distilbert_finetuned_pii_mjalg
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0531
- Overall Precision: 0.9200
- Overall Recall: 0.9427
- Overall F1: 0.9312
- Overall Accuracy: 0.9804
- Accountname F1: 0.9809
- Accountnumber F1: 0.9834
- Amount F1: 0.9331
- Bic F1: 0.8288
- Bitcoinaddress F1: 0.9118
- Buildingnumber F1: 0.6526
- City F1: 0.9876
- Company Name F1: 0.9043
- County F1: 0.9932
- Creditcardcvv F1: 0.8966
- Creditcardissuer F1: 0.9045
- Creditcardnumber F1: 0.9265
- Currency F1: 0.6492
- Currencycode F1: 0.6946
- Currencyname F1: 0.2995
- Currencysymbol F1: 0.6792
- Date F1: 0.9947
- Displayname F1: 0.4348
- Email F1: 0.9986
- Ethereumaddress F1: 0.9528
- Firstname F1: 0.8599
- Fullname F1: 0.9791
- Gender F1: 0.8283
- Iban F1: 0.98
- Ip F1: 0.2626
- Ipv4 F1: 0.8518
- Ipv6 F1: 0.6566
- Jobarea F1: 0.9366
- Jobdescriptor F1: 0.8075
- Jobtitle F1: 0.9084
- Jobtype F1: 0.8722
- Lastname F1: 0.7201
- Litecoinaddress F1: 0.8619
- Mac F1: 0.9889
- Maskednumber F1: 0.8816
- Middlename F1: 0.6604
- Name F1: 0.9927
- Nearbygpscoordinate F1: 0.1176
- Number F1: 0.8772
- Ordinaldirection F1: 0.0
- Password F1: 0.9286
- Phoneimei F1: 1.0
- Phone Number F1: 0.8670
- Pin F1: 0.7835
- Prefix F1: 0.9024
- Secondaryaddress F1: 0.9841
- Sex F1: 0.9504
- Sextype F1: 0.0
- Ssn F1: 0.8595
- State F1: 0.9938
- Street F1: 0.6691
- Streetaddress F1: 0.8492
- Suffix F1: 0.9057
- Time F1: 0.9469
- Url F1: 0.9973
- Useragent F1: 0.9692
- Username F1: 0.8986
- Vehiclevin F1: 1.0
- Vehiclevrm F1: 0.9756
- Zipcode F1: 0.8
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: 7
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 | Ordinaldirection F1 | Password F1 | Phoneimei F1 | Phone Number F1 | Pin F1 | Prefix F1 | Secondaryaddress F1 | Sex F1 | Sextype 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.0453 | 1.0 | 1337 | 0.0665 | 0.8950 | 0.9290 | 0.9117 | 0.9781 | 0.9655 | 0.9697 | 0.9342 | 0.9027 | 0.9197 | 0.5608 | 0.9710 | 0.9133 | 0.9726 | 0.8411 | 0.9215 | 0.8605 | 0.6680 | 0.6708 | 0.1017 | 0.6458 | 0.9947 | 0.25 | 0.9989 | 0.9782 | 0.8443 | 0.9832 | 0.7872 | 0.9552 | 0.1744 | 0.8438 | 0.6929 | 0.9114 | 0.6437 | 0.9123 | 0.8374 | 0.6162 | 0.8571 | 0.9399 | 0.8046 | 0.4949 | 0.9956 | 0.0 | 0.8926 | 0.0 | 0.9632 | 1.0 | 0.9118 | 0.7327 | 0.8718 | 0.9167 | 0.9220 | 0.0 | 0.9167 | 0.9912 | 0.4484 | 0.4781 | 0.8378 | 0.9274 | 0.9959 | 0.9646 | 0.8853 | 0.9231 | 0.9516 | 0.8771 |
0.0603 | 2.0 | 2674 | 0.0531 | 0.9200 | 0.9427 | 0.9312 | 0.9804 | 0.9809 | 0.9834 | 0.9331 | 0.8288 | 0.9118 | 0.6526 | 0.9876 | 0.9043 | 0.9932 | 0.8966 | 0.9045 | 0.9265 | 0.6492 | 0.6946 | 0.2995 | 0.6792 | 0.9947 | 0.4348 | 0.9986 | 0.9528 | 0.8599 | 0.9791 | 0.8283 | 0.98 | 0.2626 | 0.8518 | 0.6566 | 0.9366 | 0.8075 | 0.9084 | 0.8722 | 0.7201 | 0.8619 | 0.9889 | 0.8816 | 0.6604 | 0.9927 | 0.1176 | 0.8772 | 0.0 | 0.9286 | 1.0 | 0.8670 | 0.7835 | 0.9024 | 0.9841 | 0.9504 | 0.0 | 0.8595 | 0.9938 | 0.6691 | 0.8492 | 0.9057 | 0.9469 | 0.9973 | 0.9692 | 0.8986 | 1.0 | 0.9756 | 0.8 |
