segformer-finetuned-tt-225-2k / README.md

End of training

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	---
	library_name: transformers
	license: other
	base_model: nvidia/mit-b0
	tags:
	- image-segmentation
	- vision
	- generated_from_trainer
	model-index:
	- name: segformer-finetuned-tt-225-2k
	results: []
	---

	<!-- 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. -->

	# segformer-finetuned-tt-225-2k

	This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co./nvidia/mit-b0) on the Saumya-Mundra/text255 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.1299
	- Mean Iou: 0.4851
	- Mean Accuracy: 0.9702
	- Overall Accuracy: 0.9702
	- Accuracy Text: nan
	- Accuracy No Text: 0.9702
	- Iou Text: 0.0
	- Iou No Text: 0.9702

	## 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: 6e-05
	- train_batch_size: 8
	- eval_batch_size: 8
	- seed: 1337
	- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: polynomial
	- training_steps: 2000

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Text \| Accuracy No Text \| Iou Text \| Iou No Text \|
	\|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------:\|:----------------:\|:--------:\|:-----------:\|
	\| No log \| 1.0 \| 29 \| 0.2604 \| 0.4706 \| 0.9411 \| 0.9411 \| nan \| 0.9411 \| 0.0 \| 0.9411 \|
	\| No log \| 2.0 \| 58 \| 0.2094 \| 0.4778 \| 0.9557 \| 0.9557 \| nan \| 0.9557 \| 0.0 \| 0.9557 \|
	\| No log \| 3.0 \| 87 \| 0.1818 \| 0.4823 \| 0.9647 \| 0.9647 \| nan \| 0.9647 \| 0.0 \| 0.9647 \|
	\| 0.323 \| 4.0 \| 116 \| 0.2098 \| 0.4696 \| 0.9392 \| 0.9392 \| nan \| 0.9392 \| 0.0 \| 0.9392 \|
	\| 0.323 \| 5.0 \| 145 \| 0.1717 \| 0.4849 \| 0.9699 \| 0.9699 \| nan \| 0.9699 \| 0.0 \| 0.9699 \|
	\| 0.323 \| 6.0 \| 174 \| 0.1484 \| 0.4840 \| 0.9681 \| 0.9681 \| nan \| 0.9681 \| 0.0 \| 0.9681 \|
	\| 0.2027 \| 7.0 \| 203 \| 0.1616 \| 0.4815 \| 0.9631 \| 0.9631 \| nan \| 0.9631 \| 0.0 \| 0.9631 \|
	\| 0.2027 \| 8.0 \| 232 \| 0.1503 \| 0.4864 \| 0.9728 \| 0.9728 \| nan \| 0.9728 \| 0.0 \| 0.9728 \|
	\| 0.2027 \| 9.0 \| 261 \| 0.1628 \| 0.4783 \| 0.9566 \| 0.9566 \| nan \| 0.9566 \| 0.0 \| 0.9566 \|
