Jellywibble/CHAI_alignment_reward_model

5aef637 verified about 2 months ago

8.37 kB

	---
	license: mit
	base_model: gpt2
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: output
	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. -->

	# output

	This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.5590
	- Accuracy: 0.7005

	## 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: 1e-05
	- train_batch_size: 16
	- eval_batch_size: 64
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 3
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|
	\| 0.6976 \| 0.0268 \| 250 \| 0.6614 \| 0.6728 \|
	\| 0.6155 \| 0.0537 \| 500 \| 0.5858 \| 0.6811 \|
	\| 0.5869 \| 0.0805 \| 750 \| 0.5820 \| 0.6856 \|
	\| 0.5911 \| 0.1073 \| 1000 \| 0.5843 \| 0.6811 \|
	\| 0.5788 \| 0.1341 \| 1250 \| 0.5750 \| 0.6790 \|
	\| 0.5913 \| 0.1610 \| 1500 \| 0.5810 \| 0.6864 \|
	\| 0.5712 \| 0.1878 \| 1750 \| 0.5731 \| 0.6892 \|
	\| 0.5793 \| 0.2146 \| 2000 \| 0.5717 \| 0.6882 \|
	\| 0.5788 \| 0.2415 \| 2250 \| 0.5868 \| 0.6838 \|
	\| 0.5802 \| 0.2683 \| 2500 \| 0.5653 \| 0.6942 \|
	\| 0.583 \| 0.2951 \| 2750 \| 0.5631 \| 0.6984 \|
	\| 0.5762 \| 0.3220 \| 3000 \| 0.5654 \| 0.6916 \|
	\| 0.5678 \| 0.3488 \| 3250 \| 0.5635 \| 0.6906 \|
	\| 0.5679 \| 0.3756 \| 3500 \| 0.5706 \| 0.6838 \|
	\| 0.56 \| 0.4024 \| 3750 \| 0.5661 \| 0.6932 \|
	\| 0.562 \| 0.4293 \| 4000 \| 0.5994 \| 0.6885 \|
	\| 0.5861 \| 0.4561 \| 4250 \| 0.5659 \| 0.6979 \|
	\| 0.5845 \| 0.4829 \| 4500 \| 0.5631 \| 0.6992 \|
	\| 0.5665 \| 0.5098 \| 4750 \| 0.5621 \| 0.6987 \|
	\| 0.5795 \| 0.5366 \| 5000 \| 0.5698 \| 0.6934 \|
	\| 0.5722 \| 0.5634 \| 5250 \| 0.5615 \| 0.6895 \|
	\| 0.5765 \| 0.5903 \| 5500 \| 0.5610 \| 0.7010 \|
	\| 0.5627 \| 0.6171 \| 5750 \| 0.5594 \| 0.6932 \|
	\| 0.5761 \| 0.6439 \| 6000 \| 0.5581 \| 0.6997 \|
	\| 0.5682 \| 0.6707 \| 6250 \| 0.5693 \| 0.6856 \|
	\| 0.566 \| 0.6976 \| 6500 \| 0.5634 \| 0.6895 \|
	\| 0.5628 \| 0.7244 \| 6750 \| 0.5594 \| 0.7026 \|
	\| 0.5739 \| 0.7512 \| 7000 \| 0.5634 \| 0.6926 \|
	\| 0.5762 \| 0.7781 \| 7250 \| 0.5593 \| 0.7015 \|
	\| 0.572 \| 0.8049 \| 7500 \| 0.5612 \| 0.6853 \|
