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-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: 0.8838678328474247
	- name: F1
	type: f1
	value: 0.880922271280839
	- name: Precision
	type: precision
	value: 0.8888253617157671
	- name: Recall
	type: recall
	value: 0.8813659659148954
	---

	<!-- 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-lr0.0005-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: 0.4466
	- Accuracy: 0.8839
	- F1: 0.8809
	- Precision: 0.8888
	- Recall: 0.8814

	## 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 \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|
	\| 4.758 \| 0.0777 \| 10 \| 4.5706 \| 0.0481 \| 0.0344 \| 0.0390 \| 0.0483 \|
	\| 4.4856 \| 0.1553 \| 20 \| 4.2407 \| 0.1421 \| 0.1018 \| 0.1190 \| 0.1396 \|
	\| 4.2422 \| 0.2330 \| 30 \| 3.9118 \| 0.2655 \| 0.2120 \| 0.2525 \| 0.2553 \|
	\| 3.972 \| 0.3107 \| 40 \| 3.5875 \| 0.4096 \| 0.3445 \| 0.3996 \| 0.3933 \|
	\| 3.6632 \| 0.3883 \| 50 \| 3.2750 \| 0.5299 \| 0.4683 \| 0.5338 \| 0.5129 \|
	\| 3.4266 \| 0.4660 \| 60 \| 2.9911 \| 0.6028 \| 0.5536 \| 0.6617 \| 0.5870 \|
	\| 3.1207 \| 0.5437 \| 70 \| 2.7127 \| 0.6638 \| 0.6227 \| 0.7159 \| 0.6496 \|
	\| 2.9402 \| 0.6214 \| 80 \| 2.4853 \| 0.7043 \| 0.6716 \| 0.7387 \| 0.6923 \|
	\| 2.7683 \| 0.6990 \| 90 \| 2.2667 \| 0.7376 \| 0.7092 \| 0.7675 \| 0.7277 \|
	\| 2.5556 \| 0.7767 \| 100 \| 2.0710 \| 0.7631 \| 0.7422 \| 0.7843 \| 0.7551 \|
	\| 2.3936 \| 0.8544 \| 110 \| 1.8898 \| 0.7772 \| 0.7610 \| 0.8076 \| 0.7705 \|
	\| 2.245 \| 0.9320 \| 120 \| 1.7340 \| 0.7954 \| 0.7807 \| 0.8138 \| 0.7898 \|
	\| 2.0889 \| 1.0097 \| 130 \| 1.6027 \| 0.8073 \| 0.7970 \| 0.8314 \| 0.8024 \|
	\| 1.951 \| 1.0874 \| 140 \| 1.4715 \| 0.8197 \| 0.8094 \| 0.8361 \| 0.8132 \|
	\| 1.8414 \| 1.1650 \| 150 \| 1.3790 \| 0.8209 \| 0.8104 \| 0.8389 \| 0.8151 \|
	\| 1.7247 \| 1.2427 \| 160 \| 1.3237 \| 0.8146 \| 0.8034 \| 0.8338 \| 0.8085 \|
	\| 1.7338 \| 1.3204 \| 170 \| 1.2107 \| 0.8336 \| 0.8251 \| 0.8463 \| 0.8284 \|
	\| 1.5688 \| 1.3981 \| 180 \| 1.1370 \| 0.8401 \| 0.8314 \| 0.8531 \| 0.8354 \|
	\| 1.5471 \| 1.4757 \| 190 \| 1.0684 \| 0.8455 \| 0.8367 \| 0.8558 \| 0.8398 \|
