learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
1.5903	0.0057	10	1.0556	0.0	0.0	0.0	0.7612
1.0223	0.0114	20	0.8537	0.0	0.0	0.0	0.7614
0.6350	0.0171	30	0.7167	0.0167	0.0013	0.0025	0.7667
0.6834	0.0228	40	0.5971	0.1732	0.1010	0.1276	0.8201
0.5266	0.0285	50	0.5210	0.4280	0.3469	0.3832	0.8492
0.4736	0.0342	60	0.4302	0.4694	0.5402	0.5023	0.8838
0.4100	0.0399	70	0.3636	0.5236	0.5764	0.5487	0.9050
0.3344	0.0456	80	0.3450	0.5551	0.5996	0.5765	0.9097
0.3545	0.0513	90	0.2914	0.5520	0.6462	0.5954	0.9195
0.3044	0.0569	100	0.3129	0.6310	0.6454	0.6381	0.9248
0.4089	0.0626	110	0.2587	0.5972	0.6718	0.6323	0.9296
0.2780	0.0683	120	0.2301	0.6509	0.7200	0.6837	0.9379
0.2167	0.0740	130	0.2218	0.6622	0.7264	0.6928	0.9393
0.2005	0.0797	140	0.2080	0.6935	0.7617	0.7260	0.9432
0.1698	0.0854	150	0.1977	0.6743	0.7747	0.7210	0.9449
0.1295	0.0911	160	0.1908	0.6824	0.7728	0.7248	0.9456
0.2468	0.0968	170	0.1878	0.6876	0.7679	0.7256	0.9474
0.2283	0.1025	180	0.1635	0.7252	0.8075	0.7641	0.9527
0.2265	0.1082	190	0.1612	0.7372	0.8112	0.7724	0.9540
0.1784	0.1139	200	0.1543	0.7418	0.8098	0.7743	0.9574
0.1496	0.1196	210	0.1782	0.7602	0.8083	0.7835	0.9522
0.1882	0.1253	220	0.1663	0.6905	0.8095	0.7453	0.9516
0.1689	0.1310	230	0.1565	0.7663	0.8294	0.7966	0.9570
0.1671	0.1367	240	0.1698	0.7543	0.8219	0.7867	0.9526
0.1165	0.1424	250	0.1615	0.7524	0.8250	0.7870	0.9561
0.2523	0.1481	260	0.1442	0.7454	0.8273	0.7842	0.9586
0.1185	0.1538	270	0.1364	0.7708	0.8450	0.8062	0.9610
0.1297	0.1595	280	0.1304	0.7934	0.8623	0.8265	0.9635
0.1205	0.1651	290	0.1392	0.8191	0.8640	0.8410	0.9644
0.1280	0.1708	300	0.1310	0.8202	0.8650	0.8420	0.9656
0.1611	0.1765	310	0.1232	0.8067	0.8819	0.8426	0.9660
0.1933	0.1822	320	0.1334	0.8106	0.8593	0.8342	0.9650
0.2274	0.1879	330	0.1293	0.8170	0.8672	0.8414	0.9648
0.1298	0.1936	340	0.1244	0.8279	0.8746	0.8506	0.9674
0.1209	0.1993	350	0.1312	0.8414	0.8761	0.8584	0.9679
0.1094	0.2050	360	0.1339	0.8446	0.8800	0.8619	0.9677
0.1667	0.2107	370	0.1171	0.8329	0.8839	0.8576	0.9696
0.0983	0.2164	380	0.1264	0.8264	0.8714	0.8483	0.9664
0.1332	0.2221	390	0.1216	0.8424	0.8825	0.8620	0.9689
0.1395	0.2278	400	0.1178	0.8210	0.8883	0.8533	0.9689
0.1045	0.2335	410	0.1116	0.8339	0.8889	0.8605	0.9707
0.0726	0.2392	420	0.1087	0.8429	0.8923	0.8669	0.9708
0.2055	0.2449	430	0.1101	0.8444	0.8916	0.8674	0.9701
0.1555	0.2506	440	0.1129	0.8360	0.8830	0.8589	0.9689
0.2566	0.2563	450	0.1161	0.8150	0.8795	0.8460	0.9673
