--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert_finetuned_ai4privacy results: [] --- # distilbert_finetuned_ai4privacy This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0108 - Overall Precision: 0.9724 - Overall Recall: 0.9774 - Overall F1: 0.9749 - Overall Accuracy: 0.9977 - Accountname F1: 1.0 - Accountnumber F1: 1.0 - Amount F1: 0.9375 - Bic F1: 1.0 - Bitcoinaddress F1: 0.9880 - Buildingnumber F1: 0.95 - City F1: 0.9987 - Company Name F1: 0.9954 - County F1: 1.0 - Creditcardcvv F1: 0.9821 - Creditcardissuer F1: 0.9809 - Creditcardnumber F1: 0.9924 - Currency F1: 0.8018 - Currencycode F1: 0.8229 - Currencyname F1: 0.5534 - Currencysymbol F1: 0.3548 - Date F1: 0.9975 - Displayname F1: 0.5862 - Email F1: 1.0 - Ethereumaddress F1: 1.0 - Firstname F1: 0.9465 - Fullname F1: 0.9974 - Gender F1: 0.9574 - Iban F1: 1.0 - Ip F1: 0.5957 - Ipv4 F1: 0.8660 - Ipv6 F1: 0.9524 - Jobarea F1: 0.9789 - Jobdescriptor F1: 0.6842 - Jobtitle F1: 0.9554 - Jobtype F1: 0.8872 - Lastname F1: 0.9091 - Litecoinaddress F1: 0.9934 - Mac F1: 1.0 - Maskednumber F1: 0.9816 - Middlename F1: 0.7065 - Name F1: 0.9998 - Nearbygpscoordinate F1: 0.0 - Number F1: 1.0 - Ordinaldirection F1: 0.0 - Password F1: 1.0 - Phoneimei F1: 1.0 - Phone Number F1: 0.9811 - Pin F1: 0.9600 - Prefix F1: 0.9593 - Secondaryaddress F1: 0.9940 - Sex F1: 0.9557 - Sextype F1: 0.0 - Ssn F1: 0.9928 - State F1: 0.9991 - Street F1: 0.9701 - Streetaddress F1: 0.9813 - Suffix F1: 0.8690 - Time F1: 0.9963 - Url F1: 1.0 - Useragent F1: 1.0 - Username F1: 0.9743 - Vehiclevin F1: 1.0 - Vehiclevrm F1: 1.0 - Zipcode F1: 0.9922 ## 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: 32 - eval_batch_size: 32 - 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: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Accountname F1 | Accountnumber F1 | Amount F1 | Bic F1 | Bitcoinaddress F1 | Buildingnumber F1 | City F1 | Company Name F1 | County F1 | Creditcardcvv F1 | Creditcardissuer F1 | Creditcardnumber F1 | Currency F1 | Currencycode F1 | Currencyname F1 | Currencysymbol F1 | Date F1 | Displayname F1 | Email F1 | Ethereumaddress F1 | Firstname F1 | Fullname F1 | Gender F1 | Iban F1 | Ip F1 | Ipv4 F1 | Ipv6 F1 | Jobarea F1 | Jobdescriptor F1 | Jobtitle F1 | Jobtype F1 | Lastname F1 | Litecoinaddress F1 | Mac F1 | Maskednumber F1 | Middlename F1 | Name F1 | Nearbygpscoordinate F1 | Number F1 | Ordinaldirection F1 | Password F1 | Phoneimei F1 | Phone Number F1 | Pin F1 | Prefix F1 | Secondaryaddress F1 | Sex F1 | Sextype F1 | Ssn F1 | State F1 | Street F1 | Streetaddress F1 | Suffix F1 | Time F1 | Url F1 | Useragent F1 | Username F1 | Vehiclevin F1 | Vehiclevrm F1 | Zipcode F1 | 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| No log | 1.0 | 335 | 0.3443 | 0.6624 | 0.6759 | 0.6690 | 0.9184 | 0.0 | 0.6485 | 0.2638 | 0.0 | 0.2738 | 0.0 | 0.7150 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5613 | 0.1414 | 0.0 | 0.0 | 0.0 | 0.6308 | 0.0 | 0.9532 | 0.6853 | 0.0117 | 0.9139 | 0.0 | 0.3417 | 0.0 | 0.7449 | 0.6448 | 0.4051 | 0.0 | 0.0310 | 0.0 | 0.1213 | 0.0 | 0.7902 | 0.0 | 0.0 | 0.8767 | 0.0 | 0.3274 | 0.0 | 0.5161 | 0.6280 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0190 | 0.4854 | 0.0 | 0.6674 | 0.0 | 0.0 | 0.9036 | 0.7860 | 0.2556 | 0.4880 | 0.0 | 0.3714 | | 1.2173 | 2.0 | 670 | 0.1653 | 0.7670 | 0.8297 | 0.7971 | 0.9390 | 0.7480 | 0.8087 | 0.5992 | 0.5043 | 0.3596 | 0.3211 | 0.9374 | 0.1393 | 0.5392 | 0.0 | 0.0 | 0.6133 | 0.2427 | 0.0 | 0.1531 | 0.0 | 0.9701 | 0.0 | 0.9986 | 0.7518 | 0.7461 | 0.9780 | 0.0563 | 0.5263 | 0.2877 | 0.8325 | 0.0 | 0.7690 | 0.0 | 0.7387 | 0.0 | 0.4478 | 0.0152 | 0.9854 | 0.0 | 0.0 | 0.9681 | 0.0 | 0.4675 | 0.0 | 0.5889 | 0.9691 | 0.0 | 0.0 | 0.7262 | 0.0982 | 0.0600 | 0.0 | 0.0630 | 0.6859 | 0.2877 | 0.8083 | 0.0 | 0.9167 | 0.9909 | 0.9406 | 0.8548 | 0.6415 | 0.6057 | 0.7128 | | 0.1551 | 3.0 | 1005 | 0.0729 | 0.8510 | 0.8956 | 0.8727 | 0.9740 | 0.9416 | 0.8710 | 0.8631 | 0.9550 | 0.8028 | 0.5606 | 0.9934 | 0.8534 | 0.9547 | 0.3333 | 0.6438 | 0.0 | 0.3124 | 0.2951 | 0.1404 | 0.0 | 0.9825 | 0.0 | 0.9997 | 0.9858 | 0.8164 | 0.9880 | 0.5979 | 1.0 | 0.0 | 0.8718 | 0.4470 | 0.9217 | 0.0 | 0.8533 | 0.4524 | 0.5618 | 0.4000 | 1.0 | 0.0201 | 0.1304 | 0.9923 | 0.0 | 0.3713 | 0.0 | 0.9249 | 0.9792 | 0.6865 | 0.1538 | 0.7942 | 0.8571 | 0.8641 | 0.0 | 0.5938 | 0.9879 | 0.6811 | 0.8807 | 0.0435 | 0.9783 | 0.9939 | 0.9862 | 0.9040 | 0.9867 | 0.9323 | 0.8300 | | 0.1551 | 4.0 | 1340 | 0.0424 | 0.9312 | 0.9436 | 0.9374 | 0.9843 | 0.9921 | 0.9928 | 0.8899 | 0.9107 | 0.8121 | 0.7573 | 0.9953 | 