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	---
	library_name: transformers
	license: mit
	base_model: haryoaw/scenario-TCR-NER_data-univner_half
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: scenario-non-kd-po-ner-full-xlmr_data-univner_half44
	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-po-ner-full-xlmr_data-univner_half44

	This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_half](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_half) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.1624
	- Precision: 0.8067
	- Recall: 0.8152
	- F1: 0.8109
	- Accuracy: 0.9801

	## 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 \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 0.075 \| 0.5828 \| 500 \| 0.0769 \| 0.7627 \| 0.7648 \| 0.7638 \| 0.9772 \|
	\| 0.0416 \| 1.1655 \| 1000 \| 0.0813 \| 0.7656 \| 0.8016 \| 0.7832 \| 0.9786 \|
	\| 0.0287 \| 1.7483 \| 1500 \| 0.0833 \| 0.7729 \| 0.7989 \| 0.7857 \| 0.9785 \|
	\| 0.022 \| 2.3310 \| 2000 \| 0.1011 \| 0.7465 \| 0.7987 \| 0.7717 \| 0.9767 \|
	\| 0.0171 \| 2.9138 \| 2500 \| 0.1033 \| 0.7570 \| 0.8166 \| 0.7857 \| 0.9774 \|
	\| 0.0133 \| 3.4965 \| 3000 \| 0.0985 \| 0.7734 \| 0.8160 \| 0.7942 \| 0.9786 \|
	\| 0.0121 \| 4.0793 \| 3500 \| 0.1039 \| 0.7721 \| 0.8120 \| 0.7916 \| 0.9787 \|
	\| 0.0093 \| 4.6620 \| 4000 \| 0.1150 \| 0.7766 \| 0.8031 \| 0.7896 \| 0.9781 \|
	\| 0.0083 \| 5.2448 \| 4500 \| 0.1197 \| 0.7771 \| 0.8194 \| 0.7977 \| 0.9788 \|
	\| 0.0077 \| 5.8275 \| 5000 \| 0.1180 \| 0.7721 \| 0.8094 \| 0.7903 \| 0.9784 \|
	\| 0.0073 \| 6.4103 \| 5500 \| 0.1169 \| 0.7924 \| 0.8061 \| 0.7992 \| 0.9793 \|
	\| 0.0065 \| 6.9930 \| 6000 \| 0.1201 \| 0.8011 \| 0.8018 \| 0.8014 \| 0.9791 \|
	\| 0.0055 \| 7.5758 \| 6500 \| 0.1152 \| 0.7950 \| 0.7958 \| 0.7954 \| 0.9792 \|
	\| 0.0045 \| 8.1585 \| 7000 \| 0.1259 \| 0.7825 \| 0.8133 \| 0.7976 \| 0.9790 \|
	\| 0.0039 \| 8.7413 \| 7500 \| 0.1300 \| 0.7878 \| 0.8101 \| 0.7988 \| 0.9793 \|
	\| 0.0043 \| 9.3240 \| 8000 \| 0.1279 \| 0.8026 \| 0.7960 \| 0.7993 \| 0.9792 \|
	\| 0.0041 \| 9.9068 \| 8500 \| 0.1249 \| 0.8033 \| 0.7997 \| 0.8015 \| 0.9790 \|
	\| 0.0033 \| 10.4895 \| 9000 \| 0.1315 \| 0.7890 \| 0.8110 \| 0.7999 \| 0.9793 \|
	\| 0.0034 \| 11.0723 \| 9500 \| 0.1268 \| 0.7914 \| 0.8005 \| 0.7959 \| 0.9789 \|
	\| 0.0024 \| 11.6550 \| 10000 \| 0.1354 \| 0.7943 \| 0.8088 \| 0.8015 \| 0.9798 \|
	\| 0.0023 \| 12.2378 \| 10500 \| 0.1397 \| 0.8003 \| 0.8130 \| 0.8066 \| 0.9798 \|
