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  1. README.md +83 -105
  2. all_results.json +66 -66
  3. eval_results.json +60 -60
  4. model.safetensors +1 -1
  5. train_results.json +7 -7
  6. trainer_state.json +696 -780
README.md CHANGED
@@ -6,13 +6,6 @@ tags:
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  model-index:
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  - name: deberta-v3-base_finetuned_ai4privacy_v2
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  results: []
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- datasets:
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- - ai4privacy/pii-masking-200k
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- language:
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- - en
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- metrics:
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- - seqeval
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- pipeline_tag: token-classification
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -20,20 +13,73 @@ should probably proofread and complete it, then remove this comment. -->
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  # deberta-v3-base_finetuned_ai4privacy_v2
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23
- This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the [ai4privacy/pii-masking-200k](https://huggingface.co/ai4privacy/pii-masking-200k) dataset.
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-
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- ## Useage
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- GitHub Implementation: [Ai4Privacy](https://github.com/Sripaad/ai4privacy)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Model description
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30
- This model has been finetuned on the World's largest open source privacy dataset.
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-
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- The purpose of the trained models is to remove personally identifiable information (PII) from text, especially in the context of AI assistants and LLMs.
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-
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- The example texts have 54 PII classes (types of sensitive data), targeting 229 discussion subjects / use cases split across business, education, psychology and legal fields, and 5 interactions styles (e.g. casual conversation, formal document, emails etc...).
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-
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- Take a look at the Github implementation for specific reasearch.
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  ## Intended uses & limitations
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@@ -43,104 +89,36 @@ More information needed
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  More information needed
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- ## Training hyperparameters
 
 
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  The following hyperparameters were used during training:
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- - learning_rate: 6e-04
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- - train_batch_size: 16
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- - eval_batch_size: 16
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  - seed: 42
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- - optimizer: Adam with betas=(0.96,0.996) 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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-
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- ## Class wise metrics
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- It achieves the following results on the evaluation set:
61
-
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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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-
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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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125
  ### Training results
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127
- | 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 |
130
- | 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 |
131
- | 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 |
135
- | 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 |
136
- | 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 |
137
- | 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 |
138
- | 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 |
139
 
140
 
141
  ### Framework versions
142
 
143
  - Transformers 4.35.2
144
- - Pytorch 2.1.0+cu118
145
  - Datasets 2.15.0
146
- - Tokenizers 0.15.0
 
6
  model-index:
7
  - name: deberta-v3-base_finetuned_ai4privacy_v2
8
  results: []
 
 
 
 
 
 
 
9
  ---
10
 
11
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
13
 
14
  # deberta-v3-base_finetuned_ai4privacy_v2
15
 
16
+ This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
17
+ It achieves the following results on the evaluation set:
18
+ - Loss: 0.0693
19
+ - Overall Precision: 0.9664
20
+ - Overall Recall: 0.9732
21
+ - Overall F1: 0.9698
22
+ - Overall Accuracy: 0.9728
23
+ - Accountname F1: 1.0
24
+ - Accountnumber F1: 1.0
25
+ - Age F1: 0.9760
26
+ - Amount F1: 0.9897
27
+ - Bic F1: 0.9978
28
+ - Bitcoinaddress F1: 0.9907
29
+ - Buildingnumber F1: 0.9906
30
+ - City F1: 0.9930
31
+ - Companyname F1: 0.9994
32
+ - County F1: 0.9939
33
+ - Creditcardcvv F1: 1.0
34
+ - Creditcardissuer F1: 0.9891
35
+ - Creditcardnumber F1: 0.9590
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+ - Currency F1: 0.9052
37
+ - Currencycode F1: 0.9875
38
+ - Currencyname F1: 0.7022
39
+ - Currencysymbol F1: 0.9892
40
+ - Date F1: 0.9126
41
+ - Dob F1: 0.7438
42
+ - Email F1: 1.0
43
+ - Ethereumaddress F1: 1.0
44
+ - Eyecolor F1: 1.0
45
+ - Firstname F1: 0.9934
46
+ - Gender F1: 0.9991
47
+ - Height F1: 1.0
48
+ - Iban F1: 1.0
49
+ - Ip F1: 0.1551
50
+ - Ipv4 F1: 0.8393
51
+ - Ipv6 F1: 0.8034
52
+ - Jobarea F1: 0.9942
53
+ - Jobtitle F1: 0.9993
54
+ - Jobtype F1: 0.9928
55
+ - Lastname F1: 0.9877
56
+ - Litecoinaddress F1: 0.9770
57
+ - Mac F1: 1.0
58
+ - Maskednumber F1: 0.9451
59
+ - Middlename F1: 0.9773
60
+ - Nearbygpscoordinate F1: 1.0
61
+ - Ordinaldirection F1: 0.9924
62
+ - Password F1: 1.0
63
+ - Phoneimei F1: 1.0
64
+ - Phonenumber F1: 1.0
65
+ - Pin F1: 0.9929
66
+ - Prefix F1: 0.9722
67
+ - Secondaryaddress F1: 0.9974
68
+ - Sex F1: 0.9949
69
+ - Ssn F1: 0.9970
70
+ - State F1: 0.9941
71
+ - Street F1: 0.9972
72
+ - Time F1: 0.9967
73
+ - Url F1: 1.0
74
+ - Useragent F1: 1.0
75
+ - Username F1: 0.9991
76
+ - Vehiclevin F1: 1.0
77
+ - Vehiclevrm F1: 1.0
78
+ - Zipcode F1: 0.9890
79
 
