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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-mdeberta_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-mdeberta_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: 0.1473
	- Precision: 0.8609
	- Recall: 0.8706
	- F1: 0.8657
	- Accuracy: 0.9852

	## 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 \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 0.0061 \| 0.5828 \| 500 \| 0.0903 \| 0.8587 \| 0.8507 \| 0.8547 \| 0.9842 \|
	\| 0.0071 \| 1.1655 \| 1000 \| 0.1028 \| 0.8407 \| 0.8732 \| 0.8566 \| 0.9845 \|
	\| 0.0061 \| 1.7483 \| 1500 \| 0.0990 \| 0.8455 \| 0.8495 \| 0.8475 \| 0.9833 \|
	\| 0.0055 \| 2.3310 \| 2000 \| 0.1022 \| 0.8571 \| 0.8517 \| 0.8544 \| 0.9842 \|
	\| 0.0047 \| 2.9138 \| 2500 \| 0.0968 \| 0.8396 \| 0.8668 \| 0.8530 \| 0.9840 \|
	\| 0.0037 \| 3.4965 \| 3000 \| 0.1138 \| 0.8275 \| 0.8707 \| 0.8486 \| 0.9832 \|
	\| 0.0035 \| 4.0793 \| 3500 \| 0.1145 \| 0.8490 \| 0.8588 \| 0.8538 \| 0.9837 \|
	\| 0.0036 \| 4.6620 \| 4000 \| 0.1066 \| 0.8388 \| 0.8687 \| 0.8535 \| 0.9840 \|
	\| 0.0032 \| 5.2448 \| 4500 \| 0.1206 \| 0.8495 \| 0.8575 \| 0.8535 \| 0.9840 \|
	\| 0.0032 \| 5.8275 \| 5000 \| 0.1192 \| 0.8502 \| 0.8564 \| 0.8533 \| 0.9840 \|
	\| 0.0027 \| 6.4103 \| 5500 \| 0.1153 \| 0.8463 \| 0.8683 \| 0.8571 \| 0.9844 \|
	\| 0.0033 \| 6.9930 \| 6000 \| 0.1199 \| 0.8437 \| 0.8722 \| 0.8577 \| 0.9843 \|
	\| 0.003 \| 7.5758 \| 6500 \| 0.1121 \| 0.8533 \| 0.8525 \| 0.8529 \| 0.9843 \|
	\| 0.0023 \| 8.1585 \| 7000 \| 0.1152 \| 0.8508 \| 0.8435 \| 0.8471 \| 0.9831 \|
	\| 0.0028 \| 8.7413 \| 7500 \| 0.1131 \| 0.8368 \| 0.8536 \| 0.8451 \| 0.9835 \|
	\| 0.0026 \| 9.3240 \| 8000 \| 0.1213 \| 0.8415 \| 0.8670 \| 0.8540 \| 0.9834 \|
	\| 0.0021 \| 9.9068 \| 8500 \| 0.1207 \| 0.8313 \| 0.8716 \| 0.8510 \| 0.9833 \|
	\| 0.0015 \| 10.4895 \| 9000 \| 0.1268 \| 0.8566 \| 0.8609 \| 0.8587 \| 0.9844 \|
	\| 0.0016 \| 11.0723 \| 9500 \| 0.1223 \| 0.8525 \| 0.8528 \| 0.8527 \| 0.9839 \|
	\| 0.0021 \| 11.6550 \| 10000 \| 0.1262 \| 0.8402 \| 0.8602 \| 0.8501 \| 0.9834 \|
	\| 0.0012 \| 12.2378 \| 10500 \| 0.1237 \| 0.8575 \| 0.8453 \| 0.8514 \| 0.9837 \|
	\| 0.0014 \| 12.8205 \| 11000 \| 0.1319 \| 0.8537 \| 0.8551 \| 0.8544 \| 0.9840 \|
	\| 0.0013 \| 13.4033 \| 11500 \| 0.1308 \| 0.8316 \| 0.8619 \| 0.8465 \| 0.9830 \|
