End of training

3157baa 11 months ago

8.24 kB

	---
	license: mit
	base_model: nielsr/lilt-xlm-roberta-base
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: lilt-roberta-DocLayNet-base_lines_ml256-v1
	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. -->

	# lilt-roberta-DocLayNet-base_lines_ml256-v1

	This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.9004
	- Precision: 0.8622
	- Recall: 0.8622
	- F1: 0.8622
	- Accuracy: 0.8622

	## 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: 16
	- eval_batch_size: 32
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 5
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| No log \| 0.07 \| 300 \| 0.7371 \| 0.6945 \| 0.6945 \| 0.6945 \| 0.6945 \|
	\| 0.7701 \| 0.14 \| 600 \| 0.8573 \| 0.7488 \| 0.7488 \| 0.7488 \| 0.7488 \|
	\| 0.7701 \| 0.21 \| 900 \| 0.7687 \| 0.7606 \| 0.7606 \| 0.7606 \| 0.7606 \|
	\| 0.471 \| 0.27 \| 1200 \| 0.7057 \| 0.7750 \| 0.7750 \| 0.7750 \| 0.7750 \|
	\| 0.4183 \| 0.34 \| 1500 \| 0.6305 \| 0.7961 \| 0.7961 \| 0.7961 \| 0.7961 \|
	\| 0.4183 \| 0.41 \| 1800 \| 0.7039 \| 0.7769 \| 0.7769 \| 0.7769 \| 0.7769 \|
	\| 0.3683 \| 0.48 \| 2100 \| 0.5956 \| 0.7980 \| 0.7980 \| 0.7980 \| 0.7980 \|
	\| 0.3683 \| 0.55 \| 2400 \| 0.7312 \| 0.7864 \| 0.7864 \| 0.7864 \| 0.7864 \|
	\| 0.3429 \| 0.62 \| 2700 \| 0.5868 \| 0.8049 \| 0.8049 \| 0.8049 \| 0.8049 \|
	\| 0.3337 \| 0.69 \| 3000 \| 0.5911 \| 0.8010 \| 0.8010 \| 0.8010 \| 0.8010 \|
	\| 0.3337 \| 0.76 \| 3300 \| 0.7278 \| 0.7893 \| 0.7893 \| 0.7893 \| 0.7893 \|
	\| 0.3056 \| 0.82 \| 3600 \| 0.8030 \| 0.7908 \| 0.7908 \| 0.7908 \| 0.7908 \|
	\| 0.3056 \| 0.89 \| 3900 \| 0.6587 \| 0.7978 \| 0.7978 \| 0.7978 \| 0.7978 \|
	\| 0.2772 \| 0.96 \| 4200 \| 0.5334 \| 0.8315 \| 0.8315 \| 0.8315 \| 0.8315 \|
	\| 0.2456 \| 1.03 \| 4500 \| 0.6787 \| 0.7992 \| 0.7992 \| 0.7992 \| 0.7992 \|
	\| 0.2456 \| 1.1 \| 4800 \| 0.7325 \| 0.8037 \| 0.8037 \| 0.8037 \| 0.8037 \|
	\| 0.2183 \| 1.17 \| 5100 \| 0.7280 \| 0.7985 \| 0.7985 \| 0.7985 \| 0.7985 \|
	\| 0.2183 \| 1.24 \| 5400 \| 0.9041 \| 0.7787 \| 0.7787 \| 0.7787 \| 0.7787 \|
	\| 0.2288 \| 1.31 \| 5700 \| 0.7504 \| 0.8076 \| 0.8076 \| 0.8076 \| 0.8076 \|
