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	---
	tags:
	- generated_from_trainer
	datasets:
	- conll2003
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: twitter-roberta-base-dec2021-CoNLL
	results:
	- task:
	name: Token Classification
	type: token-classification
	dataset:
	name: conll2003
	type: conll2003
	args: conll2003
	metrics:
	- name: Precision
	type: precision
	value: 0.9552512940390716
	- name: Recall
	type: recall
	value: 0.9628071356445641
	- name: F1
	type: f1
	value: 0.9590143324113654
	- name: Accuracy
	type: accuracy
	value: 0.9926599431486313
	---

	<!-- 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. -->

	# twitter-roberta-base-dec2021-CoNLL

	This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the conll2003 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0412
	- Precision: 0.9553
	- Recall: 0.9628
	- F1: 0.9590
	- Accuracy: 0.9927

	## 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: 5e-05
	- train_batch_size: 64
	- eval_batch_size: 1024
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 10

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| No log \| 0.11 \| 25 \| 0.2126 \| 0.5639 \| 0.6067 \| 0.5845 \| 0.9349 \|
	\| No log \| 0.23 \| 50 \| 0.0849 \| 0.8259 \| 0.8612 \| 0.8431 \| 0.9765 \|
	\| No log \| 0.34 \| 75 \| 0.0640 \| 0.8752 \| 0.8957 \| 0.8853 \| 0.9820 \|
	\| No log \| 0.45 \| 100 \| 0.0572 \| 0.8848 \| 0.9034 \| 0.8940 \| 0.9832 \|
	\| No log \| 0.57 \| 125 \| 0.0469 \| 0.9071 \| 0.9239 \| 0.9155 \| 0.9866 \|
	\| No log \| 0.68 \| 150 \| 0.0442 \| 0.9198 \| 0.9278 \| 0.9238 \| 0.9877 \|
	\| No log \| 0.8 \| 175 \| 0.0424 \| 0.9192 \| 0.9322 \| 0.9256 \| 0.9881 \|
	\| No log \| 0.91 \| 200 \| 0.0407 \| 0.9170 \| 0.9414 \| 0.9291 \| 0.9891 \|
	\| No log \| 1.02 \| 225 \| 0.0402 \| 0.9264 \| 0.9403 \| 0.9333 \| 0.9894 \|
	\| No log \| 1.14 \| 250 \| 0.0399 \| 0.9329 \| 0.9446 \| 0.9387 \| 0.9897 \|
	\| No log \| 1.25 \| 275 \| 0.0384 \| 0.9278 \| 0.9413 \| 0.9345 \| 0.9897 \|
	\| No log \| 1.36 \| 300 \| 0.0363 \| 0.9379 \| 0.9477 \| 0.9427 \| 0.9906 \|
	\| No log \| 1.48 \| 325 \| 0.0362 \| 0.9380 \| 0.9493 \| 0.9436 \| 0.9905 \|
	\| No log \| 1.59 \| 350 \| 0.0364 \| 0.9397 \| 0.9497 \| 0.9447 \| 0.9905 \|
	\| No log \| 1.7 \| 375 \| 0.0367 \| 0.9324 \| 0.9475 \| 0.9399 \| 0.9899 \|
	\| No log \| 1.82 \| 400 \| 0.0372 \| 0.9350 \| 0.9460 \| 0.9404 \| 0.9899 \|
	\| No log \| 1.93 \| 425 \| 0.0339 \| 0.9411 \| 0.9514 \| 0.9462 \| 0.9909 \|
	\| No log \| 2.05 \| 450 \| 0.0336 \| 0.9419 \| 0.9529 \| 0.9474 \| 0.9911 \|
	\| No log \| 2.16 \| 475 \| 0.0336 \| 0.9447 \| 0.9537 \| 0.9492 \| 0.9914 \|
	\| 0.079 \| 2.27 \| 500 \| 0.0345 \| 0.9420 \| 0.9566 \| 0.9492 \| 0.9914 \|
