nlp-lab-2023-seq2seq
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R-facebook-bart-base-full-ft-with-tum-nlp-german-gpt2_easy-prior-pp-no-ls-4c77

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	---
	license: apache-2.0
	tags:
	- generated_from_trainer
	metrics:
	- sacrebleu
	- bleu
	- rouge
	model-index:
	- name: R-facebook-bart-base-full-ft-with-tum-nlp-german-gpt2_easy-prior-pp-no-ls-4c77
	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. -->

	# R-facebook-bart-base-full-ft-with-tum-nlp-german-gpt2_easy-prior-pp-no-ls-4c77

	This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 4.1506
	- Sacrebleu: 7.6134
	- Bleu: 0.0761
	- Rouge1: 0.3006
	- Rouge2: 0.1038
	- Rougel: 0.2079
	- Sari: 39.5909

	## 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: 4
	- eval_batch_size: 1
	- seed: 42
	- gradient_accumulation_steps: 8
	- total_train_batch_size: 32
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 100
	- num_epochs: 15
	- mixed_precision_training: Native AMP
	- label_smoothing_factor: 0.1

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Sacrebleu \| Bleu \| Rouge1 \| Rouge2 \| Rougel \| Sari \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:------:\|:------:\|:-------:\|
	\| 6.9721 \| 0.25 \| 100 \| 4.1739 \| 1.8048 \| 0.0180 \| 0.1980 \| 0.0611 \| 0.1541 \| 37.1235 \|
	\| 3.8977 \| 0.5 \| 200 \| 4.0984 \| 1.2756 \| 0.0128 \| 0.2076 \| 0.0678 \| 0.1581 \| 37.6186 \|
	\| 4.035 \| 0.75 \| 300 \| 4.0622 \| 2.6499 \| 0.0265 \| 0.2271 \| 0.0740 \| 0.1741 \| 38.1373 \|
	\| 8.2055 \| 0.99 \| 400 \| 4.0561 \| 2.7363 \| 0.0274 \| 0.2332 \| 0.0804 \| 0.1716 \| 38.0851 \|
	\| 3.6957 \| 1.24 \| 500 \| 4.0262 \| 3.5110 \| 0.0351 \| 0.2560 \| 0.0852 \| 0.1852 \| 37.9403 \|
	\| 3.0846 \| 1.49 \| 600 \| 4.0121 \| 3.2967 \| 0.0330 \| 0.2471 \| 0.0815 \| 0.1799 \| 37.5590 \|
	\| 3.283 \| 1.74 \| 700 \| 4.0510 \| 3.8512 \| 0.0385 \| 0.2602 \| 0.0917 \| 0.1951 \| 38.0037 \|
	\| 4.7429 \| 1.99 \| 800 \| 4.0048 \| 3.4891 \| 0.0349 \| 0.2524 \| 0.0850 \| 0.1877 \| 38.0324 \|
	\| 3.024 \| 2.24 \| 900 \| 3.9860 \| 3.9202 \| 0.0392 \| 0.2633 \| 0.0844 \| 0.1891 \| 37.9931 \|
	\| 5.6861 \| 2.49 \| 1000 \| 4.0493 \| 4.4801 \| 0.0448 \| 0.2622 \| 0.0878 \| 0.1926 \| 38.2052 \|
	\| 3.6185 \| 2.74 \| 1100 \| 4.0394 \| 3.6710 \| 0.0367 \| 0.2608 \| 0.0857 \| 0.1866 \| 37.9620 \|
	\| 3.3582 \| 2.98 \| 1200 \| 4.0004 \| 5.1257 \| 0.0513 \| 0.2695 \| 0.0922 \| 0.1956 \| 38.4845 \|
