PII-Detection-V2.1

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0331
• Overall Precision: 0.9482
• Overall Recall: 0.9574
• Overall F1: 0.9528
• Overall Accuracy: 0.9926
• Accountname F1: 0.9939
• Accountnumber F1: 0.9879
• Buildingnumber F1: 0.8059
• City F1: 0.9729
• Companyname F1: 0.9773
• County F1: 0.9463
• Creditcardcvv F1: 0.8970
• Creditcardissuer F1: 0.9565
• Creditcardnumber F1: 0.8770
• Email F1: 0.9981
• Firstname F1: 0.9324
• Fullname F1: 0.9851
• Iban F1: 0.9834
• Lastname F1: 0.8744
• Middlename F1: 0.8390
• Name F1: 0.9972
• Number F1: 0.9684
• Phonenumber F1: 0.9788
• Pin F1: 0.9017
• Secondaryaddress F1: 0.9892
• State F1: 0.9421
• Street F1: 0.8617
• Streetaddress F1: 0.7533
• Url F1: 0.9977
• Username F1: 0.9654

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
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Overall Precision	Overall Recall	Overall F1	Overall Accuracy	Accountname F1	Accountnumber F1	Buildingnumber F1	City F1	Companyname F1	County F1	Creditcardcvv F1	Creditcardissuer F1	Creditcardnumber F1	Email F1	Firstname F1	Fullname F1	Iban F1	Lastname F1	Middlename F1	Name F1	Number F1	Phonenumber F1	Pin F1	Secondaryaddress F1	State F1	Street F1	Streetaddress F1	Url F1	Username F1
0.0445	1.0	2031	0.0375	0.8924	0.9268	0.9093	0.9885	0.9781	0.9675	0.7260	0.9391	0.9347	0.8747	0.8221	0.8962	0.8239	0.9948	0.8764	0.9804	0.9528	0.7683	0.6548	0.9889	0.8223	0.9277	0.7938	0.9853	0.8633	0.7646	0.4597	0.9937	0.9427
0.0266	2.0	4062	0.0296	0.9245	0.9455	0.9349	0.9908	0.9900	0.9810	0.7546	0.9639	0.9574	0.9085	0.8370	0.9375	0.8809	0.9979	0.9094	0.9824	0.9785	0.8299	0.8111	0.9938	0.9247	0.9523	0.8640	0.9826	0.9163	0.7605	0.6372	0.9977	0.9599
0.0148	3.0	6093	0.0277	0.9414	0.9529	0.9471	0.9921	0.9948	0.9863	0.7876	0.9689	0.9624	0.9324	0.8883	0.9537	0.8795	0.9979	0.9252	0.9849	0.9840	0.8515	0.8310	0.9946	0.9506	0.9675	0.8685	0.9875	0.9325	0.8355	0.7560	0.9973	0.9685
0.0095	4.0	8124	0.0301	0.9438	0.9536	0.9487	0.9921	0.9913	0.9859	0.8018	0.9742	0.9652	0.9443	0.8982	0.9508	0.8784	0.9986	0.9281	0.9842	0.9828	0.8584	0.8294	0.9952	0.9681	0.9629	0.8889	0.9875	0.9374	0.8430	0.7522	0.9980	0.9457
0.0038	5.0	10155	0.0331	0.9482	0.9574	0.9528	0.9926	0.9939	0.9879	0.8059	0.9729	0.9773	0.9463	0.8970	0.9565	0.8770	0.9981	0.9324	0.9851	0.9834	0.8744	0.8390	0.9972	0.9684	0.9788	0.9017	0.9892	0.9421	0.8617	0.7533	0.9977	0.9654

Framework versions

• Transformers 4.45.2
• Pytorch 2.2.0
• Datasets 3.0.1
• Tokenizers 0.20.1