from transformers import pipeline

pipe = pipeline("token-classification", model="yonigo/deberta-v3-base-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
0.2437	1.0695	1000	0.1168	0.9421	0.8791	0.0	0.8847	0.8841	0.8507	0.8617	0.9746	0.7903	0.5186	0.0	0.7928	0.9609	0.5720	0.0	0.0	0.9128	0.7991	0.8952	0.7145	0.8960	0.8583	0.8807	0.8816	0.9170	0.9390	0.7071	0.8946	0.8053	0.8619	0.8326	0.9736
0.0841	2.1390	2000	0.0731	0.9605	0.9633	0.0	0.9526	0.9399	0.8957	0.9035	0.9819	0.9245	0.7832	0.5095	0.9001	0.9664	0.6905	0.3578	0.0	0.9417	0.8973	0.9628	0.9651	0.9592	0.9111	0.9699	0.9631	0.9419	0.9631	0.9382	0.9388	0.8858	0.9315	0.9081	0.9826
0.0592	3.2086	3000	0.0544	0.9675	0.9787	0.0	0.9630	0.9524	0.9192	0.9337	0.9844	0.9457	0.8391	0.7142	0.9139	0.9862	0.7777	0.5887	0.2644	0.9573	0.9166	0.9743	0.9682	0.9680	0.9437	0.9787	0.9420	0.9698	0.9674	0.9516	0.9491	0.9168	0.9492	0.9327	0.9874
0.0436	4.2781	4000	0.0488	0.9673	0.9821	0.0	0.9679	0.9709	0.9187	0.9487	0.9836	0.9722	0.8580	0.7335	0.9322	0.9912	0.7998	0.6667	0.5722	0.9432	0.9371	0.9791	0.9778	0.9705	0.9548	0.9831	0.9701	0.9680	0.9673	0.9543	0.9584	0.9331	0.9529	0.9429	0.9888
0.037	5.3476	5000	0.0518	0.9653	0.9811	0.0	0.9671	0.9660	0.9052	0.9392	0.9859	0.9745	0.8469	0.7616	0.9225	0.9873	0.8108	0.7059	0.6450	0.9578	0.9437	0.9772	0.9774	0.9715	0.9511	0.9827	0.9645	0.9681	0.9639	0.9617	0.9556	0.9283	0.9574	0.9426	0.9883
0.028	6.4171	6000	0.0488	0.9624	0.9842	0.0	0.9709	0.9732	0.9112	0.9437	0.9869	0.9767	0.8614	0.7818	0.9322	0.9860	0.8266	0.7344	0.7080	0.9518	0.9509	0.9797	0.9802	0.9768	0.9564	0.9831	0.9756	0.9717	0.9714	0.9610	0.9537	0.9383	0.9577	0.9479	0.9891
0.0238	7.4866	7000	0.0483	0.9625	0.9844	0.0	0.9705	0.9732	0.9144	0.9360	0.9804	0.9814	0.8654	0.7707	0.9328	0.9885	0.8234	0.7253	0.6873	0.9504	0.9372	0.9787	0.9750	0.9753	0.9523	0.9848	0.9755	0.9717	0.9730	0.9677	0.9567	0.9379	0.9554	0.9466	0.9893
0.0197	8.5561	8000	0.0517	0.9651	0.9857	0.0	0.9735	0.9735	0.9100	0.9579	0.9858	0.9679	0.8630	0.7748	0.9375	0.9858	0.8229	0.7259	0.6764	0.9496	0.9571	0.9800	0.9780	0.9753	0.9543	0.9829	0.9769	0.9763	0.9725	0.9637	0.9628	0.9409	0.9579	0.9493	0.9892
0.0164	9.6257	9000	0.0536	0.9642	0.9859	0.0	0.9707	0.9628	0.9175	0.9533	0.9857	0.9814	0.8617	0.7674	0.9377	0.9869	0.8193	0.7331	0.7110	0.9471	0.9535	0.9818	0.9756	0.9758	0.9602	0.9829	0.9746	0.9762	0.9710	0.9631	0.9596	0.9396	0.9581	0.9488	0.9893
0.0148	10.6952	10000	0.0545	0.9676	0.9849	0.0	0.9728	0.9741	0.9351	0.9563	0.9833	0.9791	0.8693	0.7877	0.9351	0.9863	0.8294	0.7536	0.7332	0.9609	0.9523	0.9808	0.9809	0.9775	0.9514	0.9837	0.9791	0.9713	0.9707	0.9624	0.9602	0.9435	0.9588	0.9511	0.9897
