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_neg
	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_neg

	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: 0.6290
	- Precision: 0.5977
	- Recall: 0.6106
	- F1: 0.6041
	- Accuracy: 0.8995

	## 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 \| 0.6820 \| 0.0 \| 0.0 \| 0.0 \| 0.7747 \|
	\| No log \| 2.22 \| 100 \| 0.5532 \| 0.0489 \| 0.0212 \| 0.0296 \| 0.8040 \|
	\| No log \| 3.33 \| 150 \| 0.4231 \| 0.1320 \| 0.1004 \| 0.1140 \| 0.8397 \|
	\| No log \| 4.44 \| 200 \| 0.3736 \| 0.2200 \| 0.1873 \| 0.2023 \| 0.8576 \|
	\| No log \| 5.56 \| 250 \| 0.3511 \| 0.3096 \| 0.2606 \| 0.2830 \| 0.8713 \|
	\| No log \| 6.67 \| 300 \| 0.2976 \| 0.3635 \| 0.4498 \| 0.4021 \| 0.8833 \|
	\| No log \| 7.78 \| 350 \| 0.2713 \| 0.3793 \| 0.4942 \| 0.4292 \| 0.8925 \|
	\| No log \| 8.89 \| 400 \| 0.2569 \| 0.4174 \| 0.5753 \| 0.4838 \| 0.9016 \|
	\| No log \| 10.0 \| 450 \| 0.2599 \| 0.4687 \| 0.5772 \| 0.5173 \| 0.9031 \|
	\| 0.389 \| 11.11 \| 500 \| 0.2775 \| 0.5008 \| 0.5849 \| 0.5396 \| 0.9084 \|
	\| 0.389 \| 12.22 \| 550 \| 0.3123 \| 0.4257 \| 0.6641 \| 0.5189 \| 0.8834 \|
	\| 0.389 \| 13.33 \| 600 \| 0.3271 \| 0.5036 \| 0.5425 \| 0.5223 \| 0.9064 \|
	\| 0.389 \| 14.44 \| 650 \| 0.3390 \| 0.5328 \| 0.5174 \| 0.5250 \| 0.9016 \|
	\| 0.389 \| 15.56 \| 700 \| 0.3688 \| 0.4788 \| 0.6313 \| 0.5445 \| 0.8988 \|
	\| 0.389 \| 16.67 \| 750 \| 0.4260 \| 0.4942 \| 0.6583 \| 0.5646 \| 0.8997 \|
	\| 0.389 \| 17.78 \| 800 \| 0.3622 \| 0.5217 \| 0.6042 \| 0.5599 \| 0.9106 \|
	\| 0.389 \| 18.89 \| 850 \| 0.4054 \| 0.5266 \| 0.6120 \| 0.5661 \| 0.9105 \|
	\| 0.389 \| 20.0 \| 900 \| 0.3988 \| 0.5070 \| 0.6255 \| 0.5601 \| 0.9049 \|
	\| 0.389 \| 21.11 \| 950 \| 0.4422 \| 0.5281 \| 0.5444 \| 0.5361 \| 0.9093 \|
	\| 0.0777 \| 22.22 \| 1000 \| 0.4207 \| 0.5635 \| 0.5656 \| 0.5645 \| 0.9117 \|
	\| 0.0777 \| 23.33 \| 1050 \| 0.4721 \| 0.5505 \| 0.5792 \| 0.5644 \| 0.9130 \|
	\| 0.0777 \| 24.44 \| 1100 \| 0.4261 \| 0.5379 \| 0.6158 \| 0.5743 \| 0.9114 \|
	\| 0.0777 \| 25.56 \| 1150 \| 0.5339 \| 0.6157 \| 0.5290 \| 0.5691 \| 0.9 \|
	\| 0.0777 \| 26.67 \| 1200 \| 0.3761 \| 0.4949 \| 0.6544 \| 0.5636 \| 0.9036 \|
	\| 0.0777 \| 27.78 \| 1250 \| 0.4250 \| 0.5650 \| 0.5792 \| 0.5720 \| 0.9110 \|
	\| 0.0777 \| 28.89 \| 1300 \| 0.3790 \| 0.5731 \| 0.6429 \| 0.6060 \| 0.9193 \|
