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	---
	library_name: transformers
	license: mit
	base_model: microsoft/mdeberta-v3-base
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: scenario-non-kd-scr-ner-half-mdeberta_data-univner_full55
	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. -->

	# scenario-non-kd-scr-ner-half-mdeberta_data-univner_full55

	This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.3355
	- Precision: 0.6133
	- Recall: 0.5878
	- F1: 0.6003
	- Accuracy: 0.9612

	## 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: 3e-05
	- train_batch_size: 32
	- eval_batch_size: 32
	- seed: 55
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 30

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 0.3541 \| 0.2910 \| 500 \| 0.2833 \| 0.3113 \| 0.1163 \| 0.1693 \| 0.9287 \|
	\| 0.2371 \| 0.5821 \| 1000 \| 0.2120 \| 0.3611 \| 0.2493 \| 0.2950 \| 0.9382 \|
	\| 0.1767 \| 0.8731 \| 1500 \| 0.1741 \| 0.4010 \| 0.3865 \| 0.3936 \| 0.9464 \|
	\| 0.1351 \| 1.1641 \| 2000 \| 0.1657 \| 0.4667 \| 0.4367 \| 0.4512 \| 0.9511 \|
	\| 0.1102 \| 1.4552 \| 2500 \| 0.1532 \| 0.4867 \| 0.5260 \| 0.5056 \| 0.9542 \|
	\| 0.1002 \| 1.7462 \| 3000 \| 0.1520 \| 0.5199 \| 0.5269 \| 0.5234 \| 0.9565 \|
	\| 0.0899 \| 2.0373 \| 3500 \| 0.1584 \| 0.5512 \| 0.5116 \| 0.5307 \| 0.9574 \|
	\| 0.0681 \| 2.3283 \| 4000 \| 0.1583 \| 0.5564 \| 0.5308 \| 0.5433 \| 0.9583 \|
	\| 0.0647 \| 2.6193 \| 4500 \| 0.1532 \| 0.5687 \| 0.5555 \| 0.5620 \| 0.9595 \|
	\| 0.0637 \| 2.9104 \| 5000 \| 0.1519 \| 0.5798 \| 0.5726 \| 0.5762 \| 0.9605 \|
	\| 0.05 \| 3.2014 \| 5500 \| 0.1600 \| 0.5696 \| 0.5904 \| 0.5798 \| 0.9596 \|
	\| 0.0449 \| 3.4924 \| 6000 \| 0.1660 \| 0.5790 \| 0.5754 \| 0.5772 \| 0.9602 \|
	\| 0.0437 \| 3.7835 \| 6500 \| 0.1589 \| 0.5820 \| 0.5885 \| 0.5853 \| 0.9610 \|
	\| 0.0396 \| 4.0745 \| 7000 \| 0.1690 \| 0.5779 \| 0.5926 \| 0.5851 \| 0.9608 \|
	\| 0.0293 \| 4.3655 \| 7500 \| 0.1803 \| 0.5815 \| 0.5845 \| 0.5830 \| 0.9609 \|
	\| 0.0325 \| 4.6566 \| 8000 \| 0.1726 \| 0.5927 \| 0.5953 \| 0.5940 \| 0.9615 \|
	\| 0.0314 \| 4.9476 \| 8500 \| 0.1743 \| 0.5756 \| 0.6148 \| 0.5945 \| 0.9604 \|
	\| 0.0215 \| 5.2386 \| 9000 \| 0.1860 \| 0.5725 \| 0.6006 \| 0.5863 \| 0.9604 \|
	\| 0.0205 \| 5.5297 \| 9500 \| 0.1973 \| 0.5849 \| 0.5838 \| 0.5843 \| 0.9604 \|
