Training complete

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	---
	base_model: DeepPavlov/rubert-base-cased
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: rubert-base-cased_pos
	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. -->

	# rubert-base-cased_pos

	This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.4707
	- Precision: 0.5814
	- Recall: 0.5824
	- F1: 0.5819
	- Accuracy: 0.9021

	## 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: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 100

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| No log \| 1.11 \| 50 \| 0.6626 \| 0.0119 \| 0.0019 \| 0.0033 \| 0.7605 \|
	\| No log \| 2.22 \| 100 \| 0.5508 \| 0.0187 \| 0.0193 \| 0.0190 \| 0.7994 \|
	\| No log \| 3.33 \| 150 \| 0.4129 \| 0.1039 \| 0.1139 \| 0.1087 \| 0.8451 \|
	\| No log \| 4.44 \| 200 \| 0.3683 \| 0.1473 \| 0.1448 \| 0.1461 \| 0.8555 \|
	\| No log \| 5.56 \| 250 \| 0.3231 \| 0.2346 \| 0.2934 \| 0.2607 \| 0.8687 \|
	\| No log \| 6.67 \| 300 \| 0.3154 \| 0.3034 \| 0.4498 \| 0.3624 \| 0.8752 \|
	\| No log \| 7.78 \| 350 \| 0.2665 \| 0.3306 \| 0.4595 \| 0.3845 \| 0.8931 \|
	\| No log \| 8.89 \| 400 \| 0.2724 \| 0.3532 \| 0.5734 \| 0.4371 \| 0.8929 \|
	\| No log \| 10.0 \| 450 \| 0.2694 \| 0.4179 \| 0.6042 \| 0.4941 \| 0.8997 \|
	\| 0.3748 \| 11.11 \| 500 \| 0.2984 \| 0.4609 \| 0.5347 \| 0.4951 \| 0.9051 \|
	\| 0.3748 \| 12.22 \| 550 \| 0.2823 \| 0.4063 \| 0.6448 \| 0.4985 \| 0.8926 \|
	\| 0.3748 \| 13.33 \| 600 \| 0.2894 \| 0.4514 \| 0.5463 \| 0.4943 \| 0.9022 \|
	\| 0.3748 \| 14.44 \| 650 \| 0.3150 \| 0.5043 \| 0.5676 \| 0.5341 \| 0.9043 \|
	\| 0.3748 \| 15.56 \| 700 \| 0.3608 \| 0.4828 \| 0.5965 \| 0.5337 \| 0.9018 \|
	\| 0.3748 \| 16.67 \| 750 \| 0.4073 \| 0.4824 \| 0.6332 \| 0.5476 \| 0.9003 \|
	\| 0.3748 \| 17.78 \| 800 \| 0.4018 \| 0.5271 \| 0.5811 \| 0.5528 \| 0.9083 \|
	\| 0.3748 \| 18.89 \| 850 \| 0.4261 \| 0.528 \| 0.6371 \| 0.5774 \| 0.9047 \|
	\| 0.3748 \| 20.0 \| 900 \| 0.4229 \| 0.5187 \| 0.6429 \| 0.5741 \| 0.9049 \|
	\| 0.3748 \| 21.11 \| 950 \| 0.4429 \| 0.4511 \| 0.6506 \| 0.5328 \| 0.8942 \|
