Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	12	2.0545	0.4101	0.2839	0.3907	0.3895	16.8125
No log	2.0	24	1.4437	0.442	0.3195	0.4261	0.4245	15.9583
No log	3.0	36	0.8267	0.5727	0.4315	0.541	0.5416	12.8125
No log	4.0	48	0.5186	0.9583	0.8429	0.9113	0.91	5.25
No log	5.0	60	0.4535	0.9739	0.8607	0.9276	0.9271	4.875
No log	6.0	72	0.4258	0.9769	0.8768	0.9365	0.9365	4.8958
No log	7.0	84	0.4014	0.9798	0.8869	0.9454	0.9464	4.9167
No log	8.0	96	0.3779	0.9798	0.8869	0.9454	0.9464	4.9167
No log	9.0	108	0.3663	0.9769	0.8726	0.9365	0.9375	4.9375
No log	10.0	120	0.3554	0.9687	0.8444	0.922	0.9226	5.0
No log	11.0	132	0.3461	0.9687	0.8444	0.922	0.9226	5.0
No log	12.0	144	0.3339	0.9716	0.8569	0.9314	0.9314	4.9792
No log	13.0	156	0.3242	0.9716	0.8569	0.9314	0.9314	4.9792
No log	14.0	168	0.3155	0.9716	0.8569	0.9314	0.9314	4.9792
No log	15.0	180	0.3030	0.9716	0.8569	0.9314	0.9314	4.9792
No log	16.0	192	0.2979	0.9676	0.8361	0.9193	0.9173	5.0
No log	17.0	204	0.2957	0.9676	0.8361	0.9193	0.9173	5.0
No log	18.0	216	0.2950	0.9676	0.8361	0.9193	0.9173	5.0
No log	19.0	228	0.2840	0.9676	0.8361	0.9193	0.9173	5.0
No log	20.0	240	0.2778	0.9676	0.8361	0.9193	0.9173	5.0
No log	21.0	252	0.2662	0.9676	0.8361	0.9193	0.9173	5.0
No log	22.0	264	0.2609	0.9676	0.8361	0.9193	0.9173	5.0
No log	23.0	276	0.2587	0.9676	0.8361	0.9193	0.9173	5.0
No log	24.0	288	0.2567	0.9676	0.8361	0.9193	0.9173	5.0
No log	25.0	300	0.2604	0.9676	0.8361	0.9193	0.9173	5.0
No log	26.0	312	0.2540	0.9676	0.8361	0.9193	0.9173	5.0
No log	27.0	324	0.2514	0.9676	0.8361	0.9193	0.9173	5.0
No log	28.0	336	0.2437	0.9676	0.8361	0.9193	0.9173	5.0
No log	29.0	348	0.2370	0.9676	0.8361	0.9193	0.9173	5.0
No log	30.0	360	0.2369	0.9676	0.8361	0.9193	0.9173	5.0
No log	31.0	372	0.2347	0.9676	0.8361	0.9193	0.9173	5.0
No log	32.0	384	0.2329	0.9676	0.8361	0.9193	0.9173	5.0
No log	33.0	396	0.2327	0.9676	0.8361	0.9193	0.9173	5.0
No log	34.0	408	0.2271	0.9676	0.8361	0.9193	0.9173	5.0
No log	35.0	420	0.2231	0.9676	0.8361	0.9193	0.9173	5.0
No log	36.0	432	0.2177	0.9676	0.8361	0.9193	0.9173	5.0
No log	37.0	444	0.2168	0.9676	0.8361	0.9193	0.9173	5.0
No log	38.0	456	0.2154	0.971	0.8468	0.9222	0.9202	4.9583
No log	39.0	468	0.2187	0.9676	0.8361	0.9193	0.9173	5.0
No log	40.0	480	0.2202	0.971	0.8468	0.9222	0.9202	4.9583
No log	41.0	492	0.2164	0.971	0.8468	0.9222	0.9202	4.9583
0.4771	42.0	504	0.2160	0.971	0.8468	0.9222	0.9202	4.9583
0.4771	43.0	516	0.2179	0.971	0.8468	0.9222	0.9202	4.9583
0.4771	44.0	528	0.2182	0.971	0.8468	0.9222	0.9202	4.9583
0.4771	45.0	540	0.2206	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	46.0	552	0.2172	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	47.0	564	0.2128	0.971	0.8468	0.9222	0.9202	4.9583
0.4771	48.0	576	0.2194	0.971	0.8468	0.9222	0.9202	4.9583
0.4771	49.0	588	0.2204	0.971	0.8468	0.9222	0.9202	4.9583
0.4771	50.0	600	0.2124	0.971	0.8468	0.9222	0.9202	4.9583
0.4771	51.0	612	0.2136	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	52.0	624	0.2119	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	53.0	636	0.2085	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	54.0	648	0.2115	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	55.0	660	0.2133	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	56.0	672	0.2087	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	57.0	684	0.2057	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	58.0	696	0.2095	0.9697	0.8445	0.9199	0.9179	4.9583
