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	---
	library_name: transformers
	license: mit
	base_model: haryoaw/scenario-TCR-NER_data-univner_full
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: scenario-non-kd-po-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-non-kd-po-ner-full_data-univner_full44

	This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_full](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_full) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.1359
	- Precision: 0.8579
	- Recall: 0.8661
	- F1: 0.8620
	- Accuracy: 0.9848

	## 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 \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 0.0095 \| 0.2910 \| 500 \| 0.0842 \| 0.8414 \| 0.8590 \| 0.8501 \| 0.9842 \|
	\| 0.0115 \| 0.5821 \| 1000 \| 0.0851 \| 0.8363 \| 0.8626 \| 0.8493 \| 0.9836 \|
	\| 0.0108 \| 0.8731 \| 1500 \| 0.0812 \| 0.8414 \| 0.8664 \| 0.8537 \| 0.9844 \|
	\| 0.0086 \| 1.1641 \| 2000 \| 0.0967 \| 0.8277 \| 0.8729 \| 0.8497 \| 0.9829 \|
	\| 0.0083 \| 1.4552 \| 2500 \| 0.0794 \| 0.8469 \| 0.8652 \| 0.8560 \| 0.9841 \|
	\| 0.0082 \| 1.7462 \| 3000 \| 0.0808 \| 0.8391 \| 0.8712 \| 0.8548 \| 0.9843 \|
	\| 0.0089 \| 2.0373 \| 3500 \| 0.0862 \| 0.8488 \| 0.8645 \| 0.8566 \| 0.9846 \|
	\| 0.0063 \| 2.3283 \| 4000 \| 0.0889 \| 0.8455 \| 0.8735 \| 0.8593 \| 0.9847 \|
	\| 0.0068 \| 2.6193 \| 4500 \| 0.0900 \| 0.8463 \| 0.8638 \| 0.8550 \| 0.9842 \|
	\| 0.0071 \| 2.9104 \| 5000 \| 0.0883 \| 0.8362 \| 0.8691 \| 0.8524 \| 0.9837 \|
	\| 0.0055 \| 3.2014 \| 5500 \| 0.0945 \| 0.8433 \| 0.8588 \| 0.8510 \| 0.9838 \|
	\| 0.0055 \| 3.4924 \| 6000 \| 0.0951 \| 0.8428 \| 0.8687 \| 0.8556 \| 0.9840 \|
	\| 0.0059 \| 3.7835 \| 6500 \| 0.0975 \| 0.8563 \| 0.8551 \| 0.8557 \| 0.9842 \|
	\| 0.0054 \| 4.0745 \| 7000 \| 0.1009 \| 0.8422 \| 0.8673 \| 0.8546 \| 0.9841 \|
	\| 0.0047 \| 4.3655 \| 7500 \| 0.0967 \| 0.8556 \| 0.8589 \| 0.8572 \| 0.9848 \|
	\| 0.0048 \| 4.6566 \| 8000 \| 0.0989 \| 0.8506 \| 0.8694 \| 0.8599 \| 0.9847 \|
	\| 0.0048 \| 4.9476 \| 8500 \| 0.0959 \| 0.8526 \| 0.8713 \| 0.8619 \| 0.9844 \|
	\| 0.0036 \| 5.2386 \| 9000 \| 0.1014 \| 0.8476 \| 0.8706 \| 0.8589 \| 0.9841 \|
	\| 0.0042 \| 5.5297 \| 9500 \| 0.1147 \| 0.8068 \| 0.8613 \| 0.8332 \| 0.9814 \|
	\| 0.0048 \| 5.8207 \| 10000 \| 0.1065 \| 0.8392 \| 0.8691 \| 0.8539 \| 0.9844 \|
