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  1. README.md +63 -59
  2. all_results.json +63 -63
  3. eval_results.json +57 -57
  4. train_results.json +7 -7
  5. trainer_state.json +769 -529
README.md CHANGED
@@ -15,66 +15,66 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0101
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- - Overall Precision: 0.9818
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- - Overall Recall: 0.9871
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- - Overall F1: 0.9844
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- - Overall Accuracy: 0.9965
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- - Accountname F1: 1.0
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- - Accountnumber F1: 1.0
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- - Age F1: 0.9951
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- - Amount F1: 1.0
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- - Bic F1: 1.0
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- - Bitcoinaddress F1: 0.9915
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- - Buildingnumber F1: 0.9969
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  - City F1: 1.0
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  - Companyname F1: 1.0
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- - County F1: 0.9985
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- - Creditcardcvv F1: 0.9831
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- - Creditcardissuer F1: 0.9964
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- - Creditcardnumber F1: 0.9889
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- - Currency F1: 0.9678
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- - Currencycode F1: 0.9949
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- - Currencyname F1: 0.9266
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- - Currencysymbol F1: 0.9984
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- - Date F1: 0.9895
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- - Dob F1: 0.9774
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- - Email F1: 0.9776
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  - Ethereumaddress F1: 1.0
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- - Eyecolor F1: 1.0
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- - Firstname F1: 1.0
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- - Gender F1: 0.9976
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  - Height F1: 1.0
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- - Iban F1: 1.0
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- - Ip F1: 0.7367
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- - Ipv4 F1: 0.8360
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- - Ipv6 F1: 0.9797
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- - Jobarea F1: 0.9667
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  - Jobtitle F1: 1.0
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- - Jobtype F1: 1.0
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- - Lastname F1: 1.0
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- - Litecoinaddress F1: 0.9688
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- - Mac F1: 1.0
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- - Maskednumber F1: 0.9887
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- - Middlename F1: 0.9583
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  - Nearbygpscoordinate F1: 1.0
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- - Ordinaldirection F1: 0.8571
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- - Password F1: 0.9949
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- - Phoneimei F1: 0.9961
