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	---
	tags:
	- generated_from_keras_callback
	model-index:
	- name: amanneo/mail-generator-mini-v2
	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. -->

	# amanneo/mail-generator-mini-v2

	This model was trained from scratch on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Train Loss: 0.5212
	- Train Accuracy: 0.0027
	- Validation Loss: 5.5781
	- Validation Accuracy: 0.0
	- Epoch: 99

	## 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -994, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
	- training_precision: mixed_float16

	### Training results

	\| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \|
	\|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\|
	\| 2.5928 \| 0.0171 \| 5.5430 \| 0.0048 \| 0 \|
	\| 2.6003 \| 0.0207 \| 5.5430 \| 0.0048 \| 1 \|
	\| 2.5954 \| 0.0171 \| 5.5508 \| 0.0048 \| 2 \|
	\| 2.5775 \| 0.0190 \| 5.5508 \| 0.0024 \| 3 \|
	\| 2.5758 \| 0.0231 \| 5.5508 \| 0.0024 \| 4 \|
	\| 2.5742 \| 0.0207 \| 5.5586 \| 0.0048 \| 5 \|
	\| 2.5547 \| 0.0209 \| 5.5586 \| 0.0048 \| 6 \|
	\| 2.5566 \| 0.0188 \| 5.5586 \| 0.0048 \| 7 \|
	\| 2.5391 \| 0.0193 \| 5.5586 \| 0.0048 \| 8 \|
	\| 2.5378 \| 0.0215 \| 5.5508 \| 0.0048 \| 9 \|
	\| 2.5238 \| 0.0188 \| 5.5469 \| 0.0048 \| 10 \|
	\| 2.5150 \| 0.0160 \| 5.5508 \| 0.0048 \| 11 \|
	\| 2.4967 \| 0.0174 \| 5.5508 \| 0.0071 \| 12 \|
	\| 2.4691 \| 0.0193 \| 5.5430 \| 0.0071 \| 13 \|
	\| 2.4626 \| 0.0163 \| 5.5430 \| 0.0071 \| 14 \|
	\| 2.4417 \| 0.0231 \| 5.5352 \| 0.0048 \| 15 \|
	\| 2.4323 \| 0.0215 \| 5.5352 \| 0.0048 \| 16 \|
	\| 2.4193 \| 0.0226 \| 5.5469 \| 0.0048 \| 17 \|
	\| 2.4170 \| 0.0185 \| 5.5469 \| 0.0048 \| 18 \|
	\| 2.3743 \| 0.0193 \| 5.5312 \| 0.0048 \| 19 \|
	\| 2.3730 \| 0.0207 \| 5.5312 \| 0.0048 \| 20 \|
	\| 2.3535 \| 0.0198 \| 5.5312 \| 0.0048 \| 21 \|
	\| 2.3372 \| 0.0182 \| 5.5312 \| 0.0071 \| 22 \|
	\| 2.3324 \| 0.0177 \| 5.5312 \| 0.0048 \| 23 \|
	\| 2.3011 \| 0.0204 \| 5.5195 \| 0.0048 \| 24 \|
	\| 2.2650 \| 0.0212 \| 5.5117 \| 0.0048 \| 25 \|
	\| 2.2568 \| 0.0198 \| 5.5078 \| 0.0048 \| 26 \|
	\| 2.2331 \| 0.0196 \| 5.5156 \| 0.0048 \| 27 \|
	\| 2.2021 \| 0.0193 \| 5.5078 \| 0.0048 \| 28 \|
	\| 2.1807 \| 0.0204 \| 5.5039 \| 0.0048 \| 29 \|
	\| 2.1691 \| 0.0190 \| 5.5 \| 0.0 \| 30 \|
	\| 2.1463 \| 0.0174 \| 5.4766 \| 0.0 \| 31 \|
	\| 2.1097 \| 0.0196 \| 5.4844 \| 0.0 \| 32 \|
	\| 2.1014 \| 0.0179 \| 5.4844 \| 0.0024 \| 33 \|
	\| 2.0833 \| 0.0177 \| 5.4844 \| 0.0024 \| 34 \|
	\| 2.0423 \| 0.0201 \| 5.4844 \| 0.0 \| 35 \|
	\| 2.0163 \| 0.0198 \| 5.4844 \| 0.0 \| 36 \|
	\| 1.9909 \| 0.0168 \| 5.4883 \| 0.0 \| 37 \|
	\| 1.9774 \| 0.0207 \| 5.4805 \| 0.0 \| 38 \|
	\| 1.9414 \| 0.0207 \| 5.4844 \| 0.0 \| 39 \|
	\| 1.9206 \| 0.0215 \| 5.4766 \| 0.0 \| 40 \|
