Nahuatl_Espanol_vn / README.md

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	---
	license: apache-2.0
	base_model: Vichentito/Nahuatl_Espanol_v2
	tags:
	- generated_from_trainer
	metrics:
	- bleu
	model-index:
	- name: Nahuatl_Espanol_vn
	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. -->

	# Nahuatl_Espanol_vn

	This model is a fine-tuned version of [Vichentito/Nahuatl_Espanol_v2](https://huggingface.co/Vichentito/Nahuatl_Espanol_v2) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.4730
	- Bleu: 12.6156
	- Gen Len: 46.1122

	## 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: 0.0003
	- train_batch_size: 32
	- eval_batch_size: 32
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 10

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\|:-------:\|
	\| No log \| 0.1064 \| 100 \| 1.9442 \| 6.1166 \| 48.8218 \|
	\| No log \| 0.2128 \| 200 \| 1.9259 \| 6.1381 \| 50.998 \|
	\| No log \| 0.3191 \| 300 \| 1.9115 \| 6.573 \| 48.1678 \|
	\| No log \| 0.4255 \| 400 \| 1.8855 \| 6.4758 \| 50.9589 \|
	\| 2.137 \| 0.5319 \| 500 \| 1.8646 \| 6.6628 \| 50.1324 \|
	\| 2.137 \| 0.6383 \| 600 \| 1.8440 \| 6.8852 \| 50.913 \|
	\| 2.137 \| 0.7447 \| 700 \| 1.8311 \| 6.879 \| 49.7563 \|
	\| 2.137 \| 0.8511 \| 800 \| 1.8105 \| 7.268 \| 48.5967 \|
	\| 2.137 \| 0.9574 \| 900 \| 1.7907 \| 7.5189 \| 47.8909 \|
	\| 2.0246 \| 1.0638 \| 1000 \| 1.7790 \| 7.8039 \| 49.7481 \|
	\| 2.0246 \| 1.1702 \| 1100 \| 1.7751 \| 7.8132 \| 47.9985 \|
	\| 2.0246 \| 1.2766 \| 1200 \| 1.7505 \| 7.9468 \| 47.9796 \|
	\| 2.0246 \| 1.3830 \| 1300 \| 1.7378 \| 8.1741 \| 47.8028 \|
	\| 2.0246 \| 1.4894 \| 1400 \| 1.7219 \| 8.1614 \| 48.2161 \|
	\| 1.8778 \| 1.5957 \| 1500 \| 1.7178 \| 8.3463 \| 47.3984 \|
	\| 1.8778 \| 1.7021 \| 1600 \| 1.7051 \| 8.8493 \| 48.1068 \|
	\| 1.8778 \| 1.8085 \| 1700 \| 1.6907 \| 8.5621 \| 48.2402 \|
	\| 1.8778 \| 1.9149 \| 1800 \| 1.6849 \| 8.7522 \| 49.7167 \|
	\| 1.8778 \| 2.0213 \| 1900 \| 1.6738 \| 8.9027 \| 47.812 \|
	\| 1.7945 \| 2.1277 \| 2000 \| 1.6718 \| 9.323 \| 47.1293 \|
	\| 1.7945 \| 2.2340 \| 2100 \| 1.6619 \| 9.1801 \| 46.7211 \|
	\| 1.7945 \| 2.3404 \| 2200 \| 1.6509 \| 9.1763 \| 47.085 \|
	\| 1.7945 \| 2.4468 \| 2300 \| 1.6394 \| 9.2575 \| 47.9275 \|