	\| 0.0041 \| 6.1118 \| 10500 \| 0.1076 \| 0.8417 \| 0.8758 \| 0.8584 \| 0.9843 \|
	\| 0.0035 \| 6.4028 \| 11000 \| 0.1029 \| 0.8505 \| 0.8732 \| 0.8617 \| 0.9848 \|
	\| 0.0036 \| 6.6938 \| 11500 \| 0.0929 \| 0.8460 \| 0.8719 \| 0.8587 \| 0.9849 \|
	\| 0.0038 \| 6.9849 \| 12000 \| 0.1019 \| 0.8494 \| 0.8631 \| 0.8562 \| 0.9846 \|
	\| 0.0031 \| 7.2759 \| 12500 \| 0.1073 \| 0.8563 \| 0.8575 \| 0.8569 \| 0.9845 \|
	\| 0.0031 \| 7.5669 \| 13000 \| 0.1013 \| 0.8431 \| 0.8696 \| 0.8561 \| 0.9847 \|
	\| 0.0034 \| 7.8580 \| 13500 \| 0.1058 \| 0.8533 \| 0.8596 \| 0.8565 \| 0.9845 \|
	\| 0.0027 \| 8.1490 \| 14000 \| 0.1154 \| 0.8431 \| 0.8719 \| 0.8572 \| 0.9845 \|
	\| 0.0027 \| 8.4400 \| 14500 \| 0.1030 \| 0.8404 \| 0.8785 \| 0.8591 \| 0.9845 \|
	\| 0.0028 \| 8.7311 \| 15000 \| 0.1132 \| 0.8559 \| 0.8510 \| 0.8534 \| 0.9846 \|
	\| 0.003 \| 9.0221 \| 15500 \| 0.1106 \| 0.8514 \| 0.8648 \| 0.8581 \| 0.9848 \|
	\| 0.0022 \| 9.3132 \| 16000 \| 0.1136 \| 0.8586 \| 0.8657 \| 0.8621 \| 0.9852 \|
	\| 0.0025 \| 9.6042 \| 16500 \| 0.1128 \| 0.8494 \| 0.8697 \| 0.8594 \| 0.9848 \|
	\| 0.002 \| 9.8952 \| 17000 \| 0.1139 \| 0.8453 \| 0.8600 \| 0.8526 \| 0.9841 \|
	\| 0.0024 \| 10.1863 \| 17500 \| 0.1124 \| 0.8541 \| 0.8658 \| 0.8599 \| 0.9849 \|
	\| 0.002 \| 10.4773 \| 18000 \| 0.1154 \| 0.8368 \| 0.8663 \| 0.8513 \| 0.9842 \|
	\| 0.0019 \| 10.7683 \| 18500 \| 0.1182 \| 0.8457 \| 0.8629 \| 0.8542 \| 0.9844 \|
	\| 0.0023 \| 11.0594 \| 19000 \| 0.1140 \| 0.8531 \| 0.8596 \| 0.8563 \| 0.9846 \|
	\| 0.0018 \| 11.3504 \| 19500 \| 0.1194 \| 0.8526 \| 0.8683 \| 0.8604 \| 0.9851 \|
	\| 0.0016 \| 11.6414 \| 20000 \| 0.1198 \| 0.8527 \| 0.8652 \| 0.8589 \| 0.9848 \|
	\| 0.0021 \| 11.9325 \| 20500 \| 0.1169 \| 0.8592 \| 0.8654 \| 0.8623 \| 0.9852 \|
	\| 0.0016 \| 12.2235 \| 21000 \| 0.1229 \| 0.8605 \| 0.8626 \| 0.8616 \| 0.9848 \|
	\| 0.0021 \| 12.5146 \| 21500 \| 0.1198 \| 0.8484 \| 0.8697 \| 0.8589 \| 0.9846 \|
	\| 0.0019 \| 12.8056 \| 22000 \| 0.1177 \| 0.8535 \| 0.8600 \| 0.8568 \| 0.9844 \|
	\| 0.0013 \| 13.0966 \| 22500 \| 0.1190 \| 0.8436 \| 0.8716 \| 0.8574 \| 0.9844 \|
	\| 0.0016 \| 13.3877 \| 23000 \| 0.1227 \| 0.8475 \| 0.8665 \| 0.8569 \| 0.9847 \|
	\| 0.0012 \| 13.6787 \| 23500 \| 0.1237 \| 0.8513 \| 0.8676 \| 0.8594 \| 0.9848 \|