	\| 0.2228 \| 1.37 \| 6000 \| 0.6672 \| 0.8042 \| 0.8042 \| 0.8042 \| 0.8042 \|
	\| 0.2228 \| 1.44 \| 6300 \| 0.5468 \| 0.8511 \| 0.8511 \| 0.8511 \| 0.8511 \|
	\| 0.1989 \| 1.51 \| 6600 \| 0.5928 \| 0.8229 \| 0.8229 \| 0.8229 \| 0.8229 \|
	\| 0.1989 \| 1.58 \| 6900 \| 0.6731 \| 0.8150 \| 0.8150 \| 0.8150 \| 0.8150 \|
	\| 0.2062 \| 1.65 \| 7200 \| 0.7504 \| 0.8030 \| 0.8030 \| 0.8030 \| 0.8030 \|
	\| 0.1971 \| 1.72 \| 7500 \| 0.6554 \| 0.8255 \| 0.8255 \| 0.8255 \| 0.8255 \|
	\| 0.1971 \| 1.79 \| 7800 \| 0.7095 \| 0.8046 \| 0.8046 \| 0.8046 \| 0.8046 \|
	\| 0.1929 \| 1.86 \| 8100 \| 0.6244 \| 0.8397 \| 0.8397 \| 0.8397 \| 0.8397 \|
	\| 0.1929 \| 1.92 \| 8400 \| 0.8521 \| 0.8067 \| 0.8067 \| 0.8067 \| 0.8067 \|
	\| 0.1788 \| 1.99 \| 8700 \| 0.7261 \| 0.8088 \| 0.8088 \| 0.8088 \| 0.8088 \|
	\| 0.1631 \| 2.06 \| 9000 \| 0.6650 \| 0.8272 \| 0.8272 \| 0.8272 \| 0.8272 \|
	\| 0.1631 \| 2.13 \| 9300 \| 0.8314 \| 0.8142 \| 0.8142 \| 0.8142 \| 0.8142 \|
	\| 0.1284 \| 2.2 \| 9600 \| 0.9010 \| 0.8113 \| 0.8113 \| 0.8113 \| 0.8113 \|
	\| 0.1284 \| 2.27 \| 9900 \| 0.9008 \| 0.8087 \| 0.8087 \| 0.8087 \| 0.8087 \|
	\| 0.1248 \| 2.34 \| 10200 \| 0.9152 \| 0.8065 \| 0.8065 \| 0.8065 \| 0.8065 \|
	\| 0.1365 \| 2.4 \| 10500 \| 0.6791 \| 0.8393 \| 0.8393 \| 0.8393 \| 0.8393 \|
	\| 0.1365 \| 2.47 \| 10800 \| 0.7301 \| 0.8185 \| 0.8185 \| 0.8185 \| 0.8185 \|
	\| 0.1194 \| 2.54 \| 11100 \| 0.8937 \| 0.8050 \| 0.8050 \| 0.8050 \| 0.8050 \|
	\| 0.1194 \| 2.61 \| 11400 \| 0.7559 \| 0.8293 \| 0.8293 \| 0.8293 \| 0.8293 \|
	\| 0.1282 \| 2.68 \| 11700 \| 0.7903 \| 0.8163 \| 0.8163 \| 0.8163 \| 0.8163 \|
	\| 0.1234 \| 2.75 \| 12000 \| 1.0103 \| 0.8090 \| 0.8090 \| 0.8090 \| 0.8090 \|
	\| 0.1234 \| 2.82 \| 12300 \| 0.9975 \| 0.8096 \| 0.8096 \| 0.8096 \| 0.8096 \|
	\| 0.1104 \| 2.89 \| 12600 \| 0.8443 \| 0.8171 \| 0.8171 \| 0.8171 \| 0.8171 \|
	\| 0.1104 \| 2.95 \| 12900 \| 0.8380 \| 0.8125 \| 0.8125 \| 0.8125 \| 0.8125 \|
	\| 0.1254 \| 3.02 \| 13200 \| 0.8283 \| 0.8223 \| 0.8223 \| 0.8223 \| 0.8223 \|
	\| 0.0806 \| 3.09 \| 13500 \| 0.9232 \| 0.8323 \| 0.8323 \| 0.8323 \| 0.8323 \|
