File size: 11,618 Bytes
4042be2 01f30f3 4042be2 74bf3b5 4042be2 74bf3b5 eb90ac6 74bf3b5 eb90ac6 74bf3b5 eb90ac6 74bf3b5 eb90ac6 74bf3b5 c8af990 74bf3b5 4042be2 c8af990 4042be2 74bf3b5 4042be2 74bf3b5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 |
---
license: cc-by-nc-4.0
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
model-index:
- name: deberta-v3-base_finetuned_ai4privacy_v2
results: []
datasets:
- ai4privacy/pii-masking-200k
- Isotonic/pii-masking-200k
language:
- en
metrics:
- seqeval
pipeline_tag: token-classification
---
# deberta-v3-base_finetuned_ai4privacy_v2
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.
## Useage
GitHub Implementation: [Ai4Privacy](https://github.com/Sripaad/ai4privacy)
## Model description
This model has been finetuned on the World's largest open source privacy dataset.
The purpose of the trained models is to remove personally identifiable information (PII) from text, especially in the context of AI assistants and LLMs.
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...).
Take a look at the Github implementation for specific reasearch.
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-04
- train_batch_size: 32
- eval_batch_size: 32
- seed: 412
- optimizer: Adam with betas=(0.96,0.996) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.22
- num_epochs: 7
- mixed_precision_training: N/A
## Class wise metrics
It achieves the following results on the evaluation set:
- Loss: 0.0211
- Overall Precision: 0.9722
- Overall Recall: 0.9792
- Overall F1: 0.9757
- Overall Accuracy: 0.9915
- Accountname F1: 0.9993
- Accountnumber F1: 0.9986
- Age F1: 0.9884
- Amount F1: 0.9984
- Bic F1: 0.9942
- Bitcoinaddress F1: 0.9974
- Buildingnumber F1: 0.9898
- City F1: 1.0
- Companyname F1: 1.0
- County F1: 0.9976
- Creditcardcvv F1: 0.9541
- Creditcardissuer F1: 0.9970
- Creditcardnumber F1: 0.9754
- Currency F1: 0.8966
- Currencycode F1: 0.9946
- Currencyname F1: 0.7697
- Currencysymbol F1: 0.9958
- Date F1: 0.9778
- Dob F1: 0.9546
- Email F1: 1.0
- Ethereumaddress F1: 1.0
- Eyecolor F1: 0.9925
- Firstname F1: 0.9947
- Gender F1: 1.0
- Height F1: 1.0
- Iban F1: 0.9978
- Ip F1: 0.5404
- Ipv4 F1: 0.8455
- Ipv6 F1: 0.8855
- Jobarea F1: 0.9091
- Jobtitle F1: 1.0
- Jobtype F1: 0.9672
- Lastname F1: 0.9855
- Litecoinaddress F1: 0.9949
- Mac F1: 0.9965
- Maskednumber F1: 0.9836
- Middlename F1: 0.7385
- Nearbygpscoordinate F1: 1.0
- Ordinaldirection F1: 1.0
- Password F1: 1.0
- Phoneimei F1: 0.9978
- Phonenumber F1: 0.9975
- Pin F1: 0.9820
- Prefix F1: 0.9872
- Secondaryaddress F1: 1.0
- Sex F1: 0.9916
- Ssn F1: 0.9960
- State F1: 0.9967
- Street F1: 0.9991
- Time F1: 1.0
- Url F1: 1.0
- Useragent F1: 0.9981
- Username F1: 1.0
- Vehiclevin F1: 0.9950
- Vehiclevrm F1: 0.9870
- Zipcode F1: 0.9966
### Training results
| 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:------:|:---------:|:------:|:-----------------:|:-----------------:|:-------:|:--------------:|:---------:|:----------------:|:-------------------:|:-------------------:|:-----------:|:---------------:|:---------------:|:-----------------:|:-------:|:------:|:--------:|:------------------:|:-----------:|:------------:|:---------:|:---------:|:-------:|:------:|:-------:|:-------:|:----------:|:-----------:|:----------:|:-----------:|:------------------:|:------:|:---------------:|:-------------:|:----------------------:|:-------------------:|:-----------:|:------------:|:--------------:|:------:|:---------:|:-------------------:|:------:|:------:|:--------:|:---------:|:-------:|:------:|:------------:|:-----------:|:-------------:|:-------------:|:----------:|
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0 |