	\| 0.079 \| 2.39 \| 525 \| 0.0364 \| 0.9436 \| 0.9522 \| 0.9479 \| 0.9913 \|
	\| 0.079 \| 2.5 \| 550 \| 0.0340 \| 0.9479 \| 0.9514 \| 0.9496 \| 0.9916 \|
	\| 0.079 \| 2.61 \| 575 \| 0.0339 \| 0.9481 \| 0.9559 \| 0.9520 \| 0.9917 \|
	\| 0.079 \| 2.73 \| 600 \| 0.0396 \| 0.9326 \| 0.9504 \| 0.9414 \| 0.9902 \|
	\| 0.079 \| 2.84 \| 625 \| 0.0348 \| 0.9461 \| 0.9544 \| 0.9502 \| 0.9915 \|
	\| 0.079 \| 2.95 \| 650 \| 0.0359 \| 0.9419 \| 0.9527 \| 0.9473 \| 0.9908 \|
	\| 0.079 \| 3.07 \| 675 \| 0.0347 \| 0.9434 \| 0.9573 \| 0.9503 \| 0.9916 \|
	\| 0.079 \| 3.18 \| 700 \| 0.0351 \| 0.9464 \| 0.9566 \| 0.9515 \| 0.9918 \|
	\| 0.079 \| 3.3 \| 725 \| 0.0370 \| 0.9446 \| 0.9536 \| 0.9491 \| 0.9911 \|
	\| 0.079 \| 3.41 \| 750 \| 0.0358 \| 0.9462 \| 0.9583 \| 0.9522 \| 0.9917 \|
	\| 0.079 \| 3.52 \| 775 \| 0.0353 \| 0.9483 \| 0.9564 \| 0.9523 \| 0.9920 \|
	\| 0.079 \| 3.64 \| 800 \| 0.0351 \| 0.9469 \| 0.9564 \| 0.9516 \| 0.9916 \|
	\| 0.079 \| 3.75 \| 825 \| 0.0361 \| 0.9479 \| 0.9579 \| 0.9529 \| 0.9919 \|
	\| 0.079 \| 3.86 \| 850 \| 0.0370 \| 0.9498 \| 0.9581 \| 0.9539 \| 0.9918 \|
	\| 0.079 \| 3.98 \| 875 \| 0.0374 \| 0.9460 \| 0.9574 \| 0.9517 \| 0.9915 \|
	\| 0.079 \| 4.09 \| 900 \| 0.0381 \| 0.9506 \| 0.9594 \| 0.9550 \| 0.9922 \|
	\| 0.079 \| 4.2 \| 925 \| 0.0415 \| 0.9460 \| 0.9557 \| 0.9509 \| 0.9912 \|
	\| 0.079 \| 4.32 \| 950 \| 0.0390 \| 0.9493 \| 0.9556 \| 0.9524 \| 0.9917 \|
	\| 0.079 \| 4.43 \| 975 \| 0.0389 \| 0.9483 \| 0.9591 \| 0.9536 \| 0.9919 \|
	\| 0.0123 \| 4.55 \| 1000 \| 0.0379 \| 0.9464 \| 0.9569 \| 0.9516 \| 0.9918 \|
	\| 0.0123 \| 4.66 \| 1025 \| 0.0376 \| 0.9463 \| 0.9579 \| 0.9521 \| 0.9920 \|
	\| 0.0123 \| 4.77 \| 1050 \| 0.0373 \| 0.9499 \| 0.9571 \| 0.9535 \| 0.9917 \|
	\| 0.0123 \| 4.89 \| 1075 \| 0.0366 \| 0.9520 \| 0.9584 \| 0.9552 \| 0.9923 \|
	\| 0.0123 \| 5.0 \| 1100 \| 0.0374 \| 0.9488 \| 0.9606 \| 0.9547 \| 0.9923 \|
	\| 0.0123 \| 5.11 \| 1125 \| 0.0393 \| 0.9516 \| 0.9589 \| 0.9552 \| 0.9920 \|
	\| 0.0123 \| 5.23 \| 1150 \| 0.0389 \| 0.9539 \| 0.9603 \| 0.9571 \| 0.9925 \|
	\| 0.0123 \| 5.34 \| 1175 \| 0.0397 \| 0.9486 \| 0.9576 \| 0.9531 \| 0.9917 \|
	\| 0.0123 \| 5.45 \| 1200 \| 0.0397 \| 0.9478 \| 0.9569 \| 0.9523 \| 0.9919 \|
	\| 0.0123 \| 5.57 \| 1225 \| 0.0388 \| 0.9483 \| 0.9593 \| 0.9537 \| 0.9920 \|
	\| 0.0123 \| 5.68 \| 1250 \| 0.0389 \| 0.9502 \| 0.9606 \| 0.9554 \| 0.9923 \|
	\| 0.0123 \| 5.8 \| 1275 \| 0.0380 \| 0.9547 \| 0.9616 \| 0.9582 \| 0.9925 \|
	\| 0.0123 \| 5.91 \| 1300 \| 0.0391 \| 0.9496 \| 0.9603 \| 0.9549 \| 0.9924 \|
	\| 0.0123 \| 6.02 \| 1325 \| 0.0381 \| 0.9548 \| 0.9603 \| 0.9575 \| 0.9924 \|
	\| 0.0123 \| 6.14 \| 1350 \| 0.0400 \| 0.9529 \| 0.9596 \| 0.9562 \| 0.9922 \|