	\| 0.5657 \| 0.8317 \| 7750 \| 0.5593 \| 0.6974 \|
	\| 0.5665 \| 0.8586 \| 8000 \| 0.5614 \| 0.6916 \|
	\| 0.578 \| 0.8854 \| 8250 \| 0.5600 \| 0.6995 \|
	\| 0.571 \| 0.9122 \| 8500 \| 0.5635 \| 0.6934 \|
	\| 0.5703 \| 0.9390 \| 8750 \| 0.5628 \| 0.7052 \|
	\| 0.5801 \| 0.9659 \| 9000 \| 0.5582 \| 0.7010 \|
	\| 0.5691 \| 0.9927 \| 9250 \| 0.5673 \| 0.6958 \|
	\| 0.551 \| 1.0195 \| 9500 \| 0.5631 \| 0.6913 \|
	\| 0.5625 \| 1.0464 \| 9750 \| 0.5583 \| 0.6987 \|
	\| 0.5679 \| 1.0732 \| 10000 \| 0.5633 \| 0.7015 \|
	\| 0.5693 \| 1.1000 \| 10250 \| 0.5590 \| 0.6934 \|
	\| 0.5649 \| 1.1269 \| 10500 \| 0.5580 \| 0.6966 \|
	\| 0.5558 \| 1.1537 \| 10750 \| 0.5661 \| 0.6879 \|
	\| 0.5674 \| 1.1805 \| 11000 \| 0.5595 \| 0.7026 \|
	\| 0.5507 \| 1.2073 \| 11250 \| 0.5594 \| 0.7015 \|
	\| 0.5656 \| 1.2342 \| 11500 \| 0.5592 \| 0.6976 \|
	\| 0.5696 \| 1.2610 \| 11750 \| 0.5604 \| 0.6926 \|
	\| 0.5605 \| 1.2878 \| 12000 \| 0.5618 \| 0.7026 \|
	\| 0.5572 \| 1.3147 \| 12250 \| 0.5649 \| 0.7000 \|
	\| 0.5553 \| 1.3415 \| 12500 \| 0.5621 \| 0.6984 \|
	\| 0.546 \| 1.3683 \| 12750 \| 0.5630 \| 0.6966 \|
	\| 0.5614 \| 1.3951 \| 13000 \| 0.5605 \| 0.6955 \|
	\| 0.5635 \| 1.4220 \| 13250 \| 0.5587 \| 0.6971 \|
	\| 0.5561 \| 1.4488 \| 13500 \| 0.5647 \| 0.6947 \|
	\| 0.5634 \| 1.4756 \| 13750 \| 0.5607 \| 0.6995 \|
	\| 0.5585 \| 1.5025 \| 14000 \| 0.5577 \| 0.7023 \|
	\| 0.5599 \| 1.5293 \| 14250 \| 0.5740 \| 0.6788 \|
	\| 0.5697 \| 1.5561 \| 14500 \| 0.5570 \| 0.7023 \|
	\| 0.5453 \| 1.5830 \| 14750 \| 0.5624 \| 0.6921 \|
	\| 0.5642 \| 1.6098 \| 15000 \| 0.5687 \| 0.6864 \|
	\| 0.5692 \| 1.6366 \| 15250 \| 0.5643 \| 0.6924 \|
	\| 0.558 \| 1.6634 \| 15500 \| 0.5625 \| 0.6961 \|
	\| 0.5465 \| 1.6903 \| 15750 \| 0.5627 \| 0.6997 \|
	\| 0.5744 \| 1.7171 \| 16000 \| 0.5594 \| 0.6992 \|
	\| 0.5683 \| 1.7439 \| 16250 \| 0.5577 \| 0.6961 \|
	\| 0.5638 \| 1.7708 \| 16500 \| 0.5579 \| 0.6961 \|
	\| 0.5512 \| 1.7976 \| 16750 \| 0.5613 \| 0.6945 \|
	\| 0.5652 \| 1.8244 \| 17000 \| 0.5596 \| 0.6987 \|
	\| 0.5771 \| 1.8513 \| 17250 \| 0.5575 \| 0.6997 \|
	\| 0.5624 \| 1.8781 \| 17500 \| 0.5628 \| 0.6971 \|
	\| 0.5719 \| 1.9049 \| 17750 \| 0.5575 \| 0.6937 \|
