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deepaksiloka
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PII-Detection-V2.1

Token Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
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  1. README.md +37 -37
  2. model.safetensors +1 -1
README.md CHANGED
@@ -16,36 +16,36 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0368
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- - Overall Precision: 0.9351
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- - Overall Recall: 0.9512
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- - Overall F1: 0.9431
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- - Overall Accuracy: 0.9916
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- - Accountname F1: 0.9871
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- - Accountnumber F1: 0.9910
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- - Buildingnumber F1: 0.7900
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- - City F1: 0.9636
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- - Companyname F1: 0.9590
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- - County F1: 0.9427
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- - Creditcardcvv F1: 0.8543
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- - Creditcardissuer F1: 0.9043
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- - Creditcardnumber F1: 0.8696
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- - Email F1: 0.9979
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- - Firstname F1: 0.9195
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- - Fullname F1: 0.9831
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- - Iban F1: 0.9658
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- - Lastname F1: 0.8370
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- - Middlename F1: 0.8452
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- - Name F1: 0.9926
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- - Number F1: 0.9157
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- - Phonenumber F1: 0.9792
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- - Pin F1: 0.8959
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- - Secondaryaddress F1: 0.9787
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- - State F1: 0.9286
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- - Street F1: 0.8457
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- - Streetaddress F1: 0.7259
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- - Url F1: 0.9980
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- - Username F1: 0.9509
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  ## Model description
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@@ -74,13 +74,13 @@ The following hyperparameters were used during training:
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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 | Buildingnumber F1 | City F1 | Companyname F1 | County F1 | Creditcardcvv F1 | Creditcardissuer F1 | Creditcardnumber F1 | Email F1 | Firstname F1 | Fullname F1 | Iban F1 | Lastname F1 | Middlename F1 | Name F1 | Number F1 | Phonenumber F1 | Pin F1 | Secondaryaddress F1 | State F1 | Street F1 | Streetaddress F1 | Url F1 | Username F1 |
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- |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:-----------------:|:-------:|:--------------:|:---------:|:----------------:|:-------------------:|:-------------------:|:--------:|:------------:|:-----------:|:-------:|:-----------:|:-------------:|:-------:|:---------:|:--------------:|:------:|:-------------------:|:--------:|:---------:|:----------------:|:------:|:-----------:|
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- | 0.0885 | 1.0 | 1361 | 0.0489 | 0.8436 | 0.8993 | 0.8705 | 0.9846 | 0.9573 | 0.9269 | 0.6452 | 0.9295 | 0.8956 | 0.8195 | 0.7291 | 0.8274 | 0.7974 | 0.9931 | 0.8206 | 0.9634 | 0.9363 | 0.7408 | 0.4829 | 0.9732 | 0.6385 | 0.9037 | 0.7326 | 0.9648 | 0.8559 | 0.6673 | 0.0246 | 0.9901 | 0.9369 |
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- | 0.0338 | 2.0 | 2722 | 0.0345 | 0.9069 | 0.9333 | 0.9199 | 0.9898 | 0.9667 | 0.9713 | 0.7312 | 0.9521 | 0.9363 | 0.9009 | 0.8538 | 0.9093 | 0.8471 | 0.9970 | 0.8864 | 0.9819 | 0.9136 | 0.7901 | 0.7787 | 0.9864 | 0.8854 | 0.9589 | 0.8097 | 0.9732 | 0.9124 | 0.7673 | 0.5046 | 0.9975 | 0.9530 |
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- | 0.0172 | 3.0 | 4083 | 0.0313 | 0.9277 | 0.9439 | 0.9358 | 0.9908 | 0.9889 | 0.9727 | 0.7592 | 0.9611 | 0.9551 | 0.9357 | 0.8656 | 0.9076 | 0.8629 | 0.9982 | 0.9062 | 0.9813 | 0.9710 | 0.8314 | 0.8107 | 0.9913 | 0.9435 | 0.9727 | 0.8847 | 0.9779 | 0.9173 | 0.7895 | 0.6915 | 0.9980 | 0.9521 |
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- | 0.0103 | 4.0 | 5444 | 0.0342 | 0.9330 | 0.9493 | 0.9411 | 0.9913 | 0.9895 | 0.9910 | 0.7939 | 0.9633 | 0.9490 | 0.9365 | 0.8577 | 0.9051 | 0.8660 | 0.9996 | 0.9149 | 0.9833 | 0.9753 | 0.8289 | 0.8430 | 0.9899 | 0.8456 | 0.9588 | 0.8824 | 0.9795 | 0.9299 | 0.8361 | 0.7332 | 0.9980 | 0.9527 |