80
  ## Model description
81
 
82
+ More information needed
 
 
 
 
 
 
83
 
84
  ## Intended uses & limitations
85
 
 
89
 
90
  More information needed
91
 
92
+ ## Training procedure
93
+
94
+ ### Training hyperparameters
95
 
96
  The following hyperparameters were used during training:
97
+ - learning_rate: 5e-05
98
+ - train_batch_size: 4
99
+ - eval_batch_size: 4
100
  - seed: 42
101
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
102
  - lr_scheduler_type: cosine_with_restarts
103
  - lr_scheduler_warmup_ratio: 0.2
104
+ - num_epochs: 7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
105
 
106
  ### Training results
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108
+ | 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 |
109
+ |:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:------:|:---------:|:------:|:-----------------:|:-----------------:|:-------:|:--------------:|:---------:|:----------------:|:-------------------:|:-------------------:|:-----------:|:---------------:|:---------------:|:-----------------:|:-------:|:------:|:--------:|:------------------:|:-----------:|:------------:|:---------:|:---------:|:-------:|:------:|:-------:|:-------:|:----------:|:-----------:|:----------:|:-----------:|:------------------:|:------:|:---------------:|:-------------:|:----------------------:|:-------------------:|:-----------:|:------------:|:--------------:|:------:|:---------:|:-------------------:|:------:|:------:|:--------:|:---------:|:-------:|:------:|:------------:|:-----------:|:-------------:|:-------------:|:----------:|
110
+ | 0.3984 | 1.0 | 2393 | 0.5120 | 0.7268 | 0.7819 | 0.7533 | 0.8741 | 0.9265 | 0.9819 | 0.8237 | 0.5053 | 0.2315 | 0.8197 | 0.7840 | 0.4886 | 0.8657 | 0.6338 | 0.8775 | 0.8575 | 0.7152 | 0.4533 | 0.0959 | 0.0 | 0.6480 | 0.7621 | 0.1884 | 0.9840 | 1.0 | 0.6194 | 0.8740 | 0.6610 | 0.9642 | 0.9039 | 0.0 | 0.8500 | 0.0220 | 0.6325 | 0.7840 | 0.6899 | 0.7667 | 0.0 | 0.2966 | 0.0 | 0.3682 | 0.9986 | 0.9387 | 0.8558 | 0.9879 | 0.9687 | 0.7455 | 0.9252 | 0.9661 | 0.9110 | 0.9771 | 0.5282 | 0.7988 | 0.8453 | 0.9648 | 0.9804 | 0.9356 | 0.7741 | 0.6780 | 0.7915 |
111
+ | 0.2097 | 2.0 | 4786 | 0.1406 | 0.8392 | 0.8913 | 0.8645 | 0.9509 | 0.9760 | 0.9114 | 0.9227 | 0.7647 | 0.9190 | 0.9554 | 0.8975 | 0.8881 | 0.9535 | 0.8414 | 0.9114 | 0.9820 | 0.8503 | 0.7525 | 0.6171 | 0.0077 | 0.8787 | 0.3161 | 0.2847 | 0.9924 | 0.9918 | 0.9495 | 0.9076 | 0.9625 | 0.9890 | 0.9870 | 0.0 | 0.8484 | 0.8007 | 0.8651 | 0.9660 | 0.9164 | 0.8695 | 0.8756 | 0.9685 | 0.7768 | 0.6697 | 0.9956 | 0.9754 | 0.9652 | 0.9976 | 0.9849 | 0.7977 | 0.9373 | 0.9923 | 0.9815 | 0.9828 | 0.8093 | 0.9445 | 0.9735 | 0.9933 | 0.9651 | 0.9854 | 0.9843 | 0.975 | 0.8123 |
112