	\| 1.461 \| 1.5534 \| 200 \| 1.0200 \| 0.8477 \| 0.8384 \| 0.8571 \| 0.8430 \|
	\| 1.4736 \| 1.6311 \| 210 \| 0.9612 \| 0.8540 \| 0.8493 \| 0.8621 \| 0.8509 \|
	\| 1.4062 \| 1.7087 \| 220 \| 0.9269 \| 0.8528 \| 0.8476 \| 0.8639 \| 0.8495 \|
	\| 1.2888 \| 1.7864 \| 230 \| 0.8914 \| 0.8516 \| 0.8456 \| 0.8626 \| 0.8478 \|
	\| 1.3353 \| 1.8641 \| 240 \| 0.8484 \| 0.8601 \| 0.8538 \| 0.8684 \| 0.8555 \|
	\| 1.2751 \| 1.9417 \| 250 \| 0.8210 \| 0.8586 \| 0.8520 \| 0.8643 \| 0.8538 \|
	\| 1.2378 \| 2.0194 \| 260 \| 0.7924 \| 0.8567 \| 0.8510 \| 0.8641 \| 0.8522 \|
	\| 1.1686 \| 2.0971 \| 270 \| 0.7683 \| 0.8579 \| 0.8529 \| 0.8670 \| 0.8543 \|
	\| 1.1625 \| 2.1748 \| 280 \| 0.7477 \| 0.8567 \| 0.8522 \| 0.8658 \| 0.8532 \|
	\| 1.1883 \| 2.2524 \| 290 \| 0.7312 \| 0.8576 \| 0.8534 \| 0.8674 \| 0.8541 \|
	\| 1.1551 \| 2.3301 \| 300 \| 0.7052 \| 0.8654 \| 0.8606 \| 0.8702 \| 0.8618 \|
	\| 1.1259 \| 2.4078 \| 310 \| 0.6901 \| 0.8627 \| 0.8580 \| 0.8720 \| 0.8597 \|
	\| 1.073 \| 2.4854 \| 320 \| 0.6793 \| 0.8654 \| 0.8600 \| 0.8722 \| 0.8612 \|
	\| 1.0587 \| 2.5631 \| 330 \| 0.6604 \| 0.8700 \| 0.8646 \| 0.8753 \| 0.8660 \|
	\| 1.0506 \| 2.6408 \| 340 \| 0.6470 \| 0.8690 \| 0.8638 \| 0.8714 \| 0.8652 \|
	\| 1.0397 \| 2.7184 \| 350 \| 0.6369 \| 0.8664 \| 0.8606 \| 0.8711 \| 0.8627 \|
	\| 1.0363 \| 2.7961 \| 360 \| 0.6373 \| 0.8664 \| 0.8610 \| 0.8797 \| 0.8623 \|
	\| 1.0408 \| 2.8738 \| 370 \| 0.6141 \| 0.8700 \| 0.8637 \| 0.8755 \| 0.8655 \|
	\| 1.0087 \| 2.9515 \| 380 \| 0.6105 \| 0.8707 \| 0.8657 \| 0.8740 \| 0.8675 \|
	\| 1.0021 \| 3.0291 \| 390 \| 0.5978 \| 0.8744 \| 0.8708 \| 0.8771 \| 0.8717 \|
	\| 0.894 \| 3.1068 \| 400 \| 0.5970 \| 0.8671 \| 0.8632 \| 0.8733 \| 0.8637 \|
	\| 0.9363 \| 3.1845 \| 410 \| 0.5860 \| 0.8683 \| 0.8640 \| 0.8754 \| 0.8653 \|
	\| 0.9678 \| 3.2621 \| 420 \| 0.5760 \| 0.8712 \| 0.8677 \| 0.8764 \| 0.8684 \|
	\| 0.9378 \| 3.3398 \| 430 \| 0.5677 \| 0.8686 \| 0.8645 \| 0.8734 \| 0.8653 \|
	\| 0.929 \| 3.4175 \| 440 \| 0.5620 \| 0.8707 \| 0.8659 \| 0.8750 \| 0.8672 \|
	\| 0.9585 \| 3.4951 \| 450 \| 0.5610 \| 0.8654 \| 0.8612 \| 0.8691 \| 0.8625 \|
	\| 0.8432 \| 3.5728 \| 460 \| 0.5557 \| 0.8690 \| 0.8638 \| 0.8715 \| 0.8665 \|
	\| 0.9423 \| 3.6505 \| 470 \| 0.5421 \| 0.8715 \| 0.8663 \| 0.8737 \| 0.8684 \|