	\| 0.0787 \| 22.22 \| 1000 \| 0.3901 \| 0.5289 \| 0.6004 \| 0.5624 \| 0.9102 \|
	\| 0.0787 \| 23.33 \| 1050 \| 0.3880 \| 0.5 \| 0.5965 \| 0.5440 \| 0.9122 \|
	\| 0.0787 \| 24.44 \| 1100 \| 0.3993 \| 0.5462 \| 0.6042 \| 0.5738 \| 0.9109 \|
	\| 0.0787 \| 25.56 \| 1150 \| 0.5128 \| 0.5087 \| 0.5097 \| 0.5092 \| 0.9010 \|
	\| 0.0787 \| 26.67 \| 1200 \| 0.3716 \| 0.5492 \| 0.6139 \| 0.5798 \| 0.9144 \|
	\| 0.0787 \| 27.78 \| 1250 \| 0.4023 \| 0.5087 \| 0.6197 \| 0.5587 \| 0.9093 \|
	\| 0.0787 \| 28.89 \| 1300 \| 0.4473 \| 0.5461 \| 0.6178 \| 0.5797 \| 0.9106 \|
	\| 0.0787 \| 30.0 \| 1350 \| 0.4449 \| 0.5739 \| 0.5772 \| 0.5756 \| 0.9067 \|
	\| 0.0787 \| 31.11 \| 1400 \| 0.5749 \| 0.5466 \| 0.5656 \| 0.5560 \| 0.9025 \|
	\| 0.0787 \| 32.22 \| 1450 \| 0.4480 \| 0.5571 \| 0.6120 \| 0.5833 \| 0.9125 \|
	\| 0.0352 \| 33.33 \| 1500 \| 0.4459 \| 0.4877 \| 0.6120 \| 0.5428 \| 0.9004 \|
	\| 0.0352 \| 34.44 \| 1550 \| 0.4265 \| 0.5554 \| 0.6293 \| 0.5900 \| 0.9148 \|
	\| 0.0352 \| 35.56 \| 1600 \| 0.3678 \| 0.5983 \| 0.5347 \| 0.5647 \| 0.9031 \|
	\| 0.0352 \| 36.67 \| 1650 \| 0.4182 \| 0.5777 \| 0.6100 \| 0.5934 \| 0.9120 \|
	\| 0.0352 \| 37.78 \| 1700 \| 0.4270 \| 0.5767 \| 0.6390 \| 0.6062 \| 0.9146 \|
	\| 0.0352 \| 38.89 \| 1750 \| 0.4614 \| 0.5283 \| 0.6486 \| 0.5823 \| 0.9045 \|
	\| 0.0352 \| 40.0 \| 1800 \| 0.4962 \| 0.5553 \| 0.5714 \| 0.5633 \| 0.9124 \|
	\| 0.0352 \| 41.11 \| 1850 \| 0.4498 \| 0.5981 \| 0.6178 \| 0.6078 \| 0.9169 \|
	\| 0.0352 \| 42.22 \| 1900 \| 0.4311 \| 0.4920 \| 0.6564 \| 0.5624 \| 0.9009 \|
	\| 0.0352 \| 43.33 \| 1950 \| 0.5744 \| 0.5065 \| 0.6023 \| 0.5503 \| 0.8935 \|
	\| 0.0269 \| 44.44 \| 2000 \| 0.4810 \| 0.5325 \| 0.5849 \| 0.5575 \| 0.9093 \|
	\| 0.0269 \| 45.56 \| 2050 \| 0.5074 \| 0.5918 \| 0.5541 \| 0.5723 \| 0.9064 \|
	\| 0.0269 \| 46.67 \| 2100 \| 0.5291 \| 0.5081 \| 0.6622 \| 0.5750 \| 0.8938 \|
	\| 0.0269 \| 47.78 \| 2150 \| 0.4815 \| 0.5904 \| 0.6429 \| 0.6155 \| 0.9092 \|
	\| 0.0269 \| 48.89 \| 2200 \| 0.5512 \| 0.6294 \| 0.5869 \| 0.6074 \| 0.9126 \|
	\| 0.0269 \| 50.0 \| 2250 \| 0.4686 \| 0.5876 \| 0.6023 \| 0.5949 \| 0.9094 \|
	\| 0.0269 \| 51.11 \| 2300 \| 0.4961 \| 0.5942 \| 0.5965 \| 0.5954 \| 0.9135 \|
