hubert-classifier-aug-fold-4 / README.md

End of training

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	---
	license: apache-2.0
	base_model: facebook/hubert-base-ls960
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	- precision
	- recall
	- f1
	model-index:
	- name: hubert-classifier-aug-fold-4
	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. -->

	# hubert-classifier-aug-fold-4

	This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.5994
	- Accuracy: 0.8908
	- Precision: 0.9054
	- Recall: 0.8908
	- F1: 0.8902
	- Binary: 0.9252

	## 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: 0.0001
	- train_batch_size: 32
	- eval_batch_size: 32
	- seed: 42
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 128
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 500
	- num_epochs: 30
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| Binary \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\|:------:\|
	\| No log \| 0.13 \| 50 \| 4.4211 \| 0.0256 \| 0.0066 \| 0.0256 \| 0.0094 \| 0.2032 \|
	\| No log \| 0.27 \| 100 \| 4.3448 \| 0.0499 \| 0.0239 \| 0.0499 \| 0.0181 \| 0.2625 \|
	\| No log \| 0.4 \| 150 \| 3.9589 \| 0.1120 \| 0.0582 \| 0.1120 \| 0.0534 \| 0.3738 \|
	\| No log \| 0.54 \| 200 \| 3.6032 \| 0.1727 \| 0.0912 \| 0.1727 \| 0.0968 \| 0.4177 \|
	\| No log \| 0.67 \| 250 \| 3.2149 \| 0.2321 \| 0.1419 \| 0.2321 \| 0.1452 \| 0.4592 \|
	\| No log \| 0.81 \| 300 \| 2.8786 \| 0.3441 \| 0.2742 \| 0.3441 \| 0.2581 \| 0.5387 \|
	\| No log \| 0.94 \| 350 \| 2.5253 \| 0.4211 \| 0.3438 \| 0.4211 \| 0.3380 \| 0.5941 \|
	\| 3.7437 \| 1.08 \| 400 \| 2.1778 \| 0.4588 \| 0.4083 \| 0.4588 \| 0.3896 \| 0.6201 \|
	\| 3.7437 \| 1.21 \| 450 \| 1.8620 \| 0.5709 \| 0.5259 \| 0.5709 \| 0.5166 \| 0.6992 \|
	\| 3.7437 \| 1.35 \| 500 \| 1.6172 \| 0.5803 \| 0.5498 \| 0.5803 \| 0.5168 \| 0.7062 \|
	\| 3.7437 \| 1.48 \| 550 \| 1.3691 \| 0.6640 \| 0.6471 \| 0.6640 \| 0.6287 \| 0.7633 \|
	\| 3.7437 \| 1.62 \| 600 \| 1.2425 \| 0.6910 \| 0.6704 \| 0.6910 \| 0.6541 \| 0.7837 \|
	\| 3.7437 \| 1.75 \| 650 \| 1.1155 \| 0.7193 \| 0.7205 \| 0.7193 \| 0.6936 \| 0.8038 \|
	\| 3.7437 \| 1.89 \| 700 \| 0.9569 \| 0.7463 \| 0.7599 \| 0.7463 \| 0.7287 \| 0.8225 \|
	\| 1.7895 \| 2.02 \| 750 \| 0.9260 \| 0.7584 \| 0.7657 \| 0.7584 \| 0.7389 \| 0.8321 \|