	\| 0.944 \| 3.7282 \| 480 \| 0.5419 \| 0.8703 \| 0.8656 \| 0.8728 \| 0.8671 \|
	\| 0.8477 \| 3.8058 \| 490 \| 0.5297 \| 0.8766 \| 0.8727 \| 0.8799 \| 0.8737 \|
	\| 0.8933 \| 3.8835 \| 500 \| 0.5263 \| 0.8739 \| 0.8707 \| 0.8790 \| 0.8716 \|
	\| 0.881 \| 3.9612 \| 510 \| 0.5197 \| 0.8768 \| 0.8739 \| 0.8817 \| 0.8746 \|
	\| 0.8603 \| 4.0388 \| 520 \| 0.5196 \| 0.8778 \| 0.8740 \| 0.8832 \| 0.8746 \|
	\| 0.8045 \| 4.1165 \| 530 \| 0.5214 \| 0.8759 \| 0.8723 \| 0.8822 \| 0.8725 \|
	\| 0.8101 \| 4.1942 \| 540 \| 0.5163 \| 0.8746 \| 0.8713 \| 0.8806 \| 0.8725 \|
	\| 0.8016 \| 4.2718 \| 550 \| 0.5149 \| 0.8766 \| 0.8729 \| 0.8813 \| 0.8735 \|
	\| 0.8403 \| 4.3495 \| 560 \| 0.5061 \| 0.8724 \| 0.8689 \| 0.8767 \| 0.8699 \|
	\| 0.8216 \| 4.4272 \| 570 \| 0.5034 \| 0.8739 \| 0.8699 \| 0.8798 \| 0.8708 \|
	\| 0.8491 \| 4.5049 \| 580 \| 0.5004 \| 0.8768 \| 0.8730 \| 0.8828 \| 0.8740 \|
	\| 0.7727 \| 4.5825 \| 590 \| 0.4990 \| 0.8766 \| 0.8722 \| 0.8810 \| 0.8736 \|
	\| 0.8475 \| 4.6602 \| 600 \| 0.4941 \| 0.8766 \| 0.8724 \| 0.8807 \| 0.8736 \|
	\| 0.854 \| 4.7379 \| 610 \| 0.4867 \| 0.8824 \| 0.8793 \| 0.8867 \| 0.8801 \|
	\| 0.8226 \| 4.8155 \| 620 \| 0.4900 \| 0.8771 \| 0.8736 \| 0.8824 \| 0.8747 \|
	\| 0.7847 \| 4.8932 \| 630 \| 0.4855 \| 0.8805 \| 0.8773 \| 0.8847 \| 0.8779 \|
	\| 0.8093 \| 4.9709 \| 640 \| 0.4820 \| 0.8805 \| 0.8775 \| 0.8844 \| 0.8777 \|
	\| 0.7667 \| 5.0485 \| 650 \| 0.4837 \| 0.8800 \| 0.8759 \| 0.8827 \| 0.8768 \|
	\| 0.7116 \| 5.1262 \| 660 \| 0.4812 \| 0.8797 \| 0.8765 \| 0.8850 \| 0.8773 \|
	\| 0.7859 \| 5.2039 \| 670 \| 0.4792 \| 0.8812 \| 0.8781 \| 0.8869 \| 0.8790 \|
	\| 0.8108 \| 5.2816 \| 680 \| 0.4772 \| 0.8797 \| 0.8766 \| 0.8862 \| 0.8771 \|
	\| 0.7425 \| 5.3592 \| 690 \| 0.4757 \| 0.8807 \| 0.8774 \| 0.8856 \| 0.8780 \|
	\| 0.7968 \| 5.4369 \| 700 \| 0.4759 \| 0.8802 \| 0.8770 \| 0.8853 \| 0.8774 \|
	\| 0.8087 \| 5.5146 \| 710 \| 0.4702 \| 0.8810 \| 0.8778 \| 0.8851 \| 0.8783 \|
	\| 0.7207 \| 5.5922 \| 720 \| 0.4716 \| 0.8805 \| 0.8771 \| 0.8845 \| 0.8776 \|
	\| 0.7776 \| 5.6699 \| 730 \| 0.4691 \| 0.8790 \| 0.8749 \| 0.8829 \| 0.8760 \|
	\| 0.8075 \| 5.7476 \| 740 \| 0.4658 \| 0.8819 \| 0.8784 \| 0.8858 \| 0.8789 \|
