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	---
	license: cc-by-nc-sa-4.0
	base_model: microsoft/layoutlmv3-base
	tags:
	- generated_from_trainer
	datasets:
	- datasetprepfrom_gcp
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: model10.0
	results:
	- task:
	name: Token Classification
	type: token-classification
	dataset:
	name: datasetprepfrom_gcp
	type: datasetprepfrom_gcp
	config: discharge
	split: test
	args: discharge
	metrics:
	- name: Precision
	type: precision
	value: 0.7824019024970273
	- name: Recall
	type: recall
	value: 0.7396028475084301
	- name: F1
	type: f1
	value: 0.7984006163328197
	- name: Accuracy
	type: accuracy
	value: 0.9480585417540451
	---

	<!-- 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. -->

	# model10.0

	This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the datasetprepfrom_gcp dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.3117
	- Precision: 0.7824
	- Recall: 0.7396
	- F1: 0.7604
	- Accuracy: 0.9481

	## 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: 1e-05
	- train_batch_size: 2
	- eval_batch_size: 2
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- training_steps: 10000

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| No log \| 0.1094 \| 100 \| 1.1070 \| 0.0820 \| 0.0037 \| 0.0072 \| 0.8202 \|
	\| No log \| 0.2188 \| 200 \| 0.9957 \| 0.0449 \| 0.0049 \| 0.0088 \| 0.8244 \|
	\| No log \| 0.3282 \| 300 \| 0.9355 \| 0.1949 \| 0.0590 \| 0.0906 \| 0.8338 \|
	\| No log \| 0.4376 \| 400 \| 0.8255 \| 0.2373 \| 0.1223 \| 0.1614 \| 0.8453 \|
	\| 1.0172 \| 0.5470 \| 500 \| 0.7511 \| 0.3185 \| 0.1510 \| 0.2049 \| 0.8489 \|
	\| 1.0172 \| 0.6565 \| 600 \| 0.7203 \| 0.3609 \| 0.1997 \| 0.2571 \| 0.8510 \|
	\| 1.0172 \| 0.7659 \| 700 \| 0.6571 \| 0.4256 \| 0.2565 \| 0.3200 \| 0.8755 \|
	\| 1.0172 \| 0.8753 \| 800 \| 0.5845 \| 0.4510 \| 0.3127 \| 0.3693 \| 0.8873 \|
	\| 1.0172 \| 0.9847 \| 900 \| 0.5614 \| 0.5053 \| 0.3477 \| 0.4119 \| 0.8948 \|
	\| 0.45 \| 1.0941 \| 1000 \| 0.5126 \| 0.5561 \| 0.3818 \| 0.4527 \| 0.9042 \|
	\| 0.45 \| 1.2035 \| 1100 \| 0.5350 \| 0.5790 \| 0.4176 \| 0.4852 \| 0.9092 \|
	\| 0.45 \| 1.3129 \| 1200 \| 0.4846 \| 0.5449 \| 0.4562 \| 0.4966 \| 0.9068 \|
	\| 0.45 \| 1.4223 \| 1300 \| 0.4681 \| 0.6153 \| 0.4843 \| 0.5420 \| 0.9149 \|
	\| 0.45 \| 1.5317 \| 1400 \| 0.4807 \| 0.6572 \| 0.4874 \| 0.5598 \| 0.9164 \|
