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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert_finetuned_ai4privacy
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_finetuned_ai4privacy
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0108
- Overall Precision: 0.9724
- Overall Recall: 0.9774
- Overall F1: 0.9749
- Overall Accuracy: 0.9977
- Accountname F1: 1.0
- Accountnumber F1: 1.0
- Amount F1: 0.9375
- Bic F1: 1.0
- Bitcoinaddress F1: 0.9880
- Buildingnumber F1: 0.95
- City F1: 0.9987
- Company Name F1: 0.9954
- County F1: 1.0
- Creditcardcvv F1: 0.9821
- Creditcardissuer F1: 0.9809
- Creditcardnumber F1: 0.9924
- Currency F1: 0.8018
- Currencycode F1: 0.8229
- Currencyname F1: 0.5534
- Currencysymbol F1: 0.3548
- Date F1: 0.9975
- Displayname F1: 0.5862
- Email F1: 1.0
- Ethereumaddress F1: 1.0
- Firstname F1: 0.9465
- Fullname F1: 0.9974
- Gender F1: 0.9574
- Iban F1: 1.0
- Ip F1: 0.5957
- Ipv4 F1: 0.8660
- Ipv6 F1: 0.9524
- Jobarea F1: 0.9789
- Jobdescriptor F1: 0.6842
- Jobtitle F1: 0.9554
- Jobtype F1: 0.8872
- Lastname F1: 0.9091
- Litecoinaddress F1: 0.9934
- Mac F1: 1.0
- Maskednumber F1: 0.9816
- Middlename F1: 0.7065
- Name F1: 0.9998
- Nearbygpscoordinate F1: 0.0
- Number F1: 1.0
- Ordinaldirection F1: 0.0
- Password F1: 1.0
- Phoneimei F1: 1.0
- Phone Number F1: 0.9811
- Pin F1: 0.9600
- Prefix F1: 0.9593
- Secondaryaddress F1: 0.9940
- Sex F1: 0.9557
- Sextype F1: 0.0
- Ssn F1: 0.9928
- State F1: 0.9991
- Street F1: 0.9701
- Streetaddress F1: 0.9813
- Suffix F1: 0.8690
- Time F1: 0.9963
- Url F1: 1.0
- Useragent F1: 1.0
- Username F1: 0.9743
- Vehiclevin F1: 1.0
- Vehiclevrm F1: 1.0
- Zipcode F1: 0.9922
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### 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
### 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 | Ordinaldirection 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 | 0.3443 | 0.6624 | 0.6759 | 0.6690 | 0.9184 | 0.0 | 0.6485 | 0.2638 | 0.0 | 0.2738 | 0.0 | 0.7150 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5613 | 0.1414 | 0.0 | 0.0 | 0.0 | 0.6308 | 0.0 | 0.9532 | 0.6853 | 0.0117 | 0.9139 | 0.0 | 0.3417 | 0.0 | 0.7449 | 0.6448 | 0.4051 | 0.0 | 0.0310 | 0.0 | 0.1213 | 0.0 | 0.7902 | 0.0 | 0.0 | 0.8767 | 0.0 | 0.3274 | 0.0 | 0.5161 | 0.6280 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0190 | 0.4854 | 0.0 | 0.6674 | 0.0 | 0.0 | 0.9036 | 0.7860 | 0.2556 | 0.4880 | 0.0 | 0.3714 |
| 1.2173 | 2.0 | 670 | 0.1653 | 0.7670 | 0.8297 | 0.7971 | 0.9390 | 0.7480 | 0.8087 | 0.5992 | 0.5043 | 0.3596 | 0.3211 | 0.9374 | 0.1393 | 0.5392 | 0.0 | 0.0 | 0.6133 | 0.2427 | 0.0 | 0.1531 | 0.0 | 0.9701 | 0.0 | 0.9986 | 0.7518 | 0.7461 | 0.9780 | 0.0563 | 0.5263 | 0.2877 | 0.8325 | 0.0 | 0.7690 | 0.0 | 0.7387 | 0.0 | 0.4478 | 0.0152 | 0.9854 | 0.0 | 0.0 | 0.9681 | 0.0 | 0.4675 | 0.0 | 0.5889 | 0.9691 | 0.0 | 0.0 | 0.7262 | 0.0982 | 0.0600 | 0.0 | 0.0630 | 0.6859 | 0.2877 | 0.8083 | 0.0 | 0.9167 | 0.9909 | 0.9406 | 0.8548 | 0.6415 | 0.6057 | 0.7128 |