	\| 0.0223 \| 5.8207 \| 10000 \| 0.1943 \| 0.6066 \| 0.5917 \| 0.5990 \| 0.9612 \|
	\| 0.0205 \| 6.1118 \| 10500 \| 0.2040 \| 0.6086 \| 0.5868 \| 0.5975 \| 0.9611 \|
	\| 0.0155 \| 6.4028 \| 11000 \| 0.2090 \| 0.5869 \| 0.5963 \| 0.5916 \| 0.9607 \|
	\| 0.0163 \| 6.6938 \| 11500 \| 0.2104 \| 0.5972 \| 0.5874 \| 0.5922 \| 0.9610 \|
	\| 0.0168 \| 6.9849 \| 12000 \| 0.2088 \| 0.5784 \| 0.5976 \| 0.5879 \| 0.9603 \|
	\| 0.0106 \| 7.2759 \| 12500 \| 0.2262 \| 0.5997 \| 0.5872 \| 0.5934 \| 0.9605 \|
	\| 0.0114 \| 7.5669 \| 13000 \| 0.2251 \| 0.6102 \| 0.5842 \| 0.5969 \| 0.9616 \|
	\| 0.0122 \| 7.8580 \| 13500 \| 0.2244 \| 0.5989 \| 0.5940 \| 0.5965 \| 0.9606 \|
	\| 0.0111 \| 8.1490 \| 14000 \| 0.2333 \| 0.5996 \| 0.5825 \| 0.5909 \| 0.9612 \|
	\| 0.0086 \| 8.4400 \| 14500 \| 0.2320 \| 0.5881 \| 0.5960 \| 0.5920 \| 0.9603 \|
	\| 0.0089 \| 8.7311 \| 15000 \| 0.2440 \| 0.6076 \| 0.5852 \| 0.5962 \| 0.9610 \|
	\| 0.0087 \| 9.0221 \| 15500 \| 0.2407 \| 0.5978 \| 0.5897 \| 0.5937 \| 0.9612 \|
	\| 0.0065 \| 9.3132 \| 16000 \| 0.2479 \| 0.6046 \| 0.5827 \| 0.5934 \| 0.9613 \|
	\| 0.0074 \| 9.6042 \| 16500 \| 0.2458 \| 0.6007 \| 0.5864 \| 0.5934 \| 0.9609 \|
	\| 0.0076 \| 9.8952 \| 17000 \| 0.2495 \| 0.5944 \| 0.5920 \| 0.5932 \| 0.9608 \|
	\| 0.0055 \| 10.1863 \| 17500 \| 0.2517 \| 0.6030 \| 0.5933 \| 0.5981 \| 0.9610 \|
	\| 0.0048 \| 10.4773 \| 18000 \| 0.2694 \| 0.5975 \| 0.5778 \| 0.5875 \| 0.9602 \|
	\| 0.0049 \| 10.7683 \| 18500 \| 0.2642 \| 0.6110 \| 0.5882 \| 0.5994 \| 0.9608 \|
	\| 0.0061 \| 11.0594 \| 19000 \| 0.2776 \| 0.6150 \| 0.5651 \| 0.5890 \| 0.9612 \|
	\| 0.0037 \| 11.3504 \| 19500 \| 0.2723 \| 0.6132 \| 0.5842 \| 0.5983 \| 0.9613 \|
	\| 0.0045 \| 11.6414 \| 20000 \| 0.2687 \| 0.6065 \| 0.5832 \| 0.5946 \| 0.9607 \|
	\| 0.0047 \| 11.9325 \| 20500 \| 0.2776 \| 0.6153 \| 0.5673 \| 0.5903 \| 0.9610 \|
	\| 0.004 \| 12.2235 \| 21000 \| 0.2806 \| 0.6030 \| 0.5763 \| 0.5893 \| 0.9612 \|
	\| 0.0033 \| 12.5146 \| 21500 \| 0.2838 \| 0.6173 \| 0.5791 \| 0.5976 \| 0.9617 \|
	\| 0.0038 \| 12.8056 \| 22000 \| 0.2884 \| 0.6175 \| 0.5705 \| 0.5931 \| 0.9611 \|
	\| 0.0034 \| 13.0966 \| 22500 \| 0.2863 \| 0.6082 \| 0.5843 \| 0.5960 \| 0.9611 \|
	\| 0.0023 \| 13.3877 \| 23000 \| 0.2905 \| 0.6222 \| 0.5806 \| 0.6007 \| 0.9618 \|
	\| 0.003 \| 13.6787 \| 23500 \| 0.2897 \| 0.6094 \| 0.5885 \| 0.5988 \| 0.9612 \|