	\| 0.2027 \| 10.0 \| 290 \| 0.1424 \| 0.4835 \| 0.9670 \| 0.9670 \| nan \| 0.9670 \| 0.0 \| 0.9670 \|
	\| 0.1693 \| 11.0 \| 319 \| 0.1236 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \|
	\| 0.1693 \| 12.0 \| 348 \| 0.1388 \| 0.4816 \| 0.9632 \| 0.9632 \| nan \| 0.9632 \| 0.0 \| 0.9632 \|
	\| 0.1693 \| 13.0 \| 377 \| 0.1554 \| 0.4788 \| 0.9577 \| 0.9577 \| nan \| 0.9577 \| 0.0 \| 0.9577 \|
	\| 0.1502 \| 14.0 \| 406 \| 0.1537 \| 0.4761 \| 0.9521 \| 0.9521 \| nan \| 0.9521 \| 0.0 \| 0.9521 \|
	\| 0.1502 \| 15.0 \| 435 \| 0.1478 \| 0.4781 \| 0.9562 \| 0.9562 \| nan \| 0.9562 \| 0.0 \| 0.9562 \|
	\| 0.1502 \| 16.0 \| 464 \| 0.1367 \| 0.4825 \| 0.9651 \| 0.9651 \| nan \| 0.9651 \| 0.0 \| 0.9651 \|
	\| 0.1502 \| 17.0 \| 493 \| 0.1330 \| 0.4851 \| 0.9703 \| 0.9703 \| nan \| 0.9703 \| 0.0 \| 0.9703 \|
	\| 0.127 \| 18.0 \| 522 \| 0.1322 \| 0.4848 \| 0.9696 \| 0.9696 \| nan \| 0.9696 \| 0.0 \| 0.9696 \|
	\| 0.127 \| 19.0 \| 551 \| 0.1341 \| 0.4842 \| 0.9684 \| 0.9684 \| nan \| 0.9684 \| 0.0 \| 0.9684 \|
	\| 0.127 \| 20.0 \| 580 \| 0.1244 \| 0.4882 \| 0.9764 \| 0.9764 \| nan \| 0.9764 \| 0.0 \| 0.9764 \|
	\| 0.1173 \| 21.0 \| 609 \| 0.1200 \| 0.4896 \| 0.9793 \| 0.9793 \| nan \| 0.9793 \| 0.0 \| 0.9793 \|
	\| 0.1173 \| 22.0 \| 638 \| 0.1374 \| 0.4826 \| 0.9653 \| 0.9653 \| nan \| 0.9653 \| 0.0 \| 0.9653 \|
	\| 0.1173 \| 23.0 \| 667 \| 0.1248 \| 0.4884 \| 0.9768 \| 0.9768 \| nan \| 0.9768 \| 0.0 \| 0.9768 \|
	\| 0.1173 \| 24.0 \| 696 \| 0.1262 \| 0.4857 \| 0.9714 \| 0.9714 \| nan \| 0.9714 \| 0.0 \| 0.9714 \|
	\| 0.1193 \| 25.0 \| 725 \| 0.1235 \| 0.4860 \| 0.9720 \| 0.9720 \| nan \| 0.9720 \| 0.0 \| 0.9720 \|
	\| 0.1193 \| 26.0 \| 754 \| 0.1323 \| 0.4838 \| 0.9677 \| 0.9677 \| nan \| 0.9677 \| 0.0 \| 0.9677 \|
	\| 0.1193 \| 27.0 \| 783 \| 0.1235 \| 0.4853 \| 0.9707 \| 0.9707 \| nan \| 0.9707 \| 0.0 \| 0.9707 \|
	\| 0.0912 \| 28.0 \| 812 \| 0.1349 \| 0.4816 \| 0.9632 \| 0.9632 \| nan \| 0.9632 \| 0.0 \| 0.9632 \|
	\| 0.0912 \| 29.0 \| 841 \| 0.1408 \| 0.4816 \| 0.9632 \| 0.9632 \| nan \| 0.9632 \| 0.0 \| 0.9632 \|
