Training Loss	Epoch	Step	Validation Loss	F1
0.1803	0.11	500	0.1939	0.9611
0.1543	0.22	1000	0.1364	0.9669
0.1213	0.32	1500	0.1487	0.9728
0.1079	0.43	2000	0.0855	0.9773
0.0975	0.54	2500	0.0844	0.9831
0.0855	0.65	3000	0.0785	0.9831
0.0844	0.76	3500	0.0679	0.9857
0.0793	0.86	4000	0.0489	0.9890
0.0864	0.97	4500	0.0399	0.9903
0.049	1.08	5000	0.0528	0.9890
0.0353	1.19	5500	0.0635	0.9877
0.0321	1.3	6000	0.0542	0.9903
0.0311	1.41	6500	0.0559	0.9896
0.0315	1.51	7000	0.0736	0.9857
0.04	1.62	7500	0.0648	0.9909
0.0265	1.73	8000	0.0608	0.9909
0.0443	1.84	8500	0.0617	0.9883
0.0443	1.95	9000	0.0555	0.9896
0.0235	2.05	9500	0.0608	0.9903
0.0139	2.16	10000	0.0613	0.9922
0.0126	2.27	10500	0.0739	0.9903
0.0164	2.38	11000	0.0679	0.9903
0.0172	2.49	11500	0.0606	0.9922
0.0175	2.59	12000	0.0442	0.9942
0.01	2.7	12500	0.0661	0.9916
0.0059	2.81	13000	0.0659	0.9929
0.0216	2.92	13500	0.0504	0.9929
0.0123	3.03	14000	0.0584	0.9929
0.0047	3.14	14500	0.0573	0.9929
0.0123	3.24	15000	0.0511	0.9935
0.0027	3.35	15500	0.0579	0.9942
0.0025	3.46	16000	0.0602	0.9935
0.0051	3.57	16500	0.0598	0.9935
0.0044	3.68	17000	0.0617	0.9929
0.0061	3.78	17500	0.0634	0.9935
0.0048	3.89	18000	0.0672	0.9929
0.0078	4.0	18500	0.0657	0.9929

juancavallotti/roberta-base-culinary-finetuned

roberta-base-culinary-finetuned