	\| 0.2388 \| 1.6411 \| 1500 \| 0.4096 \| 0.5848 \| 0.5264 \| 0.5541 \| 0.9146 \|
	\| 0.2388 \| 1.7505 \| 1600 \| 0.3991 \| 0.6344 \| 0.5474 \| 0.5877 \| 0.9203 \|
	\| 0.2388 \| 1.8600 \| 1700 \| 0.4044 \| 0.5996 \| 0.5691 \| 0.5840 \| 0.9185 \|
	\| 0.2388 \| 1.9694 \| 1800 \| 0.4065 \| 0.6607 \| 0.5869 \| 0.6216 \| 0.9258 \|
	\| 0.2388 \| 2.0788 \| 1900 \| 0.4159 \| 0.6018 \| 0.5802 \| 0.5908 \| 0.9123 \|
	\| 0.1768 \| 2.1882 \| 2000 \| 0.4203 \| 0.6846 \| 0.5822 \| 0.6293 \| 0.9268 \|
	\| 0.1768 \| 2.2976 \| 2100 \| 0.3934 \| 0.6566 \| 0.5954 \| 0.6245 \| 0.9276 \|
	\| 0.1768 \| 2.4070 \| 2200 \| 0.3879 \| 0.7137 \| 0.6038 \| 0.6542 \| 0.9313 \|
	\| 0.1768 \| 2.5164 \| 2300 \| 0.3829 \| 0.5685 \| 0.6326 \| 0.5989 \| 0.9175 \|
	\| 0.1768 \| 2.6258 \| 2400 \| 0.3508 \| 0.7175 \| 0.6191 \| 0.6647 \| 0.9328 \|
	\| 0.1399 \| 2.7352 \| 2500 \| 0.3215 \| 0.6869 \| 0.6311 \| 0.6578 \| 0.9327 \|
	\| 0.1399 \| 2.8446 \| 2600 \| 0.3271 \| 0.7248 \| 0.6171 \| 0.6666 \| 0.9358 \|
	\| 0.1399 \| 2.9540 \| 2700 \| 0.3226 \| 0.6491 \| 0.6544 \| 0.6517 \| 0.9277 \|
	\| 0.1399 \| 3.0635 \| 2800 \| 0.3336 \| 0.6596 \| 0.6386 \| 0.6490 \| 0.9278 \|
	\| 0.1399 \| 3.1729 \| 2900 \| 0.3423 \| 0.6480 \| 0.6624 \| 0.6551 \| 0.9314 \|
	\| 0.1083 \| 3.2823 \| 3000 \| 0.3698 \| 0.7509 \| 0.6566 \| 0.7006 \| 0.9372 \|
	\| 0.1083 \| 3.3917 \| 3100 \| 0.3353 \| 0.6457 \| 0.6649 \| 0.6552 \| 0.9287 \|
	\| 0.1083 \| 3.5011 \| 3200 \| 0.3391 \| 0.7518 \| 0.6626 \| 0.7044 \| 0.9383 \|
	\| 0.1083 \| 3.6105 \| 3300 \| 0.3314 \| 0.7350 \| 0.6699 \| 0.7010 \| 0.9381 \|
	\| 0.1083 \| 3.7199 \| 3400 \| 0.3338 \| 0.6728 \| 0.6832 \| 0.6779 \| 0.9347 \|
	\| 0.0988 \| 3.8293 \| 3500 \| 0.3239 \| 0.7509 \| 0.6753 \| 0.7111 \| 0.9369 \|
	\| 0.0988 \| 3.9387 \| 3600 \| 0.3481 \| 0.7555 \| 0.6564 \| 0.7025 \| 0.9395 \|
	\| 0.0988 \| 4.0481 \| 3700 \| 0.3231 \| 0.6749 \| 0.6883 \| 0.6815 \| 0.9348 \|
	\| 0.0988 \| 4.1575 \| 3800 \| 0.3581 \| 0.7669 \| 0.6699 \| 0.7151 \| 0.9411 \|
	\| 0.0988 \| 4.2670 \| 3900 \| 0.3213 \| 0.7174 \| 0.6873 \| 0.7021 \| 0.9389 \|
	\| 0.0775 \| 4.3764 \| 4000 \| 0.3244 \| 0.7433 \| 0.6738 \| 0.7069 \| 0.9387 \|
	\| 0.0775 \| 4.4858 \| 4100 \| 0.3275 \| 0.7370 \| 0.6868 \| 0.7110 \| 0.9405 \|
	\| 0.0775 \| 4.5952 \| 4200 \| 0.3197 \| 0.7405 \| 0.6997 \| 0.7195 \| 0.9413 \|
	\| 0.0775 \| 4.7046 \| 4300 \| 0.3183 \| 0.7419 \| 0.6935 \| 0.7169 \| 0.9415 \|
