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	---
	license: apache-2.0
	base_model: distilbert-base-uncased
	tags:
	- generated_from_trainer
	datasets:
	- wnut_17
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: basic_wnut
	results:
	- task:
	name: Token Classification
	type: token-classification
	dataset:
	name: wnut_17
	type: wnut_17
	config: wnut_17
	split: test
	args: wnut_17
	metrics:
	- name: Precision
	type: precision
	value: 0.5469543147208121
	- name: Recall
	type: recall
	value: 0.3994439295644115
	- name: F1
	type: f1
	value: 0.46170326727370103
	- name: Accuracy
	type: accuracy
	value: 0.9469026548672567
	---

	<!-- 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. -->

	# basic_wnut

	This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.3181
	- Precision: 0.5470
	- Recall: 0.3994
	- F1: 0.4617
	- Accuracy: 0.9469

	## 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-07
	- train_batch_size: 128
	- eval_batch_size: 128
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 100

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| No log \| 1.0 \| 27 \| 0.2984 \| 0.5557 \| 0.3930 \| 0.4604 \| 0.9463 \|
	\| No log \| 2.0 \| 54 \| 0.2991 \| 0.5547 \| 0.3902 \| 0.4581 \| 0.9462 \|
	\| No log \| 3.0 \| 81 \| 0.2993 \| 0.5557 \| 0.3930 \| 0.4604 \| 0.9463 \|
	\| No log \| 4.0 \| 108 \| 0.3011 \| 0.5550 \| 0.3883 \| 0.4569 \| 0.9461 \|
	\| No log \| 5.0 \| 135 \| 0.3015 \| 0.5532 \| 0.3902 \| 0.4576 \| 0.9462 \|
	\| No log \| 6.0 \| 162 \| 0.2997 \| 0.5467 \| 0.3957 \| 0.4591 \| 0.9463 \|
	\| No log \| 7.0 \| 189 \| 0.2997 \| 0.5487 \| 0.3967 \| 0.4605 \| 0.9462 \|
	\| No log \| 8.0 \| 216 \| 0.2998 \| 0.5439 \| 0.3957 \| 0.4582 \| 0.9463 \|
	\| No log \| 9.0 \| 243 \| 0.3024 \| 0.5501 \| 0.3920 \| 0.4578 \| 0.9462 \|
	\| No log \| 10.0 \| 270 \| 0.3021 \| 0.5470 \| 0.3939 \| 0.4580 \| 0.9462 \|
	\| No log \| 11.0 \| 297 \| 0.3027 \| 0.5471 \| 0.3930 \| 0.4574 \| 0.9463 \|
	\| No log \| 12.0 \| 324 \| 0.3023 \| 0.5453 \| 0.3957 \| 0.4586 \| 0.9463 \|
	\| No log \| 13.0 \| 351 \| 0.3028 \| 0.5481 \| 0.3957 \| 0.4596 \| 0.9463 \|
	\| No log \| 14.0 \| 378 \| 0.3028 \| 0.5467 \| 0.3957 \| 0.4591 \| 0.9463 \|
	\| No log \| 15.0 \| 405 \| 0.3034 \| 0.5444 \| 0.3976 \| 0.4596 \| 0.9464 \|
