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	---
	license: mit
	tags:
	- generated_from_trainer
	model-index:
	- name: roberta-base_ai4privacy_en
	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. -->

	# roberta-base_ai4privacy_en

	This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0962
	- Overall Precision: 0.8739
	- Overall Recall: 0.9046
	- Overall F1: 0.8890
	- Overall Accuracy: 0.9623
	- Accountname F1: 0.9898
	- Accountnumber F1: 0.9896
	- Age F1: 0.8745
	- Amount F1: 0.8663
	- Bic F1: 0.8782
	- Bitcoinaddress F1: 0.9414
	- Buildingnumber F1: 0.8279
	- City F1: 0.8312
	- Companyname F1: 0.9434
	- County F1: 0.9279
	- Creditcardcvv F1: 0.8947
	- Creditcardissuer F1: 0.9755
	- Creditcardnumber F1: 0.8770
	- Currency F1: 0.6753
	- Currencycode F1: 0.6398
	- Currencyname F1: 0.2105
	- Currencysymbol F1: 0.9223
	- Date F1: 0.8276
	- Dob F1: 0.5470
	- Email F1: 0.9840
	- Ethereumaddress F1: 0.9972
	- Eyecolor F1: 0.9027
	- Firstname F1: 0.8696
	- Gender F1: 0.9627
	- Height F1: 0.9811
	- Iban F1: 0.9912
	- Ip F1: 0.0124
	- Ipv4 F1: 0.8377
	- Ipv6 F1: 0.7585
	- Jobarea F1: 0.8212
	- Jobtitle F1: 0.9833
	- Jobtype F1: 0.9110
	- Lastname F1: 0.8305
	- Litecoinaddress F1: 0.8793
	- Mac F1: 0.9957
	- Maskednumber F1: 0.8315
	- Middlename F1: 0.9441
	- Nearbygpscoordinate F1: 0.9970
	- Ordinaldirection F1: 0.9682
	- Password F1: 0.9654
	- Phoneimei F1: 0.9944
	- Phonenumber F1: 0.9860
	- Pin F1: 0.8150
	- Prefix F1: 0.9306
	- Secondaryaddress F1: 0.9935
	- Sex F1: 0.9721
	- Ssn F1: 0.9759
	- State F1: 0.8817
	- Street F1: 0.8264
	- Time F1: 0.9485
	- Url F1: 0.9936
	- Useragent F1: 0.9976
	- Username F1: 0.9108
	- Vehiclevin F1: 0.9568
	- Vehiclevrm F1: 0.9239
	- Zipcode F1: 0.8543