	\| 0.0775 \| 4.8140 \| 4400 \| 0.2961 \| 0.7445 \| 0.6933 \| 0.7180 \| 0.9408 \|
	\| 0.0771 \| 4.9234 \| 4500 \| 0.3195 \| 0.7542 \| 0.6986 \| 0.7253 \| 0.9426 \|
	\| 0.0771 \| 5.0328 \| 4600 \| 0.3295 \| 0.7637 \| 0.7010 \| 0.7310 \| 0.9435 \|
	\| 0.0771 \| 5.1422 \| 4700 \| 0.3204 \| 0.7603 \| 0.7006 \| 0.7293 \| 0.9434 \|
	\| 0.0771 \| 5.2516 \| 4800 \| 0.2992 \| 0.7443 \| 0.6995 \| 0.7212 \| 0.9395 \|
	\| 0.0771 \| 5.3611 \| 4900 \| 0.2978 \| 0.7312 \| 0.7033 \| 0.7170 \| 0.9393 \|
	\| 0.0647 \| 5.4705 \| 5000 \| 0.3324 \| 0.7608 \| 0.7079 \| 0.7334 \| 0.9432 \|
	\| 0.0647 \| 5.5799 \| 5100 \| 0.3356 \| 0.7635 \| 0.7038 \| 0.7324 \| 0.9430 \|
	\| 0.0647 \| 5.6893 \| 5200 \| 0.3121 \| 0.7634 \| 0.7121 \| 0.7368 \| 0.9430 \|
	\| 0.0647 \| 5.7987 \| 5300 \| 0.3392 \| 0.7858 \| 0.7003 \| 0.7406 \| 0.9448 \|
	\| 0.0647 \| 5.9081 \| 5400 \| 0.2952 \| 0.7265 \| 0.7220 \| 0.7242 \| 0.9412 \|
	\| 0.0573 \| 6.0175 \| 5500 \| 0.3070 \| 0.7311 \| 0.7211 \| 0.7260 \| 0.9429 \|
	\| 0.0573 \| 6.1269 \| 5600 \| 0.3207 \| 0.7414 \| 0.7241 \| 0.7326 \| 0.9435 \|
	\| 0.0573 \| 6.2363 \| 5700 \| 0.3130 \| 0.7685 \| 0.7231 \| 0.7451 \| 0.9455 \|
	\| 0.0573 \| 6.3457 \| 5800 \| 0.3441 \| 0.7752 \| 0.7139 \| 0.7433 \| 0.9447 \|
	\| 0.0573 \| 6.4551 \| 5900 \| 0.3196 \| 0.7818 \| 0.7128 \| 0.7457 \| 0.9458 \|
	\| 0.0529 \| 6.5646 \| 6000 \| 0.3369 \| 0.7907 \| 0.7164 \| 0.7517 \| 0.9456 \|
	\| 0.0529 \| 6.6740 \| 6100 \| 0.3059 \| 0.7394 \| 0.7267 \| 0.7330 \| 0.9435 \|
	\| 0.0529 \| 6.7834 \| 6200 \| 0.3043 \| 0.7624 \| 0.7231 \| 0.7422 \| 0.9444 \|
	\| 0.0529 \| 6.8928 \| 6300 \| 0.3028 \| 0.7527 \| 0.7252 \| 0.7387 \| 0.9441 \|
	\| 0.0529 \| 7.0022 \| 6400 \| 0.3089 \| 0.7596 \| 0.7293 \| 0.7441 \| 0.9457 \|
	\| 0.0542 \| 7.1116 \| 6500 \| 0.2927 \| 0.7306 \| 0.7286 \| 0.7296 \| 0.9408 \|
	\| 0.0542 \| 7.2210 \| 6600 \| 0.3178 \| 0.7785 \| 0.7274 \| 0.7521 \| 0.9456 \|
	\| 0.0542 \| 7.3304 \| 6700 \| 0.3267 \| 0.7653 \| 0.7304 \| 0.7474 \| 0.9450 \|
	\| 0.0542 \| 7.4398 \| 6800 \| 0.3254 \| 0.7618 \| 0.7280 \| 0.7445 \| 0.9450 \|
	\| 0.0542 \| 7.5492 \| 6900 \| 0.3240 \| 0.7856 \| 0.7254 \| 0.7543 \| 0.9464 \|
	\| 0.0416 \| 7.6586 \| 7000 \| 0.3203 \| 0.7682 \| 0.7319 \| 0.7496 \| 0.9463 \|
	\| 0.0416 \| 7.7681 \| 7100 \| 0.3176 \| 0.7801 \| 0.7299 \| 0.7542 \| 0.9468 \|