	\| No log \| 16.0 \| 432 \| 0.3040 \| 0.5431 \| 0.3967 \| 0.4585 \| 0.9464 \|
	\| No log \| 17.0 \| 459 \| 0.3068 \| 0.5484 \| 0.3939 \| 0.4585 \| 0.9464 \|
	\| No log \| 18.0 \| 486 \| 0.3077 \| 0.5501 \| 0.3920 \| 0.4578 \| 0.9466 \|
	\| 0.0203 \| 19.0 \| 513 \| 0.3057 \| 0.5434 \| 0.3948 \| 0.4573 \| 0.9463 \|
	\| 0.0203 \| 20.0 \| 540 \| 0.3078 \| 0.5494 \| 0.3920 \| 0.4575 \| 0.9464 \|
	\| 0.0203 \| 21.0 \| 567 \| 0.3074 \| 0.5517 \| 0.3957 \| 0.4609 \| 0.9465 \|
	\| 0.0203 \| 22.0 \| 594 \| 0.3070 \| 0.5499 \| 0.3985 \| 0.4621 \| 0.9465 \|
	\| 0.0203 \| 23.0 \| 621 \| 0.3065 \| 0.5497 \| 0.3994 \| 0.4627 \| 0.9465 \|
	\| 0.0203 \| 24.0 \| 648 \| 0.3064 \| 0.5450 \| 0.3985 \| 0.4604 \| 0.9464 \|
	\| 0.0203 \| 25.0 \| 675 \| 0.3077 \| 0.5467 \| 0.3957 \| 0.4591 \| 0.9465 \|
	\| 0.0203 \| 26.0 \| 702 \| 0.3070 \| 0.5458 \| 0.3976 \| 0.4601 \| 0.9464 \|
	\| 0.0203 \| 27.0 \| 729 \| 0.3084 \| 0.5494 \| 0.3967 \| 0.4607 \| 0.9466 \|
	\| 0.0203 \| 28.0 \| 756 \| 0.3086 \| 0.5487 \| 0.3967 \| 0.4605 \| 0.9465 \|
	\| 0.0203 \| 29.0 \| 783 \| 0.3087 \| 0.5486 \| 0.3976 \| 0.4610 \| 0.9466 \|
	\| 0.0203 \| 30.0 \| 810 \| 0.3087 \| 0.5444 \| 0.3976 \| 0.4596 \| 0.9464 \|
	\| 0.0203 \| 31.0 \| 837 \| 0.3108 \| 0.5510 \| 0.3957 \| 0.4606 \| 0.9466 \|
	\| 0.0203 \| 32.0 \| 864 \| 0.3107 \| 0.5494 \| 0.3967 \| 0.4607 \| 0.9466 \|
	\| 0.0203 \| 33.0 \| 891 \| 0.3097 \| 0.5429 \| 0.3985 \| 0.4596 \| 0.9466 \|
	\| 0.0203 \| 34.0 \| 918 \| 0.3114 \| 0.5493 \| 0.3976 \| 0.4613 \| 0.9466 \|
	\| 0.0203 \| 35.0 \| 945 \| 0.3100 \| 0.5430 \| 0.3976 \| 0.4591 \| 0.9465 \|
	\| 0.0203 \| 36.0 \| 972 \| 0.3100 \| 0.5442 \| 0.3994 \| 0.4607 \| 0.9466 \|
	\| 0.0203 \| 37.0 \| 999 \| 0.3099 \| 0.5428 \| 0.3994 \| 0.4602 \| 0.9466 \|
	\| 0.0177 \| 38.0 \| 1026 \| 0.3109 \| 0.5450 \| 0.3985 \| 0.4604 \| 0.9465 \|
	\| 0.0177 \| 39.0 \| 1053 \| 0.3117 \| 0.5488 \| 0.3957 \| 0.4599 \| 0.9466 \|
	\| 0.0177 \| 40.0 \| 1080 \| 0.3119 \| 0.5493 \| 0.3976 \| 0.4613 \| 0.9466 \|
	\| 0.0177 \| 41.0 \| 1107 \| 0.3129 \| 0.5528 \| 0.3976 \| 0.4625 \| 0.9468 \|
	\| 0.0177 \| 42.0 \| 1134 \| 0.3124 \| 0.5473 \| 0.3967 \| 0.4600 \| 0.9467 \|
	\| 0.0177 \| 43.0 \| 1161 \| 0.3128 \| 0.55 \| 0.3976 \| 0.4615 \| 0.9468 \|
	\| 0.0177 \| 44.0 \| 1188 \| 0.3132 \| 0.5514 \| 0.3976 \| 0.4620 \| 0.9469 \|
