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xXiaobuding/
distilbert-base-uncased_ai4privacy_en

Name: distilbert-base-uncased_ai4privacy_en
Availability: InStock

generated_from_trainer

apache-2.0

autotrain_compatible

endpoints_compatible

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distilbert-base-uncased_ai4privacy_en

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

Loss: 0.0935
Overall Precision: 0.9169
Overall Recall: 0.9382
Overall F1: 0.9274
Overall Accuracy: 0.9662
Accountname F1: 0.9924
Accountnumber F1: 0.9878
Age F1: 0.9283
Amount F1: 0.9224
Bic F1: 0.9018
Bitcoinaddress F1: 0.8930
Buildingnumber F1: 0.8944
City F1: 0.9543
Companyname F1: 0.9847
County F1: 0.9807
Creditcardcvv F1: 0.9191
Creditcardissuer F1: 0.9831
Creditcardnumber F1: 0.9029
Currency F1: 0.7268
Currencycode F1: 0.8590
Currencyname F1: 0.4625
Currencysymbol F1: 0.9503
Date F1: 0.8227
Dob F1: 0.6515
Email F1: 0.9884
Ethereumaddress F1: 0.9890
Eyecolor F1: 0.9274
Firstname F1: 0.9726
Gender F1: 0.9791
Height F1: 0.9814
Iban F1: 0.9862
Ip F1: 0.1964
Ipv4 F1: 0.8063
Ipv6 F1: 0.7958
Jobarea F1: 0.9265
Jobtitle F1: 0.9965
Jobtype F1: 0.9482
Lastname F1: 0.9469
Litecoinaddress F1: 0.7767
Mac F1: 0.9892
Maskednumber F1: 0.8689
Middlename F1: 0.9628
Nearbygpscoordinate F1: 0.9955
Ordinaldirection F1: 0.9784
Password F1: 0.9503
Phoneimei F1: 0.9944
Phonenumber F1: 0.9799
Pin F1: 0.9085
Prefix F1: 0.9463
Secondaryaddress F1: 0.9902
Sex F1: 0.9752
Ssn F1: 0.9759
State F1: 0.9765
Street F1: 0.9651
Time F1: 0.9740
Url F1: 0.9889
Useragent F1: 0.9778
Username F1: 0.9885
Vehiclevin F1: 0.9621
Vehiclevrm F1: 0.9840
Zipcode F1: 0.8823

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: 2
eval_batch_size: 2
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
0.1133	1.0	17398	0.1049	0.8638	0.9087	0.8857	0.9619	0.9789	0.9552	0.8739	0.8043	0.9020	0.9265	0.8585	0.8866	0.9521	0.9754	0.8213	0.9718	0.8201	0.4143	0.7182	0.0921	0.9064	0.7717	0.4968	0.9811	0.9876	0.8715	0.9487	0.9602	0.9843	0.8432	0.0	0.8434	0.7986	0.8906	0.9808	0.9205	0.9021	0.8261	0.9741	0.7786	0.9252	0.9852	0.9458	0.9418	0.9848	0.9566	0.7740	0.9366	0.9924	0.9751	0.9822	0.9389	0.9228	0.9536	0.9772	0.9638	0.9276	0.9143	0.9043	0.8042
0.1019	2.0	34796	0.0958	0.9076	0.9315	0.9194	0.9665	0.9579	0.9564	0.9020	0.8964	0.8895	0.9511	0.8873	0.9390	0.9736	0.9761	0.9236	0.9794	0.8895	0.7610	0.8052	0.03	0.9429	0.8481	0.6713	0.9791	0.9850	0.8804	0.9651	0.9633	0.9753	0.9789	0.0046	0.8253	0.8038	0.8567	0.9903	0.9364	0.9394	0.8757	0.9764	0.8474	0.9321	0.9970	0.9641	0.9707	0.9944	0.9807	0.9198	0.8692	0.9913	0.9730	0.9594	0.9666	0.9568	0.9626	0.9842	0.9941	0.9780	0.9653	0.9368	0.8850
0.0777	3.0	52194	0.0935	0.9169	0.9382	0.9274	0.9662	0.9924	0.9878	0.9283	0.9224	0.9018	0.8930	0.8944	0.9543	0.9847	0.9807	0.9191	0.9831	0.9029	0.7268	0.8590	0.4625	0.9503	0.8227	0.6515	0.9884	0.9890	0.9274	0.9726	0.9791	0.9814	0.9862	0.1964	0.8063	0.7958	0.9265	0.9965	0.9482	0.9469	0.7767	0.9892	0.8689	0.9628	0.9955	0.9784	0.9503	0.9944	0.9799	0.9085	0.9463	0.9902	0.9752	0.9759	0.9765	0.9651	0.9740	0.9889	0.9778	0.9885	0.9621	0.9840	0.8823
0.0557	4.0	69592	0.0944	0.9285	0.9457	0.9370	0.9696	0.9941	0.9965	0.9190	0.9419	0.9765	0.9654	0.9188	0.9487	0.9787	0.9861	0.9424	0.9849	0.9001	0.7578	0.8875	0.3933	0.9647	0.8627	0.7004	0.9932	0.9876	0.9548	0.9736	0.9884	0.9968	0.9950	0.25	0.8184	0.7747	0.9206	0.9929	0.9493	0.9502	0.9075	0.9957	0.8750	0.9544	1.0	0.9784	0.9812	0.9944	0.9883	0.9309	0.9524	0.9935	0.9809	0.9859	0.9794	0.9624	0.9774	0.9937	0.9941	0.9852	0.9914	0.9865	0.9012
0.0285	5.0	86990	0.1285	0.9292	0.9448	0.9369	0.9693	0.9915	0.9948	0.9308	0.9352	0.9736	0.9674	0.9168	0.9561	0.9756	0.9843	0.9735	0.9849	0.8984	0.7356	0.8734	0.4161	0.9669	0.8510	0.7083	0.9945	0.9903	0.9632	0.9754	0.9926	0.9968	0.9975	0.3843	0.7983	0.7534	0.9237	0.9956	0.9510	0.9505	0.9240	1.0	0.8738	0.9589	0.9985	0.9784	0.9831	0.9944	0.9914	0.9480	0.9420	0.9956	0.9820	0.9860	0.9794	0.9631	0.9774	0.9937	0.9906	0.9885	0.9828	0.9946	0.9115

Framework versions

Transformers 4.26.1
Pytorch 2.0.0.post200
Datasets 2.10.1
Tokenizers 0.13.3

xXiaobuding/distilbert-base-uncased_ai4privacy_en

distilbert-base-uncased_ai4privacy_en

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

xXiaobuding/
distilbert-base-uncased_ai4privacy_en