	\| 0.0416 \| 7.8775 \| 7200 \| 0.3012 \| 0.7601 \| 0.7355 \| 0.7476 \| 0.9470 \|
	\| 0.0416 \| 7.9869 \| 7300 \| 0.3092 \| 0.7336 \| 0.7377 \| 0.7357 \| 0.9436 \|
	\| 0.0416 \| 8.0963 \| 7400 \| 0.3025 \| 0.7782 \| 0.7349 \| 0.7559 \| 0.9480 \|
	\| 0.0422 \| 8.2057 \| 7500 \| 0.3046 \| 0.7594 \| 0.7340 \| 0.7465 \| 0.9459 \|
	\| 0.0422 \| 8.3151 \| 7600 \| 0.3113 \| 0.7640 \| 0.7332 \| 0.7483 \| 0.9458 \|
	\| 0.0422 \| 8.4245 \| 7700 \| 0.3002 \| 0.7579 \| 0.7394 \| 0.7485 \| 0.9461 \|
	\| 0.0422 \| 8.5339 \| 7800 \| 0.3173 \| 0.7742 \| 0.7321 \| 0.7526 \| 0.9464 \|
	\| 0.0422 \| 8.6433 \| 7900 \| 0.3084 \| 0.7766 \| 0.7334 \| 0.7544 \| 0.9467 \|
	\| 0.041 \| 8.7527 \| 8000 \| 0.3118 \| 0.7829 \| 0.7325 \| 0.7569 \| 0.9477 \|
	\| 0.041 \| 8.8621 \| 8100 \| 0.3145 \| 0.7788 \| 0.7389 \| 0.7583 \| 0.9473 \|
	\| 0.041 \| 8.9716 \| 8200 \| 0.3123 \| 0.7788 \| 0.7366 \| 0.7571 \| 0.9480 \|
	\| 0.041 \| 9.0810 \| 8300 \| 0.3088 \| 0.7754 \| 0.7398 \| 0.7572 \| 0.9476 \|
	\| 0.041 \| 9.1904 \| 8400 \| 0.3101 \| 0.7804 \| 0.7415 \| 0.7604 \| 0.9491 \|
	\| 0.0323 \| 9.2998 \| 8500 \| 0.3152 \| 0.7829 \| 0.7357 \| 0.7785 \| 0.9482 \|
	\| 0.0323 \| 9.4092 \| 8600 \| 0.3061 \| 0.7734 \| 0.7398 \| 0.7762 \| 0.9476 \|
	\| 0.0323 \| 9.5186 \| 8700 \| 0.3086 \| 0.7636 \| 0.7437 \| 0.7735 \| 0.9476 \|
	\| 0.0323 \| 9.6280 \| 8800 \| 0.3162 \| 0.7723 \| 0.7390 \| 0.7553 \| 0.9476 \|
	\| 0.0323 \| 9.7374 \| 8900 \| 0.3070 \| 0.7605 \| 0.7419 \| 0.7811 \| 0.9467 \|
	\| 0.0357 \| 9.8468 \| 9000 \| 0.3117 \| 0.7824 \| 0.7396 \| 0.7804 \| 0.9481 \|
	\| 0.0357 \| 9.9562 \| 9100 \| 0.3130 \| 0.7750 \| 0.7396 \| 0.7869 \| 0.9472 \|
	\| 0.0357 \| 10.0656 \| 9200 \| 0.3095 \| 0.7673 \| 0.7405 \| 0.7837 \| 0.9476 \|
	\| 0.0357 \| 10.1751 \| 9300 \| 0.3179 \| 0.7868 \| 0.7357 \| 0.7804 \| 0.9477 \|
	\| 0.0357 \| 10.2845 \| 9400 \| 0.3077 \| 0.7645 \| 0.7405 \| 0.7823 \| 0.9472 \|
	\| 0.0359 \| 10.3939 \| 9500 \| 0.3128 \| 0.7798 \| 0.7366 \| 0.7876 \| 0.9476 \|
	\| 0.0359 \| 10.5033 \| 9600 \| 0.3151 \| 0.7784 \| 0.7377 \| 0.7875 \| 0.9475 \|
	\| 0.0359 \| 10.6127 \| 9700 \| 0.3138 \| 0.7744 \| 0.7420 \| 0.7879 \| 0.9478 \|
	\| 0.0359 \| 10.7221 \| 9800 \| 0.3115 \| 0.7688 \| 0.7415 \| 0.7849 \| 0.9475 \|
	\| 0.0359 \| 10.8315 \| 9900 \| 0.3097 \| 0.7673 \| 0.7411 \| 0.7840 \| 0.9472 \|
	\| 0.0301 \| 10.9409 \| 10000 \| 0.3095 \| 0.7674 \| 0.7409 \| 0.7989 \| 0.9474 \|


