313 \| 0.9669 \|
	\| 0.0058 \| 9.58 \| 575 \| 0.1321 \| 0.9669 \|
	\| 0.012 \| 9.67 \| 580 \| 0.1327 \| 0.9669 \|
	\| 0.012 \| 9.75 \| 585 \| 0.1326 \| 0.9669 \|
	\| 0.0081 \| 9.83 \| 590 \| 0.1329 \| 0.9669 \|
	\| 0.0081 \| 9.92 \| 595 \| 0.1336 \| 0.9669 \|
	\| 0.0083 \| 10.0 \| 600 \| 0.1338 \| 0.9669 \|


	### Framework versions

	- Transformers 4.38.2
	- Pytorch 2.2.1+cu121
	- Datasets 2.18.0
	- Tokenizers 0.15.2

	---
	license: apache-2.0
	base_model: nsugianto/vit-base-lcdoctypev1_session2
	tags:
	- image-classification
	- generated_from_trainer
	datasets:
	- imagefolder
	metrics:
	- accuracy
	model-index:
	- name: vit-base-lcdoctypev1_session3
	results:
	- task:
	name: Image Classification
	type: image-classification
	dataset:
	name: doctype_v1
	type: imagefolder
	config: default
	split: validation
	args: default
	metrics:
	- name: Accuracy
	type: accuracy
	value: 0.9669421487603306
	---

	<!-- 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. -->

	# vit-base-lcdoctypev1_session3

	This model is a fine-tuned version of [nsugianto/vit-base-lcdoctypev1_session2](https://huggingface.co/nsugianto/vit-base-lcdoctypev1_session2) on the doctype_v1 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.1050
	- Accuracy: 0.9669

