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	---
	library_name: transformers
	license: mit
	base_model: FacebookAI/xlm-roberta-base
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: scenario-kd-pre-ner-full_data-univner_full44
	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-kd-pre-ner-full_data-univner_full44

	This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.4381
	- Precision: 0.8004
	- Recall: 0.7801
	- F1: 0.7902
	- Accuracy: 0.9786

	## 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: 44
	- 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 \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 1.4593 \| 0.5828 \| 500 \| 0.8367 \| 0.6935 \| 0.6559 \| 0.6742 \| 0.9682 \|
	\| 0.7232 \| 1.1655 \| 1000 \| 0.7569 \| 0.7339 \| 0.6980 \| 0.7155 \| 0.9724 \|
	\| 0.594 \| 1.7483 \| 1500 \| 0.6330 \| 0.7335 \| 0.7451 \| 0.7392 \| 0.9741 \|
	\| 0.4986 \| 2.3310 \| 2000 \| 0.6003 \| 0.7291 \| 0.7552 \| 0.7419 \| 0.9746 \|
	\| 0.446 \| 2.9138 \| 2500 \| 0.5729 \| 0.7403 \| 0.7601 \| 0.7501 \| 0.9747 \|
	\| 0.385 \| 3.4965 \| 3000 \| 0.5584 \| 0.7441 \| 0.7617 \| 0.7528 \| 0.9757 \|
	\| 0.3605 \| 4.0793 \| 3500 \| 0.5602 \| 0.7615 \| 0.7575 \| 0.7595 \| 0.9758 \|
	\| 0.3172 \| 4.6620 \| 4000 \| 0.5417 \| 0.7546 \| 0.7725 \| 0.7634 \| 0.9764 \|
	\| 0.3061 \| 5.2448 \| 4500 \| 0.5329 \| 0.7884 \| 0.7485 \| 0.7680 \| 0.9769 \|
	\| 0.2856 \| 5.8275 \| 5000 \| 0.5194 \| 0.7837 \| 0.7618 \| 0.7726 \| 0.9769 \|
	\| 0.2642 \| 6.4103 \| 5500 \| 0.5154 \| 0.7622 \| 0.7780 \| 0.7700 \| 0.9765 \|
	\| 0.2592 \| 6.9930 \| 6000 \| 0.5193 \| 0.7882 \| 0.7572 \| 0.7724 \| 0.9764 \|
	\| 0.2401 \| 7.5758 \| 6500 \| 0.5123 \| 0.7727 \| 0.7599 \| 0.7663 \| 0.9763 \|
	\| 0.2344 \| 8.1585 \| 7000 \| 0.4987 \| 0.7742 \| 0.7736 \| 0.7739 \| 0.9771 \|
	\| 0.2234 \| 8.7413 \| 7500 \| 0.4914 \| 0.7894 \| 0.7640 \| 0.7764 \| 0.9777 \|
	\| 0.2131 \| 9.3240 \| 8000 \| 0.4856 \| 0.7691 \| 0.7827 \| 0.7758 \| 0.9770 \|
	\| 0.2089 \| 9.9068 \| 8500 \| 0.4898 \| 0.7895 \| 0.7655 \| 0.7773 \| 0.9773 \|
	\| 0.1972 \| 10.4895 \| 9000 \| 0.4860 \| 0.7828 \| 0.7726 \| 0.7777 \| 0.9775 \|
	\| 0.1942 \| 11.0723 \| 9500 \| 0.4787 \| 0.7807 \| 0.7807 \| 0.7807 \| 0.9776 \|
	\| 0.1854 \| 11.6550 \| 10000 \| 0.4858 \| 0.7916 \| 0.7635 \| 0.7773 \| 0.9771 \|
	\| 0.183 \| 12.2378 \| 10500 \| 0.4739 \| 0.7924 \| 0.7800 \| 0.7862 \| 0.9779 \|
	\| 0.1781 \| 12.8205 \| 11000 \| 0.4741 \| 0.7990 \| 0.7661 \| 0.7822 \| 0.9779 \|