	\| 0.0912 \| 30.0 \| 870 \| 0.1206 \| 0.4877 \| 0.9754 \| 0.9754 \| nan \| 0.9754 \| 0.0 \| 0.9754 \|
	\| 0.0912 \| 31.0 \| 899 \| 0.1347 \| 0.4823 \| 0.9646 \| 0.9646 \| nan \| 0.9646 \| 0.0 \| 0.9646 \|
	\| 0.1005 \| 32.0 \| 928 \| 0.1188 \| 0.4879 \| 0.9758 \| 0.9758 \| nan \| 0.9758 \| 0.0 \| 0.9758 \|
	\| 0.1005 \| 33.0 \| 957 \| 0.1234 \| 0.4891 \| 0.9781 \| 0.9781 \| nan \| 0.9781 \| 0.0 \| 0.9781 \|
	\| 0.1005 \| 34.0 \| 986 \| 0.1206 \| 0.4924 \| 0.9849 \| 0.9849 \| nan \| 0.9849 \| 0.0 \| 0.9849 \|
	\| 0.0933 \| 35.0 \| 1015 \| 0.1234 \| 0.4895 \| 0.9790 \| 0.9790 \| nan \| 0.9790 \| 0.0 \| 0.9790 \|
	\| 0.0933 \| 36.0 \| 1044 \| 0.1192 \| 0.4877 \| 0.9753 \| 0.9753 \| nan \| 0.9753 \| 0.0 \| 0.9753 \|
	\| 0.0933 \| 37.0 \| 1073 \| 0.1388 \| 0.4807 \| 0.9615 \| 0.9615 \| nan \| 0.9615 \| 0.0 \| 0.9615 \|
	\| 0.0937 \| 38.0 \| 1102 \| 0.1255 \| 0.4860 \| 0.9719 \| 0.9719 \| nan \| 0.9719 \| 0.0 \| 0.9719 \|
	\| 0.0937 \| 39.0 \| 1131 \| 0.1199 \| 0.4877 \| 0.9755 \| 0.9755 \| nan \| 0.9755 \| 0.0 \| 0.9755 \|
	\| 0.0937 \| 40.0 \| 1160 \| 0.1194 \| 0.4899 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \|
	\| 0.0937 \| 41.0 \| 1189 \| 0.1274 \| 0.4844 \| 0.9687 \| 0.9687 \| nan \| 0.9687 \| 0.0 \| 0.9687 \|
	\| 0.0923 \| 42.0 \| 1218 \| 0.1302 \| 0.4852 \| 0.9703 \| 0.9703 \| nan \| 0.9703 \| 0.0 \| 0.9703 \|
	\| 0.0923 \| 43.0 \| 1247 \| 0.1297 \| 0.4854 \| 0.9709 \| 0.9709 \| nan \| 0.9709 \| 0.0 \| 0.9709 \|
	\| 0.0923 \| 44.0 \| 1276 \| 0.1299 \| 0.4850 \| 0.9701 \| 0.9701 \| nan \| 0.9701 \| 0.0 \| 0.9701 \|
	\| 0.0812 \| 45.0 \| 1305 \| 0.1259 \| 0.4867 \| 0.9733 \| 0.9733 \| nan \| 0.9733 \| 0.0 \| 0.9733 \|
	\| 0.0812 \| 46.0 \| 1334 \| 0.1252 \| 0.4883 \| 0.9766 \| 0.9766 \| nan \| 0.9766 \| 0.0 \| 0.9766 \|
	\| 0.0812 \| 47.0 \| 1363 \| 0.1223 \| 0.4881 \| 0.9762 \| 0.9762 \| nan \| 0.9762 \| 0.0 \| 0.9762 \|
	\| 0.0812 \| 48.0 \| 1392 \| 0.1227 \| 0.4879 \| 0.9757 \| 0.9757 \| nan \| 0.9757 \| 0.0 \| 0.9757 \|
	\| 0.0901 \| 49.0 \| 1421 \| 0.1224 \| 0.4880 \| 0.9759 \| 0.9759 \| nan \| 0.9759 \| 0.0 \| 0.9759 \|
