Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Sacrebleu	Gen Len
14.4684	0.06	10	13.1518	0.2872	0.186	0.272	6.3114	512.0
12.7706	0.12	20	11.9531	0.2443	0.1569	0.2281	8.8122	512.0
11.7916	0.18	30	11.2548	0.2758	0.1818	0.2591	3.1927	512.0
10.9334	0.24	40	10.5034	0.4334	0.2805	0.4034	7.3173	512.0
10.2486	0.3	50	9.6013	0.2897	0.1829	0.2717	7.6318	512.0
9.3552	0.36	60	8.6828	0.1181	0.0715	0.1106	3.5634	512.0
8.511	0.42	70	7.7545	0.0752	0.0479	0.0716	2.2899	512.0
7.6434	0.48	80	6.7397	0.2452	0.1558	0.2328	9.5121	505.5873
6.5921	0.54	90	5.6501	0.5661	0.3905	0.5478	32.2949	62.8677
5.4669	0.6	100	4.4328	0.6102	0.4252	0.5925	34.814	39.0847
4.2038	0.66	110	3.1788	0.6166	0.4326	0.5996	35.9449	38.3915
2.9406	0.72	120	1.8820	0.6071	0.4296	0.5934	36.3309	37.7884
1.6293	0.78	130	0.8202	0.6135	0.4347	0.6006	36.6343	37.1164
0.6931	0.84	140	0.3292	0.6273	0.4488	0.6152	37.7992	36.8677
0.3063	0.9	150	0.2047	0.6322	0.461	0.6219	39.5968	36.8624
0.2044	0.96	160	0.1721	0.6349	0.4521	0.6207	38.1827	36.8624
0.1642	1.02	170	0.1527	0.6414	0.472	0.6312	40.84	36.8624
0.1379	1.08	180	0.1307	0.6506	0.4813	0.6424	41.2202	36.8624
0.1401	1.14	190	0.1184	0.6212	0.4465	0.6119	38.9759	36.8201
0.1234	1.2	200	0.0963	0.6719	0.4862	0.6592	42.0111	36.8677
0.1042	1.26	210	0.0820	0.6604	0.477	0.6506	41.6463	36.8836
0.0933	1.32	220	0.0821	0.6645	0.49	0.6542	42.6241	36.8624
0.095	1.38	230	0.0967	0.6581	0.4837	0.6492	42.1158	36.8624
0.1022	1.44	240	0.1332	0.6133	0.4489	0.6034	40.4641	36.873
0.7413	1.5	250	1.4191	0.0	0.0	0.0	0.0	40.0
1.3629	1.56	260	1.0858	0.0	0.0	0.0	0.0	37.8624
1.0025	1.62	270	0.8858	0.0196	0.0003	0.0196	0.0151	37.8624
0.7626	1.68	280	0.7693	0.0	0.0	0.0	0.0213	37.8624
0.7308	1.74	290	0.6842	0.0008	0.0	0.0008	0.0214	37.8624
0.6324	1.8	300	0.5928	0.0074	0.0	0.0075	0.0257	36.836
0.5934	1.86	310	0.5517	0.0	0.0	0.0	0.022	36.8783
0.5535	1.92	320	0.5225	0.0319	0.0026	0.0312	0.0565	36.8624
0.5843	1.98	330	0.5135	0.0438	0.0012	0.0435	0.0694	36.8624
0.5422	2.04	340	0.5055	0.028	0.0	0.0281	0.0237	36.8519
0.4415	2.1	350	0.2156	0.4248	0.2316	0.4101	14.9745	36.8624
0.263	2.16	360	0.2392	0.3194	0.1424	0.3038	11.6071	36.8677
0.2213	2.22	370	0.1329	0.554	0.3556	0.5421	27.6303	36.873
0.121	2.28	380	0.1066	0.5935	0.4046	0.5802	35.6023	36.8624
0.1027	2.34	390	0.0934	0.6287	0.4401	0.6155	39.0308	36.8624
0.0858	2.4	400	0.0902	0.6394	0.4678	0.6278	40.2148	36.8624
0.0937	2.46	410	0.0852	0.6721	0.4854	0.6577	41.3938	36.8624
0.0765	2.51	420	0.0798	0.7085	0.53	0.6967	43.7951	36.8624
0.0708	2.57	430	0.0770	0.703	0.5241	0.6921	42.5005	36.8624
0.0717	2.63	440	0.0775	0.7108	0.5289	0.6997	43.3111	36.8624
0.0742	2.69	450	0.0754	0.7182	0.5422	0.7088	44.229	36.8624
0.0646	2.75	460	0.0789	0.7023	0.525	0.692	43.6914	36.8624
