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	---
	license: bsd-3-clause
	base_model: Salesforce/codet5p-770m-py
	tags:
	- generated_from_trainer
	datasets:
	- mbpp
	model-index:
	- name: codet5p-770m-py-sanitized-codebleu-1-True-5e-05-0.1-lora-layer_12
	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. -->

	# codet5p-770m-py-sanitized-codebleu-1-True-5e-05-0.1-lora-layer_12

	This model is a fine-tuned version of [Salesforce/codet5p-770m-py](https://huggingface.co/Salesforce/codet5p-770m-py) on the mbpp dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.7241
	- Codebleu: 0.1012
	- Ngram Match Score: 0.0160
	- Weighted Ngram Match Score: 0.0377
	- Syntax Match Score: 0.1190
	- Dataflow Match Score: 0.1205

	## 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: 8
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 100
	- num_epochs: 64

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Codebleu \| Ngram Match Score \| Weighted Ngram Match Score \| Syntax Match Score \| Dataflow Match Score \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-----------------:\|:--------------------------:\|:------------------:\|:--------------------:\|
	\| 0.9814 \| 1.0 \| 15 \| 0.9245 \| 0.0072 \| 0.0000 \| 0.0000 \| 0.0079 \| 0.0100 \|
	\| 0.9647 \| 2.0 \| 30 \| 0.9229 \| 0.0072 \| 0.0000 \| 0.0000 \| 0.0079 \| 0.0100 \|
	\| 0.9776 \| 3.0 \| 45 \| 0.9192 \| 0.0080 \| 0.0000 \| 0.0000 \| 0.0079 \| 0.0120 \|
	\| 0.9443 \| 4.0 \| 60 \| 0.9110 \| 0.0096 \| 0.0000 \| 0.0001 \| 0.0079 \| 0.0161 \|
	\| 0.9505 \| 5.0 \| 75 \| 0.8952 \| 0.0435 \| 0.0013 \| 0.0233 \| 0.0384 \| 0.0643 \|
	\| 0.9446 \| 6.0 \| 90 \| 0.8734 \| 0.0817 \| 0.0171 \| 0.0472 \| 0.1019 \| 0.0863 \|
	\| 0.9284 \| 7.0 \| 105 \| 0.8552 \| 0.1028 \| 0.0242 \| 0.0568 \| 0.1243 \| 0.1124 \|
	\| 0.8778 \| 8.0 \| 120 \| 0.8396 \| 0.0995 \| 0.0220 \| 0.0527 \| 0.1217 \| 0.1084 \|
	\| 0.8601 \| 9.0 \| 135 \| 0.8259 \| 0.0995 \| 0.0225 \| 0.0517 \| 0.1217 \| 0.1084 \|
	\| 0.8488 \| 10.0 \| 150 \| 0.8142 \| 0.1046 \| 0.0201 \| 0.0524 \| 0.1310 \| 0.1124 \|
	\| 0.8333 \| 11.0 \| 165 \| 0.8030 \| 0.1009 \| 0.0131 \| 0.0374 \| 0.1190 \| 0.1205 \|
	\| 0.8299 \| 12.0 \| 180 \| 0.7918 \| 0.0976 \| 0.0127 \| 0.0373 \| 0.1151 \| 0.1165 \|
	\| 0.8054 \| 13.0 \| 195 \| 0.7822 \| 0.0978 \| 0.0114 \| 0.0346 \| 0.1164 \| 0.1165 \|