	\| 0.0015 \| 13.9697 \| 24000 \| 0.1198 \| 0.8407 \| 0.8709 \| 0.8555 \| 0.9843 \|
	\| 0.0012 \| 14.2608 \| 24500 \| 0.1239 \| 0.8516 \| 0.8689 \| 0.8602 \| 0.9850 \|
	\| 0.0014 \| 14.5518 \| 25000 \| 0.1261 \| 0.8432 \| 0.8634 \| 0.8532 \| 0.9843 \|
	\| 0.0015 \| 14.8428 \| 25500 \| 0.1220 \| 0.8451 \| 0.8716 \| 0.8582 \| 0.9849 \|
	\| 0.0013 \| 15.1339 \| 26000 \| 0.1209 \| 0.8608 \| 0.8598 \| 0.8603 \| 0.9847 \|
	\| 0.0011 \| 15.4249 \| 26500 \| 0.1261 \| 0.8457 \| 0.8637 \| 0.8546 \| 0.9847 \|
	\| 0.0011 \| 15.7159 \| 27000 \| 0.1273 \| 0.8510 \| 0.8616 \| 0.8563 \| 0.9846 \|
	\| 0.0022 \| 16.0070 \| 27500 \| 0.1282 \| 0.8431 \| 0.8738 \| 0.8582 \| 0.9847 \|
	\| 0.001 \| 16.2980 \| 28000 \| 0.1357 \| 0.8451 \| 0.8628 \| 0.8539 \| 0.9842 \|
	\| 0.0013 \| 16.5891 \| 28500 \| 0.1301 \| 0.8465 \| 0.8658 \| 0.8561 \| 0.9843 \|
	\| 0.0008 \| 16.8801 \| 29000 \| 0.1335 \| 0.8533 \| 0.8678 \| 0.8605 \| 0.9845 \|
	\| 0.0011 \| 17.1711 \| 29500 \| 0.1338 \| 0.8572 \| 0.8654 \| 0.8613 \| 0.9846 \|
	\| 0.0006 \| 17.4622 \| 30000 \| 0.1368 \| 0.8561 \| 0.8628 \| 0.8594 \| 0.9847 \|
	\| 0.0008 \| 17.7532 \| 30500 \| 0.1359 \| 0.8579 \| 0.8661 \| 0.8620 \| 0.9848 \|


	### 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_full
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: scenario-non-kd-po-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-non-kd-po-ner-full_data-univner_full44

	This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_full](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_full) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.1359
	- Precision: 0.8579
	- Recall: 0.8661
	- F1: 0.8620
	- Accuracy: 0.9848

	## 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 \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 0.0095 \| 0.2910 \| 500 \| 0.0842 \| 0.8414 \| 0.8590 \| 0.8501 \| 0.9842 \|
	\| 0.0115 \| 0.5821 \| 1000 \| 0.0851 \| 0.8363 \| 0.8626 \| 0.8493 \| 0.9836 \|
	\| 0.0108 \| 0.8731 \| 1500 \| 0.0812 \| 0.8414 \| 0.8664 \| 0.8537 \| 0.9844 \|
	\| 0.0086 \| 1.1641 \| 2000 \| 0.0967 \| 0.8277 \| 0.8729 \| 0.8497 \| 0.9829 \|
	\| 0.0083 \| 1.4552 \| 2500 \| 0.0794 \| 0.8469 \| 0.8652 \| 0.8560 \| 0.9841 \|
	\| 0.0082 \| 1.7462 \| 3000 \| 0.0808 \| 0.8391 \| 0.8712 \| 0.8548 \| 0.9843 \|
	\| 0.0089 \| 2.0373 \| 3500 \| 0.0862 \| 0.8488 \| 0.8645 \| 0.8566 \| 0.9846 \|
