--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-uncased_ai4privacy_en results: [] --- # 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 the English subset of [ai4privacy/pii-masking-200k](https://huggingface.co/datasets/ai4privacy/pii-masking-200k) 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