	\| 1.7945 \| 2.5532 \| 2400 \| 1.6388 \| 9.5591 \| 47.2517 \|
	\| 1.7164 \| 2.6596 \| 2500 \| 1.6336 \| 9.5656 \| 47.996 \|
	\| 1.7164 \| 2.7660 \| 2600 \| 1.6205 \| 9.767 \| 47.4039 \|
	\| 1.7164 \| 2.8723 \| 2700 \| 1.6152 \| 9.5891 \| 47.2867 \|
	\| 1.7164 \| 2.9787 \| 2800 \| 1.6074 \| 9.7122 \| 47.3419 \|
	\| 1.7164 \| 3.0851 \| 2900 \| 1.6122 \| 10.1634 \| 47.2597 \|
	\| 1.6476 \| 3.1915 \| 3000 \| 1.6016 \| 10.0543 \| 47.7276 \|
	\| 1.6476 \| 3.2979 \| 3100 \| 1.5939 \| 9.8821 \| 47.8567 \|
	\| 1.6476 \| 3.4043 \| 3200 \| 1.5922 \| 10.1382 \| 47.8498 \|
	\| 1.6476 \| 3.5106 \| 3300 \| 1.5808 \| 10.1617 \| 46.7866 \|
	\| 1.6476 \| 3.6170 \| 3400 \| 1.5780 \| 10.2872 \| 47.1357 \|
	\| 1.5916 \| 3.7234 \| 3500 \| 1.5713 \| 10.3594 \| 47.6514 \|
	\| 1.5916 \| 3.8298 \| 3600 \| 1.5657 \| 10.3745 \| 46.9836 \|
	\| 1.5916 \| 3.9362 \| 3700 \| 1.5594 \| 10.5178 \| 46.7624 \|
	\| 1.5916 \| 4.0426 \| 3800 \| 1.5704 \| 10.665 \| 46.6844 \|
	\| 1.5916 \| 4.1489 \| 3900 \| 1.5589 \| 10.6936 \| 47.1421 \|
	\| 1.5475 \| 4.2553 \| 4000 \| 1.5541 \| 10.7949 \| 46.8528 \|
	\| 1.5475 \| 4.3617 \| 4100 \| 1.5481 \| 10.631 \| 47.3707 \|
	\| 1.5475 \| 4.4681 \| 4200 \| 1.5468 \| 10.8283 \| 46.5979 \|
	\| 1.5475 \| 4.5745 \| 4300 \| 1.5403 \| 10.9811 \| 47.1724 \|
	\| 1.5475 \| 4.6809 \| 4400 \| 1.5356 \| 11.0659 \| 46.682 \|
	\| 1.4988 \| 4.7872 \| 4500 \| 1.5379 \| 11.0334 \| 46.9275 \|
	\| 1.4988 \| 4.8936 \| 4600 \| 1.5257 \| 10.9602 \| 46.5027 \|
	\| 1.4988 \| 5.0 \| 4700 \| 1.5260 \| 11.1289 \| 46.8976 \|
	\| 1.4988 \| 5.1064 \| 4800 \| 1.5311 \| 11.1567 \| 46.4451 \|
	\| 1.4988 \| 5.2128 \| 4900 \| 1.5274 \| 11.3486 \| 46.6272 \|
	\| 1.4535 \| 5.3191 \| 5000 \| 1.5259 \| 11.2413 \| 46.9351 \|
	\| 1.4535 \| 5.4255 \| 5100 \| 1.5215 \| 11.3214 \| 46.8237 \|
	\| 1.4535 \| 5.5319 \| 5200 \| 1.5129 \| 11.4718 \| 47.1328 \|
	\| 1.4535 \| 5.6383 \| 5300 \| 1.5125 \| 11.4864 \| 46.6589 \|
	\| 1.4535 \| 5.7447 \| 5400 \| 1.5121 \| 11.5694 \| 46.5577 \|
	\| 1.4219 \| 5.8511 \| 5500 \| 1.5036 \| 11.6487 \| 46.5487 \|
	\| 1.4219 \| 5.9574 \| 5600 \| 1.5000 \| 11.5189 \| 46.5733 \|
	\| 1.4219 \| 6.0638 \| 5700 \| 1.5075 \| 11.5882 \| 46.5391 \|
	\| 1.4219 \| 6.1702 \| 5800 \| 1.5096 \| 11.7659 \| 46.1593 \|
	\| 1.4219 \| 6.2766 \| 5900 \| 1.5083 \| 11.5189 \| 46.4194 \|