	\| 0.0269 \| 52.22 \| 2350 \| 0.5033 \| 0.5471 \| 0.6390 \| 0.5895 \| 0.9046 \|
	\| 0.0269 \| 53.33 \| 2400 \| 0.5583 \| 0.6308 \| 0.5212 \| 0.5708 \| 0.9021 \|
	\| 0.0269 \| 54.44 \| 2450 \| 0.4128 \| 0.5904 \| 0.5927 \| 0.5915 \| 0.9003 \|
	\| 0.0268 \| 55.56 \| 2500 \| 0.4908 \| 0.5219 \| 0.5985 \| 0.5576 \| 0.9059 \|
	\| 0.0268 \| 56.67 \| 2550 \| 0.4669 \| 0.5068 \| 0.6506 \| 0.5697 \| 0.9004 \|
	\| 0.0268 \| 57.78 \| 2600 \| 0.4738 \| 0.5692 \| 0.6351 \| 0.6004 \| 0.9004 \|
	\| 0.0268 \| 58.89 \| 2650 \| 0.4933 \| 0.6197 \| 0.6197 \| 0.6197 \| 0.9185 \|
	\| 0.0268 \| 60.0 \| 2700 \| 0.4623 \| 0.6060 \| 0.6236 \| 0.6147 \| 0.9141 \|
	\| 0.0268 \| 61.11 \| 2750 \| 0.4472 \| 0.5981 \| 0.6178 \| 0.6078 \| 0.9163 \|
	\| 0.0268 \| 62.22 \| 2800 \| 0.4988 \| 0.6410 \| 0.6274 \| 0.6341 \| 0.9132 \|
	\| 0.0268 \| 63.33 \| 2850 \| 0.4900 \| 0.6176 \| 0.5830 \| 0.5998 \| 0.9065 \|
	\| 0.0268 \| 64.44 \| 2900 \| 0.4380 \| 0.6410 \| 0.6100 \| 0.6251 \| 0.9143 \|
	\| 0.0268 \| 65.56 \| 2950 \| 0.5097 \| 0.6115 \| 0.6564 \| 0.6331 \| 0.9130 \|
	\| 0.0251 \| 66.67 \| 3000 \| 0.3672 \| 0.5845 \| 0.6409 \| 0.6114 \| 0.9088 \|
	\| 0.0251 \| 67.78 \| 3050 \| 0.4630 \| 0.5213 \| 0.6371 \| 0.5734 \| 0.9106 \|
	\| 0.0251 \| 68.89 \| 3100 \| 0.4418 \| 0.6375 \| 0.6042 \| 0.6204 \| 0.9134 \|
	\| 0.0251 \| 70.0 \| 3150 \| 0.4648 \| 0.5705 \| 0.6718 \| 0.6170 \| 0.9122 \|
	\| 0.0251 \| 71.11 \| 3200 \| 0.4314 \| 0.6388 \| 0.5907 \| 0.6138 \| 0.9138 \|
	\| 0.0251 \| 72.22 \| 3250 \| 0.4000 \| 0.5575 \| 0.6178 \| 0.5861 \| 0.9121 \|
	\| 0.0251 \| 73.33 \| 3300 \| 0.3548 \| 0.5039 \| 0.6216 \| 0.5566 \| 0.9145 \|
	\| 0.0251 \| 74.44 \| 3350 \| 0.4886 \| 0.6616 \| 0.5849 \| 0.6209 \| 0.9124 \|
	\| 0.0251 \| 75.56 \| 3400 \| 0.5781 \| 0.4402 \| 0.6602 \| 0.5282 \| 0.8803 \|
	\| 0.0251 \| 76.67 \| 3450 \| 0.3379 \| 0.5704 \| 0.6178 \| 0.5931 \| 0.9088 \|
	\| 0.0271 \| 77.78 \| 3500 \| 0.4527 \| 0.5303 \| 0.6429 \| 0.5812 \| 0.9016 \|
	\| 0.0271 \| 78.89 \| 3550 \| 0.5293 \| 0.6211 \| 0.5792 \| 0.5994 \| 0.9085 \|
	\| 0.0271 \| 80.0 \| 3600 \| 0.6486 \| 0.3684 \| 0.6351 \| 0.4663 \| 0.8263 \|