	\| 1.7895 \| 2.16 \| 800 \| 0.8667 \| 0.7787 \| 0.8008 \| 0.7787 \| 0.7639 \| 0.8452 \|
	\| 1.7895 \| 2.29 \| 850 \| 0.7438 \| 0.8138 \| 0.8159 \| 0.8138 \| 0.8047 \| 0.8696 \|
	\| 1.7895 \| 2.43 \| 900 \| 0.7958 \| 0.8016 \| 0.8175 \| 0.8016 \| 0.7917 \| 0.8602 \|
	\| 1.7895 \| 2.56 \| 950 \| 0.6627 \| 0.8327 \| 0.8449 \| 0.8327 \| 0.8296 \| 0.8829 \|
	\| 1.7895 \| 2.7 \| 1000 \| 0.7242 \| 0.7976 \| 0.8152 \| 0.7976 \| 0.7882 \| 0.8592 \|
	\| 1.7895 \| 2.83 \| 1050 \| 0.6745 \| 0.8165 \| 0.8337 \| 0.8165 \| 0.8123 \| 0.8719 \|
	\| 1.7895 \| 2.96 \| 1100 \| 0.6795 \| 0.8192 \| 0.8388 \| 0.8192 \| 0.8158 \| 0.8761 \|
	\| 1.0205 \| 3.1 \| 1150 \| 0.6546 \| 0.8354 \| 0.8575 \| 0.8354 \| 0.8319 \| 0.8835 \|
	\| 1.0205 \| 3.23 \| 1200 \| 0.6165 \| 0.8394 \| 0.8489 \| 0.8394 \| 0.8365 \| 0.8868 \|
	\| 1.0205 \| 3.37 \| 1250 \| 0.7041 \| 0.8232 \| 0.8490 \| 0.8232 \| 0.8202 \| 0.8775 \|
	\| 1.0205 \| 3.5 \| 1300 \| 0.5767 \| 0.8516 \| 0.8626 \| 0.8516 \| 0.8485 \| 0.8957 \|
	\| 1.0205 \| 3.64 \| 1350 \| 0.5831 \| 0.8448 \| 0.8609 \| 0.8448 \| 0.8404 \| 0.8910 \|
	\| 1.0205 \| 3.77 \| 1400 \| 0.5623 \| 0.8650 \| 0.8761 \| 0.8650 \| 0.8624 \| 0.9051 \|
	\| 1.0205 \| 3.91 \| 1450 \| 0.5696 \| 0.8650 \| 0.8757 \| 0.8650 \| 0.8630 \| 0.9047 \|
	\| 0.7175 \| 4.04 \| 1500 \| 0.5455 \| 0.8543 \| 0.8756 \| 0.8543 \| 0.8522 \| 0.8981 \|
	\| 0.7175 \| 4.18 \| 1550 \| 0.5209 \| 0.8650 \| 0.8785 \| 0.8650 \| 0.8592 \| 0.9053 \|
	\| 0.7175 \| 4.31 \| 1600 \| 0.6185 \| 0.8435 \| 0.8606 \| 0.8435 \| 0.8415 \| 0.8908 \|
	\| 0.7175 \| 4.45 \| 1650 \| 0.5434 \| 0.8677 \| 0.8797 \| 0.8677 \| 0.8644 \| 0.9066 \|
	\| 0.7175 \| 4.58 \| 1700 \| 0.6622 \| 0.8489 \| 0.8728 \| 0.8489 \| 0.8444 \| 0.8945 \|
	\| 0.7175 \| 4.72 \| 1750 \| 0.5668 \| 0.8677 \| 0.8798 \| 0.8677 \| 0.8662 \| 0.9070 \|
	\| 0.7175 \| 4.85 \| 1800 \| 0.5375 \| 0.8812 \| 0.8934 \| 0.8812 \| 0.8804 \| 0.9179 \|
	\| 0.7175 \| 4.99 \| 1850 \| 0.5550 \| 0.8677 \| 0.8780 \| 0.8677 \| 0.8640 \| 0.9080 \|
	\| 0.5694 \| 5.12 \| 1900 \| 0.5739 \| 0.8691 \| 0.8811 \| 0.8691 \| 0.8647 \| 0.9089 \|
	\| 0.5694 \| 5.26 \| 1950 \| 0.5325 \| 0.8826 \| 0.8923 \| 0.8826 \| 0.8818 \| 0.9174 \|
	\| 0.5694 \| 5.39 \| 2000 \| 0.5496 \| 0.8772 \| 0.8885 \| 0.8772 \| 0.8747 \| 0.9147 \|
	\| 0.5694 \| 5.53 \| 2050 \| 0.6038 \| 0.8745 \| 0.8854 \| 0.8745 \| 0.8726 \| 0.9123 \|