	\| 0.5577 \| 1.9317 \| 18000 \| 0.5686 \| 0.6895 \|
	\| 0.5599 \| 1.9586 \| 18250 \| 0.5632 \| 0.6981 \|
	\| 0.5622 \| 1.9854 \| 18500 \| 0.5574 \| 0.7008 \|
	\| 0.56 \| 2.0122 \| 18750 \| 0.5577 \| 0.7008 \|
	\| 0.5447 \| 2.0391 \| 19000 \| 0.5590 \| 0.7036 \|
	\| 0.5599 \| 2.0659 \| 19250 \| 0.5604 \| 0.7005 \|
	\| 0.5512 \| 2.0927 \| 19500 \| 0.5584 \| 0.7000 \|
	\| 0.549 \| 2.1196 \| 19750 \| 0.5593 \| 0.6987 \|
	\| 0.5485 \| 2.1464 \| 20000 \| 0.5680 \| 0.6947 \|
	\| 0.5528 \| 2.1732 \| 20250 \| 0.5619 \| 0.6955 \|
	\| 0.5549 \| 2.2000 \| 20500 \| 0.5593 \| 0.7021 \|
	\| 0.5505 \| 2.2269 \| 20750 \| 0.5608 \| 0.7029 \|
	\| 0.5424 \| 2.2537 \| 21000 \| 0.5644 \| 0.7021 \|
	\| 0.5405 \| 2.2805 \| 21250 \| 0.5607 \| 0.7013 \|
	\| 0.5492 \| 2.3074 \| 21500 \| 0.5611 \| 0.6984 \|
	\| 0.5589 \| 2.3342 \| 21750 \| 0.5621 \| 0.6961 \|
	\| 0.5531 \| 2.3610 \| 22000 \| 0.5615 \| 0.6995 \|
	\| 0.5539 \| 2.3879 \| 22250 \| 0.5623 \| 0.6950 \|
	\| 0.5479 \| 2.4147 \| 22500 \| 0.5615 \| 0.7021 \|
	\| 0.5476 \| 2.4415 \| 22750 \| 0.5600 \| 0.7015 \|
	\| 0.5589 \| 2.4683 \| 23000 \| 0.5596 \| 0.6981 \|
	\| 0.5511 \| 2.4952 \| 23250 \| 0.5603 \| 0.6997 \|
	\| 0.5517 \| 2.5220 \| 23500 \| 0.5594 \| 0.7015 \|
	\| 0.5439 \| 2.5488 \| 23750 \| 0.5623 \| 0.6947 \|
	\| 0.5442 \| 2.5757 \| 24000 \| 0.5612 \| 0.7044 \|
	\| 0.5455 \| 2.6025 \| 24250 \| 0.5596 \| 0.6966 \|
	\| 0.5525 \| 2.6293 \| 24500 \| 0.5613 \| 0.6981 \|
	\| 0.5384 \| 2.6561 \| 24750 \| 0.5622 \| 0.7010 \|
	\| 0.552 \| 2.6830 \| 25000 \| 0.5611 \| 0.6981 \|
	\| 0.5551 \| 2.7098 \| 25250 \| 0.5642 \| 0.6940 \|
	\| 0.5411 \| 2.7366 \| 25500 \| 0.5615 \| 0.7005 \|
	\| 0.5661 \| 2.7635 \| 25750 \| 0.5614 \| 0.6979 \|
	\| 0.5528 \| 2.7903 \| 26000 \| 0.5593 \| 0.7002 \|
	\| 0.5603 \| 2.8171 \| 26250 \| 0.5588 \| 0.7002 \|
	\| 0.5514 \| 2.8440 \| 26500 \| 0.5590 \| 0.7000 \|
	\| 0.5559 \| 2.8708 \| 26750 \| 0.5591 \| 0.7010 \|
	\| 0.5587 \| 2.8976 \| 27000 \| 0.5597 \| 0.6997 \|
	\| 0.5368 \| 2.9244 \| 27250 \| 0.5597 \| 0.7008 \|
	\| 0.5624 \| 2.9513 \| 27500 \| 0.5592 \| 0.7008 \|
	\| 0.571 \| 2.9781 \| 27750 \| 0.5590 \| 0.7005 \|


