Training in progress epoch 49

7ac5a94 12 months ago

20.5 kB

	---
	license: other
	base_model: nvidia/mit-b0
	tags:
	- generated_from_keras_callback
	model-index:
	- name: greathero/mit-b0-finetuned-contrails-morethanx35newercontrailsdataset
	results: []
	---

	<!-- This model card has been generated automatically according to the information Keras had access to. You should
	probably proofread and complete it, then remove this comment. -->

	# greathero/mit-b0-finetuned-contrails-morethanx35newercontrailsdataset

	This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Train Loss: 0.0016
	- Validation Loss: 0.0026
	- Validation Mean Iou: 0.9068
	- Validation Mean Accuracy: 0.9377
	- Validation Overall Accuracy: 0.9993
	- Validation Accuracy Unlabeled: 1.0
	- Validation Accuracy Notlabeled: 0.9997
	- Validation Accuracy Otherclass: 1.0
	- Validation Accuracy Contrail: 0.7513
	- Validation Iou Unlabeled: 1.0
	- Validation Iou Notlabeled: 0.9993
	- Validation Iou Otherclass: 0.9993
	- Validation Iou Contrail: 0.6286
	- Epoch: 49

	## 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:
	- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 6e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
	- training_precision: float32

	### Training results

	\| Train Loss \| Validation Loss \| Validation Mean Iou \| Validation Mean Accuracy \| Validation Overall Accuracy \| Validation Accuracy Unlabeled \| Validation Accuracy Notlabeled \| Validation Accuracy Otherclass \| Validation Accuracy Contrail \| Validation Iou Unlabeled \| Validation Iou Notlabeled \| Validation Iou Otherclass \| Validation Iou Contrail \| Epoch \|
	\|:----------:\|:---------------:\|:-------------------:\|:------------------------:\|:---------------------------:\|:-----------------------------:\|:------------------------------:\|:------------------------------:\|:----------------------------:\|:------------------------:\|:-------------------------:\|:-------------------------:\|:-----------------------:\|:-----:\|
	\| 0.2395 \| 0.0367 \| 0.2496 \| 0.25 \| 0.9983 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9983 \| 0.0 \| 0.0 \| 0 \|
	\| 0.0286 \| 0.0204 \| 0.2496 \| 0.25 \| 0.9983 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9983 \| 0.0 \| 0.0 \| 1 \|
	\| 0.0194 \| 0.0156 \| 0.2496 \| 0.25 \| 0.9983 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9983 \| 0.0 \| 0.0 \| 2 \|
	\| 0.0157 \| 0.0144 \| 0.2496 \| 0.25 \| 0.9983 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9983 \| 0.0 \| 0.0 \| 3 \|
	\| 0.0141 \| 0.0137 \| 0.2496 \| 0.25 \| 0.9983 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9983 \| 0.0 \| 0.0 \| 4 \|
	\| 0.0133 \| 0.0132 \| 0.2496 \| 0.25 \| 0.9983 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9983 \| 0.0 \| 0.0 \| 5 \|
	\| 0.0126 \| 0.0125 \| 0.2496 \| 0.25 \| 0.9983 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9983 \| 0.0 \| 0.0 \| 6 \|
