metadata

base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
model-index:
  - name: distilbert_finetuned_ai4privacy
    results: []
datasets:
  - ai4privacy/pii-masking-65k
  - ai4privacy/pii-masking-43k
language:
  - en
metrics:
  - f1
  - precision
  - recall
library_name: transformers
pipeline_tag: token-classification

distilbert_finetuned_ai4privacy

This model is a fine-tuned version of distilbert-base-uncased on the english only section of ai4privacy/pii-masking-65k dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

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: 7

Class wise metrics

It achieves the following results on the evaluation set:

Loss: 0.0106
Overall Precision: 0.9760
Overall Recall: 0.9801
Overall F1: 0.9780
Overall Accuracy: 0.9977
Accountname F1: 1.0
Accountnumber F1: 1.0
Amount F1: 0.9565
Bic F1: 1.0
Bitcoinaddress F1: 1.0
Buildingnumber F1: 0.9753
City F1: 0.9987
Company Name F1: 1.0
County F1: 1.0
Creditcardcvv F1: 0.9701
Creditcardissuer F1: 0.9939
Creditcardnumber F1: 1.0
Currency F1: 0.8668
Currencycode F1: 0.8662
Currencyname F1: 0.7582
Currencysymbol F1: 0.36
Date F1: 0.9944
Displayname F1: 0.5970
Email F1: 1.0
Ethereumaddress F1: 1.0
Firstname F1: 0.9493
Fullname F1: 0.9982
Gender F1: 0.9524
Iban F1: 1.0
Ip F1: 0.5543
Ipv4 F1: 0.8700
Ipv6 F1: 0.8863
Jobarea F1: 0.9806
Jobdescriptor F1: 0.6875
Jobtitle F1: 0.9424
Jobtype F1: 0.8811
Lastname F1: 0.9052
Litecoinaddress F1: 0.9848
Mac F1: 1.0
Maskednumber F1: 1.0
Middlename F1: 0.7364
Name F1: 0.9994
Nearbygpscoordinate F1: 0.5
Number F1: 1.0
Password F1: 1.0
Phoneimei F1: 1.0
Phone Number F1: 1.0
Pin F1: 0.9697
Prefix F1: 0.9540
Secondaryaddress F1: 0.9947
Sex F1: 0.9650
Sextype F1: 0.0
Ssn F1: 1.0
State F1: 0.9965
Street F1: 0.9810
Streetaddress F1: 0.9832
Suffix F1: 0.7928
Time F1: 0.9880
Url F1: 0.9974
Useragent F1: 1.0
Username F1: 0.9746
Vehiclevin F1: 1.0
Vehiclevrm F1: 1.0
Zipcode F1: 0.9969