0.0995	2.81	470	0.0839	0.6559	0.4866	0.6486	42.6205	36.8624
0.0705	2.87	480	0.0751	0.7197	0.5478	0.7083	44.2463	36.8624
0.076	2.93	490	0.0719	0.7296	0.5522	0.7181	44.3324	36.8624
0.0669	2.99	500	0.0706	0.7263	0.5594	0.7172	45.7606	36.8624
0.0519	3.05	510	0.0712	0.7328	0.5579	0.7233	47.2644	36.8624
0.0478	3.11	520	0.0711	0.7298	0.5574	0.7189	46.5134	36.8624
0.0487	3.17	530	0.0737	0.7258	0.558	0.7171	47.3831	36.8624
0.0508	3.23	540	0.0719	0.7269	0.5577	0.7171	46.1187	36.8624
0.0495	3.29	550	0.0719	0.7229	0.547	0.7113	46.7407	36.8624
0.0437	3.35	560	0.0703	0.7346	0.5625	0.7238	47.6379	36.8624
0.046	3.41	570	0.0688	0.7349	0.5632	0.7252	47.7757	36.8624
0.0449	3.47	580	0.0688	0.7357	0.568	0.726	47.9775	36.8624
0.0496	3.53	590	0.0690	0.7392	0.5694	0.7301	48.3888	36.8624
0.051	3.59	600	0.0675	0.7434	0.5745	0.7349	48.565	36.8624
0.0522	3.65	610	0.0677	0.7465	0.5811	0.735	49.6128	36.8624
0.0529	3.71	620	0.0671	0.7463	0.5825	0.7374	49.4657	36.8624
0.0497	3.77	630	0.0657	0.7448	0.5814	0.7348	48.9751	36.8624
0.0432	3.83	640	0.0670	0.7351	0.5648	0.7253	48.0065	36.8624
0.0468	3.89	650	0.0664	0.7451	0.5827	0.7365	49.2015	36.8624
0.0453	3.95	660	0.0656	0.7391	0.5708	0.729	48.0288	36.8624
0.0417	4.01	670	0.0662	0.7408	0.5711	0.7328	49.2061	36.8624
0.02	4.07	680	0.0719	0.743	0.582	0.7343	49.2463	36.8624
0.0302	4.13	690	0.0699	0.752	0.584	0.7423	48.8187	36.8624
0.0278	4.19	700	0.0701	0.7452	0.5796	0.7374	50.1071	36.8624
0.024	4.25	710	0.0698	0.7567	0.5966	0.7481	50.1833	36.8624
0.024	4.31	720	0.0707	0.7547	0.5932	0.7453	50.0661	36.8624
0.0302	4.37	730	0.0681	0.7572	0.5918	0.7489	50.3441	36.8624
0.0253	4.43	740	0.0683	0.7593	0.5998	0.7504	50.9046	36.8624
0.0246	4.49	750	0.0683	0.7567	0.5909	0.7467	49.9375	36.8624
0.0253	4.55	760	0.0682	0.755	0.584	0.7458	49.9308	36.8624
0.0245	4.61	770	0.0712	0.7492	0.5807	0.7403	50.1708	36.8624
0.0274	4.67	780	0.0689	0.7516	0.5846	0.7431	49.6346	36.8624
0.0261	4.73	790	0.0687	0.7477	0.5824	0.7403	50.6044	36.8624
0.0304	4.79	800	0.0666	0.7534	0.5926	0.7443	49.9827	36.8624
0.0279	4.85	810	0.0675	0.7559	0.5913	0.7452	49.9368	36.8624
0.0277	4.91	820	0.0685	0.754	0.5895	0.7432	50.8755	36.8624
0.0278	4.97	830	0.0676	0.753	0.592	0.744	50.6179	36.8624
0.0215	5.03	840	0.0690	0.7626	0.6059	0.7535	51.7703	36.8624
0.0154	5.09	850	0.0732	0.7626	0.6036	0.7543	51.3579	36.8624
0.0141	5.15	860	0.0767	0.7582	0.5963	0.75	50.8613	36.8624
0.0144	5.21	870	0.0754	0.7564	0.5948	0.7475	50.5094	36.8624
0.0174	5.27	880	0.0736	0.7613	0.6032	0.7527	51.4235	36.8624
0.0141	5.33	890	0.0746	0.7661	0.6085	0.7572	52.4218	36.8624
0.0139	5.39	900	0.0753	0.7661	0.6079	0.7576	51.9856	36.8624
0.0165	5.45	910	0.0746	0.7636	0.6063	0.7556	51.6622	36.8624
0.0129	5.51	920	0.0732	0.7661	0.6106	0.7579	51.1851	36.8624