	### Framework versions

	- Transformers 4.38.2
	- Pytorch 2.1.2
	- Datasets 2.1.0
	- Tokenizers 0.15.2

	---
	base_model: DeepPavlov/rubert-base-cased
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: rubert-base-cased_pos
	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. -->

	# rubert-base-cased_pos

	This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.4707
	- Precision: 0.5814
	- Recall: 0.5824
	- F1: 0.5819
	- Accuracy: 0.9021

	## 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: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 100

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| No log \| 1.11 \| 50 \| 0.6626 \| 0.0119 \| 0.0019 \| 0.0033 \| 0.7605 \|
	\| No log \| 2.22 \| 100 \| 0.5508 \| 0.0187 \| 0.0193 \| 0.0190 \| 0.7994 \|
	\| No log \| 3.33 \| 150 \| 0.4129 \| 0.1039 \| 0.1139 \| 0.1087 \| 0.8451 \|
	\| No log \| 4.44 \| 200 \| 0.3683 \| 0.1473 \| 0.1448 \| 0.1461 \| 0.8555 \|
	\| No log \| 5.56 \| 250 \| 0.3231 \| 0.2346 \| 0.2934 \| 0.2607 \| 0.8687 \|
	\| No log \| 6.67 \| 300 \| 0.3154 \| 0.3034 \| 0.4498 \| 0.3624 \| 0.8752 \|
	\| No log \| 7.78 \| 350 \| 0.2665 \| 0.3306 \| 0.4595 \| 0.3845 \| 0.8931 \|
	\| No log \| 8.89 \| 400 \| 0.2724 \| 0.3532 \| 0.5734 \| 0.4371 \| 0.8929 \|
	\| No log \| 10.0 \| 450 \| 0.2694 \| 0.4179 \| 0.6042 \| 0.4941 \| 0.8997 \|
	\| 0.3748 \| 11.11 \| 500 \| 0.2984 \| 0.4609 \| 0.5347 \| 0.4951 \| 0.9051 \|
	\| 0.3748 \| 12.22 \| 550 \| 0.2823 \| 0.4063 \| 0.6448 \| 0.4985 \| 0.8926 \|
	\| 0.3748 \| 13.33 \| 600 \| 0.2894 \| 0.4514 \| 0.5463 \| 0.4943 \| 0.9022 \|
	\| 0.3748 \| 14.44 \| 650 \| 0.3150 \| 0.5043 \| 0.5676 \| 0.5341 \| 0.9043 \|
	\| 0.3748 \| 15.56 \| 700 \| 0.3608 \| 0.4828 \| 0.5965 \| 0.5337 \| 0.9018 \|
	\| 0.3748 \| 16.67 \| 750 \| 0.4073 \| 0.4824 \| 0.6332 \| 0.5476 \| 0.9003 \|
	\| 0.3748 \| 17.78 \| 800 \| 0.4018 \| 0.5271 \| 0.5811 \| 0.5528 \| 0.9083 \|
	\| 0.3748 \| 18.89 \| 850 \| 0.4261 \| 0.528 \| 0.6371 \| 0.5774 \| 0.9047 \|
	\| 0.3748 \| 20.0 \| 900 \| 0.4229 \| 0.5187 \| 0.6429 \| 0.5741 \| 0.9049 \|
	\| 0.3748 \| 21.11 \| 950 \| 0.4429 \| 0.4511 \| 0.6506 \| 0.5328 \| 0.8942 \|
	\| 0.0787 \| 22.22 \| 1000 \| 0.3901 \| 0.5289 \| 0.6004 \| 0.5624 \| 0.9102 \|
