Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.3077	0.99	20	1.1683	0.5549	0.5621	0.5549	0.5286
0.9359	1.99	40	0.8573	0.6731	0.6807	0.6731	0.6535
0.7219	2.99	60	0.7106	0.7272	0.7359	0.7272	0.7246
0.6013	3.99	80	0.6445	0.7550	0.7686	0.7550	0.7558
0.5243	4.99	100	0.6717	0.7573	0.8077	0.7573	0.7584
0.4409	5.99	120	0.5315	0.8068	0.8027	0.8068	0.7989
0.3325	6.99	140	0.5159	0.8230	0.8236	0.8230	0.8158
0.2719	7.99	160	0.5250	0.8215	0.8227	0.8215	0.8202
0.242	8.99	180	0.5087	0.8277	0.8260	0.8277	0.8268
0.2247	9.99	200	0.5313	0.8215	0.8275	0.8215	0.8218
0.1955	10.99	220	0.6167	0.8130	0.8062	0.8130	0.8073
0.1567	11.99	240	0.5859	0.8168	0.8185	0.8168	0.8173
0.1479	12.99	260	0.5938	0.8215	0.8169	0.8215	0.8178
0.1241	13.99	280	0.6187	0.8261	0.8234	0.8261	0.8239
0.1114	14.99	300	0.6419	0.8261	0.8351	0.8261	0.8293
0.1022	15.99	320	0.6322	0.8323	0.8284	0.8323	0.8294
0.0941	16.99	340	0.6595	0.8269	0.8266	0.8269	0.8263
0.0935	17.99	360	0.6674	0.8269	0.8218	0.8269	0.8237
0.089	18.99	380	0.6533	0.8253	0.8222	0.8253	0.8235
0.0794	19.99	400	0.6547	0.8308	0.8272	0.8308	0.8287

jayanta/mit-b2-VF2-finetuned-memes

mit-b2-VF2-finetuned-memes