Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
19.2402	1.0	60	8.2701	2.1815	0.1429	2.2246	2.2081
12.7954	2.0	120	5.3510	3.3524	0.3929	3.3843	3.3893
8.8288	3.0	180	3.5929	4.3158	0.4242	4.2947	4.2986
6.9994	4.0	240	3.2479	4.1515	0.5195	4.1991	4.1535
5.7594	5.0	300	3.0701	4.4127	0.4838	4.4044	4.4096
5.075	6.0	360	3.0252	5.6953	0.925	5.6925	5.6771
4.6336	7.0	420	2.9917	5.8009	1.1576	5.8699	5.871
4.3993	8.0	480	2.9676	5.8763	1.1953	5.9074	5.8808
4.1863	9.0	540	2.9213	6.2006	1.3455	6.2031	6.1713
4.0672	10.0	600	2.9115	5.3167	1.2394	5.3518	5.3606
3.9671	11.0	660	2.8743	5.2749	1.2394	5.3117	5.2936
3.86	12.0	720	2.8472	5.8311	1.1505	5.9026	5.8415
3.8103	13.0	780	2.8158	6.3536	1.1505	6.3989	6.3321
3.7412	14.0	840	2.7794	6.4438	1.1505	6.4702	6.4715
3.6757	15.0	900	2.7632	6.3778	0.9616	6.4342	6.417
3.643	16.0	960	2.7335	6.2346	0.9616	6.2724	6.2393
3.5952	17.0	1020	2.7152	5.9718	0.7727	6.0017	5.9683
3.585	18.0	1080	2.6998	8.8466	0.3333	8.7787	8.7648
3.493	19.0	1140	2.6982	8.1089	0.3333	7.95	7.9352
3.4807	20.0	1200	2.6911	7.9967	0.3333	7.8437	7.843
3.451	21.0	1260	2.6885	7.9967	0.3333	7.8437	7.843
3.4368	22.0	1320	2.6945	8.2061	0.3333	8.0333	8.0097
3.4044	23.0	1380	2.6909	8.6753	0.3333	8.5901	8.4835
3.3862	24.0	1440	2.6899	8.4263	0.3333	8.2222	8.1901
3.3421	25.0	1500	2.6897	8.2061	0.3333	8.0333	8.0097
3.3414	26.0	1560	2.6801	8.2061	0.3333	8.0333	8.0097
3.3354	27.0	1620	2.6772	8.2061	0.3333	8.0333	8.0097
3.299	28.0	1680	2.6780	8.2061	0.3333	8.0333	8.0097
3.3058	29.0	1740	2.6711	8.0944	0.3333	7.9019	7.8787
3.2678	30.0	1800	2.6693	8.0944	0.3333	7.9019	7.8787
3.2538	31.0	1860	2.6661	8.0944	0.3333	7.9019	7.8787
3.2361	32.0	1920	2.6687	8.0944	0.3333	7.9019	7.8787
3.2326	33.0	1980	2.6625	8.0944	0.3333	7.9019	7.8787
3.2142	34.0	2040	2.6648	8.0526	0.3333	7.9026	7.8801
3.1875	35.0	2100	2.6634	8.5204	0.3333	8.3199	8.3352
3.1717	36.0	2160	2.6611	8.5083	0.3333	8.3228	8.3359
3.1706	37.0	2220	2.6641	8.5083	0.3333	8.3228	8.3359
3.1541	38.0	2280	2.6573	8.5083	0.3333	8.3228	8.3359
3.1468	39.0	2340	2.6626	8.5083	0.3333	8.3228	8.3359
3.1376	40.0	2400	2.6602	8.5083	0.3333	8.3228	8.3359
3.1572	41.0	2460	2.6539	7.9385	0.3333	7.8019	7.8519
3.147	42.0	2520	2.6527	7.9385	0.3333	7.8019	7.8519
3.1199	43.0	2580	2.6487	7.9385	0.3333	7.8019	7.8519
3.1286	44.0	2640	2.6493	7.6385	0.3333	7.4817	7.4859
3.1042	45.0	2700	2.6519	8.1885	0.3333	7.9894	8.0292
3.099	46.0	2760	2.6525	8.1885	0.3333	7.9894	8.0292
3.1106	47.0	2820	2.6514	8.1885	0.3333	7.9894	8.0292
3.1036	48.0	2880	2.6501	7.6385	0.3333	7.4817	7.4859
3.0934	49.0	2940	2.6501	7.6385	0.3333	7.4817	7.4859
3.0822	50.0	3000	2.6435	7.6385	0.3333	7.4817	7.4859
3.0858	51.0	3060	2.6479	7.6385	0.3333	7.4817	7.4859
3.0825	52.0	3120	2.6455	7.6385	0.3333	7.4817	7.4859
3.063	53.0	3180	2.6437	7.6385	0.3333	7.4817	7.4859
3.0641	54.0	3240	2.6429	7.6385	0.3333	7.4817	7.4859
3.0703	55.0	3300	2.6430	7.6385	0.3333	7.4817	7.4859
3.0554	56.0	3360	2.6413	7.6385	0.3333	7.4817	7.4859
3.0498	57.0	3420	2.6415	7.6385	0.3333	7.4817	7.4859
3.0668	58.0	3480	2.6411	7.6385	0.3333	7.4817	7.4859
3.0657	59.0	3540	2.6409	7.6385	0.3333	7.4817	7.4859
3.0591	60.0	3600	2.6411	7.6385	0.3333	7.4817	7.4859

Framework versions

Transformers 4.25.1
Pytorch 1.13.1+cu116
Datasets 2.8.0
Tokenizers 0.13.2

mqy/mt5-small-finetuned-19jan-5

mt5-small-finetuned-19jan-5

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mqy/
mt5-small-finetuned-19jan-5