	\| 0.0777 \| 30.0 \| 1350 \| 0.5330 \| 0.5942 \| 0.5907 \| 0.5924 \| 0.9076 \|
	\| 0.0777 \| 31.11 \| 1400 \| 0.4419 \| 0.5957 \| 0.5888 \| 0.5922 \| 0.9180 \|
	\| 0.0777 \| 32.22 \| 1450 \| 0.5531 \| 0.6008 \| 0.5695 \| 0.5847 \| 0.9088 \|
	\| 0.0362 \| 33.33 \| 1500 \| 0.4544 \| 0.5231 \| 0.6564 \| 0.5822 \| 0.9135 \|
	\| 0.0362 \| 34.44 \| 1550 \| 0.4990 \| 0.5695 \| 0.5772 \| 0.5733 \| 0.9082 \|
	\| 0.0362 \| 35.56 \| 1600 \| 0.4040 \| 0.5709 \| 0.5753 \| 0.5731 \| 0.9086 \|
	\| 0.0362 \| 36.67 \| 1650 \| 0.3807 \| 0.5989 \| 0.6255 \| 0.6119 \| 0.9123 \|
	\| 0.0362 \| 37.78 \| 1700 \| 0.5088 \| 0.5996 \| 0.6100 \| 0.6048 \| 0.9139 \|
	\| 0.0362 \| 38.89 \| 1750 \| 0.4525 \| 0.6151 \| 0.5985 \| 0.6067 \| 0.9189 \|
	\| 0.0362 \| 40.0 \| 1800 \| 0.3787 \| 0.6184 \| 0.6100 \| 0.6142 \| 0.9211 \|
	\| 0.0362 \| 41.11 \| 1850 \| 0.3974 \| 0.6097 \| 0.5849 \| 0.5970 \| 0.9162 \|
	\| 0.0362 \| 42.22 \| 1900 \| 0.3944 \| 0.5762 \| 0.6274 \| 0.6007 \| 0.9148 \|
	\| 0.0362 \| 43.33 \| 1950 \| 0.3865 \| 0.5124 \| 0.6795 \| 0.5842 \| 0.9022 \|
	\| 0.0264 \| 44.44 \| 2000 \| 0.4583 \| 0.5462 \| 0.6274 \| 0.5840 \| 0.9169 \|
	\| 0.0264 \| 45.56 \| 2050 \| 0.4640 \| 0.5635 \| 0.6429 \| 0.6005 \| 0.9105 \|
	\| 0.0264 \| 46.67 \| 2100 \| 0.5028 \| 0.5945 \| 0.5830 \| 0.5887 \| 0.9128 \|
	\| 0.0264 \| 47.78 \| 2150 \| 0.3917 \| 0.6267 \| 0.6255 \| 0.6261 \| 0.9221 \|
	\| 0.0264 \| 48.89 \| 2200 \| 0.4833 \| 0.6214 \| 0.6274 \| 0.6244 \| 0.9138 \|
	\| 0.0264 \| 50.0 \| 2250 \| 0.4147 \| 0.6130 \| 0.6390 \| 0.6257 \| 0.9190 \|
	\| 0.0264 \| 51.11 \| 2300 \| 0.4455 \| 0.6546 \| 0.5927 \| 0.6221 \| 0.9185 \|
	\| 0.0264 \| 52.22 \| 2350 \| 0.4575 \| 0.6138 \| 0.6351 \| 0.6243 \| 0.9180 \|
	\| 0.0264 \| 53.33 \| 2400 \| 0.7707 \| 0.3732 \| 0.6815 \| 0.4822 \| 0.8354 \|
	\| 0.0264 \| 54.44 \| 2450 \| 0.4440 \| 0.6015 \| 0.6236 \| 0.6123 \| 0.9130 \|
	\| 0.0248 \| 55.56 \| 2500 \| 0.4815 \| 0.5739 \| 0.6448 \| 0.6073 \| 0.9124 \|
	\| 0.0248 \| 56.67 \| 2550 \| 0.3971 \| 0.6204 \| 0.6467 \| 0.6333 \| 0.9227 \|
	\| 0.0248 \| 57.78 \| 2600 \| 0.4770 \| 0.6208 \| 0.6100 \| 0.6154 \| 0.9193 \|
	\| 0.0248 \| 58.89 \| 2650 \| 0.5450 \| 0.6699 \| 0.5367 \| 0.5959 \| 0.9109 \|
	\| 0.0248 \| 60.0 \| 2700 \| 0.5033 \| 0.5439 \| 0.6332 \| 0.5852 \| 0.9019 \|
