Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Per Category Iou	Per Category Accuracy
0.2461	0.38	15	0.3998	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.3589	0.75	30	0.3173	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.2486	1.12	45	0.3038	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.3872	1.5	60	0.2414	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.2428	1.88	75	0.2138	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.267	2.25	90	0.2384	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.1593	2.62	105	0.1965	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.1945	3.0	120	0.1901	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.1439	3.38	135	0.1763	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.1778	3.75	150	0.1817	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.1387	4.12	165	0.1603	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.1361	4.5	180	0.1420	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.1231	4.88	195	0.1482	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0922	5.25	210	0.1338	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.1217	5.62	225	0.1408	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.1138	6.0	240	0.1352	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0944	6.38	255	0.1266	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.1211	6.75	270	0.1249	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.1213	7.12	285	0.1158	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0707	7.5	300	0.1192	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0869	7.88	315	0.1146	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.1362	8.25	330	0.1101	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0748	8.62	345	0.1028	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0684	9.0	360	0.0876	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0686	9.38	375	0.0922	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0773	9.75	390	0.1011	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0694	10.12	405	0.0955	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0588	10.5	420	0.0912	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.1351	10.88	435	0.1102	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0719	11.25	450	0.0926	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0891	11.62	465	0.0895	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.048	12.0	480	0.0900	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0642	12.38	495	0.0853	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.1318	12.75	510	0.0877	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0528	13.12	525	0.0820	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.071	13.5	540	0.0885	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0938	13.88	555	0.0873	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0456	14.25	570	0.0760	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0432	14.62	585	0.0750	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0492	15.0	600	0.0751	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0469	15.38	615	0.0689	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0508	15.75	630	0.0765	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0523	16.12	645	0.0766	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.1041	16.5	660	0.0758	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0489	16.88	675	0.0734	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.047	17.25	690	0.0718	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0582	17.62	705	0.0788	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0385	18.0	720	0.0726	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0328	18.38	735	0.0689	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0464	18.75	750	0.0748	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0532	19.12	765	0.0762	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0532	19.5	780	0.0757	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0522	19.88	795	0.0745	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0375	20.25	810	0.0732	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0392	20.62	825	0.0670	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0391	21.0	840	0.0702	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0379	21.38	855	0.0658	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.094	21.75	870	0.0725	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.038	22.12	885	0.0676	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0681	22.5	900	0.0734	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0344	22.88	915	0.0653	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0363	23.25	930	0.0613	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0451	23.62	945	0.0716	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0539	24.0	960	0.0708	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.075	24.38	975	0.0781	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0427	24.75	990	0.0659	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0356	25.12	1005	0.0711	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0466	25.5	1020	0.0652	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0349	25.88	1035	0.0632	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0288	26.25	1050	0.0650	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0361	26.62	1065	0.0656	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0463	27.0	1080	0.0632	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0426	27.38	1095	0.0666	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0323	27.75	1110	0.0651	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0394	28.12	1125	0.0643	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0673	28.5	1140	0.0657	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0725	28.88	1155	0.0675	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0361	29.25	1170	0.0654	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0269	29.62	1185	0.0617	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]
0.0494	30.0	1200	0.0665	0.7633	1.0	1.0	[0.7632902145385743]	[1.0]

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3

Jalilov/doc-segment

doc-segment

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Jalilov/
doc-segment