	\| 0.0118 \| 0.0117 \| 0.2496 \| 0.25 \| 0.9983 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9983 \| 0.0 \| 0.0 \| 7 \|
	\| 0.0112 \| 0.0110 \| 0.2551 \| 0.2555 \| 0.9983 \| 0.0 \| 1.0000 \| 0.0222 \| 0.0 \| 0.0 \| 0.9983 \| 0.0220 \| 0.0 \| 8 \|
	\| 0.0106 \| 0.0103 \| 0.2744 \| 0.2756 \| 0.9983 \| 0.0180 \| 1.0000 \| 0.0832 \| 0.0013 \| 0.0179 \| 0.9983 \| 0.0801 \| 0.0013 \| 9 \|
	\| 0.0100 \| 0.0094 \| 0.3172 \| 0.3225 \| 0.9983 \| 0.0527 \| 1.0000 \| 0.2344 \| 0.0030 \| 0.0521 \| 0.9983 \| 0.2154 \| 0.0030 \| 10 \|
	\| 0.0091 \| 0.0087 \| 0.3117 \| 0.3138 \| 0.9983 \| 0.1026 \| 1.0000 \| 0.1470 \| 0.0055 \| 0.1014 \| 0.9983 \| 0.1415 \| 0.0055 \| 11 \|
	\| 0.0083 \| 0.0076 \| 0.3844 \| 0.3941 \| 0.9983 \| 0.1540 \| 1.0000 \| 0.4189 \| 0.0037 \| 0.1516 \| 0.9983 \| 0.3840 \| 0.0037 \| 12 \|
	\| 0.0073 \| 0.0070 \| 0.4402 \| 0.4781 \| 0.9983 \| 0.1997 \| 0.9999 \| 0.6616 \| 0.0511 \| 0.1978 \| 0.9983 \| 0.5167 \| 0.0479 \| 13 \|
	\| 0.0067 \| 0.0062 \| 0.5049 \| 0.5454 \| 0.9983 \| 0.4868 \| 1.0000 \| 0.6893 \| 0.0057 \| 0.4702 \| 0.9983 \| 0.5456 \| 0.0056 \| 14 \|
	\| 0.0060 \| 0.0056 \| 0.5133 \| 0.5326 \| 0.9983 \| 0.4799 \| 1.0000 \| 0.6283 \| 0.0221 \| 0.4722 \| 0.9983 \| 0.5608 \| 0.0218 \| 15 \|
	\| 0.0055 \| 0.0056 \| 0.5975 \| 0.6504 \| 0.9984 \| 0.7018 \| 0.9999 \| 0.8058 \| 0.0942 \| 0.6731 \| 0.9984 \| 0.6317 \| 0.0868 \| 16 \|
	\| 0.0051 \| 0.0050 \| 0.5800 \| 0.6003 \| 0.9984 \| 0.6893 \| 0.9999 \| 0.6644 \| 0.0476 \| 0.6707 \| 0.9984 \| 0.6048 \| 0.0462 \| 17 \|
	\| 0.0048 \| 0.0047 \| 0.6598 \| 0.6878 \| 0.9984 \| 0.8252 \| 0.9998 \| 0.7809 \| 0.1453 \| 0.8013 \| 0.9984 \| 0.7095 \| 0.1298 \| 18 \|
	\| 0.0045 \| 0.0045 \| 0.6596 \| 0.7122 \| 0.9984 \| 0.9057 \| 0.9998 \| 0.7739 \| 0.1693 \| 0.8117 \| 0.9984 \| 0.6774 \| 0.1508 \| 19 \|
	\| 0.0042 \| 0.0042 \| 0.6956 \| 0.7259 \| 0.9985 \| 0.8669 \| 0.9998 \| 0.8294 \| 0.2074 \| 0.8609 \| 0.9985 \| 0.7396 \| 0.1835 \| 20 \|
	\| 0.0039 \| 0.0045 \| 0.6824 \| 0.6971 \| 0.9985 \| 0.8710 \| 0.9999 \| 0.7933 \| 0.1243 \| 0.8579 \| 0.9985 \| 0.7546 \| 0.1186 \| 21 \|
	\| 0.0038 \| 0.0041 \| 0.7516 \| 0.7748 \| 0.9985 \| 0.9612 \| 0.9998 \| 0.8779 \| 0.2605 \| 0.9397 \| 0.9985 \| 0.8395 \| 0.2287 \| 22 \|
	\| 0.0035 \| 0.0038 \| 0.7683 \| 0.8089 \| 0.9986 \| 0.9667 \| 0.9997 \| 0.9334 \| 0.3359 \| 0.9356 \| 0.9986 \| 0.8546 \| 0.2843 \| 23 \|
	\| 0.0034 \| 0.0037 \| 0.7905 \| 0.8212 \| 0.9986 \| 0.9639 \| 0.9996 \| 0.9182 \| 0.4031 \| 0.9508 \| 0.9986 \| 0.8856 \| 0.3270 \| 24 \|
	\| 0.0032 \| 0.0037 \| 0.7938 \| 0.8154 \| 0.9987 \| 0.9515 \| 0.9997 \| 0.9112 \| 0.3992 \| 0.9469 \| 0.9987 \| 0.8939 \| 0.3358 \| 25 \|
	\| 0.0031 \| 0.0036 \| 0.8250 \| 0.8524 \| 0.9988 \| 0.9806 \| 0.9996 \| 0.9431 \| 0.4864 \| 0.9772 \| 0.9988 \| 0.9315 \| 0.3924 \| 26 \|
	\| 0.0030 \| 0.0035 \| 0.8204 \| 0.8392 \| 0.9987 \| 0.9736 \| 0.9997 \| 0.9820 \| 0.4015 \| 0.9736 \| 0.9987 \| 0.9659 \| 0.3435 \| 27 \|