0.0344 | 3.0 | 4011 | 0.0587 | 0.9400 | 0.9506 | 0.9453 | 0.9801 | 0.9508 | 0.9810 | 0.9208 | 0.8889 | 0.8550 | 0.7139 | 0.9891 | 0.9298 | 0.9966 | 0.9533 | 0.9684 | 0.8810 | 0.7254 | 0.7 | 0.1783 | 0.7429 | 0.9947 | 0.4906 | 0.9994 | 0.9692 | 0.9021 | 0.9862 | 0.9032 | 0.9557 | 0.375 | 0.8619 | 0.6489 | 0.9431 | 0.8488 | 0.9615 | 0.9020 | 0.7776 | 0.7921 | 0.9778 | 0.8284 | 0.8702 | 0.9960 | 0.7692 | 0.9561 | 0.0 | 0.9505 | 1.0 | 0.9706 | 0.9697 | 0.8871 | 0.9841 | 0.9635 | 0.0 | 0.9457 | 0.9947 | 0.6962 | 0.8274 | 0.9554 | 0.9547 | 0.9959 | 0.9474 | 0.9275 | 1.0 | 1.0 | 0.9785 |
0.0248 | 4.0 | 5348 | 0.0692 | 0.9460 | 0.9559 | 0.9509 | 0.9823 | 0.9653 | 0.9835 | 0.9595 | 0.9310 | 0.9004 | 0.7233 | 0.9907 | 0.9420 | 0.9898 | 0.9074 | 0.9442 | 0.9208 | 0.7545 | 0.7925 | 0.3784 | 0.7850 | 0.9973 | 0.4932 | 0.9994 | 0.9692 | 0.9132 | 0.9872 | 0.8632 | 0.9849 | 0.3382 | 0.8496 | 0.6429 | 0.9591 | 0.8585 | 0.9834 | 0.9038 | 0.7709 | 0.8290 | 0.9889 | 0.8831 | 0.9170 | 0.9973 | 1.0 | 0.9316 | 0.0 | 0.9530 | 1.0 | 0.9458 | 0.9126 | 0.9030 | 0.9947 | 0.9545 | 0.6667 | 0.9313 | 0.9973 | 0.6975 | 0.8742 | 0.9697 | 0.9633 | 1.0 | 0.9778 | 0.9081 | 0.9931 | 0.9672 | 0.9620 |
0.0114 | 5.0 | 6685 | 0.0754 | 0.9516 | 0.9608 | 0.9562 | 0.9829 | 0.9714 | 0.9789 | 0.9873 | 0.9138 | 0.9248 | 0.7593 | 0.9941 | 0.9278 | 0.9966 | 0.9320 | 0.9684 | 0.8839 | 0.7475 | 0.8289 | 0.5845 | 0.7407 | 0.9973 | 0.5714 | 0.9994 | 0.9520 | 0.9213 | 0.9867 | 0.8454 | 1.0 | 0.3582 | 0.8516 | 0.7377 | 0.9617 | 0.8610 | 0.9743 | 0.9108 | 0.8072 | 0.8723 | 0.9889 | 0.8364 | 0.9559 | 0.9969 | 1.0 | 0.9735 | 0.0 | 0.9701 | 1.0 | 0.9709 | 0.9434 | 0.9214 | 0.9947 | 0.9571 | 0.6667 | 0.9449 | 0.9973 | 0.7324 | 0.8750 | 0.9756 | 0.9672 | 1.0 | 0.9735 | 0.9071 | 1.0 | 0.9836 | 0.9864 |
0.0041 | 6.0 | 8022 | 0.0880 | 0.9516 | 0.9579 | 0.9547 | 0.9831 | 0.9839 | 0.9929 | 0.9703 | 0.9217 | 0.9650 | 0.7442 | 0.9938 | 0.9172 | 0.9966 | 0.9320 | 0.9538 | 0.9125 | 0.7793 | 0.8082 | 0.4848 | 0.8077 | 0.9973 | 0.5172 | 0.9994 | 1.0 | 0.9090 | 0.9870 | 0.875 | 0.9751 | 0.3254 | 0.8378 | 0.6848 | 0.9577 | 0.8812 | 0.9833 | 0.9020 | 0.7925 | 0.9222 | 0.9889 | 0.8608 | 0.9591 | 0.9958 | 1.0 | 0.9692 | 0.6667 | 0.9764 | 1.0 | 0.9756 | 0.9333 | 0.9057 | 0.9894 | 0.9565 | 0.0 | 0.9524 | 0.9973 | 0.7048 | 0.8297 | 0.9877 | 0.9672 | 1.0 | 0.9956 | 0.9331 | 0.9931 | 0.9756 | 0.9864 |
0.0029 | 7.0 | 9359 | 0.0901 | 0.9559 | 0.9607 | 0.9583 | 0.9836 | 0.9840 | 0.9929 | 0.9873 | 0.9217 | 0.9612 | 0.7711 | 0.9907 | 0.9441 | 0.9966 | 0.9333 | 0.9583 | 0.8922 | 0.7951 | 0.7973 | 0.5700 | 0.8491 | 0.9973 | 0.6102 | 0.9994 | 1.0 | 0.9261 | 0.9879 | 0.8367 | 0.9849 | 0.3628 | 0.8103 | 0.7490 | 0.9587 | 0.8725 | 0.9815 | 0.9073 | 0.7993 | 0.9162 | 0.9889 | 0.8415 | 0.9627 | 0.9979 | 1.0 | 0.9692 | 0.6667 | 0.9730 | 1.0 | 0.9756 | 0.9333 | 0.9185 | 0.9947 | 0.9559 | 0.8 | 0.976 | 0.9973 | 0.7090 | 0.8513 | 0.9816 | 0.9672 | 1.0 | 0.9956 | 0.9506 | 0.9931 | 0.9756 | 0.9891 |
Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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