Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	12	0.1051	0.9929	0.9646	0.9765	0.978	4.9792
No log	2.0	24	0.1272	0.9869	0.9319	0.9586	0.96	4.9792
No log	3.0	36	0.1472	0.9892	0.9458	0.9669	0.9684	5.0417
No log	4.0	48	0.1401	0.9892	0.9458	0.9669	0.9684	5.0417
No log	5.0	60	0.1206	0.9922	0.9655	0.9758	0.9773	5.0625
No log	6.0	72	0.1185	0.9922	0.9655	0.9758	0.9773	5.0625
No log	7.0	84	0.1177	0.9922	0.9655	0.9758	0.9773	5.0625
No log	8.0	96	0.1223	0.9922	0.9655	0.9758	0.9773	5.0625
No log	9.0	108	0.1253	0.9922	0.9655	0.9758	0.9773	5.0625
No log	10.0	120	0.1257	0.9892	0.9458	0.9669	0.9684	5.0417
No log	11.0	132	0.1289	0.9899	0.9444	0.9676	0.969	4.9583
No log	12.0	144	0.1164	0.9899	0.9444	0.9676	0.969	4.9583
No log	13.0	156	0.1188	0.9911	0.9521	0.9688	0.969	5.0
No log	14.0	168	0.1235	0.9929	0.9646	0.9765	0.978	4.9792
No log	15.0	180	0.1323	0.9899	0.9444	0.9676	0.969	4.9583
No log	16.0	192	0.1341	0.9899	0.9444	0.9676	0.969	4.9583
No log	17.0	204	0.1331	0.9899	0.9444	0.9676	0.969	4.9583
No log	18.0	216	0.1169	0.9929	0.9646	0.9765	0.978	4.9792
No log	19.0	228	0.1169	0.9929	0.9646	0.9765	0.978	4.9792
No log	20.0	240	0.1162	0.9929	0.9646	0.9765	0.978	4.9792
No log	21.0	252	0.1200	0.9929	0.9646	0.9765	0.978	4.9792
No log	22.0	264	0.1176	0.9947	0.9661	0.9792	0.9797	4.9792
No log	23.0	276	0.1110	0.997	0.9736	0.9807	0.9807	5.0
No log	24.0	288	0.1146	0.997	0.9736	0.9807	0.9807	5.0
No log	25.0	300	0.1101	0.997	0.9736	0.9807	0.9807	5.0
No log	26.0	312	0.1064	0.997	0.9736	0.9807	0.9807	5.0
No log	27.0	324	0.1059	0.994	0.9625	0.9717	0.9717	5.0208
No log	28.0	336	0.1064	0.994	0.9625	0.9717	0.9717	5.0208
No log	29.0	348	0.1047	0.994	0.9625	0.9717	0.9717	5.0208
No log	30.0	360	0.1005	0.994	0.9625	0.9717	0.9717	5.0208
No log	31.0	372	0.0986	0.994	0.9625	0.9717	0.9717	5.0208
No log	32.0	384	0.0981	0.994	0.9625	0.9717	0.9717	5.0208
No log	33.0	396	0.0989	0.994	0.9625	0.9717	0.9717	5.0208
No log	34.0	408	0.1026	0.997	0.9736	0.9807	0.9807	5.0
No log	35.0	420	0.1036	0.997	0.9736	0.9807	0.9807	5.0
No log	36.0	432	0.1033	0.997	0.9736	0.9807	0.9807	5.0
No log	37.0	444	0.0995	0.997	0.9736	0.9807	0.9807	5.0
No log	38.0	456	0.0977	0.997	0.9736	0.9807	0.9807	5.0
No log	39.0	468	0.0949	0.994	0.9625	0.9717	0.9717	5.0208
No log	40.0	480	0.0926	0.9911	0.9521	0.9688	0.969	5.0
No log	41.0	492	0.0893	0.9911	0.9521	0.9688	0.969	5.0
0.0105	42.0	504	0.0871	0.9911	0.9521	0.9688	0.969	5.0
0.0105	43.0	516	0.0863	0.9911	0.9521	0.9688	0.969	5.0
0.0105	44.0	528	0.0915	0.9911	0.9521	0.9688	0.969	5.0
0.0105	45.0	540	0.0937	0.994	0.9625	0.9717	0.9717	5.0208
0.0105	46.0	552	0.0950	0.994	0.9625	0.9717	0.9717	5.0208
0.0105	47.0	564	0.0955	0.994	0.9625	0.9717	0.9717	5.0208
0.0105	48.0	576	0.0956	0.994	0.9625	0.9717	0.9717	5.0208
0.0105	49.0	588	0.0968	0.997	0.9736	0.9807	0.9807	5.0
0.0105	50.0	600	0.0986	0.997	0.9736	0.9807	0.9807	5.0
0.0105	51.0	612	0.1001	0.997	0.9736	0.9807	0.9807	5.0