	\| 0.0029 \| 0.0033 \| 0.8431 \| 0.8689 \| 0.9988 \| 0.9917 \| 0.9997 \| 0.9834 \| 0.5011 \| 0.9910 \| 0.9988 \| 0.9653 \| 0.4172 \| 28 \|
	\| 0.0027 \| 0.0034 \| 0.8337 \| 0.8539 \| 0.9988 \| 0.9945 \| 0.9997 \| 0.9875 \| 0.4341 \| 0.9945 \| 0.9988 \| 0.9648 \| 0.3767 \| 29 \|
	\| 0.0027 \| 0.0033 \| 0.8518 \| 0.8834 \| 0.9988 \| 1.0 \| 0.9996 \| 0.9931 \| 0.5411 \| 0.9972 \| 0.9988 \| 0.9802 \| 0.4311 \| 30 \|
	\| 0.0025 \| 0.0031 \| 0.8622 \| 0.8848 \| 0.9990 \| 0.9986 \| 0.9997 \| 0.9958 \| 0.5449 \| 0.9986 \| 0.9990 \| 0.9890 \| 0.4622 \| 31 \|
	\| 0.0025 \| 0.0031 \| 0.8667 \| 0.8975 \| 0.9989 \| 0.9986 \| 0.9996 \| 0.9986 \| 0.5933 \| 0.9979 \| 0.9989 \| 0.9897 \| 0.4801 \| 32 \|
	\| 0.0024 \| 0.0031 \| 0.8607 \| 0.8791 \| 0.9990 \| 1.0 \| 0.9998 \| 0.9986 \| 0.5181 \| 1.0 \| 0.9990 \| 0.9877 \| 0.4563 \| 33 \|
	\| 0.0022 \| 0.0031 \| 0.8783 \| 0.9102 \| 0.9990 \| 1.0 \| 0.9996 \| 0.9917 \| 0.6497 \| 1.0 \| 0.9990 \| 0.9876 \| 0.5266 \| 34 \|
	\| 0.0022 \| 0.0029 \| 0.8780 \| 0.9016 \| 0.9991 \| 1.0 \| 0.9997 \| 0.9931 \| 0.6138 \| 1.0 \| 0.9991 \| 0.9903 \| 0.5225 \| 35 \|
	\| 0.0022 \| 0.0030 \| 0.8853 \| 0.9238 \| 0.9991 \| 1.0 \| 0.9996 \| 0.9986 \| 0.6969 \| 0.9972 \| 0.9991 \| 0.9965 \| 0.5484 \| 36 \|
	\| 0.0021 \| 0.0030 \| 0.8859 \| 0.9156 \| 0.9991 \| 1.0 \| 0.9996 \| 0.9986 \| 0.6641 \| 1.0 \| 0.9991 \| 0.9979 \| 0.5466 \| 37 \|
	\| 0.0021 \| 0.0029 \| 0.8865 \| 0.9165 \| 0.9991 \| 1.0 \| 0.9997 \| 1.0 \| 0.6662 \| 0.9993 \| 0.9991 \| 0.9952 \| 0.5526 \| 38 \|
	\| 0.0021 \| 0.0028 \| 0.8932 \| 0.9227 \| 0.9992 \| 1.0 \| 0.9997 \| 0.9986 \| 0.6926 \| 1.0 \| 0.9992 \| 0.9965 \| 0.5771 \| 39 \|
	\| 0.0020 \| 0.0029 \| 0.8929 \| 0.9235 \| 0.9992 \| 1.0 \| 0.9997 \| 0.9986 \| 0.6959 \| 1.0 \| 0.9992 \| 0.9979 \| 0.5747 \| 40 \|
	\| 0.0020 \| 0.0028 \| 0.8935 \| 0.9252 \| 0.9992 \| 1.0 \| 0.9996 \| 0.9986 \| 0.7026 \| 1.0 \| 0.9991 \| 0.9979 \| 0.5767 \| 41 \|
	\| 0.0019 \| 0.0028 \| 0.8934 \| 0.9144 \| 0.9992 \| 1.0 \| 0.9998 \| 0.9986 \| 0.6590 \| 1.0 \| 0.9992 \| 0.9986 \| 0.5758 \| 42 \|
	\| 0.0019 \| 0.0028 \| 0.8945 \| 0.9174 \| 0.9992 \| 1.0 \| 0.9997 \| 0.9986 \| 0.6713 \| 1.0 \| 0.9992 \| 0.9979 \| 0.5807 \| 43 \|
	\| 0.0018 \| 0.0027 \| 0.9009 \| 0.9327 \| 0.9992 \| 1.0 \| 0.9997 \| 0.9986 \| 0.7324 \| 1.0 \| 0.9992 \| 0.9979 \| 0.6065 \| 44 \|
	\| 0.0019 \| 0.0027 \| 0.9028 \| 0.9385 \| 0.9992 \| 1.0 \| 0.9996 \| 1.0 \| 0.7543 \| 1.0 \| 0.9992 \| 0.9993 \| 0.6125 \| 45 \|
	\| 0.0017 \| 0.0028 \| 0.9048 \| 0.9362 \| 0.9992 \| 1.0 \| 0.9997 \| 1.0 \| 0.7453 \| 1.0 \| 0.9992 \| 1.0 \| 0.6199 \| 46 \|
	\| 0.0017 \| 0.0029 \| 0.9043 \| 0.9362 \| 0.9992 \| 1.0 \| 0.9997 \| 0.9986 \| 0.7466 \| 1.0 \| 0.9992 \| 0.9979 \| 0.6202 \| 47 \|
	\| 0.0017 \| 0.0027 \| 0.9060 \| 0.9365 \| 0.9993 \| 1.0 \| 0.9997 \| 0.9986 \| 0.7477 \| 1.0 \| 0.9993 \| 0.9979 \| 0.6270 \| 48 \|
	\| 0.0016 \| 0.0026 \| 0.9068 \| 0.9377 \| 0.9993 \| 1.0 \| 0.9997 \| 1.0 \| 0.7513 \| 1.0 \| 0.9993 \| 0.9993 \| 0.6286 \| 49 \|