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- - Phonenumber F1: 0.9838
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- - Pin F1: 0.9963
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- - Prefix F1: 0.9949
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  - Secondaryaddress F1: 1.0
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- - Sex F1: 1.0
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- - Ssn F1: 0.9938
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- - State F1: 1.0
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- - Street F1: 0.9989
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- - Time F1: 0.9958
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  - Url F1: 1.0
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- - Useragent F1: 1.0
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  - Username F1: 1.0
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- - Vehiclevin F1: 1.0
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- - Vehiclevrm F1: 0.9929
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  - Zipcode F1: 0.9966
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  ## Model description
@@ -101,19 +101,23 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: cosine_with_restarts
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  - lr_scheduler_warmup_ratio: 0.2
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- - num_epochs: 7
 
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  ### Training results
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  | 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 |
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  |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:------:|:---------:|:------:|:-----------------:|:-----------------:|:-------:|:--------------:|:---------:|:----------------:|:-------------------:|:-------------------:|:-----------:|:---------------:|:---------------:|:-----------------:|:-------:|:------:|:--------:|:------------------:|:-----------:|:------------:|:---------:|:---------:|:-------:|:------:|:-------:|:-------:|:----------:|:-----------:|:----------:|:-----------:|:------------------:|:------:|:---------------:|:-------------:|:----------------------:|:-------------------:|:-----------:|:------------:|:--------------:|:------:|:---------:|:-------------------:|:------:|:------:|:--------:|:---------:|:-------:|:------:|:------------:|:-----------:|:-------------:|:-------------:|:----------:|
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- | 0.0484 | 1.0 | 535 | 0.1039 | 0.9213 | 0.9437 | 0.9324 | 0.9686 | 0.9973 | 1.0 | 0.9642 | 0.9918 | 0.9868 | 0.9543 | 0.9752 | 0.9978 | 0.9948 | 0.9955 | 0.9667 | 0.9964 | 0.9317 | 0.8788 | 0.9949 | 0.7526 | 0.9969 | 0.8814 | 0.6186 | 0.9987 | 0.9196 | 0.9804 | 0.9882 | 0.9988 | 1.0 | 0.9949 | 0.0922 | 0.8337 | 0.5216 | 0.9492 | 0.9983 | 0.98 | 0.9471 | 0.9351 | 0.5688 | 0.9115 | 0.1538 | 1.0 | 0.4 | 0.9732 | 0.9947 | 0.9967 | 0.9704 | 0.9806 | 0.9966 | 0.9783 | 0.9926 | 0.9715 | 0.9989 | 0.9890 | 0.9983 | 1.0 | 0.9966 | 0.9944 | 0.9976 | 0.9854 |
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- | 0.0607 | 2.0 | 1070 | 0.0445 | 0.9510 | 0.9612 | 0.9560 | 0.9797 | 0.9938 | 0.9860 | 0.9723 | 0.9887 | 0.9733 | 0.9779 | 0.9813 | 0.9946 | 0.9906 | 0.9985 | 0.9474 | 0.9929 | 0.9331 | 0.8508 | 0.9898 | 0.7176 | 0.9969 | 0.9530 | 0.9028 | 0.9969 | 1.0 | 0.9804 | 0.9847 | 0.9918 | 0.9779 | 0.9758 | 0.2269 | 0.8362 | 0.7929 | 0.8667 | 0.9941 | 0.9245 | 0.9710 | 0.9491 | 0.9954 | 0.9351 | 0.2222 | 1.0 | 0.0 | 0.9919 | 0.9961 | 0.9892 | 0.9779 | 0.9669 | 0.9989 | 0.9670 | 0.9840 | 0.9923 | 0.9979 | 0.9834 | 0.9974 | 0.9975 | 0.9923 | 0.9808 | 0.9837 | 0.9776 |