0.0105	52.0	624	0.0995	0.997	0.9736	0.9807	0.9807	5.0
0.0105	53.0	636	0.0983	0.997	0.9736	0.9807	0.9807	5.0
0.0105	54.0	648	0.0995	0.997	0.9736	0.9807	0.9807	5.0
0.0105	55.0	660	0.1024	0.997	0.9736	0.9807	0.9807	5.0
0.0105	56.0	672	0.1040	0.997	0.9736	0.9807	0.9807	5.0
0.0105	57.0	684	0.1052	0.997	0.9736	0.9807	0.9807	5.0
0.0105	58.0	696	0.1055	0.997	0.9736	0.9807	0.9807	5.0
0.0105	59.0	708	0.1061	0.997	0.9736	0.9807	0.9807	5.0
0.0105	60.0	720	0.1053	0.997	0.9736	0.9807	0.9807	5.0
0.0105	61.0	732	0.1078	0.997	0.9736	0.9807	0.9807	5.0
0.0105	62.0	744	0.1087	0.997	0.9736	0.9807	0.9807	5.0
0.0105	63.0	756	0.1074	0.997	0.9736	0.9807	0.9807	5.0
0.0105	64.0	768	0.1039	0.997	0.9736	0.9807	0.9807	5.0
0.0105	65.0	780	0.1022	0.997	0.9736	0.9807	0.9807	5.0
0.0105	66.0	792	0.1017	0.997	0.9736	0.9807	0.9807	5.0
0.0105	67.0	804	0.1026	0.997	0.9736	0.9807	0.9807	5.0
0.0105	68.0	816	0.1050	0.997	0.9736	0.9807	0.9807	5.0
0.0105	69.0	828	0.1060	0.997	0.9736	0.9807	0.9807	5.0
0.0105	70.0	840	0.1069	0.997	0.9736	0.9807	0.9807	5.0
0.0105	71.0	852	0.1070	0.997	0.9736	0.9807	0.9807	5.0
0.0105	72.0	864	0.1048	0.997	0.9736	0.9807	0.9807	5.0
0.0105	73.0	876	0.1041	0.997	0.9736	0.9807	0.9807	5.0
0.0105	74.0	888	0.1039	0.997	0.9736	0.9807	0.9807	5.0
0.0105	75.0	900	0.1042	0.997	0.9736	0.9807	0.9807	5.0
0.0105	76.0	912	0.1056	0.997	0.9736	0.9807	0.9807	5.0
0.0105	77.0	924	0.1057	0.997	0.9736	0.9807	0.9807	5.0
0.0105	78.0	936	0.1058	0.997	0.9736	0.9807	0.9807	5.0
0.0105	79.0	948	0.1062	0.997	0.9736	0.9807	0.9807	5.0
0.0105	80.0	960	0.1072	0.997	0.9736	0.9807	0.9807	5.0
0.0105	81.0	972	0.1070	0.997	0.9736	0.9807	0.9807	5.0
0.0105	82.0	984	0.1068	0.997	0.9736	0.9807	0.9807	5.0
0.0105	83.0	996	0.1064	0.997	0.9736	0.9807	0.9807	5.0
0.0053	84.0	1008	0.1078	0.997	0.9736	0.9807	0.9807	5.0
0.0053	85.0	1020	0.1077	0.997	0.9736	0.9807	0.9807	5.0
0.0053	86.0	1032	0.1086	0.997	0.9736	0.9807	0.9807	5.0
0.0053	87.0	1044	0.1087	0.997	0.9736	0.9807	0.9807	5.0
0.0053	88.0	1056	0.1088	0.997	0.9736	0.9807	0.9807	5.0
0.0053	89.0	1068	0.1081	0.997	0.9736	0.9807	0.9807	5.0
0.0053	90.0	1080	0.1081	0.997	0.9736	0.9807	0.9807	5.0
0.0053	91.0	1092	0.1085	0.997	0.9736	0.9807	0.9807	5.0
0.0053	92.0	1104	0.1089	0.997	0.9736	0.9807	0.9807	5.0
0.0053	93.0	1116	0.1093	0.997	0.9736	0.9807	0.9807	5.0
0.0053	94.0	1128	0.1098	0.997	0.9736	0.9807	0.9807	5.0
0.0053	95.0	1140	0.1102	0.997	0.9736	0.9807	0.9807	5.0
0.0053	96.0	1152	0.1106	0.997	0.9736	0.9807	0.9807	5.0
0.0053	97.0	1164	0.1108	0.997	0.9736	0.9807	0.9807	5.0
0.0053	98.0	1176	0.1109	0.997	0.9736	0.9807	0.9807	5.0
0.0053	99.0	1188	0.1107	0.997	0.9736	0.9807	0.9807	5.0
0.0053	100.0	1200	0.1106	0.997	0.9736	0.9807	0.9807	5.0

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

limaatulya/my_awesome_billsum_model_24

my_awesome_billsum_model_24

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

limaatulya/
my_awesome_billsum_model_24