Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	F1 Who	F1 What	F1 Where	F1 How
1.6596	0.15	20	1.2172	0.5510	0.6640	0.5906	0.6640	0.0	0.6409	0.3258	0.7719
1.0566	0.31	40	0.9097	0.6534	0.7087	0.6590	0.7087	0.3855	0.7020	0.5620	0.8086
0.8056	0.46	60	0.7640	0.7092	0.7570	0.7196	0.7570	0.6857	0.7709	0.6696	0.8114
0.6996	0.61	80	0.6706	0.7601	0.7931	0.7687	0.7931	0.8103	0.7743	0.7471	0.8499
0.6346	0.76	100	0.6471	0.7763	0.8032	0.7852	0.8032	0.7874	0.7813	0.7490	0.8665
0.523	0.92	120	0.6635	0.7872	0.8061	0.7865	0.8061	0.8244	0.7718	0.7692	0.8771
0.5324	1.07	140	0.6162	0.8045	0.8212	0.8110	0.8212	0.8197	0.8008	0.8033	0.8852
0.4734	1.22	160	0.6147	0.7935	0.8097	0.7978	0.8097	0.7939	0.7861	0.7698	0.8911
0.5111	1.37	180	0.6142	0.8022	0.8154	0.8051	0.8154	0.8244	0.8047	0.768	0.8909
0.4416	1.53	200	0.6204	0.8006	0.8190	0.8079	0.8190	0.8271	0.7984	0.7773	0.8886
0.5249	1.68	220	0.6239	0.7907	0.8133	0.8006	0.8133	0.8182	0.7969	0.7739	0.8776
0.4599	1.83	240	0.6458	0.7989	0.8082	0.7967	0.8082	0.8244	0.7953	0.7751	0.8853
0.4979	1.98	260	0.6390	0.8071	0.8183	0.8051	0.8183	0.7869	0.8000	0.7583	0.8871
0.393	2.14	280	0.6348	0.7994	0.8125	0.8021	0.8125	0.8271	0.7904	0.7653	0.8812
0.4079	2.29	300	0.6227	0.8002	0.8140	0.8040	0.8140	0.8182	0.7908	0.7668	0.8784
0.3731	2.44	320	0.6319	0.7887	0.8075	0.7965	0.8075	0.8030	0.7814	0.7692	0.8702
0.3987	2.6	340	0.6171	0.7922	0.8140	0.8015	0.8140	0.7907	0.7813	0.7968	0.8759
0.3865	2.75	360	0.6161	0.7968	0.8118	0.8032	0.8118	0.7846	0.7824	0.7692	0.8851
0.4222	2.9	380	0.6137	0.7955	0.8140	0.8033	0.8140	0.8060	0.7897	0.7874	0.8746
0.4164	3.05	400	0.6016	0.8017	0.8176	0.8079	0.8176	0.7846	0.7954	0.7843	0.8832
0.3505	3.21	420	0.6239	0.7912	0.8075	0.7949	0.8075	0.7846	0.7930	0.7786	0.8556
0.3834	3.36	440	0.6038	0.8022	0.8169	0.8082	0.8169	0.7907	0.7976	0.7757	0.8835
0.3139	3.51	460	0.6068	0.7978	0.8161	0.8052	0.8161	0.7970	0.7904	0.7846	0.8870
0.3679	3.66	480	0.6070	0.8026	0.8183	0.8063	0.8183	0.7907	0.7953	0.7799	0.8835
0.3387	3.82	500	0.6059	0.8025	0.8205	0.8094	0.8205	0.7879	0.7977	0.7937	0.8879
0.3208	3.97	520	0.6064	0.8015	0.8183	0.8082	0.8183	0.7970	0.7900	0.7782	0.8854
0.3008	4.12	540	0.6088	0.8020	0.8205	0.8107	0.8205	0.7970	0.7946	0.7813	0.8883
0.3014	4.27	560	0.6093	0.8032	0.8212	0.8114	0.8212	0.8120	0.7961	0.7813	0.8867
0.3486	4.43	580	0.6112	0.8042	0.8205	0.8107	0.8205	0.7939	0.7961	0.7829	0.8873
0.2793	4.58	600	0.6156	0.8047	0.8183	0.8088	0.8183	0.7846	0.7945	0.7769	0.8905
0.2943	4.73	620	0.6170	0.8044	0.8212	0.8107	0.8212	0.7846	0.7992	0.7843	0.8895
0.3314	4.89	640	0.6188	0.8040	0.8198	0.8097	0.8198	0.7939	0.7929	0.7769	0.8905

Framework versions

Transformers 4.26.0
Pytorch 1.13.1+cu117
Datasets 2.8.0
Tokenizers 0.13.2

gijs/aces-roberta-10

aces-roberta-10

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gijs/
aces-roberta-10