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- | 0.0585 | 3.0 | 1605 | 0.0391 | 0.9584 | 0.9652 | 0.9618 | 0.9834 | 1.0 | 0.9895 | 0.9706 | 0.9969 | 0.9868 | 0.9894 | 0.9788 | 0.9935 | 0.9990 | 0.9985 | 0.9825 | 0.9964 | 0.9639 | 0.8127 | 0.9897 | 0.7173 | 0.9953 | 0.9519 | 0.875 | 0.9981 | 1.0 | 1.0 | 0.9912 | 0.9988 | 0.9925 | 0.9923 | 0.3031 | 0.8371 | 0.8252 | 0.9836 | 0.9831 | 1.0 | 0.9581 | 0.9608 | 0.9954 | 0.9645 | 0.7805 | 1.0 | 0.8571 | 0.9850 | 0.9961 | 0.9967 | 0.9926 | 0.9790 | 0.9989 | 0.9890 | 0.9829 | 0.9904 | 0.9968 | 0.9903 | 1.0 | 1.0 | 0.9974 | 0.9833 | 0.9953 | 0.9818 |
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- | 0.0449 | 4.0 | 2140 | 0.0396 | 0.9668 | 0.9747 | 0.9707 | 0.9866 | 1.0 | 0.9956 | 0.9821 | 0.9928 | 0.9868 | 0.9936 | 0.9891 | 0.9989 | 0.9958 | 1.0 | 0.9655 | 0.9893 | 0.9721 | 0.9329 | 0.9949 | 0.8498 | 1.0 | 0.9763 | 0.9611 | 0.9839 | 1.0 | 0.9903 | 0.9965 | 0.9988 | 0.9925 | 0.9949 | 0.5304 | 0.8240 | 0.8382 | 0.9508 | 0.9949 | 1.0 | 0.9753 | 0.8916 | 0.9954 | 0.9666 | 0.8889 | 0.9984 | 0.75 | 1.0 | 0.9948 | 0.9967 | 0.9888 | 0.9840 | 0.9989 | 1.0 | 0.9914 | 0.9923 | 0.9989 | 0.9889 | 1.0 | 1.0 | 0.9983 | 1.0 | 0.9929 | 0.9910 |
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- | 0.0405 | 5.0 | 2675 | 0.0204 | 0.9756 | 0.9807 | 0.9782 | 0.9912 | 1.0 | 1.0 | 0.9755 | 1.0 | 0.9934 | 0.9925 | 0.9969 | 0.9978 | 1.0 | 0.9985 | 0.9831 | 1.0 | 0.9839 | 0.9449 | 0.9897 | 0.8794 | 1.0 | 0.9884 | 0.9762 | 0.9863 | 1.0 | 1.0 | 0.9982 | 0.9976 | 0.9813 | 0.9949 | 0.5933 | 0.8519 | 0.8759 | 0.9667 | 0.9882 | 1.0 | 0.9867 | 0.9791 | 1.0 | 0.9822 | 0.8636 | 1.0 | 0.5714 | 1.0 | 0.9974 | 1.0 | 1.0 | 0.9949 | 1.0 | 1.0 | 0.9975 | 0.9981 | 0.9979 | 0.9930 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9966 |
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- | 0.026 | 6.0 | 3210 | 0.0116 | 0.9819 | 0.9863 | 0.9841 | 0.9960 | 1.0 | 1.0 | 0.9951 | 1.0 | 1.0 | 0.9883 | 1.0 | 1.0 | 1.0 | 0.9985 | 0.9831 | 1.0 | 0.9828 | 0.9663 | 0.9949 | 0.9225 | 0.9984 | 0.9872 | 0.9749 | 0.9800 | 1.0 | 1.0 | 1.0 | 0.9976 | 1.0 | 1.0 | 0.7247 | 0.8389 | 0.9779 | 0.9667 | 0.9958 | 1.0 | 0.9956 | 0.9688 | 1.0 | 0.9850 | 0.9583 | 1.0 | 0.8571 | 0.9949 | 0.9961 | 0.9967 | 0.9963 | 0.9940 | 1.0 | 1.0 | 0.9975 | 0.9981 | 0.9989 | 0.9958 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9929 | 0.9977 |
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- | 0.0175 | 7.0 | 3745 | 0.0101 | 0.9818 | 0.9871 | 0.9844 | 0.9965 | 1.0 | 1.0 | 0.9951 | 1.0 | 1.0 | 0.9915 | 0.9969 | 1.0 | 1.0 | 0.9985 | 0.9831 | 0.9964 | 0.9889 | 0.9678 | 0.9949 | 0.9266 | 0.9984 | 0.9895 | 0.9774 | 0.9776 | 1.0 | 1.0 | 1.0 | 0.9976 | 1.0 | 1.0 | 0.7367 | 0.8360 | 0.9797 | 0.9667 | 1.0 | 1.0 | 1.0 | 0.9688 | 1.0 | 0.9887 | 0.9583 | 1.0 | 0.8571 | 0.9949 | 0.9961 | 0.9838 | 0.9963 | 0.9949 | 1.0 | 1.0 | 0.9938 | 1.0 | 0.9989 | 0.9958 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9929 | 0.9966 |
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0211
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+ - Overall Precision: 0.9722
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+ - Overall Recall: 0.9792
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+ - Overall F1: 0.9757
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+ - Overall Accuracy: 0.9915
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+ - Accountname F1: 0.9993
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+ - Accountnumber F1: 0.9986
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+ - Age F1: 0.9884
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+ - Amount F1: 0.9984
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+ - Bic F1: 0.9942
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+ - Bitcoinaddress F1: 0.9974