	\| 0.0034 \| 13.9697 \| 24000 \| 0.2909 \| 0.6126 \| 0.5820 \| 0.5969 \| 0.9611 \|
	\| 0.0021 \| 14.2608 \| 24500 \| 0.2951 \| 0.5846 \| 0.6029 \| 0.5936 \| 0.9604 \|
	\| 0.0028 \| 14.5518 \| 25000 \| 0.2899 \| 0.6086 \| 0.5913 \| 0.5998 \| 0.9612 \|
	\| 0.0025 \| 14.8428 \| 25500 \| 0.3014 \| 0.6205 \| 0.5719 \| 0.5952 \| 0.9610 \|
	\| 0.0024 \| 15.1339 \| 26000 \| 0.3018 \| 0.6173 \| 0.5745 \| 0.5951 \| 0.9610 \|
	\| 0.0019 \| 15.4249 \| 26500 \| 0.3058 \| 0.6235 \| 0.5738 \| 0.5976 \| 0.9614 \|
	\| 0.0021 \| 15.7159 \| 27000 \| 0.3053 \| 0.6220 \| 0.5868 \| 0.6039 \| 0.9613 \|
	\| 0.0019 \| 16.0070 \| 27500 \| 0.3142 \| 0.6098 \| 0.5689 \| 0.5886 \| 0.9608 \|
	\| 0.0018 \| 16.2980 \| 28000 \| 0.2999 \| 0.6057 \| 0.5985 \| 0.6021 \| 0.9615 \|
	\| 0.0017 \| 16.5891 \| 28500 \| 0.3096 \| 0.6015 \| 0.5822 \| 0.5917 \| 0.9605 \|
	\| 0.0017 \| 16.8801 \| 29000 \| 0.3091 \| 0.6159 \| 0.5840 \| 0.5995 \| 0.9613 \|
	\| 0.0023 \| 17.1711 \| 29500 \| 0.3051 \| 0.6161 \| 0.5913 \| 0.6034 \| 0.9615 \|
	\| 0.0012 \| 17.4622 \| 30000 \| 0.3167 \| 0.6283 \| 0.5722 \| 0.5990 \| 0.9612 \|
	\| 0.0012 \| 17.7532 \| 30500 \| 0.3246 \| 0.6197 \| 0.5682 \| 0.5928 \| 0.9612 \|
	\| 0.002 \| 18.0442 \| 31000 \| 0.3197 \| 0.6020 \| 0.5887 \| 0.5953 \| 0.9608 \|
	\| 0.0013 \| 18.3353 \| 31500 \| 0.3146 \| 0.6031 \| 0.5923 \| 0.5977 \| 0.9610 \|
	\| 0.0015 \| 18.6263 \| 32000 \| 0.3228 \| 0.6096 \| 0.5827 \| 0.5959 \| 0.9612 \|
	\| 0.0011 \| 18.9173 \| 32500 \| 0.3248 \| 0.6178 \| 0.5731 \| 0.5946 \| 0.9611 \|
	\| 0.0011 \| 19.2084 \| 33000 \| 0.3195 \| 0.6125 \| 0.5904 \| 0.6012 \| 0.9611 \|
	\| 0.0011 \| 19.4994 \| 33500 \| 0.3340 \| 0.6205 \| 0.5646 \| 0.5912 \| 0.9613 \|
	\| 0.0012 \| 19.7905 \| 34000 \| 0.3270 \| 0.6077 \| 0.5839 \| 0.5956 \| 0.9612 \|
	\| 0.0012 \| 20.0815 \| 34500 \| 0.3231 \| 0.6135 \| 0.5928 \| 0.6030 \| 0.9612 \|
	\| 0.0012 \| 20.3725 \| 35000 \| 0.3282 \| 0.6126 \| 0.5803 \| 0.5960 \| 0.9612 \|
	\| 0.001 \| 20.6636 \| 35500 \| 0.3340 \| 0.5999 \| 0.5851 \| 0.5924 \| 0.9605 \|
	\| 0.0009 \| 20.9546 \| 36000 \| 0.3358 \| 0.6126 \| 0.5706 \| 0.5909 \| 0.9608 \|
	\| 0.001 \| 21.2456 \| 36500 \| 0.3300 \| 0.6039 \| 0.5851 \| 0.5943 \| 0.9606 \|
	\| 0.0006 \| 21.5367 \| 37000 \| 0.3355 \| 0.6133 \| 0.5878 \| 0.6003 \| 0.9612 \|