	\| 0.7855 \| 14.0 \| 210 \| 0.7741 \| 0.0979 \| 0.0130 \| 0.0397 \| 0.1151 \| 0.1165 \|
	\| 0.791 \| 15.0 \| 225 \| 0.7684 \| 0.0976 \| 0.0115 \| 0.0352 \| 0.1138 \| 0.1185 \|
	\| 0.7967 \| 16.0 \| 240 \| 0.7647 \| 0.1065 \| 0.0162 \| 0.0448 \| 0.1204 \| 0.1305 \|
	\| 0.7852 \| 17.0 \| 255 \| 0.7612 \| 0.1046 \| 0.0157 \| 0.0448 \| 0.1177 \| 0.1285 \|
	\| 0.7548 \| 18.0 \| 270 \| 0.7583 \| 0.1054 \| 0.0162 \| 0.0448 \| 0.1177 \| 0.1305 \|
	\| 0.7693 \| 19.0 \| 285 \| 0.7555 \| 0.1120 \| 0.0175 \| 0.0453 \| 0.1257 \| 0.1386 \|
	\| 0.7576 \| 20.0 \| 300 \| 0.7538 \| 0.1054 \| 0.0165 \| 0.0449 \| 0.1217 \| 0.1265 \|
	\| 0.7445 \| 21.0 \| 315 \| 0.7519 \| 0.1058 \| 0.0162 \| 0.0410 \| 0.1177 \| 0.1325 \|
	\| 0.7425 \| 22.0 \| 330 \| 0.7496 \| 0.1058 \| 0.0162 \| 0.0410 \| 0.1177 \| 0.1325 \|
	\| 0.739 \| 23.0 \| 345 \| 0.7486 \| 0.1077 \| 0.0162 \| 0.0410 \| 0.1204 \| 0.1345 \|
	\| 0.7392 \| 24.0 \| 360 \| 0.7470 \| 0.1107 \| 0.0169 \| 0.0415 \| 0.1217 \| 0.1406 \|
	\| 0.7633 \| 25.0 \| 375 \| 0.7464 \| 0.1045 \| 0.0166 \| 0.0410 \| 0.1204 \| 0.1265 \|
	\| 0.7303 \| 26.0 \| 390 \| 0.7449 \| 0.1150 \| 0.0201 \| 0.0466 \| 0.1323 \| 0.1386 \|
	\| 0.7168 \| 27.0 \| 405 \| 0.7440 \| 0.1158 \| 0.0212 \| 0.0479 \| 0.1336 \| 0.1386 \|
	\| 0.7239 \| 28.0 \| 420 \| 0.7435 \| 0.1137 \| 0.0239 \| 0.0538 \| 0.1323 \| 0.1325 \|
	\| 0.7132 \| 29.0 \| 435 \| 0.7432 \| 0.1044 \| 0.0157 \| 0.0380 \| 0.1230 \| 0.1245 \|
	\| 0.709 \| 30.0 \| 450 \| 0.7418 \| 0.0992 \| 0.0139 \| 0.0334 \| 0.1138 \| 0.1225 \|
	\| 0.7067 \| 31.0 \| 465 \| 0.7408 \| 0.0969 \| 0.0154 \| 0.0380 \| 0.1164 \| 0.1124 \|
	\| 0.711 \| 32.0 \| 480 \| 0.7394 \| 0.1009 \| 0.0179 \| 0.0440 \| 0.1204 \| 0.1165 \|
	\| 0.6992 \| 33.0 \| 495 \| 0.7377 \| 0.1008 \| 0.0203 \| 0.0456 \| 0.1190 \| 0.1165 \|
	\| 0.7045 \| 34.0 \| 510 \| 0.7361 \| 0.1009 \| 0.0179 \| 0.0440 \| 0.1204 \| 0.1165 \|
	\| 0.6858 \| 35.0 \| 525 \| 0.7359 \| 0.1008 \| 0.0203 \| 0.0456 \| 0.1190 \| 0.1165 \|
	\| 0.7107 \| 36.0 \| 540 \| 0.7340 \| 0.0989 \| 0.0203 \| 0.0456 \| 0.1164 \| 0.1145 \|
	\| 0.7013 \| 37.0 \| 555 \| 0.7324 \| 0.0999 \| 0.0198 \| 0.0454 \| 0.1190 \| 0.1145 \|
	\| 0.7027 \| 38.0 \| 570 \| 0.7323 \| 0.0992 \| 0.0203 \| 0.0453 \| 0.1151 \| 0.1165 \|