	\| 0.0024 \| 12.8205 \| 11000 \| 0.1428 \| 0.7779 \| 0.8175 \| 0.7972 \| 0.9788 \|
	\| 0.0023 \| 13.4033 \| 11500 \| 0.1389 \| 0.7896 \| 0.8070 \| 0.7982 \| 0.9792 \|
	\| 0.0022 \| 13.9860 \| 12000 \| 0.1429 \| 0.8 \| 0.8051 \| 0.8025 \| 0.9794 \|
	\| 0.0017 \| 14.5688 \| 12500 \| 0.1458 \| 0.7922 \| 0.8113 \| 0.8016 \| 0.9791 \|
	\| 0.002 \| 15.1515 \| 13000 \| 0.1430 \| 0.7955 \| 0.8121 \| 0.8037 \| 0.9794 \|
	\| 0.0016 \| 15.7343 \| 13500 \| 0.1487 \| 0.7795 \| 0.8185 \| 0.7985 \| 0.9789 \|
	\| 0.0019 \| 16.3170 \| 14000 \| 0.1401 \| 0.7994 \| 0.8044 \| 0.8019 \| 0.9796 \|
	\| 0.0016 \| 16.8998 \| 14500 \| 0.1468 \| 0.7964 \| 0.8133 \| 0.8048 \| 0.9797 \|
	\| 0.0016 \| 17.4825 \| 15000 \| 0.1440 \| 0.7955 \| 0.8100 \| 0.8027 \| 0.9794 \|
	\| 0.0012 \| 18.0653 \| 15500 \| 0.1430 \| 0.8002 \| 0.8119 \| 0.8060 \| 0.9798 \|
	\| 0.0009 \| 18.6480 \| 16000 \| 0.1558 \| 0.7855 \| 0.8166 \| 0.8007 \| 0.9788 \|
	\| 0.0014 \| 19.2308 \| 16500 \| 0.1478 \| 0.8012 \| 0.8119 \| 0.8065 \| 0.9797 \|
	\| 0.001 \| 19.8135 \| 17000 \| 0.1498 \| 0.8101 \| 0.8087 \| 0.8094 \| 0.9798 \|
	\| 0.0009 \| 20.3963 \| 17500 \| 0.1515 \| 0.7946 \| 0.8116 \| 0.8030 \| 0.9795 \|
	\| 0.0008 \| 20.9790 \| 18000 \| 0.1546 \| 0.7967 \| 0.8165 \| 0.8065 \| 0.9796 \|
	\| 0.0009 \| 21.5618 \| 18500 \| 0.1553 \| 0.7953 \| 0.8162 \| 0.8056 \| 0.9796 \|
	\| 0.0008 \| 22.1445 \| 19000 \| 0.1540 \| 0.8001 \| 0.8103 \| 0.8052 \| 0.9798 \|
	\| 0.0006 \| 22.7273 \| 19500 \| 0.1620 \| 0.7877 \| 0.8208 \| 0.8039 \| 0.9793 \|
	\| 0.0007 \| 23.3100 \| 20000 \| 0.1605 \| 0.8127 \| 0.7999 \| 0.8062 \| 0.9795 \|
	\| 0.0005 \| 23.8928 \| 20500 \| 0.1589 \| 0.7974 \| 0.8132 \| 0.8052 \| 0.9799 \|
	\| 0.0005 \| 24.4755 \| 21000 \| 0.1593 \| 0.8016 \| 0.8093 \| 0.8054 \| 0.9797 \|
	\| 0.0006 \| 25.0583 \| 21500 \| 0.1561 \| 0.8060 \| 0.8114 \| 0.8087 \| 0.9800 \|
	\| 0.0005 \| 25.6410 \| 22000 \| 0.1587 \| 0.8028 \| 0.8152 \| 0.8089 \| 0.9798 \|
	\| 0.0004 \| 26.2238 \| 22500 \| 0.1582 \| 0.8058 \| 0.8101 \| 0.8080 \| 0.9802 \|
	\| 0.0004 \| 26.8065 \| 23000 \| 0.1612 \| 0.8003 \| 0.8162 \| 0.8081 \| 0.9799 \|
	\| 0.0003 \| 27.3893 \| 23500 \| 0.1609 \| 0.8066 \| 0.8142 \| 0.8104 \| 0.9801 \|
	\| 0.0003 \| 27.9720 \| 24000 \| 0.1627 \| 0.8032 \| 0.8178 \| 0.8104 \| 0.9801 \|
	\| 0.0003 \| 28.5548 \| 24500 \| 0.1616 \| 0.8081 \| 0.8127 \| 0.8104 \| 0.9801 \|
	\| 0.0003 \| 29.1375 \| 25000 \| 0.1627 \| 0.8065 \| 0.8153 \| 0.8109 \| 0.9801 \|
	\| 0.0002 \| 29.7203 \| 25500 \| 0.1624 \| 0.8067 \| 0.8152 \| 0.8109 \| 0.9801 \|


