End of training

2eddb3c about 1 year ago

8.3 kB

	---
	base_model: vinai/phobert-base
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: disfluency-large-3
	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. -->

	# disfluency-large-3

	This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 42.3155
	- Precision: 0.9886
	- Recall: 0.9862
	- F1: 0.9874
	- Accuracy: 0.9956

	## 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: 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: 100

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| No log \| 1.0 \| 140 \| 131.9614 \| 0.8037 \| 0.8438 \| 0.8232 \| 0.9411 \|
	\| No log \| 2.0 \| 280 \| 41.8031 \| 0.9487 \| 0.9549 \| 0.9518 \| 0.9855 \|
	\| No log \| 3.0 \| 420 \| 27.3502 \| 0.9664 \| 0.9681 \| 0.9673 \| 0.9906 \|
	\| 178.6738 \| 4.0 \| 560 \| 22.9255 \| 0.9741 \| 0.9730 \| 0.9735 \| 0.9925 \|
	\| 178.6738 \| 5.0 \| 700 \| 25.3163 \| 0.9676 \| 0.9688 \| 0.9682 \| 0.9919 \|
	\| 178.6738 \| 6.0 \| 840 \| 24.1142 \| 0.9723 \| 0.9718 \| 0.9720 \| 0.9925 \|
	\| 178.6738 \| 7.0 \| 980 \| 22.2517 \| 0.9777 \| 0.9766 \| 0.9771 \| 0.9938 \|
	\| 25.7318 \| 8.0 \| 1120 \| 24.4542 \| 0.9760 \| 0.9772 \| 0.9766 \| 0.9936 \|
	\| 25.7318 \| 9.0 \| 1260 \| 27.1333 \| 0.9740 \| 0.9700 \| 0.9720 \| 0.9929 \|
	\| 25.7318 \| 10.0 \| 1400 \| 24.5889 \| 0.9789 \| 0.9778 \| 0.9784 \| 0.9938 \|
	\| 16.0059 \| 11.0 \| 1540 \| 26.1038 \| 0.9819 \| 0.9808 \| 0.9814 \| 0.9936 \|
	\| 16.0059 \| 12.0 \| 1680 \| 23.3198 \| 0.9790 \| 0.9814 \| 0.9802 \| 0.9941 \|
	\| 16.0059 \| 13.0 \| 1820 \| 30.8831 \| 0.9778 \| 0.9772 \| 0.9775 \| 0.9930 \|
	\| 16.0059 \| 14.0 \| 1960 \| 28.1502 \| 0.9843 \| 0.9814 \| 0.9828 \| 0.9946 \|
	\| 11.2302 \| 15.0 \| 2100 \| 29.2842 \| 0.9790 \| 0.9808 \| 0.9799 \| 0.9937 \|
	\| 11.2302 \| 16.0 \| 2240 \| 28.5446 \| 0.9819 \| 0.9796 \| 0.9807 \| 0.9945 \|
	\| 11.2302 \| 17.0 \| 2380 \| 25.4603 \| 0.9850 \| 0.9838 \| 0.9844 \| 0.9953 \|
	\| 8.9848 \| 18.0 \| 2520 \| 29.3936 \| 0.9801 \| 0.9760 \| 0.9780 \| 0.9929 \|
	\| 8.9848 \| 19.0 \| 2660 \| 31.2320 \| 0.9796 \| 0.9796 \| 0.9796 \| 0.9944 \|
	\| 8.9848 \| 20.0 \| 2800 \| 34.0474 \| 0.9849 \| 0.9802 \| 0.9825 \| 0.9943 \|