	\| 0.6956 \| 39.0 \| 585 \| 0.7313 \| 0.1054 \| 0.0199 \| 0.0445 \| 0.1270 \| 0.1205 \|
	\| 0.6998 \| 40.0 \| 600 \| 0.7309 \| 0.1022 \| 0.0193 \| 0.0445 \| 0.1270 \| 0.1124 \|
	\| 0.6856 \| 41.0 \| 615 \| 0.7304 \| 0.1027 \| 0.0197 \| 0.0443 \| 0.1243 \| 0.1165 \|
	\| 0.682 \| 42.0 \| 630 \| 0.7296 \| 0.1038 \| 0.0197 \| 0.0443 \| 0.1270 \| 0.1165 \|
	\| 0.6819 \| 43.0 \| 645 \| 0.7295 \| 0.1027 \| 0.0197 \| 0.0443 \| 0.1243 \| 0.1165 \|
	\| 0.6897 \| 44.0 \| 660 \| 0.7288 \| 0.1053 \| 0.0208 \| 0.0449 \| 0.1243 \| 0.1225 \|
	\| 0.6863 \| 45.0 \| 675 \| 0.7287 \| 0.1053 \| 0.0208 \| 0.0449 \| 0.1243 \| 0.1225 \|
	\| 0.6794 \| 46.0 \| 690 \| 0.7281 \| 0.1053 \| 0.0208 \| 0.0449 \| 0.1243 \| 0.1225 \|
	\| 0.6907 \| 47.0 \| 705 \| 0.7273 \| 0.1053 \| 0.0208 \| 0.0453 \| 0.1243 \| 0.1225 \|
	\| 0.6838 \| 48.0 \| 720 \| 0.7267 \| 0.1053 \| 0.0208 \| 0.0453 \| 0.1243 \| 0.1225 \|
	\| 0.6848 \| 49.0 \| 735 \| 0.7261 \| 0.1024 \| 0.0171 \| 0.0386 \| 0.1217 \| 0.1205 \|
	\| 0.6712 \| 50.0 \| 750 \| 0.7263 \| 0.1024 \| 0.0171 \| 0.0386 \| 0.1217 \| 0.1205 \|
	\| 0.6962 \| 51.0 \| 765 \| 0.7262 \| 0.1024 \| 0.0170 \| 0.0388 \| 0.1217 \| 0.1205 \|
	\| 0.695 \| 52.0 \| 780 \| 0.7257 \| 0.1024 \| 0.0170 \| 0.0388 \| 0.1217 \| 0.1205 \|
	\| 0.6858 \| 53.0 \| 795 \| 0.7254 \| 0.1024 \| 0.0170 \| 0.0388 \| 0.1217 \| 0.1205 \|
	\| 0.6603 \| 54.0 \| 810 \| 0.7252 \| 0.1024 \| 0.0170 \| 0.0388 \| 0.1217 \| 0.1205 \|
	\| 0.6806 \| 55.0 \| 825 \| 0.7252 \| 0.1024 \| 0.0170 \| 0.0388 \| 0.1217 \| 0.1205 \|
	\| 0.683 \| 56.0 \| 840 \| 0.7251 \| 0.1008 \| 0.0170 \| 0.0388 \| 0.1217 \| 0.1165 \|
	\| 0.6871 \| 57.0 \| 855 \| 0.7247 \| 0.1024 \| 0.0169 \| 0.0384 \| 0.1217 \| 0.1205 \|
	\| 0.6736 \| 58.0 \| 870 \| 0.7247 \| 0.1012 \| 0.0160 \| 0.0377 \| 0.1190 \| 0.1205 \|
	\| 0.6847 \| 59.0 \| 885 \| 0.7246 \| 0.1012 \| 0.0160 \| 0.0377 \| 0.1190 \| 0.1205 \|
	\| 0.6681 \| 60.0 \| 900 \| 0.7244 \| 0.1012 \| 0.0160 \| 0.0377 \| 0.1190 \| 0.1205 \|
	\| 0.6882 \| 61.0 \| 915 \| 0.7244 \| 0.1012 \| 0.0160 \| 0.0377 \| 0.1190 \| 0.1205 \|
	\| 0.6689 \| 62.0 \| 930 \| 0.7241 \| 0.1012 \| 0.0160 \| 0.0377 \| 0.1190 \| 0.1205 \|
	\| 0.6763 \| 63.0 \| 945 \| 0.7241 \| 0.1012 \| 0.0160 \| 0.0377 \| 0.1190 \| 0.1205 \|
	\| 0.6621 \| 64.0 \| 960 \| 0.7241 \| 0.1012 \| 0.0160 \| 0.0377 \| 0.1190 \| 0.1205 \|