	\| 0.0806 \| 3.16 \| 13800 \| 1.0903 \| 0.8176 \| 0.8176 \| 0.8176 \| 0.8176 \|
	\| 0.0875 \| 3.23 \| 14100 \| 1.0781 \| 0.8110 \| 0.8110 \| 0.8110 \| 0.8110 \|
	\| 0.0875 \| 3.3 \| 14400 \| 0.8806 \| 0.8277 \| 0.8277 \| 0.8277 \| 0.8277 \|
	\| 0.0817 \| 3.37 \| 14700 \| 1.0024 \| 0.8212 \| 0.8212 \| 0.8212 \| 0.8212 \|
	\| 0.085 \| 3.44 \| 15000 \| 0.9078 \| 0.8294 \| 0.8294 \| 0.8294 \| 0.8294 \|
	\| 0.085 \| 3.5 \| 15300 \| 0.8745 \| 0.8377 \| 0.8377 \| 0.8377 \| 0.8377 \|
	\| 0.0784 \| 3.57 \| 15600 \| 0.9590 \| 0.8329 \| 0.8329 \| 0.8329 \| 0.8329 \|
	\| 0.0784 \| 3.64 \| 15900 \| 0.8027 \| 0.8500 \| 0.8500 \| 0.8500 \| 0.8500 \|
	\| 0.0785 \| 3.71 \| 16200 \| 1.0033 \| 0.8171 \| 0.8171 \| 0.8171 \| 0.8171 \|
	\| 0.0756 \| 3.78 \| 16500 \| 0.8017 \| 0.8446 \| 0.8446 \| 0.8446 \| 0.8446 \|
	\| 0.0756 \| 3.85 \| 16800 \| 1.0721 \| 0.8162 \| 0.8162 \| 0.8162 \| 0.8162 \|
	\| 0.078 \| 3.92 \| 17100 \| 1.1095 \| 0.8172 \| 0.8172 \| 0.8172 \| 0.8172 \|
	\| 0.078 \| 3.99 \| 17400 \| 1.0099 \| 0.8200 \| 0.8200 \| 0.8200 \| 0.8200 \|
	\| 0.0696 \| 4.05 \| 17700 \| 1.0189 \| 0.8249 \| 0.8249 \| 0.8249 \| 0.8249 \|
	\| 0.0456 \| 4.12 \| 18000 \| 1.2109 \| 0.8165 \| 0.8165 \| 0.8165 \| 0.8165 \|
	\| 0.0456 \| 4.19 \| 18300 \| 1.0789 \| 0.8273 \| 0.8273 \| 0.8273 \| 0.8273 \|
	\| 0.0587 \| 4.26 \| 18600 \| 1.0981 \| 0.8277 \| 0.8277 \| 0.8277 \| 0.8277 \|
	\| 0.0587 \| 4.33 \| 18900 \| 1.0236 \| 0.8395 \| 0.8395 \| 0.8395 \| 0.8395 \|
	\| 0.0485 \| 4.4 \| 19200 \| 0.9660 \| 0.8381 \| 0.8381 \| 0.8381 \| 0.8381 \|
	\| 0.056 \| 4.47 \| 19500 \| 0.9447 \| 0.8453 \| 0.8453 \| 0.8453 \| 0.8453 \|
	\| 0.056 \| 4.54 \| 19800 \| 0.9226 \| 0.8564 \| 0.8564 \| 0.8564 \| 0.8564 \|
	\| 0.0517 \| 4.6 \| 20100 \| 1.1416 \| 0.8313 \| 0.8313 \| 0.8313 \| 0.8313 \|
	\| 0.0517 \| 4.67 \| 20400 \| 0.9004 \| 0.8622 \| 0.8622 \| 0.8622 \| 0.8622 \|
	\| 0.0555 \| 4.74 \| 20700 \| 1.0452 \| 0.8416 \| 0.8416 \| 0.8416 \| 0.8416 \|
	\| 0.0578 \| 4.81 \| 21000 \| 0.9997 \| 0.8480 \| 0.8480 \| 0.8480 \| 0.8480 \|
	\| 0.0578 \| 4.88 \| 21300 \| 1.0441 \| 0.8402 \| 0.8402 \| 0.8402 \| 0.8402 \|
	\| 0.0495 \| 4.95 \| 21600 \| 1.0393 \| 0.8421 \| 0.8421 \| 0.8421 \| 0.8421 \|