	\| 0.0177 \| 45.0 \| 1215 \| 0.3119 \| 0.5457 \| 0.3985 \| 0.4606 \| 0.9467 \|
	\| 0.0177 \| 46.0 \| 1242 \| 0.3115 \| 0.5436 \| 0.3985 \| 0.4599 \| 0.9467 \|
	\| 0.0177 \| 47.0 \| 1269 \| 0.3127 \| 0.5460 \| 0.3957 \| 0.4589 \| 0.9466 \|
	\| 0.0177 \| 48.0 \| 1296 \| 0.3132 \| 0.5474 \| 0.3957 \| 0.4594 \| 0.9467 \|
	\| 0.0177 \| 49.0 \| 1323 \| 0.3137 \| 0.5469 \| 0.3948 \| 0.4586 \| 0.9467 \|
	\| 0.0177 \| 50.0 \| 1350 \| 0.3147 \| 0.5510 \| 0.3957 \| 0.4606 \| 0.9468 \|
	\| 0.0177 \| 51.0 \| 1377 \| 0.3133 \| 0.5459 \| 0.3967 \| 0.4595 \| 0.9468 \|
	\| 0.0177 \| 52.0 \| 1404 \| 0.3129 \| 0.5436 \| 0.3985 \| 0.4599 \| 0.9468 \|
	\| 0.0177 \| 53.0 \| 1431 \| 0.3138 \| 0.5431 \| 0.3967 \| 0.4585 \| 0.9467 \|
	\| 0.0177 \| 54.0 \| 1458 \| 0.3141 \| 0.5437 \| 0.3976 \| 0.4593 \| 0.9468 \|
	\| 0.0177 \| 55.0 \| 1485 \| 0.3141 \| 0.5431 \| 0.3967 \| 0.4585 \| 0.9467 \|
	\| 0.0162 \| 56.0 \| 1512 \| 0.3156 \| 0.5473 \| 0.3967 \| 0.4600 \| 0.9469 \|
	\| 0.0162 \| 57.0 \| 1539 \| 0.3147 \| 0.5463 \| 0.3994 \| 0.4615 \| 0.9469 \|
	\| 0.0162 \| 58.0 \| 1566 \| 0.3150 \| 0.5450 \| 0.3985 \| 0.4604 \| 0.9469 \|
	\| 0.0162 \| 59.0 \| 1593 \| 0.3154 \| 0.5429 \| 0.3985 \| 0.4596 \| 0.9468 \|
	\| 0.0162 \| 60.0 \| 1620 \| 0.3165 \| 0.5486 \| 0.3976 \| 0.4610 \| 0.9468 \|
	\| 0.0162 \| 61.0 \| 1647 \| 0.3150 \| 0.5435 \| 0.3994 \| 0.4605 \| 0.9468 \|
	\| 0.0162 \| 62.0 \| 1674 \| 0.3161 \| 0.5450 \| 0.3985 \| 0.4604 \| 0.9468 \|
	\| 0.0162 \| 63.0 \| 1701 \| 0.3159 \| 0.5430 \| 0.3976 \| 0.4591 \| 0.9467 \|
	\| 0.0162 \| 64.0 \| 1728 \| 0.3168 \| 0.5458 \| 0.3976 \| 0.4601 \| 0.9467 \|
	\| 0.0162 \| 65.0 \| 1755 \| 0.3168 \| 0.5471 \| 0.3985 \| 0.4611 \| 0.9468 \|
	\| 0.0162 \| 66.0 \| 1782 \| 0.3160 \| 0.5429 \| 0.3985 \| 0.4596 \| 0.9467 \|
	\| 0.0162 \| 67.0 \| 1809 \| 0.3166 \| 0.5450 \| 0.3985 \| 0.4604 \| 0.9467 \|
	\| 0.0162 \| 68.0 \| 1836 \| 0.3172 \| 0.5457 \| 0.3985 \| 0.4606 \| 0.9468 \|
	\| 0.0162 \| 69.0 \| 1863 \| 0.3168 \| 0.5476 \| 0.3994 \| 0.4620 \| 0.9468 \|
	\| 0.0162 \| 70.0 \| 1890 \| 0.3167 \| 0.5470 \| 0.3994 \| 0.4617 \| 0.9468 \|
	\| 0.0162 \| 71.0 \| 1917 \| 0.3167 \| 0.5449 \| 0.3994 \| 0.4610 \| 0.9468 \|
	\| 0.0162 \| 72.0 \| 1944 \| 0.3153 \| 0.5439 \| 0.4022 \| 0.4624 \| 0.9469 \|