	### Framework versions

	- Transformers 4.31.0
	- Pytorch 2.0.1
	- Datasets 2.14.4
	- Tokenizers 0.13.3

	---
	license: bsd-3-clause
	base_model: Salesforce/codet5p-770m-py
	tags:
	- generated_from_trainer
	datasets:
	- mbpp
	model-index:
	- name: codet5p-770m-py-sanitized-codebleu-1-True-5e-05-0.1-lora-layer_12
	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. -->

	# codet5p-770m-py-sanitized-codebleu-1-True-5e-05-0.1-lora-layer_12

	This model is a fine-tuned version of [Salesforce/codet5p-770m-py](https://huggingface.co/Salesforce/codet5p-770m-py) on the mbpp dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.7241
	- Codebleu: 0.1012
	- Ngram Match Score: 0.0160
	- Weighted Ngram Match Score: 0.0377
	- Syntax Match Score: 0.1190
	- Dataflow Match Score: 0.1205

	## 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: 8
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 100
	- num_epochs: 64

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Codebleu \| Ngram Match Score \| Weighted Ngram Match Score \| Syntax Match Score \| Dataflow Match Score \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-----------------:\|:--------------------------:\|:------------------:\|:--------------------:\|
	\| 0.9814 \| 1.0 \| 15 \| 0.9245 \| 0.0072 \| 0.0000 \| 0.0000 \| 0.0079 \| 0.0100 \|
	\| 0.9647 \| 2.0 \| 30 \| 0.9229 \| 0.0072 \| 0.0000 \| 0.0000 \| 0.0079 \| 0.0100 \|
	\| 0.9776 \| 3.0 \| 45 \| 0.9192 \| 0.0080 \| 0.0000 \| 0.0000 \| 0.0079 \| 0.0120 \|
	\| 0.9443 \| 4.0 \| 60 \| 0.9110 \| 0.0096 \| 0.0000 \| 0.0001 \| 0.0079 \| 0.0161 \|
	\| 0.9505 \| 5.0 \| 75 \| 0.8952 \| 0.0435 \| 0.0013 \| 0.0233 \| 0.0384 \| 0.0643 \|
	\| 0.9446 \| 6.0 \| 90 \| 0.8734 \| 0.0817 \| 0.0171 \| 0.0472 \| 0.1019 \| 0.0863 \|
	\| 0.9284 \| 7.0 \| 105 \| 0.8552 \| 0.1028 \| 0.0242 \| 0.0568 \| 0.1243 \| 0.1124 \|
	\| 0.8778 \| 8.0 \| 120 \| 0.8396 \| 0.0995 \| 0.0220 \| 0.0527 \| 0.1217 \| 0.1084 \|
	\| 0.8601 \| 9.0 \| 135 \| 0.8259 \| 0.0995 \| 0.0225 \| 0.0517 \| 0.1217 \| 0.1084 \|
	\| 0.8488 \| 10.0 \| 150 \| 0.8142 \| 0.1046 \| 0.0201 \| 0.0524 \| 0.1310 \| 0.1124 \|
	\| 0.8333 \| 11.0 \| 165 \| 0.8030 \| 0.1009 \| 0.0131 \| 0.0374 \| 0.1190 \| 0.1205 \|
	\| 0.8299 \| 12.0 \| 180 \| 0.7918 \| 0.0976 \| 0.0127 \| 0.0373 \| 0.1151 \| 0.1165 \|
	\| 0.8054 \| 13.0 \| 195 \| 0.7822 \| 0.0978 \| 0.0114 \| 0.0346 \| 0.1164 \| 0.1165 \|