	\| 0.1704 \| 13.4033 \| 11500 \| 0.4622 \| 0.7937 \| 0.7719 \| 0.7826 \| 0.9784 \|
	\| 0.1698 \| 13.9860 \| 12000 \| 0.4650 \| 0.8000 \| 0.7657 \| 0.7825 \| 0.9777 \|
	\| 0.1635 \| 14.5688 \| 12500 \| 0.4604 \| 0.7913 \| 0.7778 \| 0.7845 \| 0.9782 \|
	\| 0.1605 \| 15.1515 \| 13000 \| 0.4656 \| 0.7990 \| 0.7605 \| 0.7793 \| 0.9774 \|
	\| 0.1559 \| 15.7343 \| 13500 \| 0.4638 \| 0.8001 \| 0.7658 \| 0.7826 \| 0.9778 \|
	\| 0.1531 \| 16.3170 \| 14000 \| 0.4550 \| 0.7991 \| 0.7735 \| 0.7861 \| 0.9780 \|
	\| 0.1519 \| 16.8998 \| 14500 \| 0.4606 \| 0.7949 \| 0.7735 \| 0.7841 \| 0.9780 \|
	\| 0.1482 \| 17.4825 \| 15000 \| 0.4483 \| 0.7947 \| 0.7831 \| 0.7889 \| 0.9787 \|
	\| 0.1449 \| 18.0653 \| 15500 \| 0.4521 \| 0.7947 \| 0.7722 \| 0.7833 \| 0.9780 \|
	\| 0.1407 \| 18.6480 \| 16000 \| 0.4508 \| 0.7932 \| 0.7728 \| 0.7829 \| 0.9780 \|
	\| 0.1415 \| 19.2308 \| 16500 \| 0.4484 \| 0.8031 \| 0.7728 \| 0.7876 \| 0.9785 \|
	\| 0.1385 \| 19.8135 \| 17000 \| 0.4461 \| 0.7991 \| 0.7774 \| 0.7881 \| 0.9785 \|
	\| 0.1358 \| 20.3963 \| 17500 \| 0.4488 \| 0.7970 \| 0.7756 \| 0.7862 \| 0.9783 \|
	\| 0.1358 \| 20.9790 \| 18000 \| 0.4431 \| 0.8006 \| 0.7772 \| 0.7887 \| 0.9787 \|
	\| 0.1325 \| 21.5618 \| 18500 \| 0.4395 \| 0.8053 \| 0.7768 \| 0.7908 \| 0.9785 \|
	\| 0.1322 \| 22.1445 \| 19000 \| 0.4461 \| 0.7960 \| 0.7725 \| 0.7841 \| 0.9780 \|
	\| 0.1296 \| 22.7273 \| 19500 \| 0.4401 \| 0.7988 \| 0.7746 \| 0.7866 \| 0.9781 \|
	\| 0.1288 \| 23.3100 \| 20000 \| 0.4416 \| 0.7961 \| 0.7690 \| 0.7823 \| 0.9781 \|
	\| 0.1271 \| 23.8928 \| 20500 \| 0.4450 \| 0.8024 \| 0.7673 \| 0.7844 \| 0.9781 \|
	\| 0.1246 \| 24.4755 \| 21000 \| 0.4403 \| 0.7967 \| 0.7703 \| 0.7833 \| 0.9782 \|
	\| 0.1254 \| 25.0583 \| 21500 \| 0.4403 \| 0.7976 \| 0.7742 \| 0.7857 \| 0.9782 \|
	\| 0.1231 \| 25.6410 \| 22000 \| 0.4438 \| 0.8057 \| 0.7694 \| 0.7872 \| 0.9783 \|
	\| 0.1228 \| 26.2238 \| 22500 \| 0.4365 \| 0.8058 \| 0.7741 \| 0.7896 \| 0.9785 \|
	\| 0.1224 \| 26.8065 \| 23000 \| 0.4325 \| 0.7995 \| 0.7806 \| 0.7899 \| 0.9787 \|
	\| 0.1211 \| 27.3893 \| 23500 \| 0.4402 \| 0.8058 \| 0.7676 \| 0.7862 \| 0.9782 \|
	\| 0.1202 \| 27.9720 \| 24000 \| 0.4378 \| 0.8017 \| 0.7689 \| 0.7849 \| 0.9784 \|
	\| 0.1201 \| 28.5548 \| 24500 \| 0.4331 \| 0.8000 \| 0.7784 \| 0.7890 \| 0.9786 \|
	\| 0.12 \| 29.1375 \| 25000 \| 0.4317 \| 0.7999 \| 0.7794 \| 0.7895 \| 0.9787 \|
	\| 0.1194 \| 29.7203 \| 25500 \| 0.4381 \| 0.8004 \| 0.7801 \| 0.7902 \| 0.9786 \|