	\| 0.0123 \| 6.25 \| 1375 \| 0.0393 \| 0.9544 \| 0.9616 \| 0.9580 \| 0.9927 \|
	\| 0.0123 \| 6.36 \| 1400 \| 0.0419 \| 0.9514 \| 0.9621 \| 0.9567 \| 0.9924 \|
	\| 0.0123 \| 6.48 \| 1425 \| 0.0415 \| 0.9532 \| 0.9626 \| 0.9579 \| 0.9925 \|
	\| 0.0123 \| 6.59 \| 1450 \| 0.0415 \| 0.952 \| 0.9613 \| 0.9566 \| 0.9923 \|
	\| 0.0123 \| 6.7 \| 1475 \| 0.0399 \| 0.9542 \| 0.9611 \| 0.9577 \| 0.9925 \|
	\| 0.0052 \| 6.82 \| 1500 \| 0.0416 \| 0.9522 \| 0.9591 \| 0.9556 \| 0.9921 \|
	\| 0.0052 \| 6.93 \| 1525 \| 0.0410 \| 0.9502 \| 0.9599 \| 0.9550 \| 0.9919 \|
	\| 0.0052 \| 7.05 \| 1550 \| 0.0406 \| 0.9507 \| 0.9613 \| 0.9560 \| 0.9921 \|
	\| 0.0052 \| 7.16 \| 1575 \| 0.0400 \| 0.9508 \| 0.9603 \| 0.9555 \| 0.9923 \|
	\| 0.0052 \| 7.27 \| 1600 \| 0.0402 \| 0.9525 \| 0.9618 \| 0.9571 \| 0.9924 \|
	\| 0.0052 \| 7.39 \| 1625 \| 0.0401 \| 0.9550 \| 0.9633 \| 0.9591 \| 0.9925 \|
	\| 0.0052 \| 7.5 \| 1650 \| 0.0397 \| 0.9555 \| 0.9647 \| 0.9601 \| 0.9927 \|
	\| 0.0052 \| 7.61 \| 1675 \| 0.0412 \| 0.9526 \| 0.9610 \| 0.9568 \| 0.9922 \|
	\| 0.0052 \| 7.73 \| 1700 \| 0.0419 \| 0.9531 \| 0.9616 \| 0.9574 \| 0.9923 \|
	\| 0.0052 \| 7.84 \| 1725 \| 0.0407 \| 0.9555 \| 0.9621 \| 0.9588 \| 0.9927 \|
	\| 0.0052 \| 7.95 \| 1750 \| 0.0409 \| 0.9551 \| 0.9628 \| 0.9589 \| 0.9927 \|
	\| 0.0052 \| 8.07 \| 1775 \| 0.0413 \| 0.9520 \| 0.9616 \| 0.9568 \| 0.9924 \|
	\| 0.0052 \| 8.18 \| 1800 \| 0.0414 \| 0.9505 \| 0.9605 \| 0.9555 \| 0.9923 \|
	\| 0.0052 \| 8.3 \| 1825 \| 0.0410 \| 0.9542 \| 0.9605 \| 0.9573 \| 0.9924 \|
	\| 0.0052 \| 8.41 \| 1850 \| 0.0417 \| 0.9553 \| 0.9599 \| 0.9576 \| 0.9924 \|
	\| 0.0052 \| 8.52 \| 1875 \| 0.0418 \| 0.9545 \| 0.9606 \| 0.9576 \| 0.9923 \|
	\| 0.0052 \| 8.64 \| 1900 \| 0.0414 \| 0.9544 \| 0.9616 \| 0.9580 \| 0.9924 \|
	\| 0.0052 \| 8.75 \| 1925 \| 0.0419 \| 0.9555 \| 0.9620 \| 0.9587 \| 0.9925 \|
	\| 0.0052 \| 8.86 \| 1950 \| 0.0415 \| 0.9544 \| 0.9611 \| 0.9577 \| 0.9926 \|
	\| 0.0052 \| 8.98 \| 1975 \| 0.0413 \| 0.9542 \| 0.9611 \| 0.9577 \| 0.9926 \|
	\| 0.0027 \| 9.09 \| 2000 \| 0.0412 \| 0.9553 \| 0.9628 \| 0.9590 \| 0.9927 \|
	\| 0.0027 \| 9.2 \| 2025 \| 0.0408 \| 0.9554 \| 0.9630 \| 0.9592 \| 0.9927 \|
	\| 0.0027 \| 9.32 \| 2050 \| 0.0404 \| 0.9545 \| 0.9613 \| 0.9579 \| 0.9926 \|
	\| 0.0027 \| 9.43 \| 2075 \| 0.0407 \| 0.9557 \| 0.9618 \| 0.9587 \| 0.9926 \|
	\| 0.0027 \| 9.55 \| 2100 \| 0.0410 \| 0.9552 \| 0.9618 \| 0.9585 \| 0.9926 \|
	\| 0.0027 \| 9.66 \| 2125 \| 0.0412 \| 0.9552 \| 0.9620 \| 0.9586 \| 0.9925 \|
	\| 0.0027 \| 9.77 \| 2150 \| 0.0413 \| 0.9557 \| 0.9621 \| 0.9589 \| 0.9925 \|
	\| 0.0027 \| 9.89 \| 2175 \| 0.0413 \| 0.9557 \| 0.9621 \| 0.9589 \| 0.9925 \|
	\| 0.0027 \| 10.0 \| 2200 \| 0.0413 \| 0.9557 \| 0.9621 \| 0.9589 \| 0.9925 \|


