deberta-v3-base_ai4privacy_en / README.md

xXiaobuding

update model card README.md

9e3d28f 5 months ago

preview code

raw

history blame contribute delete

No virus

9.05 kB

	---
	license: mit
	tags:
	- generated_from_trainer
	model-index:
	- name: deberta-v3-base_ai4privacy_en
	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. -->

	# deberta-v3-base_ai4privacy_en

	This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.1055
	- Overall Precision: 0.8683
	- Overall Recall: 0.8949
	- Overall F1: 0.8814
	- Overall Accuracy: 0.9609
	- Accountname F1: 0.9898
	- Accountnumber F1: 0.9939
	- Age F1: 0.8397
	- Amount F1: 0.9169
	- Bic F1: 0.9012
	- Bitcoinaddress F1: 0.9583
	- Buildingnumber F1: 0.8109
	- City F1: 0.8011
	- Companyname F1: 0.9437
	- County F1: 0.8752
	- Creditcardcvv F1: 0.8635
	- Creditcardissuer F1: 0.9738
	- Creditcardnumber F1: 0.8771
	- Currency F1: 0.6542
	- Currencycode F1: 0.5566
	- Currencyname F1: 0.2214
	- Currencysymbol F1: 0.8640
	- Date F1: 0.8365
	- Dob F1: 0.5696
	- Email F1: 0.9914
	- Ethereumaddress F1: 0.9903
	- Eyecolor F1: 0.9076
	- Firstname F1: 0.8759
	- Gender F1: 0.9324
	- Height F1: 0.9046
	- Iban F1: 0.9899
	- Ip F1: 0.1137
	- Ipv4 F1: 0.8118
	- Ipv6 F1: 0.8091
	- Jobarea F1: 0.7895
	- Jobtitle F1: 0.9806
	- Jobtype F1: 0.9056
	- Lastname F1: 0.8179
	- Litecoinaddress F1: 0.8739
	- Mac F1: 1.0
	- Maskednumber F1: 0.8319
	- Middlename F1: 0.8419
	- Nearbygpscoordinate F1: 1.0
	- Ordinaldirection F1: 0.9682
	- Password F1: 0.9595
	- Phoneimei F1: 0.9930
	- Phonenumber F1: 0.9807
	- Pin F1: 0.7868
	- Prefix F1: 0.9355
	- Secondaryaddress F1: 0.9967
	- Sex F1: 0.9692
	- Ssn F1: 0.9898
	- State F1: 0.7407
	- Street F1: 0.7823
	- Time F1: 0.9500
	- Url F1: 0.9936
	- Useragent F1: 0.9976
	- Username F1: 0.9331
	- Vehiclevin F1: 0.9713
	- Vehiclevrm F1: 0.9493
	- Zipcode F1: 0.8634

	## 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: 8
	- eval_batch_size: 16
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: cosine_with_restarts
	- lr_scheduler_warmup_ratio: 0.2
	- num_epochs: 5