	### 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: FacebookAI/xlm-roberta-base
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: scenario-kd-pre-ner-full_data-univner_full44
	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-kd-pre-ner-full_data-univner_full44

	This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.4381
	- Precision: 0.8004
	- Recall: 0.7801
	- F1: 0.7902
	- Accuracy: 0.9786

	## 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: 44
	- 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 \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 1.4593 \| 0.5828 \| 500 \| 0.8367 \| 0.6935 \| 0.6559 \| 0.6742 \| 0.9682 \|
	\| 0.7232 \| 1.1655 \| 1000 \| 0.7569 \| 0.7339 \| 0.6980 \| 0.7155 \| 0.9724 \|
	\| 0.594 \| 1.7483 \| 1500 \| 0.6330 \| 0.7335 \| 0.7451 \| 0.7392 \| 0.9741 \|
	\| 0.4986 \| 2.3310 \| 2000 \| 0.6003 \| 0.7291 \| 0.7552 \| 0.7419 \| 0.9746 \|
	\| 0.446 \| 2.9138 \| 2500 \| 0.5729 \| 0.7403 \| 0.7601 \| 0.7501 \| 0.9747 \|
	\| 0.385 \| 3.4965 \| 3000 \| 0.5584 \| 0.7441 \| 0.7617 \| 0.7528 \| 0.9757 \|
	\| 0.3605 \| 4.0793 \| 3500 \| 0.5602 \| 0.7615 \| 0.7575 \| 0.7595 \| 0.9758 \|
	\| 0.3172 \| 4.6620 \| 4000 \| 0.5417 \| 0.7546 \| 0.7725 \| 0.7634 \| 0.9764 \|
	\| 0.3061 \| 5.2448 \| 4500 \| 0.5329 \| 0.7884 \| 0.7485 \| 0.7680 \| 0.9769 \|
	\| 0.2856 \| 5.8275 \| 5000 \| 0.5194 \| 0.7837 \| 0.7618 \| 0.7726 \| 0.9769 \|
	\| 0.2642 \| 6.4103 \| 5500 \| 0.5154 \| 0.7622 \| 0.7780 \| 0.7700 \| 0.9765 \|
	\| 0.2592 \| 6.9930 \| 6000 \| 0.5193 \| 0.7882 \| 0.7572 \| 0.7724 \| 0.9764 \|
	\| 0.2401 \| 7.5758 \| 6500 \| 0.5123 \| 0.7727 \| 0.7599 \| 0.7663 \| 0.9763 \|
	\| 0.2344 \| 8.1585 \| 7000 \| 0.4987 \| 0.7742 \| 0.7736 \| 0.7739 \| 0.9771 \|
	\| 0.2234 \| 8.7413 \| 7500 \| 0.4914 \| 0.7894 \| 0.7640 \| 0.7764 \| 0.9777 \|
	\| 0.2131 \| 9.3240 \| 8000 \| 0.4856 \| 0.7691 \| 0.7827 \| 0.7758 \| 0.9770 \|
	\| 0.2089 \| 9.9068 \| 8500 \| 0.4898 \| 0.7895 \| 0.7655 \| 0.7773 \| 0.9773 \|
	\| 0.1972 \| 10.4895 \| 9000 \| 0.4860 \| 0.7828 \| 0.7726 \| 0.7777 \| 0.9775 \|
	\| 0.1942 \| 11.0723 \| 9500 \| 0.4787 \| 0.7807 \| 0.7807 \| 0.7807 \| 0.9776 \|
	\| 0.1854 \| 11.6550 \| 10000 \| 0.4858 \| 0.7916 \| 0.7635 \| 0.7773 \| 0.9771 \|
	\| 0.183 \| 12.2378 \| 10500 \| 0.4739 \| 0.7924 \| 0.7800 \| 0.7862 \| 0.9779 \|
	\| 0.1781 \| 12.8205 \| 11000 \| 0.4741 \| 0.7990 \| 0.7661 \| 0.7822 \| 0.9779 \|
	\| 0.1704 \| 13.4033 \| 11500 \| 0.4622 \| 0.7937 \| 0.7719 \| 0.7826 \| 0.9784 \|