0.0968	0.2620	460	0.1073	0.8311	0.9002	0.8643	0.9690
0.1891	0.2677	470	0.1222	0.8095	0.8780	0.8423	0.9658
0.1708	0.2733	480	0.1092	0.8414	0.8859	0.8631	0.9705
0.1185	0.2790	490	0.1129	0.8238	0.8829	0.8523	0.9689
0.1960	0.2847	500	0.1053	0.8364	0.8984	0.8663	0.9715
0.1881	0.2904	510	0.1059	0.8348	0.9009	0.8666	0.9710
0.1096	0.2961	520	0.1036	0.8509	0.9000	0.8748	0.9720
0.1540	0.3018	530	0.1009	0.8494	0.9005	0.8742	0.9733
0.0972	0.3075	540	0.1037	0.8394	0.9063	0.8716	0.9721
0.1696	0.3132	550	0.1183	0.8263	0.8909	0.8574	0.9669
0.0936	0.3189	560	0.1046	0.8410	0.8940	0.8667	0.9717
0.1776	0.3246	570	0.1003	0.8506	0.8990	0.8742	0.9726
0.0887	0.3303	580	0.1013	0.8574	0.9004	0.8783	0.9717
0.0921	0.3360	590	0.1015	0.8645	0.8997	0.8817	0.9711
0.1236	0.3417	600	0.1048	0.8679	0.8955	0.8815	0.9705
0.0902	0.3474	610	0.0982	0.8591	0.8960	0.8772	0.9724
0.0785	0.3531	620	0.0966	0.8569	0.9017	0.8787	0.9737
0.0426	0.3588	630	0.0993	0.8635	0.9010	0.8819	0.9739
0.1027	0.3645	640	0.1004	0.8742	0.9041	0.8889	0.9743
0.0642	0.3702	650	0.0934	0.8720	0.9100	0.8906	0.9746
0.0958	0.3759	660	0.0968	0.8674	0.9116	0.8890	0.9732
0.1038	0.3815	670	0.0966	0.8583	0.9123	0.8845	0.9734
0.1287	0.3872	680	0.0979	0.8723	0.9069	0.8893	0.9742
0.1365	0.3929	690	0.0992	0.8756	0.9076	0.8913	0.9742
0.1110	0.3986	700	0.0919	0.8717	0.9140	0.8924	0.9744
0.0470	0.4043	710	0.0911	0.8698	0.9138	0.8913	0.9751
0.1047	0.4100	720	0.0905	0.8728	0.9132	0.8925	0.9760
0.0703	0.4157	730	0.0902	0.8752	0.9150	0.8947	0.9762
0.1617	0.4214	740	0.0891	0.8710	0.9184	0.8941	0.9763
0.0450	0.4271	750	0.0913	0.8726	0.9191	0.8952	0.9761
0.0342	0.4328	760	0.0921	0.8720	0.9140	0.8925	0.9762
0.0998	0.4385	770	0.0906	0.8789	0.9147	0.8964	0.9770
0.1439	0.4442	780	0.0852	0.8735	0.9169	0.8947	0.9773
0.1688	0.4499	790	0.0855	0.8799	0.9172	0.8982	0.9769
0.0964	0.4556	800	0.0839	0.8681	0.9191	0.8928	0.9773
0.0709	0.4613	810	0.0822	0.8700	0.9177	0.8932	0.9775
0.1235	0.4670	820	0.0824	0.8737	0.9160	0.8943	0.9773
0.1253	0.4727	830	0.0821	0.8676	0.9167	0.8915	0.9776
0.0797	0.4784	840	0.0829	0.8757	0.9155	0.8952	0.9773
0.1010	0.4841	850	0.0852	0.8758	0.9147	0.8948	0.9766
0.1138	0.4897	860	0.0835	0.8628	0.9184	0.8897	0.9768
0.1364	0.4954	870	0.0817	0.8785	0.9199	0.8987	0.9777
0.1309	0.5011	880	0.0811	0.8745	0.9206	0.8969	0.9778
0.1815	0.5068	890	0.0800	0.8828	0.9226	0.9022	0.9782
0.1240	0.5125	900	0.0764	0.8924	0.9197	0.9059	0.9779
0.1369	0.5182	910	0.0750	0.8887	0.9189	0.9035	0.9777