	### Framework versions

	- Transformers 4.20.1
	- Pytorch 1.12.0
	- Datasets 2.3.2
	- Tokenizers 0.12.1

	---
	tags:
	- generated_from_trainer
	datasets:
	- conll2003
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: twitter-roberta-base-dec2021-CoNLL
	results:
	- task:
	name: Token Classification
	type: token-classification
	dataset:
	name: conll2003
	type: conll2003
	args: conll2003
	metrics:
	- name: Precision
	type: precision
	value: 0.9552512940390716
	- name: Recall
	type: recall
	value: 0.9628071356445641
	- name: F1
	type: f1
	value: 0.9590143324113654
	- name: Accuracy
	type: accuracy
	value: 0.9926599431486313
	---

	<!-- 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. -->

	# twitter-roberta-base-dec2021-CoNLL

	This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the conll2003 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0412
	- Precision: 0.9553
	- Recall: 0.9628
	- F1: 0.9590
	- Accuracy: 0.9927

	## 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: 5e-05
	- train_batch_size: 64
	- eval_batch_size: 1024
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 10

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| No log \| 0.11 \| 25 \| 0.2126 \| 0.5639 \| 0.6067 \| 0.5845 \| 0.9349 \|
	\| No log \| 0.23 \| 50 \| 0.0849 \| 0.8259 \| 0.8612 \| 0.8431 \| 0.9765 \|
	\| No log \| 0.34 \| 75 \| 0.0640 \| 0.8752 \| 0.8957 \| 0.8853 \| 0.9820 \|
	\| No log \| 0.45 \| 100 \| 0.0572 \| 0.8848 \| 0.9034 \| 0.8940 \| 0.9832 \|
	\| No log \| 0.57 \| 125 \| 0.0469 \| 0.9071 \| 0.9239 \| 0.9155 \| 0.9866 \|
	\| No log \| 0.68 \| 150 \| 0.0442 \| 0.9198 \| 0.9278 \| 0.9238 \| 0.9877 \|
	\| No log \| 0.8 \| 175 \| 0.0424 \| 0.9192 \| 0.9322 \| 0.9256 \| 0.9881 \|
	\| No log \| 0.91 \| 200 \| 0.0407 \| 0.9170 \| 0.9414 \| 0.9291 \| 0.9891 \|
	\| No log \| 1.02 \| 225 \| 0.0402 \| 0.9264 \| 0.9403 \| 0.9333 \| 0.9894 \|
	\| No log \| 1.14 \| 250 \| 0.0399 \| 0.9329 \| 0.9446 \| 0.9387 \| 0.9897 \|
	\| No log \| 1.25 \| 275 \| 0.0384 \| 0.9278 \| 0.9413 \| 0.9345 \| 0.9897 \|