	### Framework versions

	- Transformers 4.36.0
	- Pytorch 2.0.0
	- Datasets 2.1.0
	- Tokenizers 0.15.0

	---
	license: mit
	base_model: nielsr/lilt-xlm-roberta-base
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: lilt-roberta-DocLayNet-base_lines_ml256-v1
	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. -->

	# lilt-roberta-DocLayNet-base_lines_ml256-v1

	This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.9004
	- Precision: 0.8622
	- Recall: 0.8622
	- F1: 0.8622
	- Accuracy: 0.8622

	## 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: 16
	- eval_batch_size: 32
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 5
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| No log \| 0.07 \| 300 \| 0.7371 \| 0.6945 \| 0.6945 \| 0.6945 \| 0.6945 \|
	\| 0.7701 \| 0.14 \| 600 \| 0.8573 \| 0.7488 \| 0.7488 \| 0.7488 \| 0.7488 \|
	\| 0.7701 \| 0.21 \| 900 \| 0.7687 \| 0.7606 \| 0.7606 \| 0.7606 \| 0.7606 \|
	\| 0.471 \| 0.27 \| 1200 \| 0.7057 \| 0.7750 \| 0.7750 \| 0.7750 \| 0.7750 \|
	\| 0.4183 \| 0.34 \| 1500 \| 0.6305 \| 0.7961 \| 0.7961 \| 0.7961 \| 0.7961 \|
	\| 0.4183 \| 0.41 \| 1800 \| 0.7039 \| 0.7769 \| 0.7769 \| 0.7769 \| 0.7769 \|
	\| 0.3683 \| 0.48 \| 2100 \| 0.5956 \| 0.7980 \| 0.7980 \| 0.7980 \| 0.7980 \|
	\| 0.3683 \| 0.55 \| 2400 \| 0.7312 \| 0.7864 \| 0.7864 \| 0.7864 \| 0.7864 \|
	\| 0.3429 \| 0.62 \| 2700 \| 0.5868 \| 0.8049 \| 0.8049 \| 0.8049 \| 0.8049 \|
	\| 0.3337 \| 0.69 \| 3000 \| 0.5911 \| 0.8010 \| 0.8010 \| 0.8010 \| 0.8010 \|
	\| 0.3337 \| 0.76 \| 3300 \| 0.7278 \| 0.7893 \| 0.7893 \| 0.7893 \| 0.7893 \|
	\| 0.3056 \| 0.82 \| 3600 \| 0.8030 \| 0.7908 \| 0.7908 \| 0.7908 \| 0.7908 \|
	\| 0.3056 \| 0.89 \| 3900 \| 0.6587 \| 0.7978 \| 0.7978 \| 0.7978 \| 0.7978 \|
	\| 0.2772 \| 0.96 \| 4200 \| 0.5334 \| 0.8315 \| 0.8315 \| 0.8315 \| 0.8315 \|
	\| 0.2456 \| 1.03 \| 4500 \| 0.6787 \| 0.7992 \| 0.7992 \| 0.7992 \| 0.7992 \|
	\| 0.2456 \| 1.1 \| 4800 \| 0.7325 \| 0.8037 \| 0.8037 \| 0.8037 \| 0.8037 \|
	\| 0.2183 \| 1.17 \| 5100 \| 0.7280 \| 0.7985 \| 0.7985 \| 0.7985 \| 0.7985 \|
	\| 0.2183 \| 1.24 \| 5400 \| 0.9041 \| 0.7787 \| 0.7787 \| 0.7787 \| 0.7787 \|
	\| 0.2288 \| 1.31 \| 5700 \| 0.7504 \| 0.8076 \| 0.8076 \| 0.8076 \| 0.8076 \|