	\| 0.0248 \| 61.11 \| 2750 \| 0.5185 \| 0.5187 \| 0.6699 \| 0.5847 \| 0.9010 \|
	\| 0.0248 \| 62.22 \| 2800 \| 0.4277 \| 0.6627 \| 0.6371 \| 0.6496 \| 0.9194 \|
	\| 0.0248 \| 63.33 \| 2850 \| 0.4688 \| 0.4869 \| 0.6467 \| 0.5556 \| 0.9066 \|
	\| 0.0248 \| 64.44 \| 2900 \| 0.4779 \| 0.6135 \| 0.6313 \| 0.6223 \| 0.9153 \|
	\| 0.0248 \| 65.56 \| 2950 \| 0.5012 \| 0.5852 \| 0.6100 \| 0.5974 \| 0.9079 \|
	\| 0.0232 \| 66.67 \| 3000 \| 0.4788 \| 0.5259 \| 0.6081 \| 0.5640 \| 0.9052 \|
	\| 0.0232 \| 67.78 \| 3050 \| 0.4556 \| 0.5726 \| 0.6544 \| 0.6108 \| 0.9099 \|
	\| 0.0232 \| 68.89 \| 3100 \| 0.5026 \| 0.608 \| 0.5869 \| 0.5972 \| 0.9091 \|
	\| 0.0232 \| 70.0 \| 3150 \| 0.8153 \| 0.3071 \| 0.7143 \| 0.4295 \| 0.7567 \|
	\| 0.0232 \| 71.11 \| 3200 \| 0.4670 \| 0.6169 \| 0.6062 \| 0.6115 \| 0.9113 \|
	\| 0.0232 \| 72.22 \| 3250 \| 0.5249 \| 0.5727 \| 0.6313 \| 0.6006 \| 0.9068 \|
	\| 0.0232 \| 73.33 \| 3300 \| 0.4343 \| 0.6085 \| 0.6390 \| 0.6234 \| 0.9162 \|
	\| 0.0232 \| 74.44 \| 3350 \| 0.5067 \| 0.6364 \| 0.5811 \| 0.6075 \| 0.9135 \|
	\| 0.0232 \| 75.56 \| 3400 \| 0.4415 \| 0.5812 \| 0.6429 \| 0.6104 \| 0.9149 \|
	\| 0.0232 \| 76.67 \| 3450 \| 0.4052 \| 0.5757 \| 0.6313 \| 0.6022 \| 0.9137 \|
	\| 0.0266 \| 77.78 \| 3500 \| 0.5119 \| 0.5233 \| 0.5425 \| 0.5327 \| 0.9038 \|
	\| 0.0266 \| 78.89 \| 3550 \| 0.4689 \| 0.5945 \| 0.5888 \| 0.5917 \| 0.9145 \|
	\| 0.0266 \| 80.0 \| 3600 \| 0.3973 \| 0.5609 \| 0.6313 \| 0.5940 \| 0.9154 \|
	\| 0.0266 \| 81.11 \| 3650 \| 0.4848 \| 0.5947 \| 0.6486 \| 0.6205 \| 0.9181 \|
	\| 0.0266 \| 82.22 \| 3700 \| 0.4825 \| 0.5877 \| 0.6274 \| 0.6069 \| 0.9160 \|
	\| 0.0266 \| 83.33 \| 3750 \| 0.5193 \| 0.5138 \| 0.6467 \| 0.5726 \| 0.9 \|
	\| 0.0266 \| 84.44 \| 3800 \| 0.5344 \| 0.5777 \| 0.5811 \| 0.5794 \| 0.9107 \|
	\| 0.0266 \| 85.56 \| 3850 \| 0.5227 \| 0.6591 \| 0.5637 \| 0.6077 \| 0.9107 \|
	\| 0.0266 \| 86.67 \| 3900 \| 0.4490 \| 0.5176 \| 0.6255 \| 0.5664 \| 0.9097 \|
	\| 0.0266 \| 87.78 \| 3950 \| 0.6307 \| 0.6464 \| 0.5541 \| 0.5967 \| 0.9068 \|
	\| 0.029 \| 88.89 \| 4000 \| 0.4432 \| 0.5667 \| 0.5985 \| 0.5822 \| 0.9099 \|
	\| 0.029 \| 90.0 \| 4050 \| 0.4822 \| 0.5148 \| 0.6371 \| 0.5695 \| 0.9018 \|
	\| 0.029 \| 91.11 \| 4100 \| 0.4706 \| 0.5966 \| 0.6023 \| 0.5994 \| 0.9128 \|


	### 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_neg