	### Framework versions

	- Transformers 4.35.2
	- TensorFlow 2.15.0
	- Datasets 2.15.0
	- Tokenizers 0.15.0

	---
	license: other
	base_model: nvidia/mit-b0
	tags:
	- generated_from_keras_callback
	model-index:
	- name: greathero/mit-b0-finetuned-contrails-morethanx35newercontrailsdataset
	results: []
	---

	<!-- This model card has been generated automatically according to the information Keras had access to. You should
	probably proofread and complete it, then remove this comment. -->

	# greathero/mit-b0-finetuned-contrails-morethanx35newercontrailsdataset

	This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Train Loss: 0.0016
	- Validation Loss: 0.0026
	- Validation Mean Iou: 0.9068
	- Validation Mean Accuracy: 0.9377
	- Validation Overall Accuracy: 0.9993
	- Validation Accuracy Unlabeled: 1.0
	- Validation Accuracy Notlabeled: 0.9997
	- Validation Accuracy Otherclass: 1.0
	- Validation Accuracy Contrail: 0.7513
	- Validation Iou Unlabeled: 1.0
	- Validation Iou Notlabeled: 0.9993
	- Validation Iou Otherclass: 0.9993
	- Validation Iou Contrail: 0.6286
	- Epoch: 49

	## 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:
	- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 6e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
	- training_precision: float32