	### Framework versions

	- Transformers 4.41.0.dev0
	- Pytorch 2.2.1+cu121
	- Datasets 2.19.1
	- Tokenizers 0.19.1

	---
	license: cc-by-nc-sa-4.0
	base_model: microsoft/layoutlmv3-base
	tags:
	- generated_from_trainer
	datasets:
	- datasetprepfrom_gcp
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: model10.0
	results:
	- task:
	name: Token Classification
	type: token-classification
	dataset:
	name: datasetprepfrom_gcp
	type: datasetprepfrom_gcp
	config: discharge
	split: test
	args: discharge
	metrics:
	- name: Precision
	type: precision
	value: 0.7824019024970273
	- name: Recall
	type: recall
	value: 0.7396028475084301
	- name: F1
	type: f1
	value: 0.7984006163328197
	- name: Accuracy
	type: accuracy
	value: 0.9480585417540451
	---

	<!-- 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. -->

	# model10.0

	This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the datasetprepfrom_gcp dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.3117
	- Precision: 0.7824
	- Recall: 0.7396
	- F1: 0.7604
	- Accuracy: 0.9481

	## 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: 1e-05
	- train_batch_size: 2
	- eval_batch_size: 2
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- training_steps: 10000

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| No log \| 0.1094 \| 100 \| 1.1070 \| 0.0820 \| 0.0037 \| 0.0072 \| 0.8202 \|
	\| No log \| 0.2188 \| 200 \| 0.9957 \| 0.0449 \| 0.0049 \| 0.0088 \| 0.8244 \|
	\| No log \| 0.3282 \| 300 \| 0.9355 \| 0.1949 \| 0.0590 \| 0.0906 \| 0.8338 \|
	\| No log \| 0.4376 \| 400 \| 0.8255 \| 0.2373 \| 0.1223 \| 0.1614 \| 0.8453 \|
	\| 1.0172 \| 0.5470 \| 500 \| 0.7511 \| 0.3185 \| 0.1510 \| 0.2049 \| 0.8489 \|
	\| 1.0172 \| 0.6565 \| 600 \| 0.7203 \| 0.3609 \| 0.1997 \| 0.2571 \| 0.8510 \|
	\| 1.0172 \| 0.7659 \| 700 \| 0.6571 \| 0.4256 \| 0.2565 \| 0.3200 \| 0.8755 \|
	\| 1.0172 \| 0.8753 \| 800 \| 0.5845 \| 0.4510 \| 0.3127 \| 0.3693 \| 0.8873 \|
	\| 1.0172 \| 0.9847 \| 900 \| 0.5614 \| 0.5053 \| 0.3477 \| 0.4119 \| 0.8948 \|
	\| 0.45 \| 1.0941 \| 1000 \| 0.5126 \| 0.5561 \| 0.3818 \| 0.4527 \| 0.9042 \|
	\| 0.45 \| 1.2035 \| 1100 \| 0.5350 \| 0.5790 \| 0.4176 \| 0.4852 \| 0.9092 \|
	\| 0.45 \| 1.3129 \| 1200 \| 0.4846 \| 0.5449 \| 0.4562 \| 0.4966 \| 0.9068 \|
	\| 0.45 \| 1.4223 \| 1300 \| 0.4681 \| 0.6153 \| 0.4843 \| 0.5420 \| 0.9149 \|
	\| 0.45 \| 1.5317 \| 1400 \| 0.4807 \| 0.6572 \| 0.4874 \| 0.5598 \| 0.9164 \|