	\| 5.0036 \| 3.23 \| 1300 \| 4.0223 \| 5.3256 \| 0.0533 \| 0.2752 \| 0.0938 \| 0.1975 \| 38.6943 \|
	\| 3.9904 \| 3.48 \| 1400 \| 4.0040 \| 5.0070 \| 0.0501 \| 0.2744 \| 0.0927 \| 0.1951 \| 38.5338 \|
	\| 3.1496 \| 3.73 \| 1500 \| 4.0282 \| 5.9234 \| 0.0592 \| 0.2803 \| 0.0907 \| 0.2002 \| 38.2119 \|
	\| 3.9604 \| 3.98 \| 1600 \| 4.0253 \| 5.1875 \| 0.0519 \| 0.2658 \| 0.0864 \| 0.1920 \| 38.2336 \|
	\| 2.9813 \| 4.23 \| 1700 \| 4.0148 \| 5.9589 \| 0.0596 \| 0.2891 \| 0.0976 \| 0.2028 \| 38.8216 \|
	\| 3.5448 \| 4.48 \| 1800 \| 4.0071 \| 5.2759 \| 0.0528 \| 0.2736 \| 0.0867 \| 0.1894 \| 37.8800 \|
	\| 3.6836 \| 4.72 \| 1900 \| 4.0105 \| 5.1414 \| 0.0514 \| 0.2750 \| 0.0894 \| 0.1982 \| 38.3898 \|
	\| 4.0471 \| 4.97 \| 2000 \| 3.9788 \| 5.5747 \| 0.0557 \| 0.2792 \| 0.0932 \| 0.1973 \| 38.5705 \|
	\| 3.3437 \| 5.22 \| 2100 \| 4.0057 \| 5.3969 \| 0.0540 \| 0.2827 \| 0.0926 \| 0.1978 \| 38.3453 \|
	\| 3.1657 \| 5.47 \| 2200 \| 4.0439 \| 5.4820 \| 0.0548 \| 0.2861 \| 0.0946 \| 0.2071 \| 38.4004 \|
	\| 2.5486 \| 5.72 \| 2300 \| 4.0315 \| 6.1738 \| 0.0617 \| 0.2896 \| 0.0966 \| 0.2048 \| 38.5404 \|
	\| 3.6148 \| 5.97 \| 2400 \| 4.0056 \| 6.5570 \| 0.0656 \| 0.2941 \| 0.1046 \| 0.2072 \| 39.0698 \|
	\| 3.1477 \| 6.22 \| 2500 \| 4.0612 \| 6.2221 \| 0.0622 \| 0.2806 \| 0.0932 \| 0.1998 \| 38.5211 \|
	\| 3.175 \| 6.47 \| 2600 \| 4.0126 \| 6.6920 \| 0.0669 \| 0.2916 \| 0.1037 \| 0.2122 \| 39.1438 \|
	\| 4.6616 \| 6.71 \| 2700 \| 4.0467 \| 6.0344 \| 0.0603 \| 0.2804 \| 0.0953 \| 0.1983 \| 38.4171 \|
	\| 3.109 \| 6.96 \| 2800 \| 4.0420 \| 5.8656 \| 0.0587 \| 0.2864 \| 0.0983 \| 0.2034 \| 38.7225 \|
	\| 3.0659 \| 7.21 \| 2900 \| 4.0613 \| 5.6029 \| 0.0560 \| 0.2839 \| 0.0938 \| 0.1980 \| 38.7136 \|
	\| 2.658 \| 7.46 \| 3000 \| 4.0726 \| 6.2791 \| 0.0628 \| 0.2824 \| 0.0947 \| 0.1972 \| 38.6330 \|
	\| 3.178 \| 7.71 \| 3100 \| 4.0437 \| 6.4351 \| 0.0644 \| 0.2924 \| 0.0956 \| 0.2032 \| 38.6577 \|
	\| 4.0606 \| 7.96 \| 3200 \| 4.0644 \| 6.6271 \| 0.0663 \| 0.2966 \| 0.1019 \| 0.2088 \| 39.1513 \|
	\| 3.664 \| 8.21 \| 3300 \| 4.0615 \| 6.3354 \| 0.0634 \| 0.2961 \| 0.0981 \| 0.2024 \| 38.6904 \|
	\| 2.8457 \| 8.46 \| 3400 \| 4.0861 \| 7.4278 \| 0.0743 \| 0.2975 \| 0.1025 \| 0.2017 \| 39.0452 \|
	\| 3.3883 \| 8.7 \| 3500 \| 4.1037 \| 6.4498 \| 0.0645 \| 0.2826 \| 0.0955 \| 0.2008 \| 38.5961 \|
	\| 5.4189 \| 8.95 \| 3600 \| 4.1099 \| 6.0065 \| 0.0601 \| 0.2946 \| 0.0952 \| 0.2020 \| 38.6177 \|