	\| 8.9848 \| 21.0 \| 2940 \| 32.9968 \| 0.9849 \| 0.9826 \| 0.9838 \| 0.9948 \|
	\| 8.2401 \| 22.0 \| 3080 \| 39.6873 \| 0.9819 \| 0.9808 \| 0.9814 \| 0.9946 \|
	\| 8.2401 \| 23.0 \| 3220 \| 42.7506 \| 0.9819 \| 0.9802 \| 0.9811 \| 0.9945 \|
	\| 8.2401 \| 24.0 \| 3360 \| 33.8886 \| 0.9856 \| 0.9862 \| 0.9859 \| 0.9954 \|
	\| 7.099 \| 25.0 \| 3500 \| 36.8275 \| 0.9819 \| 0.9808 \| 0.9814 \| 0.9941 \|
	\| 7.099 \| 26.0 \| 3640 \| 36.7838 \| 0.9831 \| 0.9814 \| 0.9823 \| 0.9951 \|
	\| 7.099 \| 27.0 \| 3780 \| 39.2226 \| 0.9813 \| 0.9790 \| 0.9801 \| 0.9947 \|
	\| 7.099 \| 28.0 \| 3920 \| 39.2492 \| 0.9843 \| 0.9820 \| 0.9832 \| 0.9949 \|
	\| 5.6646 \| 29.0 \| 4060 \| 41.4139 \| 0.9790 \| 0.9790 \| 0.9790 \| 0.9944 \|
	\| 5.6646 \| 30.0 \| 4200 \| 41.4583 \| 0.9838 \| 0.9826 \| 0.9832 \| 0.9949 \|
	\| 5.6646 \| 31.0 \| 4340 \| 47.1872 \| 0.9801 \| 0.9778 \| 0.9789 \| 0.9941 \|
	\| 5.6646 \| 32.0 \| 4480 \| 41.3073 \| 0.9862 \| 0.9844 \| 0.9853 \| 0.9956 \|
	\| 5.304 \| 33.0 \| 4620 \| 44.8882 \| 0.9796 \| 0.9790 \| 0.9793 \| 0.9945 \|
	\| 5.304 \| 34.0 \| 4760 \| 52.3203 \| 0.9783 \| 0.9772 \| 0.9778 \| 0.9941 \|
	\| 5.304 \| 35.0 \| 4900 \| 43.9140 \| 0.9825 \| 0.9808 \| 0.9817 \| 0.9951 \|
	\| 4.7574 \| 36.0 \| 5040 \| 46.8215 \| 0.9819 \| 0.9802 \| 0.9811 \| 0.9947 \|
	\| 4.7574 \| 37.0 \| 5180 \| 39.5738 \| 0.9867 \| 0.9844 \| 0.9856 \| 0.9959 \|
	\| 4.7574 \| 38.0 \| 5320 \| 39.9370 \| 0.9837 \| 0.9814 \| 0.9826 \| 0.9955 \|
	\| 4.7574 \| 39.0 \| 5460 \| 40.4614 \| 0.9856 \| 0.9844 \| 0.9850 \| 0.9956 \|
	\| 3.8125 \| 40.0 \| 5600 \| 38.6418 \| 0.9885 \| 0.9850 \| 0.9868 \| 0.9959 \|
	\| 3.8125 \| 41.0 \| 5740 \| 42.7438 \| 0.9813 \| 0.9796 \| 0.9805 \| 0.9947 \|
	\| 3.8125 \| 42.0 \| 5880 \| 52.7676 \| 0.9689 \| 0.9730 \| 0.9709 \| 0.9940 \|
	\| 3.2902 \| 43.0 \| 6020 \| 38.5737 \| 0.9825 \| 0.9808 \| 0.9817 \| 0.9953 \|
	\| 3.2902 \| 44.0 \| 6160 \| 42.4615 \| 0.9868 \| 0.9850 \| 0.9859 \| 0.9952 \|
	\| 3.2902 \| 45.0 \| 6300 \| 43.5099 \| 0.9856 \| 0.9838 \| 0.9847 \| 0.9956 \|
	\| 3.2902 \| 46.0 \| 6440 \| 45.0846 \| 0.9837 \| 0.9820 \| 0.9829 \| 0.9952 \|
	\| 4.0467 \| 47.0 \| 6580 \| 41.7571 \| 0.9862 \| 0.9850 \| 0.9856 \| 0.9955 \|