	\| No log \| 1.36 \| 300 \| 0.0363 \| 0.9379 \| 0.9477 \| 0.9427 \| 0.9906 \|
	\| No log \| 1.48 \| 325 \| 0.0362 \| 0.9380 \| 0.9493 \| 0.9436 \| 0.9905 \|
	\| No log \| 1.59 \| 350 \| 0.0364 \| 0.9397 \| 0.9497 \| 0.9447 \| 0.9905 \|
	\| No log \| 1.7 \| 375 \| 0.0367 \| 0.9324 \| 0.9475 \| 0.9399 \| 0.9899 \|
	\| No log \| 1.82 \| 400 \| 0.0372 \| 0.9350 \| 0.9460 \| 0.9404 \| 0.9899 \|
	\| No log \| 1.93 \| 425 \| 0.0339 \| 0.9411 \| 0.9514 \| 0.9462 \| 0.9909 \|
	\| No log \| 2.05 \| 450 \| 0.0336 \| 0.9419 \| 0.9529 \| 0.9474 \| 0.9911 \|
	\| No log \| 2.16 \| 475 \| 0.0336 \| 0.9447 \| 0.9537 \| 0.9492 \| 0.9914 \|
	\| 0.079 \| 2.27 \| 500 \| 0.0345 \| 0.9420 \| 0.9566 \| 0.9492 \| 0.9914 \|
	\| 0.079 \| 2.39 \| 525 \| 0.0364 \| 0.9436 \| 0.9522 \| 0.9479 \| 0.9913 \|
	\| 0.079 \| 2.5 \| 550 \| 0.0340 \| 0.9479 \| 0.9514 \| 0.9496 \| 0.9916 \|
	\| 0.079 \| 2.61 \| 575 \| 0.0339 \| 0.9481 \| 0.9559 \| 0.9520 \| 0.9917 \|
	\| 0.079 \| 2.73 \| 600 \| 0.0396 \| 0.9326 \| 0.9504 \| 0.9414 \| 0.9902 \|
	\| 0.079 \| 2.84 \| 625 \| 0.0348 \| 0.9461 \| 0.9544 \| 0.9502 \| 0.9915 \|
	\| 0.079 \| 2.95 \| 650 \| 0.0359 \| 0.9419 \| 0.9527 \| 0.9473 \| 0.9908 \|
	\| 0.079 \| 3.07 \| 675 \| 0.0347 \| 0.9434 \| 0.9573 \| 0.9503 \| 0.9916 \|
	\| 0.079 \| 3.18 \| 700 \| 0.0351 \| 0.9464 \| 0.9566 \| 0.9515 \| 0.9918 \|
	\| 0.079 \| 3.3 \| 725 \| 0.0370 \| 0.9446 \| 0.9536 \| 0.9491 \| 0.9911 \|
	\| 0.079 \| 3.41 \| 750 \| 0.0358 \| 0.9462 \| 0.9583 \| 0.9522 \| 0.9917 \|
	\| 0.079 \| 3.52 \| 775 \| 0.0353 \| 0.9483 \| 0.9564 \| 0.9523 \| 0.9920 \|
	\| 0.079 \| 3.64 \| 800 \| 0.0351 \| 0.9469 \| 0.9564 \| 0.9516 \| 0.9916 \|
	\| 0.079 \| 3.75 \| 825 \| 0.0361 \| 0.9479 \| 0.9579 \| 0.9529 \| 0.9919 \|
	\| 0.079 \| 3.86 \| 850 \| 0.0370 \| 0.9498 \| 0.9581 \| 0.9539 \| 0.9918 \|
	\| 0.079 \| 3.98 \| 875 \| 0.0374 \| 0.9460 \| 0.9574 \| 0.9517 \| 0.9915 \|
	\| 0.079 \| 4.09 \| 900 \| 0.0381 \| 0.9506 \| 0.9594 \| 0.9550 \| 0.9922 \|
	\| 0.079 \| 4.2 \| 925 \| 0.0415 \| 0.9460 \| 0.9557 \| 0.9509 \| 0.9912 \|
	\| 0.079 \| 4.32 \| 950 \| 0.0390 \| 0.9493 \| 0.9556 \| 0.9524 \| 0.9917 \|