	### 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: haryoaw/scenario-TCR-NER_data-univner_half
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: scenario-non-kd-po-ner-full-xlmr_data-univner_half44
	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-po-ner-full-xlmr_data-univner_half44

	This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_half](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_half) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.1624
	- Precision: 0.8067
	- Recall: 0.8152
	- F1: 0.8109
	- Accuracy: 0.9801

	## 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 \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 0.075 \| 0.5828 \| 500 \| 0.0769 \| 0.7627 \| 0.7648 \| 0.7638 \| 0.9772 \|
	\| 0.0416 \| 1.1655 \| 1000 \| 0.0813 \| 0.7656 \| 0.8016 \| 0.7832 \| 0.9786 \|
	\| 0.0287 \| 1.7483 \| 1500 \| 0.0833 \| 0.7729 \| 0.7989 \| 0.7857 \| 0.9785 \|
	\| 0.022 \| 2.3310 \| 2000 \| 0.1011 \| 0.7465 \| 0.7987 \| 0.7717 \| 0.9767 \|
	\| 0.0171 \| 2.9138 \| 2500 \| 0.1033 \| 0.7570 \| 0.8166 \| 0.7857 \| 0.9774 \|
	\| 0.0133 \| 3.4965 \| 3000 \| 0.0985 \| 0.7734 \| 0.8160 \| 0.7942 \| 0.9786 \|
	\| 0.0121 \| 4.0793 \| 3500 \| 0.1039 \| 0.7721 \| 0.8120 \| 0.7916 \| 0.9787 \|
	\| 0.0093 \| 4.6620 \| 4000 \| 0.1150 \| 0.7766 \| 0.8031 \| 0.7896 \| 0.9781 \|
	\| 0.0083 \| 5.2448 \| 4500 \| 0.1197 \| 0.7771 \| 0.8194 \| 0.7977 \| 0.9788 \|
	\| 0.0077 \| 5.8275 \| 5000 \| 0.1180 \| 0.7721 \| 0.8094 \| 0.7903 \| 0.9784 \|
	\| 0.0073 \| 6.4103 \| 5500 \| 0.1169 \| 0.7924 \| 0.8061 \| 0.7992 \| 0.9793 \|
	\| 0.0065 \| 6.9930 \| 6000 \| 0.1201 \| 0.8011 \| 0.8018 \| 0.8014 \| 0.9791 \|
	\| 0.0055 \| 7.5758 \| 6500 \| 0.1152 \| 0.7950 \| 0.7958 \| 0.7954 \| 0.9792 \|
	\| 0.0045 \| 8.1585 \| 7000 \| 0.1259 \| 0.7825 \| 0.8133 \| 0.7976 \| 0.9790 \|
	\| 0.0039 \| 8.7413 \| 7500 \| 0.1300 \| 0.7878 \| 0.8101 \| 0.7988 \| 0.9793 \|
	\| 0.0043 \| 9.3240 \| 8000 \| 0.1279 \| 0.8026 \| 0.7960 \| 0.7993 \| 0.9792 \|
	\| 0.0041 \| 9.9068 \| 8500 \| 0.1249 \| 0.8033 \| 0.7997 \| 0.8015 \| 0.9790 \|
	\| 0.0033 \| 10.4895 \| 9000 \| 0.1315 \| 0.7890 \| 0.8110 \| 0.7999 \| 0.9793 \|
	\| 0.0034 \| 11.0723 \| 9500 \| 0.1268 \| 0.7914 \| 0.8005 \| 0.7959 \| 0.9789 \|
	\| 0.0024 \| 11.6550 \| 10000 \| 0.1354 \| 0.7943 \| 0.8088 \| 0.8015 \| 0.9798 \|
	\| 0.0023 \| 12.2378 \| 10500 \| 0.1397 \| 0.8003 \| 0.8130 \| 0.8066 \| 0.9798 \|
	\| 0.0024 \| 12.8205 \| 11000 \| 0.1428 \| 0.7779 \| 0.8175 \| 0.7972 \| 0.9788 \|