	\| 3.2093 \| 9.2 \| 3700 \| 4.1074 \| 6.2514 \| 0.0625 \| 0.2933 \| 0.0942 \| 0.2014 \| 38.7227 \|
	\| 3.9625 \| 9.45 \| 3800 \| 4.0937 \| 6.6653 \| 0.0667 \| 0.2912 \| 0.0970 \| 0.2020 \| 38.4853 \|
	\| 2.7172 \| 9.7 \| 3900 \| 4.1130 \| 6.1736 \| 0.0617 \| 0.2860 \| 0.0898 \| 0.1948 \| 38.5064 \|
	\| 2.4973 \| 9.95 \| 4000 \| 4.0737 \| 7.4889 \| 0.0749 \| 0.2986 \| 0.1023 \| 0.2060 \| 39.2124 \|
	\| 2.7371 \| 10.2 \| 4100 \| 4.1032 \| 6.4897 \| 0.0649 \| 0.2985 \| 0.0990 \| 0.2031 \| 38.3514 \|
	\| 3.9244 \| 10.44 \| 4200 \| 4.0880 \| 6.7268 \| 0.0673 \| 0.2906 \| 0.1006 \| 0.2012 \| 38.6404 \|
	\| 3.2153 \| 10.69 \| 4300 \| 4.0961 \| 6.7780 \| 0.0678 \| 0.2953 \| 0.0977 \| 0.2008 \| 38.7091 \|
	\| 3.0715 \| 10.94 \| 4400 \| 4.1005 \| 7.1435 \| 0.0714 \| 0.2870 \| 0.0937 \| 0.1950 \| 38.5542 \|
	\| 2.7833 \| 11.19 \| 4500 \| 4.1112 \| 7.5856 \| 0.0759 \| 0.3008 \| 0.1037 \| 0.2063 \| 38.8659 \|
	\| 5.6278 \| 11.44 \| 4600 \| 4.0988 \| 7.8870 \| 0.0789 \| 0.2962 \| 0.1019 \| 0.2025 \| 38.8174 \|
	\| 4.3557 \| 11.69 \| 4700 \| 4.1049 \| 7.9121 \| 0.0791 \| 0.3105 \| 0.1076 \| 0.2106 \| 39.2476 \|
	\| 3.4938 \| 11.94 \| 4800 \| 4.1067 \| 7.1602 \| 0.0716 \| 0.2961 \| 0.1009 \| 0.2039 \| 38.9165 \|
	\| 5.6848 \| 12.19 \| 4900 \| 4.1140 \| 7.8746 \| 0.0787 \| 0.2951 \| 0.0996 \| 0.2005 \| 38.7719 \|
	\| 3.4738 \| 12.43 \| 5000 \| 4.0969 \| 7.8672 \| 0.0787 \| 0.3055 \| 0.1087 \| 0.2092 \| 39.0808 \|
	\| 2.9039 \| 12.68 \| 5100 \| 4.1185 \| 7.6696 \| 0.0767 \| 0.3033 \| 0.1071 \| 0.2092 \| 39.0788 \|
	\| 4.4091 \| 12.93 \| 5200 \| 4.1346 \| 7.9896 \| 0.0799 \| 0.3014 \| 0.1046 \| 0.2070 \| 39.2032 \|
	\| 3.102 \| 13.18 \| 5300 \| 4.1308 \| 7.2969 \| 0.0730 \| 0.3030 \| 0.1032 \| 0.2039 \| 39.1031 \|
	\| 2.9972 \| 13.43 \| 5400 \| 4.1518 \| 7.7779 \| 0.0778 \| 0.3017 \| 0.1053 \| 0.2090 \| 39.4092 \|
	\| 2.7672 \| 13.68 \| 5500 \| 4.1515 \| 7.7545 \| 0.0775 \| 0.3010 \| 0.1079 \| 0.2091 \| 39.0093 \|
	\| 3.7358 \| 13.93 \| 5600 \| 4.1360 \| 7.5980 \| 0.0760 \| 0.2970 \| 0.1036 \| 0.2080 \| 39.0873 \|
	\| 3.4363 \| 14.17 \| 5700 \| 4.1367 \| 7.2901 \| 0.0729 \| 0.3013 \| 0.1057 \| 0.2084 \| 39.3389 \|
	\| 3.3451 \| 14.42 \| 5800 \| 4.1500 \| 7.5605 \| 0.0756 \| 0.2984 \| 0.0979 \| 0.2074 \| 39.0107 \|
	\| 2.8616 \| 14.67 \| 5900 \| 4.1447 \| 7.8204 \| 0.0782 \| 0.3020 \| 0.1059 \| 0.2127 \| 39.7465 \|
	\| 3.1149 \| 14.92 \| 6000 \| 4.1506 \| 7.6134 \| 0.0761 \| 0.3006 \| 0.1038 \| 0.2079 \| 39.5909 \|