	\| 0.079 \| 4.43 \| 975 \| 0.0389 \| 0.9483 \| 0.9591 \| 0.9536 \| 0.9919 \|
	\| 0.0123 \| 4.55 \| 1000 \| 0.0379 \| 0.9464 \| 0.9569 \| 0.9516 \| 0.9918 \|
	\| 0.0123 \| 4.66 \| 1025 \| 0.0376 \| 0.9463 \| 0.9579 \| 0.9521 \| 0.9920 \|
	\| 0.0123 \| 4.77 \| 1050 \| 0.0373 \| 0.9499 \| 0.9571 \| 0.9535 \| 0.9917 \|
	\| 0.0123 \| 4.89 \| 1075 \| 0.0366 \| 0.9520 \| 0.9584 \| 0.9552 \| 0.9923 \|
	\| 0.0123 \| 5.0 \| 1100 \| 0.0374 \| 0.9488 \| 0.9606 \| 0.9547 \| 0.9923 \|
	\| 0.0123 \| 5.11 \| 1125 \| 0.0393 \| 0.9516 \| 0.9589 \| 0.9552 \| 0.9920 \|
	\| 0.0123 \| 5.23 \| 1150 \| 0.0389 \| 0.9539 \| 0.9603 \| 0.9571 \| 0.9925 \|
	\| 0.0123 \| 5.34 \| 1175 \| 0.0397 \| 0.9486 \| 0.9576 \| 0.9531 \| 0.9917 \|
	\| 0.0123 \| 5.45 \| 1200 \| 0.0397 \| 0.9478 \| 0.9569 \| 0.9523 \| 0.9919 \|
	\| 0.0123 \| 5.57 \| 1225 \| 0.0388 \| 0.9483 \| 0.9593 \| 0.9537 \| 0.9920 \|
	\| 0.0123 \| 5.68 \| 1250 \| 0.0389 \| 0.9502 \| 0.9606 \| 0.9554 \| 0.9923 \|
	\| 0.0123 \| 5.8 \| 1275 \| 0.0380 \| 0.9547 \| 0.9616 \| 0.9582 \| 0.9925 \|
	\| 0.0123 \| 5.91 \| 1300 \| 0.0391 \| 0.9496 \| 0.9603 \| 0.9549 \| 0.9924 \|
	\| 0.0123 \| 6.02 \| 1325 \| 0.0381 \| 0.9548 \| 0.9603 \| 0.9575 \| 0.9924 \|
	\| 0.0123 \| 6.14 \| 1350 \| 0.0400 \| 0.9529 \| 0.9596 \| 0.9562 \| 0.9922 \|
	\| 0.0123 \| 6.25 \| 1375 \| 0.0393 \| 0.9544 \| 0.9616 \| 0.9580 \| 0.9927 \|
	\| 0.0123 \| 6.36 \| 1400 \| 0.0419 \| 0.9514 \| 0.9621 \| 0.9567 \| 0.9924 \|
	\| 0.0123 \| 6.48 \| 1425 \| 0.0415 \| 0.9532 \| 0.9626 \| 0.9579 \| 0.9925 \|
	\| 0.0123 \| 6.59 \| 1450 \| 0.0415 \| 0.952 \| 0.9613 \| 0.9566 \| 0.9923 \|
	\| 0.0123 \| 6.7 \| 1475 \| 0.0399 \| 0.9542 \| 0.9611 \| 0.9577 \| 0.9925 \|
	\| 0.0052 \| 6.82 \| 1500 \| 0.0416 \| 0.9522 \| 0.9591 \| 0.9556 \| 0.9921 \|
	\| 0.0052 \| 6.93 \| 1525 \| 0.0410 \| 0.9502 \| 0.9599 \| 0.9550 \| 0.9919 \|
	\| 0.0052 \| 7.05 \| 1550 \| 0.0406 \| 0.9507 \| 0.9613 \| 0.9560 \| 0.9921 \|
	\| 0.0052 \| 7.16 \| 1575 \| 0.0400 \| 0.9508 \| 0.9603 \| 0.9555 \| 0.9923 \|
	\| 0.0052 \| 7.27 \| 1600 \| 0.0402 \| 0.9525 \| 0.9618 \| 0.9571 \| 0.9924 \|