	\| 0.0014 \| 13.9860 \| 12000 \| 0.1237 \| 0.8615 \| 0.8536 \| 0.8575 \| 0.9843 \|
	\| 0.0008 \| 14.5688 \| 12500 \| 0.1361 \| 0.8436 \| 0.8638 \| 0.8536 \| 0.9839 \|
	\| 0.0013 \| 15.1515 \| 13000 \| 0.1383 \| 0.8466 \| 0.8576 \| 0.8521 \| 0.9839 \|
	\| 0.0011 \| 15.7343 \| 13500 \| 0.1356 \| 0.8595 \| 0.8527 \| 0.8561 \| 0.9843 \|
	\| 0.0007 \| 16.3170 \| 14000 \| 0.1366 \| 0.8533 \| 0.8618 \| 0.8575 \| 0.9843 \|
	\| 0.0008 \| 16.8998 \| 14500 \| 0.1334 \| 0.8608 \| 0.8625 \| 0.8616 \| 0.9846 \|
	\| 0.0007 \| 17.4825 \| 15000 \| 0.1382 \| 0.8549 \| 0.8644 \| 0.8596 \| 0.9847 \|
	\| 0.0007 \| 18.0653 \| 15500 \| 0.1432 \| 0.8682 \| 0.8488 \| 0.8584 \| 0.9845 \|
	\| 0.0006 \| 18.6480 \| 16000 \| 0.1408 \| 0.8551 \| 0.8613 \| 0.8582 \| 0.9844 \|
	\| 0.0009 \| 19.2308 \| 16500 \| 0.1366 \| 0.8551 \| 0.8567 \| 0.8559 \| 0.9844 \|
	\| 0.0007 \| 19.8135 \| 17000 \| 0.1320 \| 0.8518 \| 0.8641 \| 0.8579 \| 0.9845 \|
	\| 0.0005 \| 20.3963 \| 17500 \| 0.1399 \| 0.8506 \| 0.8655 \| 0.8580 \| 0.9844 \|
	\| 0.0005 \| 20.9790 \| 18000 \| 0.1399 \| 0.8510 \| 0.8631 \| 0.8570 \| 0.9845 \|
	\| 0.0005 \| 21.5618 \| 18500 \| 0.1430 \| 0.8633 \| 0.8622 \| 0.8628 \| 0.9849 \|
	\| 0.0005 \| 22.1445 \| 19000 \| 0.1368 \| 0.8549 \| 0.8673 \| 0.8611 \| 0.9848 \|
	\| 0.0004 \| 22.7273 \| 19500 \| 0.1364 \| 0.8548 \| 0.8678 \| 0.8613 \| 0.9848 \|
	\| 0.0005 \| 23.3100 \| 20000 \| 0.1400 \| 0.8616 \| 0.8650 \| 0.8633 \| 0.9848 \|
	\| 0.0003 \| 23.8928 \| 20500 \| 0.1452 \| 0.8458 \| 0.8723 \| 0.8589 \| 0.9843 \|
	\| 0.0002 \| 24.4755 \| 21000 \| 0.1398 \| 0.8552 \| 0.8665 \| 0.8608 \| 0.9849 \|
	\| 0.0003 \| 25.0583 \| 21500 \| 0.1405 \| 0.8578 \| 0.8710 \| 0.8643 \| 0.9849 \|
	\| 0.0002 \| 25.6410 \| 22000 \| 0.1408 \| 0.8601 \| 0.8691 \| 0.8646 \| 0.9851 \|
	\| 0.0002 \| 26.2238 \| 22500 \| 0.1453 \| 0.8578 \| 0.8730 \| 0.8654 \| 0.9852 \|
	\| 0.0002 \| 26.8065 \| 23000 \| 0.1463 \| 0.8574 \| 0.8736 \| 0.8654 \| 0.9851 \|
	\| 0.0002 \| 27.3893 \| 23500 \| 0.1458 \| 0.8637 \| 0.8714 \| 0.8676 \| 0.9854 \|
	\| 0.0001 \| 27.9720 \| 24000 \| 0.1465 \| 0.8565 \| 0.8732 \| 0.8648 \| 0.9851 \|
	\| 0.0001 \| 28.5548 \| 24500 \| 0.1464 \| 0.8602 \| 0.8727 \| 0.8664 \| 0.9852 \|
	\| 0.0002 \| 29.1375 \| 25000 \| 0.1474 \| 0.8598 \| 0.8714 \| 0.8656 \| 0.9852 \|
	\| 0.0001 \| 29.7203 \| 25500 \| 0.1473 \| 0.8609 \| 0.8706 \| 0.8657 \| 0.9852 \|