	### Framework versions

	- Transformers 4.29.2
	- Pytorch 2.0.0+cu117
	- Datasets 2.12.0
	- Tokenizers 0.13.3

	---
	license: apache-2.0
	tags:
	- generated_from_trainer
	metrics:
	- sacrebleu
	- bleu
	- rouge
	model-index:
	- name: R-facebook-bart-base-full-ft-with-tum-nlp-german-gpt2_easy-prior-pp-no-ls-4c77
	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. -->

	# R-facebook-bart-base-full-ft-with-tum-nlp-german-gpt2_easy-prior-pp-no-ls-4c77

	This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 4.1506
	- Sacrebleu: 7.6134
	- Bleu: 0.0761
	- Rouge1: 0.3006
	- Rouge2: 0.1038
	- Rougel: 0.2079
	- Sari: 39.5909

	## 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: 4
	- eval_batch_size: 1
	- seed: 42
	- gradient_accumulation_steps: 8
	- total_train_batch_size: 32
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 100
	- num_epochs: 15
	- mixed_precision_training: Native AMP
	- label_smoothing_factor: 0.1

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Sacrebleu \| Bleu \| Rouge1 \| Rouge2 \| Rougel \| Sari \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:------:\|:------:\|:-------:\|
	\| 6.9721 \| 0.25 \| 100 \| 4.1739 \| 1.8048 \| 0.0180 \| 0.1980 \| 0.0611 \| 0.1541 \| 37.1235 \|
	\| 3.8977 \| 0.5 \| 200 \| 4.0984 \| 1.2756 \| 0.0128 \| 0.2076 \| 0.0678 \| 0.1581 \| 37.6186 \|
	\| 4.035 \| 0.75 \| 300 \| 4.0622 \| 2.6499 \| 0.0265 \| 0.2271 \| 0.0740 \| 0.1741 \| 38.1373 \|
	\| 8.2055 \| 0.99 \| 400 \| 4.0561 \| 2.7363 \| 0.0274 \| 0.2332 \| 0.0804 \| 0.1716 \| 38.0851 \|
	\| 3.6957 \| 1.24 \| 500 \| 4.0262 \| 3.5110 \| 0.0351 \| 0.2560 \| 0.0852 \| 0.1852 \| 37.9403 \|
	\| 3.0846 \| 1.49 \| 600 \| 4.0121 \| 3.2967 \| 0.0330 \| 0.2471 \| 0.0815 \| 0.1799 \| 37.5590 \|
	\| 3.283 \| 1.74 \| 700 \| 4.0510 \| 3.8512 \| 0.0385 \| 0.2602 \| 0.0917 \| 0.1951 \| 38.0037 \|