	\| 1.8849 \| 0.0182 \| 5.4805 \| 0.0 \| 41 \|
	\| 1.8732 \| 0.0193 \| 5.4648 \| 0.0 \| 42 \|
	\| 1.8460 \| 0.0160 \| 5.4609 \| 0.0 \| 43 \|
	\| 1.8171 \| 0.0168 \| 5.4648 \| 0.0 \| 44 \|
	\| 1.7791 \| 0.0201 \| 5.4531 \| 0.0 \| 45 \|
	\| 1.7583 \| 0.0158 \| 5.4570 \| 0.0 \| 46 \|
	\| 1.7360 \| 0.0171 \| 5.4570 \| 0.0 \| 47 \|
	\| 1.7061 \| 0.0120 \| 5.4297 \| 0.0 \| 48 \|
	\| 1.6802 \| 0.0155 \| 5.4258 \| 0.0 \| 49 \|
	\| 1.6551 \| 0.0182 \| 5.4141 \| 0.0 \| 50 \|
	\| 1.6289 \| 0.0130 \| 5.4219 \| 0.0 \| 51 \|
	\| 1.5981 \| 0.0130 \| 5.3945 \| 0.0 \| 52 \|
	\| 1.5656 \| 0.0128 \| 5.4297 \| 0.0 \| 53 \|
	\| 1.5535 \| 0.0168 \| 5.4219 \| 0.0 \| 54 \|
	\| 1.5184 \| 0.0141 \| 5.4102 \| 0.0 \| 55 \|
	\| 1.4943 \| 0.0149 \| 5.4023 \| 0.0 \| 56 \|
	\| 1.4616 \| 0.0122 \| 5.4062 \| 0.0 \| 57 \|
	\| 1.4344 \| 0.0111 \| 5.4062 \| 0.0 \| 58 \|
	\| 1.3965 \| 0.0111 \| 5.4141 \| 0.0 \| 59 \|
	\| 1.3643 \| 0.0122 \| 5.4375 \| 0.0 \| 60 \|
	\| 1.3309 \| 0.0087 \| 5.4453 \| 0.0 \| 61 \|
	\| 1.3215 \| 0.0090 \| 5.4648 \| 0.0 \| 62 \|
	\| 1.3058 \| 0.0084 \| 5.4727 \| 0.0 \| 63 \|
	\| 1.2700 \| 0.0109 \| 5.4453 \| 0.0 \| 64 \|
	\| 1.2396 \| 0.0079 \| 5.4609 \| 0.0 \| 65 \|
	\| 1.2189 \| 0.0092 \| 5.4375 \| 0.0 \| 66 \|
	\| 1.1855 \| 0.0079 \| 5.4375 \| 0.0 \| 67 \|
	\| 1.1592 \| 0.0073 \| 5.4375 \| 0.0 \| 68 \|
	\| 1.1219 \| 0.0071 \| 5.4648 \| 0.0 \| 69 \|
	\| 1.1071 \| 0.0065 \| 5.4570 \| 0.0 \| 70 \|
	\| 1.0848 \| 0.0060 \| 5.4375 \| 0.0 \| 71 \|
	\| 1.0581 \| 0.0076 \| 5.4453 \| 0.0 \| 72 \|
	\| 1.0316 \| 0.0090 \| 5.4570 \| 0.0 \| 73 \|
	\| 1.0068 \| 0.0063 \| 5.4219 \| 0.0 \| 74 \|
	\| 0.9832 \| 0.0060 \| 5.4570 \| 0.0 \| 75 \|
	\| 0.9534 \| 0.0046 \| 5.4570 \| 0.0 \| 76 \|
	\| 0.9378 \| 0.0057 \| 5.4648 \| 0.0 \| 77 \|
	\| 0.9170 \| 0.0033 \| 5.4844 \| 0.0 \| 78 \|
	\| 0.8941 \| 0.0041 \| 5.4883 \| 0.0 \| 79 \|
	\| 0.8666 \| 0.0030 \| 5.4922 \| 0.0 \| 80 \|
	\| 0.8419 \| 0.0054 \| 5.4375 \| 0.0 \| 81 \|
	\| 0.8200 \| 0.0035 \| 5.4492 \| 0.0 \| 82 \|
	\| 0.8020 \| 0.0022 \| 5.4648 \| 0.0 \| 83 \|
	\| 0.7785 \| 0.0057 \| 5.4883 \| 0.0 \| 84 \|
	\| 0.7607 \| 0.0052 \| 5.4648 \| 0.0 \| 85 \|
	\| 0.7454 \| 0.0041 \| 5.5078 \| 0.0 \| 86 \|
	\| 0.7208 \| 0.0024 \| 5.5078 \| 0.0 \| 87 \|
	\| 0.7040 \| 0.0027 \| 5.5078 \| 0.0 \| 88 \|
	\| 0.6799 \| 0.0041 \| 5.5156 \| 0.0 \| 89 \|
	\| 0.6594 \| 0.0030 \| 5.5312 \| 0.0 \| 90 \|
	\| 0.6397 \| 0.0030 \| 5.5312 \| 0.0 \| 91 \|
	\| 0.6217 \| 0.0030 \| 5.5195 \| 0.0 \| 92 \|
	\| 0.6112 \| 0.0033 \| 5.5195 \| 0.0 \| 93 \|
	\| 0.5937 \| 0.0046 \| 5.5625 \| 0.0 \| 94 \|
	\| 0.5745 \| 0.0035 \| 5.5625 \| 0.0 \| 95 \|
	\| 0.5616 \| 0.0027 \| 5.5586 \| 0.0 \| 96 \|
	\| 0.5468 \| 0.0043 \| 5.5742 \| 0.0 \| 97 \|
	\| 0.5354 \| 0.0027 \| 5.5781 \| 0.0 \| 98 \|
	\| 0.5212 \| 0.0027 \| 5.5781 \| 0.0 \| 99 \|