	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_neg

	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: 0.6290
	- Precision: 0.5977
	- Recall: 0.6106
	- F1: 0.6041
	- Accuracy: 0.8995

	## 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 \| 0.6820 \| 0.0 \| 0.0 \| 0.0 \| 0.7747 \|
	\| No log \| 2.22 \| 100 \| 0.5532 \| 0.0489 \| 0.0212 \| 0.0296 \| 0.8040 \|
	\| No log \| 3.33 \| 150 \| 0.4231 \| 0.1320 \| 0.1004 \| 0.1140 \| 0.8397 \|
	\| No log \| 4.44 \| 200 \| 0.3736 \| 0.2200 \| 0.1873 \| 0.2023 \| 0.8576 \|
	\| No log \| 5.56 \| 250 \| 0.3511 \| 0.3096 \| 0.2606 \| 0.2830 \| 0.8713 \|
	\| No log \| 6.67 \| 300 \| 0.2976 \| 0.3635 \| 0.4498 \| 0.4021 \| 0.8833 \|
	\| No log \| 7.78 \| 350 \| 0.2713 \| 0.3793 \| 0.4942 \| 0.4292 \| 0.8925 \|
	\| No log \| 8.89 \| 400 \| 0.2569 \| 0.4174 \| 0.5753 \| 0.4838 \| 0.9016 \|
	\| No log \| 10.0 \| 450 \| 0.2599 \| 0.4687 \| 0.5772 \| 0.5173 \| 0.9031 \|
	\| 0.389 \| 11.11 \| 500 \| 0.2775 \| 0.5008 \| 0.5849 \| 0.5396 \| 0.9084 \|
	\| 0.389 \| 12.22 \| 550 \| 0.3123 \| 0.4257 \| 0.6641 \| 0.5189 \| 0.8834 \|
	\| 0.389 \| 13.33 \| 600 \| 0.3271 \| 0.5036 \| 0.5425 \| 0.5223 \| 0.9064 \|
	\| 0.389 \| 14.44 \| 650 \| 0.3390 \| 0.5328 \| 0.5174 \| 0.5250 \| 0.9016 \|
	\| 0.389 \| 15.56 \| 700 \| 0.3688 \| 0.4788 \| 0.6313 \| 0.5445 \| 0.8988 \|
	\| 0.389 \| 16.67 \| 750 \| 0.4260 \| 0.4942 \| 0.6583 \| 0.5646 \| 0.8997 \|
	\| 0.389 \| 17.78 \| 800 \| 0.3622 \| 0.5217 \| 0.6042 \| 0.5599 \| 0.9106 \|
	\| 0.389 \| 18.89 \| 850 \| 0.4054 \| 0.5266 \| 0.6120 \| 0.5661 \| 0.9105 \|
	\| 0.389 \| 20.0 \| 900 \| 0.3988 \| 0.5070 \| 0.6255 \| 0.5601 \| 0.9049 \|
	\| 0.389 \| 21.11 \| 950 \| 0.4422 \| 0.5281 \| 0.5444 \| 0.5361 \| 0.9093 \|
	\| 0.0777 \| 22.22 \| 1000 \| 0.4207 \| 0.5635 \| 0.5656 \| 0.5645 \| 0.9117 \|
	\| 0.0777 \| 23.33 \| 1050 \| 0.4721 \| 0.5505 \| 0.5792 \| 0.5644 \| 0.9130 \|
	\| 0.0777 \| 24.44 \| 1100 \| 0.4261 \| 0.5379 \| 0.6158 \| 0.5743 \| 0.9114 \|
	\| 0.0777 \| 25.56 \| 1150 \| 0.5339 \| 0.6157 \| 0.5290 \| 0.5691 \| 0.9 \|
	\| 0.0777 \| 26.67 \| 1200 \| 0.3761 \| 0.4949 \| 0.6544 \| 0.5636 \| 0.9036 \|
	\| 0.0777 \| 27.78 \| 1250 \| 0.4250 \| 0.5650 \| 0.5792 \| 0.5720 \| 0.9110 \|
	\| 0.0777 \| 28.89 \| 1300 \| 0.3790 \| 0.5731 \| 0.6429 \| 0.6060 \| 0.9193 \|