	\| 0.7855 \| 14.0 \| 210 \| 0.7741 \| 0.0979 \| 0.0130 \| 0.0397 \| 0.1151 \| 0.1165 \|
	\| 0.791 \| 15.0 \| 225 \| 0.7684 \| 0.0976 \| 0.0115 \| 0.0352 \| 0.1138 \| 0.1185 \|
	\| 0.7967 \| 16.0 \| 240 \| 0.7647 \| 0.1065 \| 0.0162 \| 0.0448 \| 0.1204 \| 0.1305 \|
	\| 0.7852 \| 17.0 \| 255 \| 0.7612 \| 0.1046 \| 0.0157 \| 0.0448 \| 0.1177 \| 0.1285 \|
	\| 0.7548 \| 18.0 \| 270 \| 0.7583 \| 0.1054 \| 0.0162 \| 0.0448 \| 0.1177 \| 0.1305 \|
	\| 0.7693 \| 19.0 \| 285 \| 0.7555 \| 0.1120 \| 0.0175 \| 0.0453 \| 0.1257 \| 0.1386 \|
	\| 0.7576 \| 20.0 \| 300 \| 0.7538 \| 0.1054 \| 0.0165 \| 0.0449 \| 0.1217 \| 0.1265 \|
	\| 0.7445 \| 21.0 \| 315 \| 0.7519 \| 0.1058 \| 0.0162 \| 0.0410 \| 0.1177 \| 0.1325 \|
	\| 0.7425 \| 22.0 \| 330 \| 0.7496 \| 0.1058 \| 0.0162 \| 0.0410 \| 0.1177 \| 0.1325 \|
	\| 0.739 \| 23.0 \| 345 \| 0.7486 \| 0.1077 \| 0.0162 \| 0.0410 \| 0.1204 \| 0.1345 \|
	\| 0.7392 \| 24.0 \| 360 \| 0.7470 \| 0.1107 \| 0.0169 \| 0.0415 \| 0.1217 \| 0.1406 \|
	\| 0.7633 \| 25.0 \| 375 \| 0.7464 \| 0.1045 \| 0.0166 \| 0.0410 \| 0.1204 \| 0.1265 \|
	\| 0.7303 \| 26.0 \| 390 \| 0.7449 \| 0.1150 \| 0.0201 \| 0.0466 \| 0.1323 \| 0.1386 \|
	\| 0.7168 \| 27.0 \| 405 \| 0.7440 \| 0.1158 \| 0.0212 \| 0.0479 \| 0.1336 \| 0.1386 \|
	\| 0.7239 \| 28.0 \| 420 \| 0.7435 \| 0.1137 \| 0.0239 \| 0.0538 \| 0.1323 \| 0.1325 \|
	\| 0.7132 \| 29.0 \| 435 \| 0.7432 \| 0.1044 \| 0.0157 \| 0.0380 \| 0.1230 \| 0.1245 \|
	\| 0.709 \| 30.0 \| 450 \| 0.7418 \| 0.0992 \| 0.0139 \| 0.0334 \| 0.1138 \| 0.1225 \|
	\| 0.7067 \| 31.0 \| 465 \| 0.7408 \| 0.0969 \| 0.0154 \| 0.0380 \| 0.1164 \| 0.1124 \|
	\| 0.711 \| 32.0 \| 480 \| 0.7394 \| 0.1009 \| 0.0179 \| 0.0440 \| 0.1204 \| 0.1165 \|
	\| 0.6992 \| 33.0 \| 495 \| 0.7377 \| 0.1008 \| 0.0203 \| 0.0456 \| 0.1190 \| 0.1165 \|
	\| 0.7045 \| 34.0 \| 510 \| 0.7361 \| 0.1009 \| 0.0179 \| 0.0440 \| 0.1204 \| 0.1165 \|
	\| 0.6858 \| 35.0 \| 525 \| 0.7359 \| 0.1008 \| 0.0203 \| 0.0456 \| 0.1190 \| 0.1165 \|
	\| 0.7107 \| 36.0 \| 540 \| 0.7340 \| 0.0989 \| 0.0203 \| 0.0456 \| 0.1164 \| 0.1145 \|
	\| 0.7013 \| 37.0 \| 555 \| 0.7324 \| 0.0999 \| 0.0198 \| 0.0454 \| 0.1190 \| 0.1145 \|
	\| 0.7027 \| 38.0 \| 570 \| 0.7323 \| 0.0992 \| 0.0203 \| 0.0453 \| 0.1151 \| 0.1165 \|
	\| 0.6956 \| 39.0 \| 585 \| 0.7313 \| 0.1054 \| 0.0199 \| 0.0445 \| 0.1270 \| 0.1205 \|
