End of training

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	---
	license: apache-2.0
	base_model: google/vit-base-patch16-224
	tags:
	- generated_from_trainer
	datasets:
	- stanford-dogs
	metrics:
	- accuracy
	- f1
	- precision
	- recall
	model-index:
	- name: google-vit-base-patch16-224-batch32-lr5e-05-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: 0.8865403304178814
	- name: F1
	type: f1
	value: 0.8829055367708631
	- name: Precision
	type: precision
	value: 0.8892817099907323
	- name: Recall
	type: recall
	value: 0.8836513270735221
	---

	<!-- 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. -->

	# google-vit-base-patch16-224-batch32-lr5e-05-standford-dogs

	This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the stanford-dogs dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.4497
	- Accuracy: 0.8865
	- F1: 0.8829
	- Precision: 0.8893
	- Recall: 0.8837

	## 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 \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|
	\| 4.7916 \| 0.0777 \| 10 \| 4.5904 \| 0.0328 \| 0.0240 \| 0.0321 \| 0.0343 \|
	\| 4.5526 \| 0.1553 \| 20 \| 4.2901 \| 0.1118 \| 0.0891 \| 0.1068 \| 0.1134 \|
	\| 4.2946 \| 0.2330 \| 30 \| 3.9659 \| 0.2602 \| 0.2124 \| 0.2287 \| 0.2522 \|
	\| 3.9673 \| 0.3107 \| 40 \| 3.6288 \| 0.4351 \| 0.3666 \| 0.4093 \| 0.4189 \|
	\| 3.69 \| 0.3883 \| 50 \| 3.3225 \| 0.5394 \| 0.4751 \| 0.5232 \| 0.5244 \|
	\| 3.4705 \| 0.4660 \| 60 \| 3.0343 \| 0.6261 \| 0.5750 \| 0.6563 \| 0.6139 \|
	\| 3.2239 \| 0.5437 \| 70 \| 2.7671 \| 0.6842 \| 0.6503 \| 0.7272 \| 0.6743 \|
	\| 2.9986 \| 0.6214 \| 80 \| 2.5191 \| 0.7262 \| 0.6971 \| 0.7601 \| 0.7161 \|
	\| 2.7575 \| 0.6990 \| 90 \| 2.2953 \| 0.7430 \| 0.7162 \| 0.7735 \| 0.7333 \|
	\| 2.5923 \| 0.7767 \| 100 \| 2.1008 \| 0.7694 \| 0.7470 \| 0.7956 \| 0.7600 \|
	\| 2.4265 \| 0.8544 \| 110 \| 1.9250 \| 0.7949 \| 0.7762 \| 0.8094 \| 0.7863 \|
	\| 2.3049 \| 0.9320 \| 120 \| 1.7636 \| 0.8054 \| 0.7861 \| 0.8173 \| 0.7971 \|
	\| 2.1243 \| 1.0097 \| 130 \| 1.6290 \| 0.8200 \| 0.8056 \| 0.8382 \| 0.8125 \|
	\| 1.9721 \| 1.0874 \| 140 \| 1.5121 \| 0.8226 \| 0.8084 \| 0.8396 \| 0.8149 \|
	\| 1.848 \| 1.1650 \| 150 \| 1.4282 \| 0.8163 \| 0.8002 \| 0.8362 \| 0.8083 \|
	\| 1.775 \| 1.2427 \| 160 \| 1.3034 \| 0.8304 \| 0.8171 \| 0.8438 \| 0.8238 \|