	### Training results

	\| Train Loss \| Validation Loss \| Validation Mean Iou \| Validation Mean Accuracy \| Validation Overall Accuracy \| Validation Accuracy Unlabeled \| Validation Accuracy Notlabeled \| Validation Accuracy Otherclass \| Validation Accuracy Contrail \| Validation Iou Unlabeled \| Validation Iou Notlabeled \| Validation Iou Otherclass \| Validation Iou Contrail \| Epoch \|
	\|:----------:\|:---------------:\|:-------------------:\|:------------------------:\|:---------------------------:\|:-----------------------------:\|:------------------------------:\|:------------------------------:\|:----------------------------:\|:------------------------:\|:-------------------------:\|:-------------------------:\|:-----------------------:\|:-----:\|
	\| 0.2395 \| 0.0367 \| 0.2496 \| 0.25 \| 0.9983 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9983 \| 0.0 \| 0.0 \| 0 \|
	\| 0.0286 \| 0.0204 \| 0.2496 \| 0.25 \| 0.9983 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9983 \| 0.0 \| 0.0 \| 1 \|
	\| 0.0194 \| 0.0156 \| 0.2496 \| 0.25 \| 0.9983 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9983 \| 0.0 \| 0.0 \| 2 \|
	\| 0.0157 \| 0.0144 \| 0.2496 \| 0.25 \| 0.9983 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9983 \| 0.0 \| 0.0 \| 3 \|
	\| 0.0141 \| 0.0137 \| 0.2496 \| 0.25 \| 0.9983 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9983 \| 0.0 \| 0.0 \| 4 \|
	\| 0.0133 \| 0.0132 \| 0.2496 \| 0.25 \| 0.9983 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9983 \| 0.0 \| 0.0 \| 5 \|
	\| 0.0126 \| 0.0125 \| 0.2496 \| 0.25 \| 0.9983 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9983 \| 0.0 \| 0.0 \| 6 \|
	\| 0.0118 \| 0.0117 \| 0.2496 \| 0.25 \| 0.9983 \| 0.0 \| 1.0 \| 0.0 \| 0.0 \| 0.0 \| 0.9983 \| 0.0 \| 0.0 \| 7 \|
	\| 0.0112 \| 0.0110 \| 0.2551 \| 0.2555 \| 0.9983 \| 0.0 \| 1.0000 \| 0.0222 \| 0.0 \| 0.0 \| 0.9983 \| 0.0220 \| 0.0 \| 8 \|
	\| 0.0106 \| 0.0103 \| 0.2744 \| 0.2756 \| 0.9983 \| 0.0180 \| 1.0000 \| 0.0832 \| 0.0013 \| 0.0179 \| 0.9983 \| 0.0801 \| 0.0013 \| 9 \|
	\| 0.0100 \| 0.0094 \| 0.3172 \| 0.3225 \| 0.9983 \| 0.0527 \| 1.0000 \| 0.2344 \| 0.0030 \| 0.0521 \| 0.9983 \| 0.2154 \| 0.0030 \| 10 \|
	\| 0.0091 \| 0.0087 \| 0.3117 \| 0.3138 \| 0.9983 \| 0.1026 \| 1.0000 \| 0.1470 \| 0.0055 \| 0.1014 \| 0.9983 \| 0.1415 \| 0.0055 \| 11 \|
	\| 0.0083 \| 0.0076 \| 0.3844 \| 0.3941 \| 0.9983 \| 0.1540 \| 1.0000 \| 0.4189 \| 0.0037 \| 0.1516 \| 0.9983 \| 0.3840 \| 0.0037 \| 12 \|
	\| 0.0073 \| 0.0070 \| 0.4402 \| 0.4781 \| 0.9983 \| 0.1997 \| 0.9999 \| 0.6616 \| 0.0511 \| 0.1978 \| 0.9983 \| 0.5167 \| 0.0479 \| 13 \|
