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distilbert_finetuned_ai4privacy

Token Classification
Transformers
PyTorch
ONNX
Safetensors
English
distilbert
Generated from Trainer
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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
widget:
  - text: >-
      'The fake charges put forth in their sham indictment are an outrageous
      criminalization of political speech,' the former president said. 'They’re
      trying to make it illegal to question the results of a bad election. It
      was a very bad election,' said Trump, who refuses to accept he lost the
      2020 election and regularly promotes election conspiracy theories.
  - text: >-
      Donald Trump’s campaign released a new video Friday attacking the
      prosecutors who have brought cases against or are investigating the former
      president one day after Trump was arrested and arraigned for a third time.
      The video attacks federal special prosecutor Jack Smith, New York Attorney
      General Letitia James, Manhattan District Attorney Alvin Bragg and Fulton
      County District Attorney Fani Willis and dubs the group the 'Fraud Squad.'
      'Meet the cast of unscrupulous accomplices he’s assembled to get Trump,'
      the narrator says in the video.

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 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
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 0.0 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 0.0 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 0.0 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 0.0 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 0.0 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 0.0 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 0.0 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