LIM-0.75 / README.md

End of training

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	---
	license: apache-2.0
	base_model: google-bert/bert-base-uncased
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: 20240327211222_nice_straka
	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. -->

	# 20240327211222_nice_straka

	This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0208
	- Precision: 0.9848
	- Recall: 0.9853
	- F1: 0.9850
	- Accuracy: 0.9923

	## 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: 0.0005
	- train_batch_size: 32
	- eval_batch_size: 32
	- seed: 69
	- gradient_accumulation_steps: 8
	- total_train_batch_size: 256
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 350
	- num_epochs: 5

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 0.0519 \| 0.09 \| 300 \| 0.0367 \| 0.9736 \| 0.9691 \| 0.9713 \| 0.9856 \|
	\| 0.0518 \| 0.17 \| 600 \| 0.0379 \| 0.9717 \| 0.9709 \| 0.9713 \| 0.9855 \|
	\| 0.048 \| 0.26 \| 900 \| 0.0357 \| 0.9742 \| 0.9692 \| 0.9717 \| 0.9858 \|
	\| 0.0478 \| 0.34 \| 1200 \| 0.0350 \| 0.9736 \| 0.9724 \| 0.9730 \| 0.9863 \|
	\| 0.0495 \| 0.43 \| 1500 \| 0.0366 \| 0.9734 \| 0.9703 \| 0.9718 \| 0.9856 \|
	\| 0.0457 \| 0.51 \| 1800 \| 0.0344 \| 0.9719 \| 0.9749 \| 0.9734 \| 0.9863 \|
	\| 0.0464 \| 0.6 \| 2100 \| 0.0347 \| 0.9731 \| 0.9717 \| 0.9724 \| 0.9861 \|
	\| 0.0447 \| 0.68 \| 2400 \| 0.0329 \| 0.9743 \| 0.9739 \| 0.9741 \| 0.9868 \|
	\| 0.0435 \| 0.77 \| 2700 \| 0.0332 \| 0.9738 \| 0.9748 \| 0.9743 \| 0.9868 \|
	\| 0.0414 \| 0.85 \| 3000 \| 0.0324 \| 0.9729 \| 0.9771 \| 0.9750 \| 0.9871 \|
	\| 0.0412 \| 0.94 \| 3300 \| 0.0312 \| 0.9759 \| 0.9756 \| 0.9758 \| 0.9875 \|
	\| 0.0352 \| 1.02 \| 3600 \| 0.0312 \| 0.9749 \| 0.9760 \| 0.9754 \| 0.9875 \|
	\| 0.0353 \| 1.11 \| 3900 \| 0.0304 \| 0.9767 \| 0.9759 \| 0.9763 \| 0.9878 \|
	\| 0.0348 \| 1.19 \| 4200 \| 0.0305 \| 0.9765 \| 0.9748 \| 0.9757 \| 0.9877 \|
	\| 0.0362 \| 1.28 \| 4500 \| 0.0313 \| 0.9768 \| 0.9738 \| 0.9753 \| 0.9876 \|
	\| 0.0352 \| 1.36 \| 4800 \| 0.0304 \| 0.9764 \| 0.9771 \| 0.9767 \| 0.9880 \|
	\| 0.0344 \| 1.45 \| 5100 \| 0.0306 \| 0.9778 \| 0.9744 \| 0.9761 \| 0.9880 \|
	\| 0.0337 \| 1.54 \| 5400 \| 0.0288 \| 0.9779 \| 0.9769 \| 0.9774 \| 0.9886 \|
	\| 0.0328 \| 1.62 \| 5700 \| 0.0284 \| 0.9776 \| 0.9777 \| 0.9776 \| 0.9888 \|
	\| 0.0335 \| 1.71 \| 6000 \| 0.0277 \| 0.9783 \| 0.9779 \| 0.9781 \| 0.9887 \|
	\| 0.0329 \| 1.79 \| 6300 \| 0.0284 \| 0.9791 \| 0.9752 \| 0.9772 \| 0.9886 \|
	\| 0.0328 \| 1.88 \| 6600 \| 0.0292 \| 0.9764 \| 0.9773 \| 0.9768 \| 0.9882 \|
	\| 0.0316 \| 1.96 \| 6900 \| 0.0268 \| 0.9785 \| 0.9773 \| 0.9779 \| 0.9890 \|
	\| 0.0264 \| 2.05 \| 7200 \| 0.0272 \| 0.9776 \| 0.9803 \| 0.9789 \| 0.9892 \|