	\| 0.7759 \| 5.8252 \| 750 \| 0.4637 \| 0.8807 \| 0.8771 \| 0.8838 \| 0.8778 \|
	\| 0.6963 \| 5.9029 \| 760 \| 0.4656 \| 0.8795 \| 0.8765 \| 0.8847 \| 0.8769 \|
	\| 0.7245 \| 5.9806 \| 770 \| 0.4644 \| 0.8827 \| 0.8796 \| 0.8872 \| 0.8800 \|
	\| 0.692 \| 6.0583 \| 780 \| 0.4602 \| 0.8819 \| 0.8789 \| 0.8861 \| 0.8792 \|
	\| 0.6859 \| 6.1359 \| 790 \| 0.4594 \| 0.8827 \| 0.8797 \| 0.8872 \| 0.8798 \|
	\| 0.7221 \| 6.2136 \| 800 \| 0.4593 \| 0.8814 \| 0.8783 \| 0.8861 \| 0.8788 \|
	\| 0.701 \| 6.2913 \| 810 \| 0.4599 \| 0.8807 \| 0.8777 \| 0.8852 \| 0.8778 \|
	\| 0.7151 \| 6.3689 \| 820 \| 0.4564 \| 0.8824 \| 0.8794 \| 0.8869 \| 0.8797 \|
	\| 0.7038 \| 6.4466 \| 830 \| 0.4573 \| 0.8812 \| 0.8780 \| 0.8860 \| 0.8783 \|
	\| 0.7182 \| 6.5243 \| 840 \| 0.4553 \| 0.8824 \| 0.8793 \| 0.8866 \| 0.8795 \|
	\| 0.6964 \| 6.6019 \| 850 \| 0.4535 \| 0.8822 \| 0.8788 \| 0.8861 \| 0.8794 \|
	\| 0.6805 \| 6.6796 \| 860 \| 0.4556 \| 0.8819 \| 0.8789 \| 0.8878 \| 0.8791 \|
	\| 0.6209 \| 6.7573 \| 870 \| 0.4518 \| 0.8836 \| 0.8804 \| 0.8883 \| 0.8810 \|
	\| 0.6665 \| 6.8350 \| 880 \| 0.4524 \| 0.8829 \| 0.8798 \| 0.8881 \| 0.8800 \|
	\| 0.7334 \| 6.9126 \| 890 \| 0.4507 \| 0.8805 \| 0.8776 \| 0.8859 \| 0.8779 \|
	\| 0.6889 \| 6.9903 \| 900 \| 0.4503 \| 0.8822 \| 0.8791 \| 0.8872 \| 0.8796 \|
	\| 0.6854 \| 7.0680 \| 910 \| 0.4488 \| 0.8846 \| 0.8816 \| 0.8887 \| 0.8824 \|
	\| 0.6855 \| 7.1456 \| 920 \| 0.4485 \| 0.8829 \| 0.8800 \| 0.8877 \| 0.8804 \|
	\| 0.6644 \| 7.2233 \| 930 \| 0.4477 \| 0.8846 \| 0.8814 \| 0.8888 \| 0.8822 \|
	\| 0.6556 \| 7.3010 \| 940 \| 0.4469 \| 0.8841 \| 0.8811 \| 0.8887 \| 0.8818 \|
	\| 0.7299 \| 7.3786 \| 950 \| 0.4480 \| 0.8841 \| 0.8813 \| 0.8894 \| 0.8817 \|
	\| 0.6425 \| 7.4563 \| 960 \| 0.4467 \| 0.8829 \| 0.8798 \| 0.8876 \| 0.8805 \|
	\| 0.6582 \| 7.5340 \| 970 \| 0.4470 \| 0.8831 \| 0.8801 \| 0.8879 \| 0.8806 \|
	\| 0.7499 \| 7.6117 \| 980 \| 0.4466 \| 0.8831 \| 0.8801 \| 0.8878 \| 0.8806 \|
	\| 0.6396 \| 7.6893 \| 990 \| 0.4466 \| 0.8839 \| 0.8809 \| 0.8887 \| 0.8813 \|
	\| 0.6864 \| 7.7670 \| 1000 \| 0.4466 \| 0.8839 \| 0.8809 \| 0.8888 \| 0.8814 \|