	\| 4.0467 \| 48.0 \| 6720 \| 50.8592 \| 0.9807 \| 0.9778 \| 0.9792 \| 0.9945 \|
	\| 4.0467 \| 49.0 \| 6860 \| 42.3155 \| 0.9886 \| 0.9862 \| 0.9874 \| 0.9956 \|
	\| 2.3503 \| 50.0 \| 7000 \| 45.7602 \| 0.9873 \| 0.9850 \| 0.9862 \| 0.9952 \|
	\| 2.3503 \| 51.0 \| 7140 \| 43.4314 \| 0.9856 \| 0.9838 \| 0.9847 \| 0.9953 \|
	\| 2.3503 \| 52.0 \| 7280 \| 47.4167 \| 0.9813 \| 0.9790 \| 0.9801 \| 0.9949 \|
	\| 2.3503 \| 53.0 \| 7420 \| 46.8868 \| 0.9838 \| 0.9826 \| 0.9832 \| 0.9952 \|
	\| 2.841 \| 54.0 \| 7560 \| 50.8428 \| 0.9843 \| 0.9814 \| 0.9828 \| 0.9950 \|
	\| 2.841 \| 55.0 \| 7700 \| 49.0097 \| 0.9825 \| 0.9808 \| 0.9817 \| 0.9949 \|
	\| 2.841 \| 56.0 \| 7840 \| 49.0165 \| 0.9831 \| 0.9802 \| 0.9816 \| 0.9950 \|
	\| 2.841 \| 57.0 \| 7980 \| 46.3213 \| 0.9838 \| 0.9826 \| 0.9832 \| 0.9953 \|
	\| 1.8064 \| 58.0 \| 8120 \| 49.3268 \| 0.9825 \| 0.9790 \| 0.9807 \| 0.9946 \|
	\| 1.8064 \| 59.0 \| 8260 \| 48.1988 \| 0.9849 \| 0.9814 \| 0.9831 \| 0.9952 \|
	\| 1.8064 \| 60.0 \| 8400 \| 46.5527 \| 0.9838 \| 0.9826 \| 0.9832 \| 0.9955 \|
	\| 1.5941 \| 61.0 \| 8540 \| 57.5747 \| 0.9807 \| 0.9790 \| 0.9798 \| 0.9942 \|
	\| 1.5941 \| 62.0 \| 8680 \| 56.6894 \| 0.9801 \| 0.9790 \| 0.9796 \| 0.9945 \|
	\| 1.5941 \| 63.0 \| 8820 \| 58.1243 \| 0.9808 \| 0.9802 \| 0.9805 \| 0.9945 \|
	\| 1.5941 \| 64.0 \| 8960 \| 53.2165 \| 0.9837 \| 0.9808 \| 0.9822 \| 0.9951 \|
	\| 1.5057 \| 65.0 \| 9100 \| 52.2484 \| 0.9832 \| 0.9820 \| 0.9826 \| 0.9949 \|
	\| 1.5057 \| 66.0 \| 9240 \| 49.2435 \| 0.9837 \| 0.9814 \| 0.9826 \| 0.9951 \|
	\| 1.5057 \| 67.0 \| 9380 \| 51.2186 \| 0.9796 \| 0.9790 \| 0.9793 \| 0.9948 \|
	\| 1.5084 \| 68.0 \| 9520 \| 54.1799 \| 0.9825 \| 0.9808 \| 0.9817 \| 0.9947 \|
	\| 1.5084 \| 69.0 \| 9660 \| 56.3696 \| 0.9807 \| 0.9778 \| 0.9792 \| 0.9945 \|
	\| 1.5084 \| 70.0 \| 9800 \| 52.6295 \| 0.9837 \| 0.9802 \| 0.9819 \| 0.9948 \|
	\| 1.5084 \| 71.0 \| 9940 \| 51.2577 \| 0.9825 \| 0.9790 \| 0.9807 \| 0.9950 \|
	\| 1.0448 \| 72.0 \| 10080 \| 56.0093 \| 0.9807 \| 0.9790 \| 0.9798 \| 0.9945 \|
	\| 1.0448 \| 73.0 \| 10220 \| 50.7540 \| 0.9831 \| 0.9808 \| 0.9819 \| 0.9951 \|
	\| 1.0448 \| 74.0 \| 10360 \| 52.9783 \| 0.9819 \| 0.9790 \| 0.9804 \| 0.9947 \|