0.0166	5.57	930	0.0721	0.7665	0.6108	0.7588	51.9259	36.8624
0.0137	5.63	940	0.0702	0.7607	0.6007	0.7521	51.835	36.8624
0.0153	5.69	950	0.0731	0.765	0.605	0.7563	52.1179	36.8624
0.0165	5.75	960	0.0737	0.7641	0.6045	0.7559	52.2019	36.8624
0.0117	5.81	970	0.0742	0.7648	0.6057	0.7559	51.9825	36.8624
0.015	5.87	980	0.0735	0.7635	0.6061	0.7537	52.2846	36.8624
0.0171	5.93	990	0.0719	0.764	0.6031	0.7546	51.4322	36.8624
0.0143	5.99	1000	0.0733	0.7661	0.611	0.7572	51.9965	36.8624
0.0079	6.05	1010	0.0773	0.7635	0.6114	0.7554	52.298	36.8624
0.0077	6.11	1020	0.0801	0.7608	0.6046	0.7532	52.0944	36.8624
0.0094	6.17	1030	0.0806	0.7593	0.6008	0.7508	52.0165	36.8624
0.0078	6.23	1040	0.0798	0.7634	0.6064	0.7549	52.6951	36.8624
0.0078	6.29	1050	0.0790	0.7604	0.6039	0.7514	51.9559	36.8624
0.0088	6.35	1060	0.0783	0.7604	0.5987	0.7522	50.9347	36.8624
0.0095	6.41	1070	0.0787	0.7686	0.6095	0.7599	52.0715	36.8624
0.0093	6.47	1080	0.0794	0.7647	0.6098	0.757	52.3564	36.8624
0.009	6.53	1090	0.0786	0.7591	0.6012	0.75	52.058	36.8624
0.0099	6.59	1100	0.0770	0.7537	0.5954	0.7454	52.2023	36.8624
0.0091	6.65	1110	0.0770	0.7583	0.6003	0.7507	52.3379	36.8624
0.0085	6.71	1120	0.0777	0.7583	0.5992	0.7503	52.3369	36.8624
0.0085	6.77	1130	0.0787	0.76	0.6014	0.7523	52.5729	36.8624
0.0089	6.83	1140	0.0779	0.7629	0.6028	0.7553	52.0656	36.8624
0.0096	6.89	1150	0.0777	0.7606	0.5998	0.7527	51.8477	36.8624
0.0087	6.95	1160	0.0783	0.768	0.6076	0.7593	52.1456	36.8624
0.0081	7.01	1170	0.0791	0.7705	0.6152	0.7624	52.803	36.8624
0.0054	7.07	1180	0.0798	0.7667	0.6084	0.7584	52.7433	36.8624
0.0045	7.13	1190	0.0815	0.7646	0.6055	0.7565	53.0281	36.8624
0.0051	7.19	1200	0.0828	0.7609	0.5999	0.7526	52.6461	36.8624
0.0055	7.25	1210	0.0841	0.7608	0.5994	0.7529	52.6382	36.8624
0.0047	7.31	1220	0.0847	0.7637	0.6035	0.7554	52.5827	36.8624
0.0049	7.37	1230	0.0851	0.7673	0.6088	0.7589	52.9688	36.8624
0.0045	7.43	1240	0.0853	0.7661	0.6083	0.7575	52.9357	36.8624
0.0045	7.49	1250	0.0856	0.7667	0.6107	0.7583	53.1254	36.8624
0.0054	7.54	1260	0.0857	0.7691	0.615	0.7604	53.3457	36.8624
0.0052	7.6	1270	0.0857	0.7703	0.6158	0.7617	53.3061	36.8624
0.0055	7.66	1280	0.0858	0.7729	0.6205	0.7644	53.4336	36.8624
0.0053	7.72	1290	0.0858	0.7718	0.6163	0.7636	53.3378	36.8624
0.0052	7.78	1300	0.0859	0.7724	0.6206	0.7643	53.248	36.8624
0.0043	7.84	1310	0.0858	0.7715	0.6194	0.7633	53.441	36.8624
0.0048	7.9	1320	0.0857	0.7715	0.6194	0.7632	53.3966	36.8624
0.0053	7.96	1330	0.0856	0.7721	0.6212	0.7638	53.4548	36.8624

Framework versions

Transformers 4.33.0
Pytorch 2.1.2+cu121
Datasets 2.14.4
Tokenizers 0.13.3

eglkan1/mBART-07-TextSimp-LT-BatchSize8-lr1e-4

mBART-07-TextSimp-LT-BatchSize8-lr1e-4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

eglkan1/
mBART-07-TextSimp-LT-BatchSize8-lr1e-4