	## 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: 32
	- 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: 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 \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\|:--------------:\|:----------------:\|:------:\|:---------:\|:------:\|:-----------------:\|:-----------------:\|:-------:\|:--------------:\|:---------:\|:----------------:\|:-------------------:\|:-------------------:\|:-----------:\|:---------------:\|:---------------:\|:-----------------:\|:-------:\|:------:\|:--------:\|:------------------:\|:-----------:\|:------------:\|:---------:\|:---------:\|:-------:\|:------:\|:-------:\|:-------:\|:----------:\|:-----------:\|:----------:\|:-----------:\|:------------------:\|:------:\|:---------------:\|:-------------:\|:----------------------:\|:-------------------:\|:-----------:\|:------------:\|:--------------:\|:------:\|:---------:\|:-------------------:\|:------:\|:------:\|:--------:\|:---------:\|:-------:\|:------:\|:------------:\|:-----------:\|:-------------:\|:-------------:\|:----------:\|
	\| 0.3911 \| 1.0 \| 2175 \| 0.2642 \| 0.6000 \| 0.6420 \| 0.6203 \| 0.9119 \| 0.9177 \| 0.8043 \| 0.6417 \| 0.2664 \| 0.4444 \| 0.7762 \| 0.2639 \| 0.3614 \| 0.6320 \| 0.5282 \| 0.5097 \| 0.8493 \| 0.4381 \| 0.2180 \| 0.0 \| 0.0 \| 0.4754 \| 0.6817 \| 0.0 \| 0.9518 \| 0.9710 \| 0.5435 \| 0.5869 \| 0.6865 \| 0.5455 \| 0.7382 \| 0.0 \| 0.7780 \| 0.7260 \| 0.3064 \| 0.6099 \| 0.5096 \| 0.4491 \| 0.5872 \| 0.8521 \| 0.4547 \| 0.0365 \| 0.9822 \| 0.7915 \| 0.7728 \| 0.9553 \| 0.8337 \| 0.1159 \| 0.8853 \| 0.8930 \| 0.9520 \| 0.7916 \| 0.1731 \| 0.2881 \| 0.7877 \| 0.9793 \| 0.9202 \| 0.6108 \| 0.7374 \| 0.4551 \| 0.5222 \|
	\| 0.1748 \| 2.0 \| 4350 \| 0.1410 \| 0.7732 \| 0.8035 \| 0.7880 \| 0.9479 \| 0.9831 \| 0.9575 \| 0.8131 \| 0.6771 \| 0.8161 \| 0.8795 \| 0.6822 \| 0.6022 \| 0.8531 \| 0.6954 \| 0.8056 \| 0.9663 \| 0.8012 \| 0.5330 \| 0.3009 \| 0.0571 \| 0.8293 \| 0.7760 \| 0.3798 \| 0.9646 \| 0.9675 \| 0.8677 \| 0.6901 \| 0.9239 \| 0.9655 \| 0.9073 \| 0.0 \| 0.8345 \| 0.7913 \| 0.7190 \| 0.9331 \| 0.8958 \| 0.5220 \| 0.7748 \| 0.9913 \| 0.7372 \| 0.4010 \| 0.9925 \| 0.9558 \| 0.8982 \| 0.9359 \| 0.9586 \| 0.6094 \| 0.8621 \| 0.9891 \| 0.9702 \| 0.9464 \| 0.5906 \| 0.5943 \| 0.9437 \| 0.9936 \| 0.9369 \| 0.8300 \| 0.9157 \| 0.8207 \| 0.7405 \|
	\| 0.1081 \| 3.0 \| 6525 \| 0.1143 \| 0.8376 \| 0.8825 \| 0.8595 \| 0.9559 \| 0.9865 \| 0.9803 \| 0.8586 \| 0.7828 \| 0.8154 \| 0.8297 \| 0.7920 \| 0.7957 \| 0.9143 \| 0.8413 \| 0.8218 \| 0.9628 \| 0.8634 \| 0.6290 \| 0.5636 \| 0.1324 \| 0.8788 \| 0.8283 \| 0.4895 \| 0.9797 \| 0.9917 \| 0.8895 \| 0.8303 \| 0.9294 \| 0.9718 \| 0.9746 \| 0.0 \| 0.8325 \| 0.7976 \| 0.7521 \| 0.9647 \| 0.9140 \| 0.7495 \| 0.7371 \| 0.9848 \| 0.7944 \| 0.8836 \| 0.9955 \| 0.9701 \| 0.9227 \| 0.9944 \| 0.9785 \| 0.7427 \| 0.9254 \| 0.9924 \| 0.9701 \| 0.9527 \| 0.8031 \| 0.7519 \| 0.9288 \| 0.9929 \| 0.9848 \| 0.8880 \| 0.9391 \| 0.9251 \| 0.8403 \|
	\| 0.0804 \| 4.0 \| 8700 \| 0.0962 \| 0.8739 \| 0.9046 \| 0.8890 \| 0.9623 \| 0.9898 \| 0.9896 \| 0.8745 \| 0.8663 \| 0.8782 \| 0.9414 \| 0.8279 \| 0.8312 \| 0.9434 \| 0.9279 \| 0.8947 \| 0.9755 \| 0.8770 \| 0.6753 \| 0.6398 \| 0.2105 \| 0.9223 \| 0.8276 \| 0.5470 \| 0.9840 \| 0.9972 \| 0.9027 \| 0.8696 \| 0.9627 \| 0.9811 \| 0.9912 \| 0.0124 \| 0.8377 \| 0.7585 \| 0.8212 \| 0.9833 \| 0.9110 \| 0.8305 \| 0.8793 \| 0.9957 \| 0.8315 \| 0.9441 \| 0.9970 \| 0.9682 \| 0.9654 \| 0.9944 \| 0.9860 \| 0.8150 \| 0.9306 \| 0.9935 \| 0.9721 \| 0.9759 \| 0.8817 \| 0.8264 \| 0.9485 \| 0.9936 \| 0.9976 \| 0.9108 \| 0.9568 \| 0.9239 \| 0.8543 \|
	\| 0.0663 \| 5.0 \| 10875 \| 0.0965 \| 0.8761 \| 0.9089 \| 0.8922 \| 0.9632 \| 0.9882 \| 0.9896 \| 0.8845 \| 0.8676 \| 0.8750 \| 0.9463 \| 0.8280 \| 0.8415 \| 0.9482 \| 0.9365 \| 0.8954 \| 0.9774 \| 0.8897 \| 0.6571 \| 0.6773 \| 0.2690 \| 0.9217 \| 0.8259 \| 0.6135 \| 0.9859 \| 0.9972 \| 0.9180 \| 0.8840 \| 0.9708 \| 0.975 \| 0.9667 \| 0.1201 \| 0.8109 \| 0.7064 \| 0.8298 \| 0.9885 \| 0.9265 \| 0.8520 \| 0.8844 \| 0.9935 \| 0.8523 \| 0.9462 \| 0.9985 \| 0.9744 \| 0.9682 \| 0.9958 \| 0.9881 \| 0.8166 \| 0.9333 \| 0.9935 \| 0.9721 \| 0.9772 \| 0.8889 \| 0.8294 \| 0.9632 \| 0.9952 \| 0.9976 \| 0.9172 \| 0.9538 \| 0.9418 \| 0.8638 \|


	### Framework versions

	- Transformers 4.26.1
	- Pytorch 2.0.0.post200
	- Datasets 2.10.1
	- Tokenizers 0.13.3