	\| 4.7429 \| 1.99 \| 800 \| 4.0048 \| 3.4891 \| 0.0349 \| 0.2524 \| 0.0850 \| 0.1877 \| 38.0324 \|
	\| 3.024 \| 2.24 \| 900 \| 3.9860 \| 3.9202 \| 0.0392 \| 0.2633 \| 0.0844 \| 0.1891 \| 37.9931 \|
	\| 5.6861 \| 2.49 \| 1000 \| 4.0493 \| 4.4801 \| 0.0448 \| 0.2622 \| 0.0878 \| 0.1926 \| 38.2052 \|
	\| 3.6185 \| 2.74 \| 1100 \| 4.0394 \| 3.6710 \| 0.0367 \| 0.2608 \| 0.0857 \| 0.1866 \| 37.9620 \|
	\| 3.3582 \| 2.98 \| 1200 \| 4.0004 \| 5.1257 \| 0.0513 \| 0.2695 \| 0.0922 \| 0.1956 \| 38.4845 \|
	\| 5.0036 \| 3.23 \| 1300 \| 4.0223 \| 5.3256 \| 0.0533 \| 0.2752 \| 0.0938 \| 0.1975 \| 38.6943 \|
	\| 3.9904 \| 3.48 \| 1400 \| 4.0040 \| 5.0070 \| 0.0501 \| 0.2744 \| 0.0927 \| 0.1951 \| 38.5338 \|
	\| 3.1496 \| 3.73 \| 1500 \| 4.0282 \| 5.9234 \| 0.0592 \| 0.2803 \| 0.0907 \| 0.2002 \| 38.2119 \|
	\| 3.9604 \| 3.98 \| 1600 \| 4.0253 \| 5.1875 \| 0.0519 \| 0.2658 \| 0.0864 \| 0.1920 \| 38.2336 \|
	\| 2.9813 \| 4.23 \| 1700 \| 4.0148 \| 5.9589 \| 0.0596 \| 0.2891 \| 0.0976 \| 0.2028 \| 38.8216 \|
	\| 3.5448 \| 4.48 \| 1800 \| 4.0071 \| 5.2759 \| 0.0528 \| 0.2736 \| 0.0867 \| 0.1894 \| 37.8800 \|
	\| 3.6836 \| 4.72 \| 1900 \| 4.0105 \| 5.1414 \| 0.0514 \| 0.2750 \| 0.0894 \| 0.1982 \| 38.3898 \|
	\| 4.0471 \| 4.97 \| 2000 \| 3.9788 \| 5.5747 \| 0.0557 \| 0.2792 \| 0.0932 \| 0.1973 \| 38.5705 \|
	\| 3.3437 \| 5.22 \| 2100 \| 4.0057 \| 5.3969 \| 0.0540 \| 0.2827 \| 0.0926 \| 0.1978 \| 38.3453 \|
	\| 3.1657 \| 5.47 \| 2200 \| 4.0439 \| 5.4820 \| 0.0548 \| 0.2861 \| 0.0946 \| 0.2071 \| 38.4004 \|
	\| 2.5486 \| 5.72 \| 2300 \| 4.0315 \| 6.1738 \| 0.0617 \| 0.2896 \| 0.0966 \| 0.2048 \| 38.5404 \|
	\| 3.6148 \| 5.97 \| 2400 \| 4.0056 \| 6.5570 \| 0.0656 \| 0.2941 \| 0.1046 \| 0.2072 \| 39.0698 \|
	\| 3.1477 \| 6.22 \| 2500 \| 4.0612 \| 6.2221 \| 0.0622 \| 0.2806 \| 0.0932 \| 0.1998 \| 38.5211 \|