0.9450 | 0.9966 | 0.9138 | 0.8718 | 0.8514 | 0.6389 | 0.5921 | 0.0182 | 0.0 | 0.9874 | 0.0 | 0.9989 | 0.9581 | 0.8856 | 0.9903 | 0.6739 | 0.9778 | 0.0163 | 0.8696 | 0.7732 | 0.9492 | 0.2037 | 0.9128 | 0.7073 | 0.7077 | 0.6705 | 0.9901 | 0.6875 | 0.5193 | 0.9972 | 0.0 | 0.9749 | 0.0 | 0.9595 | 0.9895 | 0.9132 | 0.6575 | 0.8811 | 0.9822 | 0.8920 | 0.0 | 1.0 | 0.9956 | 0.8383 | 0.9181 | 0.5694 | 0.9963 | 1.0 | 1.0 | 0.9481 | 0.9396 | 0.9921 | 0.9846 | | 0.065 | 5.0 | 1675 | 0.0341 | 0.9446 | 0.9582 | 0.9514 | 0.9894 | 0.992 | 0.9976 | 0.9165 | 1.0 | 0.9421 | 0.8397 | 0.9987 | 0.9815 | 1.0 | 0.9244 | 0.9419 | 0.9438 | 0.6724 | 0.7447 | 0.3274 | 0.1481 | 0.9874 | 0.0727 | 1.0 | 1.0 | 0.9071 | 0.9915 | 0.9032 | 1.0 | 0.0979 | 0.8645 | 0.7668 | 0.9753 | 0.5180 | 0.9170 | 0.7790 | 0.7843 | 0.8917 | 1.0 | 0.8765 | 0.5761 | 0.9992 | 0.0 | 0.9898 | 0.0 | 0.9842 | 1.0 | 0.9630 | 0.7632 | 0.9101 | 0.9822 | 0.9372 | 0.0 | 1.0 | 0.9983 | 0.8399 | 0.9347 | 0.6581 | 0.9963 | 1.0 | 0.9630 | 0.9447 | 0.9933 | 1.0 | 0.9948 | | 0.0346 | 6.0 | 2010 | 0.0136 | 0.9671 | 0.9736 | 0.9703 | 0.9968 | 0.9921 | 1.0 | 0.9327 | 1.0 | 0.9880 | 0.9182 | 0.9984 | 0.9907 | 1.0 | 0.9649 | 0.9809 | 0.9403 | 0.7669 | 0.8046 | 0.4433 | 0.3051 | 0.9899 | 0.4483 | 0.9997 | 1.0 | 0.9379 | 0.9973 | 0.9474 | 1.0 | 0.6272 | 0.8673 | 0.9639 | 0.9789 | 0.6928 | 0.9453 | 0.8617 | 0.8995 | 0.9934 | 1.0 | 0.9186 | 0.6632 | 0.9996 | 0.0 | 0.9898 | 0.0 | 0.9937 | 0.9947 | 0.9765 | 0.9067 | 0.9507 | 0.9881 | 0.9505 | 0.0 | 1.0 | 0.9983 | 0.9222 | 0.9633 | 0.8571 | 0.9963 | 1.0 | 1.0 | 0.9689 | 1.0 | 1.0 | 0.9896 | | 0.0346 | 7.0 | 2345 | 0.0108 | 0.9724 | 0.9774 | 0.9749 | 0.9977 | 1.0 | 1.0 | 0.9375 | 1.0 | 0.9880 | 0.95 | 0.9987 | 0.9954 | 1.0 | 0.9821 | 0.9809 | 0.9924 | 0.8018 | 0.8229 | 0.5534 | 0.3548 | 0.9975 | 0.5862 | 1.0 | 1.0 | 0.9465 | 0.9974 | 0.9574 | 1.0 | 0.5957 | 0.8660 | 0.9524 | 0.9789 | 0.6842 | 0.9554 | 0.8872 | 0.9091 | 0.9934 | 1.0 | 0.9816 | 0.7065 | 0.9998 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 | 0.9811 | 0.9600 | 0.9593 | 0.9940 | 0.9557 | 0.0 | 0.9928 | 0.9991 | 0.9701 | 0.9813 | 0.8690 | 0.9963 | 1.0 | 1.0 | 0.9743 | 1.0 | 1.0 | 0.9922 | ### Framework versions - Transformers 4.33.1 - Pytorch 1.13.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3