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| Accountname F1 \| Accountnumber F1 \| Age F1 \| Amount F1 \| Bic F1 \| Bitcoinaddress F1 \| Buildingnumber F1 \| City F1 \| Companyname F1 \| County F1 \| Creditcardcvv F1 \| Creditcardissuer F1 \| Creditcardnumber F1 \| Currency F1 \| Currencycode F1 \| Currencyname F1 \| Currencysymbol F1 \| Date F1 \| Dob F1 \| Email F1 \| Ethereumaddress F1 \| Eyecolor F1 \| Firstname F1 \| Gender F1 \| Height F1 \| Iban F1 \| Ip F1 \| Ipv4 F1 \| Ipv6 F1 \| Jobarea F1 \| Jobtitle F1 \| Jobtype F1 \| Lastname F1 \| Litecoinaddress F1 \| Mac F1 \| Maskednumber F1 \| Middlename F1 \| Nearbygpscoordinate F1 \| Ordinaldirection F1 \| Password F1 \| Phoneimei F1 \| Phonenumber F1 \| Pin F1 \| Prefix F1 \| Secondaryaddress F1 \| Sex F1 \| Ssn F1 \| State F1 \| Street F1 \| Time F1 \| Url F1 \| Useragent F1 \| Username F1 \| Vehiclevin F1 \| Vehiclevrm F1 \| Zipcode F1 \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\|:--------------:\|:----------------:\|:------:\|:---------:\|:------:\|:-----------------:\|:-----------------:\|:-------:\|:--------------:\|:---------:\|:----------------:\|:-------------------:\|:-------------------:\|:-----------:\|:---------------:\|:---------------:\|:-----------------:\|:-------:\|:------:\|:--------:\|:------------------:\|:-----------:\|:------------:\|:---------:\|:---------:\|:-------:\|:------:\|:-------:\|:-------:\|:----------:\|:-----------:\|:----------:\|:-----------:\|:------------------:\|:------:\|:---------------:\|:-------------:\|:----------------------:\|:-------------------:\|:-----------:\|:------------:\|:--------------:\|:------:\|:---------:\|:-------------------:\|:------:\|:------:\|:--------:\|:---------:\|:-------:\|:------:\|:------------:\|:-----------:\|:-------------:\|:-------------:\|:----------:\|
	\| 0.463 \| 1.0 \| 4350 \| 0.3229 \| 0.5378 \| 0.5277 \| 0.5327 \| 0.8941 \| 0.8722 \| 0.7667 \| 0.5849 \| 0.2284 \| 0.5391 \| 0.7502 \| 0.3143 \| 0.1514 \| 0.2844 \| 0.2640 \| 0.0086 \| 0.5288 \| 0.0 \| 0.0956 \| 0.0 \| 0.0 \| 0.3410 \| 0.7146 \| 0.0169 \| 0.8043 \| 0.9458 \| 0.0090 \| 0.4894 \| 0.1550 \| 0.0 \| 0.8653 \| 0.0 \| 0.8168 \| 0.7474 \| 0.1611 \| 0.4548 \| 0.0035 \| 0.3781 \| 0.1472 \| 0.8989 \| 0.4641 \| 0.0035 \| 0.9955 \| 0.0 \| 0.7959 \| 0.9464 \| 0.7831 \| 0.2258 \| 0.7847 \| 0.8639 \| 0.5481 \| 0.7480 \| 0.0643 \| 0.1795 \| 0.7463 \| 0.9683 \| 0.9080 \| 0.4569 \| 0.8724 \| 0.5152 \| 0.5458 \|