	### Framework versions

	- Transformers 4.44.2
	- Pytorch 2.1.1+cu121
	- Datasets 2.14.5
	- Tokenizers 0.19.1

	---
	library_name: transformers
	license: mit
	base_model: microsoft/mdeberta-v3-base
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: scenario-non-kd-scr-ner-half-mdeberta_data-univner_full55
	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. -->

	# scenario-non-kd-scr-ner-half-mdeberta_data-univner_full55

	This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.3355
	- Precision: 0.6133
	- Recall: 0.5878
	- F1: 0.6003
	- Accuracy: 0.9612

	## 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: 3e-05
	- train_batch_size: 32
	- eval_batch_size: 32
	- seed: 55
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 30

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 0.3541 \| 0.2910 \| 500 \| 0.2833 \| 0.3113 \| 0.1163 \| 0.1693 \| 0.9287 \|
	\| 0.2371 \| 0.5821 \| 1000 \| 0.2120 \| 0.3611 \| 0.2493 \| 0.2950 \| 0.9382 \|
	\| 0.1767 \| 0.8731 \| 1500 \| 0.1741 \| 0.4010 \| 0.3865 \| 0.3936 \| 0.9464 \|
	\| 0.1351 \| 1.1641 \| 2000 \| 0.1657 \| 0.4667 \| 0.4367 \| 0.4512 \| 0.9511 \|
	\| 0.1102 \| 1.4552 \| 2500 \| 0.1532 \| 0.4867 \| 0.5260 \| 0.5056 \| 0.9542 \|
	\| 0.1002 \| 1.7462 \| 3000 \| 0.1520 \| 0.5199 \| 0.5269 \| 0.5234 \| 0.9565 \|
	\| 0.0899 \| 2.0373 \| 3500 \| 0.1584 \| 0.5512 \| 0.5116 \| 0.5307 \| 0.9574 \|
	\| 0.0681 \| 2.3283 \| 4000 \| 0.1583 \| 0.5564 \| 0.5308 \| 0.5433 \| 0.9583 \|
	\| 0.0647 \| 2.6193 \| 4500 \| 0.1532 \| 0.5687 \| 0.5555 \| 0.5620 \| 0.9595 \|
	\| 0.0637 \| 2.9104 \| 5000 \| 0.1519 \| 0.5798 \| 0.5726 \| 0.5762 \| 0.9605 \|
	\| 0.05 \| 3.2014 \| 5500 \| 0.1600 \| 0.5696 \| 0.5904 \| 0.5798 \| 0.9596 \|
	\| 0.0449 \| 3.4924 \| 6000 \| 0.1660 \| 0.5790 \| 0.5754 \| 0.5772 \| 0.9602 \|
	\| 0.0437 \| 3.7835 \| 6500 \| 0.1589 \| 0.5820 \| 0.5885 \| 0.5853 \| 0.9610 \|
	\| 0.0396 \| 4.0745 \| 7000 \| 0.1690 \| 0.5779 \| 0.5926 \| 0.5851 \| 0.9608 \|
	\| 0.0293 \| 4.3655 \| 7500 \| 0.1803 \| 0.5815 \| 0.5845 \| 0.5830 \| 0.9609 \|
	\| 0.0325 \| 4.6566 \| 8000 \| 0.1726 \| 0.5927 \| 0.5953 \| 0.5940 \| 0.9615 \|
	\| 0.0314 \| 4.9476 \| 8500 \| 0.1743 \| 0.5756 \| 0.6148 \| 0.5945 \| 0.9604 \|
	\| 0.0215 \| 5.2386 \| 9000 \| 0.1860 \| 0.5725 \| 0.6006 \| 0.5863 \| 0.9604 \|
	\| 0.0205 \| 5.5297 \| 9500 \| 0.1973 \| 0.5849 \| 0.5838 \| 0.5843 \| 0.9604 \|
