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	---
	base_model: haryoaw/scenario-TCR-NER_data-univner_half
	library_name: transformers
	license: mit
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	tags:
	- generated_from_trainer
	model-index:
	- name: scenario-non-kd-po-ner-full-xlmr_data-univner_half55
	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_half55

	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: 0.1639
	- Precision: 0.8020
	- Recall: 0.8084
	- F1: 0.8052
	- Accuracy: 0.9798

	## 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 \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 0.0755 \| 0.5828 \| 500 \| 0.0755 \| 0.7536 \| 0.7986 \| 0.7754 \| 0.9776 \|
	\| 0.0412 \| 1.1655 \| 1000 \| 0.0813 \| 0.7809 \| 0.7912 \| 0.7860 \| 0.9783 \|
	\| 0.0296 \| 1.7483 \| 1500 \| 0.0840 \| 0.7778 \| 0.8042 \| 0.7908 \| 0.9788 \|
	\| 0.02 \| 2.3310 \| 2000 \| 0.0938 \| 0.7895 \| 0.8042 \| 0.7968 \| 0.9791 \|
	\| 0.0182 \| 2.9138 \| 2500 \| 0.0984 \| 0.7760 \| 0.7937 \| 0.7847 \| 0.9777 \|
	\| 0.0131 \| 3.4965 \| 3000 \| 0.1027 \| 0.7937 \| 0.8016 \| 0.7976 \| 0.9791 \|
	\| 0.0119 \| 4.0793 \| 3500 \| 0.1057 \| 0.7955 \| 0.7895 \| 0.7925 \| 0.9785 \|
	\| 0.0092 \| 4.6620 \| 4000 \| 0.1141 \| 0.7852 \| 0.7855 \| 0.7853 \| 0.9780 \|
	\| 0.0085 \| 5.2448 \| 4500 \| 0.1148 \| 0.7781 \| 0.8173 \| 0.7972 \| 0.9787 \|
	\| 0.0078 \| 5.8275 \| 5000 \| 0.1116 \| 0.8016 \| 0.7834 \| 0.7924 \| 0.9790 \|
	\| 0.0063 \| 6.4103 \| 5500 \| 0.1147 \| 0.7951 \| 0.8041 \| 0.7996 \| 0.9795 \|
	\| 0.0063 \| 6.9930 \| 6000 \| 0.1211 \| 0.7885 \| 0.8032 \| 0.7958 \| 0.9791 \|
	\| 0.0048 \| 7.5758 \| 6500 \| 0.1206 \| 0.7957 \| 0.8038 \| 0.7997 \| 0.9794 \|
	\| 0.0053 \| 8.1585 \| 7000 \| 0.1313 \| 0.8020 \| 0.7979 \| 0.7999 \| 0.9789 \|
	\| 0.0044 \| 8.7413 \| 7500 \| 0.1250 \| 0.8067 \| 0.7930 \| 0.7998 \| 0.9793 \|
	\| 0.0041 \| 9.3240 \| 8000 \| 0.1293 \| 0.7933 \| 0.8045 \| 0.7989 \| 0.9789 \|
	\| 0.004 \| 9.9068 \| 8500 \| 0.1255 \| 0.8019 \| 0.8029 \| 0.8024 \| 0.9798 \|
	\| 0.0032 \| 10.4895 \| 9000 \| 0.1303 \| 0.8018 \| 0.8025 \| 0.8021 \| 0.9794 \|
	\| 0.0029 \| 11.0723 \| 9500 \| 0.1367 \| 0.7926 \| 0.8121 \| 0.8023 \| 0.9796 \|
	\| 0.0029 \| 11.6550 \| 10000 \| 0.1362 \| 0.8021 \| 0.7992 \| 0.8006 \| 0.9795 \|
	\| 0.0026 \| 12.2378 \| 10500 \| 0.1369 \| 0.8011 \| 0.8081 \| 0.8046 \| 0.9794 \|
	\| 0.0028 \| 12.8205 \| 11000 \| 0.1408 \| 0.7972 \| 0.8010 \| 0.7991 \| 0.9791 \|