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- | 0.0058 | 5.0 | 6805 | 0.0368 | 0.9351 | 0.9512 | 0.9431 | 0.9916 | 0.9871 | 0.9910 | 0.7900 | 0.9636 | 0.9590 | 0.9427 | 0.8543 | 0.9043 | 0.8696 | 0.9979 | 0.9195 | 0.9831 | 0.9658 | 0.8370 | 0.8452 | 0.9926 | 0.9157 | 0.9792 | 0.8959 | 0.9787 | 0.9286 | 0.8457 | 0.7259 | 0.9980 | 0.9509 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0331
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+ - Overall Precision: 0.9482
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+ - Overall Recall: 0.9574
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+ - Overall F1: 0.9528
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+ - Overall Accuracy: 0.9926
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+ - Accountname F1: 0.9939
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+ - Accountnumber F1: 0.9879
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+ - Buildingnumber F1: 0.8059
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+ - City F1: 0.9729
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+ - Companyname F1: 0.9773
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+ - County F1: 0.9463
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+ - Creditcardcvv F1: 0.8970
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+ - Creditcardissuer F1: 0.9565
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+ - Creditcardnumber F1: 0.8770
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+ - Email F1: 0.9981
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+ - Firstname F1: 0.9324
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+ - Fullname F1: 0.9851
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+ - Iban F1: 0.9834
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+ - Lastname F1: 0.8744
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+ - Middlename F1: 0.8390
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+ - Name F1: 0.9972
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+ - Number F1: 0.9684
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+ - Phonenumber F1: 0.9788
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+ - Pin F1: 0.9017
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+ - Secondaryaddress F1: 0.9892
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+ - State F1: 0.9421
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+ - Street F1: 0.8617
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+ - Streetaddress F1: 0.7533
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+ - Url F1: 0.9977
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+ - Username F1: 0.9654
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  ## Model description
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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 | Buildingnumber F1 | City F1 | Companyname F1 | County F1 | Creditcardcvv F1 | Creditcardissuer F1 | Creditcardnumber F1 | Email F1 | Firstname F1 | Fullname F1 | Iban F1 | Lastname F1 | Middlename F1 | Name F1 | Number F1 | Phonenumber F1 | Pin F1 | Secondaryaddress F1 | State F1 | Street F1 | Streetaddress F1 | Url F1 | Username F1 |
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+ |:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:-----------------:|:-------:|:--------------:|:---------:|:----------------:|:-------------------:|:-------------------:|:--------:|:------------:|:-----------:|:-------:|:-----------:|:-------------:|:-------:|:---------:|:--------------:|:------:|:-------------------:|:--------:|:---------:|:----------------:|:------:|:-----------:|
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+ | 0.0445 | 1.0 | 2031 | 0.0375 | 0.8924 | 0.9268 | 0.9093 | 0.9885 | 0.9781 | 0.9675 | 0.7260 | 0.9391 | 0.9347 | 0.8747 | 0.8221 | 0.8962 | 0.8239 | 0.9948 | 0.8764 | 0.9804 | 0.9528 | 0.7683 | 0.6548 | 0.9889 | 0.8223 | 0.9277 | 0.7938 | 0.9853 | 0.8633 | 0.7646 | 0.4597 | 0.9937 | 0.9427 |
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+ | 0.0266 | 2.0 | 4062 | 0.0296 | 0.9245 | 0.9455 | 0.9349 | 0.9908 | 0.9900 | 0.9810 | 0.7546 | 0.9639 | 0.9574 | 0.9085 | 0.8370 | 0.9375 | 0.8809 | 0.9979 | 0.9094 | 0.9824 | 0.9785 | 0.8299 | 0.8111 | 0.9938 | 0.9247 | 0.9523 | 0.8640 | 0.9826 | 0.9163 | 0.7605 | 0.6372 | 0.9977 | 0.9599 |
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+ | 0.0148 | 3.0 | 6093 | 0.0277 | 0.9414 | 0.9529 | 0.9471 | 0.9921 | 0.9948 | 0.9863 | 0.7876 | 0.9689 | 0.9624 | 0.9324 | 0.8883 | 0.9537 | 0.8795 | 0.9979 | 0.9252 | 0.9849 | 0.9840 | 0.8515 | 0.8310 | 0.9946 | 0.9506 | 0.9675 | 0.8685 | 0.9875 | 0.9325 | 0.8355 | 0.7560 | 0.9973 | 0.9685 |
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+ | 0.0095 | 4.0 | 8124 | 0.0301 | 0.9438 | 0.9536 | 0.9487 | 0.9921 | 0.9913 | 0.9859 | 0.8018 | 0.9742 | 0.9652 | 0.9443 | 0.8982 | 0.9508 | 0.8784 | 0.9986 | 0.9281 | 0.9842 | 0.9828 | 0.8584 | 0.8294 | 0.9952 | 0.9681 | 0.9629 | 0.8889 | 0.9875 | 0.9374 | 0.8430 | 0.7522 | 0.9980 | 0.9457 |
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+ | 0.0038 | 5.0 | 10155 | 0.0331 | 0.9482 | 0.9574 | 0.9528 | 0.9926 | 0.9939 | 0.9879 | 0.8059 | 0.9729 | 0.9773 | 0.9463 | 0.8970 | 0.9565 | 0.8770 | 0.9981 | 0.9324 | 0.9851 | 0.9834 | 0.8744 | 0.8390 | 0.9972 | 0.9684 | 0.9788 | 0.9017 | 0.9892 | 0.9421 | 0.8617 | 0.7533 | 0.9977 | 0.9654 |
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  ### Framework versions
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