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+ - Buildingnumber F1: 0.9898
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  - City F1: 1.0
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  - Companyname F1: 1.0
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+ - County F1: 0.9976
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+ - Creditcardcvv F1: 0.9541
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+ - Creditcardissuer F1: 0.9970
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+ - Creditcardnumber F1: 0.9754
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+ - Currency F1: 0.8966
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+ - Currencycode F1: 0.9946
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+ - Currencyname F1: 0.7697
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+ - Currencysymbol F1: 0.9958
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+ - Date F1: 0.9778
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+ - Dob F1: 0.9546
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+ - Email F1: 1.0
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  - Ethereumaddress F1: 1.0
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+ - Eyecolor F1: 0.9925
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+ - Firstname F1: 0.9947
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+ - Gender F1: 1.0
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  - Height F1: 1.0
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+ - Iban F1: 0.9978
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+ - Ip F1: 0.5404
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+ - Ipv4 F1: 0.8455
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+ - Ipv6 F1: 0.8855
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+ - Jobarea F1: 0.9091
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  - Jobtitle F1: 1.0
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+ - Jobtype F1: 0.9672
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+ - Lastname F1: 0.9855
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+ - Litecoinaddress F1: 0.9949
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+ - Mac F1: 0.9965
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+ - Maskednumber F1: 0.9836
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+ - Middlename F1: 0.7385
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  - Nearbygpscoordinate F1: 1.0
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+ - Ordinaldirection F1: 1.0
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+ - Password F1: 1.0
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+ - Phoneimei F1: 0.9978
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+ - Phonenumber F1: 0.9975
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+ - Pin F1: 0.9820
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+ - Prefix F1: 0.9872
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  - Secondaryaddress F1: 1.0
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+ - Sex F1: 0.9916
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+ - Ssn F1: 0.9960
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+ - State F1: 0.9967
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+ - Street F1: 0.9991
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+ - Time F1: 1.0
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  - Url F1: 1.0
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+ - Useragent F1: 0.9981
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  - Username F1: 1.0
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+ - Vehiclevin F1: 0.9950
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+ - Vehiclevrm F1: 0.9870
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  - Zipcode F1: 0.9966
