Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	12	0.3399	0.9769	0.8778	0.9325	0.9335	5.0
No log	2.0	24	0.3413	0.9795	0.8917	0.941	0.9417	5.0208
No log	3.0	36	0.3375	0.9769	0.8778	0.9325	0.9335	5.0
No log	4.0	48	0.3497	0.9795	0.8917	0.941	0.9417	5.0208
No log	5.0	60	0.3593	0.9732	0.8592	0.9226	0.9246	5.0625
No log	6.0	72	0.3610	0.9732	0.8592	0.9226	0.9246	5.0625
No log	7.0	84	0.3733	0.9732	0.8592	0.9226	0.9246	5.0625
No log	8.0	96	0.3603	0.9735	0.8668	0.9236	0.9241	5.0208
No log	9.0	108	0.3482	0.9735	0.8668	0.9236	0.9241	5.0208
No log	10.0	120	0.3502	0.9735	0.8668	0.9236	0.9241	5.0208
No log	11.0	132	0.3529	0.9769	0.8778	0.9325	0.9335	5.0
No log	12.0	144	0.3542	0.9735	0.8668	0.9236	0.9241	5.0208
No log	13.0	156	0.3619	0.9765	0.8799	0.932	0.933	5.0417
No log	14.0	168	0.3750	0.9765	0.8799	0.932	0.933	5.0417
No log	15.0	180	0.3778	0.9765	0.8799	0.932	0.933	5.0417
No log	16.0	192	0.3731	0.9765	0.8799	0.932	0.933	5.0417
No log	17.0	204	0.3651	0.9765	0.8799	0.932	0.933	5.0417
No log	18.0	216	0.3695	0.9765	0.8799	0.932	0.933	5.0417
No log	19.0	228	0.3884	0.9765	0.8799	0.932	0.933	5.0417
No log	20.0	240	0.3913	0.9765	0.8799	0.932	0.933	5.0417
No log	21.0	252	0.3775	0.9795	0.8917	0.941	0.9417	5.0208
No log	22.0	264	0.3539	0.9769	0.8778	0.9325	0.9335	5.0
No log	23.0	276	0.3635	0.9769	0.8778	0.9325	0.9335	5.0
No log	24.0	288	0.3701	0.9769	0.8778	0.9325	0.9335	5.0
No log	25.0	300	0.3684	0.9769	0.8778	0.9325	0.9335	5.0
No log	26.0	312	0.3642	0.9792	0.8868	0.9405	0.94	4.9792
No log	27.0	324	0.3627	0.9792	0.8868	0.9405	0.94	4.9792
No log	28.0	336	0.3648	0.9769	0.8778	0.9325	0.9335	5.0
No log	29.0	348	0.3650	0.9769	0.8778	0.9325	0.9335	5.0
No log	30.0	360	0.3776	0.9792	0.8868	0.9405	0.94	4.9792
No log	31.0	372	0.3823	0.9792	0.8868	0.9405	0.94	4.9792
No log	32.0	384	0.3647	0.9792	0.8868	0.9405	0.94	4.9792
No log	33.0	396	0.3687	0.9792	0.8868	0.9405	0.94	4.9792
No log	34.0	408	0.3808	0.9792	0.8868	0.9405	0.94	4.9792
No log	35.0	420	0.3876	0.9821	0.9007	0.9479	0.9494	5.0
No log	36.0	432	0.3691	0.9821	0.9007	0.9479	0.9494	5.0
No log	37.0	444	0.3604	0.9851	0.9236	0.9573	0.9583	4.9792
No log	38.0	456	0.3620	0.9851	0.9236	0.9573	0.9583	4.9792
No log	39.0	468	0.3672	0.9821	0.9111	0.9474	0.9484	5.0
No log	40.0	480	0.3753	0.9792	0.8903	0.9395	0.9405	5.0208
No log	41.0	492	0.3718	0.9821	0.9111	0.9474	0.9484	5.0
0.0456	42.0	504	0.3747	0.9821	0.9007	0.9479	0.9494	5.0
0.0456	43.0	516	0.3900	0.9821	0.9007	0.9479	0.9494	5.0
0.0456	44.0	528	0.3961	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	45.0	540	0.3949	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	46.0	552	0.3953	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	47.0	564	0.3953	0.9821	0.9007	0.9479	0.9494	5.0
0.0456	48.0	576	0.3891	0.9821	0.9007	0.9479	0.9494	5.0
0.0456	49.0	588	0.3811	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	50.0	600	0.3826	0.9821	0.9007	0.9479	0.9494	5.0
0.0456	51.0	612	0.3850	0.9821	0.9007	0.9479	0.9494	5.0
0.0456	52.0	624	0.3851	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	53.0	636	0.3937	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	54.0	648	0.3990	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	55.0	660	0.4056	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	56.0	672	0.4101	0.9792	0.8903	0.9395	0.9405	5.0208