	\| 0.0901 \| 50.0 \| 1450 \| 0.1350 \| 0.4818 \| 0.9636 \| 0.9636 \| nan \| 0.9636 \| 0.0 \| 0.9636 \|
	\| 0.0901 \| 51.0 \| 1479 \| 0.1285 \| 0.4859 \| 0.9719 \| 0.9719 \| nan \| 0.9719 \| 0.0 \| 0.9719 \|
	\| 0.083 \| 52.0 \| 1508 \| 0.1286 \| 0.4848 \| 0.9695 \| 0.9695 \| nan \| 0.9695 \| 0.0 \| 0.9695 \|
	\| 0.083 \| 53.0 \| 1537 \| 0.1285 \| 0.4850 \| 0.9700 \| 0.9700 \| nan \| 0.9700 \| 0.0 \| 0.9700 \|
	\| 0.083 \| 54.0 \| 1566 \| 0.1252 \| 0.4888 \| 0.9775 \| 0.9775 \| nan \| 0.9775 \| 0.0 \| 0.9775 \|
	\| 0.083 \| 55.0 \| 1595 \| 0.1259 \| 0.4867 \| 0.9734 \| 0.9734 \| nan \| 0.9734 \| 0.0 \| 0.9734 \|
	\| 0.0822 \| 56.0 \| 1624 \| 0.1373 \| 0.4821 \| 0.9642 \| 0.9642 \| nan \| 0.9642 \| 0.0 \| 0.9642 \|
	\| 0.0822 \| 57.0 \| 1653 \| 0.1248 \| 0.4860 \| 0.9719 \| 0.9719 \| nan \| 0.9719 \| 0.0 \| 0.9719 \|
	\| 0.0822 \| 58.0 \| 1682 \| 0.1228 \| 0.4881 \| 0.9761 \| 0.9761 \| nan \| 0.9761 \| 0.0 \| 0.9761 \|
	\| 0.0777 \| 59.0 \| 1711 \| 0.1315 \| 0.4838 \| 0.9677 \| 0.9677 \| nan \| 0.9677 \| 0.0 \| 0.9677 \|
	\| 0.0777 \| 60.0 \| 1740 \| 0.1254 \| 0.4883 \| 0.9766 \| 0.9766 \| nan \| 0.9766 \| 0.0 \| 0.9766 \|
	\| 0.0777 \| 61.0 \| 1769 \| 0.1341 \| 0.4841 \| 0.9682 \| 0.9682 \| nan \| 0.9682 \| 0.0 \| 0.9682 \|
	\| 0.0777 \| 62.0 \| 1798 \| 0.1269 \| 0.4863 \| 0.9726 \| 0.9726 \| nan \| 0.9726 \| 0.0 \| 0.9726 \|
	\| 0.079 \| 63.0 \| 1827 \| 0.1266 \| 0.4878 \| 0.9755 \| 0.9755 \| nan \| 0.9755 \| 0.0 \| 0.9755 \|
	\| 0.079 \| 64.0 \| 1856 \| 0.1304 \| 0.4853 \| 0.9706 \| 0.9706 \| nan \| 0.9706 \| 0.0 \| 0.9706 \|
	\| 0.079 \| 65.0 \| 1885 \| 0.1253 \| 0.4873 \| 0.9747 \| 0.9747 \| nan \| 0.9747 \| 0.0 \| 0.9747 \|
	\| 0.0781 \| 66.0 \| 1914 \| 0.1283 \| 0.4866 \| 0.9731 \| 0.9731 \| nan \| 0.9731 \| 0.0 \| 0.9731 \|
	\| 0.0781 \| 67.0 \| 1943 \| 0.1290 \| 0.4892 \| 0.9784 \| 0.9784 \| nan \| 0.9784 \| 0.0 \| 0.9784 \|
	\| 0.0781 \| 68.0 \| 1972 \| 0.1363 \| 0.4835 \| 0.9669 \| 0.9669 \| nan \| 0.9669 \| 0.0 \| 0.9669 \|
	\| 0.0826 \| 68.9655 \| 2000 \| 0.1299 \| 0.4851 \| 0.9702 \| 0.9702 \| nan \| 0.9702 \| 0.0 \| 0.9702 \|