	\| 0.0052 \| 7.39 \| 1625 \| 0.0401 \| 0.9550 \| 0.9633 \| 0.9591 \| 0.9925 \|
	\| 0.0052 \| 7.5 \| 1650 \| 0.0397 \| 0.9555 \| 0.9647 \| 0.9601 \| 0.9927 \|
	\| 0.0052 \| 7.61 \| 1675 \| 0.0412 \| 0.9526 \| 0.9610 \| 0.9568 \| 0.9922 \|
	\| 0.0052 \| 7.73 \| 1700 \| 0.0419 \| 0.9531 \| 0.9616 \| 0.9574 \| 0.9923 \|
	\| 0.0052 \| 7.84 \| 1725 \| 0.0407 \| 0.9555 \| 0.9621 \| 0.9588 \| 0.9927 \|
	\| 0.0052 \| 7.95 \| 1750 \| 0.0409 \| 0.9551 \| 0.9628 \| 0.9589 \| 0.9927 \|
	\| 0.0052 \| 8.07 \| 1775 \| 0.0413 \| 0.9520 \| 0.9616 \| 0.9568 \| 0.9924 \|
	\| 0.0052 \| 8.18 \| 1800 \| 0.0414 \| 0.9505 \| 0.9605 \| 0.9555 \| 0.9923 \|
	\| 0.0052 \| 8.3 \| 1825 \| 0.0410 \| 0.9542 \| 0.9605 \| 0.9573 \| 0.9924 \|
	\| 0.0052 \| 8.41 \| 1850 \| 0.0417 \| 0.9553 \| 0.9599 \| 0.9576 \| 0.9924 \|
	\| 0.0052 \| 8.52 \| 1875 \| 0.0418 \| 0.9545 \| 0.9606 \| 0.9576 \| 0.9923 \|
	\| 0.0052 \| 8.64 \| 1900 \| 0.0414 \| 0.9544 \| 0.9616 \| 0.9580 \| 0.9924 \|
	\| 0.0052 \| 8.75 \| 1925 \| 0.0419 \| 0.9555 \| 0.9620 \| 0.9587 \| 0.9925 \|
	\| 0.0052 \| 8.86 \| 1950 \| 0.0415 \| 0.9544 \| 0.9611 \| 0.9577 \| 0.9926 \|
	\| 0.0052 \| 8.98 \| 1975 \| 0.0413 \| 0.9542 \| 0.9611 \| 0.9577 \| 0.9926 \|
	\| 0.0027 \| 9.09 \| 2000 \| 0.0412 \| 0.9553 \| 0.9628 \| 0.9590 \| 0.9927 \|
	\| 0.0027 \| 9.2 \| 2025 \| 0.0408 \| 0.9554 \| 0.9630 \| 0.9592 \| 0.9927 \|
	\| 0.0027 \| 9.32 \| 2050 \| 0.0404 \| 0.9545 \| 0.9613 \| 0.9579 \| 0.9926 \|
	\| 0.0027 \| 9.43 \| 2075 \| 0.0407 \| 0.9557 \| 0.9618 \| 0.9587 \| 0.9926 \|
	\| 0.0027 \| 9.55 \| 2100 \| 0.0410 \| 0.9552 \| 0.9618 \| 0.9585 \| 0.9926 \|
	\| 0.0027 \| 9.66 \| 2125 \| 0.0412 \| 0.9552 \| 0.9620 \| 0.9586 \| 0.9925 \|
	\| 0.0027 \| 9.77 \| 2150 \| 0.0413 \| 0.9557 \| 0.9621 \| 0.9589 \| 0.9925 \|
	\| 0.0027 \| 9.89 \| 2175 \| 0.0413 \| 0.9557 \| 0.9621 \| 0.9589 \| 0.9925 \|
	\| 0.0027 \| 10.0 \| 2200 \| 0.0413 \| 0.9557 \| 0.9621 \| 0.9589 \| 0.9925 \|


	### Framework versions

	- Transformers 4.20.1
	- Pytorch 1.12.0
	- Datasets 2.3.2
	- Tokenizers 0.12.1