0.0456	57.0	684	0.4103	0.9792	0.8903	0.9395	0.9405	5.0208
0.0456	58.0	696	0.4083	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	59.0	708	0.4045	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	60.0	720	0.4109	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	61.0	732	0.4154	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	62.0	744	0.4149	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	63.0	756	0.4133	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	64.0	768	0.4194	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	65.0	780	0.4339	0.9762	0.8691	0.9311	0.9311	5.0
0.0456	66.0	792	0.4413	0.9762	0.8691	0.9311	0.9311	5.0
0.0456	67.0	804	0.4265	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	68.0	816	0.4261	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	69.0	828	0.4187	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	70.0	840	0.4231	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	71.0	852	0.4243	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	72.0	864	0.4159	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	73.0	876	0.4133	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	74.0	888	0.4130	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	75.0	900	0.4112	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	76.0	912	0.4096	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	77.0	924	0.4079	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	78.0	936	0.4056	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	79.0	948	0.4030	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	80.0	960	0.4078	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	81.0	972	0.4078	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	82.0	984	0.4074	0.9792	0.8868	0.9405	0.94	4.9792
0.0456	83.0	996	0.4099	0.9792	0.8868	0.9405	0.94	4.9792
0.0238	84.0	1008	0.4148	0.9792	0.8868	0.9405	0.94	4.9792
0.0238	85.0	1020	0.4180	0.9792	0.8868	0.9405	0.94	4.9792
0.0238	86.0	1032	0.4147	0.9792	0.8868	0.9405	0.94	4.9792
0.0238	87.0	1044	0.4145	0.9792	0.8868	0.9405	0.94	4.9792
0.0238	88.0	1056	0.4168	0.9792	0.8868	0.9405	0.94	4.9792
0.0238	89.0	1068	0.4171	0.9792	0.8868	0.9405	0.94	4.9792
0.0238	90.0	1080	0.4158	0.9792	0.8868	0.9405	0.94	4.9792
0.0238	91.0	1092	0.4154	0.9792	0.8868	0.9405	0.94	4.9792
0.0238	92.0	1104	0.4155	0.9792	0.8868	0.9405	0.94	4.9792
0.0238	93.0	1116	0.4157	0.9792	0.8868	0.9405	0.94	4.9792
0.0238	94.0	1128	0.4165	0.9792	0.8868	0.9405	0.94	4.9792
0.0238	95.0	1140	0.4165	0.9792	0.8868	0.9405	0.94	4.9792
0.0238	96.0	1152	0.4151	0.9792	0.8868	0.9405	0.94	4.9792
0.0238	97.0	1164	0.4149	0.9792	0.8868	0.9405	0.94	4.9792
0.0238	98.0	1176	0.4149	0.9792	0.8868	0.9405	0.94	4.9792
0.0238	99.0	1188	0.4150	0.9792	0.8868	0.9405	0.94	4.9792
0.0238	100.0	1200	0.4150	0.9792	0.8868	0.9405	0.94	4.9792

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

limaatulya/my_awesome_billsum_model_76

my_awesome_billsum_model_76

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

limaatulya/
my_awesome_billsum_model_76