	\| 1.717 \| 1.3204 \| 170 \| 1.2343 \| 0.8275 \| 0.8126 \| 0.8460 \| 0.8207 \|
	\| 1.6203 \| 1.3981 \| 180 \| 1.1554 \| 0.8387 \| 0.8259 \| 0.8552 \| 0.8323 \|
	\| 1.5739 \| 1.4757 \| 190 \| 1.0944 \| 0.8484 \| 0.8384 \| 0.8593 \| 0.8420 \|
	\| 1.5508 \| 1.5534 \| 200 \| 1.0400 \| 0.8484 \| 0.8394 \| 0.8574 \| 0.8431 \|
	\| 1.4549 \| 1.6311 \| 210 \| 0.9943 \| 0.8452 \| 0.8340 \| 0.8497 \| 0.8399 \|
	\| 1.3907 \| 1.7087 \| 220 \| 0.9427 \| 0.8596 \| 0.8480 \| 0.8627 \| 0.8542 \|
	\| 1.3497 \| 1.7864 \| 230 \| 0.8936 \| 0.8569 \| 0.8461 \| 0.8647 \| 0.8516 \|
	\| 1.2618 \| 1.8641 \| 240 \| 0.8619 \| 0.8613 \| 0.8503 \| 0.8671 \| 0.8560 \|
	\| 1.3014 \| 1.9417 \| 250 \| 0.8324 \| 0.8603 \| 0.8508 \| 0.8737 \| 0.8553 \|
	\| 1.2209 \| 2.0194 \| 260 \| 0.8015 \| 0.8591 \| 0.8503 \| 0.8645 \| 0.8537 \|
	\| 1.2139 \| 2.0971 \| 270 \| 0.7824 \| 0.8596 \| 0.8517 \| 0.8656 \| 0.8544 \|
	\| 1.1364 \| 2.1748 \| 280 \| 0.7544 \| 0.8603 \| 0.8513 \| 0.8611 \| 0.8556 \|
	\| 1.1811 \| 2.2524 \| 290 \| 0.7283 \| 0.8683 \| 0.8605 \| 0.8785 \| 0.8637 \|
	\| 1.1316 \| 2.3301 \| 300 \| 0.7169 \| 0.8635 \| 0.8550 \| 0.8653 \| 0.8590 \|
	\| 1.1246 \| 2.4078 \| 310 \| 0.6900 \| 0.8686 \| 0.8610 \| 0.8739 \| 0.8645 \|
	\| 1.1027 \| 2.4854 \| 320 \| 0.6862 \| 0.8627 \| 0.8548 \| 0.8730 \| 0.8582 \|
	\| 1.0911 \| 2.5631 \| 330 \| 0.6667 \| 0.8693 \| 0.8632 \| 0.8730 \| 0.8653 \|
	\| 1.0158 \| 2.6408 \| 340 \| 0.6544 \| 0.8695 \| 0.8628 \| 0.8751 \| 0.8651 \|
	\| 1.0805 \| 2.7184 \| 350 \| 0.6342 \| 0.8703 \| 0.8634 \| 0.8733 \| 0.8663 \|
	\| 1.0679 \| 2.7961 \| 360 \| 0.6276 \| 0.8754 \| 0.8689 \| 0.8797 \| 0.8713 \|
	\| 1.0611 \| 2.8738 \| 370 \| 0.6223 \| 0.8746 \| 0.8692 \| 0.8807 \| 0.8705 \|
	\| 0.9996 \| 2.9515 \| 380 \| 0.6055 \| 0.8724 \| 0.8661 \| 0.8758 \| 0.8683 \|
	\| 1.0838 \| 3.0291 \| 390 \| 0.6039 \| 0.8715 \| 0.8652 \| 0.8769 \| 0.8677 \|
	\| 0.9396 \| 3.1068 \| 400 \| 0.5946 \| 0.8737 \| 0.8676 \| 0.8791 \| 0.8699 \|
	\| 0.8466 \| 3.1845 \| 410 \| 0.5810 \| 0.8717 \| 0.8653 \| 0.8775 \| 0.8673 \|
	\| 0.9588 \| 3.2621 \| 420 \| 0.5819 \| 0.8710 \| 0.8651 \| 0.8766 \| 0.8671 \|
	\| 0.9784 \| 3.3398 \| 430 \| 0.5742 \| 0.8754 \| 0.8684 \| 0.8788 \| 0.8716 \|
	\| 0.9289 \| 3.4175 \| 440 \| 0.5667 \| 0.8768 \| 0.8703 \| 0.8792 \| 0.8731 \|
