Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1169	0.25	7000	0.1114	0.9639	0.9581	0.9610	0.9575
0.1171	0.5	14000	0.1150	0.9655	0.9534	0.9594	0.9554
0.1202	0.75	21000	0.1058	0.9644	0.9578	0.9611	0.9575
0.1105	0.99	28000	0.1098	0.9664	0.9549	0.9606	0.9566
0.0935	1.24	35000	0.1270	0.9643	0.9570	0.9606	0.9570
0.0999	1.49	42000	0.1112	0.9626	0.9605	0.9615	0.9580
0.0948	1.74	49000	0.1114	0.9670	0.9606	0.9638	0.9603
0.1015	1.99	56000	0.1146	0.9680	0.9589	0.9634	0.9597
0.0816	2.24	63000	0.1244	0.9670	0.9607	0.9638	0.9603
0.0855	2.49	70000	0.1107	0.9675	0.9623	0.9649	0.9614
0.0814	2.73	77000	0.1047	0.9661	0.9630	0.9645	0.9611
0.0827	2.98	84000	0.1082	0.9665	0.9631	0.9648	0.9614
0.0655	3.23	91000	0.1485	0.9690	0.9615	0.9653	0.9618
0.0631	3.48	98000	0.1314	0.9683	0.9639	0.9661	0.9627
0.0667	3.73	105000	0.1164	0.9683	0.9643	0.9663	0.9629
0.0652	3.98	112000	0.1297	0.9681	0.9653	0.9667	0.9633
0.0485	4.23	119000	0.1441	0.9697	0.9645	0.9671	0.9636
0.0505	4.47	126000	0.1350	0.9700	0.9651	0.9675	0.9642
0.0498	4.72	133000	0.1243	0.9691	0.9657	0.9674	0.9640
0.0463	4.97	140000	0.1392	0.9699	0.9660	0.9679	0.9645
0.0371	5.22	147000	0.1527	0.9709	0.9658	0.9683	0.9649
0.0363	5.47	154000	0.1490	0.9703	0.9667	0.9685	0.9651
0.0341	5.72	161000	0.1538	0.9712	0.9666	0.9689	0.9656
0.0338	5.97	168000	0.1488	0.9705	0.9668	0.9687	0.9653

surrey-nlp/roberta-large-finetuned-abbr-filtered-plod

roberta-large-finetuned-abbr-filtered-plod