	\| 0.2228 \| 1.37 \| 6000 \| 0.6672 \| 0.8042 \| 0.8042 \| 0.8042 \| 0.8042 \|
	\| 0.2228 \| 1.44 \| 6300 \| 0.5468 \| 0.8511 \| 0.8511 \| 0.8511 \| 0.8511 \|
	\| 0.1989 \| 1.51 \| 6600 \| 0.5928 \| 0.8229 \| 0.8229 \| 0.8229 \| 0.8229 \|
	\| 0.1989 \| 1.58 \| 6900 \| 0.6731 \| 0.8150 \| 0.8150 \| 0.8150 \| 0.8150 \|
	\| 0.2062 \| 1.65 \| 7200 \| 0.7504 \| 0.8030 \| 0.8030 \| 0.8030 \| 0.8030 \|
	\| 0.1971 \| 1.72 \| 7500 \| 0.6554 \| 0.8255 \| 0.8255 \| 0.8255 \| 0.8255 \|
	\| 0.1971 \| 1.79 \| 7800 \| 0.7095 \| 0.8046 \| 0.8046 \| 0.8046 \| 0.8046 \|
	\| 0.1929 \| 1.86 \| 8100 \| 0.6244 \| 0.8397 \| 0.8397 \| 0.8397 \| 0.8397 \|
	\| 0.1929 \| 1.92 \| 8400 \| 0.8521 \| 0.8067 \| 0.8067 \| 0.8067 \| 0.8067 \|
	\| 0.1788 \| 1.99 \| 8700 \| 0.7261 \| 0.8088 \| 0.8088 \| 0.8088 \| 0.8088 \|
	\| 0.1631 \| 2.06 \| 9000 \| 0.6650 \| 0.8272 \| 0.8272 \| 0.8272 \| 0.8272 \|
	\| 0.1631 \| 2.13 \| 9300 \| 0.8314 \| 0.8142 \| 0.8142 \| 0.8142 \| 0.8142 \|
	\| 0.1284 \| 2.2 \| 9600 \| 0.9010 \| 0.8113 \| 0.8113 \| 0.8113 \| 0.8113 \|
	\| 0.1284 \| 2.27 \| 9900 \| 0.9008 \| 0.8087 \| 0.8087 \| 0.8087 \| 0.8087 \|
	\| 0.1248 \| 2.34 \| 10200 \| 0.9152 \| 0.8065 \| 0.8065 \| 0.8065 \| 0.8065 \|
	\| 0.1365 \| 2.4 \| 10500 \| 0.6791 \| 0.8393 \| 0.8393 \| 0.8393 \| 0.8393 \|
	\| 0.1365 \| 2.47 \| 10800 \| 0.7301 \| 0.8185 \| 0.8185 \| 0.8185 \| 0.8185 \|
	\| 0.1194 \| 2.54 \| 11100 \| 0.8937 \| 0.8050 \| 0.8050 \| 0.8050 \| 0.8050 \|
	\| 0.1194 \| 2.61 \| 11400 \| 0.7559 \| 0.8293 \| 0.8293 \| 0.8293 \| 0.8293 \|
	\| 0.1282 \| 2.68 \| 11700 \| 0.7903 \| 0.8163 \| 0.8163 \| 0.8163 \| 0.8163 \|
	\| 0.1234 \| 2.75 \| 12000 \| 1.0103 \| 0.8090 \| 0.8090 \| 0.8090 \| 0.8090 \|
	\| 0.1234 \| 2.82 \| 12300 \| 0.9975 \| 0.8096 \| 0.8096 \| 0.8096 \| 0.8096 \|
	\| 0.1104 \| 2.89 \| 12600 \| 0.8443 \| 0.8171 \| 0.8171 \| 0.8171 \| 0.8171 \|
	\| 0.1104 \| 2.95 \| 12900 \| 0.8380 \| 0.8125 \| 0.8125 \| 0.8125 \| 0.8125 \|
	\| 0.1254 \| 3.02 \| 13200 \| 0.8283 \| 0.8223 \| 0.8223 \| 0.8223 \| 0.8223 \|
	\| 0.0806 \| 3.09 \| 13500 \| 0.9232 \| 0.8323 \| 0.8323 \| 0.8323 \| 0.8323 \|
	\| 0.0806 \| 3.16 \| 13800 \| 1.0903 \| 0.8176 \| 0.8176 \| 0.8176 \| 0.8176 \|