	\| 0.0162 \| 73.0 \| 1971 \| 0.3155 \| 0.5439 \| 0.4022 \| 0.4624 \| 0.9469 \|
	\| 0.0162 \| 74.0 \| 1998 \| 0.3160 \| 0.5428 \| 0.3994 \| 0.4602 \| 0.9468 \|
	\| 0.0153 \| 75.0 \| 2025 \| 0.3167 \| 0.5435 \| 0.3994 \| 0.4605 \| 0.9469 \|
	\| 0.0153 \| 76.0 \| 2052 \| 0.3171 \| 0.5449 \| 0.3994 \| 0.4610 \| 0.9469 \|
	\| 0.0153 \| 77.0 \| 2079 \| 0.3176 \| 0.5463 \| 0.3994 \| 0.4615 \| 0.9469 \|
	\| 0.0153 \| 78.0 \| 2106 \| 0.3177 \| 0.5463 \| 0.3994 \| 0.4615 \| 0.9469 \|
	\| 0.0153 \| 79.0 \| 2133 \| 0.3172 \| 0.5449 \| 0.3994 \| 0.4610 \| 0.9469 \|
	\| 0.0153 \| 80.0 \| 2160 \| 0.3171 \| 0.5443 \| 0.3985 \| 0.4601 \| 0.9469 \|
	\| 0.0153 \| 81.0 \| 2187 \| 0.3171 \| 0.5443 \| 0.3985 \| 0.4601 \| 0.9469 \|
	\| 0.0153 \| 82.0 \| 2214 \| 0.3173 \| 0.5457 \| 0.3985 \| 0.4606 \| 0.9469 \|
	\| 0.0153 \| 83.0 \| 2241 \| 0.3174 \| 0.5450 \| 0.3985 \| 0.4604 \| 0.9468 \|
	\| 0.0153 \| 84.0 \| 2268 \| 0.3174 \| 0.5436 \| 0.3985 \| 0.4599 \| 0.9467 \|
	\| 0.0153 \| 85.0 \| 2295 \| 0.3170 \| 0.5442 \| 0.3994 \| 0.4607 \| 0.9467 \|
	\| 0.0153 \| 86.0 \| 2322 \| 0.3172 \| 0.5449 \| 0.3994 \| 0.4610 \| 0.9468 \|
	\| 0.0153 \| 87.0 \| 2349 \| 0.3181 \| 0.5456 \| 0.3994 \| 0.4612 \| 0.9468 \|
	\| 0.0153 \| 88.0 \| 2376 \| 0.3179 \| 0.5463 \| 0.3994 \| 0.4615 \| 0.9468 \|
	\| 0.0153 \| 89.0 \| 2403 \| 0.3181 \| 0.5470 \| 0.3994 \| 0.4617 \| 0.9469 \|
	\| 0.0153 \| 90.0 \| 2430 \| 0.3179 \| 0.5470 \| 0.3994 \| 0.4617 \| 0.9469 \|
	\| 0.0153 \| 91.0 \| 2457 \| 0.3181 \| 0.5470 \| 0.3994 \| 0.4617 \| 0.9469 \|
	\| 0.0153 \| 92.0 \| 2484 \| 0.3182 \| 0.5463 \| 0.3994 \| 0.4615 \| 0.9468 \|
	\| 0.0145 \| 93.0 \| 2511 \| 0.3182 \| 0.5470 \| 0.3994 \| 0.4617 \| 0.9469 \|
	\| 0.0145 \| 94.0 \| 2538 \| 0.3181 \| 0.5470 \| 0.3994 \| 0.4617 \| 0.9469 \|
	\| 0.0145 \| 95.0 \| 2565 \| 0.3182 \| 0.5463 \| 0.3994 \| 0.4615 \| 0.9468 \|
	\| 0.0145 \| 96.0 \| 2592 \| 0.3180 \| 0.5470 \| 0.3994 \| 0.4617 \| 0.9469 \|
	\| 0.0145 \| 97.0 \| 2619 \| 0.3180 \| 0.5463 \| 0.3994 \| 0.4615 \| 0.9469 \|
	\| 0.0145 \| 98.0 \| 2646 \| 0.3180 \| 0.5463 \| 0.3994 \| 0.4615 \| 0.9469 \|
	\| 0.0145 \| 99.0 \| 2673 \| 0.3181 \| 0.5470 \| 0.3994 \| 0.4617 \| 0.9469 \|
	\| 0.0145 \| 100.0 \| 2700 \| 0.3181 \| 0.5470 \| 0.3994 \| 0.4617 \| 0.9469 \|