	\| 0.0223 \| 5.8207 \| 10000 \| 0.1943 \| 0.6066 \| 0.5917 \| 0.5990 \| 0.9612 \|
	\| 0.0205 \| 6.1118 \| 10500 \| 0.2040 \| 0.6086 \| 0.5868 \| 0.5975 \| 0.9611 \|
	\| 0.0155 \| 6.4028 \| 11000 \| 0.2090 \| 0.5869 \| 0.5963 \| 0.5916 \| 0.9607 \|
	\| 0.0163 \| 6.6938 \| 11500 \| 0.2104 \| 0.5972 \| 0.5874 \| 0.5922 \| 0.9610 \|
	\| 0.0168 \| 6.9849 \| 12000 \| 0.2088 \| 0.5784 \| 0.5976 \| 0.5879 \| 0.9603 \|
	\| 0.0106 \| 7.2759 \| 12500 \| 0.2262 \| 0.5997 \| 0.5872 \| 0.5934 \| 0.9605 \|
	\| 0.0114 \| 7.5669 \| 13000 \| 0.2251 \| 0.6102 \| 0.5842 \| 0.5969 \| 0.9616 \|
	\| 0.0122 \| 7.8580 \| 13500 \| 0.2244 \| 0.5989 \| 0.5940 \| 0.5965 \| 0.9606 \|
	\| 0.0111 \| 8.1490 \| 14000 \| 0.2333 \| 0.5996 \| 0.5825 \| 0.5909 \| 0.9612 \|
	\| 0.0086 \| 8.4400 \| 14500 \| 0.2320 \| 0.5881 \| 0.5960 \| 0.5920 \| 0.9603 \|
	\| 0.0089 \| 8.7311 \| 15000 \| 0.2440 \| 0.6076 \| 0.5852 \| 0.5962 \| 0.9610 \|
	\| 0.0087 \| 9.0221 \| 15500 \| 0.2407 \| 0.5978 \| 0.5897 \| 0.5937 \| 0.9612 \|
	\| 0.0065 \| 9.3132 \| 16000 \| 0.2479 \| 0.6046 \| 0.5827 \| 0.5934 \| 0.9613 \|
	\| 0.0074 \| 9.6042 \| 16500 \| 0.2458 \| 0.6007 \| 0.5864 \| 0.5934 \| 0.9609 \|
	\| 0.0076 \| 9.8952 \| 17000 \| 0.2495 \| 0.5944 \| 0.5920 \| 0.5932 \| 0.9608 \|
	\| 0.0055 \| 10.1863 \| 17500 \| 0.2517 \| 0.6030 \| 0.5933 \| 0.5981 \| 0.9610 \|
	\| 0.0048 \| 10.4773 \| 18000 \| 0.2694 \| 0.5975 \| 0.5778 \| 0.5875 \| 0.9602 \|
	\| 0.0049 \| 10.7683 \| 18500 \| 0.2642 \| 0.6110 \| 0.5882 \| 0.5994 \| 0.9608 \|
	\| 0.0061 \| 11.0594 \| 19000 \| 0.2776 \| 0.6150 \| 0.5651 \| 0.5890 \| 0.9612 \|
	\| 0.0037 \| 11.3504 \| 19500 \| 0.2723 \| 0.6132 \| 0.5842 \| 0.5983 \| 0.9613 \|
	\| 0.0045 \| 11.6414 \| 20000 \| 0.2687 \| 0.6065 \| 0.5832 \| 0.5946 \| 0.9607 \|
	\| 0.0047 \| 11.9325 \| 20500 \| 0.2776 \| 0.6153 \| 0.5673 \| 0.5903 \| 0.9610 \|
	\| 0.004 \| 12.2235 \| 21000 \| 0.2806 \| 0.6030 \| 0.5763 \| 0.5893 \| 0.9612 \|
	\| 0.0033 \| 12.5146 \| 21500 \| 0.2838 \| 0.6173 \| 0.5791 \| 0.5976 \| 0.9617 \|
	\| 0.0038 \| 12.8056 \| 22000 \| 0.2884 \| 0.6175 \| 0.5705 \| 0.5931 \| 0.9611 \|
	\| 0.0034 \| 13.0966 \| 22500 \| 0.2863 \| 0.6082 \| 0.5843 \| 0.5960 \| 0.9611 \|
	\| 0.0023 \| 13.3877 \| 23000 \| 0.2905 \| 0.6222 \| 0.5806 \| 0.6007 \| 0.9618 \|
	\| 0.003 \| 13.6787 \| 23500 \| 0.2897 \| 0.6094 \| 0.5885 \| 0.5988 \| 0.9612 \|