0.0434	0.5239	920	0.0754	0.8864	0.9192	0.9025	0.9778
0.1834	0.5296	930	0.0743	0.8933	0.9229	0.9079	0.9791
0.0630	0.5353	940	0.0747	0.8859	0.9222	0.9037	0.9793
0.1441	0.5410	950	0.0767	0.8762	0.9199	0.8975	0.9780
0.1221	0.5467	960	0.0747	0.8819	0.9228	0.9019	0.9784
0.0639	0.5524	970	0.0799	0.8735	0.9216	0.8969	0.9771
0.0593	0.5581	980	0.0815	0.8891	0.9211	0.9048	0.9781
0.1228	0.5638	990	0.0795	0.8931	0.9214	0.9071	0.9789
0.0945	0.5695	1000	0.0757	0.8916	0.9236	0.9073	0.9793
0.0593	0.5752	1010	0.0750	0.8863	0.9253	0.9054	0.9796
0.0586	0.5809	1020	0.0745	0.8839	0.9248	0.9039	0.9795
0.0608	0.5866	1030	0.0747	0.8868	0.9256	0.9058	0.9791
0.0486	0.5923	1040	0.0766	0.8931	0.9248	0.9086	0.9792
0.0858	0.5979	1050	0.0784	0.8978	0.9239	0.9107	0.9792
0.0341	0.6036	1060	0.0795	0.9015	0.9239	0.9126	0.9793
0.0963	0.6093	1070	0.0791	0.9009	0.9236	0.9121	0.9797
0.1173	0.6150	1080	0.0747	0.8984	0.9241	0.9111	0.9800
0.0431	0.6207	1090	0.0769	0.8987	0.9246	0.9115	0.9797
0.0299	0.6264	1100	0.0797	0.8949	0.9253	0.9098	0.9795
0.0470	0.6321	1110	0.0808	0.8920	0.9258	0.9086	0.9793
0.0875	0.6378	1120	0.0791	0.8918	0.9251	0.9081	0.9792
0.1278	0.6435	1130	0.0772	0.8964	0.9260	0.9109	0.9798
0.1377	0.6492	1140	0.0759	0.8925	0.9281	0.9100	0.9801
0.0578	0.6549	1150	0.0754	0.8989	0.9276	0.9130	0.9804
0.0971	0.6606	1160	0.0745	0.8969	0.9265	0.9114	0.9803
0.0831	0.6663	1170	0.0737	0.8945	0.9256	0.9098	0.9798
0.1006	0.6720	1180	0.0737	0.8944	0.9246	0.9092	0.9800
0.0858	0.6777	1190	0.0740	0.8986	0.9260	0.9121	0.9800
0.0832	0.6834	1200	0.0737	0.8958	0.9263	0.9108	0.9802
0.1260	0.6891	1210	0.0724	0.8975	0.9256	0.9114	0.9802
0.0360	0.6948	1220	0.0716	0.8983	0.9265	0.9122	0.9804
0.0909	0.7005	1230	0.0719	0.8955	0.9276	0.9113	0.9805
0.0878	0.7062	1240	0.0725	0.8993	0.9283	0.9135	0.9807
0.0864	0.7118	1250	0.0709	0.9004	0.9268	0.9134	0.9811
0.1352	0.7175	1260	0.0694	0.8978	0.9271	0.9122	0.9812
0.0765	0.7232	1270	0.0687	0.8966	0.9276	0.9118	0.9810
0.0831	0.7289	1280	0.0698	0.8944	0.9275	0.9106	0.9805
0.0693	0.7346	1290	0.0722	0.8904	0.9297	0.9096	0.9797
0.1066	0.7403	1300	0.0690	0.8975	0.9297	0.9133	0.9809
0.0810	0.7460	1310	0.0686	0.8971	0.9298	0.9131	0.9813
0.0306	0.7517	1320	0.0686	0.8974	0.9317	0.9142	0.9814
0.0719	0.7574	1330	0.0686	0.8943	0.9322	0.9128	0.9812
0.0472	0.7631	1340	0.0686	0.8966	0.9323	0.9141	0.9814
0.0859	0.7688	1350	0.0696	0.8984	0.9313	0.9146	0.9811