	### Framework versions

	- Transformers 4.35.2
	- Pytorch 2.1.0+cu118
	- Datasets 2.15.0
	- Tokenizers 0.15.0

	---
	license: apache-2.0
	base_model: distilbert-base-uncased
	tags:
	- generated_from_trainer
	datasets:
	- wnut_17
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: basic_wnut
	results:
	- task:
	name: Token Classification
	type: token-classification
	dataset:
	name: wnut_17
	type: wnut_17
	config: wnut_17
	split: test
	args: wnut_17
	metrics:
	- name: Precision
	type: precision
	value: 0.5469543147208121
	- name: Recall
	type: recall
	value: 0.3994439295644115
	- name: F1
	type: f1
	value: 0.46170326727370103
	- name: Accuracy
	type: accuracy
	value: 0.9469026548672567
	---

	<!-- 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. -->

	# basic_wnut

	This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.3181
	- Precision: 0.5470
	- Recall: 0.3994
	- F1: 0.4617
	- Accuracy: 0.9469

	## 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-07
	- train_batch_size: 128
	- eval_batch_size: 128
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 100

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| No log \| 1.0 \| 27 \| 0.2984 \| 0.5557 \| 0.3930 \| 0.4604 \| 0.9463 \|
	\| No log \| 2.0 \| 54 \| 0.2991 \| 0.5547 \| 0.3902 \| 0.4581 \| 0.9462 \|
	\| No log \| 3.0 \| 81 \| 0.2993 \| 0.5557 \| 0.3930 \| 0.4604 \| 0.9463 \|
	\| No log \| 4.0 \| 108 \| 0.3011 \| 0.5550 \| 0.3883 \| 0.4569 \| 0.9461 \|
	\| No log \| 5.0 \| 135 \| 0.3015 \| 0.5532 \| 0.3902 \| 0.4576 \| 0.9462 \|
	\| No log \| 6.0 \| 162 \| 0.2997 \| 0.5467 \| 0.3957 \| 0.4591 \| 0.9463 \|
	\| No log \| 7.0 \| 189 \| 0.2997 \| 0.5487 \| 0.3967 \| 0.4605 \| 0.9462 \|
	\| No log \| 8.0 \| 216 \| 0.2998 \| 0.5439 \| 0.3957 \| 0.4582 \| 0.9463 \|
	\| No log \| 9.0 \| 243 \| 0.3024 \| 0.5501 \| 0.3920 \| 0.4578 \| 0.9462 \|
	\| No log \| 10.0 \| 270 \| 0.3021 \| 0.5470 \| 0.3939 \| 0.4580 \| 0.9462 \|
	\| No log \| 11.0 \| 297 \| 0.3027 \| 0.5471 \| 0.3930 \| 0.4574 \| 0.9463 \|
	\| No log \| 12.0 \| 324 \| 0.3023 \| 0.5453 \| 0.3957 \| 0.4586 \| 0.9463 \|
	\| No log \| 13.0 \| 351 \| 0.3028 \| 0.5481 \| 0.3957 \| 0.4596 \| 0.9463 \|
	\| No log \| 14.0 \| 378 \| 0.3028 \| 0.5467 \| 0.3957 \| 0.4591 \| 0.9463 \|
	\| No log \| 15.0 \| 405 \| 0.3034 \| 0.5444 \| 0.3976 \| 0.4596 \| 0.9464 \|