	### Framework versions

	- Transformers 4.32.0
	- Pytorch 2.0.1+cu118
	- Datasets 2.14.4
	- Tokenizers 0.13.3

	---
	base_model: vinai/phobert-base
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: disfluency-large-3
	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. -->

	# disfluency-large-3

	This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 42.3155
	- Precision: 0.9886
	- Recall: 0.9862
	- F1: 0.9874
	- Accuracy: 0.9956

	## 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: 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: 100

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| No log \| 1.0 \| 140 \| 131.9614 \| 0.8037 \| 0.8438 \| 0.8232 \| 0.9411 \|
	\| No log \| 2.0 \| 280 \| 41.8031 \| 0.9487 \| 0.9549 \| 0.9518 \| 0.9855 \|
	\| No log \| 3.0 \| 420 \| 27.3502 \| 0.9664 \| 0.9681 \| 0.9673 \| 0.9906 \|
	\| 178.6738 \| 4.0 \| 560 \| 22.9255 \| 0.9741 \| 0.9730 \| 0.9735 \| 0.9925 \|
	\| 178.6738 \| 5.0 \| 700 \| 25.3163 \| 0.9676 \| 0.9688 \| 0.9682 \| 0.9919 \|
	\| 178.6738 \| 6.0 \| 840 \| 24.1142 \| 0.9723 \| 0.9718 \| 0.9720 \| 0.9925 \|
	\| 178.6738 \| 7.0 \| 980 \| 22.2517 \| 0.9777 \| 0.9766 \| 0.9771 \| 0.9938 \|
	\| 25.7318 \| 8.0 \| 1120 \| 24.4542 \| 0.9760 \| 0.9772 \| 0.9766 \| 0.9936 \|
	\| 25.7318 \| 9.0 \| 1260 \| 27.1333 \| 0.9740 \| 0.9700 \| 0.9720 \| 0.9929 \|
	\| 25.7318 \| 10.0 \| 1400 \| 24.5889 \| 0.9789 \| 0.9778 \| 0.9784 \| 0.9938 \|
	\| 16.0059 \| 11.0 \| 1540 \| 26.1038 \| 0.9819 \| 0.9808 \| 0.9814 \| 0.9936 \|
	\| 16.0059 \| 12.0 \| 1680 \| 23.3198 \| 0.9790 \| 0.9814 \| 0.9802 \| 0.9941 \|
	\| 16.0059 \| 13.0 \| 1820 \| 30.8831 \| 0.9778 \| 0.9772 \| 0.9775 \| 0.9930 \|
	\| 16.0059 \| 14.0 \| 1960 \| 28.1502 \| 0.9843 \| 0.9814 \| 0.9828 \| 0.9946 \|
	\| 11.2302 \| 15.0 \| 2100 \| 29.2842 \| 0.9790 \| 0.9808 \| 0.9799 \| 0.9937 \|
	\| 11.2302 \| 16.0 \| 2240 \| 28.5446 \| 0.9819 \| 0.9796 \| 0.9807 \| 0.9945 \|
	\| 11.2302 \| 17.0 \| 2380 \| 25.4603 \| 0.9850 \| 0.9838 \| 0.9844 \| 0.9953 \|
	\| 8.9848 \| 18.0 \| 2520 \| 29.3936 \| 0.9801 \| 0.9760 \| 0.9780 \| 0.9929 \|
	\| 8.9848 \| 19.0 \| 2660 \| 31.2320 \| 0.9796 \| 0.9796 \| 0.9796 \| 0.9944 \|
	\| 8.9848 \| 20.0 \| 2800 \| 34.0474 \| 0.9849 \| 0.9802 \| 0.9825 \| 0.9943 \|
