ai4privacy_v2_adapter_en

This model is a fine-tuned version of distilbert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1079
Overall Precision: 0.8136
Overall Recall: 0.8836
Overall F1: 0.8472
Overall Accuracy: 0.9560
Accountname F1: 0.9712
Accountnumber F1: 0.9677
Age F1: 0.8432
Amount F1: 0.7929
Bic F1: 0.9689
Bitcoinaddress F1: 0.9343
Buildingnumber F1: 0.8405
City F1: 0.8139
Companyname F1: 0.9310
County F1: 0.8406
Creditcardcvv F1: 0.8889
Creditcardissuer F1: 0.9661
Creditcardnumber F1: 0.8404
Currency F1: 0.5968
Currencycode F1: 0.6556
Currencyname F1: 0.0182
Currencysymbol F1: 0.8176
Date F1: 0.7858
Dob F1: 0.4636
Email F1: 0.9928
Ethereumaddress F1: 0.9941
Eyecolor F1: 0.7859
Firstname F1: 0.8699
Gender F1: 0.8945
Height F1: 0.9227
Iban F1: 0.9730
Ip F1: 0.0497
Ipv4 F1: 0.8345
Ipv6 F1: 0.4659
Jobarea F1: 0.9131
Jobtitle F1: 0.9599
Jobtype F1: 0.9303
Lastname F1: 0.8016
Litecoinaddress F1: 0.8483
Mac F1: 0.9817
Maskednumber F1: 0.7049
Middlename F1: 0.5006
Nearbygpscoordinate F1: 0.9969
Ordinaldirection F1: 0.9519
Password F1: 0.9780
Phoneimei F1: 0.9928
Phonenumber F1: 0.9752
Pin F1: 0.7525
Prefix F1: 0.9121
Secondaryaddress F1: 0.9474
Sex F1: 0.9770
Ssn F1: 0.9771
State F1: 0.8680
Street F1: 0.8677
Time F1: 0.9516
Url F1: 0.9975
Useragent F1: 0.9844
Username F1: 0.9669
Vehiclevin F1: 0.9775
Vehiclevrm F1: 0.9888
Zipcode F1: 0.8203

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.2
num_epochs: 6

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
0.9109	1.0	1088	0.5093	0.2688	0.3282	0.2956	0.8499	0.2034	0.3040	0.0752	0.1147	0.0935	0.5972	0.0673	0.0	0.0024	0.0	0.0	0.0	0.1141	0.0	0.0	0.0	0.0112	0.7137	0.0	0.9095	0.9119	0.0	0.3152	0.0	0.0	0.8248	0.0	0.6785	0.4199	0.0	0.0475	0.0	0.0167	0.0907	0.6333	0.0011	0.0	0.9525	0.0	0.6438	0.3918	0.2703	0.0	0.0099	0.2395	0.0	0.0220	0.0	0.0276	0.5557	0.9658	0.8157	0.0415	0.3955	0.0106	0.0181
0.2793	2.0	2176	0.1903	0.6974	0.7582	0.7265	0.9309	0.9684	0.8789	0.7561	0.3604	0.7609	0.8715	0.6492	0.4956	0.8028	0.5558	0.7756	0.6575	0.5363	0.4671	0.128	0.0	0.5910	0.7731	0.0186	0.9838	0.9465	0.4482	0.7689	0.5160	0.7446	0.9234	0.0	0.8226	0.8007	0.5589	0.8712	0.6553	0.6634	0.7056	0.9315	0.0382	0.0661	0.9954	0.8828	0.9270	0.9693	0.8621	0.1481	0.8783	0.9120	0.9262	0.8707	0.6693	0.7378	0.9152	0.9886	0.9524	0.8859	0.8352	0.8976	0.6466
0.1707	3.0	3264	0.1388	0.7704	0.8319	0.8000	0.9467	0.9702	0.9396	0.8120	0.6102	0.9298	0.9034	0.7409	0.6205	0.8922	0.7128	0.8446	0.9218	0.6983	0.5504	0.5380	0.0106	0.6877	0.6125	0.3259	0.9892	0.9956	0.6647	0.8358	0.7540	0.8859	0.9430	0.0	0.8313	0.7993	0.8235	0.9241	0.8191	0.7504	0.7882	0.9412	0.5129	0.4005	0.9969	0.9452	0.9618	0.9900	0.9452	0.5018	0.8959	0.9348	0.9406	0.9524	0.7643	0.7747	0.9497	0.9943	0.9714	0.9274	0.9048	0.9646	0.7387
0.1373	4.0	4352	0.1197	0.7751	0.8645	0.8174	0.9509	0.9651	0.9510	0.8263	0.7169	0.9592	0.9254	0.8151	0.7795	0.9148	0.8060	0.8808	0.9524	0.8007	0.5676	0.5656	0.0249	0.8000	0.7858	0.3425	0.9910	0.9869	0.7552	0.8609	0.8397	0.8981	0.9779	0.0278	0.8309	0.3179	0.8643	0.9475	0.9000	0.7812	0.8415	0.9749	0.6191	0.4617	0.9969	0.9519	0.9711	0.9914	0.9700	0.6733	0.9088	0.9421	0.9732	0.9671	0.8360	0.8494	0.9442	0.9943	0.9618	0.9487	0.9838	0.9672	0.8093
0.1202	5.0	5440	0.1106	0.8264	0.8796	0.8522	0.9564	0.9720	0.9634	0.8360	0.7423	0.9756	0.9327	0.8268	0.8031	0.9134	0.8404	0.8792	0.9660	0.7957	0.5723	0.6159	0.0167	0.8132	0.7914	0.4301	0.9928	0.9941	0.7544	0.8659	0.8871	0.9121	0.9695	0.0137	0.8361	0.7008	0.9108	0.9623	0.9126	0.7783	0.8459	0.9749	0.6770	0.4944	0.9985	0.9519	0.9754	0.9900	0.9673	0.7647	0.9115	0.9484	0.9760	0.9771	0.8588	0.8590	0.9526	0.9975	0.9820	0.9547	0.9871	0.9916	0.8133
0.1065	6.0	6528	0.1079	0.8136	0.8836	0.8472	0.9560	0.9712	0.9677	0.8432	0.7929	0.9689	0.9343	0.8405	0.8139	0.9310	0.8406	0.8889	0.9661	0.8404	0.5968	0.6556	0.0182	0.8176	0.7858	0.4636	0.9928	0.9941	0.7859	0.8699	0.8945	0.9227	0.9730	0.0497	0.8345	0.4659	0.9131	0.9599	0.9303	0.8016	0.8483	0.9817	0.7049	0.5006	0.9969	0.9519	0.9780	0.9928	0.9752	0.7525	0.9121	0.9474	0.9770	0.9771	0.8680	0.8677	0.9516	0.9975	0.9844	0.9669	0.9775	0.9888	0.8203

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.17.0
Tokenizers 0.15.1

Isotonic/ai4privacy_v2_adapter_en

ai4privacy_v2_adapter_en

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

为此模型做种