Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	12	0.3628	0.9844	0.9417	0.9576	0.9576	5.25
No log	2.0	24	0.3725	0.9844	0.9417	0.9576	0.9576	5.25
No log	3.0	36	0.3888	0.9844	0.9417	0.9576	0.9576	5.25
No log	4.0	48	0.4046	0.9779	0.9378	0.9561	0.9561	5.2083
No log	5.0	60	0.4100	0.9844	0.9417	0.9576	0.9576	5.25
No log	6.0	72	0.3963	0.9844	0.9417	0.9576	0.9576	5.25
No log	7.0	84	0.3786	0.9844	0.9417	0.9576	0.9576	5.25
No log	8.0	96	0.3765	0.9844	0.9417	0.9576	0.9576	5.25
No log	9.0	108	0.3928	0.9844	0.9417	0.9576	0.9576	5.25
No log	10.0	120	0.3881	0.9844	0.9417	0.9576	0.9576	5.25
No log	11.0	132	0.3780	0.9866	0.9486	0.9628	0.9628	5.2292
No log	12.0	144	0.3859	0.9866	0.9486	0.9628	0.9628	5.2292
No log	13.0	156	0.3843	0.9866	0.9486	0.9628	0.9628	5.2292
No log	14.0	168	0.3782	0.9866	0.9486	0.9628	0.9628	5.2292
No log	15.0	180	0.3802	0.9844	0.9417	0.9576	0.9576	5.25
No log	16.0	192	0.3542	0.9844	0.9417	0.9576	0.9576	5.25
No log	17.0	204	0.3478	0.9844	0.9417	0.9576	0.9576	5.25
No log	18.0	216	0.3549	0.9844	0.9417	0.9576	0.9576	5.25
No log	19.0	228	0.3581	0.9844	0.9417	0.9576	0.9576	5.25
No log	20.0	240	0.3675	0.9844	0.9417	0.9576	0.9576	5.25
No log	21.0	252	0.3728	0.9844	0.9417	0.9576	0.9576	5.25
No log	22.0	264	0.3606	0.9844	0.9417	0.9576	0.9576	5.25
No log	23.0	276	0.3327	0.9866	0.9486	0.9628	0.9628	5.2292
No log	24.0	288	0.3361	0.9844	0.9417	0.9576	0.9576	5.25
No log	25.0	300	0.3485	0.9844	0.9417	0.9576	0.9576	5.25
No log	26.0	312	0.3550	0.9844	0.9417	0.9576	0.9576	5.25
No log	27.0	324	0.3590	0.9844	0.9417	0.9576	0.9576	5.25
No log	28.0	336	0.3670	0.9844	0.9417	0.9576	0.9576	5.25
No log	29.0	348	0.3715	0.9844	0.9417	0.9576	0.9576	5.25
No log	30.0	360	0.3780	0.9844	0.9417	0.9576	0.9576	5.25
No log	31.0	372	0.3968	0.9844	0.9417	0.9576	0.9576	5.25
No log	32.0	384	0.4152	0.9844	0.9417	0.9576	0.9576	5.25
No log	33.0	396	0.4171	0.9844	0.9417	0.9576	0.9576	5.25
No log	34.0	408	0.4122	0.9844	0.9417	0.9576	0.9576	5.25
No log	35.0	420	0.4035	0.9844	0.9417	0.9576	0.9576	5.25
No log	36.0	432	0.3880	0.9844	0.9417	0.9576	0.9576	5.25
No log	37.0	444	0.3796	0.9844	0.9417	0.9576	0.9576	5.25
No log	38.0	456	0.3713	0.9844	0.9417	0.9576	0.9576	5.25
No log	39.0	468	0.3801	0.9844	0.9417	0.9576	0.9576	5.25
No log	40.0	480	0.3973	0.9844	0.9417	0.9576	0.9576	5.25
No log	41.0	492	0.3983	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	42.0	504	0.4107	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	43.0	516	0.4200	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	44.0	528	0.4209	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	45.0	540	0.4172	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	46.0	552	0.4136	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	47.0	564	0.4100	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	48.0	576	0.3916	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	49.0	588	0.3910	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	50.0	600	0.3989	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	51.0	612	0.4052	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	52.0	624	0.4111	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	53.0	636	0.4099	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	54.0	648	0.4135	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	55.0	660	0.4160	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	56.0	672	0.4088	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	57.0	684	0.3945	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	58.0	696	0.3872	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	59.0	708	0.3690	0.9866	0.9486	0.9628	0.9628	5.2292
0.0033	60.0	720	0.3610	0.9866	0.9486	0.9628	0.9628	5.2292
0.0033	61.0	732	0.3652	0.9866	0.9486	0.9628	0.9628	5.2292
0.0033	62.0	744	0.3710	0.9866	0.9486	0.9628	0.9628	5.2292
0.0033	63.0	756	0.3731	0.9866	0.9486	0.9628	0.9628	5.2292
0.0033	64.0	768	0.3884	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	65.0	780	0.3859	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	66.0	792	0.3844	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	67.0	804	0.3839	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	68.0	816	0.3891	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	69.0	828	0.3926	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	70.0	840	0.3991	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	71.0	852	0.4008	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	72.0	864	0.4135	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	73.0	876	0.4268	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	74.0	888	0.4344	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	75.0	900	0.4383	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	76.0	912	0.4366	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	77.0	924	0.4270	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	78.0	936	0.4260	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	79.0	948	0.4327	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	80.0	960	0.4291	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	81.0	972	0.4221	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	82.0	984	0.4191	0.9844	0.9417	0.9576	0.9576	5.25
0.0033	83.0	996	0.4193	0.9844	0.9417	0.9576	0.9576	5.25
0.0025	84.0	1008	0.4208	0.9844	0.9417	0.9576	0.9576	5.25
0.0025	85.0	1020	0.4211	0.9844	0.9417	0.9576	0.9576	5.25
0.0025	86.0	1032	0.4207	0.9844	0.9417	0.9576	0.9576	5.25
0.0025	87.0	1044	0.4190	0.9844	0.9417	0.9576	0.9576	5.25
0.0025	88.0	1056	0.4182	0.9844	0.9417	0.9576	0.9576	5.25
0.0025	89.0	1068	0.4178	0.9844	0.9417	0.9576	0.9576	5.25
0.0025	90.0	1080	0.4173	0.9844	0.9417	0.9576	0.9576	5.25
0.0025	91.0	1092	0.4149	0.9844	0.9417	0.9576	0.9576	5.25
0.0025	92.0	1104	0.4130	0.9844	0.9417	0.9576	0.9576	5.25
0.0025	93.0	1116	0.4123	0.9844	0.9417	0.9576	0.9576	5.25
0.0025	94.0	1128	0.4127	0.9844	0.9417	0.9576	0.9576	5.25
0.0025	95.0	1140	0.4119	0.9844	0.9417	0.9576	0.9576	5.25
0.0025	96.0	1152	0.4122	0.9844	0.9417	0.9576	0.9576	5.25
0.0025	97.0	1164	0.4135	0.9844	0.9417	0.9576	0.9576	5.25
0.0025	98.0	1176	0.4148	0.9844	0.9417	0.9576	0.9576	5.25
0.0025	99.0	1188	0.4152	0.9844	0.9417	0.9576	0.9576	5.25
0.0025	100.0	1200	0.4150	0.9844	0.9417	0.9576	0.9576	5.25

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

limaatulya/my_awesome_billsum_model_30

my_awesome_billsum_model_30

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

limaatulya/
my_awesome_billsum_model_30