	\| 8.9848 \| 21.0 \| 2940 \| 32.9968 \| 0.9849 \| 0.9826 \| 0.9838 \| 0.9948 \|
	\| 8.2401 \| 22.0 \| 3080 \| 39.6873 \| 0.9819 \| 0.9808 \| 0.9814 \| 0.9946 \|
	\| 8.2401 \| 23.0 \| 3220 \| 42.7506 \| 0.9819 \| 0.9802 \| 0.9811 \| 0.9945 \|
	\| 8.2401 \| 24.0 \| 3360 \| 33.8886 \| 0.9856 \| 0.9862 \| 0.9859 \| 0.9954 \|
	\| 7.099 \| 25.0 \| 3500 \| 36.8275 \| 0.9819 \| 0.9808 \| 0.9814 \| 0.9941 \|
	\| 7.099 \| 26.0 \| 3640 \| 36.7838 \| 0.9831 \| 0.9814 \| 0.9823 \| 0.9951 \|
	\| 7.099 \| 27.0 \| 3780 \| 39.2226 \| 0.9813 \| 0.9790 \| 0.9801 \| 0.9947 \|
	\| 7.099 \| 28.0 \| 3920 \| 39.2492 \| 0.9843 \| 0.9820 \| 0.9832 \| 0.9949 \|
	\| 5.6646 \| 29.0 \| 4060 \| 41.4139 \| 0.9790 \| 0.9790 \| 0.9790 \| 0.9944 \|
	\| 5.6646 \| 30.0 \| 4200 \| 41.4583 \| 0.9838 \| 0.9826 \| 0.9832 \| 0.9949 \|
	\| 5.6646 \| 31.0 \| 4340 \| 47.1872 \| 0.9801 \| 0.9778 \| 0.9789 \| 0.9941 \|
	\| 5.6646 \| 32.0 \| 4480 \| 41.3073 \| 0.9862 \| 0.9844 \| 0.9853 \| 0.9956 \|
	\| 5.304 \| 33.0 \| 4620 \| 44.8882 \| 0.9796 \| 0.9790 \| 0.9793 \| 0.9945 \|
	\| 5.304 \| 34.0 \| 4760 \| 52.3203 \| 0.9783 \| 0.9772 \| 0.9778 \| 0.9941 \|
	\| 5.304 \| 35.0 \| 4900 \| 43.9140 \| 0.9825 \| 0.9808 \| 0.9817 \| 0.9951 \|
	\| 4.7574 \| 36.0 \| 5040 \| 46.8215 \| 0.9819 \| 0.9802 \| 0.9811 \| 0.9947 \|
	\| 4.7574 \| 37.0 \| 5180 \| 39.5738 \| 0.9867 \| 0.9844 \| 0.9856 \| 0.9959 \|
	\| 4.7574 \| 38.0 \| 5320 \| 39.9370 \| 0.9837 \| 0.9814 \| 0.9826 \| 0.9955 \|
	\| 4.7574 \| 39.0 \| 5460 \| 40.4614 \| 0.9856 \| 0.9844 \| 0.9850 \| 0.9956 \|
	\| 3.8125 \| 40.0 \| 5600 \| 38.6418 \| 0.9885 \| 0.9850 \| 0.9868 \| 0.9959 \|
	\| 3.8125 \| 41.0 \| 5740 \| 42.7438 \| 0.9813 \| 0.9796 \| 0.9805 \| 0.9947 \|
	\| 3.8125 \| 42.0 \| 5880 \| 52.7676 \| 0.9689 \| 0.9730 \| 0.9709 \| 0.9940 \|
	\| 3.2902 \| 43.0 \| 6020 \| 38.5737 \| 0.9825 \| 0.9808 \| 0.9817 \| 0.9953 \|
	\| 3.2902 \| 44.0 \| 6160 \| 42.4615 \| 0.9868 \| 0.9850 \| 0.9859 \| 0.9952 \|
	\| 3.2902 \| 45.0 \| 6300 \| 43.5099 \| 0.9856 \| 0.9838 \| 0.9847 \| 0.9956 \|
	\| 3.2902 \| 46.0 \| 6440 \| 45.0846 \| 0.9837 \| 0.9820 \| 0.9829 \| 0.9952 \|
	\| 4.0467 \| 47.0 \| 6580 \| 41.7571 \| 0.9862 \| 0.9850 \| 0.9856 \| 0.9955 \|
	\| 4.0467 \| 48.0 \| 6720 \| 50.8592 \| 0.9807 \| 0.9778 \| 0.9792 \| 0.9945 \|