	### Framework versions

	- Transformers 4.23.1
	- TensorFlow 2.9.2
	- Datasets 2.6.1
	- Tokenizers 0.13.1

	---
	tags:
	- generated_from_keras_callback
	model-index:
	- name: amanneo/mail-generator-mini-v2
	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. -->

	# amanneo/mail-generator-mini-v2

	This model was trained from scratch on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Train Loss: 0.5212
	- Train Accuracy: 0.0027
	- Validation Loss: 5.5781
	- Validation Accuracy: 0.0
	- Epoch: 99

	## 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -994, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
	- training_precision: mixed_float16

	### Training results

	\| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \|
	\|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\|
	\| 2.5928 \| 0.0171 \| 5.5430 \| 0.0048 \| 0 \|
	\| 2.6003 \| 0.0207 \| 5.5430 \| 0.0048 \| 1 \|
	\| 2.5954 \| 0.0171 \| 5.5508 \| 0.0048 \| 2 \|
	\| 2.5775 \| 0.0190 \| 5.5508 \| 0.0024 \| 3 \|
	\| 2.5758 \| 0.0231 \| 5.5508 \| 0.0024 \| 4 \|
	\| 2.5742 \| 0.0207 \| 5.5586 \| 0.0048 \| 5 \|
	\| 2.5547 \| 0.0209 \| 5.5586 \| 0.0048 \| 6 \|
	\| 2.5566 \| 0.0188 \| 5.5586 \| 0.0048 \| 7 \|
	\| 2.5391 \| 0.0193 \| 5.5586 \| 0.0048 \| 8 \|
	\| 2.5378 \| 0.0215 \| 5.5508 \| 0.0048 \| 9 \|
	\| 2.5238 \| 0.0188 \| 5.5469 \| 0.0048 \| 10 \|
	\| 2.5150 \| 0.0160 \| 5.5508 \| 0.0048 \| 11 \|
	\| 2.4967 \| 0.0174 \| 5.5508 \| 0.0071 \| 12 \|
	\| 2.4691 \| 0.0193 \| 5.5430 \| 0.0071 \| 13 \|
	\| 2.4626 \| 0.0163 \| 5.5430 \| 0.0071 \| 14 \|
	\| 2.4417 \| 0.0231 \| 5.5352 \| 0.0048 \| 15 \|
	\| 2.4323 \| 0.0215 \| 5.5352 \| 0.0048 \| 16 \|
	\| 2.4193 \| 0.0226 \| 5.5469 \| 0.0048 \| 17 \|
	\| 2.4170 \| 0.0185 \| 5.5469 \| 0.0048 \| 18 \|
	\| 2.3743 \| 0.0193 \| 5.5312 \| 0.0048 \| 19 \|
	\| 2.3730 \| 0.0207 \| 5.5312 \| 0.0048 \| 20 \|
	\| 2.3535 \| 0.0198 \| 5.5312 \| 0.0048 \| 21 \|