	\| 0.8917 \| 3.4951 \| 450 \| 0.5615 \| 0.8724 \| 0.8672 \| 0.8762 \| 0.8690 \|
	\| 0.8646 \| 3.5728 \| 460 \| 0.5537 \| 0.8737 \| 0.8681 \| 0.8761 \| 0.8702 \|
	\| 0.9029 \| 3.6505 \| 470 \| 0.5538 \| 0.8732 \| 0.8694 \| 0.8771 \| 0.8698 \|
	\| 0.9551 \| 3.7282 \| 480 \| 0.5440 \| 0.8766 \| 0.8720 \| 0.8809 \| 0.8735 \|
	\| 0.8787 \| 3.8058 \| 490 \| 0.5448 \| 0.8751 \| 0.8704 \| 0.8791 \| 0.8712 \|
	\| 0.9128 \| 3.8835 \| 500 \| 0.5354 \| 0.8751 \| 0.8701 \| 0.8799 \| 0.8712 \|
	\| 0.8566 \| 3.9612 \| 510 \| 0.5262 \| 0.8776 \| 0.8715 \| 0.8846 \| 0.8738 \|
	\| 0.8624 \| 4.0388 \| 520 \| 0.5252 \| 0.8754 \| 0.8692 \| 0.8840 \| 0.8715 \|
	\| 0.799 \| 4.1165 \| 530 \| 0.5197 \| 0.8763 \| 0.8702 \| 0.8817 \| 0.8723 \|
	\| 0.7912 \| 4.1942 \| 540 \| 0.5213 \| 0.8751 \| 0.8695 \| 0.8815 \| 0.8709 \|
	\| 0.874 \| 4.2718 \| 550 \| 0.5142 \| 0.8778 \| 0.8730 \| 0.8862 \| 0.8742 \|
	\| 0.766 \| 4.3495 \| 560 \| 0.5019 \| 0.8817 \| 0.8770 \| 0.8864 \| 0.8783 \|
	\| 0.8902 \| 4.4272 \| 570 \| 0.5011 \| 0.8831 \| 0.8785 \| 0.8887 \| 0.8798 \|
	\| 0.8038 \| 4.5049 \| 580 \| 0.5014 \| 0.8800 \| 0.8742 \| 0.8878 \| 0.8762 \|
	\| 0.8893 \| 4.5825 \| 590 \| 0.5062 \| 0.8797 \| 0.8744 \| 0.8851 \| 0.8759 \|
	\| 0.7868 \| 4.6602 \| 600 \| 0.4926 \| 0.8827 \| 0.8785 \| 0.8867 \| 0.8791 \|
	\| 0.7733 \| 4.7379 \| 610 \| 0.4957 \| 0.8783 \| 0.8749 \| 0.8816 \| 0.8755 \|
	\| 0.8275 \| 4.8155 \| 620 \| 0.4871 \| 0.8817 \| 0.8781 \| 0.8847 \| 0.8785 \|
	\| 0.7944 \| 4.8932 \| 630 \| 0.4855 \| 0.8858 \| 0.8823 \| 0.8880 \| 0.8829 \|
	\| 0.8483 \| 4.9709 \| 640 \| 0.4849 \| 0.8836 \| 0.8797 \| 0.8858 \| 0.8803 \|
	\| 0.7297 \| 5.0485 \| 650 \| 0.4833 \| 0.8814 \| 0.8779 \| 0.8845 \| 0.8784 \|
	\| 0.754 \| 5.1262 \| 660 \| 0.4824 \| 0.8814 \| 0.8775 \| 0.8844 \| 0.8782 \|
	\| 0.698 \| 5.2039 \| 670 \| 0.4806 \| 0.8851 \| 0.8818 \| 0.8878 \| 0.8821 \|
	\| 0.7515 \| 5.2816 \| 680 \| 0.4777 \| 0.8824 \| 0.8791 \| 0.8855 \| 0.8796 \|
	\| 0.7527 \| 5.3592 \| 690 \| 0.4711 \| 0.8841 \| 0.8806 \| 0.8869 \| 0.8808 \|
	\| 0.7287 \| 5.4369 \| 700 \| 0.4718 \| 0.8853 \| 0.8819 \| 0.8873 \| 0.8824 \|
	\| 0.8134 \| 5.5146 \| 710 \| 0.4680 \| 0.8856 \| 0.8826 \| 0.8885 \| 0.8828 \|
	\| 0.7655 \| 5.5922 \| 720 \| 0.4688 \| 0.8836 \| 0.8795 \| 0.8862 \| 0.8800 \|