	### Framework versions

	- Transformers 4.49.0.dev0
	- Pytorch 2.5.1+cu124
	- Datasets 3.2.0
	- Tokenizers 0.21.0

	---
	library_name: transformers
	license: other
	base_model: nvidia/mit-b0
	tags:
	- image-segmentation
	- vision
	- generated_from_trainer
	model-index:
	- name: segformer-finetuned-tt-225-2k
	results: []
	---

	<!-- 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. -->

	# segformer-finetuned-tt-225-2k

	This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co./nvidia/mit-b0) on the Saumya-Mundra/text255 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.1299
	- Mean Iou: 0.4851
	- Mean Accuracy: 0.9702
	- Overall Accuracy: 0.9702
	- Accuracy Text: nan
	- Accuracy No Text: 0.9702
	- Iou Text: 0.0
	- Iou No Text: 0.9702

	## 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: 6e-05
	- train_batch_size: 8
	- eval_batch_size: 8
	- seed: 1337
	- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: polynomial
	- training_steps: 2000

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Text \| Accuracy No Text \| Iou Text \| Iou No Text \|
	\|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------:\|:----------------:\|:--------:\|:-----------:\|
	\| No log \| 1.0 \| 29 \| 0.2604 \| 0.4706 \| 0.9411 \| 0.9411 \| nan \| 0.9411 \| 0.0 \| 0.9411 \|
	\| No log \| 2.0 \| 58 \| 0.2094 \| 0.4778 \| 0.9557 \| 0.9557 \| nan \| 0.9557 \| 0.0 \| 0.9557 \|
	\| No log \| 3.0 \| 87 \| 0.1818 \| 0.4823 \| 0.9647 \| 0.9647 \| nan \| 0.9647 \| 0.0 \| 0.9647 \|
	\| 0.323 \| 4.0 \| 116 \| 0.2098 \| 0.4696 \| 0.9392 \| 0.9392 \| nan \| 0.9392 \| 0.0 \| 0.9392 \|
	\| 0.323 \| 5.0 \| 145 \| 0.1717 \| 0.4849 \| 0.9699 \| 0.9699 \| nan \| 0.9699 \| 0.0 \| 0.9699 \|
	\| 0.323 \| 6.0 \| 174 \| 0.1484 \| 0.4840 \| 0.9681 \| 0.9681 \| nan \| 0.9681 \| 0.0 \| 0.9681 \|
	\| 0.2027 \| 7.0 \| 203 \| 0.1616 \| 0.4815 \| 0.9631 \| 0.9631 \| nan \| 0.9631 \| 0.0 \| 0.9631 \|
	\| 0.2027 \| 8.0 \| 232 \| 0.1503 \| 0.4864 \| 0.9728 \| 0.9728 \| nan \| 0.9728 \| 0.0 \| 0.9728 \|
	\| 0.2027 \| 9.0 \| 261 \| 0.1628 \| 0.4783 \| 0.9566 \| 0.9566 \| nan \| 0.9566 \| 0.0 \| 0.9566 \|