| 0.1551 | 3.0 | 1005 | 0.0729 | 0.8510 | 0.8956 | 0.8727 | 0.9740 | 0.9416 | 0.8710 | 0.8631 | 0.9550 | 0.8028 | 0.5606 | 0.9934 | 0.8534 | 0.9547 | 0.3333 | 0.6438 | 0.0 | 0.3124 | 0.2951 | 0.1404 | 0.0 | 0.9825 | 0.0 | 0.9997 | 0.9858 | 0.8164 | 0.9880 | 0.5979 | 1.0 | 0.0 | 0.8718 | 0.4470 | 0.9217 | 0.0 | 0.8533 | 0.4524 | 0.5618 | 0.4000 | 1.0 | 0.0201 | 0.1304 | 0.9923 | 0.0 | 0.3713 | 0.0 | 0.9249 | 0.9792 | 0.6865 | 0.1538 | 0.7942 | 0.8571 | 0.8641 | 0.0 | 0.5938 | 0.9879 | 0.6811 | 0.8807 | 0.0435 | 0.9783 | 0.9939 | 0.9862 | 0.9040 | 0.9867 | 0.9323 | 0.8300 |
| 0.1551 | 4.0 | 1340 | 0.0424 | 0.9312 | 0.9436 | 0.9374 | 0.9843 | 0.9921 | 0.9928 | 0.8899 | 0.9107 | 0.8121 | 0.7573 | 0.9953 | 0.9450 | 0.9966 | 0.9138 | 0.8718 | 0.8514 | 0.6389 | 0.5921 | 0.0182 | 0.0 | 0.9874 | 0.0 | 0.9989 | 0.9581 | 0.8856 | 0.9903 | 0.6739 | 0.9778 | 0.0163 | 0.8696 | 0.7732 | 0.9492 | 0.2037 | 0.9128 | 0.7073 | 0.7077 | 0.6705 | 0.9901 | 0.6875 | 0.5193 | 0.9972 | 0.0 | 0.9749 | 0.0 | 0.9595 | 0.9895 | 0.9132 | 0.6575 | 0.8811 | 0.9822 | 0.8920 | 0.0 | 1.0 | 0.9956 | 0.8383 | 0.9181 | 0.5694 | 0.9963 | 1.0 | 1.0 | 0.9481 | 0.9396 | 0.9921 | 0.9846 |
| 0.065 | 5.0 | 1675 | 0.0341 | 0.9446 | 0.9582 | 0.9514 | 0.9894 | 0.992 | 0.9976 | 0.9165 | 1.0 | 0.9421 | 0.8397 | 0.9987 | 0.9815 | 1.0 | 0.9244 | 0.9419 | 0.9438 | 0.6724 | 0.7447 | 0.3274 | 0.1481 | 0.9874 | 0.0727 | 1.0 | 1.0 | 0.9071 | 0.9915 | 0.9032 | 1.0 | 0.0979 | 0.8645 | 0.7668 | 0.9753 | 0.5180 | 0.9170 | 0.7790 | 0.7843 | 0.8917 | 1.0 | 0.8765 | 0.5761 | 0.9992 | 0.0 | 0.9898 | 0.0 | 0.9842 | 1.0 | 0.9630 | 0.7632 | 0.9101 | 0.9822 | 0.9372 | 0.0 | 1.0 | 0.9983 | 0.8399 | 0.9347 | 0.6581 | 0.9963 | 1.0 | 0.9630 | 0.9447 | 0.9933 | 1.0 | 0.9948 |
| 0.0346 | 6.0 | 2010 | 0.0136 | 0.9671 | 0.9736 | 0.9703 | 0.9968 | 0.9921 | 1.0 | 0.9327 | 1.0 | 0.9880 | 0.9182 | 0.9984 | 0.9907 | 1.0 | 0.9649 | 0.9809 | 0.9403 | 0.7669 | 0.8046 | 0.4433 | 0.3051 | 0.9899 | 0.4483 | 0.9997 | 1.0 | 0.9379 | 0.9973 | 0.9474 | 1.0 | 0.6272 | 0.8673 | 0.9639 | 0.9789 | 0.6928 | 0.9453 | 0.8617 | 0.8995 | 0.9934 | 1.0 | 0.9186 | 0.6632 | 0.9996 | 0.0 | 0.9898 | 0.0 | 0.9937 | 0.9947 | 0.9765 | 0.9067 | 0.9507 | 0.9881 | 0.9505 | 0.0 | 1.0 | 0.9983 | 0.9222 | 0.9633 | 0.8571 | 0.9963 | 1.0 | 1.0 | 0.9689 | 1.0 | 1.0 | 0.9896 |
| 0.0346 | 7.0 | 2345 | 0.0108 | 0.9724 | 0.9774 | 0.9749 | 0.9977 | 1.0 | 1.0 | 0.9375 | 1.0 | 0.9880 | 0.95 | 0.9987 | 0.9954 | 1.0 | 0.9821 | 0.9809 | 0.9924 | 0.8018 | 0.8229 | 0.5534 | 0.3548 | 0.9975 | 0.5862 | 1.0 | 1.0 | 0.9465 | 0.9974 | 0.9574 | 1.0 | 0.5957 | 0.8660 | 0.9524 | 0.9789 | 0.6842 | 0.9554 | 0.8872 | 0.9091 | 0.9934 | 1.0 | 0.9816 | 0.7065 | 0.9998 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 | 0.9811 | 0.9600 | 0.9593 | 0.9940 | 0.9557 | 0.0 | 0.9928 | 0.9991 | 0.9701 | 0.9813 | 0.8690 | 0.9963 | 1.0 | 1.0 | 0.9743 | 1.0 | 1.0 | 0.9922 |
### Framework versions
- Transformers 4.33.1
- Pytorch 1.13.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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