	\| 2.3372 \| 0.0182 \| 5.5312 \| 0.0071 \| 22 \|
	\| 2.3324 \| 0.0177 \| 5.5312 \| 0.0048 \| 23 \|
	\| 2.3011 \| 0.0204 \| 5.5195 \| 0.0048 \| 24 \|
	\| 2.2650 \| 0.0212 \| 5.5117 \| 0.0048 \| 25 \|
	\| 2.2568 \| 0.0198 \| 5.5078 \| 0.0048 \| 26 \|
	\| 2.2331 \| 0.0196 \| 5.5156 \| 0.0048 \| 27 \|
	\| 2.2021 \| 0.0193 \| 5.5078 \| 0.0048 \| 28 \|
	\| 2.1807 \| 0.0204 \| 5.5039 \| 0.0048 \| 29 \|
	\| 2.1691 \| 0.0190 \| 5.5 \| 0.0 \| 30 \|
	\| 2.1463 \| 0.0174 \| 5.4766 \| 0.0 \| 31 \|
	\| 2.1097 \| 0.0196 \| 5.4844 \| 0.0 \| 32 \|
	\| 2.1014 \| 0.0179 \| 5.4844 \| 0.0024 \| 33 \|
	\| 2.0833 \| 0.0177 \| 5.4844 \| 0.0024 \| 34 \|
	\| 2.0423 \| 0.0201 \| 5.4844 \| 0.0 \| 35 \|
	\| 2.0163 \| 0.0198 \| 5.4844 \| 0.0 \| 36 \|
	\| 1.9909 \| 0.0168 \| 5.4883 \| 0.0 \| 37 \|
	\| 1.9774 \| 0.0207 \| 5.4805 \| 0.0 \| 38 \|
	\| 1.9414 \| 0.0207 \| 5.4844 \| 0.0 \| 39 \|
	\| 1.9206 \| 0.0215 \| 5.4766 \| 0.0 \| 40 \|
	\| 1.8849 \| 0.0182 \| 5.4805 \| 0.0 \| 41 \|
	\| 1.8732 \| 0.0193 \| 5.4648 \| 0.0 \| 42 \|
	\| 1.8460 \| 0.0160 \| 5.4609 \| 0.0 \| 43 \|
	\| 1.8171 \| 0.0168 \| 5.4648 \| 0.0 \| 44 \|
	\| 1.7791 \| 0.0201 \| 5.4531 \| 0.0 \| 45 \|
	\| 1.7583 \| 0.0158 \| 5.4570 \| 0.0 \| 46 \|
	\| 1.7360 \| 0.0171 \| 5.4570 \| 0.0 \| 47 \|
	\| 1.7061 \| 0.0120 \| 5.4297 \| 0.0 \| 48 \|
	\| 1.6802 \| 0.0155 \| 5.4258 \| 0.0 \| 49 \|
	\| 1.6551 \| 0.0182 \| 5.4141 \| 0.0 \| 50 \|
	\| 1.6289 \| 0.0130 \| 5.4219 \| 0.0 \| 51 \|
	\| 1.5981 \| 0.0130 \| 5.3945 \| 0.0 \| 52 \|
	\| 1.5656 \| 0.0128 \| 5.4297 \| 0.0 \| 53 \|
	\| 1.5535 \| 0.0168 \| 5.4219 \| 0.0 \| 54 \|
	\| 1.5184 \| 0.0141 \| 5.4102 \| 0.0 \| 55 \|
	\| 1.4943 \| 0.0149 \| 5.4023 \| 0.0 \| 56 \|
	\| 1.4616 \| 0.0122 \| 5.4062 \| 0.0 \| 57 \|
	\| 1.4344 \| 0.0111 \| 5.4062 \| 0.0 \| 58 \|
	\| 1.3965 \| 0.0111 \| 5.4141 \| 0.0 \| 59 \|
	\| 1.3643 \| 0.0122 \| 5.4375 \| 0.0 \| 60 \|
	\| 1.3309 \| 0.0087 \| 5.4453 \| 0.0 \| 61 \|