	\| 0.0018 \| 13.4033 \| 11500 \| 0.1502 \| 0.7928 \| 0.8093 \| 0.8009 \| 0.9788 \|
	\| 0.0024 \| 13.9860 \| 12000 \| 0.1519 \| 0.7828 \| 0.8162 \| 0.7991 \| 0.9786 \|
	\| 0.002 \| 14.5688 \| 12500 \| 0.1452 \| 0.7958 \| 0.8055 \| 0.8006 \| 0.9793 \|
	\| 0.0019 \| 15.1515 \| 13000 \| 0.1498 \| 0.7976 \| 0.8068 \| 0.8022 \| 0.9789 \|
	\| 0.0018 \| 15.7343 \| 13500 \| 0.1494 \| 0.7998 \| 0.8051 \| 0.8024 \| 0.9790 \|
	\| 0.0016 \| 16.3170 \| 14000 \| 0.1466 \| 0.7947 \| 0.8098 \| 0.8022 \| 0.9790 \|
	\| 0.0015 \| 16.8998 \| 14500 \| 0.1488 \| 0.7897 \| 0.8146 \| 0.8019 \| 0.9788 \|
	\| 0.0014 \| 17.4825 \| 15000 \| 0.1535 \| 0.7979 \| 0.8149 \| 0.8063 \| 0.9794 \|
	\| 0.0016 \| 18.0653 \| 15500 \| 0.1567 \| 0.7935 \| 0.8072 \| 0.8003 \| 0.9790 \|
	\| 0.0012 \| 18.6480 \| 16000 \| 0.1483 \| 0.8011 \| 0.8052 \| 0.8031 \| 0.9795 \|
	\| 0.0011 \| 19.2308 \| 16500 \| 0.1506 \| 0.7951 \| 0.8106 \| 0.8027 \| 0.9797 \|
	\| 0.0009 \| 19.8135 \| 17000 \| 0.1552 \| 0.8057 \| 0.8035 \| 0.8046 \| 0.9797 \|
	\| 0.0007 \| 20.3963 \| 17500 \| 0.1566 \| 0.8007 \| 0.8045 \| 0.8026 \| 0.9794 \|
	\| 0.0011 \| 20.9790 \| 18000 \| 0.1528 \| 0.8072 \| 0.8036 \| 0.8054 \| 0.9798 \|
	\| 0.0008 \| 21.5618 \| 18500 \| 0.1592 \| 0.7966 \| 0.8046 \| 0.8006 \| 0.9789 \|
	\| 0.0006 \| 22.1445 \| 19000 \| 0.1582 \| 0.8021 \| 0.8059 \| 0.8040 \| 0.9794 \|
	\| 0.0006 \| 22.7273 \| 19500 \| 0.1667 \| 0.7953 \| 0.8104 \| 0.8028 \| 0.9789 \|
	\| 0.0007 \| 23.3100 \| 20000 \| 0.1618 \| 0.8067 \| 0.8028 \| 0.8047 \| 0.9795 \|
	\| 0.0006 \| 23.8928 \| 20500 \| 0.1632 \| 0.8004 \| 0.8046 \| 0.8025 \| 0.9795 \|
	\| 0.0006 \| 24.4755 \| 21000 \| 0.1661 \| 0.7889 \| 0.8140 \| 0.8012 \| 0.9787 \|
	\| 0.0006 \| 25.0583 \| 21500 \| 0.1629 \| 0.8034 \| 0.8097 \| 0.8066 \| 0.9795 \|
	\| 0.0006 \| 25.6410 \| 22000 \| 0.1630 \| 0.8075 \| 0.8070 \| 0.8072 \| 0.9799 \|
	\| 0.0005 \| 26.2238 \| 22500 \| 0.1636 \| 0.7975 \| 0.8165 \| 0.8069 \| 0.9796 \|
	\| 0.0004 \| 26.8065 \| 23000 \| 0.1614 \| 0.8037 \| 0.8113 \| 0.8075 \| 0.9800 \|
	\| 0.0003 \| 27.3893 \| 23500 \| 0.1645 \| 0.8024 \| 0.8093 \| 0.8058 \| 0.9797 \|
	\| 0.0003 \| 27.9720 \| 24000 \| 0.1637 \| 0.8073 \| 0.8038 \| 0.8055 \| 0.9798 \|
	\| 0.0003 \| 28.5548 \| 24500 \| 0.1638 \| 0.8025 \| 0.8072 \| 0.8049 \| 0.9796 \|
	\| 0.0003 \| 29.1375 \| 25000 \| 0.1633 \| 0.8016 \| 0.8093 \| 0.8054 \| 0.9797 \|
	\| 0.0003 \| 29.7203 \| 25500 \| 0.1639 \| 0.8020 \| 0.8084 \| 0.8052 \| 0.9798 \|


