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---
license: cc-by-nc-4.0
base_model: distilbert-base-german-cased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-german-cased_finetuned_ai4privacy_v2
results: []
datasets:
- ai4privacy/pii-masking-200k
- Isotonic/pii-masking-200k
language:
- de
metrics:
- accuracy
- f1
- precision
- recall
pipeline_tag: token-classification
---
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# distilbert-base-german-cased_finetuned_ai4privacy_v2
This model is a fine-tuned version of [distilbert-base-german-cased](https://huggingface.co/distilbert-base-german-cased) on the German subset of [pii-masking-200k](https://huggingface.co/ai4privacy/pii-masking-200k) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0821
- Overall Precision: 0.9086
- Overall Recall: 0.9379
- Overall F1: 0.9230
- Overall Accuracy: 0.9679
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 5
### 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:------:|:---------:|:------:|:-----------------:|:-----------------:|:-------:|:--------------:|:---------:|:----------------:|:-------------------:|:-------------------:|:-----------:|:---------------:|:---------------:|:-----------------:|:-------:|:------:|:--------:|:------------------:|:-----------:|:------------:|:---------:|:---------:|:-------:|:------:|:-------:|:-------:|:----------:|:-----------:|:----------:|:-----------:|:------------------:|:------:|:---------------:|:-------------:|:----------------------:|:-------------------:|:-----------:|:------------:|:--------------:|:------:|:---------:|:-------------------:|:------:|:------:|:--------:|:---------:|:-------:|:------:|:------------:|:-----------:|:-------------:|:-------------:|:----------:|
| 0.1449 | 1.0 | 5282 | 0.1365 | 0.8213 | 0.8741 | 0.8469 | 0.9504 | 0.9954 | 0.9180 | 0.9509 | 0.7478 | 0.8315 | 0.8265 | 0.7908 | 0.8030 | 0.9011 | 0.9118 | 0.8669 | 0.9831 | 0.8053 | 0.4935 | 0.6482 | 0.0 | 0.8430 | 0.7672 | 0.4751 | 0.9870 | 0.9103 | 0.9501 | 0.8810 | 0.9552 | 0.9507 | 0.9086 | 0.0 | 0.8124 | 0.7776 | 0.8698 | 0.9758 | 0.9445 | 0.8140 | 0.5210 | 0.9819 | 0.6555 | 0.4114 | 1.0 | 0.9837 | 0.8093 | 0.9761 | 0.9254 | 0.7705 | 0.8613 | 0.9676 | 0.9978 | 0.9570 | 0.8585 | 0.8164 | 0.9643 | 0.9879 | 0.9534 | 0.9415 | 0.8778 | 0.9716 | 0.7313 |
| 0.1039 | 2.0 | 10564 | 0.0841 | 0.8875 | 0.9213 | 0.9041 | 0.9649 | 0.9923 | 0.9598 | 0.9721 | 0.8979 | 0.9240 | 0.9218 | 0.8937 | 0.8803 | 0.9648 | 0.9595 | 0.9563 | 0.9848 | 0.8427 | 0.5724 | 0.7677 | 0.2210 | 0.9244 | 0.8003 | 0.5866 | 0.9932 | 0.9636 | 0.9835 | 0.9473 | 0.9794 | 0.9753 | 0.9644 | 0.0173 | 0.7042 | 0.7564 | 0.9439 | 0.9911 | 0.9710 | 0.8988 | 0.7288 | 0.9801 | 0.7913 | 0.8977 | 0.9978 | 0.9853 | 0.9581 | 0.9937 | 0.9761 | 0.9146 | 0.9166 | 0.9741 | 0.9978 | 0.9787 | 0.9448 | 0.9031 | 0.9591 | 0.9968 | 0.9638 | 0.9719 | 0.9455 | 0.9829 | 0.8863 |
| 0.0804 | 3.0 | 15846 | 0.0821 | 0.9086 | 0.9379 | 0.9230 | 0.9679 | 0.9985 | 0.9849 | 0.9792 | 0.9387 | 0.9641 | 0.9637 | 0.9011 | 0.9260 | 0.9782 | 0.9778 | 0.9543 | 1.0 | 0.8796 | 0.7027 | 0.8328 | 0.3466 | 0.9420 | 0.8156 | 0.6575 | 0.9971 | 0.9947 | 0.9833 | 0.9614 | 0.9881 | 0.9842 | 0.9819 | 0.2023 | 0.6631 | 0.7243 | 0.9722 | 0.9904 | 0.9725 | 0.9185 | 0.8545 | 0.9780 | 0.8365 | 0.9156 | 1.0 | 0.9853 | 0.9782 | 0.9947 | 0.9883 | 0.9189 | 0.9594 | 0.9831 | 0.9993 | 0.9898 | 0.9739 | 0.9355 | 0.9764 | 0.9984 | 0.9885 | 0.9798 | 0.9614 | 1.0 | 0.9100 |
| 0.0622 | 4.0 | 21128 | 0.0848 | 0.9095 | 0.9420 | 0.9255 | 0.9713 | 0.9977 | 0.9932 | 0.9815 | 0.9566 | 0.9550 | 0.9704 | 0.9187 | 0.9277 | 0.9735 | 0.9756 | 0.9679 | 0.9966 | 0.8885 | 0.6985 | 0.8598 | 0.4217 | 0.9602 | 0.8262 | 0.6809 | 0.9960 | 0.9947 | 0.9852 | 0.9641 | 0.9952 | 0.9955 | 0.9909 | 0.3053 | 0.7067 | 0.6156 | 0.9784 | 0.9948 | 0.9773 | 0.9176 | 0.8856 | 0.9880 | 0.8598 | 0.9186 | 1.0 | 0.9886 | 0.9871 | 0.9968 | 0.9916 | 0.9419 | 0.9621 | 0.9887 | 1.0 | 0.9926 | 0.9717 | 0.9441 | 0.9835 | 0.9992 | 0.9858 | 0.9838 | 0.9818 | 0.9856 | 0.8972 |
| 0.032 | 5.0 | 26410 | 0.0998 | 0.9210 | 0.9497 | 0.9351 | 0.9741 | 0.9985 | 0.9962 | 0.9847 | 0.9622 | 0.9614 | 0.9738 | 0.9269 | 0.9431 | 0.9782 | 0.9749 | 0.9708 | 0.9949 | 0.8990 | 0.7116 | 0.8447 | 0.4615 | 0.9646 | 0.8296 | 0.7235 | 0.9966 | 0.9947 | 0.9853 | 0.9672 | 0.9929 | 0.9932 | 0.9919 | 0.3706 | 0.7690 | 0.6836 | 0.9838 | 0.9941 | 0.9789 | 0.9252 | 0.8876 | 0.9960 | 0.8849 | 0.9172 | 1.0 | 0.9886 | 0.9847 | 0.9958 | 0.9925 | 0.9483 | 0.9700 | 0.9912 | 1.0 | 0.9944 | 0.9756 | 0.9468 | 0.99 | 0.9984 | 0.9947 | 0.9806 | 0.9939 | 1.0 | 0.9108 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0 |