	\| No log \| 16.0 \| 432 \| 0.3040 \| 0.5431 \| 0.3967 \| 0.4585 \| 0.9464 \|
	\| No log \| 17.0 \| 459 \| 0.3068 \| 0.5484 \| 0.3939 \| 0.4585 \| 0.9464 \|
	\| No log \| 18.0 \| 486 \| 0.3077 \| 0.5501 \| 0.3920 \| 0.4578 \| 0.9466 \|
	\| 0.0203 \| 19.0 \| 513 \| 0.3057 \| 0.5434 \| 0.3948 \| 0.4573 \| 0.9463 \|
	\| 0.0203 \| 20.0 \| 540 \| 0.3078 \| 0.5494 \| 0.3920 \| 0.4575 \| 0.9464 \|
	\| 0.0203 \| 21.0 \| 567 \| 0.3074 \| 0.5517 \| 0.3957 \| 0.4609 \| 0.9465 \|
	\| 0.0203 \| 22.0 \| 594 \| 0.3070 \| 0.5499 \| 0.3985 \| 0.4621 \| 0.9465 \|
	\| 0.0203 \| 23.0 \| 621 \| 0.3065 \| 0.5497 \| 0.3994 \| 0.4627 \| 0.9465 \|
	\| 0.0203 \| 24.0 \| 648 \| 0.3064 \| 0.5450 \| 0.3985 \| 0.4604 \| 0.9464 \|
	\| 0.0203 \| 25.0 \| 675 \| 0.3077 \| 0.5467 \| 0.3957 \| 0.4591 \| 0.9465 \|
	\| 0.0203 \| 26.0 \| 702 \| 0.3070 \| 0.5458 \| 0.3976 \| 0.4601 \| 0.9464 \|
	\| 0.0203 \| 27.0 \| 729 \| 0.3084 \| 0.5494 \| 0.3967 \| 0.4607 \| 0.9466 \|
	\| 0.0203 \| 28.0 \| 756 \| 0.3086 \| 0.5487 \| 0.3967 \| 0.4605 \| 0.9465 \|
	\| 0.0203 \| 29.0 \| 783 \| 0.3087 \| 0.5486 \| 0.3976 \| 0.4610 \| 0.9466 \|
	\| 0.0203 \| 30.0 \| 810 \| 0.3087 \| 0.5444 \| 0.3976 \| 0.4596 \| 0.9464 \|
	\| 0.0203 \| 31.0 \| 837 \| 0.3108 \| 0.5510 \| 0.3957 \| 0.4606 \| 0.9466 \|
	\| 0.0203 \| 32.0 \| 864 \| 0.3107 \| 0.5494 \| 0.3967 \| 0.4607 \| 0.9466 \|
	\| 0.0203 \| 33.0 \| 891 \| 0.3097 \| 0.5429 \| 0.3985 \| 0.4596 \| 0.9466 \|
	\| 0.0203 \| 34.0 \| 918 \| 0.3114 \| 0.5493 \| 0.3976 \| 0.4613 \| 0.9466 \|
	\| 0.0203 \| 35.0 \| 945 \| 0.3100 \| 0.5430 \| 0.3976 \| 0.4591 \| 0.9465 \|
	\| 0.0203 \| 36.0 \| 972 \| 0.3100 \| 0.5442 \| 0.3994 \| 0.4607 \| 0.9466 \|
	\| 0.0203 \| 37.0 \| 999 \| 0.3099 \| 0.5428 \| 0.3994 \| 0.4602 \| 0.9466 \|
	\| 0.0177 \| 38.0 \| 1026 \| 0.3109 \| 0.5450 \| 0.3985 \| 0.4604 \| 0.9465 \|
	\| 0.0177 \| 39.0 \| 1053 \| 0.3117 \| 0.5488 \| 0.3957 \| 0.4599 \| 0.9466 \|
	\| 0.0177 \| 40.0 \| 1080 \| 0.3119 \| 0.5493 \| 0.3976 \| 0.4613 \| 0.9466 \|
	\| 0.0177 \| 41.0 \| 1107 \| 0.3129 \| 0.5528 \| 0.3976 \| 0.4625 \| 0.9468 \|
	\| 0.0177 \| 42.0 \| 1134 \| 0.3124 \| 0.5473 \| 0.3967 \| 0.4600 \| 0.9467 \|
	\| 0.0177 \| 43.0 \| 1161 \| 0.3128 \| 0.55 \| 0.3976 \| 0.4615 \| 0.9468 \|
	\| 0.0177 \| 44.0 \| 1188 \| 0.3132 \| 0.5514 \| 0.3976 \| 0.4620 \| 0.9469 \|