	\| 0.0067 \| 0.0062 \| 0.5049 \| 0.5454 \| 0.9983 \| 0.4868 \| 1.0000 \| 0.6893 \| 0.0057 \| 0.4702 \| 0.9983 \| 0.5456 \| 0.0056 \| 14 \|
	\| 0.0060 \| 0.0056 \| 0.5133 \| 0.5326 \| 0.9983 \| 0.4799 \| 1.0000 \| 0.6283 \| 0.0221 \| 0.4722 \| 0.9983 \| 0.5608 \| 0.0218 \| 15 \|
	\| 0.0055 \| 0.0056 \| 0.5975 \| 0.6504 \| 0.9984 \| 0.7018 \| 0.9999 \| 0.8058 \| 0.0942 \| 0.6731 \| 0.9984 \| 0.6317 \| 0.0868 \| 16 \|
	\| 0.0051 \| 0.0050 \| 0.5800 \| 0.6003 \| 0.9984 \| 0.6893 \| 0.9999 \| 0.6644 \| 0.0476 \| 0.6707 \| 0.9984 \| 0.6048 \| 0.0462 \| 17 \|
	\| 0.0048 \| 0.0047 \| 0.6598 \| 0.6878 \| 0.9984 \| 0.8252 \| 0.9998 \| 0.7809 \| 0.1453 \| 0.8013 \| 0.9984 \| 0.7095 \| 0.1298 \| 18 \|
	\| 0.0045 \| 0.0045 \| 0.6596 \| 0.7122 \| 0.9984 \| 0.9057 \| 0.9998 \| 0.7739 \| 0.1693 \| 0.8117 \| 0.9984 \| 0.6774 \| 0.1508 \| 19 \|
	\| 0.0042 \| 0.0042 \| 0.6956 \| 0.7259 \| 0.9985 \| 0.8669 \| 0.9998 \| 0.8294 \| 0.2074 \| 0.8609 \| 0.9985 \| 0.7396 \| 0.1835 \| 20 \|
	\| 0.0039 \| 0.0045 \| 0.6824 \| 0.6971 \| 0.9985 \| 0.8710 \| 0.9999 \| 0.7933 \| 0.1243 \| 0.8579 \| 0.9985 \| 0.7546 \| 0.1186 \| 21 \|
	\| 0.0038 \| 0.0041 \| 0.7516 \| 0.7748 \| 0.9985 \| 0.9612 \| 0.9998 \| 0.8779 \| 0.2605 \| 0.9397 \| 0.9985 \| 0.8395 \| 0.2287 \| 22 \|
	\| 0.0035 \| 0.0038 \| 0.7683 \| 0.8089 \| 0.9986 \| 0.9667 \| 0.9997 \| 0.9334 \| 0.3359 \| 0.9356 \| 0.9986 \| 0.8546 \| 0.2843 \| 23 \|
	\| 0.0034 \| 0.0037 \| 0.7905 \| 0.8212 \| 0.9986 \| 0.9639 \| 0.9996 \| 0.9182 \| 0.4031 \| 0.9508 \| 0.9986 \| 0.8856 \| 0.3270 \| 24 \|
	\| 0.0032 \| 0.0037 \| 0.7938 \| 0.8154 \| 0.9987 \| 0.9515 \| 0.9997 \| 0.9112 \| 0.3992 \| 0.9469 \| 0.9987 \| 0.8939 \| 0.3358 \| 25 \|
	\| 0.0031 \| 0.0036 \| 0.8250 \| 0.8524 \| 0.9988 \| 0.9806 \| 0.9996 \| 0.9431 \| 0.4864 \| 0.9772 \| 0.9988 \| 0.9315 \| 0.3924 \| 26 \|
	\| 0.0030 \| 0.0035 \| 0.8204 \| 0.8392 \| 0.9987 \| 0.9736 \| 0.9997 \| 0.9820 \| 0.4015 \| 0.9736 \| 0.9987 \| 0.9659 \| 0.3435 \| 27 \|
	\| 0.0029 \| 0.0033 \| 0.8431 \| 0.8689 \| 0.9988 \| 0.9917 \| 0.9997 \| 0.9834 \| 0.5011 \| 0.9910 \| 0.9988 \| 0.9653 \| 0.4172 \| 28 \|
	\| 0.0027 \| 0.0034 \| 0.8337 \| 0.8539 \| 0.9988 \| 0.9945 \| 0.9997 \| 0.9875 \| 0.4341 \| 0.9945 \| 0.9988 \| 0.9648 \| 0.3767 \| 29 \|
	\| 0.0027 \| 0.0033 \| 0.8518 \| 0.8834 \| 0.9988 \| 1.0 \| 0.9996 \| 0.9931 \| 0.5411 \| 0.9972 \| 0.9988 \| 0.9802 \| 0.4311 \| 30 \|
	\| 0.0025 \| 0.0031 \| 0.8622 \| 0.8848 \| 0.9990 \| 0.9986 \| 0.9997 \| 0.9958 \| 0.5449 \| 0.9986 \| 0.9990 \| 0.9890 \| 0.4622 \| 31 \|