	\| 0.2388 \| 1.6411 \| 1500 \| 0.4096 \| 0.5848 \| 0.5264 \| 0.5541 \| 0.9146 \|
	\| 0.2388 \| 1.7505 \| 1600 \| 0.3991 \| 0.6344 \| 0.5474 \| 0.5877 \| 0.9203 \|
	\| 0.2388 \| 1.8600 \| 1700 \| 0.4044 \| 0.5996 \| 0.5691 \| 0.5840 \| 0.9185 \|
	\| 0.2388 \| 1.9694 \| 1800 \| 0.4065 \| 0.6607 \| 0.5869 \| 0.6216 \| 0.9258 \|
	\| 0.2388 \| 2.0788 \| 1900 \| 0.4159 \| 0.6018 \| 0.5802 \| 0.5908 \| 0.9123 \|
	\| 0.1768 \| 2.1882 \| 2000 \| 0.4203 \| 0.6846 \| 0.5822 \| 0.6293 \| 0.9268 \|
	\| 0.1768 \| 2.2976 \| 2100 \| 0.3934 \| 0.6566 \| 0.5954 \| 0.6245 \| 0.9276 \|
	\| 0.1768 \| 2.4070 \| 2200 \| 0.3879 \| 0.7137 \| 0.6038 \| 0.6542 \| 0.9313 \|
	\| 0.1768 \| 2.5164 \| 2300 \| 0.3829 \| 0.5685 \| 0.6326 \| 0.5989 \| 0.9175 \|
	\| 0.1768 \| 2.6258 \| 2400 \| 0.3508 \| 0.7175 \| 0.6191 \| 0.6647 \| 0.9328 \|
	\| 0.1399 \| 2.7352 \| 2500 \| 0.3215 \| 0.6869 \| 0.6311 \| 0.6578 \| 0.9327 \|
	\| 0.1399 \| 2.8446 \| 2600 \| 0.3271 \| 0.7248 \| 0.6171 \| 0.6666 \| 0.9358 \|
	\| 0.1399 \| 2.9540 \| 2700 \| 0.3226 \| 0.6491 \| 0.6544 \| 0.6517 \| 0.9277 \|
	\| 0.1399 \| 3.0635 \| 2800 \| 0.3336 \| 0.6596 \| 0.6386 \| 0.6490 \| 0.9278 \|
	\| 0.1399 \| 3.1729 \| 2900 \| 0.3423 \| 0.6480 \| 0.6624 \| 0.6551 \| 0.9314 \|
	\| 0.1083 \| 3.2823 \| 3000 \| 0.3698 \| 0.7509 \| 0.6566 \| 0.7006 \| 0.9372 \|
	\| 0.1083 \| 3.3917 \| 3100 \| 0.3353 \| 0.6457 \| 0.6649 \| 0.6552 \| 0.9287 \|
	\| 0.1083 \| 3.5011 \| 3200 \| 0.3391 \| 0.7518 \| 0.6626 \| 0.7044 \| 0.9383 \|
	\| 0.1083 \| 3.6105 \| 3300 \| 0.3314 \| 0.7350 \| 0.6699 \| 0.7010 \| 0.9381 \|
	\| 0.1083 \| 3.7199 \| 3400 \| 0.3338 \| 0.6728 \| 0.6832 \| 0.6779 \| 0.9347 \|
	\| 0.0988 \| 3.8293 \| 3500 \| 0.3239 \| 0.7509 \| 0.6753 \| 0.7111 \| 0.9369 \|
	\| 0.0988 \| 3.9387 \| 3600 \| 0.3481 \| 0.7555 \| 0.6564 \| 0.7025 \| 0.9395 \|
	\| 0.0988 \| 4.0481 \| 3700 \| 0.3231 \| 0.6749 \| 0.6883 \| 0.6815 \| 0.9348 \|
	\| 0.0988 \| 4.1575 \| 3800 \| 0.3581 \| 0.7669 \| 0.6699 \| 0.7151 \| 0.9411 \|
	\| 0.0988 \| 4.2670 \| 3900 \| 0.3213 \| 0.7174 \| 0.6873 \| 0.7021 \| 0.9389 \|
	\| 0.0775 \| 4.3764 \| 4000 \| 0.3244 \| 0.7433 \| 0.6738 \| 0.7069 \| 0.9387 \|
	\| 0.0775 \| 4.4858 \| 4100 \| 0.3275 \| 0.7370 \| 0.6868 \| 0.7110 \| 0.9405 \|
	\| 0.0775 \| 4.5952 \| 4200 \| 0.3197 \| 0.7405 \| 0.6997 \| 0.7195 \| 0.9413 \|
	\| 0.0775 \| 4.7046 \| 4300 \| 0.3183 \| 0.7419 \| 0.6935 \| 0.7169 \| 0.9415 \|