Training results

Training Loss	Epoch	Step	Validation Loss	Overall Precision	Overall Recall	Overall F1	Overall Accuracy	Accountname F1	Accountnumber F1	Amount F1	Bic F1	Bitcoinaddress F1	Buildingnumber F1	City F1	Company Name F1	County F1	Creditcardcvv F1	Creditcardissuer F1	Creditcardnumber F1	Currency F1	Currencycode F1	Currencyname F1	Currencysymbol F1	Date F1	Displayname F1	Email F1	Ethereumaddress F1	Firstname F1	Fullname F1	Gender F1	Iban F1	Ip F1	Ipv4 F1	Ipv6 F1	Jobarea F1	Jobdescriptor F1	Jobtitle F1	Jobtype F1	Lastname F1	Litecoinaddress F1	Mac F1	Maskednumber F1	Middlename F1	Name F1	Nearbygpscoordinate F1	Number F1	Password F1	Phoneimei F1	Phone Number F1	Pin F1	Prefix F1	Secondaryaddress F1	Sex F1	Ssn F1	State F1	Street F1	Streetaddress F1	Suffix F1	Time F1	Url F1	Useragent F1	Username F1	Vehiclevin F1	Vehiclevrm F1	Zipcode F1
No log	1.0	335	0.3836	0.6166	0.6314	0.6239	0.9080	0.0	0.5534	0.1940	0.0	0.4890	0.0	0.6856	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.3306	0.0	0.9420	0.4869	0.0704	0.9094	0.0	0.0877	0.0	0.6112	0.6779	0.0	0.0	0.0066	0.0	0.0	0.0	0.5589	0.3733	0.0	0.8152	0.0	0.0137	0.4013	0.3786	0.1117	0.0	0.0	0.0	0.0	0.0	0.0104	0.0	0.5657	0.0	0.1786	0.7969	0.7734	0.0710	0.2662	0.0	0.2335
1.2518	2.0	670	0.1360	0.7806	0.8283	0.8037	0.9571	0.7286	0.6427	0.6429	0.5102	0.6207	0.1322	0.9476	0.1031	0.7823	0.0303	0.0	0.4403	0.5190	0.0	0.0144	0.0	0.9125	0.0	0.9908	0.7273	0.7199	0.9762	0.0	0.2890	0.0	0.8519	0.5472	0.8354	0.0	0.7228	0.0	0.3513	0.0	0.8381	0.0117	0.0	0.9740	0.0	0.3070	0.7378	0.8857	0.4724	0.0	0.3978	0.4541	0.0278	0.2254	0.7361	0.0205	0.7132	0.0	0.9032	0.9870	0.9540	0.7943	0.6036	0.6184	0.6923
0.1589	3.0	1005	0.0721	0.8615	0.9008	0.8807	0.9770	0.9164	0.9765	0.8283	0.5200	0.8077	0.6461	0.9790	0.6881	0.9592	0.5217	0.6769	0.5950	0.4094	0.5758	0.2397	0.0	0.9672	0.0	0.9994	0.9484	0.8170	0.9836	0.6437	0.9492	0.0	0.8424	0.8056	0.8999	0.0	0.7921	0.2667	0.5761	0.0	0.9841	0.0103	0.2147	0.9880	0.0	0.8051	0.8299	0.9947	0.7793	0.5161	0.7444	0.9894	0.7692	0.8182	0.9939	0.5244	0.4451	0.0	0.9762	0.9896	1.0	0.9008	0.9349	0.9605	0.9337
0.1589	4.0	1340	0.0386	0.9175	0.9445	0.9308	0.9876	0.9597	0.9921	0.9041	0.9691	0.7944	0.7662	0.9940	0.9864	0.9801	0.7463	0.9560	0.8562	0.7383	0.7308	0.4286	0.0	0.9861	0.0	1.0	1.0	0.8726	0.9916	0.8434	0.9884	0.0382	0.8700	0.4811	0.9517	0.0741	0.8927	0.6732	0.7251	0.5629	1.0	0.6341	0.3353	0.9968	0.0	0.9648	0.9532	0.9947	0.9725	0.7719	0.8683	0.9947	0.9028	0.9302	0.9957	0.8287	0.8698	0.1389	0.9841	0.9974	0.9832	0.9303	0.9639	0.9673	0.9573
0.0637	5.0	1675	0.0226	0.9402	0.9627	0.9513	0.9936	1.0	1.0	0.9355	0.9796	0.9813	0.8643	0.9987	0.9640	1.0	0.9197	0.9693	0.9368	0.7273	0.8052	0.5455	0.1395	0.9916	0.0615	1.0	0.9952	0.9051	0.9933	0.9048	1.0	0.2069	0.8700	0.5124	0.9728	0.4444	0.9107	0.7753	0.8147	0.9023	0.9741	0.8521	0.5990	0.9978	0.0	1.0	0.9970	1.0	0.9953	0.8713	0.8913	0.9735	0.9583	0.9924	0.9974	0.9041	0.9192	0.5053	0.9801	0.9974	1.0	0.9521	1.0	0.9934	0.975
0.0333	6.0	2010	0.0136	0.9683	0.9774	0.9728	0.9966	0.9963	1.0	0.9454	1.0	1.0	0.9670	0.9987	1.0	1.0	0.9481	0.9880	1.0	0.8475	0.8701	0.7174	0.36	0.9944	0.4776	1.0	1.0	0.9441	0.9982	0.9398	1.0	0.3661	0.8519	0.7309	0.9785	0.7108	0.9474	0.8722	0.8909	0.9848	0.9895	1.0	0.7	0.9994	0.5	1.0	1.0	1.0	1.0	0.96	0.9535	0.9947	0.9718	1.0	0.9974	0.9810	0.9815	0.7037	0.9880	0.9974	1.0	0.9681	1.0	1.0	0.9938
0.0333	7.0	2345	0.0106	0.9760	0.9801	0.9780	0.9977	1.0	1.0	0.9565	1.0	1.0	0.9753	0.9987	1.0	1.0	0.9701	0.9939	1.0	0.8668	0.8662	0.7582	0.36	0.9944	0.5970	1.0	1.0	0.9493	0.9982	0.9524	1.0	0.5543	0.8700	0.8863	0.9806	0.6875	0.9424	0.8811	0.9052	0.9848	1.0	1.0	0.7364	0.9994	0.5	1.0	1.0	1.0	1.0	0.9697	0.9540	0.9947	0.9650	1.0	0.9965	0.9810	0.9832	0.7928	0.9880	0.9974	1.0	0.9746	1.0	1.0	0.9969

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3