	## 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: 0.0002
	- 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: 10
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|
	\| No log \| 0.08 \| 5 \| 0.3159 \| 0.9091 \|
	\| 0.1798 \| 0.17 \| 10 \| 0.2262 \| 0.9339 \|
	\| 0.1798 \| 0.25 \| 15 \| 0.9910 \| 0.7769 \|
	\| 0.3815 \| 0.33 \| 20 \| 0.3035 \| 0.9008 \|
	\| 0.3815 \| 0.42 \| 25 \| 0.2177 \| 0.9339 \|
	\| 0.1429 \| 0.5 \| 30 \| 0.4909 \| 0.8843 \|
	\| 0.1429 \| 0.58 \| 35 \| 0.3096 \| 0.9256 \|
	\| 0.2424 \| 0.67 \| 40 \| 0.3270 \| 0.9174 \|
	\| 0.2424 \| 0.75 \| 45 \| 0.2555 \| 0.9174 \|
	\| 0.1172 \| 0.83 \| 50 \| 0.2309 \| 0.9174 \|
	\| 0.1172 \| 0.92 \| 55 \| 0.2952 \| 0.9174 \|
	\| 0.1185 \| 1.0 \| 60 \| 0.2957 \| 0.9174 \|
	\| 0.1185 \| 1.08 \| 65 \| 0.3724 \| 0.8926 \|
	\| 0.1594 \| 1.17 \| 70 \| 0.4216 \| 0.8843 \|
	\| 0.1594 \| 1.25 \| 75 \| 0.3475 \| 0.9174 \|
	\| 0.1231 \| 1.33 \| 80 \| 0.3234 \| 0.8926 \|
	\| 0.1231 \| 1.42 \| 85 \| 0.4310 \| 0.8843 \|
	\| 0.0875 \| 1.5 \| 90 \| 0.3598 \| 0.9256 \|
	\| 0.0875 \| 1.58 \| 95 \| 0.3038 \| 0.9256 \|
	\| 0.0897 \| 1.67 \| 100 \| 0.2599 \| 0.9339 \|
	\| 0.0897 \| 1.75 \| 105 \| 0.1684 \| 0.9587 \|
	\| 0.1797 \| 1.83 \| 110 \| 0.1412 \| 0.9504 \|
	\| 0.1797 \| 1.92 \| 115 \| 0.1453 \| 0.9587 \|
	\| 0.1178 \| 2.0 \| 120 \| 0.3831 \| 0.8926 \|
	\| 0.1178 \| 2.08 \| 125 \| 0.3321 \| 0.9091 \|
	\| 0.1969 \| 2.17 \| 130 \| 0.2546 \| 0.9091 \|
	\| 0.1969 \| 2.25 \| 135 \| 0.1839 \| 0.9504 \|
	\| 0.0362 \| 2.33 \| 140 \| 0.2027 \| 0.9587 \|
	\| 0.0362 \| 2.42 \| 145 \| 0.2877 \| 0.9091 \|
	\| 0.1047 \| 2.5 \| 150 \| 0.4504 \| 0.8926 \|
	\| 0.1047 \| 2.58 \| 155 \| 0.1811 \| 0.9504 \|
	\| 0.1232 \| 2.67 \| 160 \| 0.2107 \| 0.9421 \|
	\| 0.1232 \| 2.75 \| 165 \| 0.2086 \| 0.9504 \|
	\| 0.0611 \| 2.83 \| 170 \| 0.2971 \| 0.9339 \|
	\| 0.0611 \| 2.92 \| 175 \| 0.2732 \| 0.9339 \|
	\| 0.0815 \| 3.0 \| 180 \| 0.1679 \| 0.9587 \|
	\| 0.0815 \| 3.08 \| 185 \| 0.2416 \| 0.9339 \|
	\| 0.0469 \| 3.17 \| 190 \| 0.2927 \| 0.9256 \|
	\| 0.0469 \| 3.25 \| 195 \| 0.2831 \| 0.9339 \|
	\| 0.0443 \| 3.33 \| 200 \| 0.2745 \| 0.9421 \|
	\| 0.0443 \| 3.42 \| 205 \| 0.4193 \| 0.8926 \|
	\| 0.0823 \| 3.5 \| 210 \| 0.3746 \| 0.9174 \|
	\| 0.0823 \| 3.58 \| 215 \| 0.3030 \| 0.9421 \|
	\| 0.0101 \| 3.67 \| 220 \| 0.2146 \| 0.9504 \|
	\| 0.0101 \| 3.75 \| 225 \| 0.2514 \| 0.9421 \|
	\| 0.16 \| 3.83 \| 230 \| 0.2552 \| 0.9421 \|
	\| 0.16 \| 3.92 \| 235 \| 0.2239 \| 0.9421 \|
	\| 0.1687 \| 4.0 \| 240 \| 0.2571 \| 0.9256 \|
	\| 0.1687 \| 4.08 \| 245 \| 0.1357 \| 0.9752 \|
	\| 0.0758 \| 4.17 \| 250 \| 0.1734 \| 0.9504 \|
	\| 0.0758 \| 4.25 \| 255 \| 0.1197 \| 0.9752 \|
	\| 0.042 \| 4.33 \| 260 \| 0.2339 \| 0.9421 \|
	\| 0.042 \| 4.42 \| 265 \| 0.2924 \| 0.9174 \|
	\| 0.0114 \| 4.5 \| 270 \| 0.2318 \| 0.9504 \|
	\| 0.0114 \| 4.58 \| 275 \| 0.1765 \| 0.9587 \|
	\| 0.0197 \| 4.67 \| 280 \| 0.1263 \| 0.9669 \|
	\| 0.0197 \| 4.75 \| 285 \| 0.1253 \| 0.9669 \|
	\| 0.0283 \| 4.83 \| 290 \| 0.1239 \| 0.9669 \|
	\| 0.0283 \| 4.92 \| 295 \| 0.1278 \| 0.9669 \|
	\| 0.1115 \| 5.0 \| 300 \| 0.2528 \| 0.9339 \|