	\| 0.5694 \| 5.66 \| 2100 \| 0.5606 \| 0.8826 \| 0.8936 \| 0.8826 \| 0.8816 \| 0.9194 \|
	\| 0.5694 \| 5.8 \| 2150 \| 0.5655 \| 0.8745 \| 0.8885 \| 0.8745 \| 0.8741 \| 0.9128 \|
	\| 0.5694 \| 5.93 \| 2200 \| 0.5588 \| 0.8785 \| 0.8912 \| 0.8785 \| 0.8775 \| 0.9157 \|
	\| 0.4761 \| 6.06 \| 2250 \| 0.6021 \| 0.8637 \| 0.8802 \| 0.8637 \| 0.8617 \| 0.9047 \|
	\| 0.4761 \| 6.2 \| 2300 \| 0.5785 \| 0.8839 \| 0.8956 \| 0.8839 \| 0.8840 \| 0.9194 \|
	\| 0.4761 \| 6.33 \| 2350 \| 0.6397 \| 0.8691 \| 0.8831 \| 0.8691 \| 0.8677 \| 0.9090 \|
	\| 0.4761 \| 6.47 \| 2400 \| 0.5376 \| 0.8880 \| 0.8998 \| 0.8880 \| 0.8866 \| 0.9238 \|
	\| 0.4761 \| 6.6 \| 2450 \| 0.5669 \| 0.8920 \| 0.9025 \| 0.8920 \| 0.8904 \| 0.9255 \|
	\| 0.4761 \| 6.74 \| 2500 \| 0.6968 \| 0.8543 \| 0.8723 \| 0.8543 \| 0.8522 \| 0.8987 \|
	\| 0.4761 \| 6.87 \| 2550 \| 0.5628 \| 0.8839 \| 0.8952 \| 0.8839 \| 0.8829 \| 0.9194 \|
	\| 0.4178 \| 7.01 \| 2600 \| 0.5975 \| 0.8772 \| 0.8861 \| 0.8772 \| 0.8755 \| 0.9167 \|
	\| 0.4178 \| 7.14 \| 2650 \| 0.5967 \| 0.8853 \| 0.8919 \| 0.8853 \| 0.8834 \| 0.9219 \|
	\| 0.4178 \| 7.28 \| 2700 \| 0.6271 \| 0.8799 \| 0.8921 \| 0.8799 \| 0.8783 \| 0.9166 \|
	\| 0.4178 \| 7.41 \| 2750 \| 0.6047 \| 0.8799 \| 0.8916 \| 0.8799 \| 0.8784 \| 0.9170 \|
	\| 0.4178 \| 7.55 \| 2800 \| 0.5336 \| 0.8853 \| 0.8978 \| 0.8853 \| 0.8829 \| 0.9204 \|
	\| 0.4178 \| 7.68 \| 2850 \| 0.5722 \| 0.8988 \| 0.9097 \| 0.8988 \| 0.8988 \| 0.9298 \|
	\| 0.4178 \| 7.82 \| 2900 \| 0.5478 \| 0.8866 \| 0.8987 \| 0.8866 \| 0.8853 \| 0.9213 \|
	\| 0.4178 \| 7.95 \| 2950 \| 0.5176 \| 0.8907 \| 0.9016 \| 0.8907 \| 0.8897 \| 0.9242 \|
	\| 0.3642 \| 8.09 \| 3000 \| 0.5172 \| 0.8947 \| 0.9030 \| 0.8947 \| 0.8938 \| 0.9279 \|
	\| 0.3642 \| 8.22 \| 3050 \| 0.6341 \| 0.8799 \| 0.8932 \| 0.8799 \| 0.8787 \| 0.9157 \|
	\| 0.3642 \| 8.36 \| 3100 \| 0.6011 \| 0.8812 \| 0.8897 \| 0.8812 \| 0.8797 \| 0.9181 \|
	\| 0.3642 \| 8.49 \| 3150 \| 0.5807 \| 0.8745 \| 0.8864 \| 0.8745 \| 0.8733 \| 0.9132 \|
	\| 0.3642 \| 8.63 \| 3200 \| 0.5931 \| 0.8799 \| 0.8942 \| 0.8799 \| 0.8795 \| 0.9157 \|
	\| 0.3642 \| 8.76 \| 3250 \| 0.6045 \| 0.8812 \| 0.8955 \| 0.8812 \| 0.8818 \| 0.9175 \|
	\| 0.3642 \| 8.89 \| 3300 \| 0.5473 \| 0.8934 \| 0.9047 \| 0.8934 \| 0.8927 \| 0.9260 \|
	\| 0.3326 \| 9.03 \| 3350 \| 0.5111 \| 0.8934 \| 0.9058 \| 0.8934 \| 0.8924 \| 0.9266 \|