	\| 1.3736 \| 6.3830 \| 6000 \| 1.4987 \| 11.7254 \| 46.3748 \|
	\| 1.3736 \| 6.4894 \| 6100 \| 1.4974 \| 11.709 \| 46.7318 \|
	\| 1.3736 \| 6.5957 \| 6200 \| 1.4940 \| 11.7516 \| 46.5484 \|
	\| 1.3736 \| 6.7021 \| 6300 \| 1.4918 \| 11.828 \| 46.4844 \|
	\| 1.3736 \| 6.8085 \| 6400 \| 1.4933 \| 11.9539 \| 46.5024 \|
	\| 1.3705 \| 6.9149 \| 6500 \| 1.4856 \| 11.8196 \| 46.6158 \|
	\| 1.3705 \| 7.0213 \| 6600 \| 1.4959 \| 11.8671 \| 46.5148 \|
	\| 1.3705 \| 7.1277 \| 6700 \| 1.4959 \| 11.9404 \| 46.1803 \|
	\| 1.3705 \| 7.2340 \| 6800 \| 1.4974 \| 12.0784 \| 46.2473 \|
	\| 1.3705 \| 7.3404 \| 6900 \| 1.4882 \| 12.3014 \| 46.27 \|
	\| 1.3223 \| 7.4468 \| 7000 \| 1.4813 \| 12.0859 \| 46.5862 \|
	\| 1.3223 \| 7.5532 \| 7100 \| 1.4846 \| 12.1787 \| 46.0993 \|
	\| 1.3223 \| 7.6596 \| 7200 \| 1.4853 \| 12.1633 \| 46.142 \|
	\| 1.3223 \| 7.7660 \| 7300 \| 1.4811 \| 12.1962 \| 46.4309 \|
	\| 1.3223 \| 7.8723 \| 7400 \| 1.4819 \| 12.1183 \| 46.0882 \|
	\| 1.3154 \| 7.9787 \| 7500 \| 1.4757 \| 12.2428 \| 46.2431 \|
	\| 1.3154 \| 8.0851 \| 7600 \| 1.4811 \| 12.2027 \| 46.3626 \|
	\| 1.3154 \| 8.1915 \| 7700 \| 1.4803 \| 12.3011 \| 46.328 \|
	\| 1.3154 \| 8.2979 \| 7800 \| 1.4830 \| 12.2846 \| 46.3101 \|
	\| 1.3154 \| 8.4043 \| 7900 \| 1.4808 \| 12.3297 \| 45.987 \|
	\| 1.2766 \| 8.5106 \| 8000 \| 1.4789 \| 12.3831 \| 46.1575 \|
	\| 1.2766 \| 8.6170 \| 8100 \| 1.4774 \| 12.4203 \| 46.2323 \|
	\| 1.2766 \| 8.7234 \| 8200 \| 1.4737 \| 12.5194 \| 46.2774 \|
	\| 1.2766 \| 8.8298 \| 8300 \| 1.4738 \| 12.3472 \| 46.2114 \|
	\| 1.2766 \| 8.9362 \| 8400 \| 1.4687 \| 12.3894 \| 46.3324 \|
	\| 1.2752 \| 9.0426 \| 8500 \| 1.4748 \| 12.4876 \| 46.0959 \|
	\| 1.2752 \| 9.1489 \| 8600 \| 1.4792 \| 12.597 \| 45.985 \|
	\| 1.2752 \| 9.2553 \| 8700 \| 1.4761 \| 12.5547 \| 46.2209 \|
	\| 1.2752 \| 9.3617 \| 8800 \| 1.4759 \| 12.5615 \| 46.0812 \|
	\| 1.2752 \| 9.4681 \| 8900 \| 1.4752 \| 12.5736 \| 46.1437 \|
	\| 1.2454 \| 9.5745 \| 9000 \| 1.4765 \| 12.5976 \| 46.0358 \|
	\| 1.2454 \| 9.6809 \| 9100 \| 1.4745 \| 12.5204 \| 46.1139 \|
	\| 1.2454 \| 9.7872 \| 9200 \| 1.4735 \| 12.5765 \| 46.107 \|
	\| 1.2454 \| 9.8936 \| 9300 \| 1.4732 \| 12.5875 \| 46.1734 \|
	\| 1.2454 \| 10.0 \| 9400 \| 1.4730 \| 12.6156 \| 46.1122 \|