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  ## Model description
 
101
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: cosine_with_restarts
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  - lr_scheduler_warmup_ratio: 0.2
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+ - num_epochs: 10
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+ - mixed_precision_training: Native AMP
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  ### Training results
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  | 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 |
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  |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:------:|:---------:|:------:|:-----------------:|:-----------------:|:-------:|:--------------:|:---------:|:----------------:|:-------------------:|:-------------------:|:-----------:|:---------------:|:---------------:|:-----------------:|:-------:|:------:|:--------:|:------------------:|:-----------:|:------------:|:---------:|:---------:|:-------:|:------:|:-------:|:-------:|:----------:|:-----------:|:----------:|:-----------:|:------------------:|:------:|:---------------:|:-------------:|:----------------------:|:-------------------:|:-----------:|:------------:|:--------------:|:------:|:---------:|:-------------------:|:------:|:------:|:--------:|:---------:|:-------:|:------:|:------------:|:-----------:|:-------------:|:-------------:|:----------:|
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+ | 1.5081 | 1.0 | 669 | 0.1618 | 0.7172 | 0.7690 | 0.7422 | 0.9464 | 0.9134 | 0.7806 | 0.7878 | 0.3447 | 0.6647 | 0.8155 | 0.7413 | 0.6679 | 0.8354 | 0.7719 | 0.0 | 0.0 | 0.3622 | 0.4985 | 0.0 | 0.0 | 0.0166 | 0.8014 | 0.3108 | 0.9665 | 0.9878 | 0.0 | 0.7814 | 0.7444 | 0.9244 | 0.9059 | 0.0 | 0.8207 | 0.7064 | 0.0 | 0.8649 | 0.0 | 0.2521 | 0.0 | 0.9755 | 0.5147 | 0.0 | 0.9918 | 0.0 | 0.9443 | 0.9839 | 0.9700 | 0.1919 | 0.6695 | 0.8664 | 0.0 | 0.8882 | 0.4056 | 0.6466 | 0.9538 | 0.9870 | 0.9519 | 0.7703 | 0.9380 | 0.7398 | 0.6931 |
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+ | 0.1884 | 2.0 | 1338 | 0.1022 | 0.8709 | 0.8877 | 0.8792 | 0.9648 | 0.9565 | 0.9263 | 0.8838 | 0.9202 | 0.8272 | 0.8935 | 0.8713 | 0.9326 | 0.9596 | 0.9403 | 0.0 | 0.9699 | 0.8038 | 0.6842 | 0.4724 | 0.3972 | 0.7884 | 0.8431 | 0.5134 | 0.9913 | 0.9830 | 0.8794 | 0.9270 | 0.9272 | 0.9718 | 0.9341 | 0.0 | 0.8180 | 0.8136 | 0.0 | 0.9206 | 0.0882 | 0.5360 | 0.7641 | 0.9789 | 0.6638 | 0.0 | 0.9930 | 0.0 | 0.9487 | 0.9646 | 0.9624 | 0.8082 | 0.9381 | 0.9679 | 0.9333 | 0.9392 | 0.8885 | 0.9371 | 0.9681 | 0.9883 | 0.9481 | 0.9683 | 0.7937 | 0.7858 | 0.8649 |
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+ | 0.0915 | 3.0 | 2007 | 0.0748 | 0.8934 | 0.9163 | 0.9047 | 0.9702 | 0.9783 | 0.9562 | 0.8920 | 0.9582 | 0.9474 | 0.9315 | 0.9056 | 0.9587 | 0.9785 | 0.9757 | 0.6190 | 0.9699 | 0.8209 | 0.7763 | 0.8342 | 0.0359 | 0.9223 | 0.7257 | 0.6549 | 0.9954 | 0.9818 | 0.8056 | 0.9518 | 0.9519 | 0.9887 | 0.9806 | 0.0039 | 0.8328 | 0.7923 | 0.0 | 0.9657 | 0.7634 | 0.6688 | 0.8525 | 0.9790 | 0.7631 | 0.0 | 0.9883 | 0.0 | 0.9646 | 0.9957 | 0.9829 | 0.9054 | 0.9413 | 0.9776 | 0.9421 | 0.9663 | 0.9659 | 0.9498 | 0.9682 | 0.9925 | 0.9944 | 0.9913 | 0.9497 | 0.9762 | 0.9148 |
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+ | 0.0782 | 4.0 | 2676 | 0.0632 | 0.9004 | 0.9286 | 0.9143 | 0.9622 | 0.9958 | 0.9832 | 0.9433 | 0.9646 | 0.9855 | 0.9655 | 0.9527 | 0.9808 | 0.9904 | 0.9707 | 0.5823 | 0.9670 | 0.8964 | 0.7717 | 0.8830 | 0.3387 | 0.9780 | 0.8780 | 0.6913 | 0.9776 | 0.9951 | 0.8857 | 0.9675 | 0.9865 | 0.9859 | 0.9742 | 0.2961 | 0.8395 | 0.2833 | 0.6471 | 0.9952 | 0.9355 | 0.8277 | 0.8808 | 0.9756 | 0.8859 | 0.0 | 0.9883 | 0.0 | 0.9838 | 0.9946 | 0.9853 | 0.9066 | 0.9351 | 0.9942 | 0.9134 | 0.9911 | 0.9493 | 0.9869 | 0.9768 | 0.9979 | 0.9944 | 0.9953 | 0.9572 | 0.9848 | 0.9489 |