	### Framework versions

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

	---
	base_model: haryoaw/scenario-TCR-NER_data-univner_half
	library_name: transformers
	license: mit
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	tags:
	- generated_from_trainer
	model-index:
	- name: scenario-non-kd-po-ner-full-xlmr_data-univner_half55
	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_half55

	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: 0.1639
	- Precision: 0.8020
	- Recall: 0.8084
	- F1: 0.8052
	- Accuracy: 0.9798

	## 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 \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 0.0755 \| 0.5828 \| 500 \| 0.0755 \| 0.7536 \| 0.7986 \| 0.7754 \| 0.9776 \|
	\| 0.0412 \| 1.1655 \| 1000 \| 0.0813 \| 0.7809 \| 0.7912 \| 0.7860 \| 0.9783 \|
	\| 0.0296 \| 1.7483 \| 1500 \| 0.0840 \| 0.7778 \| 0.8042 \| 0.7908 \| 0.9788 \|
	\| 0.02 \| 2.3310 \| 2000 \| 0.0938 \| 0.7895 \| 0.8042 \| 0.7968 \| 0.9791 \|
	\| 0.0182 \| 2.9138 \| 2500 \| 0.0984 \| 0.7760 \| 0.7937 \| 0.7847 \| 0.9777 \|
	\| 0.0131 \| 3.4965 \| 3000 \| 0.1027 \| 0.7937 \| 0.8016 \| 0.7976 \| 0.9791 \|
	\| 0.0119 \| 4.0793 \| 3500 \| 0.1057 \| 0.7955 \| 0.7895 \| 0.7925 \| 0.9785 \|
	\| 0.0092 \| 4.6620 \| 4000 \| 0.1141 \| 0.7852 \| 0.7855 \| 0.7853 \| 0.9780 \|
	\| 0.0085 \| 5.2448 \| 4500 \| 0.1148 \| 0.7781 \| 0.8173 \| 0.7972 \| 0.9787 \|
	\| 0.0078 \| 5.8275 \| 5000 \| 0.1116 \| 0.8016 \| 0.7834 \| 0.7924 \| 0.9790 \|
	\| 0.0063 \| 6.4103 \| 5500 \| 0.1147 \| 0.7951 \| 0.8041 \| 0.7996 \| 0.9795 \|
	\| 0.0063 \| 6.9930 \| 6000 \| 0.1211 \| 0.7885 \| 0.8032 \| 0.7958 \| 0.9791 \|
	\| 0.0048 \| 7.5758 \| 6500 \| 0.1206 \| 0.7957 \| 0.8038 \| 0.7997 \| 0.9794 \|
	\| 0.0053 \| 8.1585 \| 7000 \| 0.1313 \| 0.8020 \| 0.7979 \| 0.7999 \| 0.9789 \|
	\| 0.0044 \| 8.7413 \| 7500 \| 0.1250 \| 0.8067 \| 0.7930 \| 0.7998 \| 0.9793 \|
	\| 0.0041 \| 9.3240 \| 8000 \| 0.1293 \| 0.7933 \| 0.8045 \| 0.7989 \| 0.9789 \|
	\| 0.004 \| 9.9068 \| 8500 \| 0.1255 \| 0.8019 \| 0.8029 \| 0.8024 \| 0.9798 \|
	\| 0.0032 \| 10.4895 \| 9000 \| 0.1303 \| 0.8018 \| 0.8025 \| 0.8021 \| 0.9794 \|
	\| 0.0029 \| 11.0723 \| 9500 \| 0.1367 \| 0.7926 \| 0.8121 \| 0.8023 \| 0.9796 \|
	\| 0.0029 \| 11.6550 \| 10000 \| 0.1362 \| 0.8021 \| 0.7992 \| 0.8006 \| 0.9795 \|
	\| 0.0026 \| 12.2378 \| 10500 \| 0.1369 \| 0.8011 \| 0.8081 \| 0.8046 \| 0.9794 \|
	\| 0.0028 \| 12.8205 \| 11000 \| 0.1408 \| 0.7972 \| 0.8010 \| 0.7991 \| 0.9791 \|