	\| 0.0269 \| 2.13 \| 7500 \| 0.0274 \| 0.9792 \| 0.9782 \| 0.9787 \| 0.9891 \|
	\| 0.027 \| 2.22 \| 7800 \| 0.0291 \| 0.9774 \| 0.9782 \| 0.9778 \| 0.9889 \|
	\| 0.0262 \| 2.3 \| 8100 \| 0.0249 \| 0.9809 \| 0.9807 \| 0.9808 \| 0.9902 \|
	\| 0.0258 \| 2.39 \| 8400 \| 0.0255 \| 0.9808 \| 0.9805 \| 0.9806 \| 0.9900 \|
	\| 0.0261 \| 2.47 \| 8700 \| 0.0251 \| 0.9808 \| 0.9800 \| 0.9804 \| 0.9900 \|
	\| 0.0251 \| 2.56 \| 9000 \| 0.0250 \| 0.9814 \| 0.9788 \| 0.9801 \| 0.9901 \|
	\| 0.0248 \| 2.64 \| 9300 \| 0.0248 \| 0.9813 \| 0.9791 \| 0.9802 \| 0.9901 \|
	\| 0.0246 \| 2.73 \| 9600 \| 0.0248 \| 0.9800 \| 0.9817 \| 0.9809 \| 0.9902 \|
	\| 0.0243 \| 2.82 \| 9900 \| 0.0239 \| 0.9793 \| 0.9819 \| 0.9806 \| 0.9900 \|
	\| 0.0241 \| 2.9 \| 10200 \| 0.0236 \| 0.9805 \| 0.9823 \| 0.9814 \| 0.9904 \|
	\| 0.0238 \| 2.99 \| 10500 \| 0.0231 \| 0.9822 \| 0.9799 \| 0.9811 \| 0.9907 \|
	\| 0.0187 \| 3.07 \| 10800 \| 0.0259 \| 0.9782 \| 0.9823 \| 0.9802 \| 0.9901 \|
	\| 0.0188 \| 3.16 \| 11100 \| 0.0231 \| 0.9821 \| 0.9827 \| 0.9824 \| 0.9909 \|
	\| 0.0189 \| 3.24 \| 11400 \| 0.0229 \| 0.9830 \| 0.9802 \| 0.9816 \| 0.9910 \|
	\| 0.0191 \| 3.33 \| 11700 \| 0.0220 \| 0.9815 \| 0.9827 \| 0.9821 \| 0.9910 \|
	\| 0.0187 \| 3.41 \| 12000 \| 0.0223 \| 0.9821 \| 0.9834 \| 0.9828 \| 0.9912 \|
	\| 0.018 \| 3.5 \| 12300 \| 0.0224 \| 0.9802 \| 0.9829 \| 0.9815 \| 0.9909 \|
	\| 0.0183 \| 3.58 \| 12600 \| 0.0217 \| 0.9823 \| 0.9831 \| 0.9827 \| 0.9911 \|
	\| 0.0176 \| 3.67 \| 12900 \| 0.0214 \| 0.9840 \| 0.9824 \| 0.9832 \| 0.9916 \|
	\| 0.0177 \| 3.75 \| 13200 \| 0.0211 \| 0.9837 \| 0.9834 \| 0.9835 \| 0.9916 \|
	\| 0.0173 \| 3.84 \| 13500 \| 0.0210 \| 0.9828 \| 0.9840 \| 0.9834 \| 0.9916 \|
	\| 0.017 \| 3.92 \| 13800 \| 0.0207 \| 0.9832 \| 0.9839 \| 0.9836 \| 0.9916 \|
	\| 0.0141 \| 4.01 \| 14100 \| 0.0213 \| 0.9844 \| 0.9838 \| 0.9841 \| 0.9919 \|
	\| 0.0129 \| 4.09 \| 14400 \| 0.0213 \| 0.9837 \| 0.9849 \| 0.9843 \| 0.9919 \|
	\| 0.013 \| 4.18 \| 14700 \| 0.0228 \| 0.9831 \| 0.9834 \| 0.9833 \| 0.9915 \|
	\| 0.0128 \| 4.27 \| 15000 \| 0.0210 \| 0.9844 \| 0.9846 \| 0.9845 \| 0.9920 \|
	\| 0.0126 \| 4.35 \| 15300 \| 0.0212 \| 0.9843 \| 0.9842 \| 0.9842 \| 0.9920 \|
	\| 0.0125 \| 4.44 \| 15600 \| 0.0214 \| 0.9845 \| 0.9844 \| 0.9844 \| 0.9920 \|
	\| 0.0121 \| 4.52 \| 15900 \| 0.0217 \| 0.9844 \| 0.9846 \| 0.9845 \| 0.9921 \|
	\| 0.012 \| 4.61 \| 16200 \| 0.0211 \| 0.9847 \| 0.9848 \| 0.9847 \| 0.9922 \|
	\| 0.0119 \| 4.69 \| 16500 \| 0.0209 \| 0.9845 \| 0.9852 \| 0.9848 \| 0.9922 \|
	\| 0.0116 \| 4.78 \| 16800 \| 0.0211 \| 0.9845 \| 0.9847 \| 0.9846 \| 0.9922 \|
	\| 0.0115 \| 4.86 \| 17100 \| 0.0210 \| 0.9850 \| 0.9844 \| 0.9847 \| 0.9923 \|
	\| 0.0115 \| 4.95 \| 17400 \| 0.0208 \| 0.9848 \| 0.9853 \| 0.9850 \| 0.9923 \|