0.0115	11.7647	11000	0.0546	0.9661	0.9849	0.0	0.9757	0.9661	0.9133	0.9579	0.9800	0.9769	0.8661	0.7935	0.9439	0.9894	0.8292	0.7485	0.7126	0.9513	0.9607	0.9793	0.9815	0.9770	0.9581	0.9851	0.9803	0.9711	0.9645	0.9672	0.9588	0.9413	0.9597	0.9504	0.9896
0.0101	12.8342	12000	0.0573	0.9634	0.9861	0.0	0.9742	0.9693	0.9234	0.9574	0.9850	0.9837	0.8602	0.7854	0.9391	0.9898	0.8220	0.7470	0.7056	0.9515	0.9586	0.9834	0.9803	0.9787	0.9617	0.9841	0.9773	0.9753	0.9691	0.9649	0.9594	0.9459	0.9560	0.9509	0.9898
0.0084	13.9037	13000	0.0597	0.9657	0.9861	0.0	0.9761	0.9733	0.9136	0.9542	0.9828	0.9813	0.8672	0.7989	0.9418	0.9889	0.8326	0.7458	0.7409	0.9556	0.9573	0.9815	0.9797	0.9772	0.9616	0.9866	0.9810	0.9784	0.9644	0.9658	0.9609	0.9467	0.9568	0.9517	0.9897
0.0065	14.9733	14000	0.0621	0.9684	0.9859	0.0	0.9726	0.9741	0.9277	0.9539	0.9789	0.9814	0.8696	0.7879	0.9348	0.9868	0.8368	0.7542	0.7456	0.9487	0.9543	0.9805	0.9809	0.9780	0.9582	0.9863	0.9801	0.9763	0.9716	0.9629	0.9580	0.9439	0.9590	0.9514	0.9896
0.0059	16.0428	15000	0.0613	0.9679	0.9874	0.0	0.9770	0.9694	0.9347	0.9621	0.9786	0.9791	0.8723	0.7857	0.9403	0.9891	0.8414	0.7594	0.7371	0.9508	0.9595	0.9813	0.9797	0.9775	0.9562	0.9856	0.9790	0.9805	0.9725	0.9677	0.9554	0.9457	0.9609	0.9532	0.9901
0.005	17.1123	16000	0.0639	0.9693	0.9839	0.0	0.9781	0.9735	0.9264	0.9631	0.9827	0.9791	0.8731	0.7996	0.9437	0.9869	0.8406	0.7714	0.7593	0.9547	0.9559	0.9813	0.9809	0.9782	0.9502	0.9849	0.9810	0.9795	0.9731	0.9654	0.9617	0.9460	0.9616	0.9537	0.9901
0.0038	18.1818	17000	0.0651	0.9681	0.9869	0.0	0.9785	0.9747	0.9311	0.9606	0.9831	0.9837	0.8749	0.7899	0.9366	0.9889	0.8331	0.7520	0.7230	0.9582	0.9596	0.9805	0.9802	0.9784	0.9609	0.9858	0.9805	0.9800	0.9756	0.9663	0.9614	0.9494	0.9586	0.9540	0.9902
0.0035	19.2513	18000	0.0716	0.9661	0.9857	0.0	0.9791	0.9715	0.9319	0.9607	0.9829	0.9791	0.8707	0.8026	0.9385	0.9859	0.8354	0.7557	0.7374	0.9564	0.9580	0.9795	0.9803	0.9767	0.9563	0.9871	0.9823	0.9750	0.9745	0.9654	0.9574	0.9450	0.9610	0.9529	0.9896
0.0023	20.3209	19000	0.0682	0.9686	0.9857	0.0	0.9789	0.9755	0.9310	0.9621	0.9850	0.9837	0.8777	0.7974	0.9430	0.9880	0.8424	0.7600	0.7545	0.9566	0.9628	0.9813	0.9773	0.9765	0.9620	0.9863	0.9813	0.9743	0.9742	0.9660	0.9567	0.9474	0.9622	0.9548	0.9901
0.002	21.3904	20000	0.0727	0.9696	0.9857	0.0	0.9759	0.9742	0.9315	0.9636	0.9814	0.9814	0.8791	0.8011	0.9427	0.9898	0.8383	0.7556	0.7419	0.9588	0.9575	0.9826	0.9756	0.9756	0.9519	0.9853	0.9802	0.9733	0.9749	0.9618	0.9569	0.9459	0.9614	0.9536	0.9900
