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
license: cc-by-nc-4.0

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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.

Latest Model: Isotonic/mdeberta-v3-base_finetuned_ai4privacy_v2

Useage

GitHub Implementation: Ai4Privacy

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

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