	### Framework versions

	- Transformers 4.41.2
	- Pytorch 2.0.1+cu118
	- Datasets 2.19.2
	- Tokenizers 0.19.1

	---
	license: mit
	base_model: gpt2
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: output
	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. -->

	# output

	This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.5590
	- Accuracy: 0.7005

	## 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: 1e-05
	- train_batch_size: 16
	- eval_batch_size: 64
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 3
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|
	\| 0.6976 \| 0.0268 \| 250 \| 0.6614 \| 0.6728 \|
	\| 0.6155 \| 0.0537 \| 500 \| 0.5858 \| 0.6811 \|
	\| 0.5869 \| 0.0805 \| 750 \| 0.5820 \| 0.6856 \|
	\| 0.5911 \| 0.1073 \| 1000 \| 0.5843 \| 0.6811 \|
	\| 0.5788 \| 0.1341 \| 1250 \| 0.5750 \| 0.6790 \|
	\| 0.5913 \| 0.1610 \| 1500 \| 0.5810 \| 0.6864 \|
	\| 0.5712 \| 0.1878 \| 1750 \| 0.5731 \| 0.6892 \|
	\| 0.5793 \| 0.2146 \| 2000 \| 0.5717 \| 0.6882 \|
	\| 0.5788 \| 0.2415 \| 2250 \| 0.5868 \| 0.6838 \|
	\| 0.5802 \| 0.2683 \| 2500 \| 0.5653 \| 0.6942 \|
	\| 0.583 \| 0.2951 \| 2750 \| 0.5631 \| 0.6984 \|
	\| 0.5762 \| 0.3220 \| 3000 \| 0.5654 \| 0.6916 \|
	\| 0.5678 \| 0.3488 \| 3250 \| 0.5635 \| 0.6906 \|
	\| 0.5679 \| 0.3756 \| 3500 \| 0.5706 \| 0.6838 \|
	\| 0.56 \| 0.4024 \| 3750 \| 0.5661 \| 0.6932 \|
	\| 0.562 \| 0.4293 \| 4000 \| 0.5994 \| 0.6885 \|
	\| 0.5861 \| 0.4561 \| 4250 \| 0.5659 \| 0.6979 \|
	\| 0.5845 \| 0.4829 \| 4500 \| 0.5631 \| 0.6992 \|
	\| 0.5665 \| 0.5098 \| 4750 \| 0.5621 \| 0.6987 \|
	\| 0.5795 \| 0.5366 \| 5000 \| 0.5698 \| 0.6934 \|
	\| 0.5722 \| 0.5634 \| 5250 \| 0.5615 \| 0.6895 \|
	\| 0.5765 \| 0.5903 \| 5500 \| 0.5610 \| 0.7010 \|
	\| 0.5627 \| 0.6171 \| 5750 \| 0.5594 \| 0.6932 \|
	\| 0.5761 \| 0.6439 \| 6000 \| 0.5581 \| 0.6997 \|
	\| 0.5682 \| 0.6707 \| 6250 \| 0.5693 \| 0.6856 \|
	\| 0.566 \| 0.6976 \| 6500 \| 0.5634 \| 0.6895 \|
	\| 0.5628 \| 0.7244 \| 6750 \| 0.5594 \| 0.7026 \|
	\| 0.5739 \| 0.7512 \| 7000 \| 0.5634 \| 0.6926 \|
	\| 0.5762 \| 0.7781 \| 7250 \| 0.5593 \| 0.7015 \|
	\| 0.572 \| 0.8049 \| 7500 \| 0.5612 \| 0.6853 \|
	\| 0.5657 \| 0.8317 \| 7750 \| 0.5593 \| 0.6974 \|