	\| 0.0777 \| 30.0 \| 1350 \| 0.5330 \| 0.5942 \| 0.5907 \| 0.5924 \| 0.9076 \|
	\| 0.0777 \| 31.11 \| 1400 \| 0.4419 \| 0.5957 \| 0.5888 \| 0.5922 \| 0.9180 \|
	\| 0.0777 \| 32.22 \| 1450 \| 0.5531 \| 0.6008 \| 0.5695 \| 0.5847 \| 0.9088 \|
	\| 0.0362 \| 33.33 \| 1500 \| 0.4544 \| 0.5231 \| 0.6564 \| 0.5822 \| 0.9135 \|
	\| 0.0362 \| 34.44 \| 1550 \| 0.4990 \| 0.5695 \| 0.5772 \| 0.5733 \| 0.9082 \|
	\| 0.0362 \| 35.56 \| 1600 \| 0.4040 \| 0.5709 \| 0.5753 \| 0.5731 \| 0.9086 \|
	\| 0.0362 \| 36.67 \| 1650 \| 0.3807 \| 0.5989 \| 0.6255 \| 0.6119 \| 0.9123 \|
	\| 0.0362 \| 37.78 \| 1700 \| 0.5088 \| 0.5996 \| 0.6100 \| 0.6048 \| 0.9139 \|
	\| 0.0362 \| 38.89 \| 1750 \| 0.4525 \| 0.6151 \| 0.5985 \| 0.6067 \| 0.9189 \|
	\| 0.0362 \| 40.0 \| 1800 \| 0.3787 \| 0.6184 \| 0.6100 \| 0.6142 \| 0.9211 \|
	\| 0.0362 \| 41.11 \| 1850 \| 0.3974 \| 0.6097 \| 0.5849 \| 0.5970 \| 0.9162 \|
	\| 0.0362 \| 42.22 \| 1900 \| 0.3944 \| 0.5762 \| 0.6274 \| 0.6007 \| 0.9148 \|
	\| 0.0362 \| 43.33 \| 1950 \| 0.3865 \| 0.5124 \| 0.6795 \| 0.5842 \| 0.9022 \|
	\| 0.0264 \| 44.44 \| 2000 \| 0.4583 \| 0.5462 \| 0.6274 \| 0.5840 \| 0.9169 \|
	\| 0.0264 \| 45.56 \| 2050 \| 0.4640 \| 0.5635 \| 0.6429 \| 0.6005 \| 0.9105 \|
	\| 0.0264 \| 46.67 \| 2100 \| 0.5028 \| 0.5945 \| 0.5830 \| 0.5887 \| 0.9128 \|
	\| 0.0264 \| 47.78 \| 2150 \| 0.3917 \| 0.6267 \| 0.6255 \| 0.6261 \| 0.9221 \|
	\| 0.0264 \| 48.89 \| 2200 \| 0.4833 \| 0.6214 \| 0.6274 \| 0.6244 \| 0.9138 \|
	\| 0.0264 \| 50.0 \| 2250 \| 0.4147 \| 0.6130 \| 0.6390 \| 0.6257 \| 0.9190 \|
	\| 0.0264 \| 51.11 \| 2300 \| 0.4455 \| 0.6546 \| 0.5927 \| 0.6221 \| 0.9185 \|
	\| 0.0264 \| 52.22 \| 2350 \| 0.4575 \| 0.6138 \| 0.6351 \| 0.6243 \| 0.9180 \|
	\| 0.0264 \| 53.33 \| 2400 \| 0.7707 \| 0.3732 \| 0.6815 \| 0.4822 \| 0.8354 \|
	\| 0.0264 \| 54.44 \| 2450 \| 0.4440 \| 0.6015 \| 0.6236 \| 0.6123 \| 0.9130 \|
	\| 0.0248 \| 55.56 \| 2500 \| 0.4815 \| 0.5739 \| 0.6448 \| 0.6073 \| 0.9124 \|
	\| 0.0248 \| 56.67 \| 2550 \| 0.3971 \| 0.6204 \| 0.6467 \| 0.6333 \| 0.9227 \|
	\| 0.0248 \| 57.78 \| 2600 \| 0.4770 \| 0.6208 \| 0.6100 \| 0.6154 \| 0.9193 \|
	\| 0.0248 \| 58.89 \| 2650 \| 0.5450 \| 0.6699 \| 0.5367 \| 0.5959 \| 0.9109 \|
	\| 0.0248 \| 60.0 \| 2700 \| 0.5033 \| 0.5439 \| 0.6332 \| 0.5852 \| 0.9019 \|