	\| 3.175 \| 6.47 \| 2600 \| 4.0126 \| 6.6920 \| 0.0669 \| 0.2916 \| 0.1037 \| 0.2122 \| 39.1438 \|
	\| 4.6616 \| 6.71 \| 2700 \| 4.0467 \| 6.0344 \| 0.0603 \| 0.2804 \| 0.0953 \| 0.1983 \| 38.4171 \|
	\| 3.109 \| 6.96 \| 2800 \| 4.0420 \| 5.8656 \| 0.0587 \| 0.2864 \| 0.0983 \| 0.2034 \| 38.7225 \|
	\| 3.0659 \| 7.21 \| 2900 \| 4.0613 \| 5.6029 \| 0.0560 \| 0.2839 \| 0.0938 \| 0.1980 \| 38.7136 \|
	\| 2.658 \| 7.46 \| 3000 \| 4.0726 \| 6.2791 \| 0.0628 \| 0.2824 \| 0.0947 \| 0.1972 \| 38.6330 \|
	\| 3.178 \| 7.71 \| 3100 \| 4.0437 \| 6.4351 \| 0.0644 \| 0.2924 \| 0.0956 \| 0.2032 \| 38.6577 \|
	\| 4.0606 \| 7.96 \| 3200 \| 4.0644 \| 6.6271 \| 0.0663 \| 0.2966 \| 0.1019 \| 0.2088 \| 39.1513 \|
	\| 3.664 \| 8.21 \| 3300 \| 4.0615 \| 6.3354 \| 0.0634 \| 0.2961 \| 0.0981 \| 0.2024 \| 38.6904 \|
	\| 2.8457 \| 8.46 \| 3400 \| 4.0861 \| 7.4278 \| 0.0743 \| 0.2975 \| 0.1025 \| 0.2017 \| 39.0452 \|
	\| 3.3883 \| 8.7 \| 3500 \| 4.1037 \| 6.4498 \| 0.0645 \| 0.2826 \| 0.0955 \| 0.2008 \| 38.5961 \|
	\| 5.4189 \| 8.95 \| 3600 \| 4.1099 \| 6.0065 \| 0.0601 \| 0.2946 \| 0.0952 \| 0.2020 \| 38.6177 \|
	\| 3.2093 \| 9.2 \| 3700 \| 4.1074 \| 6.2514 \| 0.0625 \| 0.2933 \| 0.0942 \| 0.2014 \| 38.7227 \|
	\| 3.9625 \| 9.45 \| 3800 \| 4.0937 \| 6.6653 \| 0.0667 \| 0.2912 \| 0.0970 \| 0.2020 \| 38.4853 \|
	\| 2.7172 \| 9.7 \| 3900 \| 4.1130 \| 6.1736 \| 0.0617 \| 0.2860 \| 0.0898 \| 0.1948 \| 38.5064 \|
	\| 2.4973 \| 9.95 \| 4000 \| 4.0737 \| 7.4889 \| 0.0749 \| 0.2986 \| 0.1023 \| 0.2060 \| 39.2124 \|
	\| 2.7371 \| 10.2 \| 4100 \| 4.1032 \| 6.4897 \| 0.0649 \| 0.2985 \| 0.0990 \| 0.2031 \| 38.3514 \|
	\| 3.9244 \| 10.44 \| 4200 \| 4.0880 \| 6.7268 \| 0.0673 \| 0.2906 \| 0.1006 \| 0.2012 \| 38.6404 \|
	\| 3.2153 \| 10.69 \| 4300 \| 4.0961 \| 6.7780 \| 0.0678 \| 0.2953 \| 0.0977 \| 0.2008 \| 38.7091 \|
