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---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased_ai4privacy_en
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-base-uncased_ai4privacy_en
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0935
- Overall Precision: 0.9169
- Overall Recall: 0.9382
- Overall F1: 0.9274
- Overall Accuracy: 0.9662
- Accountname F1: 0.9924
- Accountnumber F1: 0.9878
- Age F1: 0.9283
- Amount F1: 0.9224
- Bic F1: 0.9018
- Bitcoinaddress F1: 0.8930
- Buildingnumber F1: 0.8944
- City F1: 0.9543
- Companyname F1: 0.9847
- County F1: 0.9807
- Creditcardcvv F1: 0.9191
- Creditcardissuer F1: 0.9831
- Creditcardnumber F1: 0.9029
- Currency F1: 0.7268
- Currencycode F1: 0.8590
- Currencyname F1: 0.4625
- Currencysymbol F1: 0.9503
- Date F1: 0.8227
- Dob F1: 0.6515
- Email F1: 0.9884
- Ethereumaddress F1: 0.9890
- Eyecolor F1: 0.9274
- Firstname F1: 0.9726
- Gender F1: 0.9791
- Height F1: 0.9814
- Iban F1: 0.9862
- Ip F1: 0.1964
- Ipv4 F1: 0.8063
- Ipv6 F1: 0.7958
- Jobarea F1: 0.9265
- Jobtitle F1: 0.9965
- Jobtype F1: 0.9482
- Lastname F1: 0.9469
- Litecoinaddress F1: 0.7767
- Mac F1: 0.9892
- Maskednumber F1: 0.8689
- Middlename F1: 0.9628
- Nearbygpscoordinate F1: 0.9955
- Ordinaldirection F1: 0.9784
- Password F1: 0.9503
- Phoneimei F1: 0.9944
- Phonenumber F1: 0.9799
- Pin F1: 0.9085
- Prefix F1: 0.9463
- Secondaryaddress F1: 0.9902
- Sex F1: 0.9752
- Ssn F1: 0.9759
- State F1: 0.9765
- Street F1: 0.9651
- Time F1: 0.9740
- Url F1: 0.9889
- Useragent F1: 0.9778
- Username F1: 0.9885
- Vehiclevin F1: 0.9621
- Vehiclevrm F1: 0.9840
- Zipcode F1: 0.8823
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:------:|:---------:|:------:|:-----------------:|:-----------------:|:-------:|:--------------:|:---------:|:----------------:|:-------------------:|:-------------------:|:-----------:|:---------------:|:---------------:|:-----------------:|:-------:|:------:|:--------:|:------------------:|:-----------:|:------------:|:---------:|:---------:|:-------:|:------:|:-------:|:-------:|:----------:|:-----------:|:----------:|:-----------:|:------------------:|:------:|:---------------:|:-------------:|:----------------------:|:-------------------:|:-----------:|:------------:|:--------------:|:------:|:---------:|:-------------------:|:------:|:------:|:--------:|:---------:|:-------:|:------:|:------------:|:-----------:|:-------------:|:-------------:|:----------:|
