Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	56	1.5406	43.3416	31.084	43.0687	42.9946	10.6
No log	2.0	112	1.1462	64.0552	47.7355	64.151	64.0941	7.0131
No log	3.0	168	0.9162	66.5132	53.877	66.55	66.4972	7.0492
No log	4.0	224	0.7621	67.2802	55.5159	67.3364	67.2804	7.2656
No log	5.0	280	0.6620	67.4693	55.799	67.5037	67.4546	7.1016
No log	6.0	336	0.5712	68.0598	56.7188	68.1216	68.1066	7.3902
No log	7.0	392	0.5079	69.0251	59.176	68.6058	68.6522	7.6033
No log	8.0	448	0.4657	74.7698	65.6322	74.5062	74.493	7.423
1.0151	9.0	504	0.4322	76.9011	69.4338	76.6186	76.5937	7.3934
1.0151	10.0	560	0.4101	77.5182	70.1625	77.171	77.2435	7.4393
1.0151	11.0	616	0.3828	81.6474	75.4109	81.6711	81.7906	7.2131
1.0151	12.0	672	0.3666	81.2489	74.6281	81.232	81.4677	7.3148
1.0151	13.0	728	0.3527	82.3918	75.0412	81.9976	82.1857	7.3541
1.0151	14.0	784	0.3394	82.9197	75.9783	82.5519	82.7218	7.4885
1.0151	15.0	840	0.3258	84.0647	78.0372	84.0255	84.1354	7.4492
1.0151	16.0	896	0.3171	84.5067	78.0453	84.1956	84.3144	7.4492
1.0151	17.0	952	0.3106	84.7725	79.5598	84.7029	84.7864	7.4361
0.413	18.0	1008	0.3010	84.4725	79.0308	84.3942	84.4988	7.4197
0.413	19.0	1064	0.2913	85.4699	80.5213	85.46	85.5262	7.4
0.413	20.0	1120	0.2886	85.5393	80.7955	85.4693	85.5732	7.3672
0.413	21.0	1176	0.2875	86.1586	81.5805	86.1426	86.185	7.4328
0.413	22.0	1232	0.2827	87.2461	82.8164	87.2022	87.2368	7.4131
0.413	23.0	1288	0.2784	87.8228	83.2401	87.6811	87.7369	7.3967
0.413	24.0	1344	0.2794	88.2089	83.8529	88.1541	88.1459	7.3607
0.413	25.0	1400	0.2782	88.1461	83.6076	88.1074	88.1156	7.3475
0.413	26.0	1456	0.2749	89.2327	85.2551	89.0845	89.1578	7.3607
0.2859	27.0	1512	0.2725	89.6924	85.8808	89.5475	89.6202	7.3311
0.2859	28.0	1568	0.2793	88.9947	85.1463	88.917	88.9471	7.3443
0.2859	29.0	1624	0.2695	88.8896	84.747	88.7592	88.8463	7.3279
0.2859	30.0	1680	0.2703	89.0948	85.2637	88.9155	88.9607	7.3344
0.2859	31.0	1736	0.2758	88.7993	84.6864	88.6857	88.7443	7.3869
0.2859	32.0	1792	0.2639	89.4105	85.351	89.2585	89.3354	7.3672
0.2859	33.0	1848	0.2677	89.1001	84.8211	88.9673	89.0328	7.3672
0.2859	34.0	1904	0.2671	89.1583	85.0989	89.0433	89.0619	7.3475
0.2859	35.0	1960	0.2655	89.2276	85.0053	89.1094	89.152	7.3738
0.2231	36.0	2016	0.2627	89.4831	85.4103	89.3925	89.4464	7.3934
0.2231	37.0	2072	0.2616	89.2111	85.1176	89.1383	89.1287	7.377
0.2231	38.0	2128	0.2602	89.1662	85.1415	89.0434	89.0755	7.3541
0.2231	39.0	2184	0.2616	89.1137	85.0139	89.0406	88.9993	7.3607
0.2231	40.0	2240	0.2659	89.3719	85.205	89.3022	89.2528	7.3672
0.2231	41.0	2296	0.2654	89.361	85.2269	89.2932	89.2493	7.3607
0.2231	42.0	2352	0.2649	89.3592	85.2114	89.2514	89.2198	7.3639
0.2231	43.0	2408	0.2620	89.3706	85.0113	89.2818	89.275	7.4131
0.2231	44.0	2464	0.2622	89.8559	85.784	89.7113	89.7674	7.4131
0.184	45.0	2520	0.2606	89.5451	85.52	89.4465	89.4138	7.3574
0.184	46.0	2576	0.2596	89.2498	85.0315	89.1214	89.1948	7.3803
0.184	47.0	2632	0.2615	89.6342	85.4441	89.5363	89.5743	7.3836
0.184	48.0	2688	0.2640	89.2974	85.0091	89.1747	89.2362	7.4066
0.184	49.0	2744	0.2638	89.4178	85.1524	89.2826	89.3557	7.3902
0.184	50.0	2800	0.2668	89.7496	86.0205	89.6675	89.6583	7.3738
0.184	51.0	2856	0.2741	89.5245	85.8589	89.4872	89.4965	7.377
0.184	52.0	2912	0.2671	89.82	85.6109	89.7265	89.7363	7.4066
0.184	53.0	2968	0.2697	89.9136	85.6378	89.8346	89.7737	7.3672
0.1563	54.0	3024	0.2716	89.4994	85.2523	89.4243	89.4203	7.3934
0.1563	55.0	3080	0.2743	89.4049	85.3106	89.4046	89.356	7.4066
0.1563	56.0	3136	0.2762	89.0641	84.8059	89.0163	89.0039	7.4033

Framework versions

Transformers 4.42.3
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

mgkamalesh7/t5-small-wsd-finetuned-cve-reason_1

t5-small-wsd-finetuned-cve-reason_1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mgkamalesh7/
t5-small-wsd-finetuned-cve-reason_1