	\| 0.2027 \| 10.0 \| 290 \| 0.1424 \| 0.4835 \| 0.9670 \| 0.9670 \| nan \| 0.9670 \| 0.0 \| 0.9670 \|
	\| 0.1693 \| 11.0 \| 319 \| 0.1236 \| 0.4903 \| 0.9806 \| 0.9806 \| nan \| 0.9806 \| 0.0 \| 0.9806 \|
	\| 0.1693 \| 12.0 \| 348 \| 0.1388 \| 0.4816 \| 0.9632 \| 0.9632 \| nan \| 0.9632 \| 0.0 \| 0.9632 \|
	\| 0.1693 \| 13.0 \| 377 \| 0.1554 \| 0.4788 \| 0.9577 \| 0.9577 \| nan \| 0.9577 \| 0.0 \| 0.9577 \|
	\| 0.1502 \| 14.0 \| 406 \| 0.1537 \| 0.4761 \| 0.9521 \| 0.9521 \| nan \| 0.9521 \| 0.0 \| 0.9521 \|
	\| 0.1502 \| 15.0 \| 435 \| 0.1478 \| 0.4781 \| 0.9562 \| 0.9562 \| nan \| 0.9562 \| 0.0 \| 0.9562 \|
	\| 0.1502 \| 16.0 \| 464 \| 0.1367 \| 0.4825 \| 0.9651 \| 0.9651 \| nan \| 0.9651 \| 0.0 \| 0.9651 \|
	\| 0.1502 \| 17.0 \| 493 \| 0.1330 \| 0.4851 \| 0.9703 \| 0.9703 \| nan \| 0.9703 \| 0.0 \| 0.9703 \|
	\| 0.127 \| 18.0 \| 522 \| 0.1322 \| 0.4848 \| 0.9696 \| 0.9696 \| nan \| 0.9696 \| 0.0 \| 0.9696 \|
	\| 0.127 \| 19.0 \| 551 \| 0.1341 \| 0.4842 \| 0.9684 \| 0.9684 \| nan \| 0.9684 \| 0.0 \| 0.9684 \|
	\| 0.127 \| 20.0 \| 580 \| 0.1244 \| 0.4882 \| 0.9764 \| 0.9764 \| nan \| 0.9764 \| 0.0 \| 0.9764 \|
	\| 0.1173 \| 21.0 \| 609 \| 0.1200 \| 0.4896 \| 0.9793 \| 0.9793 \| nan \| 0.9793 \| 0.0 \| 0.9793 \|
	\| 0.1173 \| 22.0 \| 638 \| 0.1374 \| 0.4826 \| 0.9653 \| 0.9653 \| nan \| 0.9653 \| 0.0 \| 0.9653 \|
	\| 0.1173 \| 23.0 \| 667 \| 0.1248 \| 0.4884 \| 0.9768 \| 0.9768 \| nan \| 0.9768 \| 0.0 \| 0.9768 \|
	\| 0.1173 \| 24.0 \| 696 \| 0.1262 \| 0.4857 \| 0.9714 \| 0.9714 \| nan \| 0.9714 \| 0.0 \| 0.9714 \|
	\| 0.1193 \| 25.0 \| 725 \| 0.1235 \| 0.4860 \| 0.9720 \| 0.9720 \| nan \| 0.9720 \| 0.0 \| 0.9720 \|
	\| 0.1193 \| 26.0 \| 754 \| 0.1323 \| 0.4838 \| 0.9677 \| 0.9677 \| nan \| 0.9677 \| 0.0 \| 0.9677 \|
	\| 0.1193 \| 27.0 \| 783 \| 0.1235 \| 0.4853 \| 0.9707 \| 0.9707 \| nan \| 0.9707 \| 0.0 \| 0.9707 \|
	\| 0.0912 \| 28.0 \| 812 \| 0.1349 \| 0.4816 \| 0.9632 \| 0.9632 \| nan \| 0.9632 \| 0.0 \| 0.9632 \|
	\| 0.0912 \| 29.0 \| 841 \| 0.1408 \| 0.4816 \| 0.9632 \| 0.9632 \| nan \| 0.9632 \| 0.0 \| 0.9632 \|