	\| 0.7904 \| 5.6699 \| 730 \| 0.4671 \| 0.8878 \| 0.8841 \| 0.8901 \| 0.8846 \|
	\| 0.7257 \| 5.7476 \| 740 \| 0.4704 \| 0.8824 \| 0.8790 \| 0.8872 \| 0.8796 \|
	\| 0.7342 \| 5.8252 \| 750 \| 0.4641 \| 0.8841 \| 0.8802 \| 0.8889 \| 0.8810 \|
	\| 0.7075 \| 5.9029 \| 760 \| 0.4654 \| 0.8824 \| 0.8782 \| 0.8865 \| 0.8791 \|
	\| 0.7924 \| 5.9806 \| 770 \| 0.4619 \| 0.8868 \| 0.8829 \| 0.8899 \| 0.8839 \|
	\| 0.7176 \| 6.0583 \| 780 \| 0.4597 \| 0.8861 \| 0.8815 \| 0.8889 \| 0.8829 \|
	\| 0.6768 \| 6.1359 \| 790 \| 0.4595 \| 0.8858 \| 0.8820 \| 0.8910 \| 0.8827 \|
	\| 0.722 \| 6.2136 \| 800 \| 0.4605 \| 0.8836 \| 0.8796 \| 0.8882 \| 0.8803 \|
	\| 0.7429 \| 6.2913 \| 810 \| 0.4594 \| 0.8865 \| 0.8823 \| 0.8912 \| 0.8833 \|
	\| 0.6904 \| 6.3689 \| 820 \| 0.4611 \| 0.8856 \| 0.8821 \| 0.8892 \| 0.8825 \|
	\| 0.7617 \| 6.4466 \| 830 \| 0.4592 \| 0.8856 \| 0.8816 \| 0.8879 \| 0.8826 \|
	\| 0.7285 \| 6.5243 \| 840 \| 0.4576 \| 0.8863 \| 0.8822 \| 0.8895 \| 0.8832 \|
	\| 0.686 \| 6.6019 \| 850 \| 0.4561 \| 0.8875 \| 0.8834 \| 0.8923 \| 0.8844 \|
	\| 0.6546 \| 6.6796 \| 860 \| 0.4561 \| 0.8865 \| 0.8824 \| 0.8903 \| 0.8835 \|
	\| 0.6526 \| 6.7573 \| 870 \| 0.4543 \| 0.8875 \| 0.8830 \| 0.8917 \| 0.8844 \|
	\| 0.7534 \| 6.8350 \| 880 \| 0.4537 \| 0.8885 \| 0.8845 \| 0.8927 \| 0.8855 \|
	\| 0.7065 \| 6.9126 \| 890 \| 0.4535 \| 0.8870 \| 0.8831 \| 0.8912 \| 0.8841 \|
	\| 0.774 \| 6.9903 \| 900 \| 0.4528 \| 0.8878 \| 0.8842 \| 0.8924 \| 0.8849 \|
	\| 0.7185 \| 7.0680 \| 910 \| 0.4516 \| 0.8880 \| 0.8840 \| 0.8913 \| 0.8849 \|
	\| 0.6321 \| 7.1456 \| 920 \| 0.4526 \| 0.8868 \| 0.8830 \| 0.8900 \| 0.8838 \|
	\| 0.6957 \| 7.2233 \| 930 \| 0.4517 \| 0.8865 \| 0.8825 \| 0.8901 \| 0.8834 \|
	\| 0.6774 \| 7.3010 \| 940 \| 0.4523 \| 0.8863 \| 0.8823 \| 0.8895 \| 0.8833 \|
	\| 0.6915 \| 7.3786 \| 950 \| 0.4528 \| 0.8853 \| 0.8814 \| 0.8890 \| 0.8822 \|
	\| 0.6738 \| 7.4563 \| 960 \| 0.4520 \| 0.8868 \| 0.8829 \| 0.8901 \| 0.8838 \|
	\| 0.7021 \| 7.5340 \| 970 \| 0.4510 \| 0.8863 \| 0.8826 \| 0.8897 \| 0.8834 \|
	\| 0.7053 \| 7.6117 \| 980 \| 0.4501 \| 0.8863 \| 0.8827 \| 0.8885 \| 0.8835 \|
	\| 0.7241 \| 7.6893 \| 990 \| 0.4498 \| 0.8865 \| 0.8829 \| 0.8893 \| 0.8837 \|
	\| 0.703 \| 7.7670 \| 1000 \| 0.4497 \| 0.8865 \| 0.8829 \| 0.8893 \| 0.8837 \|


	### Framework versions