	\| 0.0775 \| 4.8140 \| 4400 \| 0.2961 \| 0.7445 \| 0.6933 \| 0.7180 \| 0.9408 \|
	\| 0.0771 \| 4.9234 \| 4500 \| 0.3195 \| 0.7542 \| 0.6986 \| 0.7253 \| 0.9426 \|
	\| 0.0771 \| 5.0328 \| 4600 \| 0.3295 \| 0.7637 \| 0.7010 \| 0.7310 \| 0.9435 \|
	\| 0.0771 \| 5.1422 \| 4700 \| 0.3204 \| 0.7603 \| 0.7006 \| 0.7293 \| 0.9434 \|
	\| 0.0771 \| 5.2516 \| 4800 \| 0.2992 \| 0.7443 \| 0.6995 \| 0.7212 \| 0.9395 \|
	\| 0.0771 \| 5.3611 \| 4900 \| 0.2978 \| 0.7312 \| 0.7033 \| 0.7170 \| 0.9393 \|
	\| 0.0647 \| 5.4705 \| 5000 \| 0.3324 \| 0.7608 \| 0.7079 \| 0.7334 \| 0.9432 \|
	\| 0.0647 \| 5.5799 \| 5100 \| 0.3356 \| 0.7635 \| 0.7038 \| 0.7324 \| 0.9430 \|
	\| 0.0647 \| 5.6893 \| 5200 \| 0.3121 \| 0.7634 \| 0.7121 \| 0.7368 \| 0.9430 \|
	\| 0.0647 \| 5.7987 \| 5300 \| 0.3392 \| 0.7858 \| 0.7003 \| 0.7406 \| 0.9448 \|
	\| 0.0647 \| 5.9081 \| 5400 \| 0.2952 \| 0.7265 \| 0.7220 \| 0.7242 \| 0.9412 \|
	\| 0.0573 \| 6.0175 \| 5500 \| 0.3070 \| 0.7311 \| 0.7211 \| 0.7260 \| 0.9429 \|
	\| 0.0573 \| 6.1269 \| 5600 \| 0.3207 \| 0.7414 \| 0.7241 \| 0.7326 \| 0.9435 \|
	\| 0.0573 \| 6.2363 \| 5700 \| 0.3130 \| 0.7685 \| 0.7231 \| 0.7451 \| 0.9455 \|
	\| 0.0573 \| 6.3457 \| 5800 \| 0.3441 \| 0.7752 \| 0.7139 \| 0.7433 \| 0.9447 \|
	\| 0.0573 \| 6.4551 \| 5900 \| 0.3196 \| 0.7818 \| 0.7128 \| 0.7457 \| 0.9458 \|
	\| 0.0529 \| 6.5646 \| 6000 \| 0.3369 \| 0.7907 \| 0.7164 \| 0.7517 \| 0.9456 \|
	\| 0.0529 \| 6.6740 \| 6100 \| 0.3059 \| 0.7394 \| 0.7267 \| 0.7330 \| 0.9435 \|
	\| 0.0529 \| 6.7834 \| 6200 \| 0.3043 \| 0.7624 \| 0.7231 \| 0.7422 \| 0.9444 \|
	\| 0.0529 \| 6.8928 \| 6300 \| 0.3028 \| 0.7527 \| 0.7252 \| 0.7387 \| 0.9441 \|
	\| 0.0529 \| 7.0022 \| 6400 \| 0.3089 \| 0.7596 \| 0.7293 \| 0.7441 \| 0.9457 \|
	\| 0.0542 \| 7.1116 \| 6500 \| 0.2927 \| 0.7306 \| 0.7286 \| 0.7296 \| 0.9408 \|
	\| 0.0542 \| 7.2210 \| 6600 \| 0.3178 \| 0.7785 \| 0.7274 \| 0.7521 \| 0.9456 \|
	\| 0.0542 \| 7.3304 \| 6700 \| 0.3267 \| 0.7653 \| 0.7304 \| 0.7474 \| 0.9450 \|
	\| 0.0542 \| 7.4398 \| 6800 \| 0.3254 \| 0.7618 \| 0.7280 \| 0.7445 \| 0.9450 \|
	\| 0.0542 \| 7.5492 \| 6900 \| 0.3240 \| 0.7856 \| 0.7254 \| 0.7543 \| 0.9464 \|
	\| 0.0416 \| 7.6586 \| 7000 \| 0.3203 \| 0.7682 \| 0.7319 \| 0.7496 \| 0.9463 \|
	\| 0.0416 \| 7.7681 \| 7100 \| 0.3176 \| 0.7801 \| 0.7299 \| 0.7542 \| 0.9468 \|
	\| 0.0416 \| 7.8775 \| 7200 \| 0.3012 \| 0.7601 \| 0.7355 \| 0.7476 \| 0.9470 \|