	\| 0.0912 \| 30.0 \| 870 \| 0.1206 \| 0.4877 \| 0.9754 \| 0.9754 \| nan \| 0.9754 \| 0.0 \| 0.9754 \|
	\| 0.0912 \| 31.0 \| 899 \| 0.1347 \| 0.4823 \| 0.9646 \| 0.9646 \| nan \| 0.9646 \| 0.0 \| 0.9646 \|
	\| 0.1005 \| 32.0 \| 928 \| 0.1188 \| 0.4879 \| 0.9758 \| 0.9758 \| nan \| 0.9758 \| 0.0 \| 0.9758 \|
	\| 0.1005 \| 33.0 \| 957 \| 0.1234 \| 0.4891 \| 0.9781 \| 0.9781 \| nan \| 0.9781 \| 0.0 \| 0.9781 \|
	\| 0.1005 \| 34.0 \| 986 \| 0.1206 \| 0.4924 \| 0.9849 \| 0.9849 \| nan \| 0.9849 \| 0.0 \| 0.9849 \|
	\| 0.0933 \| 35.0 \| 1015 \| 0.1234 \| 0.4895 \| 0.9790 \| 0.9790 \| nan \| 0.9790 \| 0.0 \| 0.9790 \|
	\| 0.0933 \| 36.0 \| 1044 \| 0.1192 \| 0.4877 \| 0.9753 \| 0.9753 \| nan \| 0.9753 \| 0.0 \| 0.9753 \|
	\| 0.0933 \| 37.0 \| 1073 \| 0.1388 \| 0.4807 \| 0.9615 \| 0.9615 \| nan \| 0.9615 \| 0.0 \| 0.9615 \|
	\| 0.0937 \| 38.0 \| 1102 \| 0.1255 \| 0.4860 \| 0.9719 \| 0.9719 \| nan \| 0.9719 \| 0.0 \| 0.9719 \|
	\| 0.0937 \| 39.0 \| 1131 \| 0.1199 \| 0.4877 \| 0.9755 \| 0.9755 \| nan \| 0.9755 \| 0.0 \| 0.9755 \|
	\| 0.0937 \| 40.0 \| 1160 \| 0.1194 \| 0.4899 \| 0.9799 \| 0.9799 \| nan \| 0.9799 \| 0.0 \| 0.9799 \|
	\| 0.0937 \| 41.0 \| 1189 \| 0.1274 \| 0.4844 \| 0.9687 \| 0.9687 \| nan \| 0.9687 \| 0.0 \| 0.9687 \|
	\| 0.0923 \| 42.0 \| 1218 \| 0.1302 \| 0.4852 \| 0.9703 \| 0.9703 \| nan \| 0.9703 \| 0.0 \| 0.9703 \|
	\| 0.0923 \| 43.0 \| 1247 \| 0.1297 \| 0.4854 \| 0.9709 \| 0.9709 \| nan \| 0.9709 \| 0.0 \| 0.9709 \|
	\| 0.0923 \| 44.0 \| 1276 \| 0.1299 \| 0.4850 \| 0.9701 \| 0.9701 \| nan \| 0.9701 \| 0.0 \| 0.9701 \|
	\| 0.0812 \| 45.0 \| 1305 \| 0.1259 \| 0.4867 \| 0.9733 \| 0.9733 \| nan \| 0.9733 \| 0.0 \| 0.9733 \|
	\| 0.0812 \| 46.0 \| 1334 \| 0.1252 \| 0.4883 \| 0.9766 \| 0.9766 \| nan \| 0.9766 \| 0.0 \| 0.9766 \|
	\| 0.0812 \| 47.0 \| 1363 \| 0.1223 \| 0.4881 \| 0.9762 \| 0.9762 \| nan \| 0.9762 \| 0.0 \| 0.9762 \|
	\| 0.0812 \| 48.0 \| 1392 \| 0.1227 \| 0.4879 \| 0.9757 \| 0.9757 \| nan \| 0.9757 \| 0.0 \| 0.9757 \|
	\| 0.0901 \| 49.0 \| 1421 \| 0.1224 \| 0.4880 \| 0.9759 \| 0.9759 \| nan \| 0.9759 \| 0.0 \| 0.9759 \|
	\| 0.0901 \| 50.0 \| 1450 \| 0.1350 \| 0.4818 \| 0.9636 \| 0.9636 \| nan \| 0.9636 \| 0.0 \| 0.9636 \|