	### 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-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: 0.8838678328474247
	- name: F1
	type: f1
	value: 0.880922271280839
	- name: Precision
	type: precision
	value: 0.8888253617157671
	- name: Recall
	type: recall
	value: 0.8813659659148954
	---

	<!-- 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-lr0.0005-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: 0.4466
	- Accuracy: 0.8839
	- F1: 0.8809
	- Precision: 0.8888
	- Recall: 0.8814

	## 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 \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|
	\| 4.758 \| 0.0777 \| 10 \| 4.5706 \| 0.0481 \| 0.0344 \| 0.0390 \| 0.0483 \|
	\| 4.4856 \| 0.1553 \| 20 \| 4.2407 \| 0.1421 \| 0.1018 \| 0.1190 \| 0.1396 \|
	\| 4.2422 \| 0.2330 \| 30 \| 3.9118 \| 0.2655 \| 0.2120 \| 0.2525 \| 0.2553 \|
	\| 3.972 \| 0.3107 \| 40 \| 3.5875 \| 0.4096 \| 0.3445 \| 0.3996 \| 0.3933 \|
	\| 3.6632 \| 0.3883 \| 50 \| 3.2750 \| 0.5299 \| 0.4683 \| 0.5338 \| 0.5129 \|
	\| 3.4266 \| 0.4660 \| 60 \| 2.9911 \| 0.6028 \| 0.5536 \| 0.6617 \| 0.5870 \|
	\| 3.1207 \| 0.5437 \| 70 \| 2.7127 \| 0.6638 \| 0.6227 \| 0.7159 \| 0.6496 \|
	\| 2.9402 \| 0.6214 \| 80 \| 2.4853 \| 0.7043 \| 0.6716 \| 0.7387 \| 0.6923 \|
	\| 2.7683 \| 0.6990 \| 90 \| 2.2667 \| 0.7376 \| 0.7092 \| 0.7675 \| 0.7277 \|
	\| 2.5556 \| 0.7767 \| 100 \| 2.0710 \| 0.7631 \| 0.7422 \| 0.7843 \| 0.7551 \|
	\| 2.3936 \| 0.8544 \| 110 \| 1.8898 \| 0.7772 \| 0.7610 \| 0.8076 \| 0.7705 \|
	\| 2.245 \| 0.9320 \| 120 \| 1.7340 \| 0.7954 \| 0.7807 \| 0.8138 \| 0.7898 \|
	\| 2.0889 \| 1.0097 \| 130 \| 1.6027 \| 0.8073 \| 0.7970 \| 0.8314 \| 0.8024 \|
	\| 1.951 \| 1.0874 \| 140 \| 1.4715 \| 0.8197 \| 0.8094 \| 0.8361 \| 0.8132 \|
	\| 1.8414 \| 1.1650 \| 150 \| 1.3790 \| 0.8209 \| 0.8104 \| 0.8389 \| 0.8151 \|
	\| 1.7247 \| 1.2427 \| 160 \| 1.3237 \| 0.8146 \| 0.8034 \| 0.8338 \| 0.8085 \|
	\| 1.7338 \| 1.3204 \| 170 \| 1.2107 \| 0.8336 \| 0.8251 \| 0.8463 \| 0.8284 \|
	\| 1.5688 \| 1.3981 \| 180 \| 1.1370 \| 0.8401 \| 0.8314 \| 0.8531 \| 0.8354 \|
	\| 1.5471 \| 1.4757 \| 190 \| 1.0684 \| 0.8455 \| 0.8367 \| 0.8558 \| 0.8398 \|
	\| 1.461 \| 1.5534 \| 200 \| 1.0200 \| 0.8477 \| 0.8384 \| 0.8571 \| 0.8430 \|
