Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.6617	0.16	100	0.3490	0.7259	0.7730	0.7487	0.8983
0.341	0.32	200	0.2942	0.7665	0.8052	0.7854	0.9121
0.3052	0.48	300	0.2821	0.7694	0.8021	0.7854	0.9152
0.2938	0.64	400	0.2700	0.7897	0.8122	0.8008	0.9206
0.2685	0.8	500	0.2482	0.7901	0.8253	0.8073	0.9242
0.2622	0.96	600	0.2478	0.7989	0.8298	0.8141	0.9250
0.2154	1.12	700	0.2456	0.8126	0.8365	0.8244	0.9273
0.2046	1.28	800	0.2429	0.8079	0.8335	0.8205	0.9270
0.2114	1.44	900	0.2377	0.8125	0.8415	0.8268	0.9300
0.2111	1.6	1000	0.2381	0.8231	0.8397	0.8313	0.9309
0.1934	1.76	1100	0.2349	0.8179	0.8485	0.8329	0.9308
0.1972	1.92	1200	0.2293	0.8287	0.8446	0.8366	0.9332
0.1858	2.08	1300	0.2366	0.8280	0.8463	0.8371	0.9327
0.1506	2.24	1400	0.2392	0.8255	0.8505	0.8378	0.9332
0.1508	2.4	1500	0.2346	0.8266	0.8465	0.8364	0.9334
0.1674	2.56	1600	0.2329	0.8249	0.8487	0.8366	0.9329
0.1584	2.72	1700	0.2309	0.8316	0.8508	0.8411	0.9341
0.154	2.88	1800	0.2299	0.8327	0.8515	0.8420	0.9347

MayaGalvez/bert-base-multilingual-cased-finetuned-ner

bert-base-multilingual-cased-finetuned-ner