	\| 0.1698 \| 13.9860 \| 12000 \| 0.4650 \| 0.8000 \| 0.7657 \| 0.7825 \| 0.9777 \|
	\| 0.1635 \| 14.5688 \| 12500 \| 0.4604 \| 0.7913 \| 0.7778 \| 0.7845 \| 0.9782 \|
	\| 0.1605 \| 15.1515 \| 13000 \| 0.4656 \| 0.7990 \| 0.7605 \| 0.7793 \| 0.9774 \|
	\| 0.1559 \| 15.7343 \| 13500 \| 0.4638 \| 0.8001 \| 0.7658 \| 0.7826 \| 0.9778 \|
	\| 0.1531 \| 16.3170 \| 14000 \| 0.4550 \| 0.7991 \| 0.7735 \| 0.7861 \| 0.9780 \|
	\| 0.1519 \| 16.8998 \| 14500 \| 0.4606 \| 0.7949 \| 0.7735 \| 0.7841 \| 0.9780 \|
	\| 0.1482 \| 17.4825 \| 15000 \| 0.4483 \| 0.7947 \| 0.7831 \| 0.7889 \| 0.9787 \|
	\| 0.1449 \| 18.0653 \| 15500 \| 0.4521 \| 0.7947 \| 0.7722 \| 0.7833 \| 0.9780 \|
	\| 0.1407 \| 18.6480 \| 16000 \| 0.4508 \| 0.7932 \| 0.7728 \| 0.7829 \| 0.9780 \|
	\| 0.1415 \| 19.2308 \| 16500 \| 0.4484 \| 0.8031 \| 0.7728 \| 0.7876 \| 0.9785 \|
	\| 0.1385 \| 19.8135 \| 17000 \| 0.4461 \| 0.7991 \| 0.7774 \| 0.7881 \| 0.9785 \|
	\| 0.1358 \| 20.3963 \| 17500 \| 0.4488 \| 0.7970 \| 0.7756 \| 0.7862 \| 0.9783 \|
	\| 0.1358 \| 20.9790 \| 18000 \| 0.4431 \| 0.8006 \| 0.7772 \| 0.7887 \| 0.9787 \|
	\| 0.1325 \| 21.5618 \| 18500 \| 0.4395 \| 0.8053 \| 0.7768 \| 0.7908 \| 0.9785 \|
	\| 0.1322 \| 22.1445 \| 19000 \| 0.4461 \| 0.7960 \| 0.7725 \| 0.7841 \| 0.9780 \|
	\| 0.1296 \| 22.7273 \| 19500 \| 0.4401 \| 0.7988 \| 0.7746 \| 0.7866 \| 0.9781 \|
	\| 0.1288 \| 23.3100 \| 20000 \| 0.4416 \| 0.7961 \| 0.7690 \| 0.7823 \| 0.9781 \|
	\| 0.1271 \| 23.8928 \| 20500 \| 0.4450 \| 0.8024 \| 0.7673 \| 0.7844 \| 0.9781 \|
	\| 0.1246 \| 24.4755 \| 21000 \| 0.4403 \| 0.7967 \| 0.7703 \| 0.7833 \| 0.9782 \|
	\| 0.1254 \| 25.0583 \| 21500 \| 0.4403 \| 0.7976 \| 0.7742 \| 0.7857 \| 0.9782 \|
	\| 0.1231 \| 25.6410 \| 22000 \| 0.4438 \| 0.8057 \| 0.7694 \| 0.7872 \| 0.9783 \|
	\| 0.1228 \| 26.2238 \| 22500 \| 0.4365 \| 0.8058 \| 0.7741 \| 0.7896 \| 0.9785 \|
	\| 0.1224 \| 26.8065 \| 23000 \| 0.4325 \| 0.7995 \| 0.7806 \| 0.7899 \| 0.9787 \|
	\| 0.1211 \| 27.3893 \| 23500 \| 0.4402 \| 0.8058 \| 0.7676 \| 0.7862 \| 0.9782 \|
	\| 0.1202 \| 27.9720 \| 24000 \| 0.4378 \| 0.8017 \| 0.7689 \| 0.7849 \| 0.9784 \|
	\| 0.1201 \| 28.5548 \| 24500 \| 0.4331 \| 0.8000 \| 0.7784 \| 0.7890 \| 0.9786 \|
	\| 0.12 \| 29.1375 \| 25000 \| 0.4317 \| 0.7999 \| 0.7794 \| 0.7895 \| 0.9787 \|
	\| 0.1194 \| 29.7203 \| 25500 \| 0.4381 \| 0.8004 \| 0.7801 \| 0.7902 \| 0.9786 \|


	### Framework versions

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