	\| 4.0467 \| 49.0 \| 6860 \| 42.3155 \| 0.9886 \| 0.9862 \| 0.9874 \| 0.9956 \|
	\| 2.3503 \| 50.0 \| 7000 \| 45.7602 \| 0.9873 \| 0.9850 \| 0.9862 \| 0.9952 \|
	\| 2.3503 \| 51.0 \| 7140 \| 43.4314 \| 0.9856 \| 0.9838 \| 0.9847 \| 0.9953 \|
	\| 2.3503 \| 52.0 \| 7280 \| 47.4167 \| 0.9813 \| 0.9790 \| 0.9801 \| 0.9949 \|
	\| 2.3503 \| 53.0 \| 7420 \| 46.8868 \| 0.9838 \| 0.9826 \| 0.9832 \| 0.9952 \|
	\| 2.841 \| 54.0 \| 7560 \| 50.8428 \| 0.9843 \| 0.9814 \| 0.9828 \| 0.9950 \|
	\| 2.841 \| 55.0 \| 7700 \| 49.0097 \| 0.9825 \| 0.9808 \| 0.9817 \| 0.9949 \|
	\| 2.841 \| 56.0 \| 7840 \| 49.0165 \| 0.9831 \| 0.9802 \| 0.9816 \| 0.9950 \|
	\| 2.841 \| 57.0 \| 7980 \| 46.3213 \| 0.9838 \| 0.9826 \| 0.9832 \| 0.9953 \|
	\| 1.8064 \| 58.0 \| 8120 \| 49.3268 \| 0.9825 \| 0.9790 \| 0.9807 \| 0.9946 \|
	\| 1.8064 \| 59.0 \| 8260 \| 48.1988 \| 0.9849 \| 0.9814 \| 0.9831 \| 0.9952 \|
	\| 1.8064 \| 60.0 \| 8400 \| 46.5527 \| 0.9838 \| 0.9826 \| 0.9832 \| 0.9955 \|
	\| 1.5941 \| 61.0 \| 8540 \| 57.5747 \| 0.9807 \| 0.9790 \| 0.9798 \| 0.9942 \|
	\| 1.5941 \| 62.0 \| 8680 \| 56.6894 \| 0.9801 \| 0.9790 \| 0.9796 \| 0.9945 \|
	\| 1.5941 \| 63.0 \| 8820 \| 58.1243 \| 0.9808 \| 0.9802 \| 0.9805 \| 0.9945 \|
	\| 1.5941 \| 64.0 \| 8960 \| 53.2165 \| 0.9837 \| 0.9808 \| 0.9822 \| 0.9951 \|
	\| 1.5057 \| 65.0 \| 9100 \| 52.2484 \| 0.9832 \| 0.9820 \| 0.9826 \| 0.9949 \|
	\| 1.5057 \| 66.0 \| 9240 \| 49.2435 \| 0.9837 \| 0.9814 \| 0.9826 \| 0.9951 \|
	\| 1.5057 \| 67.0 \| 9380 \| 51.2186 \| 0.9796 \| 0.9790 \| 0.9793 \| 0.9948 \|
	\| 1.5084 \| 68.0 \| 9520 \| 54.1799 \| 0.9825 \| 0.9808 \| 0.9817 \| 0.9947 \|
	\| 1.5084 \| 69.0 \| 9660 \| 56.3696 \| 0.9807 \| 0.9778 \| 0.9792 \| 0.9945 \|
	\| 1.5084 \| 70.0 \| 9800 \| 52.6295 \| 0.9837 \| 0.9802 \| 0.9819 \| 0.9948 \|
	\| 1.5084 \| 71.0 \| 9940 \| 51.2577 \| 0.9825 \| 0.9790 \| 0.9807 \| 0.9950 \|
	\| 1.0448 \| 72.0 \| 10080 \| 56.0093 \| 0.9807 \| 0.9790 \| 0.9798 \| 0.9945 \|
	\| 1.0448 \| 73.0 \| 10220 \| 50.7540 \| 0.9831 \| 0.9808 \| 0.9819 \| 0.9951 \|
	\| 1.0448 \| 74.0 \| 10360 \| 52.9783 \| 0.9819 \| 0.9790 \| 0.9804 \| 0.9947 \|


	### Framework versions

	- Transformers 4.32.0
	- Pytorch 2.0.1+cu118
	- Datasets 2.14.4
	- Tokenizers 0.13.3