	\| 1.4736 \| 1.6311 \| 210 \| 0.9612 \| 0.8540 \| 0.8493 \| 0.8621 \| 0.8509 \|
	\| 1.4062 \| 1.7087 \| 220 \| 0.9269 \| 0.8528 \| 0.8476 \| 0.8639 \| 0.8495 \|
	\| 1.2888 \| 1.7864 \| 230 \| 0.8914 \| 0.8516 \| 0.8456 \| 0.8626 \| 0.8478 \|
	\| 1.3353 \| 1.8641 \| 240 \| 0.8484 \| 0.8601 \| 0.8538 \| 0.8684 \| 0.8555 \|
	\| 1.2751 \| 1.9417 \| 250 \| 0.8210 \| 0.8586 \| 0.8520 \| 0.8643 \| 0.8538 \|
	\| 1.2378 \| 2.0194 \| 260 \| 0.7924 \| 0.8567 \| 0.8510 \| 0.8641 \| 0.8522 \|
	\| 1.1686 \| 2.0971 \| 270 \| 0.7683 \| 0.8579 \| 0.8529 \| 0.8670 \| 0.8543 \|
	\| 1.1625 \| 2.1748 \| 280 \| 0.7477 \| 0.8567 \| 0.8522 \| 0.8658 \| 0.8532 \|
	\| 1.1883 \| 2.2524 \| 290 \| 0.7312 \| 0.8576 \| 0.8534 \| 0.8674 \| 0.8541 \|
	\| 1.1551 \| 2.3301 \| 300 \| 0.7052 \| 0.8654 \| 0.8606 \| 0.8702 \| 0.8618 \|
	\| 1.1259 \| 2.4078 \| 310 \| 0.6901 \| 0.8627 \| 0.8580 \| 0.8720 \| 0.8597 \|
	\| 1.073 \| 2.4854 \| 320 \| 0.6793 \| 0.8654 \| 0.8600 \| 0.8722 \| 0.8612 \|
	\| 1.0587 \| 2.5631 \| 330 \| 0.6604 \| 0.8700 \| 0.8646 \| 0.8753 \| 0.8660 \|
	\| 1.0506 \| 2.6408 \| 340 \| 0.6470 \| 0.8690 \| 0.8638 \| 0.8714 \| 0.8652 \|
	\| 1.0397 \| 2.7184 \| 350 \| 0.6369 \| 0.8664 \| 0.8606 \| 0.8711 \| 0.8627 \|
	\| 1.0363 \| 2.7961 \| 360 \| 0.6373 \| 0.8664 \| 0.8610 \| 0.8797 \| 0.8623 \|
	\| 1.0408 \| 2.8738 \| 370 \| 0.6141 \| 0.8700 \| 0.8637 \| 0.8755 \| 0.8655 \|
	\| 1.0087 \| 2.9515 \| 380 \| 0.6105 \| 0.8707 \| 0.8657 \| 0.8740 \| 0.8675 \|
	\| 1.0021 \| 3.0291 \| 390 \| 0.5978 \| 0.8744 \| 0.8708 \| 0.8771 \| 0.8717 \|
	\| 0.894 \| 3.1068 \| 400 \| 0.5970 \| 0.8671 \| 0.8632 \| 0.8733 \| 0.8637 \|
	\| 0.9363 \| 3.1845 \| 410 \| 0.5860 \| 0.8683 \| 0.8640 \| 0.8754 \| 0.8653 \|
	\| 0.9678 \| 3.2621 \| 420 \| 0.5760 \| 0.8712 \| 0.8677 \| 0.8764 \| 0.8684 \|
	\| 0.9378 \| 3.3398 \| 430 \| 0.5677 \| 0.8686 \| 0.8645 \| 0.8734 \| 0.8653 \|
	\| 0.929 \| 3.4175 \| 440 \| 0.5620 \| 0.8707 \| 0.8659 \| 0.8750 \| 0.8672 \|
	\| 0.9585 \| 3.4951 \| 450 \| 0.5610 \| 0.8654 \| 0.8612 \| 0.8691 \| 0.8625 \|
	\| 0.8432 \| 3.5728 \| 460 \| 0.5557 \| 0.8690 \| 0.8638 \| 0.8715 \| 0.8665 \|
	\| 0.9423 \| 3.6505 \| 470 \| 0.5421 \| 0.8715 \| 0.8663 \| 0.8737 \| 0.8684 \|
