bert-base-uncased_ai4privacy_en

xXiaobuding/bert-base-uncased_ai4privacy_en

bert-base-uncased_ai4privacy_en is a token classification model from xXiaobuding. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Mar 9, 2024
Updated: Mar 10, 2024
SHA: 76c72e15

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Model card

From the Hugging Face model README

bert-base-uncased_ai4privacy_en

This model is a fine-tuned version of google-bert/bert-base-uncased on the English subset of 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

Task: Token Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Mar 9, 2024
Updated: Mar 10, 2024