| 0.1133 | 1.0 | 17398 | 0.1049 | 0.8638 | 0.9087 | 0.8857 | 0.9619 | 0.9789 | 0.9552 | 0.8739 | 0.8043 | 0.9020 | 0.9265 | 0.8585 | 0.8866 | 0.9521 | 0.9754 | 0.8213 | 0.9718 | 0.8201 | 0.4143 | 0.7182 | 0.0921 | 0.9064 | 0.7717 | 0.4968 | 0.9811 | 0.9876 | 0.8715 | 0.9487 | 0.9602 | 0.9843 | 0.8432 | 0.0 | 0.8434 | 0.7986 | 0.8906 | 0.9808 | 0.9205 | 0.9021 | 0.8261 | 0.9741 | 0.7786 | 0.9252 | 0.9852 | 0.9458 | 0.9418 | 0.9848 | 0.9566 | 0.7740 | 0.9366 | 0.9924 | 0.9751 | 0.9822 | 0.9389 | 0.9228 | 0.9536 | 0.9772 | 0.9638 | 0.9276 | 0.9143 | 0.9043 | 0.8042 |
| 0.1019 | 2.0 | 34796 | 0.0958 | 0.9076 | 0.9315 | 0.9194 | 0.9665 | 0.9579 | 0.9564 | 0.9020 | 0.8964 | 0.8895 | 0.9511 | 0.8873 | 0.9390 | 0.9736 | 0.9761 | 0.9236 | 0.9794 | 0.8895 | 0.7610 | 0.8052 | 0.03 | 0.9429 | 0.8481 | 0.6713 | 0.9791 | 0.9850 | 0.8804 | 0.9651 | 0.9633 | 0.9753 | 0.9789 | 0.0046 | 0.8253 | 0.8038 | 0.8567 | 0.9903 | 0.9364 | 0.9394 | 0.8757 | 0.9764 | 0.8474 | 0.9321 | 0.9970 | 0.9641 | 0.9707 | 0.9944 | 0.9807 | 0.9198 | 0.8692 | 0.9913 | 0.9730 | 0.9594 | 0.9666 | 0.9568 | 0.9626 | 0.9842 | 0.9941 | 0.9780 | 0.9653 | 0.9368 | 0.8850 |
| 0.0777 | 3.0 | 52194 | 0.0935 | 0.9169 | 0.9382 | 0.9274 | 0.9662 | 0.9924 | 0.9878 | 0.9283 | 0.9224 | 0.9018 | 0.8930 | 0.8944 | 0.9543 | 0.9847 | 0.9807 | 0.9191 | 0.9831 | 0.9029 | 0.7268 | 0.8590 | 0.4625 | 0.9503 | 0.8227 | 0.6515 | 0.9884 | 0.9890 | 0.9274 | 0.9726 | 0.9791 | 0.9814 | 0.9862 | 0.1964 | 0.8063 | 0.7958 | 0.9265 | 0.9965 | 0.9482 | 0.9469 | 0.7767 | 0.9892 | 0.8689 | 0.9628 | 0.9955 | 0.9784 | 0.9503 | 0.9944 | 0.9799 | 0.9085 | 0.9463 | 0.9902 | 0.9752 | 0.9759 | 0.9765 | 0.9651 | 0.9740 | 0.9889 | 0.9778 | 0.9885 | 0.9621 | 0.9840 | 0.8823 |
| 0.0557 | 4.0 | 69592 | 0.0944 | 0.9285 | 0.9457 | 0.9370 | 0.9696 | 0.9941 | 0.9965 | 0.9190 | 0.9419 | 0.9765 | 0.9654 | 0.9188 | 0.9487 | 0.9787 | 0.9861 | 0.9424 | 0.9849 | 0.9001 | 0.7578 | 0.8875 | 0.3933 | 0.9647 | 0.8627 | 0.7004 | 0.9932 | 0.9876 | 0.9548 | 0.9736 | 0.9884 | 0.9968 | 0.9950 | 0.25 | 0.8184 | 0.7747 | 0.9206 | 0.9929 | 0.9493 | 0.9502 | 0.9075 | 0.9957 | 0.8750 | 0.9544 | 1.0 | 0.9784 | 0.9812 | 0.9944 | 0.9883 | 0.9309 | 0.9524 | 0.9935 | 0.9809 | 0.9859 | 0.9794 | 0.9624 | 0.9774 | 0.9937 | 0.9941 | 0.9852 | 0.9914 | 0.9865 | 0.9012 |
| 0.0285 | 5.0 | 86990 | 0.1285 | 0.9292 | 0.9448 | 0.9369 | 0.9693 | 0.9915 | 0.9948 | 0.9308 | 0.9352 | 0.9736 | 0.9674 | 0.9168 | 0.9561 | 0.9756 | 0.9843 | 0.9735 | 0.9849 | 0.8984 | 0.7356 | 0.8734 | 0.4161 | 0.9669 | 0.8510 | 0.7083 | 0.9945 | 0.9903 | 0.9632 | 0.9754 | 0.9926 | 0.9968 | 0.9975 | 0.3843 | 0.7983 | 0.7534 | 0.9237 | 0.9956 | 0.9510 | 0.9505 | 0.9240 | 1.0 | 0.8738 | 0.9589 | 0.9985 | 0.9784 | 0.9831 | 0.9944 | 0.9914 | 0.9480 | 0.9420 | 0.9956 | 0.9820 | 0.9860 | 0.9794 | 0.9631 | 0.9774 | 0.9937 | 0.9906 | 0.9885 | 0.9828 | 0.9946 | 0.9115 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0.post200
- Datasets 2.10.1
- Tokenizers 0.13.3
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