	\| 3.0715 \| 10.94 \| 4400 \| 4.1005 \| 7.1435 \| 0.0714 \| 0.2870 \| 0.0937 \| 0.1950 \| 38.5542 \|
	\| 2.7833 \| 11.19 \| 4500 \| 4.1112 \| 7.5856 \| 0.0759 \| 0.3008 \| 0.1037 \| 0.2063 \| 38.8659 \|
	\| 5.6278 \| 11.44 \| 4600 \| 4.0988 \| 7.8870 \| 0.0789 \| 0.2962 \| 0.1019 \| 0.2025 \| 38.8174 \|
	\| 4.3557 \| 11.69 \| 4700 \| 4.1049 \| 7.9121 \| 0.0791 \| 0.3105 \| 0.1076 \| 0.2106 \| 39.2476 \|
	\| 3.4938 \| 11.94 \| 4800 \| 4.1067 \| 7.1602 \| 0.0716 \| 0.2961 \| 0.1009 \| 0.2039 \| 38.9165 \|
	\| 5.6848 \| 12.19 \| 4900 \| 4.1140 \| 7.8746 \| 0.0787 \| 0.2951 \| 0.0996 \| 0.2005 \| 38.7719 \|
	\| 3.4738 \| 12.43 \| 5000 \| 4.0969 \| 7.8672 \| 0.0787 \| 0.3055 \| 0.1087 \| 0.2092 \| 39.0808 \|
	\| 2.9039 \| 12.68 \| 5100 \| 4.1185 \| 7.6696 \| 0.0767 \| 0.3033 \| 0.1071 \| 0.2092 \| 39.0788 \|
	\| 4.4091 \| 12.93 \| 5200 \| 4.1346 \| 7.9896 \| 0.0799 \| 0.3014 \| 0.1046 \| 0.2070 \| 39.2032 \|
	\| 3.102 \| 13.18 \| 5300 \| 4.1308 \| 7.2969 \| 0.0730 \| 0.3030 \| 0.1032 \| 0.2039 \| 39.1031 \|
	\| 2.9972 \| 13.43 \| 5400 \| 4.1518 \| 7.7779 \| 0.0778 \| 0.3017 \| 0.1053 \| 0.2090 \| 39.4092 \|
	\| 2.7672 \| 13.68 \| 5500 \| 4.1515 \| 7.7545 \| 0.0775 \| 0.3010 \| 0.1079 \| 0.2091 \| 39.0093 \|
	\| 3.7358 \| 13.93 \| 5600 \| 4.1360 \| 7.5980 \| 0.0760 \| 0.2970 \| 0.1036 \| 0.2080 \| 39.0873 \|
	\| 3.4363 \| 14.17 \| 5700 \| 4.1367 \| 7.2901 \| 0.0729 \| 0.3013 \| 0.1057 \| 0.2084 \| 39.3389 \|
	\| 3.3451 \| 14.42 \| 5800 \| 4.1500 \| 7.5605 \| 0.0756 \| 0.2984 \| 0.0979 \| 0.2074 \| 39.0107 \|
	\| 2.8616 \| 14.67 \| 5900 \| 4.1447 \| 7.8204 \| 0.0782 \| 0.3020 \| 0.1059 \| 0.2127 \| 39.7465 \|
	\| 3.1149 \| 14.92 \| 6000 \| 4.1506 \| 7.6134 \| 0.0761 \| 0.3006 \| 0.1038 \| 0.2079 \| 39.5909 \|


	### Framework versions

	- Transformers 4.29.2
	- Pytorch 2.0.0+cu117
	- Datasets 2.12.0
	- Tokenizers 0.13.3