	\| 0.6998 \| 40.0 \| 600 \| 0.7309 \| 0.1022 \| 0.0193 \| 0.0445 \| 0.1270 \| 0.1124 \|
	\| 0.6856 \| 41.0 \| 615 \| 0.7304 \| 0.1027 \| 0.0197 \| 0.0443 \| 0.1243 \| 0.1165 \|
	\| 0.682 \| 42.0 \| 630 \| 0.7296 \| 0.1038 \| 0.0197 \| 0.0443 \| 0.1270 \| 0.1165 \|
	\| 0.6819 \| 43.0 \| 645 \| 0.7295 \| 0.1027 \| 0.0197 \| 0.0443 \| 0.1243 \| 0.1165 \|
	\| 0.6897 \| 44.0 \| 660 \| 0.7288 \| 0.1053 \| 0.0208 \| 0.0449 \| 0.1243 \| 0.1225 \|
	\| 0.6863 \| 45.0 \| 675 \| 0.7287 \| 0.1053 \| 0.0208 \| 0.0449 \| 0.1243 \| 0.1225 \|
	\| 0.6794 \| 46.0 \| 690 \| 0.7281 \| 0.1053 \| 0.0208 \| 0.0449 \| 0.1243 \| 0.1225 \|
	\| 0.6907 \| 47.0 \| 705 \| 0.7273 \| 0.1053 \| 0.0208 \| 0.0453 \| 0.1243 \| 0.1225 \|
	\| 0.6838 \| 48.0 \| 720 \| 0.7267 \| 0.1053 \| 0.0208 \| 0.0453 \| 0.1243 \| 0.1225 \|
	\| 0.6848 \| 49.0 \| 735 \| 0.7261 \| 0.1024 \| 0.0171 \| 0.0386 \| 0.1217 \| 0.1205 \|
	\| 0.6712 \| 50.0 \| 750 \| 0.7263 \| 0.1024 \| 0.0171 \| 0.0386 \| 0.1217 \| 0.1205 \|
	\| 0.6962 \| 51.0 \| 765 \| 0.7262 \| 0.1024 \| 0.0170 \| 0.0388 \| 0.1217 \| 0.1205 \|
	\| 0.695 \| 52.0 \| 780 \| 0.7257 \| 0.1024 \| 0.0170 \| 0.0388 \| 0.1217 \| 0.1205 \|
	\| 0.6858 \| 53.0 \| 795 \| 0.7254 \| 0.1024 \| 0.0170 \| 0.0388 \| 0.1217 \| 0.1205 \|
	\| 0.6603 \| 54.0 \| 810 \| 0.7252 \| 0.1024 \| 0.0170 \| 0.0388 \| 0.1217 \| 0.1205 \|
	\| 0.6806 \| 55.0 \| 825 \| 0.7252 \| 0.1024 \| 0.0170 \| 0.0388 \| 0.1217 \| 0.1205 \|
	\| 0.683 \| 56.0 \| 840 \| 0.7251 \| 0.1008 \| 0.0170 \| 0.0388 \| 0.1217 \| 0.1165 \|
	\| 0.6871 \| 57.0 \| 855 \| 0.7247 \| 0.1024 \| 0.0169 \| 0.0384 \| 0.1217 \| 0.1205 \|
	\| 0.6736 \| 58.0 \| 870 \| 0.7247 \| 0.1012 \| 0.0160 \| 0.0377 \| 0.1190 \| 0.1205 \|
	\| 0.6847 \| 59.0 \| 885 \| 0.7246 \| 0.1012 \| 0.0160 \| 0.0377 \| 0.1190 \| 0.1205 \|
	\| 0.6681 \| 60.0 \| 900 \| 0.7244 \| 0.1012 \| 0.0160 \| 0.0377 \| 0.1190 \| 0.1205 \|
	\| 0.6882 \| 61.0 \| 915 \| 0.7244 \| 0.1012 \| 0.0160 \| 0.0377 \| 0.1190 \| 0.1205 \|
	\| 0.6689 \| 62.0 \| 930 \| 0.7241 \| 0.1012 \| 0.0160 \| 0.0377 \| 0.1190 \| 0.1205 \|
	\| 0.6763 \| 63.0 \| 945 \| 0.7241 \| 0.1012 \| 0.0160 \| 0.0377 \| 0.1190 \| 0.1205 \|
	\| 0.6621 \| 64.0 \| 960 \| 0.7241 \| 0.1012 \| 0.0160 \| 0.0377 \| 0.1190 \| 0.1205 \|


	### Framework versions

	- Transformers 4.31.0
	- Pytorch 2.0.1
	- Datasets 2.14.4
	- Tokenizers 0.13.3