Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
1.3484	0.05	500	0.5395	0.1841	0.1976	0.1906	0.8465
0.3948	0.09	1000	0.2910	0.6138	0.7113	0.6590	0.9263
0.2388	0.14	1500	0.2030	0.6628	0.7797	0.7165	0.9414
0.1864	0.18	2000	0.1729	0.7490	0.7935	0.7706	0.9498
0.1754	0.23	2500	0.1641	0.7415	0.7869	0.7635	0.9505
0.1558	0.28	3000	0.1532	0.7680	0.8002	0.7838	0.9530
0.1497	0.32	3500	0.1424	0.7865	0.8282	0.8068	0.9555
0.1488	0.37	4000	0.1373	0.7887	0.8111	0.7997	0.9553
0.1361	0.42	4500	0.1311	0.7942	0.8382	0.8156	0.9590
0.1335	0.46	5000	0.1264	0.7948	0.8423	0.8179	0.9596
0.1296	0.51	5500	0.1242	0.8129	0.8416	0.8270	0.9603
0.1338	0.55	6000	0.1315	0.7910	0.8588	0.8235	0.9586
0.1267	0.6	6500	0.1193	0.8092	0.8399	0.8243	0.9609
0.1207	0.65	7000	0.1205	0.8021	0.8469	0.8239	0.9601
0.1214	0.69	7500	0.1201	0.7969	0.8489	0.8220	0.9605
0.1168	0.74	8000	0.1134	0.8087	0.8607	0.8339	0.9620
0.1162	0.78	8500	0.1127	0.8177	0.8492	0.8331	0.9625
0.1202	0.83	9000	0.1283	0.7986	0.8550	0.8259	0.9580
0.1135	0.88	9500	0.1101	0.8213	0.8572	0.8389	0.9638
0.1121	0.92	10000	0.1097	0.8190	0.8588	0.8384	0.9635
0.1091	0.97	10500	0.1088	0.8180	0.8521	0.8347	0.9632
0.1058	1.02	11000	0.1085	0.8136	0.8716	0.8416	0.9630
0.0919	1.06	11500	0.1079	0.8309	0.8566	0.8436	0.9646
0.0914	1.11	12000	0.1079	0.8423	0.8542	0.8482	0.9656
0.0921	1.15	12500	0.1109	0.8312	0.8647	0.8476	0.9646
0.0926	1.2	13000	0.1240	0.8413	0.8488	0.8451	0.9637
0.0914	1.25	13500	0.1040	0.8336	0.8666	0.8498	0.9652
0.0917	1.29	14000	0.1032	0.8352	0.8707	0.8526	0.9662
0.0928	1.34	14500	0.1052	0.8347	0.8656	0.8498	0.9651
0.0906	1.38	15000	0.1032	0.8399	0.8619	0.8507	0.9662
0.0903	1.43	15500	0.1074	0.8180	0.8708	0.8436	0.9651
0.0889	1.48	16000	0.0990	0.8367	0.8713	0.8537	0.9670
0.0914	1.52	16500	0.1055	0.8508	0.8506	0.8507	0.9661
0.0934	1.57	17000	0.0979	0.8326	0.8740	0.8528	0.9669
0.0898	1.62	17500	0.1022	0.8393	0.8615	0.8502	0.9668
0.0869	1.66	18000	0.0962	0.8484	0.8762	0.8621	0.9682
0.089	1.71	18500	0.1008	0.8447	0.8714	0.8579	0.9674
0.0927	1.75	19000	0.0986	0.8379	0.8749	0.8560	0.9673
0.0883	1.8	19500	0.0965	0.8518	0.8749	0.8632	0.9688
0.0965	1.85	20000	0.0937	0.8412	0.8766	0.8585	0.9682
0.0834	1.89	20500	0.0920	0.8451	0.8862	0.8652	0.9687
0.0817	1.94	21000	0.0943	0.8439	0.8800	0.8616	0.9686
0.088	1.99	21500	0.0927	0.8483	0.8762	0.8620	0.9683
0.0705	2.03	22000	0.0993	0.8525	0.8783	0.8652	0.9690
0.0709	2.08	22500	0.0976	0.8610	0.8697	0.8653	0.9689
0.0655	2.12	23000	0.0997	0.8585	0.8665	0.8625	0.9683
0.0656	2.17	23500	0.0966	0.8569	0.8822	0.8694	0.9695
0.0698	2.22	24000	0.0955	0.8604	0.8775	0.8689	0.9696
0.065	2.26	24500	0.0971	0.8614	0.8780	0.8696	0.9697
0.0653	2.31	25000	0.0959	0.8600	0.8787	0.8692	0.9698
0.0685	2.35	25500	0.1001	0.8610	0.8710	0.8659	0.9690
0.0684	2.4	26000	0.0969	0.8490	0.8877	0.8679	0.9690
0.0657	2.45	26500	0.0954	0.8532	0.8832	0.8680	0.9696
0.0668	2.49	27000	0.0947	0.8604	0.8793	0.8698	0.9695
0.0644	2.54	27500	0.0989	0.8527	0.8790	0.8656	0.9696
0.0685	2.59	28000	0.0955	0.8596	0.8772	0.8683	0.9700
0.0702	2.63	28500	0.0937	0.8585	0.8837	0.8709	0.9700
0.0644	2.68	29000	0.0946	0.8605	0.8830	0.8716	0.9702
0.065	2.72	29500	0.0953	0.8617	0.8822	0.8719	0.9701
0.063	2.77	30000	0.0943	0.8597	0.8848	0.8721	0.9701
0.0638	2.82	30500	0.0941	0.8619	0.8846	0.8731	0.9702
0.066	2.86	31000	0.0942	0.8608	0.8847	0.8726	0.9701
0.0589	2.91	31500	0.0952	0.8632	0.8836	0.8733	0.9704
0.0568	2.95	32000	0.0948	0.8603	0.8871	0.8735	0.9704

Framework versions

Transformers 4.18.0
Pytorch 1.10.0+cu111
Datasets 2.0.0
Tokenizers 0.11.6

yihsuan/albert-base-chinese-0407-ner

albert-base-chinese-0407-ner

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

yihsuan/
albert-base-chinese-0407-ner