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+ | 0.067 | 5.0 | 3345 | 0.0517 | 0.9389 | 0.9496 | 0.9442 | 0.9767 | 0.9936 | 0.9867 | 0.9579 | 0.9584 | 0.9884 | 0.9746 | 0.9542 | 0.9958 | 0.9887 | 0.9903 | 0.8952 | 0.9970 | 0.9397 | 0.8107 | 0.9140 | 0.6029 | 0.9848 | 0.9160 | 0.8132 | 0.9969 | 0.9951 | 0.9412 | 0.9731 | 0.9942 | 0.9915 | 0.9870 | 0.2084 | 0.7981 | 0.8064 | 0.7568 | 0.9827 | 0.9134 | 0.8692 | 0.8289 | 0.9877 | 0.9392 | 0.0513 | 0.9883 | 0.6667 | 0.9722 | 0.9904 | 0.9943 | 0.9591 | 0.9676 | 0.9903 | 0.9748 | 0.9921 | 0.9790 | 0.9904 | 0.9787 | 0.9952 | 0.9926 | 0.9893 | 0.9749 | 0.9891 | 0.9653 |
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+ | 0.0568 | 6.0 | 4014 | 0.0472 | 0.9539 | 0.9594 | 0.9567 | 0.9797 | 0.9986 | 0.9986 | 0.9508 | 0.9703 | 0.9884 | 0.9722 | 0.9755 | 0.9975 | 0.9965 | 0.9904 | 0.84 | 0.9670 | 0.95 | 0.8498 | 0.9565 | 0.6278 | 0.9944 | 0.9165 | 0.8625 | 0.9985 | 0.9927 | 0.9630 | 0.9894 | 0.9962 | 0.9944 | 0.9913 | 0.1921 | 0.8345 | 0.8222 | 0.8293 | 0.9979 | 0.9516 | 0.9465 | 0.9340 | 0.9772 | 0.9563 | 0.6667 | 0.9977 | 0.6667 | 0.9985 | 0.9978 | 0.9975 | 0.9704 | 0.9686 | 0.9971 | 0.9748 | 0.9931 | 0.9886 | 0.9948 | 0.9892 | 1.0 | 0.9963 | 0.9980 | 0.9749 | 0.9935 | 0.9830 |
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+ | 0.0482 | 7.0 | 4683 | 0.0426 | 0.9632 | 0.9678 | 0.9655 | 0.9826 | 0.9993 | 0.9972 | 0.9707 | 0.9926 | 0.9942 | 0.9602 | 0.9847 | 0.9992 | 0.9983 | 0.9952 | 0.9444 | 0.9970 | 0.9627 | 0.8634 | 0.9730 | 0.72 | 0.9958 | 0.9586 | 0.9102 | 1.0 | 0.9951 | 0.9925 | 0.9917 | 0.9943 | 1.0 | 0.9935 | 0.2256 | 0.8430 | 0.8488 | 0.8571 | 1.0 | 0.9516 | 0.9499 | 0.9198 | 0.9930 | 0.9704 | 0.6866 | 0.9977 | 0.9091 | 1.0 | 0.9978 | 0.9975 | 0.9880 | 0.9797 | 0.9971 | 0.9915 | 0.9940 | 0.9951 | 1.0 | 0.9873 | 1.0 | 0.9963 | 0.9993 | 0.9950 | 0.9870 | 0.9863 |
118
+ | 0.042 | 8.0 | 5352 | 0.0288 | 0.9697 | 0.9749 | 0.9723 | 0.9874 | 0.9993 | 0.9986 | 0.9808 | 0.9959 | 0.9942 | 0.9932 | 0.9911 | 1.0 | 1.0 | 0.9976 | 0.9444 | 0.9970 | 0.9716 | 0.8907 | 0.9730 | 0.7561 | 0.9944 | 0.9725 | 0.9429 | 1.0 | 0.9976 | 0.9925 | 0.9927 | 0.9971 | 1.0 | 0.9957 | 0.4056 | 0.8498 | 0.8565 | 0.9091 | 1.0 | 0.9672 | 0.9757 | 0.9848 | 0.9965 | 0.9808 | 0.7164 | 1.0 | 1.0 | 1.0 | 0.9978 | 0.9975 | 0.9880 | 0.9872 | 0.9971 | 0.9831 | 0.9960 | 0.9967 | 0.9991 | 0.9941 | 1.0 | 0.9981 | 1.0 | 0.9950 | 0.9892 | 0.9966 |
119
+ | 0.0327 | 9.0 | 6021 | 0.0237 | 0.9688 | 0.9778 | 0.9733 | 0.9899 | 0.9993 | 0.9986 | 0.9872 | 0.9984 | 0.9942 | 0.9957 | 0.9898 | 1.0 | 1.0 | 1.0 | 0.9444 | 0.9970 | 0.9746 | 0.8958 | 0.9946 | 0.7761 | 0.9958 | 0.9787 | 0.9557 | 1.0 | 1.0 | 0.9925 | 0.9947 | 1.0 | 1.0 | 0.9957 | 0.5173 | 0.8455 | 0.8317 | 0.9091 | 1.0 | 0.9672 | 0.9871 | 0.9949 | 0.9965 | 0.9818 | 0.7576 | 1.0 | 1.0 | 1.0 | 0.9978 | 0.9975 | 0.9820 | 0.9879 | 0.9990 | 0.9916 | 0.9960 | 0.9967 | 0.9991 | 0.9971 | 1.0 | 0.9981 | 1.0 | 0.9950 | 0.9913 | 0.9966 |
120
+ | 0.0267 | 10.0 | 6690 | 0.0211 | 0.9722 | 0.9792 | 0.9757 | 0.9915 | 0.9993 | 0.9986 | 0.9884 | 0.9984 | 0.9942 | 0.9974 | 0.9898 | 1.0 | 1.0 | 0.9976 | 0.9541 | 0.9970 | 0.9754 | 0.8966 | 0.9946 | 0.7697 | 0.9958 | 0.9778 | 0.9546 | 1.0 | 1.0 | 0.9925 | 0.9947 | 1.0 | 1.0 | 0.9978 | 0.5404 | 0.8455 | 0.8855 | 0.9091 | 1.0 | 0.9672 | 0.9855 | 0.9949 | 0.9965 | 0.9836 | 0.7385 | 1.0 | 1.0 | 1.0 | 0.9978 | 0.9975 | 0.9820 | 0.9872 | 1.0 | 0.9916 | 0.9960 | 0.9967 | 0.9991 | 1.0 | 1.0 | 0.9981 | 1.0 | 0.9950 | 0.9870 | 0.9966 |
121
 
122
 
123
  ### Framework versions
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  }
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