	\| 0.0018 \| 13.4033 \| 11500 \| 0.1502 \| 0.7928 \| 0.8093 \| 0.8009 \| 0.9788 \|
	\| 0.0024 \| 13.9860 \| 12000 \| 0.1519 \| 0.7828 \| 0.8162 \| 0.7991 \| 0.9786 \|
	\| 0.002 \| 14.5688 \| 12500 \| 0.1452 \| 0.7958 \| 0.8055 \| 0.8006 \| 0.9793 \|
	\| 0.0019 \| 15.1515 \| 13000 \| 0.1498 \| 0.7976 \| 0.8068 \| 0.8022 \| 0.9789 \|
	\| 0.0018 \| 15.7343 \| 13500 \| 0.1494 \| 0.7998 \| 0.8051 \| 0.8024 \| 0.9790 \|
	\| 0.0016 \| 16.3170 \| 14000 \| 0.1466 \| 0.7947 \| 0.8098 \| 0.8022 \| 0.9790 \|
	\| 0.0015 \| 16.8998 \| 14500 \| 0.1488 \| 0.7897 \| 0.8146 \| 0.8019 \| 0.9788 \|
	\| 0.0014 \| 17.4825 \| 15000 \| 0.1535 \| 0.7979 \| 0.8149 \| 0.8063 \| 0.9794 \|
	\| 0.0016 \| 18.0653 \| 15500 \| 0.1567 \| 0.7935 \| 0.8072 \| 0.8003 \| 0.9790 \|
	\| 0.0012 \| 18.6480 \| 16000 \| 0.1483 \| 0.8011 \| 0.8052 \| 0.8031 \| 0.9795 \|
	\| 0.0011 \| 19.2308 \| 16500 \| 0.1506 \| 0.7951 \| 0.8106 \| 0.8027 \| 0.9797 \|
	\| 0.0009 \| 19.8135 \| 17000 \| 0.1552 \| 0.8057 \| 0.8035 \| 0.8046 \| 0.9797 \|
	\| 0.0007 \| 20.3963 \| 17500 \| 0.1566 \| 0.8007 \| 0.8045 \| 0.8026 \| 0.9794 \|
	\| 0.0011 \| 20.9790 \| 18000 \| 0.1528 \| 0.8072 \| 0.8036 \| 0.8054 \| 0.9798 \|
	\| 0.0008 \| 21.5618 \| 18500 \| 0.1592 \| 0.7966 \| 0.8046 \| 0.8006 \| 0.9789 \|
	\| 0.0006 \| 22.1445 \| 19000 \| 0.1582 \| 0.8021 \| 0.8059 \| 0.8040 \| 0.9794 \|
	\| 0.0006 \| 22.7273 \| 19500 \| 0.1667 \| 0.7953 \| 0.8104 \| 0.8028 \| 0.9789 \|
	\| 0.0007 \| 23.3100 \| 20000 \| 0.1618 \| 0.8067 \| 0.8028 \| 0.8047 \| 0.9795 \|
	\| 0.0006 \| 23.8928 \| 20500 \| 0.1632 \| 0.8004 \| 0.8046 \| 0.8025 \| 0.9795 \|
	\| 0.0006 \| 24.4755 \| 21000 \| 0.1661 \| 0.7889 \| 0.8140 \| 0.8012 \| 0.9787 \|
	\| 0.0006 \| 25.0583 \| 21500 \| 0.1629 \| 0.8034 \| 0.8097 \| 0.8066 \| 0.9795 \|
	\| 0.0006 \| 25.6410 \| 22000 \| 0.1630 \| 0.8075 \| 0.8070 \| 0.8072 \| 0.9799 \|
	\| 0.0005 \| 26.2238 \| 22500 \| 0.1636 \| 0.7975 \| 0.8165 \| 0.8069 \| 0.9796 \|
	\| 0.0004 \| 26.8065 \| 23000 \| 0.1614 \| 0.8037 \| 0.8113 \| 0.8075 \| 0.9800 \|
	\| 0.0003 \| 27.3893 \| 23500 \| 0.1645 \| 0.8024 \| 0.8093 \| 0.8058 \| 0.9797 \|
	\| 0.0003 \| 27.9720 \| 24000 \| 0.1637 \| 0.8073 \| 0.8038 \| 0.8055 \| 0.9798 \|
	\| 0.0003 \| 28.5548 \| 24500 \| 0.1638 \| 0.8025 \| 0.8072 \| 0.8049 \| 0.9796 \|
	\| 0.0003 \| 29.1375 \| 25000 \| 0.1633 \| 0.8016 \| 0.8093 \| 0.8054 \| 0.9797 \|
	\| 0.0003 \| 29.7203 \| 25500 \| 0.1639 \| 0.8020 \| 0.8084 \| 0.8052 \| 0.9798 \|


	### Framework versions

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