	### Framework versions

	- Transformers 4.38.2
	- Pytorch 2.3.0
	- Datasets 2.19.1
	- Tokenizers 0.15.1

	---
	license: apache-2.0
	base_model: facebook/hubert-base-ls960
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	- precision
	- recall
	- f1
	model-index:
	- name: hubert-classifier-aug-fold-4
	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. -->

	# hubert-classifier-aug-fold-4

	This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.5994
	- Accuracy: 0.8908
	- Precision: 0.9054
	- Recall: 0.8908
	- F1: 0.8902
	- Binary: 0.9252

	## 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: 0.0001
	- train_batch_size: 32
	- eval_batch_size: 32
	- seed: 42
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 128
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 500
	- num_epochs: 30
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| Binary \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\|:------:\|
	\| No log \| 0.13 \| 50 \| 4.4211 \| 0.0256 \| 0.0066 \| 0.0256 \| 0.0094 \| 0.2032 \|
	\| No log \| 0.27 \| 100 \| 4.3448 \| 0.0499 \| 0.0239 \| 0.0499 \| 0.0181 \| 0.2625 \|
	\| No log \| 0.4 \| 150 \| 3.9589 \| 0.1120 \| 0.0582 \| 0.1120 \| 0.0534 \| 0.3738 \|
	\| No log \| 0.54 \| 200 \| 3.6032 \| 0.1727 \| 0.0912 \| 0.1727 \| 0.0968 \| 0.4177 \|
	\| No log \| 0.67 \| 250 \| 3.2149 \| 0.2321 \| 0.1419 \| 0.2321 \| 0.1452 \| 0.4592 \|
	\| No log \| 0.81 \| 300 \| 2.8786 \| 0.3441 \| 0.2742 \| 0.3441 \| 0.2581 \| 0.5387 \|
	\| No log \| 0.94 \| 350 \| 2.5253 \| 0.4211 \| 0.3438 \| 0.4211 \| 0.3380 \| 0.5941 \|
	\| 3.7437 \| 1.08 \| 400 \| 2.1778 \| 0.4588 \| 0.4083 \| 0.4588 \| 0.3896 \| 0.6201 \|
	\| 3.7437 \| 1.21 \| 450 \| 1.8620 \| 0.5709 \| 0.5259 \| 0.5709 \| 0.5166 \| 0.6992 \|
	\| 3.7437 \| 1.35 \| 500 \| 1.6172 \| 0.5803 \| 0.5498 \| 0.5803 \| 0.5168 \| 0.7062 \|
	\| 3.7437 \| 1.48 \| 550 \| 1.3691 \| 0.6640 \| 0.6471 \| 0.6640 \| 0.6287 \| 0.7633 \|
	\| 3.7437 \| 1.62 \| 600 \| 1.2425 \| 0.6910 \| 0.6704 \| 0.6910 \| 0.6541 \| 0.7837 \|
	\| 3.7437 \| 1.75 \| 650 \| 1.1155 \| 0.7193 \| 0.7205 \| 0.7193 \| 0.6936 \| 0.8038 \|
	\| 3.7437 \| 1.89 \| 700 \| 0.9569 \| 0.7463 \| 0.7599 \| 0.7463 \| 0.7287 \| 0.8225 \|
	\| 1.7895 \| 2.02 \| 750 \| 0.9260 \| 0.7584 \| 0.7657 \| 0.7584 \| 0.7389 \| 0.8321 \|
	\| 1.7895 \| 2.16 \| 800 \| 0.8667 \| 0.7787 \| 0.8008 \| 0.7787 \| 0.7639 \| 0.8452 \|