	\| 0.0177 \| 45.0 \| 1215 \| 0.3119 \| 0.5457 \| 0.3985 \| 0.4606 \| 0.9467 \|
	\| 0.0177 \| 46.0 \| 1242 \| 0.3115 \| 0.5436 \| 0.3985 \| 0.4599 \| 0.9467 \|
	\| 0.0177 \| 47.0 \| 1269 \| 0.3127 \| 0.5460 \| 0.3957 \| 0.4589 \| 0.9466 \|
	\| 0.0177 \| 48.0 \| 1296 \| 0.3132 \| 0.5474 \| 0.3957 \| 0.4594 \| 0.9467 \|
	\| 0.0177 \| 49.0 \| 1323 \| 0.3137 \| 0.5469 \| 0.3948 \| 0.4586 \| 0.9467 \|
	\| 0.0177 \| 50.0 \| 1350 \| 0.3147 \| 0.5510 \| 0.3957 \| 0.4606 \| 0.9468 \|
	\| 0.0177 \| 51.0 \| 1377 \| 0.3133 \| 0.5459 \| 0.3967 \| 0.4595 \| 0.9468 \|
	\| 0.0177 \| 52.0 \| 1404 \| 0.3129 \| 0.5436 \| 0.3985 \| 0.4599 \| 0.9468 \|
	\| 0.0177 \| 53.0 \| 1431 \| 0.3138 \| 0.5431 \| 0.3967 \| 0.4585 \| 0.9467 \|
	\| 0.0177 \| 54.0 \| 1458 \| 0.3141 \| 0.5437 \| 0.3976 \| 0.4593 \| 0.9468 \|
	\| 0.0177 \| 55.0 \| 1485 \| 0.3141 \| 0.5431 \| 0.3967 \| 0.4585 \| 0.9467 \|
	\| 0.0162 \| 56.0 \| 1512 \| 0.3156 \| 0.5473 \| 0.3967 \| 0.4600 \| 0.9469 \|
	\| 0.0162 \| 57.0 \| 1539 \| 0.3147 \| 0.5463 \| 0.3994 \| 0.4615 \| 0.9469 \|
	\| 0.0162 \| 58.0 \| 1566 \| 0.3150 \| 0.5450 \| 0.3985 \| 0.4604 \| 0.9469 \|
	\| 0.0162 \| 59.0 \| 1593 \| 0.3154 \| 0.5429 \| 0.3985 \| 0.4596 \| 0.9468 \|
	\| 0.0162 \| 60.0 \| 1620 \| 0.3165 \| 0.5486 \| 0.3976 \| 0.4610 \| 0.9468 \|
	\| 0.0162 \| 61.0 \| 1647 \| 0.3150 \| 0.5435 \| 0.3994 \| 0.4605 \| 0.9468 \|
	\| 0.0162 \| 62.0 \| 1674 \| 0.3161 \| 0.5450 \| 0.3985 \| 0.4604 \| 0.9468 \|
	\| 0.0162 \| 63.0 \| 1701 \| 0.3159 \| 0.5430 \| 0.3976 \| 0.4591 \| 0.9467 \|
	\| 0.0162 \| 64.0 \| 1728 \| 0.3168 \| 0.5458 \| 0.3976 \| 0.4601 \| 0.9467 \|
	\| 0.0162 \| 65.0 \| 1755 \| 0.3168 \| 0.5471 \| 0.3985 \| 0.4611 \| 0.9468 \|
	\| 0.0162 \| 66.0 \| 1782 \| 0.3160 \| 0.5429 \| 0.3985 \| 0.4596 \| 0.9467 \|
	\| 0.0162 \| 67.0 \| 1809 \| 0.3166 \| 0.5450 \| 0.3985 \| 0.4604 \| 0.9467 \|
	\| 0.0162 \| 68.0 \| 1836 \| 0.3172 \| 0.5457 \| 0.3985 \| 0.4606 \| 0.9468 \|
	\| 0.0162 \| 69.0 \| 1863 \| 0.3168 \| 0.5476 \| 0.3994 \| 0.4620 \| 0.9468 \|
	\| 0.0162 \| 70.0 \| 1890 \| 0.3167 \| 0.5470 \| 0.3994 \| 0.4617 \| 0.9468 \|
	\| 0.0162 \| 71.0 \| 1917 \| 0.3167 \| 0.5449 \| 0.3994 \| 0.4610 \| 0.9468 \|
	\| 0.0162 \| 72.0 \| 1944 \| 0.3153 \| 0.5439 \| 0.4022 \| 0.4624 \| 0.9469 \|
	\| 0.0162 \| 73.0 \| 1971 \| 0.3155 \| 0.5439 \| 0.4022 \| 0.4624 \| 0.9469 \|