	\| 0.0901 \| 51.0 \| 1479 \| 0.1285 \| 0.4859 \| 0.9719 \| 0.9719 \| nan \| 0.9719 \| 0.0 \| 0.9719 \|
	\| 0.083 \| 52.0 \| 1508 \| 0.1286 \| 0.4848 \| 0.9695 \| 0.9695 \| nan \| 0.9695 \| 0.0 \| 0.9695 \|
	\| 0.083 \| 53.0 \| 1537 \| 0.1285 \| 0.4850 \| 0.9700 \| 0.9700 \| nan \| 0.9700 \| 0.0 \| 0.9700 \|
	\| 0.083 \| 54.0 \| 1566 \| 0.1252 \| 0.4888 \| 0.9775 \| 0.9775 \| nan \| 0.9775 \| 0.0 \| 0.9775 \|
	\| 0.083 \| 55.0 \| 1595 \| 0.1259 \| 0.4867 \| 0.9734 \| 0.9734 \| nan \| 0.9734 \| 0.0 \| 0.9734 \|
	\| 0.0822 \| 56.0 \| 1624 \| 0.1373 \| 0.4821 \| 0.9642 \| 0.9642 \| nan \| 0.9642 \| 0.0 \| 0.9642 \|
	\| 0.0822 \| 57.0 \| 1653 \| 0.1248 \| 0.4860 \| 0.9719 \| 0.9719 \| nan \| 0.9719 \| 0.0 \| 0.9719 \|
	\| 0.0822 \| 58.0 \| 1682 \| 0.1228 \| 0.4881 \| 0.9761 \| 0.9761 \| nan \| 0.9761 \| 0.0 \| 0.9761 \|
	\| 0.0777 \| 59.0 \| 1711 \| 0.1315 \| 0.4838 \| 0.9677 \| 0.9677 \| nan \| 0.9677 \| 0.0 \| 0.9677 \|
	\| 0.0777 \| 60.0 \| 1740 \| 0.1254 \| 0.4883 \| 0.9766 \| 0.9766 \| nan \| 0.9766 \| 0.0 \| 0.9766 \|
	\| 0.0777 \| 61.0 \| 1769 \| 0.1341 \| 0.4841 \| 0.9682 \| 0.9682 \| nan \| 0.9682 \| 0.0 \| 0.9682 \|
	\| 0.0777 \| 62.0 \| 1798 \| 0.1269 \| 0.4863 \| 0.9726 \| 0.9726 \| nan \| 0.9726 \| 0.0 \| 0.9726 \|
	\| 0.079 \| 63.0 \| 1827 \| 0.1266 \| 0.4878 \| 0.9755 \| 0.9755 \| nan \| 0.9755 \| 0.0 \| 0.9755 \|
	\| 0.079 \| 64.0 \| 1856 \| 0.1304 \| 0.4853 \| 0.9706 \| 0.9706 \| nan \| 0.9706 \| 0.0 \| 0.9706 \|
	\| 0.079 \| 65.0 \| 1885 \| 0.1253 \| 0.4873 \| 0.9747 \| 0.9747 \| nan \| 0.9747 \| 0.0 \| 0.9747 \|
	\| 0.0781 \| 66.0 \| 1914 \| 0.1283 \| 0.4866 \| 0.9731 \| 0.9731 \| nan \| 0.9731 \| 0.0 \| 0.9731 \|
	\| 0.0781 \| 67.0 \| 1943 \| 0.1290 \| 0.4892 \| 0.9784 \| 0.9784 \| nan \| 0.9784 \| 0.0 \| 0.9784 \|
	\| 0.0781 \| 68.0 \| 1972 \| 0.1363 \| 0.4835 \| 0.9669 \| 0.9669 \| nan \| 0.9669 \| 0.0 \| 0.9669 \|
	\| 0.0826 \| 68.9655 \| 2000 \| 0.1299 \| 0.4851 \| 0.9702 \| 0.9702 \| nan \| 0.9702 \| 0.0 \| 0.9702 \|


	### Framework versions

	- Transformers 4.49.0.dev0
	- Pytorch 2.5.1+cu124
	- Datasets 3.2.0
	- Tokenizers 0.21.0