	### Framework versions

	- Transformers 4.39.1
	- Pytorch 2.2.0a0+6a974be
	- Datasets 2.18.0
	- Tokenizers 0.15.2

	---
	license: apache-2.0
	base_model: google-bert/bert-base-uncased
	tags:
	- generated_from_trainer
	metrics:
	- precision
	- recall
	- f1
	- accuracy
	model-index:
	- name: 20240327211222_nice_straka
	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. -->

	# 20240327211222_nice_straka

	This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0208
	- Precision: 0.9848
	- Recall: 0.9853
	- F1: 0.9850
	- Accuracy: 0.9923

	## 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: 0.0005
	- train_batch_size: 32
	- eval_batch_size: 32
	- seed: 69
	- gradient_accumulation_steps: 8
	- total_train_batch_size: 256
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 350
	- num_epochs: 5

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|
	\| 0.0519 \| 0.09 \| 300 \| 0.0367 \| 0.9736 \| 0.9691 \| 0.9713 \| 0.9856 \|
	\| 0.0518 \| 0.17 \| 600 \| 0.0379 \| 0.9717 \| 0.9709 \| 0.9713 \| 0.9855 \|
	\| 0.048 \| 0.26 \| 900 \| 0.0357 \| 0.9742 \| 0.9692 \| 0.9717 \| 0.9858 \|
	\| 0.0478 \| 0.34 \| 1200 \| 0.0350 \| 0.9736 \| 0.9724 \| 0.9730 \| 0.9863 \|
	\| 0.0495 \| 0.43 \| 1500 \| 0.0366 \| 0.9734 \| 0.9703 \| 0.9718 \| 0.9856 \|
	\| 0.0457 \| 0.51 \| 1800 \| 0.0344 \| 0.9719 \| 0.9749 \| 0.9734 \| 0.9863 \|
	\| 0.0464 \| 0.6 \| 2100 \| 0.0347 \| 0.9731 \| 0.9717 \| 0.9724 \| 0.9861 \|
	\| 0.0447 \| 0.68 \| 2400 \| 0.0329 \| 0.9743 \| 0.9739 \| 0.9741 \| 0.9868 \|
	\| 0.0435 \| 0.77 \| 2700 \| 0.0332 \| 0.9738 \| 0.9748 \| 0.9743 \| 0.9868 \|
	\| 0.0414 \| 0.85 \| 3000 \| 0.0324 \| 0.9729 \| 0.9771 \| 0.9750 \| 0.9871 \|
	\| 0.0412 \| 0.94 \| 3300 \| 0.0312 \| 0.9759 \| 0.9756 \| 0.9758 \| 0.9875 \|
	\| 0.0352 \| 1.02 \| 3600 \| 0.0312 \| 0.9749 \| 0.9760 \| 0.9754 \| 0.9875 \|
	\| 0.0353 \| 1.11 \| 3900 \| 0.0304 \| 0.9767 \| 0.9759 \| 0.9763 \| 0.9878 \|