	\| 0.0875 \| 3.23 \| 14100 \| 1.0781 \| 0.8110 \| 0.8110 \| 0.8110 \| 0.8110 \|
	\| 0.0875 \| 3.3 \| 14400 \| 0.8806 \| 0.8277 \| 0.8277 \| 0.8277 \| 0.8277 \|
	\| 0.0817 \| 3.37 \| 14700 \| 1.0024 \| 0.8212 \| 0.8212 \| 0.8212 \| 0.8212 \|
	\| 0.085 \| 3.44 \| 15000 \| 0.9078 \| 0.8294 \| 0.8294 \| 0.8294 \| 0.8294 \|
	\| 0.085 \| 3.5 \| 15300 \| 0.8745 \| 0.8377 \| 0.8377 \| 0.8377 \| 0.8377 \|
	\| 0.0784 \| 3.57 \| 15600 \| 0.9590 \| 0.8329 \| 0.8329 \| 0.8329 \| 0.8329 \|
	\| 0.0784 \| 3.64 \| 15900 \| 0.8027 \| 0.8500 \| 0.8500 \| 0.8500 \| 0.8500 \|
	\| 0.0785 \| 3.71 \| 16200 \| 1.0033 \| 0.8171 \| 0.8171 \| 0.8171 \| 0.8171 \|
	\| 0.0756 \| 3.78 \| 16500 \| 0.8017 \| 0.8446 \| 0.8446 \| 0.8446 \| 0.8446 \|
	\| 0.0756 \| 3.85 \| 16800 \| 1.0721 \| 0.8162 \| 0.8162 \| 0.8162 \| 0.8162 \|
	\| 0.078 \| 3.92 \| 17100 \| 1.1095 \| 0.8172 \| 0.8172 \| 0.8172 \| 0.8172 \|
	\| 0.078 \| 3.99 \| 17400 \| 1.0099 \| 0.8200 \| 0.8200 \| 0.8200 \| 0.8200 \|
	\| 0.0696 \| 4.05 \| 17700 \| 1.0189 \| 0.8249 \| 0.8249 \| 0.8249 \| 0.8249 \|
	\| 0.0456 \| 4.12 \| 18000 \| 1.2109 \| 0.8165 \| 0.8165 \| 0.8165 \| 0.8165 \|
	\| 0.0456 \| 4.19 \| 18300 \| 1.0789 \| 0.8273 \| 0.8273 \| 0.8273 \| 0.8273 \|
	\| 0.0587 \| 4.26 \| 18600 \| 1.0981 \| 0.8277 \| 0.8277 \| 0.8277 \| 0.8277 \|
	\| 0.0587 \| 4.33 \| 18900 \| 1.0236 \| 0.8395 \| 0.8395 \| 0.8395 \| 0.8395 \|
	\| 0.0485 \| 4.4 \| 19200 \| 0.9660 \| 0.8381 \| 0.8381 \| 0.8381 \| 0.8381 \|
	\| 0.056 \| 4.47 \| 19500 \| 0.9447 \| 0.8453 \| 0.8453 \| 0.8453 \| 0.8453 \|
	\| 0.056 \| 4.54 \| 19800 \| 0.9226 \| 0.8564 \| 0.8564 \| 0.8564 \| 0.8564 \|
	\| 0.0517 \| 4.6 \| 20100 \| 1.1416 \| 0.8313 \| 0.8313 \| 0.8313 \| 0.8313 \|
	\| 0.0517 \| 4.67 \| 20400 \| 0.9004 \| 0.8622 \| 0.8622 \| 0.8622 \| 0.8622 \|
	\| 0.0555 \| 4.74 \| 20700 \| 1.0452 \| 0.8416 \| 0.8416 \| 0.8416 \| 0.8416 \|
	\| 0.0578 \| 4.81 \| 21000 \| 0.9997 \| 0.8480 \| 0.8480 \| 0.8480 \| 0.8480 \|
	\| 0.0578 \| 4.88 \| 21300 \| 1.0441 \| 0.8402 \| 0.8402 \| 0.8402 \| 0.8402 \|
	\| 0.0495 \| 4.95 \| 21600 \| 1.0393 \| 0.8421 \| 0.8421 \| 0.8421 \| 0.8421 \|


	### Framework versions

	- Transformers 4.36.0
	- Pytorch 2.0.0
	- Datasets 2.1.0
	- Tokenizers 0.15.0