0.0665	0.7745	1360	0.0696	0.8967	0.9325	0.9143	0.9812
0.0857	0.7802	1370	0.0700	0.8955	0.9320	0.9134	0.9810
0.0466	0.7859	1380	0.0694	0.8937	0.9322	0.9125	0.9810
0.0242	0.7916	1390	0.0696	0.8971	0.9322	0.9143	0.9813
0.0887	0.7973	1400	0.0684	0.9009	0.9329	0.9166	0.9818
0.0524	0.8030	1410	0.0682	0.8982	0.9322	0.9149	0.9815
0.0713	0.8087	1420	0.0687	0.9011	0.9318	0.9162	0.9816
0.0227	0.8144	1430	0.0691	0.9011	0.9307	0.9156	0.9817
0.0782	0.8200	1440	0.0695	0.9019	0.9310	0.9162	0.9816
0.0720	0.8257	1450	0.0700	0.9030	0.9310	0.9168	0.9815
0.0494	0.8314	1460	0.0701	0.9036	0.9308	0.9170	0.9815
0.0807	0.8371	1470	0.0694	0.9061	0.9320	0.9189	0.9815
0.0652	0.8428	1480	0.0689	0.9071	0.9317	0.9192	0.9816
0.0682	0.8485	1490	0.0684	0.9084	0.9315	0.9198	0.9817
0.1060	0.8542	1500	0.0678	0.9077	0.9315	0.9194	0.9818
0.0819	0.8599	1510	0.0666	0.9032	0.9327	0.9177	0.9819
0.0624	0.8656	1520	0.0665	0.8998	0.9312	0.9152	0.9816
0.0446	0.8713	1530	0.0661	0.9036	0.9318	0.9175	0.9815
0.0786	0.8770	1540	0.0653	0.9031	0.9318	0.9173	0.9818
0.0718	0.8827	1550	0.0647	0.9042	0.9325	0.9181	0.9820
0.1019	0.8884	1560	0.0648	0.9052	0.9335	0.9191	0.9820
0.0557	0.8941	1570	0.0651	0.9026	0.9329	0.9175	0.9819
0.0380	0.8998	1580	0.0650	0.9036	0.9325	0.9178	0.9819
0.0712	0.9055	1590	0.0649	0.9026	0.9329	0.9175	0.9819
0.0893	0.9112	1600	0.0653	0.8990	0.9320	0.9152	0.9817
0.1792	0.9169	1610	0.0655	0.8988	0.9315	0.9149	0.9815
0.0253	0.9226	1620	0.0656	0.8984	0.9317	0.9147	0.9815
0.1265	0.9282	1630	0.0657	0.8986	0.9317	0.9148	0.9817
0.0291	0.9339	1640	0.0652	0.8987	0.9317	0.9149	0.9816
0.0714	0.9396	1650	0.0653	0.8984	0.9317	0.9147	0.9816
0.0390	0.9453	1660	0.0655	0.8999	0.9318	0.9156	0.9815
0.0695	0.9510	1670	0.0655	0.8997	0.9318	0.9155	0.9815
0.1013	0.9567	1680	0.0654	0.9013	0.9323	0.9165	0.9817
0.0622	0.9624	1690	0.0653	0.9017	0.9323	0.9168	0.9817
0.0750	0.9681	1700	0.0650	0.9035	0.9323	0.9177	0.9818
0.0567	0.9738	1710	0.0649	0.9040	0.9322	0.9179	0.9819
0.0263	0.9795	1720	0.0647	0.9044	0.9327	0.9183	0.9819
0.1022	0.9852	1730	0.0646	0.9050	0.9330	0.9188	0.9820
0.1157	0.9909	1740	0.0645	0.9047	0.9327	0.9185	0.9820
0.0935	0.9966	1750	0.0645	0.9048	0.9327	0.9185	0.9820
0.0935	1.0	1756	0.0645	0.9042	0.9327	0.9182	0.9820

Framework versions

Transformers 5.6.2
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

YarBar/bert-finetuned-ner

bert-finetuned-ner

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

YarBar/
bert-finetuned-ner