	\| 0.0348 \| 1.19 \| 4200 \| 0.0305 \| 0.9765 \| 0.9748 \| 0.9757 \| 0.9877 \|
	\| 0.0362 \| 1.28 \| 4500 \| 0.0313 \| 0.9768 \| 0.9738 \| 0.9753 \| 0.9876 \|
	\| 0.0352 \| 1.36 \| 4800 \| 0.0304 \| 0.9764 \| 0.9771 \| 0.9767 \| 0.9880 \|
	\| 0.0344 \| 1.45 \| 5100 \| 0.0306 \| 0.9778 \| 0.9744 \| 0.9761 \| 0.9880 \|
	\| 0.0337 \| 1.54 \| 5400 \| 0.0288 \| 0.9779 \| 0.9769 \| 0.9774 \| 0.9886 \|
	\| 0.0328 \| 1.62 \| 5700 \| 0.0284 \| 0.9776 \| 0.9777 \| 0.9776 \| 0.9888 \|
	\| 0.0335 \| 1.71 \| 6000 \| 0.0277 \| 0.9783 \| 0.9779 \| 0.9781 \| 0.9887 \|
	\| 0.0329 \| 1.79 \| 6300 \| 0.0284 \| 0.9791 \| 0.9752 \| 0.9772 \| 0.9886 \|
	\| 0.0328 \| 1.88 \| 6600 \| 0.0292 \| 0.9764 \| 0.9773 \| 0.9768 \| 0.9882 \|
	\| 0.0316 \| 1.96 \| 6900 \| 0.0268 \| 0.9785 \| 0.9773 \| 0.9779 \| 0.9890 \|
	\| 0.0264 \| 2.05 \| 7200 \| 0.0272 \| 0.9776 \| 0.9803 \| 0.9789 \| 0.9892 \|
	\| 0.0269 \| 2.13 \| 7500 \| 0.0274 \| 0.9792 \| 0.9782 \| 0.9787 \| 0.9891 \|
	\| 0.027 \| 2.22 \| 7800 \| 0.0291 \| 0.9774 \| 0.9782 \| 0.9778 \| 0.9889 \|
	\| 0.0262 \| 2.3 \| 8100 \| 0.0249 \| 0.9809 \| 0.9807 \| 0.9808 \| 0.9902 \|
	\| 0.0258 \| 2.39 \| 8400 \| 0.0255 \| 0.9808 \| 0.9805 \| 0.9806 \| 0.9900 \|
	\| 0.0261 \| 2.47 \| 8700 \| 0.0251 \| 0.9808 \| 0.9800 \| 0.9804 \| 0.9900 \|
	\| 0.0251 \| 2.56 \| 9000 \| 0.0250 \| 0.9814 \| 0.9788 \| 0.9801 \| 0.9901 \|
	\| 0.0248 \| 2.64 \| 9300 \| 0.0248 \| 0.9813 \| 0.9791 \| 0.9802 \| 0.9901 \|
	\| 0.0246 \| 2.73 \| 9600 \| 0.0248 \| 0.9800 \| 0.9817 \| 0.9809 \| 0.9902 \|
	\| 0.0243 \| 2.82 \| 9900 \| 0.0239 \| 0.9793 \| 0.9819 \| 0.9806 \| 0.9900 \|
	\| 0.0241 \| 2.9 \| 10200 \| 0.0236 \| 0.9805 \| 0.9823 \| 0.9814 \| 0.9904 \|
	\| 0.0238 \| 2.99 \| 10500 \| 0.0231 \| 0.9822 \| 0.9799 \| 0.9811 \| 0.9907 \|
	\| 0.0187 \| 3.07 \| 10800 \| 0.0259 \| 0.9782 \| 0.9823 \| 0.9802 \| 0.9901 \|
