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---
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
model-index:
- name: mdeberta-v3-base_finetuned_ai4privacy_v2
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. -->
# 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
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