	\| 1.7895 \| 2.29 \| 850 \| 0.7438 \| 0.8138 \| 0.8159 \| 0.8138 \| 0.8047 \| 0.8696 \|
	\| 1.7895 \| 2.43 \| 900 \| 0.7958 \| 0.8016 \| 0.8175 \| 0.8016 \| 0.7917 \| 0.8602 \|
	\| 1.7895 \| 2.56 \| 950 \| 0.6627 \| 0.8327 \| 0.8449 \| 0.8327 \| 0.8296 \| 0.8829 \|
	\| 1.7895 \| 2.7 \| 1000 \| 0.7242 \| 0.7976 \| 0.8152 \| 0.7976 \| 0.7882 \| 0.8592 \|
	\| 1.7895 \| 2.83 \| 1050 \| 0.6745 \| 0.8165 \| 0.8337 \| 0.8165 \| 0.8123 \| 0.8719 \|
	\| 1.7895 \| 2.96 \| 1100 \| 0.6795 \| 0.8192 \| 0.8388 \| 0.8192 \| 0.8158 \| 0.8761 \|
	\| 1.0205 \| 3.1 \| 1150 \| 0.6546 \| 0.8354 \| 0.8575 \| 0.8354 \| 0.8319 \| 0.8835 \|
	\| 1.0205 \| 3.23 \| 1200 \| 0.6165 \| 0.8394 \| 0.8489 \| 0.8394 \| 0.8365 \| 0.8868 \|
	\| 1.0205 \| 3.37 \| 1250 \| 0.7041 \| 0.8232 \| 0.8490 \| 0.8232 \| 0.8202 \| 0.8775 \|
	\| 1.0205 \| 3.5 \| 1300 \| 0.5767 \| 0.8516 \| 0.8626 \| 0.8516 \| 0.8485 \| 0.8957 \|
	\| 1.0205 \| 3.64 \| 1350 \| 0.5831 \| 0.8448 \| 0.8609 \| 0.8448 \| 0.8404 \| 0.8910 \|
	\| 1.0205 \| 3.77 \| 1400 \| 0.5623 \| 0.8650 \| 0.8761 \| 0.8650 \| 0.8624 \| 0.9051 \|
	\| 1.0205 \| 3.91 \| 1450 \| 0.5696 \| 0.8650 \| 0.8757 \| 0.8650 \| 0.8630 \| 0.9047 \|
	\| 0.7175 \| 4.04 \| 1500 \| 0.5455 \| 0.8543 \| 0.8756 \| 0.8543 \| 0.8522 \| 0.8981 \|
	\| 0.7175 \| 4.18 \| 1550 \| 0.5209 \| 0.8650 \| 0.8785 \| 0.8650 \| 0.8592 \| 0.9053 \|
	\| 0.7175 \| 4.31 \| 1600 \| 0.6185 \| 0.8435 \| 0.8606 \| 0.8435 \| 0.8415 \| 0.8908 \|
	\| 0.7175 \| 4.45 \| 1650 \| 0.5434 \| 0.8677 \| 0.8797 \| 0.8677 \| 0.8644 \| 0.9066 \|
	\| 0.7175 \| 4.58 \| 1700 \| 0.6622 \| 0.8489 \| 0.8728 \| 0.8489 \| 0.8444 \| 0.8945 \|
	\| 0.7175 \| 4.72 \| 1750 \| 0.5668 \| 0.8677 \| 0.8798 \| 0.8677 \| 0.8662 \| 0.9070 \|
	\| 0.7175 \| 4.85 \| 1800 \| 0.5375 \| 0.8812 \| 0.8934 \| 0.8812 \| 0.8804 \| 0.9179 \|
	\| 0.7175 \| 4.99 \| 1850 \| 0.5550 \| 0.8677 \| 0.8780 \| 0.8677 \| 0.8640 \| 0.9080 \|
	\| 0.5694 \| 5.12 \| 1900 \| 0.5739 \| 0.8691 \| 0.8811 \| 0.8691 \| 0.8647 \| 0.9089 \|
	\| 0.5694 \| 5.26 \| 1950 \| 0.5325 \| 0.8826 \| 0.8923 \| 0.8826 \| 0.8818 \| 0.9174 \|
	\| 0.5694 \| 5.39 \| 2000 \| 0.5496 \| 0.8772 \| 0.8885 \| 0.8772 \| 0.8747 \| 0.9147 \|
	\| 0.5694 \| 5.53 \| 2050 \| 0.6038 \| 0.8745 \| 0.8854 \| 0.8745 \| 0.8726 \| 0.9123 \|
	\| 0.5694 \| 5.66 \| 2100 \| 0.5606 \| 0.8826 \| 0.8936 \| 0.8826 \| 0.8816 \| 0.9194 \|