	\| 0.5665 \| 0.8586 \| 8000 \| 0.5614 \| 0.6916 \|
	\| 0.578 \| 0.8854 \| 8250 \| 0.5600 \| 0.6995 \|
	\| 0.571 \| 0.9122 \| 8500 \| 0.5635 \| 0.6934 \|
	\| 0.5703 \| 0.9390 \| 8750 \| 0.5628 \| 0.7052 \|
	\| 0.5801 \| 0.9659 \| 9000 \| 0.5582 \| 0.7010 \|
	\| 0.5691 \| 0.9927 \| 9250 \| 0.5673 \| 0.6958 \|
	\| 0.551 \| 1.0195 \| 9500 \| 0.5631 \| 0.6913 \|
	\| 0.5625 \| 1.0464 \| 9750 \| 0.5583 \| 0.6987 \|
	\| 0.5679 \| 1.0732 \| 10000 \| 0.5633 \| 0.7015 \|
	\| 0.5693 \| 1.1000 \| 10250 \| 0.5590 \| 0.6934 \|
	\| 0.5649 \| 1.1269 \| 10500 \| 0.5580 \| 0.6966 \|
	\| 0.5558 \| 1.1537 \| 10750 \| 0.5661 \| 0.6879 \|
	\| 0.5674 \| 1.1805 \| 11000 \| 0.5595 \| 0.7026 \|
	\| 0.5507 \| 1.2073 \| 11250 \| 0.5594 \| 0.7015 \|
	\| 0.5656 \| 1.2342 \| 11500 \| 0.5592 \| 0.6976 \|
	\| 0.5696 \| 1.2610 \| 11750 \| 0.5604 \| 0.6926 \|
	\| 0.5605 \| 1.2878 \| 12000 \| 0.5618 \| 0.7026 \|
	\| 0.5572 \| 1.3147 \| 12250 \| 0.5649 \| 0.7000 \|
	\| 0.5553 \| 1.3415 \| 12500 \| 0.5621 \| 0.6984 \|
	\| 0.546 \| 1.3683 \| 12750 \| 0.5630 \| 0.6966 \|
	\| 0.5614 \| 1.3951 \| 13000 \| 0.5605 \| 0.6955 \|
	\| 0.5635 \| 1.4220 \| 13250 \| 0.5587 \| 0.6971 \|
	\| 0.5561 \| 1.4488 \| 13500 \| 0.5647 \| 0.6947 \|
	\| 0.5634 \| 1.4756 \| 13750 \| 0.5607 \| 0.6995 \|
	\| 0.5585 \| 1.5025 \| 14000 \| 0.5577 \| 0.7023 \|
	\| 0.5599 \| 1.5293 \| 14250 \| 0.5740 \| 0.6788 \|
	\| 0.5697 \| 1.5561 \| 14500 \| 0.5570 \| 0.7023 \|
	\| 0.5453 \| 1.5830 \| 14750 \| 0.5624 \| 0.6921 \|
	\| 0.5642 \| 1.6098 \| 15000 \| 0.5687 \| 0.6864 \|
	\| 0.5692 \| 1.6366 \| 15250 \| 0.5643 \| 0.6924 \|
	\| 0.558 \| 1.6634 \| 15500 \| 0.5625 \| 0.6961 \|
	\| 0.5465 \| 1.6903 \| 15750 \| 0.5627 \| 0.6997 \|
	\| 0.5744 \| 1.7171 \| 16000 \| 0.5594 \| 0.6992 \|
	\| 0.5683 \| 1.7439 \| 16250 \| 0.5577 \| 0.6961 \|
	\| 0.5638 \| 1.7708 \| 16500 \| 0.5579 \| 0.6961 \|
	\| 0.5512 \| 1.7976 \| 16750 \| 0.5613 \| 0.6945 \|
	\| 0.5652 \| 1.8244 \| 17000 \| 0.5596 \| 0.6987 \|
	\| 0.5771 \| 1.8513 \| 17250 \| 0.5575 \| 0.6997 \|
	\| 0.5624 \| 1.8781 \| 17500 \| 0.5628 \| 0.6971 \|
	\| 0.5719 \| 1.9049 \| 17750 \| 0.5575 \| 0.6937 \|
	\| 0.5577 \| 1.9317 \| 18000 \| 0.5686 \| 0.6895 \|