0.002	22.4599	21000	0.0756	0.9690	0.9859	0.0	0.9770	0.9752	0.9225	0.9626	0.9829	0.9814	0.8734	0.7850	0.9417	0.9878	0.8312	0.7560	0.7405	0.9570	0.9591	0.9805	0.9814	0.9768	0.9614	0.9858	0.9795	0.9758	0.9700	0.9643	0.9596	0.9452	0.9610	0.9530	0.9898
0.0014	23.5294	22000	0.0746	0.9694	0.9874	0.0	0.9779	0.9749	0.9309	0.9653	0.9869	0.9814	0.8739	0.8019	0.9439	0.9884	0.8365	0.7684	0.7559	0.9570	0.9587	0.9808	0.9797	0.9777	0.9570	0.9861	0.9807	0.9758	0.9732	0.9658	0.9625	0.9476	0.9621	0.9548	0.9902
0.0012	24.5989	23000	0.0762	0.9696	0.9864	1.0	0.9784	0.9761	0.9389	0.9666	0.9844	0.9814	0.8718	0.7860	0.9440	0.9880	0.8296	0.7611	0.7513	0.9579	0.9621	0.9826	0.9797	0.9777	0.9584	0.9856	0.9810	0.9757	0.9754	0.9673	0.9615	0.9484	0.9614	0.9548	0.9902
0.0011	25.6684	24000	0.0744	0.9698	0.9862	0.0	0.9783	0.9785	0.9353	0.9666	0.9832	0.9837	0.8775	0.8007	0.9454	0.9878	0.8417	0.7705	0.7680	0.9592	0.9629	0.9821	0.9785	0.9785	0.9625	0.9858	0.9831	0.9779	0.9756	0.9688	0.9615	0.9502	0.9626	0.9564	0.9904
0.001	26.7380	25000	0.0750	0.9702	0.9869	1.0	0.9803	0.9752	0.9335	0.9666	0.9831	0.9791	0.8836	0.8048	0.9451	0.9871	0.8435	0.7724	0.7708	0.9589	0.9624	0.9821	0.9774	0.9782	0.9605	0.9871	0.9828	0.9760	0.9762	0.9653	0.9611	0.9499	0.9630	0.9564	0.9904
0.0009	27.8075	26000	0.0764	0.9695	0.9877	1.0	0.9798	0.9767	0.9379	0.9647	0.9825	0.9746	0.8781	0.7989	0.9463	0.9878	0.8417	0.7605	0.7708	0.9595	0.9626	0.9826	0.9780	0.9782	0.9609	0.9881	0.9810	0.974	0.9761	0.9663	0.9606	0.9493	0.9627	0.9560	0.9903
0.0008	28.8770	27000	0.0767	0.9699	0.9867	1.0	0.9788	0.9773	0.9356	0.9654	0.9844	0.9746	0.8798	0.7989	0.9441	0.9880	0.8411	0.7651	0.7676	0.9603	0.9627	0.9815	0.9797	0.9782	0.9592	0.9881	0.9815	0.9765	0.9767	0.9666	0.9604	0.9496	0.9626	0.9561	0.9904
0.0008	29.9465	28000	0.0765	0.9707	0.9869	1.0	0.9785	0.9773	0.9381	0.9671	0.9835	0.9746	0.8823	0.7963	0.9426	0.9857	0.8426	0.7621	0.7708	0.9610	0.9621	0.9815	0.9791	0.9782	0.9590	0.9878	0.9815	0.9760	0.9770	0.9668	0.9606	0.9501	0.9623	0.9562	0.9904
0.0009	31.0160	29000	0.0768	0.9705	0.9867	1.0	0.9781	0.9773	0.9376	0.9641	0.9852	0.9769	0.8812	0.7989	0.9441	0.9875	0.8435	0.7673	0.7676	0.9610	0.9623	0.9820	0.9791	0.9782	0.9617	0.9871	0.9818	0.9767	0.9767	0.9668	0.9608	0.9504	0.9626	0.9565	0.9904
0.0007	32.0856	30000	0.0767	0.9705	0.9869	1.0	0.9781	0.9773	0.9374	0.9645	0.9850	0.9769	0.8810	0.7996	0.9443	0.9873	0.8433	0.7641	0.7696	0.9603	0.9619	0.9820	0.9791	0.9782	0.9615	0.9878	0.9815	0.9767	0.9762	0.9668	0.9606	0.9504	0.9625	0.9564	0.9904

Framework versions

Transformers 4.41.2
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1