	\| 0.0025 \| 0.0031 \| 0.8667 \| 0.8975 \| 0.9989 \| 0.9986 \| 0.9996 \| 0.9986 \| 0.5933 \| 0.9979 \| 0.9989 \| 0.9897 \| 0.4801 \| 32 \|
	\| 0.0024 \| 0.0031 \| 0.8607 \| 0.8791 \| 0.9990 \| 1.0 \| 0.9998 \| 0.9986 \| 0.5181 \| 1.0 \| 0.9990 \| 0.9877 \| 0.4563 \| 33 \|
	\| 0.0022 \| 0.0031 \| 0.8783 \| 0.9102 \| 0.9990 \| 1.0 \| 0.9996 \| 0.9917 \| 0.6497 \| 1.0 \| 0.9990 \| 0.9876 \| 0.5266 \| 34 \|
	\| 0.0022 \| 0.0029 \| 0.8780 \| 0.9016 \| 0.9991 \| 1.0 \| 0.9997 \| 0.9931 \| 0.6138 \| 1.0 \| 0.9991 \| 0.9903 \| 0.5225 \| 35 \|
	\| 0.0022 \| 0.0030 \| 0.8853 \| 0.9238 \| 0.9991 \| 1.0 \| 0.9996 \| 0.9986 \| 0.6969 \| 0.9972 \| 0.9991 \| 0.9965 \| 0.5484 \| 36 \|
	\| 0.0021 \| 0.0030 \| 0.8859 \| 0.9156 \| 0.9991 \| 1.0 \| 0.9996 \| 0.9986 \| 0.6641 \| 1.0 \| 0.9991 \| 0.9979 \| 0.5466 \| 37 \|
	\| 0.0021 \| 0.0029 \| 0.8865 \| 0.9165 \| 0.9991 \| 1.0 \| 0.9997 \| 1.0 \| 0.6662 \| 0.9993 \| 0.9991 \| 0.9952 \| 0.5526 \| 38 \|
	\| 0.0021 \| 0.0028 \| 0.8932 \| 0.9227 \| 0.9992 \| 1.0 \| 0.9997 \| 0.9986 \| 0.6926 \| 1.0 \| 0.9992 \| 0.9965 \| 0.5771 \| 39 \|
	\| 0.0020 \| 0.0029 \| 0.8929 \| 0.9235 \| 0.9992 \| 1.0 \| 0.9997 \| 0.9986 \| 0.6959 \| 1.0 \| 0.9992 \| 0.9979 \| 0.5747 \| 40 \|
	\| 0.0020 \| 0.0028 \| 0.8935 \| 0.9252 \| 0.9992 \| 1.0 \| 0.9996 \| 0.9986 \| 0.7026 \| 1.0 \| 0.9991 \| 0.9979 \| 0.5767 \| 41 \|
	\| 0.0019 \| 0.0028 \| 0.8934 \| 0.9144 \| 0.9992 \| 1.0 \| 0.9998 \| 0.9986 \| 0.6590 \| 1.0 \| 0.9992 \| 0.9986 \| 0.5758 \| 42 \|
	\| 0.0019 \| 0.0028 \| 0.8945 \| 0.9174 \| 0.9992 \| 1.0 \| 0.9997 \| 0.9986 \| 0.6713 \| 1.0 \| 0.9992 \| 0.9979 \| 0.5807 \| 43 \|
	\| 0.0018 \| 0.0027 \| 0.9009 \| 0.9327 \| 0.9992 \| 1.0 \| 0.9997 \| 0.9986 \| 0.7324 \| 1.0 \| 0.9992 \| 0.9979 \| 0.6065 \| 44 \|
	\| 0.0019 \| 0.0027 \| 0.9028 \| 0.9385 \| 0.9992 \| 1.0 \| 0.9996 \| 1.0 \| 0.7543 \| 1.0 \| 0.9992 \| 0.9993 \| 0.6125 \| 45 \|
	\| 0.0017 \| 0.0028 \| 0.9048 \| 0.9362 \| 0.9992 \| 1.0 \| 0.9997 \| 1.0 \| 0.7453 \| 1.0 \| 0.9992 \| 1.0 \| 0.6199 \| 46 \|
	\| 0.0017 \| 0.0029 \| 0.9043 \| 0.9362 \| 0.9992 \| 1.0 \| 0.9997 \| 0.9986 \| 0.7466 \| 1.0 \| 0.9992 \| 0.9979 \| 0.6202 \| 47 \|
	\| 0.0017 \| 0.0027 \| 0.9060 \| 0.9365 \| 0.9993 \| 1.0 \| 0.9997 \| 0.9986 \| 0.7477 \| 1.0 \| 0.9993 \| 0.9979 \| 0.6270 \| 48 \|
	\| 0.0016 \| 0.0026 \| 0.9068 \| 0.9377 \| 0.9993 \| 1.0 \| 0.9997 \| 1.0 \| 0.7513 \| 1.0 \| 0.9993 \| 0.9993 \| 0.6286 \| 49 \|


	### Framework versions

	- Transformers 4.35.2
	- TensorFlow 2.15.0
	- Datasets 2.15.0
	- Tokenizers 0.15.0