	### 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-mdeberta_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-mdeberta_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: 0.1473
	- Precision: 0.8609
	- Recall: 0.8706
	- F1: 0.8657
	- Accuracy: 0.9852

	## 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 \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 0.0061 \| 0.5828 \| 500 \| 0.0903 \| 0.8587 \| 0.8507 \| 0.8547 \| 0.9842 \|
	\| 0.0071 \| 1.1655 \| 1000 \| 0.1028 \| 0.8407 \| 0.8732 \| 0.8566 \| 0.9845 \|
	\| 0.0061 \| 1.7483 \| 1500 \| 0.0990 \| 0.8455 \| 0.8495 \| 0.8475 \| 0.9833 \|
	\| 0.0055 \| 2.3310 \| 2000 \| 0.1022 \| 0.8571 \| 0.8517 \| 0.8544 \| 0.9842 \|
	\| 0.0047 \| 2.9138 \| 2500 \| 0.0968 \| 0.8396 \| 0.8668 \| 0.8530 \| 0.9840 \|
	\| 0.0037 \| 3.4965 \| 3000 \| 0.1138 \| 0.8275 \| 0.8707 \| 0.8486 \| 0.9832 \|
	\| 0.0035 \| 4.0793 \| 3500 \| 0.1145 \| 0.8490 \| 0.8588 \| 0.8538 \| 0.9837 \|
	\| 0.0036 \| 4.6620 \| 4000 \| 0.1066 \| 0.8388 \| 0.8687 \| 0.8535 \| 0.9840 \|
	\| 0.0032 \| 5.2448 \| 4500 \| 0.1206 \| 0.8495 \| 0.8575 \| 0.8535 \| 0.9840 \|
	\| 0.0032 \| 5.8275 \| 5000 \| 0.1192 \| 0.8502 \| 0.8564 \| 0.8533 \| 0.9840 \|
	\| 0.0027 \| 6.4103 \| 5500 \| 0.1153 \| 0.8463 \| 0.8683 \| 0.8571 \| 0.9844 \|
	\| 0.0033 \| 6.9930 \| 6000 \| 0.1199 \| 0.8437 \| 0.8722 \| 0.8577 \| 0.9843 \|
	\| 0.003 \| 7.5758 \| 6500 \| 0.1121 \| 0.8533 \| 0.8525 \| 0.8529 \| 0.9843 \|
	\| 0.0023 \| 8.1585 \| 7000 \| 0.1152 \| 0.8508 \| 0.8435 \| 0.8471 \| 0.9831 \|
	\| 0.0028 \| 8.7413 \| 7500 \| 0.1131 \| 0.8368 \| 0.8536 \| 0.8451 \| 0.9835 \|
	\| 0.0026 \| 9.3240 \| 8000 \| 0.1213 \| 0.8415 \| 0.8670 \| 0.8540 \| 0.9834 \|
	\| 0.0021 \| 9.9068 \| 8500 \| 0.1207 \| 0.8313 \| 0.8716 \| 0.8510 \| 0.9833 \|
	\| 0.0015 \| 10.4895 \| 9000 \| 0.1268 \| 0.8566 \| 0.8609 \| 0.8587 \| 0.9844 \|
	\| 0.0016 \| 11.0723 \| 9500 \| 0.1223 \| 0.8525 \| 0.8528 \| 0.8527 \| 0.9839 \|
	\| 0.0021 \| 11.6550 \| 10000 \| 0.1262 \| 0.8402 \| 0.8602 \| 0.8501 \| 0.9834 \|
	\| 0.0012 \| 12.2378 \| 10500 \| 0.1237 \| 0.8575 \| 0.8453 \| 0.8514 \| 0.9837 \|
	\| 0.0014 \| 12.8205 \| 11000 \| 0.1319 \| 0.8537 \| 0.8551 \| 0.8544 \| 0.9840 \|
	\| 0.0013 \| 13.4033 \| 11500 \| 0.1308 \| 0.8316 \| 0.8619 \| 0.8465 \| 0.9830 \|
	\| 0.0014 \| 13.9860 \| 12000 \| 0.1237 \| 0.8615 \| 0.8536 \| 0.8575 \| 0.9843 \|
