--- license: mit base_model: microsoft/mdeberta-v3-base tags: - generated_from_trainer model-index: - name: mdeberta-v3-base_finetuned_ai4privacy_v2 results: [] --- # mdeberta-v3-base_finetuned_ai4privacy_v2 This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0323 - Overall Precision: 0.9636 - Overall Recall: 0.9731 - Overall F1: 0.9683 - Overall Accuracy: 0.9896 - Accountname F1: 0.9998 - Accountnumber F1: 0.9973 - Age F1: 0.9878 - Amount F1: 0.9495 - Bic F1: 0.9932 - Bitcoinaddress F1: 0.9704 - Buildingnumber F1: 0.9648 - City F1: 0.9887 - Companyname F1: 0.9942 - County F1: 0.9940 - Creditcardcvv F1: 0.9820 - Creditcardissuer F1: 0.9985 - Creditcardnumber F1: 0.9570 - Currency F1: 0.8750 - Currencycode F1: 0.9888 - Currencyname F1: 0.7416 - Currencysymbol F1: 0.9819 - Date F1: 0.9295 - Dob F1: 0.8946 - Email F1: 0.9998 - Ethereumaddress F1: 0.9965 - Eyecolor F1: 0.9984 - Firstname F1: 0.9886 - Gender F1: 0.9962 - Height F1: 1.0 - Iban F1: 0.9966 - Ip F1: 0.6284 - Ipv4 F1: 0.8884 - Ipv6 F1: 0.8015 - Jobarea F1: 0.9940 - Jobtitle F1: 0.9973 - Jobtype F1: 0.9970 - Lastname F1: 0.9653 - Litecoinaddress F1: 0.9109 - Mac F1: 0.9992 - Maskednumber F1: 0.9524 - Middlename F1: 0.9347 - Nearbygpscoordinate F1: 1.0 - Ordinaldirection F1: 0.9984 - Password F1: 0.9936 - Phoneimei F1: 0.9998 - Phonenumber F1: 0.9992 - Pin F1: 0.9857 - Prefix F1: 0.9801 - Secondaryaddress F1: 0.9988 - Sex F1: 0.9979 - Ssn F1: 0.9983 - State F1: 0.9944 - Street F1: 0.9953 - Time F1: 0.9974 - Url F1: 1.0 - Useragent F1: 1.0 - Username F1: 0.9966 - Vehiclevin F1: 0.9936 - Vehiclevrm F1: 0.9917 - Zipcode F1: 0.9727 ## 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: 16 - eval_batch_size: 16 - 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: 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 | |:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:------:|:---------:|:------:|:-----------------:|:-----------------:|:-------:|:--------------:|:---------:|:----------------:|:-------------------:|:-------------------:|:-----------:|:---------------:|:---------------:|:-----------------:|:-------:|:------:|:--------:|:------------------:|:-----------:|:------------:|:---------:|:---------:|:-------:|:------:|:-------:|:-------:|:----------:|:-----------:|:----------:|:-----------:|:------------------:|:------:|:---------------:|:-------------:|:----------------------:|:-------------------:|:-----------:|:------------:|:--------------:|:------:|:---------:|:-------------------:|:------:|:------:|:--------:|:---------:|:-------:|:------:|:------------:|:-----------:|:-------------:|:-------------:|:----------:| | 0.0622 | 1.0 | 10463 | 0.0541 | 0.9247 | 0.9384 | 0.9315 | 0.9770 | 0.9949 | 0.9917 | 0.9812 | 0.9224 | 0.9847 | 0.9592 | 0.9056 | 0.9595 | 0.9802 | 0.9775 | 0.9350 | 0.9971 | 0.8939 | 0.7380 | 0.9664 | 0.0843 | 0.9721 | 0.7784 | 0.6363 | 0.9993 | 0.9877 | 0.9833 | 0.9696 | 0.9866 | 0.9716 | 0.9914 | 0.0 | 0.8238 | 0.8025 | 0.9882 | 0.9874 | 0.9878 | 0.8820 | 0.9085 | 0.9869 | 0.8831 | 0.8686 | 0.9984 | 0.9958 | 0.9786 | 0.9971 | 0.9885 | 0.9482 | 0.9455 | 0.9934 | 0.9956 | 0.9860 | 0.9673 | 0.9799 | 0.9916 | 0.9958 | 0.9995 | 0.9875 | 0.9555 | 0.9819 | 0.9311 | | 0.0492 | 2.0 | 20926 | 0.0445 | 0.9376 | 0.9494 | 0.9434 | 0.9788 | 0.9970 | 0.9979 | 0.9883 | 0.9492 | 0.9949 | 0.9626 | 0.9548 | 0.9819 | 0.9911 | 0.9922 | 0.9740 | 0.9985 | 0.9057 | 0.5805 | 0.9771 | 0.4872 | 0.9734 | 0.8257 | 0.7479 | 0.9989 | 0.9944 | 0.9960 | 0.9819 | 0.9933 | 0.9958 | 0.9962 | 0.1521 | 0.7969 | 0.8083 | 0.9957 | 0.9972 | 0.9970 | 0.9335 | 0.8953 | 0.9967 | 0.8786 | 0.9232 | 1.0 | 0.9980 | 0.9868 | 0.9985 | 0.9967 | 0.9808 | 0.9708 | 0.9969 | 0.9979 | 0.9980 | 0.9921 | 0.9913 | 0.9979 | 1.0 | 1.0 | 0.9914 | 0.9788 | 0.9859 | 0.9622 | | 0.0392 | 3.0 | 31389 | 0.0395 | 0.9458 | 0.9584 | 0.9521 | 0.9815 | 0.9988 | 0.9971 | 0.9861 | 0.9573 | 0.9955 | 0.9764 | 0.9518 | 0.9836 | 0.9914 | 0.9921 | 0.9802 | 0.9985 | 0.9300 | 0.8115 | 0.9862 | 0.5935 | 0.9821 | 0.8684 | 0.7870 | 0.9994 | 0.9972 | 0.9976 | 0.9860 | 0.9956 | 0.9981 | 0.9964 | 0.3002 | 0.8302 | 0.7274 | 0.9863 | 0.9968 | 0.9979 | 0.9528 | 0.9208 | 0.9881 | 0.9195 | 0.9269 | 0.9992 | 0.9980 | 0.9894 | 0.9998 | 0.9984 | 0.9750 | 0.9767 | 0.9971 | 0.9972 | 0.9978 | 0.9927 | 0.9952 | 0.9966 | 1.0 | 0.9997 | 0.9962 | 0.9942 | 0.9937 | 0.9653 | | 0.0311 | 4.0 | 41852 | 0.0341 | 0.9537 | 0.9667 | 0.9601 | 0.9855 | 0.9998 | 0.9986 | 0.9869 | 0.9545 | 0.9961 | 0.9774 | 0.9632 | 0.9885 | 0.9952 | 0.9940 | 0.9838 | 0.9985 | 0.9392 | 0.8567 | 0.9863 | 0.7015 | 0.9853 | 0.9158 | 0.8687 | 1.0 | 0.9979 | 0.9984 | 0.9875 | 0.9971 | 1.0 | 0.9962 | 0.4882 | 0.8664 | 0.6966 | 0.9927 | 0.9973 | 0.9967 | 0.9583 | 0.9251 | 0.9979 | 0.9275 | 0.9351 | 1.0 | 0.9988 | 0.9932 | 0.9998 | 0.9980 | 0.9852 | 0.9760 | 0.9983 | 0.9980 | 0.9985 | 0.9932 | 0.9952 | 0.9966 | 0.9996 | 1.0 | 0.9969 | 0.9955 | 0.9942 | 0.9685 | | 0.0196 | 5.0 | 52315 | 0.0323 | 0.9636 | 0.9731 | 0.9683 | 0.9896 | 0.9998 | 0.9973 | 0.9878 | 0.9495 | 0.9932 | 0.9704 | 0.9648 | 0.9887 | 0.9942 | 0.9940 | 0.9820 | 0.9985 | 0.9570 | 0.8750 | 0.9888 | 0.7416 | 0.9819 | 0.9295 | 0.8946 | 0.9998 | 0.9965 | 0.9984 | 0.9886 | 0.9962 | 1.0 | 0.9966 | 0.6284 | 0.8884 | 0.8015 | 0.9940 | 0.9973 | 0.9970 | 0.9653 | 0.9109 | 0.9992 | 0.9524 | 0.9347 | 1.0 | 0.9984 | 0.9936 | 0.9998 | 0.9992 | 0.9857 | 0.9801 | 0.9988 | 0.9979 | 0.9983 | 0.9944 | 0.9953 | 0.9974 | 1.0 | 1.0 | 0.9966 | 0.9936 | 0.9917 | 0.9727 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0