	\| 0.0188 \| 3.16 \| 11100 \| 0.0231 \| 0.9821 \| 0.9827 \| 0.9824 \| 0.9909 \|
	\| 0.0189 \| 3.24 \| 11400 \| 0.0229 \| 0.9830 \| 0.9802 \| 0.9816 \| 0.9910 \|
	\| 0.0191 \| 3.33 \| 11700 \| 0.0220 \| 0.9815 \| 0.9827 \| 0.9821 \| 0.9910 \|
	\| 0.0187 \| 3.41 \| 12000 \| 0.0223 \| 0.9821 \| 0.9834 \| 0.9828 \| 0.9912 \|
	\| 0.018 \| 3.5 \| 12300 \| 0.0224 \| 0.9802 \| 0.9829 \| 0.9815 \| 0.9909 \|
	\| 0.0183 \| 3.58 \| 12600 \| 0.0217 \| 0.9823 \| 0.9831 \| 0.9827 \| 0.9911 \|
	\| 0.0176 \| 3.67 \| 12900 \| 0.0214 \| 0.9840 \| 0.9824 \| 0.9832 \| 0.9916 \|
	\| 0.0177 \| 3.75 \| 13200 \| 0.0211 \| 0.9837 \| 0.9834 \| 0.9835 \| 0.9916 \|
	\| 0.0173 \| 3.84 \| 13500 \| 0.0210 \| 0.9828 \| 0.9840 \| 0.9834 \| 0.9916 \|
	\| 0.017 \| 3.92 \| 13800 \| 0.0207 \| 0.9832 \| 0.9839 \| 0.9836 \| 0.9916 \|
	\| 0.0141 \| 4.01 \| 14100 \| 0.0213 \| 0.9844 \| 0.9838 \| 0.9841 \| 0.9919 \|
	\| 0.0129 \| 4.09 \| 14400 \| 0.0213 \| 0.9837 \| 0.9849 \| 0.9843 \| 0.9919 \|
	\| 0.013 \| 4.18 \| 14700 \| 0.0228 \| 0.9831 \| 0.9834 \| 0.9833 \| 0.9915 \|
	\| 0.0128 \| 4.27 \| 15000 \| 0.0210 \| 0.9844 \| 0.9846 \| 0.9845 \| 0.9920 \|
	\| 0.0126 \| 4.35 \| 15300 \| 0.0212 \| 0.9843 \| 0.9842 \| 0.9842 \| 0.9920 \|
	\| 0.0125 \| 4.44 \| 15600 \| 0.0214 \| 0.9845 \| 0.9844 \| 0.9844 \| 0.9920 \|
	\| 0.0121 \| 4.52 \| 15900 \| 0.0217 \| 0.9844 \| 0.9846 \| 0.9845 \| 0.9921 \|
	\| 0.012 \| 4.61 \| 16200 \| 0.0211 \| 0.9847 \| 0.9848 \| 0.9847 \| 0.9922 \|
	\| 0.0119 \| 4.69 \| 16500 \| 0.0209 \| 0.9845 \| 0.9852 \| 0.9848 \| 0.9922 \|
	\| 0.0116 \| 4.78 \| 16800 \| 0.0211 \| 0.9845 \| 0.9847 \| 0.9846 \| 0.9922 \|
	\| 0.0115 \| 4.86 \| 17100 \| 0.0210 \| 0.9850 \| 0.9844 \| 0.9847 \| 0.9923 \|
	\| 0.0115 \| 4.95 \| 17400 \| 0.0208 \| 0.9848 \| 0.9853 \| 0.9850 \| 0.9923 \|


	### Framework versions

	- Transformers 4.39.1
	- Pytorch 2.2.0a0+6a974be
	- Datasets 2.18.0
	- Tokenizers 0.15.2