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

	---
	license: apache-2.0
	base_model: google/vit-base-patch16-224
	tags:
	- generated_from_trainer
	datasets:
	- stanford-dogs
	metrics:
	- accuracy
	- f1
	- precision
	- recall
	model-index:
	- name: google-vit-base-patch16-224-batch32-lr5e-05-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: 0.8865403304178814
	- name: F1
	type: f1
	value: 0.8829055367708631
	- name: Precision
	type: precision
	value: 0.8892817099907323
	- name: Recall
	type: recall
	value: 0.8836513270735221
	---

	<!-- 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. -->

	# google-vit-base-patch16-224-batch32-lr5e-05-standford-dogs

	This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the stanford-dogs dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.4497
	- Accuracy: 0.8865
	- F1: 0.8829
	- Precision: 0.8893
	- Recall: 0.8837

	## 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 \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|
	\| 4.7916 \| 0.0777 \| 10 \| 4.5904 \| 0.0328 \| 0.0240 \| 0.0321 \| 0.0343 \|
	\| 4.5526 \| 0.1553 \| 20 \| 4.2901 \| 0.1118 \| 0.0891 \| 0.1068 \| 0.1134 \|
	\| 4.2946 \| 0.2330 \| 30 \| 3.9659 \| 0.2602 \| 0.2124 \| 0.2287 \| 0.2522 \|
	\| 3.9673 \| 0.3107 \| 40 \| 3.6288 \| 0.4351 \| 0.3666 \| 0.4093 \| 0.4189 \|
	\| 3.69 \| 0.3883 \| 50 \| 3.3225 \| 0.5394 \| 0.4751 \| 0.5232 \| 0.5244 \|
	\| 3.4705 \| 0.4660 \| 60 \| 3.0343 \| 0.6261 \| 0.5750 \| 0.6563 \| 0.6139 \|
	\| 3.2239 \| 0.5437 \| 70 \| 2.7671 \| 0.6842 \| 0.6503 \| 0.7272 \| 0.6743 \|
	\| 2.9986 \| 0.6214 \| 80 \| 2.5191 \| 0.7262 \| 0.6971 \| 0.7601 \| 0.7161 \|
	\| 2.7575 \| 0.6990 \| 90 \| 2.2953 \| 0.7430 \| 0.7162 \| 0.7735 \| 0.7333 \|
	\| 2.5923 \| 0.7767 \| 100 \| 2.1008 \| 0.7694 \| 0.7470 \| 0.7956 \| 0.7600 \|
	\| 2.4265 \| 0.8544 \| 110 \| 1.9250 \| 0.7949 \| 0.7762 \| 0.8094 \| 0.7863 \|
	\| 2.3049 \| 0.9320 \| 120 \| 1.7636 \| 0.8054 \| 0.7861 \| 0.8173 \| 0.7971 \|
	\| 2.1243 \| 1.0097 \| 130 \| 1.6290 \| 0.8200 \| 0.8056 \| 0.8382 \| 0.8125 \|
	\| 1.9721 \| 1.0874 \| 140 \| 1.5121 \| 0.8226 \| 0.8084 \| 0.8396 \| 0.8149 \|
	\| 1.848 \| 1.1650 \| 150 \| 1.4282 \| 0.8163 \| 0.8002 \| 0.8362 \| 0.8083 \|
	\| 1.775 \| 1.2427 \| 160 \| 1.3034 \| 0.8304 \| 0.8171 \| 0.8438 \| 0.8238 \|