	\| 1.3215 \| 0.0090 \| 5.4648 \| 0.0 \| 62 \|
	\| 1.3058 \| 0.0084 \| 5.4727 \| 0.0 \| 63 \|
	\| 1.2700 \| 0.0109 \| 5.4453 \| 0.0 \| 64 \|
	\| 1.2396 \| 0.0079 \| 5.4609 \| 0.0 \| 65 \|
	\| 1.2189 \| 0.0092 \| 5.4375 \| 0.0 \| 66 \|
	\| 1.1855 \| 0.0079 \| 5.4375 \| 0.0 \| 67 \|
	\| 1.1592 \| 0.0073 \| 5.4375 \| 0.0 \| 68 \|
	\| 1.1219 \| 0.0071 \| 5.4648 \| 0.0 \| 69 \|
	\| 1.1071 \| 0.0065 \| 5.4570 \| 0.0 \| 70 \|
	\| 1.0848 \| 0.0060 \| 5.4375 \| 0.0 \| 71 \|
	\| 1.0581 \| 0.0076 \| 5.4453 \| 0.0 \| 72 \|
	\| 1.0316 \| 0.0090 \| 5.4570 \| 0.0 \| 73 \|
	\| 1.0068 \| 0.0063 \| 5.4219 \| 0.0 \| 74 \|
	\| 0.9832 \| 0.0060 \| 5.4570 \| 0.0 \| 75 \|
	\| 0.9534 \| 0.0046 \| 5.4570 \| 0.0 \| 76 \|
	\| 0.9378 \| 0.0057 \| 5.4648 \| 0.0 \| 77 \|
	\| 0.9170 \| 0.0033 \| 5.4844 \| 0.0 \| 78 \|
	\| 0.8941 \| 0.0041 \| 5.4883 \| 0.0 \| 79 \|
	\| 0.8666 \| 0.0030 \| 5.4922 \| 0.0 \| 80 \|
	\| 0.8419 \| 0.0054 \| 5.4375 \| 0.0 \| 81 \|
	\| 0.8200 \| 0.0035 \| 5.4492 \| 0.0 \| 82 \|
	\| 0.8020 \| 0.0022 \| 5.4648 \| 0.0 \| 83 \|
	\| 0.7785 \| 0.0057 \| 5.4883 \| 0.0 \| 84 \|
	\| 0.7607 \| 0.0052 \| 5.4648 \| 0.0 \| 85 \|
	\| 0.7454 \| 0.0041 \| 5.5078 \| 0.0 \| 86 \|
	\| 0.7208 \| 0.0024 \| 5.5078 \| 0.0 \| 87 \|
	\| 0.7040 \| 0.0027 \| 5.5078 \| 0.0 \| 88 \|
	\| 0.6799 \| 0.0041 \| 5.5156 \| 0.0 \| 89 \|
	\| 0.6594 \| 0.0030 \| 5.5312 \| 0.0 \| 90 \|
	\| 0.6397 \| 0.0030 \| 5.5312 \| 0.0 \| 91 \|
	\| 0.6217 \| 0.0030 \| 5.5195 \| 0.0 \| 92 \|
	\| 0.6112 \| 0.0033 \| 5.5195 \| 0.0 \| 93 \|
	\| 0.5937 \| 0.0046 \| 5.5625 \| 0.0 \| 94 \|
	\| 0.5745 \| 0.0035 \| 5.5625 \| 0.0 \| 95 \|
	\| 0.5616 \| 0.0027 \| 5.5586 \| 0.0 \| 96 \|
	\| 0.5468 \| 0.0043 \| 5.5742 \| 0.0 \| 97 \|
	\| 0.5354 \| 0.0027 \| 5.5781 \| 0.0 \| 98 \|
	\| 0.5212 \| 0.0027 \| 5.5781 \| 0.0 \| 99 \|


	### Framework versions

	- Transformers 4.23.1
	- TensorFlow 2.9.2
	- Datasets 2.6.1
	- Tokenizers 0.13.1