	\| 0.0008 \| 14.5688 \| 12500 \| 0.1361 \| 0.8436 \| 0.8638 \| 0.8536 \| 0.9839 \|
	\| 0.0013 \| 15.1515 \| 13000 \| 0.1383 \| 0.8466 \| 0.8576 \| 0.8521 \| 0.9839 \|
	\| 0.0011 \| 15.7343 \| 13500 \| 0.1356 \| 0.8595 \| 0.8527 \| 0.8561 \| 0.9843 \|
	\| 0.0007 \| 16.3170 \| 14000 \| 0.1366 \| 0.8533 \| 0.8618 \| 0.8575 \| 0.9843 \|
	\| 0.0008 \| 16.8998 \| 14500 \| 0.1334 \| 0.8608 \| 0.8625 \| 0.8616 \| 0.9846 \|
	\| 0.0007 \| 17.4825 \| 15000 \| 0.1382 \| 0.8549 \| 0.8644 \| 0.8596 \| 0.9847 \|
	\| 0.0007 \| 18.0653 \| 15500 \| 0.1432 \| 0.8682 \| 0.8488 \| 0.8584 \| 0.9845 \|
	\| 0.0006 \| 18.6480 \| 16000 \| 0.1408 \| 0.8551 \| 0.8613 \| 0.8582 \| 0.9844 \|
	\| 0.0009 \| 19.2308 \| 16500 \| 0.1366 \| 0.8551 \| 0.8567 \| 0.8559 \| 0.9844 \|
	\| 0.0007 \| 19.8135 \| 17000 \| 0.1320 \| 0.8518 \| 0.8641 \| 0.8579 \| 0.9845 \|
	\| 0.0005 \| 20.3963 \| 17500 \| 0.1399 \| 0.8506 \| 0.8655 \| 0.8580 \| 0.9844 \|
	\| 0.0005 \| 20.9790 \| 18000 \| 0.1399 \| 0.8510 \| 0.8631 \| 0.8570 \| 0.9845 \|
	\| 0.0005 \| 21.5618 \| 18500 \| 0.1430 \| 0.8633 \| 0.8622 \| 0.8628 \| 0.9849 \|
	\| 0.0005 \| 22.1445 \| 19000 \| 0.1368 \| 0.8549 \| 0.8673 \| 0.8611 \| 0.9848 \|
	\| 0.0004 \| 22.7273 \| 19500 \| 0.1364 \| 0.8548 \| 0.8678 \| 0.8613 \| 0.9848 \|
	\| 0.0005 \| 23.3100 \| 20000 \| 0.1400 \| 0.8616 \| 0.8650 \| 0.8633 \| 0.9848 \|
	\| 0.0003 \| 23.8928 \| 20500 \| 0.1452 \| 0.8458 \| 0.8723 \| 0.8589 \| 0.9843 \|
	\| 0.0002 \| 24.4755 \| 21000 \| 0.1398 \| 0.8552 \| 0.8665 \| 0.8608 \| 0.9849 \|
	\| 0.0003 \| 25.0583 \| 21500 \| 0.1405 \| 0.8578 \| 0.8710 \| 0.8643 \| 0.9849 \|
	\| 0.0002 \| 25.6410 \| 22000 \| 0.1408 \| 0.8601 \| 0.8691 \| 0.8646 \| 0.9851 \|
	\| 0.0002 \| 26.2238 \| 22500 \| 0.1453 \| 0.8578 \| 0.8730 \| 0.8654 \| 0.9852 \|
	\| 0.0002 \| 26.8065 \| 23000 \| 0.1463 \| 0.8574 \| 0.8736 \| 0.8654 \| 0.9851 \|
	\| 0.0002 \| 27.3893 \| 23500 \| 0.1458 \| 0.8637 \| 0.8714 \| 0.8676 \| 0.9854 \|
	\| 0.0001 \| 27.9720 \| 24000 \| 0.1465 \| 0.8565 \| 0.8732 \| 0.8648 \| 0.9851 \|
	\| 0.0001 \| 28.5548 \| 24500 \| 0.1464 \| 0.8602 \| 0.8727 \| 0.8664 \| 0.9852 \|
	\| 0.0002 \| 29.1375 \| 25000 \| 0.1474 \| 0.8598 \| 0.8714 \| 0.8656 \| 0.9852 \|
	\| 0.0001 \| 29.7203 \| 25500 \| 0.1473 \| 0.8609 \| 0.8706 \| 0.8657 \| 0.9852 \|


	### Framework versions

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