	\| 1.717 \| 1.3204 \| 170 \| 1.2343 \| 0.8275 \| 0.8126 \| 0.8460 \| 0.8207 \|
	\| 1.6203 \| 1.3981 \| 180 \| 1.1554 \| 0.8387 \| 0.8259 \| 0.8552 \| 0.8323 \|
	\| 1.5739 \| 1.4757 \| 190 \| 1.0944 \| 0.8484 \| 0.8384 \| 0.8593 \| 0.8420 \|
	\| 1.5508 \| 1.5534 \| 200 \| 1.0400 \| 0.8484 \| 0.8394 \| 0.8574 \| 0.8431 \|
	\| 1.4549 \| 1.6311 \| 210 \| 0.9943 \| 0.8452 \| 0.8340 \| 0.8497 \| 0.8399 \|
	\| 1.3907 \| 1.7087 \| 220 \| 0.9427 \| 0.8596 \| 0.8480 \| 0.8627 \| 0.8542 \|
	\| 1.3497 \| 1.7864 \| 230 \| 0.8936 \| 0.8569 \| 0.8461 \| 0.8647 \| 0.8516 \|
	\| 1.2618 \| 1.8641 \| 240 \| 0.8619 \| 0.8613 \| 0.8503 \| 0.8671 \| 0.8560 \|
	\| 1.3014 \| 1.9417 \| 250 \| 0.8324 \| 0.8603 \| 0.8508 \| 0.8737 \| 0.8553 \|
	\| 1.2209 \| 2.0194 \| 260 \| 0.8015 \| 0.8591 \| 0.8503 \| 0.8645 \| 0.8537 \|
	\| 1.2139 \| 2.0971 \| 270 \| 0.7824 \| 0.8596 \| 0.8517 \| 0.8656 \| 0.8544 \|
	\| 1.1364 \| 2.1748 \| 280 \| 0.7544 \| 0.8603 \| 0.8513 \| 0.8611 \| 0.8556 \|
	\| 1.1811 \| 2.2524 \| 290 \| 0.7283 \| 0.8683 \| 0.8605 \| 0.8785 \| 0.8637 \|
	\| 1.1316 \| 2.3301 \| 300 \| 0.7169 \| 0.8635 \| 0.8550 \| 0.8653 \| 0.8590 \|
	\| 1.1246 \| 2.4078 \| 310 \| 0.6900 \| 0.8686 \| 0.8610 \| 0.8739 \| 0.8645 \|
	\| 1.1027 \| 2.4854 \| 320 \| 0.6862 \| 0.8627 \| 0.8548 \| 0.8730 \| 0.8582 \|
	\| 1.0911 \| 2.5631 \| 330 \| 0.6667 \| 0.8693 \| 0.8632 \| 0.8730 \| 0.8653 \|
	\| 1.0158 \| 2.6408 \| 340 \| 0.6544 \| 0.8695 \| 0.8628 \| 0.8751 \| 0.8651 \|
	\| 1.0805 \| 2.7184 \| 350 \| 0.6342 \| 0.8703 \| 0.8634 \| 0.8733 \| 0.8663 \|
	\| 1.0679 \| 2.7961 \| 360 \| 0.6276 \| 0.8754 \| 0.8689 \| 0.8797 \| 0.8713 \|
	\| 1.0611 \| 2.8738 \| 370 \| 0.6223 \| 0.8746 \| 0.8692 \| 0.8807 \| 0.8705 \|
	\| 0.9996 \| 2.9515 \| 380 \| 0.6055 \| 0.8724 \| 0.8661 \| 0.8758 \| 0.8683 \|
	\| 1.0838 \| 3.0291 \| 390 \| 0.6039 \| 0.8715 \| 0.8652 \| 0.8769 \| 0.8677 \|
	\| 0.9396 \| 3.1068 \| 400 \| 0.5946 \| 0.8737 \| 0.8676 \| 0.8791 \| 0.8699 \|
	\| 0.8466 \| 3.1845 \| 410 \| 0.5810 \| 0.8717 \| 0.8653 \| 0.8775 \| 0.8673 \|
	\| 0.9588 \| 3.2621 \| 420 \| 0.5819 \| 0.8710 \| 0.8651 \| 0.8766 \| 0.8671 \|
	\| 0.9784 \| 3.3398 \| 430 \| 0.5742 \| 0.8754 \| 0.8684 \| 0.8788 \| 0.8716 \|
	\| 0.9289 \| 3.4175 \| 440 \| 0.5667 \| 0.8768 \| 0.8703 \| 0.8792 \| 0.8731 \|
