distilbert-base-cased-pii-en

This model is a fine-tuned version of distilbert-base-cased on English samples from ai4privacy/pii-masking-300k.

Usage:

from transformers import pipeline

pipe = pipeline("token-classification", model="yonigo/distilbert-base-cased-pii-en", aggregation_strategy="first")
pipe("My name is Yoni Go and I live in Israel. My phone number is 054-1234567")

training code git

Training results

Training Loss	Epoch	Step	Validation Loss	Bod F1	Building F1	Cardissuer F1	City F1	Country F1	Date F1	Driverlicense F1	Email F1	Geocoord F1	Givenname1 F1	Givenname2 F1	Idcard F1	Ip F1	Lastname1 F1	Lastname2 F1	Lastname3 F1	Pass F1	Passport F1	Postcode F1	Secaddress F1	Sex F1	Socialnumber F1	State F1	Street F1	Tel F1	Time F1	Title F1	Username F1	Precision	Recall	F1	Accuracy
0.23	2.1368	1000	0.1111	0.8804	0.9218	0.0	0.5854	0.8890	0.8009	0.5896	0.9469	0.7002	0.4149	0.0	0.5740	0.9056	0.5230	0.0	0.0	0.6551	0.6301	0.7819	0.5871	0.8396	0.7137	0.8771	0.6466	0.8567	0.9185	0.7411	0.8202	0.7127	0.7640	0.7374	0.9723
0.0687	4.2735	2000	0.0541	0.9365	0.9688	0.0	0.9115	0.9434	0.8841	0.8530	0.9768	0.9698	0.7276	0.3073	0.8720	0.9701	0.6430	0.1854	0.0	0.8169	0.8857	0.9638	0.9617	0.9312	0.8927	0.9418	0.9231	0.9438	0.9461	0.9232	0.9138	0.8698	0.9020	0.8856	0.9862
0.0452	6.4103	3000	0.0469	0.9550	0.9753	0.0	0.9404	0.9620	0.9014	0.9004	0.9831	0.9721	0.7664	0.4606	0.9099	0.97	0.6838	0.3818	0.0	0.8611	0.9079	0.9757	0.9715	0.9661	0.9197	0.9674	0.9435	0.9448	0.96	0.9354	0.9357	0.8975	0.9212	0.9092	0.9883
0.0311	8.5470	4000	0.0434	0.9581	0.9753	0.0	0.9481	0.9641	0.8973	0.9217	0.9833	0.9745	0.8085	0.6085	0.9232	0.9781	0.7519	0.4891	0.0849	0.8685	0.9280	0.9771	0.9704	0.9659	0.9278	0.9729	0.9549	0.9563	0.9594	0.9564	0.9407	0.9117	0.9344	0.9229	0.9897
0.023	10.6838	5000	0.0416	0.9578	0.9778	0.0	0.9503	0.9639	0.9043	0.9245	0.9804	0.9767	0.8277	0.6607	0.9031	0.9836	0.7790	0.5779	0.25	0.8791	0.9276	0.9767	0.9660	0.9708	0.9353	0.9749	0.9610	0.9681	0.9611	0.9545	0.9382	0.9185	0.9363	0.9273	0.9903
0.016	12.8205	6000	0.0436	0.9607	0.9778	0.0	0.9516	0.9698	0.9027	0.9274	0.9781	0.9677	0.8472	0.6913	0.9259	0.9877	0.7857	0.5925	0.3819	0.8853	0.9377	0.9731	0.9688	0.9752	0.9461	0.9756	0.9594	0.9613	0.9568	0.9545	0.9447	0.9247	0.9396	0.9321	0.9907
0.012	14.9573	7000	0.0447	0.9598	0.9808	0.0	0.9558	0.9737	0.9070	0.9339	0.9719	0.9654	0.8482	0.7009	0.9280	0.9859	0.7879	0.6027	0.4729	0.8909	0.9413	0.9781	0.9750	0.9740	0.9431	0.9778	0.9624	0.9623	0.9665	0.9555	0.9403	0.9275	0.9413	0.9344	0.9911
0.0086	17.0940	8000	0.0487	0.9584	0.9814	0.0	0.9529	0.9733	0.9114	0.9350	0.9857	0.9767	0.8493	0.7059	0.9306	0.9829	0.7918	0.6216	0.5292	0.8934	0.9330	0.9790	0.9766	0.9750	0.9395	0.9747	0.9624	0.9605	0.9631	0.9580	0.9399	0.9282	0.9423	0.9352	0.9908
0.0062	19.2308	9000	0.0509	0.9596	0.9795	0.0	0.9594	0.9708	0.9128	0.9299	0.9872	0.9837	0.8491	0.7188	0.9270	0.9859	0.7923	0.6468	0.5371	0.8919	0.9369	0.9783	0.9756	0.9749	0.9382	0.9778	0.9650	0.9647	0.9653	0.9559	0.9461	0.9337	0.9394	0.9365	0.9911