	\| 0.0248 \| 61.11 \| 2750 \| 0.5185 \| 0.5187 \| 0.6699 \| 0.5847 \| 0.9010 \|
	\| 0.0248 \| 62.22 \| 2800 \| 0.4277 \| 0.6627 \| 0.6371 \| 0.6496 \| 0.9194 \|
	\| 0.0248 \| 63.33 \| 2850 \| 0.4688 \| 0.4869 \| 0.6467 \| 0.5556 \| 0.9066 \|
	\| 0.0248 \| 64.44 \| 2900 \| 0.4779 \| 0.6135 \| 0.6313 \| 0.6223 \| 0.9153 \|
	\| 0.0248 \| 65.56 \| 2950 \| 0.5012 \| 0.5852 \| 0.6100 \| 0.5974 \| 0.9079 \|
	\| 0.0232 \| 66.67 \| 3000 \| 0.4788 \| 0.5259 \| 0.6081 \| 0.5640 \| 0.9052 \|
	\| 0.0232 \| 67.78 \| 3050 \| 0.4556 \| 0.5726 \| 0.6544 \| 0.6108 \| 0.9099 \|
	\| 0.0232 \| 68.89 \| 3100 \| 0.5026 \| 0.608 \| 0.5869 \| 0.5972 \| 0.9091 \|
	\| 0.0232 \| 70.0 \| 3150 \| 0.8153 \| 0.3071 \| 0.7143 \| 0.4295 \| 0.7567 \|
	\| 0.0232 \| 71.11 \| 3200 \| 0.4670 \| 0.6169 \| 0.6062 \| 0.6115 \| 0.9113 \|
	\| 0.0232 \| 72.22 \| 3250 \| 0.5249 \| 0.5727 \| 0.6313 \| 0.6006 \| 0.9068 \|
	\| 0.0232 \| 73.33 \| 3300 \| 0.4343 \| 0.6085 \| 0.6390 \| 0.6234 \| 0.9162 \|
	\| 0.0232 \| 74.44 \| 3350 \| 0.5067 \| 0.6364 \| 0.5811 \| 0.6075 \| 0.9135 \|
	\| 0.0232 \| 75.56 \| 3400 \| 0.4415 \| 0.5812 \| 0.6429 \| 0.6104 \| 0.9149 \|
	\| 0.0232 \| 76.67 \| 3450 \| 0.4052 \| 0.5757 \| 0.6313 \| 0.6022 \| 0.9137 \|
	\| 0.0266 \| 77.78 \| 3500 \| 0.5119 \| 0.5233 \| 0.5425 \| 0.5327 \| 0.9038 \|
	\| 0.0266 \| 78.89 \| 3550 \| 0.4689 \| 0.5945 \| 0.5888 \| 0.5917 \| 0.9145 \|
	\| 0.0266 \| 80.0 \| 3600 \| 0.3973 \| 0.5609 \| 0.6313 \| 0.5940 \| 0.9154 \|
	\| 0.0266 \| 81.11 \| 3650 \| 0.4848 \| 0.5947 \| 0.6486 \| 0.6205 \| 0.9181 \|
	\| 0.0266 \| 82.22 \| 3700 \| 0.4825 \| 0.5877 \| 0.6274 \| 0.6069 \| 0.9160 \|
	\| 0.0266 \| 83.33 \| 3750 \| 0.5193 \| 0.5138 \| 0.6467 \| 0.5726 \| 0.9 \|
	\| 0.0266 \| 84.44 \| 3800 \| 0.5344 \| 0.5777 \| 0.5811 \| 0.5794 \| 0.9107 \|
	\| 0.0266 \| 85.56 \| 3850 \| 0.5227 \| 0.6591 \| 0.5637 \| 0.6077 \| 0.9107 \|
	\| 0.0266 \| 86.67 \| 3900 \| 0.4490 \| 0.5176 \| 0.6255 \| 0.5664 \| 0.9097 \|
	\| 0.0266 \| 87.78 \| 3950 \| 0.6307 \| 0.6464 \| 0.5541 \| 0.5967 \| 0.9068 \|
	\| 0.029 \| 88.89 \| 4000 \| 0.4432 \| 0.5667 \| 0.5985 \| 0.5822 \| 0.9099 \|
	\| 0.029 \| 90.0 \| 4050 \| 0.4822 \| 0.5148 \| 0.6371 \| 0.5695 \| 0.9018 \|
	\| 0.029 \| 91.11 \| 4100 \| 0.4706 \| 0.5966 \| 0.6023 \| 0.5994 \| 0.9128 \|


	### Framework versions

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