0.4771	59.0	708	0.2105	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	60.0	720	0.2123	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	61.0	732	0.2120	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	62.0	744	0.2132	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	63.0	756	0.2117	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	64.0	768	0.2068	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	65.0	780	0.2049	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	66.0	792	0.2054	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	67.0	804	0.2029	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	68.0	816	0.1995	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	69.0	828	0.1946	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	70.0	840	0.1975	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	71.0	852	0.1995	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	72.0	864	0.2009	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	73.0	876	0.2050	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	74.0	888	0.2039	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	75.0	900	0.2040	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	76.0	912	0.2020	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	77.0	924	0.2003	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	78.0	936	0.1992	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	79.0	948	0.1984	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	80.0	960	0.1971	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	81.0	972	0.1995	0.9675	0.8359	0.9136	0.9111	4.9792
0.4771	82.0	984	0.2007	0.9697	0.8445	0.9199	0.9179	4.9583
0.4771	83.0	996	0.2020	0.9697	0.8445	0.9199	0.9179	4.9583
0.113	84.0	1008	0.2007	0.9697	0.8445	0.9199	0.9179	4.9583
0.113	85.0	1020	0.1967	0.9697	0.8445	0.9199	0.9179	4.9583
0.113	86.0	1032	0.1975	0.9697	0.8445	0.9199	0.9179	4.9583
0.113	87.0	1044	0.1967	0.9697	0.8445	0.9199	0.9179	4.9583
0.113	88.0	1056	0.1947	0.9697	0.8445	0.9199	0.9179	4.9583
0.113	89.0	1068	0.1925	0.9697	0.8445	0.9199	0.9179	4.9583
0.113	90.0	1080	0.1926	0.9697	0.8445	0.9199	0.9179	4.9583
0.113	91.0	1092	0.1937	0.9697	0.8445	0.9199	0.9179	4.9583
0.113	92.0	1104	0.1934	0.9697	0.8445	0.9199	0.9179	4.9583
0.113	93.0	1116	0.1929	0.9697	0.8445	0.9199	0.9179	4.9583
0.113	94.0	1128	0.1929	0.9697	0.8445	0.9199	0.9179	4.9583
0.113	95.0	1140	0.1928	0.9697	0.8445	0.9199	0.9179	4.9583
0.113	96.0	1152	0.1927	0.9697	0.8445	0.9199	0.9179	4.9583
0.113	97.0	1164	0.1927	0.9697	0.8445	0.9199	0.9179	4.9583
0.113	98.0	1176	0.1925	0.9697	0.8445	0.9199	0.9179	4.9583
0.113	99.0	1188	0.1925	0.9697	0.8445	0.9199	0.9179	4.9583
0.113	100.0	1200	0.1923	0.9697	0.8445	0.9199	0.9179	4.9583

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

limaatulya/my_awesome_billsum_model_80

my_awesome_billsum_model_80

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

limaatulya/
my_awesome_billsum_model_80