	\| 0.0787 \| 23.33 \| 1050 \| 0.3880 \| 0.5 \| 0.5965 \| 0.5440 \| 0.9122 \|
	\| 0.0787 \| 24.44 \| 1100 \| 0.3993 \| 0.5462 \| 0.6042 \| 0.5738 \| 0.9109 \|
	\| 0.0787 \| 25.56 \| 1150 \| 0.5128 \| 0.5087 \| 0.5097 \| 0.5092 \| 0.9010 \|
	\| 0.0787 \| 26.67 \| 1200 \| 0.3716 \| 0.5492 \| 0.6139 \| 0.5798 \| 0.9144 \|
	\| 0.0787 \| 27.78 \| 1250 \| 0.4023 \| 0.5087 \| 0.6197 \| 0.5587 \| 0.9093 \|
	\| 0.0787 \| 28.89 \| 1300 \| 0.4473 \| 0.5461 \| 0.6178 \| 0.5797 \| 0.9106 \|
	\| 0.0787 \| 30.0 \| 1350 \| 0.4449 \| 0.5739 \| 0.5772 \| 0.5756 \| 0.9067 \|
	\| 0.0787 \| 31.11 \| 1400 \| 0.5749 \| 0.5466 \| 0.5656 \| 0.5560 \| 0.9025 \|
	\| 0.0787 \| 32.22 \| 1450 \| 0.4480 \| 0.5571 \| 0.6120 \| 0.5833 \| 0.9125 \|
	\| 0.0352 \| 33.33 \| 1500 \| 0.4459 \| 0.4877 \| 0.6120 \| 0.5428 \| 0.9004 \|
	\| 0.0352 \| 34.44 \| 1550 \| 0.4265 \| 0.5554 \| 0.6293 \| 0.5900 \| 0.9148 \|
	\| 0.0352 \| 35.56 \| 1600 \| 0.3678 \| 0.5983 \| 0.5347 \| 0.5647 \| 0.9031 \|
	\| 0.0352 \| 36.67 \| 1650 \| 0.4182 \| 0.5777 \| 0.6100 \| 0.5934 \| 0.9120 \|
	\| 0.0352 \| 37.78 \| 1700 \| 0.4270 \| 0.5767 \| 0.6390 \| 0.6062 \| 0.9146 \|
	\| 0.0352 \| 38.89 \| 1750 \| 0.4614 \| 0.5283 \| 0.6486 \| 0.5823 \| 0.9045 \|
	\| 0.0352 \| 40.0 \| 1800 \| 0.4962 \| 0.5553 \| 0.5714 \| 0.5633 \| 0.9124 \|
	\| 0.0352 \| 41.11 \| 1850 \| 0.4498 \| 0.5981 \| 0.6178 \| 0.6078 \| 0.9169 \|
	\| 0.0352 \| 42.22 \| 1900 \| 0.4311 \| 0.4920 \| 0.6564 \| 0.5624 \| 0.9009 \|
	\| 0.0352 \| 43.33 \| 1950 \| 0.5744 \| 0.5065 \| 0.6023 \| 0.5503 \| 0.8935 \|
	\| 0.0269 \| 44.44 \| 2000 \| 0.4810 \| 0.5325 \| 0.5849 \| 0.5575 \| 0.9093 \|
	\| 0.0269 \| 45.56 \| 2050 \| 0.5074 \| 0.5918 \| 0.5541 \| 0.5723 \| 0.9064 \|
	\| 0.0269 \| 46.67 \| 2100 \| 0.5291 \| 0.5081 \| 0.6622 \| 0.5750 \| 0.8938 \|
	\| 0.0269 \| 47.78 \| 2150 \| 0.4815 \| 0.5904 \| 0.6429 \| 0.6155 \| 0.9092 \|
	\| 0.0269 \| 48.89 \| 2200 \| 0.5512 \| 0.6294 \| 0.5869 \| 0.6074 \| 0.9126 \|
	\| 0.0269 \| 50.0 \| 2250 \| 0.4686 \| 0.5876 \| 0.6023 \| 0.5949 \| 0.9094 \|
	\| 0.0269 \| 51.11 \| 2300 \| 0.4961 \| 0.5942 \| 0.5965 \| 0.5954 \| 0.9135 \|