	\| 0.5694 \| 5.8 \| 2150 \| 0.5655 \| 0.8745 \| 0.8885 \| 0.8745 \| 0.8741 \| 0.9128 \|
	\| 0.5694 \| 5.93 \| 2200 \| 0.5588 \| 0.8785 \| 0.8912 \| 0.8785 \| 0.8775 \| 0.9157 \|
	\| 0.4761 \| 6.06 \| 2250 \| 0.6021 \| 0.8637 \| 0.8802 \| 0.8637 \| 0.8617 \| 0.9047 \|
	\| 0.4761 \| 6.2 \| 2300 \| 0.5785 \| 0.8839 \| 0.8956 \| 0.8839 \| 0.8840 \| 0.9194 \|
	\| 0.4761 \| 6.33 \| 2350 \| 0.6397 \| 0.8691 \| 0.8831 \| 0.8691 \| 0.8677 \| 0.9090 \|
	\| 0.4761 \| 6.47 \| 2400 \| 0.5376 \| 0.8880 \| 0.8998 \| 0.8880 \| 0.8866 \| 0.9238 \|
	\| 0.4761 \| 6.6 \| 2450 \| 0.5669 \| 0.8920 \| 0.9025 \| 0.8920 \| 0.8904 \| 0.9255 \|
	\| 0.4761 \| 6.74 \| 2500 \| 0.6968 \| 0.8543 \| 0.8723 \| 0.8543 \| 0.8522 \| 0.8987 \|
	\| 0.4761 \| 6.87 \| 2550 \| 0.5628 \| 0.8839 \| 0.8952 \| 0.8839 \| 0.8829 \| 0.9194 \|
	\| 0.4178 \| 7.01 \| 2600 \| 0.5975 \| 0.8772 \| 0.8861 \| 0.8772 \| 0.8755 \| 0.9167 \|
	\| 0.4178 \| 7.14 \| 2650 \| 0.5967 \| 0.8853 \| 0.8919 \| 0.8853 \| 0.8834 \| 0.9219 \|
	\| 0.4178 \| 7.28 \| 2700 \| 0.6271 \| 0.8799 \| 0.8921 \| 0.8799 \| 0.8783 \| 0.9166 \|
	\| 0.4178 \| 7.41 \| 2750 \| 0.6047 \| 0.8799 \| 0.8916 \| 0.8799 \| 0.8784 \| 0.9170 \|
	\| 0.4178 \| 7.55 \| 2800 \| 0.5336 \| 0.8853 \| 0.8978 \| 0.8853 \| 0.8829 \| 0.9204 \|
	\| 0.4178 \| 7.68 \| 2850 \| 0.5722 \| 0.8988 \| 0.9097 \| 0.8988 \| 0.8988 \| 0.9298 \|
	\| 0.4178 \| 7.82 \| 2900 \| 0.5478 \| 0.8866 \| 0.8987 \| 0.8866 \| 0.8853 \| 0.9213 \|
	\| 0.4178 \| 7.95 \| 2950 \| 0.5176 \| 0.8907 \| 0.9016 \| 0.8907 \| 0.8897 \| 0.9242 \|
	\| 0.3642 \| 8.09 \| 3000 \| 0.5172 \| 0.8947 \| 0.9030 \| 0.8947 \| 0.8938 \| 0.9279 \|
	\| 0.3642 \| 8.22 \| 3050 \| 0.6341 \| 0.8799 \| 0.8932 \| 0.8799 \| 0.8787 \| 0.9157 \|
	\| 0.3642 \| 8.36 \| 3100 \| 0.6011 \| 0.8812 \| 0.8897 \| 0.8812 \| 0.8797 \| 0.9181 \|
	\| 0.3642 \| 8.49 \| 3150 \| 0.5807 \| 0.8745 \| 0.8864 \| 0.8745 \| 0.8733 \| 0.9132 \|
	\| 0.3642 \| 8.63 \| 3200 \| 0.5931 \| 0.8799 \| 0.8942 \| 0.8799 \| 0.8795 \| 0.9157 \|
	\| 0.3642 \| 8.76 \| 3250 \| 0.6045 \| 0.8812 \| 0.8955 \| 0.8812 \| 0.8818 \| 0.9175 \|
	\| 0.3642 \| 8.89 \| 3300 \| 0.5473 \| 0.8934 \| 0.9047 \| 0.8934 \| 0.8927 \| 0.9260 \|
	\| 0.3326 \| 9.03 \| 3350 \| 0.5111 \| 0.8934 \| 0.9058 \| 0.8934 \| 0.8924 \| 0.9266 \|


	### Framework versions

	- Transformers 4.38.2
	- Pytorch 2.3.0
	- Datasets 2.19.1
	- Tokenizers 0.15.1