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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bert-base-uncased_ai4privacy_en |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-base-uncased_ai4privacy_en |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0702 |
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- Overall Precision: 0.9259 |
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- Overall Recall: 0.9464 |
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- Overall F1: 0.9360 |
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- Overall Accuracy: 0.9706 |
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- Accountname F1: 0.9924 |
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- Accountnumber F1: 0.9905 |
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- Age F1: 0.9339 |
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- Amount F1: 0.9278 |
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- Bic F1: 0.9598 |
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- Bitcoinaddress F1: 0.9801 |
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- Buildingnumber F1: 0.9091 |
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- City F1: 0.9564 |
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- Companyname F1: 0.9908 |
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- County F1: 0.9853 |
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- Creditcardcvv F1: 0.9639 |
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- Creditcardissuer F1: 0.9868 |
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- Creditcardnumber F1: 0.8929 |
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- Currency F1: 0.7726 |
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- Currencycode F1: 0.8608 |
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- Currencyname F1: 0.3650 |
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- Currencysymbol F1: 0.9536 |
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- Date F1: 0.8590 |
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- Dob F1: 0.6490 |
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- Email F1: 0.9945 |
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- Ethereumaddress F1: 0.9986 |
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- Eyecolor F1: 0.9688 |
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- Firstname F1: 0.9790 |
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- Gender F1: 0.9832 |
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- Height F1: 0.9906 |
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- Iban F1: 1.0 |
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- Ip F1: 0.1025 |
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- Ipv4 F1: 0.8217 |
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- Ipv6 F1: 0.7506 |
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- Jobarea F1: 0.9306 |
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- Jobtitle F1: 0.9938 |
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- Jobtype F1: 0.9508 |
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- Lastname F1: 0.9480 |
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- Litecoinaddress F1: 0.9345 |
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- Mac F1: 1.0 |
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- Maskednumber F1: 0.8609 |
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- Middlename F1: 0.9601 |
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- Nearbygpscoordinate F1: 1.0 |
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- Ordinaldirection F1: 0.9784 |
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- Password F1: 0.9839 |
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- Phoneimei F1: 0.9986 |
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- Phonenumber F1: 0.9903 |
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- Pin F1: 0.9390 |
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- Prefix F1: 0.9441 |
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- Secondaryaddress F1: 0.9945 |
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- Sex F1: 0.9780 |
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- Ssn F1: 0.9898 |
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- State F1: 0.9805 |
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- Street F1: 0.9693 |
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- Time F1: 0.9843 |
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- Url F1: 0.9984 |
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- Useragent F1: 0.9918 |
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- Username F1: 0.9909 |
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- Vehiclevin F1: 0.9856 |
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- Vehiclevrm F1: 0.9653 |
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- Zipcode F1: 0.8990 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine_with_restarts |
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- lr_scheduler_warmup_ratio: 0.2 |
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- num_epochs: 2 |
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### Training results |
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| 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 | |
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|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:------:|:---------:|:------:|:-----------------:|:-----------------:|:-------:|:--------------:|:---------:|:----------------:|:-------------------:|:-------------------:|:-----------:|:---------------:|:---------------:|:-----------------:|:-------:|:------:|:--------:|:------------------:|:-----------:|:------------:|:---------:|:---------:|:-------:|:------:|:-------:|:-------:|:----------:|:-----------:|:----------:|:-----------:|:------------------:|:------:|:---------------:|:-------------:|:----------------------:|:-------------------:|:-----------:|:------------:|:--------------:|:------:|:---------:|:-------------------:|:------:|:------:|:--------:|:---------:|:-------:|:------:|:------------:|:-----------:|:-------------:|:-------------:|:----------:| |
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| 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 | |
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| 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 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 2.0.0.post101 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.3 |
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