	\| 0.0034 \| 13.9697 \| 24000 \| 0.2909 \| 0.6126 \| 0.5820 \| 0.5969 \| 0.9611 \|
	\| 0.0021 \| 14.2608 \| 24500 \| 0.2951 \| 0.5846 \| 0.6029 \| 0.5936 \| 0.9604 \|
	\| 0.0028 \| 14.5518 \| 25000 \| 0.2899 \| 0.6086 \| 0.5913 \| 0.5998 \| 0.9612 \|
	\| 0.0025 \| 14.8428 \| 25500 \| 0.3014 \| 0.6205 \| 0.5719 \| 0.5952 \| 0.9610 \|
	\| 0.0024 \| 15.1339 \| 26000 \| 0.3018 \| 0.6173 \| 0.5745 \| 0.5951 \| 0.9610 \|
	\| 0.0019 \| 15.4249 \| 26500 \| 0.3058 \| 0.6235 \| 0.5738 \| 0.5976 \| 0.9614 \|
	\| 0.0021 \| 15.7159 \| 27000 \| 0.3053 \| 0.6220 \| 0.5868 \| 0.6039 \| 0.9613 \|
	\| 0.0019 \| 16.0070 \| 27500 \| 0.3142 \| 0.6098 \| 0.5689 \| 0.5886 \| 0.9608 \|
	\| 0.0018 \| 16.2980 \| 28000 \| 0.2999 \| 0.6057 \| 0.5985 \| 0.6021 \| 0.9615 \|
	\| 0.0017 \| 16.5891 \| 28500 \| 0.3096 \| 0.6015 \| 0.5822 \| 0.5917 \| 0.9605 \|
	\| 0.0017 \| 16.8801 \| 29000 \| 0.3091 \| 0.6159 \| 0.5840 \| 0.5995 \| 0.9613 \|
	\| 0.0023 \| 17.1711 \| 29500 \| 0.3051 \| 0.6161 \| 0.5913 \| 0.6034 \| 0.9615 \|
	\| 0.0012 \| 17.4622 \| 30000 \| 0.3167 \| 0.6283 \| 0.5722 \| 0.5990 \| 0.9612 \|
	\| 0.0012 \| 17.7532 \| 30500 \| 0.3246 \| 0.6197 \| 0.5682 \| 0.5928 \| 0.9612 \|
	\| 0.002 \| 18.0442 \| 31000 \| 0.3197 \| 0.6020 \| 0.5887 \| 0.5953 \| 0.9608 \|
	\| 0.0013 \| 18.3353 \| 31500 \| 0.3146 \| 0.6031 \| 0.5923 \| 0.5977 \| 0.9610 \|
	\| 0.0015 \| 18.6263 \| 32000 \| 0.3228 \| 0.6096 \| 0.5827 \| 0.5959 \| 0.9612 \|
	\| 0.0011 \| 18.9173 \| 32500 \| 0.3248 \| 0.6178 \| 0.5731 \| 0.5946 \| 0.9611 \|
	\| 0.0011 \| 19.2084 \| 33000 \| 0.3195 \| 0.6125 \| 0.5904 \| 0.6012 \| 0.9611 \|
	\| 0.0011 \| 19.4994 \| 33500 \| 0.3340 \| 0.6205 \| 0.5646 \| 0.5912 \| 0.9613 \|
	\| 0.0012 \| 19.7905 \| 34000 \| 0.3270 \| 0.6077 \| 0.5839 \| 0.5956 \| 0.9612 \|
	\| 0.0012 \| 20.0815 \| 34500 \| 0.3231 \| 0.6135 \| 0.5928 \| 0.6030 \| 0.9612 \|
	\| 0.0012 \| 20.3725 \| 35000 \| 0.3282 \| 0.6126 \| 0.5803 \| 0.5960 \| 0.9612 \|
	\| 0.001 \| 20.6636 \| 35500 \| 0.3340 \| 0.5999 \| 0.5851 \| 0.5924 \| 0.9605 \|
	\| 0.0009 \| 20.9546 \| 36000 \| 0.3358 \| 0.6126 \| 0.5706 \| 0.5909 \| 0.9608 \|
	\| 0.001 \| 21.2456 \| 36500 \| 0.3300 \| 0.6039 \| 0.5851 \| 0.5943 \| 0.9606 \|
	\| 0.0006 \| 21.5367 \| 37000 \| 0.3355 \| 0.6133 \| 0.5878 \| 0.6003 \| 0.9612 \|


	### Framework versions

	- Transformers 4.44.2
	- Pytorch 2.1.1+cu121
	- Datasets 2.14.5
	- Tokenizers 0.19.1