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---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-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. -->
# bert-base-uncased_ai4privacy_en
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0702
- Overall Precision: 0.9259
- Overall Recall: 0.9464
- Overall F1: 0.9360
- Overall Accuracy: 0.9706
- Accountname F1: 0.9924
- Accountnumber F1: 0.9905
- Age F1: 0.9339
- Amount F1: 0.9278
- Bic F1: 0.9598
- Bitcoinaddress F1: 0.9801
- Buildingnumber F1: 0.9091
- City F1: 0.9564
- Companyname F1: 0.9908
- County F1: 0.9853
- Creditcardcvv F1: 0.9639
- Creditcardissuer F1: 0.9868
- Creditcardnumber F1: 0.8929
- Currency F1: 0.7726
- Currencycode F1: 0.8608
- Currencyname F1: 0.3650
- Currencysymbol F1: 0.9536
- Date F1: 0.8590
- Dob F1: 0.6490
- Email F1: 0.9945
- Ethereumaddress F1: 0.9986
- Eyecolor F1: 0.9688
- Firstname F1: 0.9790
- Gender F1: 0.9832
- Height F1: 0.9906
- Iban F1: 1.0
- Ip F1: 0.1025
- Ipv4 F1: 0.8217
- Ipv6 F1: 0.7506
- Jobarea F1: 0.9306
- Jobtitle F1: 0.9938
- Jobtype F1: 0.9508
- Lastname F1: 0.9480
- Litecoinaddress F1: 0.9345
- Mac F1: 1.0
- Maskednumber F1: 0.8609
- Middlename F1: 0.9601
- Nearbygpscoordinate F1: 1.0
- Ordinaldirection F1: 0.9784
- Password F1: 0.9839
- Phoneimei F1: 0.9986
- Phonenumber F1: 0.9903
- Pin F1: 0.9390
- Prefix F1: 0.9441
- Secondaryaddress F1: 0.9945
- Sex F1: 0.9780
- Ssn F1: 0.9898
- State F1: 0.9805
- Street F1: 0.9693
- Time F1: 0.9843
- Url F1: 0.9984
- Useragent F1: 0.9918
- Username F1: 0.9909
- Vehiclevin F1: 0.9856
- Vehiclevrm F1: 0.9653
- Zipcode F1: 0.8990
## 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: 8
- eval_batch_size: 16
- 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: 2
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:------:|:---------:|:------:|:-----------------:|:-----------------:|:-------:|:--------------:|:---------:|:----------------:|:-------------------:|:-------------------:|:-----------:|:---------------:|:---------------:|:-----------------:|:-------:|:------:|:--------:|:------------------:|:-----------:|:------------:|:---------:|:---------:|:-------:|:------:|:-------:|:-------:|:----------:|:-----------:|:----------:|:-----------:|:------------------:|:------:|:---------------:|:-------------:|:----------------------:|:-------------------:|:-----------:|:------------:|:--------------:|:------:|:---------:|:-------------------:|:------:|:------:|:--------:|:---------:|:-------:|:------:|:------------:|:-----------:|:-------------:|:-------------:|:----------:|
| 0.0939 | 1.0 | 4350 | 0.0801 | 0.8951 | 0.9309 | 0.9126 | 0.9666 | 0.9840 | 0.9896 | 0.9182 | 0.8769 | 0.9127 | 0.9627 | 0.8770 | 0.9616 | 0.9847 | 0.9712 | 0.9373 | 0.9813 | 0.8406 | 0.3934 | 0.7451 | 0.1372 | 0.9266 | 0.8354 | 0.5796 | 0.9920 | 0.9877 | 0.9037 | 0.9642 | 0.9789 | 0.9906 | 0.9874 | 0.0 | 0.8416 | 0.8087 | 0.8854 | 0.9825 | 0.9426 | 0.9213 | 0.9015 | 0.9806 | 0.7978 | 0.9543 | 1.0 | 0.9828 | 0.9689 | 0.9917 | 0.9777 | 0.8764 | 0.9340 | 0.9913 | 0.9761 | 0.9949 | 0.9553 | 0.9561 | 0.9723 | 0.9921 | 0.9906 | 0.9779 | 0.9942 | 0.9684 | 0.8522 |
| 0.0644 | 2.0 | 8700 | 0.0702 | 0.9259 | 0.9464 | 0.9360 | 0.9706 | 0.9924 | 0.9905 | 0.9339 | 0.9278 | 0.9598 | 0.9801 | 0.9091 | 0.9564 | 0.9908 | 0.9853 | 0.9639 | 0.9868 | 0.8929 | 0.7726 | 0.8608 | 0.3650 | 0.9536 | 0.8590 | 0.6490 | 0.9945 | 0.9986 | 0.9688 | 0.9790 | 0.9832 | 0.9906 | 1.0 | 0.1025 | 0.8217 | 0.7506 | 0.9306 | 0.9938 | 0.9508 | 0.9480 | 0.9345 | 1.0 | 0.8609 | 0.9601 | 1.0 | 0.9784 | 0.9839 | 0.9986 | 0.9903 | 0.9390 | 0.9441 | 0.9945 | 0.9780 | 0.9898 | 0.9805 | 0.9693 | 0.9843 | 0.9984 | 0.9918 | 0.9909 | 0.9856 | 0.9653 | 0.8990 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0.post101
- Datasets 2.10.1
- Tokenizers 0.13.3
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