	\| 0.8917 \| 3.4951 \| 450 \| 0.5615 \| 0.8724 \| 0.8672 \| 0.8762 \| 0.8690 \|
	\| 0.8646 \| 3.5728 \| 460 \| 0.5537 \| 0.8737 \| 0.8681 \| 0.8761 \| 0.8702 \|
	\| 0.9029 \| 3.6505 \| 470 \| 0.5538 \| 0.8732 \| 0.8694 \| 0.8771 \| 0.8698 \|
	\| 0.9551 \| 3.7282 \| 480 \| 0.5440 \| 0.8766 \| 0.8720 \| 0.8809 \| 0.8735 \|
	\| 0.8787 \| 3.8058 \| 490 \| 0.5448 \| 0.8751 \| 0.8704 \| 0.8791 \| 0.8712 \|
	\| 0.9128 \| 3.8835 \| 500 \| 0.5354 \| 0.8751 \| 0.8701 \| 0.8799 \| 0.8712 \|
	\| 0.8566 \| 3.9612 \| 510 \| 0.5262 \| 0.8776 \| 0.8715 \| 0.8846 \| 0.8738 \|
	\| 0.8624 \| 4.0388 \| 520 \| 0.5252 \| 0.8754 \| 0.8692 \| 0.8840 \| 0.8715 \|
	\| 0.799 \| 4.1165 \| 530 \| 0.5197 \| 0.8763 \| 0.8702 \| 0.8817 \| 0.8723 \|
	\| 0.7912 \| 4.1942 \| 540 \| 0.5213 \| 0.8751 \| 0.8695 \| 0.8815 \| 0.8709 \|
	\| 0.874 \| 4.2718 \| 550 \| 0.5142 \| 0.8778 \| 0.8730 \| 0.8862 \| 0.8742 \|
	\| 0.766 \| 4.3495 \| 560 \| 0.5019 \| 0.8817 \| 0.8770 \| 0.8864 \| 0.8783 \|
	\| 0.8902 \| 4.4272 \| 570 \| 0.5011 \| 0.8831 \| 0.8785 \| 0.8887 \| 0.8798 \|
	\| 0.8038 \| 4.5049 \| 580 \| 0.5014 \| 0.8800 \| 0.8742 \| 0.8878 \| 0.8762 \|
	\| 0.8893 \| 4.5825 \| 590 \| 0.5062 \| 0.8797 \| 0.8744 \| 0.8851 \| 0.8759 \|
	\| 0.7868 \| 4.6602 \| 600 \| 0.4926 \| 0.8827 \| 0.8785 \| 0.8867 \| 0.8791 \|
	\| 0.7733 \| 4.7379 \| 610 \| 0.4957 \| 0.8783 \| 0.8749 \| 0.8816 \| 0.8755 \|
	\| 0.8275 \| 4.8155 \| 620 \| 0.4871 \| 0.8817 \| 0.8781 \| 0.8847 \| 0.8785 \|
	\| 0.7944 \| 4.8932 \| 630 \| 0.4855 \| 0.8858 \| 0.8823 \| 0.8880 \| 0.8829 \|
	\| 0.8483 \| 4.9709 \| 640 \| 0.4849 \| 0.8836 \| 0.8797 \| 0.8858 \| 0.8803 \|
	\| 0.7297 \| 5.0485 \| 650 \| 0.4833 \| 0.8814 \| 0.8779 \| 0.8845 \| 0.8784 \|
	\| 0.754 \| 5.1262 \| 660 \| 0.4824 \| 0.8814 \| 0.8775 \| 0.8844 \| 0.8782 \|
	\| 0.698 \| 5.2039 \| 670 \| 0.4806 \| 0.8851 \| 0.8818 \| 0.8878 \| 0.8821 \|
	\| 0.7515 \| 5.2816 \| 680 \| 0.4777 \| 0.8824 \| 0.8791 \| 0.8855 \| 0.8796 \|
	\| 0.7527 \| 5.3592 \| 690 \| 0.4711 \| 0.8841 \| 0.8806 \| 0.8869 \| 0.8808 \|
	\| 0.7287 \| 5.4369 \| 700 \| 0.4718 \| 0.8853 \| 0.8819 \| 0.8873 \| 0.8824 \|
	\| 0.8134 \| 5.5146 \| 710 \| 0.4680 \| 0.8856 \| 0.8826 \| 0.8885 \| 0.8828 \|
	\| 0.7655 \| 5.5922 \| 720 \| 0.4688 \| 0.8836 \| 0.8795 \| 0.8862 \| 0.8800 \|
	\| 0.7904 \| 5.6699 \| 730 \| 0.4671 \| 0.8878 \| 0.8841 \| 0.8901 \| 0.8846 \|