	\| 0.944 \| 3.7282 \| 480 \| 0.5419 \| 0.8703 \| 0.8656 \| 0.8728 \| 0.8671 \|
	\| 0.8477 \| 3.8058 \| 490 \| 0.5297 \| 0.8766 \| 0.8727 \| 0.8799 \| 0.8737 \|
	\| 0.8933 \| 3.8835 \| 500 \| 0.5263 \| 0.8739 \| 0.8707 \| 0.8790 \| 0.8716 \|
	\| 0.881 \| 3.9612 \| 510 \| 0.5197 \| 0.8768 \| 0.8739 \| 0.8817 \| 0.8746 \|
	\| 0.8603 \| 4.0388 \| 520 \| 0.5196 \| 0.8778 \| 0.8740 \| 0.8832 \| 0.8746 \|
	\| 0.8045 \| 4.1165 \| 530 \| 0.5214 \| 0.8759 \| 0.8723 \| 0.8822 \| 0.8725 \|
	\| 0.8101 \| 4.1942 \| 540 \| 0.5163 \| 0.8746 \| 0.8713 \| 0.8806 \| 0.8725 \|
	\| 0.8016 \| 4.2718 \| 550 \| 0.5149 \| 0.8766 \| 0.8729 \| 0.8813 \| 0.8735 \|
	\| 0.8403 \| 4.3495 \| 560 \| 0.5061 \| 0.8724 \| 0.8689 \| 0.8767 \| 0.8699 \|
	\| 0.8216 \| 4.4272 \| 570 \| 0.5034 \| 0.8739 \| 0.8699 \| 0.8798 \| 0.8708 \|
	\| 0.8491 \| 4.5049 \| 580 \| 0.5004 \| 0.8768 \| 0.8730 \| 0.8828 \| 0.8740 \|
	\| 0.7727 \| 4.5825 \| 590 \| 0.4990 \| 0.8766 \| 0.8722 \| 0.8810 \| 0.8736 \|
	\| 0.8475 \| 4.6602 \| 600 \| 0.4941 \| 0.8766 \| 0.8724 \| 0.8807 \| 0.8736 \|
	\| 0.854 \| 4.7379 \| 610 \| 0.4867 \| 0.8824 \| 0.8793 \| 0.8867 \| 0.8801 \|
	\| 0.8226 \| 4.8155 \| 620 \| 0.4900 \| 0.8771 \| 0.8736 \| 0.8824 \| 0.8747 \|
	\| 0.7847 \| 4.8932 \| 630 \| 0.4855 \| 0.8805 \| 0.8773 \| 0.8847 \| 0.8779 \|
	\| 0.8093 \| 4.9709 \| 640 \| 0.4820 \| 0.8805 \| 0.8775 \| 0.8844 \| 0.8777 \|
	\| 0.7667 \| 5.0485 \| 650 \| 0.4837 \| 0.8800 \| 0.8759 \| 0.8827 \| 0.8768 \|
	\| 0.7116 \| 5.1262 \| 660 \| 0.4812 \| 0.8797 \| 0.8765 \| 0.8850 \| 0.8773 \|
	\| 0.7859 \| 5.2039 \| 670 \| 0.4792 \| 0.8812 \| 0.8781 \| 0.8869 \| 0.8790 \|
	\| 0.8108 \| 5.2816 \| 680 \| 0.4772 \| 0.8797 \| 0.8766 \| 0.8862 \| 0.8771 \|
	\| 0.7425 \| 5.3592 \| 690 \| 0.4757 \| 0.8807 \| 0.8774 \| 0.8856 \| 0.8780 \|
	\| 0.7968 \| 5.4369 \| 700 \| 0.4759 \| 0.8802 \| 0.8770 \| 0.8853 \| 0.8774 \|
	\| 0.8087 \| 5.5146 \| 710 \| 0.4702 \| 0.8810 \| 0.8778 \| 0.8851 \| 0.8783 \|
	\| 0.7207 \| 5.5922 \| 720 \| 0.4716 \| 0.8805 \| 0.8771 \| 0.8845 \| 0.8776 \|
	\| 0.7776 \| 5.6699 \| 730 \| 0.4691 \| 0.8790 \| 0.8749 \| 0.8829 \| 0.8760 \|
	\| 0.8075 \| 5.7476 \| 740 \| 0.4658 \| 0.8819 \| 0.8784 \| 0.8858 \| 0.8789 \|