	\| 0.0162 \| 74.0 \| 1998 \| 0.3160 \| 0.5428 \| 0.3994 \| 0.4602 \| 0.9468 \|
	\| 0.0153 \| 75.0 \| 2025 \| 0.3167 \| 0.5435 \| 0.3994 \| 0.4605 \| 0.9469 \|
	\| 0.0153 \| 76.0 \| 2052 \| 0.3171 \| 0.5449 \| 0.3994 \| 0.4610 \| 0.9469 \|
	\| 0.0153 \| 77.0 \| 2079 \| 0.3176 \| 0.5463 \| 0.3994 \| 0.4615 \| 0.9469 \|
	\| 0.0153 \| 78.0 \| 2106 \| 0.3177 \| 0.5463 \| 0.3994 \| 0.4615 \| 0.9469 \|
	\| 0.0153 \| 79.0 \| 2133 \| 0.3172 \| 0.5449 \| 0.3994 \| 0.4610 \| 0.9469 \|
	\| 0.0153 \| 80.0 \| 2160 \| 0.3171 \| 0.5443 \| 0.3985 \| 0.4601 \| 0.9469 \|
	\| 0.0153 \| 81.0 \| 2187 \| 0.3171 \| 0.5443 \| 0.3985 \| 0.4601 \| 0.9469 \|
	\| 0.0153 \| 82.0 \| 2214 \| 0.3173 \| 0.5457 \| 0.3985 \| 0.4606 \| 0.9469 \|
	\| 0.0153 \| 83.0 \| 2241 \| 0.3174 \| 0.5450 \| 0.3985 \| 0.4604 \| 0.9468 \|
	\| 0.0153 \| 84.0 \| 2268 \| 0.3174 \| 0.5436 \| 0.3985 \| 0.4599 \| 0.9467 \|
	\| 0.0153 \| 85.0 \| 2295 \| 0.3170 \| 0.5442 \| 0.3994 \| 0.4607 \| 0.9467 \|
	\| 0.0153 \| 86.0 \| 2322 \| 0.3172 \| 0.5449 \| 0.3994 \| 0.4610 \| 0.9468 \|
	\| 0.0153 \| 87.0 \| 2349 \| 0.3181 \| 0.5456 \| 0.3994 \| 0.4612 \| 0.9468 \|
	\| 0.0153 \| 88.0 \| 2376 \| 0.3179 \| 0.5463 \| 0.3994 \| 0.4615 \| 0.9468 \|
	\| 0.0153 \| 89.0 \| 2403 \| 0.3181 \| 0.5470 \| 0.3994 \| 0.4617 \| 0.9469 \|
	\| 0.0153 \| 90.0 \| 2430 \| 0.3179 \| 0.5470 \| 0.3994 \| 0.4617 \| 0.9469 \|
	\| 0.0153 \| 91.0 \| 2457 \| 0.3181 \| 0.5470 \| 0.3994 \| 0.4617 \| 0.9469 \|
	\| 0.0153 \| 92.0 \| 2484 \| 0.3182 \| 0.5463 \| 0.3994 \| 0.4615 \| 0.9468 \|
	\| 0.0145 \| 93.0 \| 2511 \| 0.3182 \| 0.5470 \| 0.3994 \| 0.4617 \| 0.9469 \|
	\| 0.0145 \| 94.0 \| 2538 \| 0.3181 \| 0.5470 \| 0.3994 \| 0.4617 \| 0.9469 \|
	\| 0.0145 \| 95.0 \| 2565 \| 0.3182 \| 0.5463 \| 0.3994 \| 0.4615 \| 0.9468 \|
	\| 0.0145 \| 96.0 \| 2592 \| 0.3180 \| 0.5470 \| 0.3994 \| 0.4617 \| 0.9469 \|
	\| 0.0145 \| 97.0 \| 2619 \| 0.3180 \| 0.5463 \| 0.3994 \| 0.4615 \| 0.9469 \|
	\| 0.0145 \| 98.0 \| 2646 \| 0.3180 \| 0.5463 \| 0.3994 \| 0.4615 \| 0.9469 \|
	\| 0.0145 \| 99.0 \| 2673 \| 0.3181 \| 0.5470 \| 0.3994 \| 0.4617 \| 0.9469 \|
	\| 0.0145 \| 100.0 \| 2700 \| 0.3181 \| 0.5470 \| 0.3994 \| 0.4617 \| 0.9469 \|


	### Framework versions

	- Transformers 4.35.2
	- Pytorch 2.1.0+cu118
	- Datasets 2.15.0
	- Tokenizers 0.15.0