	\| 0.0063 \| 2.3283 \| 4000 \| 0.0889 \| 0.8455 \| 0.8735 \| 0.8593 \| 0.9847 \|
	\| 0.0068 \| 2.6193 \| 4500 \| 0.0900 \| 0.8463 \| 0.8638 \| 0.8550 \| 0.9842 \|
	\| 0.0071 \| 2.9104 \| 5000 \| 0.0883 \| 0.8362 \| 0.8691 \| 0.8524 \| 0.9837 \|
	\| 0.0055 \| 3.2014 \| 5500 \| 0.0945 \| 0.8433 \| 0.8588 \| 0.8510 \| 0.9838 \|
	\| 0.0055 \| 3.4924 \| 6000 \| 0.0951 \| 0.8428 \| 0.8687 \| 0.8556 \| 0.9840 \|
	\| 0.0059 \| 3.7835 \| 6500 \| 0.0975 \| 0.8563 \| 0.8551 \| 0.8557 \| 0.9842 \|
	\| 0.0054 \| 4.0745 \| 7000 \| 0.1009 \| 0.8422 \| 0.8673 \| 0.8546 \| 0.9841 \|
	\| 0.0047 \| 4.3655 \| 7500 \| 0.0967 \| 0.8556 \| 0.8589 \| 0.8572 \| 0.9848 \|
	\| 0.0048 \| 4.6566 \| 8000 \| 0.0989 \| 0.8506 \| 0.8694 \| 0.8599 \| 0.9847 \|
	\| 0.0048 \| 4.9476 \| 8500 \| 0.0959 \| 0.8526 \| 0.8713 \| 0.8619 \| 0.9844 \|
	\| 0.0036 \| 5.2386 \| 9000 \| 0.1014 \| 0.8476 \| 0.8706 \| 0.8589 \| 0.9841 \|
	\| 0.0042 \| 5.5297 \| 9500 \| 0.1147 \| 0.8068 \| 0.8613 \| 0.8332 \| 0.9814 \|
	\| 0.0048 \| 5.8207 \| 10000 \| 0.1065 \| 0.8392 \| 0.8691 \| 0.8539 \| 0.9844 \|
	\| 0.0041 \| 6.1118 \| 10500 \| 0.1076 \| 0.8417 \| 0.8758 \| 0.8584 \| 0.9843 \|
	\| 0.0035 \| 6.4028 \| 11000 \| 0.1029 \| 0.8505 \| 0.8732 \| 0.8617 \| 0.9848 \|
	\| 0.0036 \| 6.6938 \| 11500 \| 0.0929 \| 0.8460 \| 0.8719 \| 0.8587 \| 0.9849 \|
	\| 0.0038 \| 6.9849 \| 12000 \| 0.1019 \| 0.8494 \| 0.8631 \| 0.8562 \| 0.9846 \|
	\| 0.0031 \| 7.2759 \| 12500 \| 0.1073 \| 0.8563 \| 0.8575 \| 0.8569 \| 0.9845 \|
	\| 0.0031 \| 7.5669 \| 13000 \| 0.1013 \| 0.8431 \| 0.8696 \| 0.8561 \| 0.9847 \|
	\| 0.0034 \| 7.8580 \| 13500 \| 0.1058 \| 0.8533 \| 0.8596 \| 0.8565 \| 0.9845 \|
	\| 0.0027 \| 8.1490 \| 14000 \| 0.1154 \| 0.8431 \| 0.8719 \| 0.8572 \| 0.9845 \|
	\| 0.0027 \| 8.4400 \| 14500 \| 0.1030 \| 0.8404 \| 0.8785 \| 0.8591 \| 0.9845 \|
	\| 0.0028 \| 8.7311 \| 15000 \| 0.1132 \| 0.8559 \| 0.8510 \| 0.8534 \| 0.9846 \|
	\| 0.003 \| 9.0221 \| 15500 \| 0.1106 \| 0.8514 \| 0.8648 \| 0.8581 \| 0.9848 \|
	\| 0.0022 \| 9.3132 \| 16000 \| 0.1136 \| 0.8586 \| 0.8657 \| 0.8621 \| 0.9852 \|
	\| 0.0025 \| 9.6042 \| 16500 \| 0.1128 \| 0.8494 \| 0.8697 \| 0.8594 \| 0.9848 \|
	\| 0.002 \| 9.8952 \| 17000 \| 0.1139 \| 0.8453 \| 0.8600 \| 0.8526 \| 0.9841 \|
	\| 0.0024 \| 10.1863 \| 17500 \| 0.1124 \| 0.8541 \| 0.8658 \| 0.8599 \| 0.9849 \|