	### Framework versions

	- Transformers 4.40.2
	- Pytorch 2.2.1+cu121
	- Datasets 2.19.1
	- Tokenizers 0.19.1

	---
	license: apache-2.0
	base_model: Vichentito/Nahuatl_Espanol_v2
	tags:
	- generated_from_trainer
	metrics:
	- bleu
	model-index:
	- name: Nahuatl_Espanol_vn
	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. -->

	# Nahuatl_Espanol_vn

	This model is a fine-tuned version of [Vichentito/Nahuatl_Espanol_v2](https://huggingface.co/Vichentito/Nahuatl_Espanol_v2) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.4730
	- Bleu: 12.6156
	- Gen Len: 46.1122

	## 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: 0.0003
	- train_batch_size: 32
	- eval_batch_size: 32
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 10

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\|:-------:\|
	\| No log \| 0.1064 \| 100 \| 1.9442 \| 6.1166 \| 48.8218 \|
	\| No log \| 0.2128 \| 200 \| 1.9259 \| 6.1381 \| 50.998 \|
	\| No log \| 0.3191 \| 300 \| 1.9115 \| 6.573 \| 48.1678 \|
	\| No log \| 0.4255 \| 400 \| 1.8855 \| 6.4758 \| 50.9589 \|
	\| 2.137 \| 0.5319 \| 500 \| 1.8646 \| 6.6628 \| 50.1324 \|
	\| 2.137 \| 0.6383 \| 600 \| 1.8440 \| 6.8852 \| 50.913 \|
	\| 2.137 \| 0.7447 \| 700 \| 1.8311 \| 6.879 \| 49.7563 \|
	\| 2.137 \| 0.8511 \| 800 \| 1.8105 \| 7.268 \| 48.5967 \|
	\| 2.137 \| 0.9574 \| 900 \| 1.7907 \| 7.5189 \| 47.8909 \|
	\| 2.0246 \| 1.0638 \| 1000 \| 1.7790 \| 7.8039 \| 49.7481 \|
	\| 2.0246 \| 1.1702 \| 1100 \| 1.7751 \| 7.8132 \| 47.9985 \|
	\| 2.0246 \| 1.2766 \| 1200 \| 1.7505 \| 7.9468 \| 47.9796 \|
	\| 2.0246 \| 1.3830 \| 1300 \| 1.7378 \| 8.1741 \| 47.8028 \|
	\| 2.0246 \| 1.4894 \| 1400 \| 1.7219 \| 8.1614 \| 48.2161 \|
	\| 1.8778 \| 1.5957 \| 1500 \| 1.7178 \| 8.3463 \| 47.3984 \|
	\| 1.8778 \| 1.7021 \| 1600 \| 1.7051 \| 8.8493 \| 48.1068 \|
	\| 1.8778 \| 1.8085 \| 1700 \| 1.6907 \| 8.5621 \| 48.2402 \|
	\| 1.8778 \| 1.9149 \| 1800 \| 1.6849 \| 8.7522 \| 49.7167 \|
	\| 1.8778 \| 2.0213 \| 1900 \| 1.6738 \| 8.9027 \| 47.812 \|
	\| 1.7945 \| 2.1277 \| 2000 \| 1.6718 \| 9.323 \| 47.1293 \|
	\| 1.7945 \| 2.2340 \| 2100 \| 1.6619 \| 9.1801 \| 46.7211 \|
	\| 1.7945 \| 2.3404 \| 2200 \| 1.6509 \| 9.1763 \| 47.085 \|
	\| 1.7945 \| 2.4468 \| 2300 \| 1.6394 \| 9.2575 \| 47.9275 \|
	\| 1.7945 \| 2.5532 \| 2400 \| 1.6388 \| 9.5591 \| 47.2517 \|