	\| 0.5599 \| 1.9586 \| 18250 \| 0.5632 \| 0.6981 \|
	\| 0.5622 \| 1.9854 \| 18500 \| 0.5574 \| 0.7008 \|
	\| 0.56 \| 2.0122 \| 18750 \| 0.5577 \| 0.7008 \|
	\| 0.5447 \| 2.0391 \| 19000 \| 0.5590 \| 0.7036 \|
	\| 0.5599 \| 2.0659 \| 19250 \| 0.5604 \| 0.7005 \|
	\| 0.5512 \| 2.0927 \| 19500 \| 0.5584 \| 0.7000 \|
	\| 0.549 \| 2.1196 \| 19750 \| 0.5593 \| 0.6987 \|
	\| 0.5485 \| 2.1464 \| 20000 \| 0.5680 \| 0.6947 \|
	\| 0.5528 \| 2.1732 \| 20250 \| 0.5619 \| 0.6955 \|
	\| 0.5549 \| 2.2000 \| 20500 \| 0.5593 \| 0.7021 \|
	\| 0.5505 \| 2.2269 \| 20750 \| 0.5608 \| 0.7029 \|
	\| 0.5424 \| 2.2537 \| 21000 \| 0.5644 \| 0.7021 \|
	\| 0.5405 \| 2.2805 \| 21250 \| 0.5607 \| 0.7013 \|
	\| 0.5492 \| 2.3074 \| 21500 \| 0.5611 \| 0.6984 \|
	\| 0.5589 \| 2.3342 \| 21750 \| 0.5621 \| 0.6961 \|
	\| 0.5531 \| 2.3610 \| 22000 \| 0.5615 \| 0.6995 \|
	\| 0.5539 \| 2.3879 \| 22250 \| 0.5623 \| 0.6950 \|
	\| 0.5479 \| 2.4147 \| 22500 \| 0.5615 \| 0.7021 \|
	\| 0.5476 \| 2.4415 \| 22750 \| 0.5600 \| 0.7015 \|
	\| 0.5589 \| 2.4683 \| 23000 \| 0.5596 \| 0.6981 \|
	\| 0.5511 \| 2.4952 \| 23250 \| 0.5603 \| 0.6997 \|
	\| 0.5517 \| 2.5220 \| 23500 \| 0.5594 \| 0.7015 \|
	\| 0.5439 \| 2.5488 \| 23750 \| 0.5623 \| 0.6947 \|
	\| 0.5442 \| 2.5757 \| 24000 \| 0.5612 \| 0.7044 \|
	\| 0.5455 \| 2.6025 \| 24250 \| 0.5596 \| 0.6966 \|
	\| 0.5525 \| 2.6293 \| 24500 \| 0.5613 \| 0.6981 \|
	\| 0.5384 \| 2.6561 \| 24750 \| 0.5622 \| 0.7010 \|
	\| 0.552 \| 2.6830 \| 25000 \| 0.5611 \| 0.6981 \|
	\| 0.5551 \| 2.7098 \| 25250 \| 0.5642 \| 0.6940 \|
	\| 0.5411 \| 2.7366 \| 25500 \| 0.5615 \| 0.7005 \|
	\| 0.5661 \| 2.7635 \| 25750 \| 0.5614 \| 0.6979 \|
	\| 0.5528 \| 2.7903 \| 26000 \| 0.5593 \| 0.7002 \|
	\| 0.5603 \| 2.8171 \| 26250 \| 0.5588 \| 0.7002 \|
	\| 0.5514 \| 2.8440 \| 26500 \| 0.5590 \| 0.7000 \|
	\| 0.5559 \| 2.8708 \| 26750 \| 0.5591 \| 0.7010 \|
	\| 0.5587 \| 2.8976 \| 27000 \| 0.5597 \| 0.6997 \|
	\| 0.5368 \| 2.9244 \| 27250 \| 0.5597 \| 0.7008 \|
	\| 0.5624 \| 2.9513 \| 27500 \| 0.5592 \| 0.7008 \|
	\| 0.571 \| 2.9781 \| 27750 \| 0.5590 \| 0.7005 \|


	### Framework versions

	- Transformers 4.41.2
	- Pytorch 2.0.1+cu118
	- Datasets 2.19.2
	- Tokenizers 0.19.1