	\| 0.002 \| 10.4773 \| 18000 \| 0.1154 \| 0.8368 \| 0.8663 \| 0.8513 \| 0.9842 \|
	\| 0.0019 \| 10.7683 \| 18500 \| 0.1182 \| 0.8457 \| 0.8629 \| 0.8542 \| 0.9844 \|
	\| 0.0023 \| 11.0594 \| 19000 \| 0.1140 \| 0.8531 \| 0.8596 \| 0.8563 \| 0.9846 \|
	\| 0.0018 \| 11.3504 \| 19500 \| 0.1194 \| 0.8526 \| 0.8683 \| 0.8604 \| 0.9851 \|
	\| 0.0016 \| 11.6414 \| 20000 \| 0.1198 \| 0.8527 \| 0.8652 \| 0.8589 \| 0.9848 \|
	\| 0.0021 \| 11.9325 \| 20500 \| 0.1169 \| 0.8592 \| 0.8654 \| 0.8623 \| 0.9852 \|
	\| 0.0016 \| 12.2235 \| 21000 \| 0.1229 \| 0.8605 \| 0.8626 \| 0.8616 \| 0.9848 \|
	\| 0.0021 \| 12.5146 \| 21500 \| 0.1198 \| 0.8484 \| 0.8697 \| 0.8589 \| 0.9846 \|
	\| 0.0019 \| 12.8056 \| 22000 \| 0.1177 \| 0.8535 \| 0.8600 \| 0.8568 \| 0.9844 \|
	\| 0.0013 \| 13.0966 \| 22500 \| 0.1190 \| 0.8436 \| 0.8716 \| 0.8574 \| 0.9844 \|
	\| 0.0016 \| 13.3877 \| 23000 \| 0.1227 \| 0.8475 \| 0.8665 \| 0.8569 \| 0.9847 \|
	\| 0.0012 \| 13.6787 \| 23500 \| 0.1237 \| 0.8513 \| 0.8676 \| 0.8594 \| 0.9848 \|
	\| 0.0015 \| 13.9697 \| 24000 \| 0.1198 \| 0.8407 \| 0.8709 \| 0.8555 \| 0.9843 \|
	\| 0.0012 \| 14.2608 \| 24500 \| 0.1239 \| 0.8516 \| 0.8689 \| 0.8602 \| 0.9850 \|
	\| 0.0014 \| 14.5518 \| 25000 \| 0.1261 \| 0.8432 \| 0.8634 \| 0.8532 \| 0.9843 \|
	\| 0.0015 \| 14.8428 \| 25500 \| 0.1220 \| 0.8451 \| 0.8716 \| 0.8582 \| 0.9849 \|
	\| 0.0013 \| 15.1339 \| 26000 \| 0.1209 \| 0.8608 \| 0.8598 \| 0.8603 \| 0.9847 \|
	\| 0.0011 \| 15.4249 \| 26500 \| 0.1261 \| 0.8457 \| 0.8637 \| 0.8546 \| 0.9847 \|
	\| 0.0011 \| 15.7159 \| 27000 \| 0.1273 \| 0.8510 \| 0.8616 \| 0.8563 \| 0.9846 \|
	\| 0.0022 \| 16.0070 \| 27500 \| 0.1282 \| 0.8431 \| 0.8738 \| 0.8582 \| 0.9847 \|
	\| 0.001 \| 16.2980 \| 28000 \| 0.1357 \| 0.8451 \| 0.8628 \| 0.8539 \| 0.9842 \|
	\| 0.0013 \| 16.5891 \| 28500 \| 0.1301 \| 0.8465 \| 0.8658 \| 0.8561 \| 0.9843 \|
	\| 0.0008 \| 16.8801 \| 29000 \| 0.1335 \| 0.8533 \| 0.8678 \| 0.8605 \| 0.9845 \|
	\| 0.0011 \| 17.1711 \| 29500 \| 0.1338 \| 0.8572 \| 0.8654 \| 0.8613 \| 0.9846 \|
	\| 0.0006 \| 17.4622 \| 30000 \| 0.1368 \| 0.8561 \| 0.8628 \| 0.8594 \| 0.9847 \|
	\| 0.0008 \| 17.7532 \| 30500 \| 0.1359 \| 0.8579 \| 0.8661 \| 0.8620 \| 0.9848 \|


	### Framework versions

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