	\| 0.7257 \| 5.7476 \| 740 \| 0.4704 \| 0.8824 \| 0.8790 \| 0.8872 \| 0.8796 \|
	\| 0.7342 \| 5.8252 \| 750 \| 0.4641 \| 0.8841 \| 0.8802 \| 0.8889 \| 0.8810 \|
	\| 0.7075 \| 5.9029 \| 760 \| 0.4654 \| 0.8824 \| 0.8782 \| 0.8865 \| 0.8791 \|
	\| 0.7924 \| 5.9806 \| 770 \| 0.4619 \| 0.8868 \| 0.8829 \| 0.8899 \| 0.8839 \|
	\| 0.7176 \| 6.0583 \| 780 \| 0.4597 \| 0.8861 \| 0.8815 \| 0.8889 \| 0.8829 \|
	\| 0.6768 \| 6.1359 \| 790 \| 0.4595 \| 0.8858 \| 0.8820 \| 0.8910 \| 0.8827 \|
	\| 0.722 \| 6.2136 \| 800 \| 0.4605 \| 0.8836 \| 0.8796 \| 0.8882 \| 0.8803 \|
	\| 0.7429 \| 6.2913 \| 810 \| 0.4594 \| 0.8865 \| 0.8823 \| 0.8912 \| 0.8833 \|
	\| 0.6904 \| 6.3689 \| 820 \| 0.4611 \| 0.8856 \| 0.8821 \| 0.8892 \| 0.8825 \|
	\| 0.7617 \| 6.4466 \| 830 \| 0.4592 \| 0.8856 \| 0.8816 \| 0.8879 \| 0.8826 \|
	\| 0.7285 \| 6.5243 \| 840 \| 0.4576 \| 0.8863 \| 0.8822 \| 0.8895 \| 0.8832 \|
	\| 0.686 \| 6.6019 \| 850 \| 0.4561 \| 0.8875 \| 0.8834 \| 0.8923 \| 0.8844 \|
	\| 0.6546 \| 6.6796 \| 860 \| 0.4561 \| 0.8865 \| 0.8824 \| 0.8903 \| 0.8835 \|
	\| 0.6526 \| 6.7573 \| 870 \| 0.4543 \| 0.8875 \| 0.8830 \| 0.8917 \| 0.8844 \|
	\| 0.7534 \| 6.8350 \| 880 \| 0.4537 \| 0.8885 \| 0.8845 \| 0.8927 \| 0.8855 \|
	\| 0.7065 \| 6.9126 \| 890 \| 0.4535 \| 0.8870 \| 0.8831 \| 0.8912 \| 0.8841 \|
	\| 0.774 \| 6.9903 \| 900 \| 0.4528 \| 0.8878 \| 0.8842 \| 0.8924 \| 0.8849 \|
	\| 0.7185 \| 7.0680 \| 910 \| 0.4516 \| 0.8880 \| 0.8840 \| 0.8913 \| 0.8849 \|
	\| 0.6321 \| 7.1456 \| 920 \| 0.4526 \| 0.8868 \| 0.8830 \| 0.8900 \| 0.8838 \|
	\| 0.6957 \| 7.2233 \| 930 \| 0.4517 \| 0.8865 \| 0.8825 \| 0.8901 \| 0.8834 \|
	\| 0.6774 \| 7.3010 \| 940 \| 0.4523 \| 0.8863 \| 0.8823 \| 0.8895 \| 0.8833 \|
	\| 0.6915 \| 7.3786 \| 950 \| 0.4528 \| 0.8853 \| 0.8814 \| 0.8890 \| 0.8822 \|
	\| 0.6738 \| 7.4563 \| 960 \| 0.4520 \| 0.8868 \| 0.8829 \| 0.8901 \| 0.8838 \|
	\| 0.7021 \| 7.5340 \| 970 \| 0.4510 \| 0.8863 \| 0.8826 \| 0.8897 \| 0.8834 \|
	\| 0.7053 \| 7.6117 \| 980 \| 0.4501 \| 0.8863 \| 0.8827 \| 0.8885 \| 0.8835 \|
	\| 0.7241 \| 7.6893 \| 990 \| 0.4498 \| 0.8865 \| 0.8829 \| 0.8893 \| 0.8837 \|
	\| 0.703 \| 7.7670 \| 1000 \| 0.4497 \| 0.8865 \| 0.8829 \| 0.8893 \| 0.8837 \|


	### Framework versions

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