+ | 0.1271 | 3.0 | 7179 | 0.1049 | 0.9218 | 0.9312 | 0.9265 | 0.9618 | 0.9950 | 0.9880 | 0.9172 | 0.9309 | 0.9652 | 0.8222 | 0.9160 | 0.9364 | 0.9749 | 0.9556 | 0.9211 | 0.9856 | 0.8939 | 0.8237 | 0.76 | 0.0080 | 0.9360 | 0.8735 | 0.5567 | 0.9993 | 0.9973 | 0.9872 | 0.9547 | 0.9773 | 0.9574 | 0.9694 | 0.0 | 0.8510 | 0.8032 | 0.9404 | 0.9844 | 0.9522 | 0.9294 | 0.8584 | 1.0 | 0.8603 | 0.8908 | 1.0 | 0.9829 | 0.9513 | 1.0 | 0.9792 | 0.8579 | 0.9413 | 0.9968 | 0.9513 | 0.9929 | 0.9278 | 0.9484 | 0.9862 | 0.9940 | 0.8884 | 0.9943 | 0.9616 | 0.9648 | 0.9395 |
113
+ | 0.1345 | 4.0 | 9572 | 0.0941 | 0.9463 | 0.9580 | 0.9521 | 0.9659 | 0.9975 | 0.9979 | 0.9356 | 0.9597 | 0.9084 | 0.9569 | 0.9827 | 0.9734 | 0.9835 | 0.9780 | 0.9634 | 0.9904 | 0.9393 | 0.8542 | 0.8915 | 0.4069 | 0.9636 | 0.8873 | 0.6572 | 0.9993 | 1.0 | 0.9923 | 0.9796 | 0.9983 | 0.9917 | 0.9972 | 0.0 | 0.8515 | 0.8027 | 0.9689 | 0.9943 | 0.9685 | 0.9668 | 0.8162 | 0.9912 | 0.9110 | 0.9364 | 1.0 | 0.9848 | 0.9734 | 0.9976 | 0.9949 | 0.9739 | 0.9609 | 0.9968 | 0.9906 | 0.9899 | 0.9772 | 0.9875 | 0.9855 | 0.9978 | 1.0 | 0.9972 | 0.9867 | 0.9817 | 0.9780 |
114
+ | 0.1067 | 5.0 | 11965 | 0.0724 | 0.9556 | 0.9659 | 0.9607 | 0.9699 | 0.9967 | 0.9965 | 0.9705 | 0.9742 | 0.9892 | 0.9736 | 0.9891 | 0.9794 | 0.9951 | 0.9860 | 0.9897 | 0.9892 | 0.9517 | 0.8386 | 0.9770 | 0.4186 | 0.9822 | 0.8869 | 0.7016 | 1.0 | 1.0 | 0.9949 | 0.9859 | 0.9983 | 1.0 | 0.9954 | 0.0075 | 0.8569 | 0.8012 | 0.9819 | 0.9979 | 0.9856 | 0.9843 | 0.9383 | 1.0 | 0.9318 | 0.9461 | 1.0 | 0.9905 | 1.0 | 1.0 | 0.9978 | 0.9906 | 0.9646 | 0.9981 | 0.9924 | 0.9970 | 0.9862 | 0.9966 | 0.9951 | 0.9970 | 1.0 | 0.9981 | 0.9933 | 1.0 | 0.9913 |
115
+ | 0.0808 | 6.0 | 14358 | 0.0693 | 0.9664 | 0.9732 | 0.9698 | 0.9728 | 1.0 | 1.0 | 0.9760 | 0.9897 | 0.9978 | 0.9907 | 0.9906 | 0.9930 | 0.9994 | 0.9939 | 1.0 | 0.9891 | 0.9590 | 0.9052 | 0.9875 | 0.7022 | 0.9892 | 0.9126 | 0.7438 | 1.0 | 1.0 | 1.0 | 0.9934 | 0.9991 | 1.0 | 1.0 | 0.1551 | 0.8393 | 0.8034 | 0.9942 | 0.9993 | 0.9928 | 0.9877 | 0.9770 | 1.0 | 0.9451 | 0.9773 | 1.0 | 0.9924 | 1.0 | 1.0 | 1.0 | 0.9929 | 0.9722 | 0.9974 | 0.9949 | 0.9970 | 0.9941 | 0.9972 | 0.9967 | 1.0 | 1.0 | 0.9991 | 1.0 | 1.0 | 0.9890 |
116
+ | 0.0779 | 7.0 | 16751 | 0.0697 | 0.9698 | 0.9756 | 0.9727 | 0.9739 | 0.9983 | 1.0 | 0.9815 | 0.9904 | 1.0 | 0.9938 | 0.9935 | 0.9930 | 0.9994 | 0.9935 | 1.0 | 0.9903 | 0.9584 | 0.9206 | 0.9917 | 0.7753 | 0.9914 | 0.9315 | 0.8305 | 1.0 | 1.0 | 1.0 | 0.9939 | 1.0 | 1.0 | 1.0 | 0.1404 | 0.8382 | 0.8029 | 0.9958 | 1.0 | 0.9944 | 0.9910 | 0.9875 | 1.0 | 0.9480 | 0.9788 | 1.0 | 0.9924 | 1.0 | 1.0 | 1.0 | 0.9929 | 0.9747 | 0.9961 | 0.9949 | 0.9970 | 0.9925 | 0.9983 | 0.9967 | 1.0 | 1.0 | 0.9991 | 1.0 | 1.0 | 0.9953 |
 
 
 
117
 
118
 
119
  ### Framework versions
120
 
121
  - Transformers 4.35.2
122
+ - Pytorch 2.1.0+cu121
123
  - Datasets 2.15.0
124
+ - Tokenizers 0.15.0
all_results.json CHANGED
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  }
 
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  }
eval_results.json CHANGED
@@ -1,68 +1,68 @@
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