	\| 0.1944 \| 2.0 \| 8700 \| 0.1709 \| 0.7179 \| 0.7495 \| 0.7334 \| 0.9387 \| 0.9789 \| 0.9718 \| 0.6535 \| 0.4640 \| 0.6039 \| 0.9240 \| 0.6723 \| 0.4777 \| 0.8654 \| 0.6234 \| 0.7241 \| 0.8713 \| 0.6077 \| 0.4598 \| 0.0698 \| 0.0104 \| 0.6163 \| 0.7518 \| 0.4439 \| 0.9803 \| 0.9848 \| 0.6276 \| 0.6714 \| 0.7937 \| 0.6295 \| 0.9538 \| 0.0 \| 0.8285 \| 0.7976 \| 0.5304 \| 0.9253 \| 0.6957 \| 0.4694 \| 0.7181 \| 0.9892 \| 0.6301 \| 0.2027 \| 0.9865 \| 0.8016 \| 0.7931 \| 0.9888 \| 0.9658 \| 0.3231 \| 0.8959 \| 0.9721 \| 0.8506 \| 0.9692 \| 0.3841 \| 0.4389 \| 0.9064 \| 0.9905 \| 0.9670 \| 0.8341 \| 0.9563 \| 0.8449 \| 0.7487 \|
	\| 0.1275 \| 3.0 \| 13050 \| 0.1174 \| 0.8276 \| 0.8506 \| 0.8390 \| 0.9559 \| 0.9881 \| 0.9896 \| 0.7347 \| 0.8484 \| 0.8214 \| 0.9571 \| 0.7815 \| 0.7437 \| 0.9289 \| 0.7794 \| 0.8323 \| 0.9754 \| 0.8624 \| 0.4890 \| 0.4318 \| 0.2006 \| 0.8043 \| 0.8066 \| 0.5459 \| 0.9858 \| 0.9903 \| 0.8511 \| 0.8071 \| 0.8187 \| 0.8657 \| 0.9486 \| 0.0 \| 0.8396 \| 0.8049 \| 0.7326 \| 0.9720 \| 0.8699 \| 0.6714 \| 0.8655 \| 0.9957 \| 0.8194 \| 0.6478 \| 1.0 \| 0.9660 \| 0.9331 \| 0.9916 \| 0.9711 \| 0.6899 \| 0.9302 \| 0.9902 \| 0.9413 \| 0.9847 \| 0.5684 \| 0.7259 \| 0.9381 \| 0.9929 \| 0.9953 \| 0.9094 \| 0.9598 \| 0.9115 \| 0.8324 \|
	\| 0.0976 \| 4.0 \| 17400 \| 0.1065 \| 0.8624 \| 0.8877 \| 0.8749 \| 0.9598 \| 0.9907 \| 0.9939 \| 0.8312 \| 0.9141 \| 0.8689 \| 0.9511 \| 0.8027 \| 0.8014 \| 0.9538 \| 0.8827 \| 0.8599 \| 0.9701 \| 0.8634 \| 0.6637 \| 0.5488 \| 0.1181 \| 0.8541 \| 0.8224 \| 0.5333 \| 0.9926 \| 0.9876 \| 0.9041 \| 0.8664 \| 0.9303 \| 0.9207 \| 0.9861 \| 0.0591 \| 0.8174 \| 0.8098 \| 0.7798 \| 0.9686 \| 0.9013 \| 0.7845 \| 0.8661 \| 1.0 \| 0.8091 \| 0.8103 \| 1.0 \| 0.9785 \| 0.9430 \| 0.9916 \| 0.9806 \| 0.7778 \| 0.9354 \| 0.9913 \| 0.9692 \| 0.9885 \| 0.7476 \| 0.7658 \| 0.9427 \| 0.9889 \| 0.9976 \| 0.9346 \| 0.9797 \| 0.9570 \| 0.8362 \|
	\| 0.0886 \| 5.0 \| 21750 \| 0.1055 \| 0.8683 \| 0.8949 \| 0.8814 \| 0.9609 \| 0.9898 \| 0.9939 \| 0.8397 \| 0.9169 \| 0.9012 \| 0.9583 \| 0.8109 \| 0.8011 \| 0.9437 \| 0.8752 \| 0.8635 \| 0.9738 \| 0.8771 \| 0.6542 \| 0.5566 \| 0.2214 \| 0.8640 \| 0.8365 \| 0.5696 \| 0.9914 \| 0.9903 \| 0.9076 \| 0.8759 \| 0.9324 \| 0.9046 \| 0.9899 \| 0.1137 \| 0.8118 \| 0.8091 \| 0.7895 \| 0.9806 \| 0.9056 \| 0.8179 \| 0.8739 \| 1.0 \| 0.8319 \| 0.8419 \| 1.0 \| 0.9682 \| 0.9595 \| 0.9930 \| 0.9807 \| 0.7868 \| 0.9355 \| 0.9967 \| 0.9692 \| 0.9898 \| 0.7407 \| 0.7823 \| 0.9500 \| 0.9936 \| 0.9976 \| 0.9331 \| 0.9713 \| 0.9493 \| 0.8634 \|


	### Framework versions

	- Transformers 4.26.1
	- Pytorch 2.0.0.post101
	- Datasets 2.10.1
	- Tokenizers 0.13.3