0.0045	21.3675	10000	0.0548	0.9559	0.9774	0.0	0.9524	0.9720	0.9080	0.9348	0.9827	0.9814	0.8446	0.7117	0.9271	0.9800	0.7977	0.6428	0.5266	0.8964	0.9351	0.9754	0.9716	0.9728	0.9478	0.9757	0.9584	0.9698	0.9587	0.9548	0.9423	0.9242	0.9457	0.9348	0.9907
0.0036	23.5043	11000	0.0560	0.9594	0.9781	0.0	0.9575	0.9720	0.9121	0.9367	0.9814	0.9814	0.8504	0.7209	0.9317	0.9807	0.7922	0.6507	0.5918	0.8864	0.9380	0.9769	0.9722	0.9745	0.9399	0.9771	0.9628	0.9675	0.9618	0.9581	0.9446	0.9288	0.9434	0.9361	0.9910
0.0026	25.6410	12000	0.0576	0.9596	0.9798	0.0	0.9575	0.9732	0.9130	0.9308	0.9831	0.9791	0.8471	0.7104	0.9268	0.9836	0.7967	0.6563	0.6222	0.8982	0.9345	0.9771	0.9739	0.9733	0.9402	0.9771	0.9673	0.9656	0.9642	0.9576	0.9480	0.9288	0.9446	0.9366	0.9910
0.002	27.7778	13000	0.0608	0.9555	0.9796	0.0	0.9561	0.9717	0.9045	0.9319	0.9848	0.9791	0.8488	0.7157	0.9268	0.9852	0.7909	0.6580	0.6039	0.8900	0.9360	0.9802	0.9717	0.9750	0.9361	0.9778	0.9646	0.9683	0.9615	0.9565	0.9465	0.9279	0.9433	0.9355	0.9909
0.0016	29.9145	14000	0.0601	0.9573	0.9801	0.0	0.9589	0.9722	0.9135	0.9353	0.9848	0.9837	0.8499	0.7202	0.9316	0.9871	0.7942	0.6677	0.6432	0.9017	0.9402	0.9799	0.9744	0.9740	0.9455	0.9783	0.9631	0.9719	0.9645	0.9600	0.9482	0.9331	0.9443	0.9387	0.9913
0.0013	32.0513	15000	0.0613	0.9606	0.9798	0.0	0.9571	0.9739	0.9155	0.9365	0.9840	0.9791	0.8466	0.7221	0.9321	0.9861	0.7948	0.6622	0.6281	0.9017	0.9407	0.9787	0.9739	0.9740	0.9475	0.9774	0.9632	0.9693	0.9644	0.9594	0.9469	0.9313	0.9457	0.9384	0.9912
0.001	34.1880	16000	0.0639	0.9611	0.9808	0.0	0.9601	0.9729	0.9149	0.9337	0.9867	0.9814	0.8483	0.7269	0.9307	0.9838	0.7956	0.6627	0.6154	0.9025	0.9397	0.9797	0.9733	0.9738	0.9383	0.9776	0.9636	0.9674	0.9637	0.9576	0.9480	0.9304	0.9457	0.9380	0.9911
0.0009	36.3248	17000	0.0621	0.9622	0.9811	0.0	0.9604	0.9741	0.9156	0.9359	0.9855	0.9814	0.8510	0.7273	0.9319	0.9859	0.7991	0.6646	0.6413	0.8999	0.9393	0.9789	0.9739	0.9740	0.9427	0.9789	0.9653	0.9687	0.9637	0.9597	0.9460	0.9324	0.9456	0.9390	0.9913
0.0008	38.4615	18000	0.0631	0.9620	0.9801	0.0	0.9582	0.9744	0.9190	0.9350	0.9853	0.9814	0.8514	0.7253	0.9298	0.9848	0.7992	0.6677	0.6434	0.8992	0.9401	0.9797	0.9739	0.9731	0.9421	0.9789	0.9653	0.9681	0.9643	0.9592	0.9462	0.9319	0.9457	0.9388	0.9913
0.0007	40.5983	19000	0.0633	0.9615	0.9806	0.0	0.9589	0.9741	0.9170	0.9358	0.9861	0.9814	0.8501	0.7268	0.9316	0.9857	0.7973	0.6662	0.6393	0.9002	0.9405	0.9797	0.9745	0.9738	0.9442	0.9796	0.9658	0.9679	0.9644	0.9586	0.9469	0.9323	0.9459	0.9391	0.9913
0.0008	42.7350	20000	0.0635	0.9622	0.9808	0.0	0.9589	0.9741	0.9176	0.9355	0.9855	0.9814	0.8496	0.7261	0.9306	0.9850	0.7974	0.6672	0.6393	0.9005	0.9405	0.9794	0.9745	0.9738	0.9437	0.9794	0.9651	0.9674	0.9638	0.9587	0.9469	0.9318	0.9460	0.9388	0.9913

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.1+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1