	\| 0.7759 \| 5.8252 \| 750 \| 0.4637 \| 0.8807 \| 0.8771 \| 0.8838 \| 0.8778 \|
	\| 0.6963 \| 5.9029 \| 760 \| 0.4656 \| 0.8795 \| 0.8765 \| 0.8847 \| 0.8769 \|
	\| 0.7245 \| 5.9806 \| 770 \| 0.4644 \| 0.8827 \| 0.8796 \| 0.8872 \| 0.8800 \|
	\| 0.692 \| 6.0583 \| 780 \| 0.4602 \| 0.8819 \| 0.8789 \| 0.8861 \| 0.8792 \|
	\| 0.6859 \| 6.1359 \| 790 \| 0.4594 \| 0.8827 \| 0.8797 \| 0.8872 \| 0.8798 \|
	\| 0.7221 \| 6.2136 \| 800 \| 0.4593 \| 0.8814 \| 0.8783 \| 0.8861 \| 0.8788 \|
	\| 0.701 \| 6.2913 \| 810 \| 0.4599 \| 0.8807 \| 0.8777 \| 0.8852 \| 0.8778 \|
	\| 0.7151 \| 6.3689 \| 820 \| 0.4564 \| 0.8824 \| 0.8794 \| 0.8869 \| 0.8797 \|
	\| 0.7038 \| 6.4466 \| 830 \| 0.4573 \| 0.8812 \| 0.8780 \| 0.8860 \| 0.8783 \|
	\| 0.7182 \| 6.5243 \| 840 \| 0.4553 \| 0.8824 \| 0.8793 \| 0.8866 \| 0.8795 \|
	\| 0.6964 \| 6.6019 \| 850 \| 0.4535 \| 0.8822 \| 0.8788 \| 0.8861 \| 0.8794 \|
	\| 0.6805 \| 6.6796 \| 860 \| 0.4556 \| 0.8819 \| 0.8789 \| 0.8878 \| 0.8791 \|
	\| 0.6209 \| 6.7573 \| 870 \| 0.4518 \| 0.8836 \| 0.8804 \| 0.8883 \| 0.8810 \|
	\| 0.6665 \| 6.8350 \| 880 \| 0.4524 \| 0.8829 \| 0.8798 \| 0.8881 \| 0.8800 \|
	\| 0.7334 \| 6.9126 \| 890 \| 0.4507 \| 0.8805 \| 0.8776 \| 0.8859 \| 0.8779 \|
	\| 0.6889 \| 6.9903 \| 900 \| 0.4503 \| 0.8822 \| 0.8791 \| 0.8872 \| 0.8796 \|
	\| 0.6854 \| 7.0680 \| 910 \| 0.4488 \| 0.8846 \| 0.8816 \| 0.8887 \| 0.8824 \|
	\| 0.6855 \| 7.1456 \| 920 \| 0.4485 \| 0.8829 \| 0.8800 \| 0.8877 \| 0.8804 \|
	\| 0.6644 \| 7.2233 \| 930 \| 0.4477 \| 0.8846 \| 0.8814 \| 0.8888 \| 0.8822 \|
	\| 0.6556 \| 7.3010 \| 940 \| 0.4469 \| 0.8841 \| 0.8811 \| 0.8887 \| 0.8818 \|
	\| 0.7299 \| 7.3786 \| 950 \| 0.4480 \| 0.8841 \| 0.8813 \| 0.8894 \| 0.8817 \|
	\| 0.6425 \| 7.4563 \| 960 \| 0.4467 \| 0.8829 \| 0.8798 \| 0.8876 \| 0.8805 \|
	\| 0.6582 \| 7.5340 \| 970 \| 0.4470 \| 0.8831 \| 0.8801 \| 0.8879 \| 0.8806 \|
	\| 0.7499 \| 7.6117 \| 980 \| 0.4466 \| 0.8831 \| 0.8801 \| 0.8878 \| 0.8806 \|
	\| 0.6396 \| 7.6893 \| 990 \| 0.4466 \| 0.8839 \| 0.8809 \| 0.8887 \| 0.8813 \|
	\| 0.6864 \| 7.7670 \| 1000 \| 0.4466 \| 0.8839 \| 0.8809 \| 0.8888 \| 0.8814 \|


	### Framework versions

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