	\| 0.1115 \| 5.08 \| 305 \| 0.3164 \| 0.9339 \|
	\| 0.0404 \| 5.17 \| 310 \| 0.2842 \| 0.9339 \|
	\| 0.0404 \| 5.25 \| 315 \| 0.1713 \| 0.9504 \|
	\| 0.0719 \| 5.33 \| 320 \| 0.1896 \| 0.9339 \|
	\| 0.0719 \| 5.42 \| 325 \| 0.1855 \| 0.9256 \|
	\| 0.0435 \| 5.5 \| 330 \| 0.1541 \| 0.9669 \|
	\| 0.0435 \| 5.58 \| 335 \| 0.1050 \| 0.9669 \|
	\| 0.0129 \| 5.67 \| 340 \| 0.1063 \| 0.9587 \|
	\| 0.0129 \| 5.75 \| 345 \| 0.1138 \| 0.9587 \|
	\| 0.0222 \| 5.83 \| 350 \| 0.1144 \| 0.9587 \|
	\| 0.0222 \| 5.92 \| 355 \| 0.1238 \| 0.9669 \|
	\| 0.0431 \| 6.0 \| 360 \| 0.1343 \| 0.9752 \|
	\| 0.0431 \| 6.08 \| 365 \| 0.1441 \| 0.9669 \|
	\| 0.0064 \| 6.17 \| 370 \| 0.1471 \| 0.9669 \|
	\| 0.0064 \| 6.25 \| 375 \| 0.1361 \| 0.9752 \|
	\| 0.0576 \| 6.33 \| 380 \| 0.1316 \| 0.9752 \|
	\| 0.0576 \| 6.42 \| 385 \| 0.1232 \| 0.9669 \|
	\| 0.0298 \| 6.5 \| 390 \| 0.1255 \| 0.9669 \|
	\| 0.0298 \| 6.58 \| 395 \| 0.1359 \| 0.9669 \|
	\| 0.0097 \| 6.67 \| 400 \| 0.1435 \| 0.9669 \|
	\| 0.0097 \| 6.75 \| 405 \| 0.1451 \| 0.9669 \|
	\| 0.0153 \| 6.83 \| 410 \| 0.1439 \| 0.9669 \|
	\| 0.0153 \| 6.92 \| 415 \| 0.1353 \| 0.9752 \|
	\| 0.0406 \| 7.0 \| 420 \| 0.1316 \| 0.9752 \|
	\| 0.0406 \| 7.08 \| 425 \| 0.1309 \| 0.9752 \|
	\| 0.0154 \| 7.17 \| 430 \| 0.1305 \| 0.9752 \|
	\| 0.0154 \| 7.25 \| 435 \| 0.1310 \| 0.9752 \|
	\| 0.0209 \| 7.33 \| 440 \| 0.1301 \| 0.9752 \|
	\| 0.0209 \| 7.42 \| 445 \| 0.1459 \| 0.9587 \|
	\| 0.0298 \| 7.5 \| 450 \| 0.1663 \| 0.9587 \|
	\| 0.0298 \| 7.58 \| 455 \| 0.1559 \| 0.9587 \|
	\| 0.0052 \| 7.67 \| 460 \| 0.1516 \| 0.9587 \|
	\| 0.0052 \| 7.75 \| 465 \| 0.1396 \| 0.9587 \|
	\| 0.0172 \| 7.83 \| 470 \| 0.1330 \| 0.9587 \|
	\| 0.0172 \| 7.92 \| 475 \| 0.1236 \| 0.9752 \|
	\| 0.0348 \| 8.0 \| 480 \| 0.1210 \| 0.9752 \|
	\| 0.0348 \| 8.08 \| 485 \| 0.1175 \| 0.9752 \|
	\| 0.0068 \| 8.17 \| 490 \| 0.1185 \| 0.9752 \|
	\| 0.0068 \| 8.25 \| 495 \| 0.1229 \| 0.9752 \|
	\| 0.0305 \| 8.33 \| 500 \| 0.1230 \| 0.9752 \|
	\| 0.0305 \| 8.42 \| 505 \| 0.1205 \| 0.9752 \|
	\| 0.0154 \| 8.5 \| 510 \| 0.1197 \| 0.9752 \|
	\| 0.0154 \| 8.58 \| 515 \| 0.1217 \| 0.9752 \|
	\| 0.0177 \| 8.67 \| 520 \| 0.1239 \| 0.9752 \|
	\| 0.0177 \| 8.75 \| 525 \| 0.1244 \| 0.9752 \|
	\| 0.0123 \| 8.83 \| 530 \| 0.1271 \| 0.9669 \|
	\| 0.0123 \| 8.92 \| 535 \| 0.1300 \| 0.9669 \|
	\| 0.0154 \| 9.0 \| 540 \| 0.1314 \| 0.9669 \|
	\| 0.0154 \| 9.08 \| 545 \| 0.1296 \| 0.9669 \|
	\| 0.0331 \| 9.17 \| 550 \| 0.1251 \| 0.9752 \|
	\| 0.0331 \| 9.25 \| 555 \| 0.1269 \| 0.9752 \|
	\| 0.0196 \| 9.33 \| 560 \| 0.1284 \| 0.9752 \|
	\| 0.0196 \| 9.42 \| 565 \| 0.1298 \| 0.9669 \|
	\| 0.0058 \| 9.5 \| 570 \| 0.1313 \| 0.9669 \|
	\| 0.0058 \| 9.58 \| 575 \| 0.1321 \| 0.9669 \|
	\| 0.012 \| 9.67 \| 580 \| 0.1327 \| 0.9669 \|
	\| 0.012 \| 9.75 \| 585 \| 0.1326 \| 0.9669 \|
	\| 0.0081 \| 9.83 \| 590 \| 0.1329 \| 0.9669 \|
	\| 0.0081 \| 9.92 \| 595 \| 0.1336 \| 0.9669 \|
	\| 0.0083 \| 10.0 \| 600 \| 0.1338 \| 0.9669 \|


	### Framework versions

	- Transformers 4.38.2
	- Pytorch 2.2.1+cu121
	- Datasets 2.18.0
	- Tokenizers 0.15.2