	\| 0.0269 \| 52.22 \| 2350 \| 0.5033 \| 0.5471 \| 0.6390 \| 0.5895 \| 0.9046 \|
	\| 0.0269 \| 53.33 \| 2400 \| 0.5583 \| 0.6308 \| 0.5212 \| 0.5708 \| 0.9021 \|
	\| 0.0269 \| 54.44 \| 2450 \| 0.4128 \| 0.5904 \| 0.5927 \| 0.5915 \| 0.9003 \|
	\| 0.0268 \| 55.56 \| 2500 \| 0.4908 \| 0.5219 \| 0.5985 \| 0.5576 \| 0.9059 \|
	\| 0.0268 \| 56.67 \| 2550 \| 0.4669 \| 0.5068 \| 0.6506 \| 0.5697 \| 0.9004 \|
	\| 0.0268 \| 57.78 \| 2600 \| 0.4738 \| 0.5692 \| 0.6351 \| 0.6004 \| 0.9004 \|
	\| 0.0268 \| 58.89 \| 2650 \| 0.4933 \| 0.6197 \| 0.6197 \| 0.6197 \| 0.9185 \|
	\| 0.0268 \| 60.0 \| 2700 \| 0.4623 \| 0.6060 \| 0.6236 \| 0.6147 \| 0.9141 \|
	\| 0.0268 \| 61.11 \| 2750 \| 0.4472 \| 0.5981 \| 0.6178 \| 0.6078 \| 0.9163 \|
	\| 0.0268 \| 62.22 \| 2800 \| 0.4988 \| 0.6410 \| 0.6274 \| 0.6341 \| 0.9132 \|
	\| 0.0268 \| 63.33 \| 2850 \| 0.4900 \| 0.6176 \| 0.5830 \| 0.5998 \| 0.9065 \|
	\| 0.0268 \| 64.44 \| 2900 \| 0.4380 \| 0.6410 \| 0.6100 \| 0.6251 \| 0.9143 \|
	\| 0.0268 \| 65.56 \| 2950 \| 0.5097 \| 0.6115 \| 0.6564 \| 0.6331 \| 0.9130 \|
	\| 0.0251 \| 66.67 \| 3000 \| 0.3672 \| 0.5845 \| 0.6409 \| 0.6114 \| 0.9088 \|
	\| 0.0251 \| 67.78 \| 3050 \| 0.4630 \| 0.5213 \| 0.6371 \| 0.5734 \| 0.9106 \|
	\| 0.0251 \| 68.89 \| 3100 \| 0.4418 \| 0.6375 \| 0.6042 \| 0.6204 \| 0.9134 \|
	\| 0.0251 \| 70.0 \| 3150 \| 0.4648 \| 0.5705 \| 0.6718 \| 0.6170 \| 0.9122 \|
	\| 0.0251 \| 71.11 \| 3200 \| 0.4314 \| 0.6388 \| 0.5907 \| 0.6138 \| 0.9138 \|
	\| 0.0251 \| 72.22 \| 3250 \| 0.4000 \| 0.5575 \| 0.6178 \| 0.5861 \| 0.9121 \|
	\| 0.0251 \| 73.33 \| 3300 \| 0.3548 \| 0.5039 \| 0.6216 \| 0.5566 \| 0.9145 \|
	\| 0.0251 \| 74.44 \| 3350 \| 0.4886 \| 0.6616 \| 0.5849 \| 0.6209 \| 0.9124 \|
	\| 0.0251 \| 75.56 \| 3400 \| 0.5781 \| 0.4402 \| 0.6602 \| 0.5282 \| 0.8803 \|
	\| 0.0251 \| 76.67 \| 3450 \| 0.3379 \| 0.5704 \| 0.6178 \| 0.5931 \| 0.9088 \|
	\| 0.0271 \| 77.78 \| 3500 \| 0.4527 \| 0.5303 \| 0.6429 \| 0.5812 \| 0.9016 \|
	\| 0.0271 \| 78.89 \| 3550 \| 0.5293 \| 0.6211 \| 0.5792 \| 0.5994 \| 0.9085 \|
	\| 0.0271 \| 80.0 \| 3600 \| 0.6486 \| 0.3684 \| 0.6351 \| 0.4663 \| 0.8263 \|


	### Framework versions

	- Transformers 4.38.2
	- Pytorch 2.1.2
	- Datasets 2.1.0
	- Tokenizers 0.15.2