	\| 0.0023 \| 13.4033 \| 11500 \| 0.1389 \| 0.7896 \| 0.8070 \| 0.7982 \| 0.9792 \|
	\| 0.0022 \| 13.9860 \| 12000 \| 0.1429 \| 0.8 \| 0.8051 \| 0.8025 \| 0.9794 \|
	\| 0.0017 \| 14.5688 \| 12500 \| 0.1458 \| 0.7922 \| 0.8113 \| 0.8016 \| 0.9791 \|
	\| 0.002 \| 15.1515 \| 13000 \| 0.1430 \| 0.7955 \| 0.8121 \| 0.8037 \| 0.9794 \|
	\| 0.0016 \| 15.7343 \| 13500 \| 0.1487 \| 0.7795 \| 0.8185 \| 0.7985 \| 0.9789 \|
	\| 0.0019 \| 16.3170 \| 14000 \| 0.1401 \| 0.7994 \| 0.8044 \| 0.8019 \| 0.9796 \|
	\| 0.0016 \| 16.8998 \| 14500 \| 0.1468 \| 0.7964 \| 0.8133 \| 0.8048 \| 0.9797 \|
	\| 0.0016 \| 17.4825 \| 15000 \| 0.1440 \| 0.7955 \| 0.8100 \| 0.8027 \| 0.9794 \|
	\| 0.0012 \| 18.0653 \| 15500 \| 0.1430 \| 0.8002 \| 0.8119 \| 0.8060 \| 0.9798 \|
	\| 0.0009 \| 18.6480 \| 16000 \| 0.1558 \| 0.7855 \| 0.8166 \| 0.8007 \| 0.9788 \|
	\| 0.0014 \| 19.2308 \| 16500 \| 0.1478 \| 0.8012 \| 0.8119 \| 0.8065 \| 0.9797 \|
	\| 0.001 \| 19.8135 \| 17000 \| 0.1498 \| 0.8101 \| 0.8087 \| 0.8094 \| 0.9798 \|
	\| 0.0009 \| 20.3963 \| 17500 \| 0.1515 \| 0.7946 \| 0.8116 \| 0.8030 \| 0.9795 \|
	\| 0.0008 \| 20.9790 \| 18000 \| 0.1546 \| 0.7967 \| 0.8165 \| 0.8065 \| 0.9796 \|
	\| 0.0009 \| 21.5618 \| 18500 \| 0.1553 \| 0.7953 \| 0.8162 \| 0.8056 \| 0.9796 \|
	\| 0.0008 \| 22.1445 \| 19000 \| 0.1540 \| 0.8001 \| 0.8103 \| 0.8052 \| 0.9798 \|
	\| 0.0006 \| 22.7273 \| 19500 \| 0.1620 \| 0.7877 \| 0.8208 \| 0.8039 \| 0.9793 \|
	\| 0.0007 \| 23.3100 \| 20000 \| 0.1605 \| 0.8127 \| 0.7999 \| 0.8062 \| 0.9795 \|
	\| 0.0005 \| 23.8928 \| 20500 \| 0.1589 \| 0.7974 \| 0.8132 \| 0.8052 \| 0.9799 \|
	\| 0.0005 \| 24.4755 \| 21000 \| 0.1593 \| 0.8016 \| 0.8093 \| 0.8054 \| 0.9797 \|
	\| 0.0006 \| 25.0583 \| 21500 \| 0.1561 \| 0.8060 \| 0.8114 \| 0.8087 \| 0.9800 \|
	\| 0.0005 \| 25.6410 \| 22000 \| 0.1587 \| 0.8028 \| 0.8152 \| 0.8089 \| 0.9798 \|
	\| 0.0004 \| 26.2238 \| 22500 \| 0.1582 \| 0.8058 \| 0.8101 \| 0.8080 \| 0.9802 \|
	\| 0.0004 \| 26.8065 \| 23000 \| 0.1612 \| 0.8003 \| 0.8162 \| 0.8081 \| 0.9799 \|
	\| 0.0003 \| 27.3893 \| 23500 \| 0.1609 \| 0.8066 \| 0.8142 \| 0.8104 \| 0.9801 \|
	\| 0.0003 \| 27.9720 \| 24000 \| 0.1627 \| 0.8032 \| 0.8178 \| 0.8104 \| 0.9801 \|
	\| 0.0003 \| 28.5548 \| 24500 \| 0.1616 \| 0.8081 \| 0.8127 \| 0.8104 \| 0.9801 \|
	\| 0.0003 \| 29.1375 \| 25000 \| 0.1627 \| 0.8065 \| 0.8153 \| 0.8109 \| 0.9801 \|
	\| 0.0002 \| 29.7203 \| 25500 \| 0.1624 \| 0.8067 \| 0.8152 \| 0.8109 \| 0.9801 \|


	### Framework versions

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