	\| 0.0416 \| 7.9869 \| 7300 \| 0.3092 \| 0.7336 \| 0.7377 \| 0.7357 \| 0.9436 \|
	\| 0.0416 \| 8.0963 \| 7400 \| 0.3025 \| 0.7782 \| 0.7349 \| 0.7559 \| 0.9480 \|
	\| 0.0422 \| 8.2057 \| 7500 \| 0.3046 \| 0.7594 \| 0.7340 \| 0.7465 \| 0.9459 \|
	\| 0.0422 \| 8.3151 \| 7600 \| 0.3113 \| 0.7640 \| 0.7332 \| 0.7483 \| 0.9458 \|
	\| 0.0422 \| 8.4245 \| 7700 \| 0.3002 \| 0.7579 \| 0.7394 \| 0.7485 \| 0.9461 \|
	\| 0.0422 \| 8.5339 \| 7800 \| 0.3173 \| 0.7742 \| 0.7321 \| 0.7526 \| 0.9464 \|
	\| 0.0422 \| 8.6433 \| 7900 \| 0.3084 \| 0.7766 \| 0.7334 \| 0.7544 \| 0.9467 \|
	\| 0.041 \| 8.7527 \| 8000 \| 0.3118 \| 0.7829 \| 0.7325 \| 0.7569 \| 0.9477 \|
	\| 0.041 \| 8.8621 \| 8100 \| 0.3145 \| 0.7788 \| 0.7389 \| 0.7583 \| 0.9473 \|
	\| 0.041 \| 8.9716 \| 8200 \| 0.3123 \| 0.7788 \| 0.7366 \| 0.7571 \| 0.9480 \|
	\| 0.041 \| 9.0810 \| 8300 \| 0.3088 \| 0.7754 \| 0.7398 \| 0.7572 \| 0.9476 \|
	\| 0.041 \| 9.1904 \| 8400 \| 0.3101 \| 0.7804 \| 0.7415 \| 0.7604 \| 0.9491 \|
	\| 0.0323 \| 9.2998 \| 8500 \| 0.3152 \| 0.7829 \| 0.7357 \| 0.7785 \| 0.9482 \|
	\| 0.0323 \| 9.4092 \| 8600 \| 0.3061 \| 0.7734 \| 0.7398 \| 0.7762 \| 0.9476 \|
	\| 0.0323 \| 9.5186 \| 8700 \| 0.3086 \| 0.7636 \| 0.7437 \| 0.7735 \| 0.9476 \|
	\| 0.0323 \| 9.6280 \| 8800 \| 0.3162 \| 0.7723 \| 0.7390 \| 0.7553 \| 0.9476 \|
	\| 0.0323 \| 9.7374 \| 8900 \| 0.3070 \| 0.7605 \| 0.7419 \| 0.7811 \| 0.9467 \|
	\| 0.0357 \| 9.8468 \| 9000 \| 0.3117 \| 0.7824 \| 0.7396 \| 0.7804 \| 0.9481 \|
	\| 0.0357 \| 9.9562 \| 9100 \| 0.3130 \| 0.7750 \| 0.7396 \| 0.7869 \| 0.9472 \|
	\| 0.0357 \| 10.0656 \| 9200 \| 0.3095 \| 0.7673 \| 0.7405 \| 0.7837 \| 0.9476 \|
	\| 0.0357 \| 10.1751 \| 9300 \| 0.3179 \| 0.7868 \| 0.7357 \| 0.7804 \| 0.9477 \|
	\| 0.0357 \| 10.2845 \| 9400 \| 0.3077 \| 0.7645 \| 0.7405 \| 0.7823 \| 0.9472 \|
	\| 0.0359 \| 10.3939 \| 9500 \| 0.3128 \| 0.7798 \| 0.7366 \| 0.7876 \| 0.9476 \|
	\| 0.0359 \| 10.5033 \| 9600 \| 0.3151 \| 0.7784 \| 0.7377 \| 0.7875 \| 0.9475 \|
	\| 0.0359 \| 10.6127 \| 9700 \| 0.3138 \| 0.7744 \| 0.7420 \| 0.7879 \| 0.9478 \|
	\| 0.0359 \| 10.7221 \| 9800 \| 0.3115 \| 0.7688 \| 0.7415 \| 0.7849 \| 0.9475 \|
	\| 0.0359 \| 10.8315 \| 9900 \| 0.3097 \| 0.7673 \| 0.7411 \| 0.7840 \| 0.9472 \|
	\| 0.0301 \| 10.9409 \| 10000 \| 0.3095 \| 0.7674 \| 0.7409 \| 0.7989 \| 0.9474 \|


	### Framework versions

	- Transformers 4.41.0.dev0
	- Pytorch 2.2.1+cu121
	- Datasets 2.19.1
	- Tokenizers 0.19.1