	\| 1.7164 \| 2.6596 \| 2500 \| 1.6336 \| 9.5656 \| 47.996 \|
	\| 1.7164 \| 2.7660 \| 2600 \| 1.6205 \| 9.767 \| 47.4039 \|
	\| 1.7164 \| 2.8723 \| 2700 \| 1.6152 \| 9.5891 \| 47.2867 \|
	\| 1.7164 \| 2.9787 \| 2800 \| 1.6074 \| 9.7122 \| 47.3419 \|
	\| 1.7164 \| 3.0851 \| 2900 \| 1.6122 \| 10.1634 \| 47.2597 \|
	\| 1.6476 \| 3.1915 \| 3000 \| 1.6016 \| 10.0543 \| 47.7276 \|
	\| 1.6476 \| 3.2979 \| 3100 \| 1.5939 \| 9.8821 \| 47.8567 \|
	\| 1.6476 \| 3.4043 \| 3200 \| 1.5922 \| 10.1382 \| 47.8498 \|
	\| 1.6476 \| 3.5106 \| 3300 \| 1.5808 \| 10.1617 \| 46.7866 \|
	\| 1.6476 \| 3.6170 \| 3400 \| 1.5780 \| 10.2872 \| 47.1357 \|
	\| 1.5916 \| 3.7234 \| 3500 \| 1.5713 \| 10.3594 \| 47.6514 \|
	\| 1.5916 \| 3.8298 \| 3600 \| 1.5657 \| 10.3745 \| 46.9836 \|
	\| 1.5916 \| 3.9362 \| 3700 \| 1.5594 \| 10.5178 \| 46.7624 \|
	\| 1.5916 \| 4.0426 \| 3800 \| 1.5704 \| 10.665 \| 46.6844 \|
	\| 1.5916 \| 4.1489 \| 3900 \| 1.5589 \| 10.6936 \| 47.1421 \|
	\| 1.5475 \| 4.2553 \| 4000 \| 1.5541 \| 10.7949 \| 46.8528 \|
	\| 1.5475 \| 4.3617 \| 4100 \| 1.5481 \| 10.631 \| 47.3707 \|
	\| 1.5475 \| 4.4681 \| 4200 \| 1.5468 \| 10.8283 \| 46.5979 \|
	\| 1.5475 \| 4.5745 \| 4300 \| 1.5403 \| 10.9811 \| 47.1724 \|
	\| 1.5475 \| 4.6809 \| 4400 \| 1.5356 \| 11.0659 \| 46.682 \|
	\| 1.4988 \| 4.7872 \| 4500 \| 1.5379 \| 11.0334 \| 46.9275 \|
	\| 1.4988 \| 4.8936 \| 4600 \| 1.5257 \| 10.9602 \| 46.5027 \|
	\| 1.4988 \| 5.0 \| 4700 \| 1.5260 \| 11.1289 \| 46.8976 \|
	\| 1.4988 \| 5.1064 \| 4800 \| 1.5311 \| 11.1567 \| 46.4451 \|
	\| 1.4988 \| 5.2128 \| 4900 \| 1.5274 \| 11.3486 \| 46.6272 \|
	\| 1.4535 \| 5.3191 \| 5000 \| 1.5259 \| 11.2413 \| 46.9351 \|
	\| 1.4535 \| 5.4255 \| 5100 \| 1.5215 \| 11.3214 \| 46.8237 \|
	\| 1.4535 \| 5.5319 \| 5200 \| 1.5129 \| 11.4718 \| 47.1328 \|
	\| 1.4535 \| 5.6383 \| 5300 \| 1.5125 \| 11.4864 \| 46.6589 \|
	\| 1.4535 \| 5.7447 \| 5400 \| 1.5121 \| 11.5694 \| 46.5577 \|
	\| 1.4219 \| 5.8511 \| 5500 \| 1.5036 \| 11.6487 \| 46.5487 \|
	\| 1.4219 \| 5.9574 \| 5600 \| 1.5000 \| 11.5189 \| 46.5733 \|
	\| 1.4219 \| 6.0638 \| 5700 \| 1.5075 \| 11.5882 \| 46.5391 \|
	\| 1.4219 \| 6.1702 \| 5800 \| 1.5096 \| 11.7659 \| 46.1593 \|
	\| 1.4219 \| 6.2766 \| 5900 \| 1.5083 \| 11.5189 \| 46.4194 \|
	\| 1.3736 \| 6.3830 \| 6000 \| 1.4987 \| 11.7254 \| 46.3748 \|
	\| 1.3736 \| 6.4894 \| 6100 \| 1.4974 \| 11.709 \| 46.7318 \|
	\| 1.3736 \| 6.5957 \| 6200 \| 1.4940 \| 11.7516 \| 46.5484 \|
	\| 1.3736 \| 6.7021 \| 6300 \| 1.4918 \| 11.828 \| 46.4844 \|
	\| 1.3736 \| 6.8085 \| 6400 \| 1.4933 \| 11.9539 \| 46.5024 \|
	\| 1.3705 \| 6.9149 \| 6500 \| 1.4856 \| 11.8196 \| 46.6158 \|
	\| 1.3705 \| 7.0213 \| 6600 \| 1.4959 \| 11.8671 \| 46.5148 \|
	\| 1.3705 \| 7.1277 \| 6700 \| 1.4959 \| 11.9404 \| 46.1803 \|
	\| 1.3705 \| 7.2340 \| 6800 \| 1.4974 \| 12.0784 \| 46.2473 \|
	\| 1.3705 \| 7.3404 \| 6900 \| 1.4882 \| 12.3014 \| 46.27 \|
	\| 1.3223 \| 7.4468 \| 7000 \| 1.4813 \| 12.0859 \| 46.5862 \|
	\| 1.3223 \| 7.5532 \| 7100 \| 1.4846 \| 12.1787 \| 46.0993 \|
	\| 1.3223 \| 7.6596 \| 7200 \| 1.4853 \| 12.1633 \| 46.142 \|
	\| 1.3223 \| 7.7660 \| 7300 \| 1.4811 \| 12.1962 \| 46.4309 \|
	\| 1.3223 \| 7.8723 \| 7400 \| 1.4819 \| 12.1183 \| 46.0882 \|
	\| 1.3154 \| 7.9787 \| 7500 \| 1.4757 \| 12.2428 \| 46.2431 \|
	\| 1.3154 \| 8.0851 \| 7600 \| 1.4811 \| 12.2027 \| 46.3626 \|
	\| 1.3154 \| 8.1915 \| 7700 \| 1.4803 \| 12.3011 \| 46.328 \|
	\| 1.3154 \| 8.2979 \| 7800 \| 1.4830 \| 12.2846 \| 46.3101 \|
	\| 1.3154 \| 8.4043 \| 7900 \| 1.4808 \| 12.3297 \| 45.987 \|
	\| 1.2766 \| 8.5106 \| 8000 \| 1.4789 \| 12.3831 \| 46.1575 \|
	\| 1.2766 \| 8.6170 \| 8100 \| 1.4774 \| 12.4203 \| 46.2323 \|
	\| 1.2766 \| 8.7234 \| 8200 \| 1.4737 \| 12.5194 \| 46.2774 \|
	\| 1.2766 \| 8.8298 \| 8300 \| 1.4738 \| 12.3472 \| 46.2114 \|
	\| 1.2766 \| 8.9362 \| 8400 \| 1.4687 \| 12.3894 \| 46.3324 \|
	\| 1.2752 \| 9.0426 \| 8500 \| 1.4748 \| 12.4876 \| 46.0959 \|
	\| 1.2752 \| 9.1489 \| 8600 \| 1.4792 \| 12.597 \| 45.985 \|
	\| 1.2752 \| 9.2553 \| 8700 \| 1.4761 \| 12.5547 \| 46.2209 \|
	\| 1.2752 \| 9.3617 \| 8800 \| 1.4759 \| 12.5615 \| 46.0812 \|
	\| 1.2752 \| 9.4681 \| 8900 \| 1.4752 \| 12.5736 \| 46.1437 \|
	\| 1.2454 \| 9.5745 \| 9000 \| 1.4765 \| 12.5976 \| 46.0358 \|
	\| 1.2454 \| 9.6809 \| 9100 \| 1.4745 \| 12.5204 \| 46.1139 \|
	\| 1.2454 \| 9.7872 \| 9200 \| 1.4735 \| 12.5765 \| 46.107 \|
	\| 1.2454 \| 9.8936 \| 9300 \| 1.4732 \| 12.5875 \| 46.1734 \|
	\| 1.2454 \| 10.0 \| 9400 \| 1.4730 \| 12.6156 \| 46.1122 \|


	### Framework versions

	- Transformers 4.40.2
	- Pytorch 2.2.1+cu121
	- Datasets 2.19.1
	- Tokenizers 0.19.1