Training Loss	Epoch	Step	Validation Loss	Rouge1 Fmeasure	Rouge1 Precision	Rouge1 Recall
2.0529	0.13	10	2.6191	0.3014	0.2948	0.324
1.778	0.26	20	2.4690	0.2947	0.2802	0.3213
1.7425	0.38	30	2.3989	0.3037	0.2734	0.3524
1.7006	0.51	40	2.3216	0.2941	0.2665	0.3386
1.6751	0.64	50	2.3027	0.3101	0.282	0.3551
1.6887	0.77	60	2.2911	0.3058	0.2731	0.3577
1.6008	0.89	70	2.2476	0.3016	0.272	0.3487
1.5767	1.02	80	2.2167	0.3043	0.2775	0.3465
1.5046	1.15	90	2.2185	0.3004	0.2721	0.3458
1.5394	1.28	100	2.1977	0.2991	0.2696	0.3463
1.5449	1.41	110	2.1823	0.2978	0.2704	0.341
1.5073	1.53	120	2.1832	0.3057	0.276	0.3527
1.5232	0.42	130	2.2091	0.2955	0.2664	0.3424
1.4896	0.45	140	2.2069	0.2905	0.2574	0.3424
1.4848	0.48	150	2.1913	0.2868	0.2567	0.3356
1.5084	0.51	160	2.1826	0.3006	0.2755	0.3406
1.4322	0.54	170	2.2525	0.3049	0.2716	0.3582
1.4672	0.58	180	2.1890	0.2919	0.2663	0.3322
1.4543	0.61	190	2.1487	0.3022	0.276	0.344
1.5446	0.64	200	2.1496	0.2993	0.273	0.3418
1.412	0.67	210	2.1837	0.2976	0.268	0.3439
1.5241	0.7	220	2.1423	0.2913	0.2665	0.3305
1.4806	0.74	230	2.1303	0.2997	0.2736	0.3411
1.5405	0.77	240	2.1205	0.2966	0.2668	0.3428
1.4287	0.8	250	2.1322	0.2976	0.268	0.3442
1.4977	0.83	260	2.1334	0.2979	0.2665	0.3477
1.4171	0.86	270	2.1184	0.3043	0.2741	0.3509
1.4491	0.9	280	2.1038	0.2868	0.2628	0.3253
1.4316	0.93	290	2.1254	0.2958	0.2678	0.3393
1.4689	0.96	300	2.1052	0.299	0.2685	0.3471
1.4347	0.99	310	2.0815	0.3019	0.273	0.3476
1.3285	1.02	320	2.0877	0.2981	0.2695	0.3427
1.2636	1.06	330	2.0740	0.2933	0.2645	0.3382
1.32	1.09	340	2.0755	0.2997	0.2689	0.3487
1.357	1.12	350	2.0594	0.301	0.2743	0.3434
1.3412	1.15	360	2.0660	0.2961	0.2677	0.3405
1.327	1.18	370	2.0649	0.2912	0.263	0.335
1.3193	1.22	380	2.0842	0.2952	0.2673	0.3392
1.2961	1.25	390	2.0749	0.2957	0.2705	0.3342
1.3093	1.28	400	2.0715	0.2997	0.272	0.3441
1.3403	1.31	410	2.0671	0.3119	0.2823	0.3584
1.3685	1.34	420	2.0580	0.2973	0.2695	0.3409
1.2913	1.38	430	2.0685	0.2926	0.2632	0.339
1.3796	1.41	440	2.0339	0.2962	0.2697	0.3387
1.354	1.44	450	2.0371	0.2953	0.2665	0.3412
1.3268	1.47	460	2.0309	0.2957	0.2681	0.3395
1.3706	1.5	470	2.0215	0.2932	0.2685	0.3315
1.3288	1.54	480	2.0044	0.2948	0.2674	0.3374
1.4102	1.57	490	2.0046	0.2998	0.271	0.3446
1.3952	1.6	500	2.0044	0.3063	0.2794	0.3487
1.2994	1.63	510	1.9993	0.3052	0.2787	0.3461
1.2948	1.66	520	2.0168	0.3	0.2743	0.3406
1.2972	1.7	530	2.0290	0.3003	0.2734	0.342
1.3181	1.73	540	2.0234	0.2949	0.2676	0.338
1.3505	1.76	550	1.9942	0.301	0.2737	0.3436
1.3163	1.79	560	1.9983	0.2963	0.2705	0.3366
1.2876	1.82	570	2.0206	0.303	0.2739	0.3486
1.2895	1.86	580	2.0131	0.2958	0.2652	0.3443
1.3257	1.89	590	1.9888	0.3022	0.2743	0.3455
1.2891	1.92	600	1.9928	0.2972	0.2694	0.3408
1.3152	1.95	610	1.9785	0.292	0.2653	0.334
1.2834	1.98	620	2.0105	0.3039	0.2735	0.3511
1.2373	2.02	630	2.0023	0.3019	0.2735	0.346
1.2569	2.05	640	2.0006	0.3029	0.2753	0.3463
1.2337	2.08	650	1.9919	0.3006	0.2746	0.3416
1.1274	2.11	660	2.0095	0.3015	0.2732	0.3457
1.2178	2.14	670	1.9974	0.3031	0.275	0.3475
1.22	2.18	680	1.9924	0.3059	0.2777	0.3501
1.2913	2.21	690	1.9880	0.3044	0.2745	0.351
1.2441	2.24	700	1.9886	0.299	0.2721	0.3412
1.3258	2.27	710	1.9772	0.2956	0.2686	0.3377
1.158	2.3	720	2.0003	0.2983	0.2702	0.3424
1.1908	2.34	730	1.9845	0.2975	0.2705	0.3398
1.2411	2.37	740	1.9768	0.304	0.275	0.3493
1.1936	2.4	750	2.0065	0.293	0.2628	0.3403
1.1578	2.44	760	2.0199	0.301	0.2713	0.3473
1.2086	2.47	770	1.9949	0.2921	0.2664	0.3323
1.2574	2.5	780	1.9806	0.297	0.2693	0.3405
1.2331	2.53	790	2.0100	0.3012	0.2733	0.3446
1.2522	2.56	800	1.9969	0.301	0.2716	0.3468
1.2508	2.6	810	1.9931	0.3016	0.2719	0.3471
1.1558	2.63	820	1.9873	0.2986	0.2725	0.3402
1.2721	2.66	830	1.9763	0.2988	0.2671	0.348
1.2817	2.69	840	1.9713	0.2961	0.2688	0.3388
1.2183	2.72	850	1.9783	0.2985	0.2709	0.3416
1.2278	2.76	860	1.9757	0.2964	0.2681	0.3402
1.2087	2.79	870	1.9818	0.304	0.2735	0.3516
1.1838	2.82	880	1.9845	0.2916	0.2659	0.3312
1.1185	2.85	890	1.9912	0.3044	0.2759	0.3492
1.1214	2.88	900	1.9838	0.2995	0.2692	0.3468
1.2341	2.92	910	1.9685	0.296	0.2713	0.3344
1.1808	2.95	920	1.9803	0.3008	0.2725	0.345
1.2843	2.98	930	1.9645	0.3041	0.2745	0.3504
1.1824	3.01	940	1.9750	0.2985	0.2713	0.3412
1.1399	3.04	950	1.9762	0.2943	0.264	0.3416
1.1347	3.08	960	1.9841	0.2971	0.2685	0.3419
1.2298	3.11	970	1.9526	0.2993	0.2701	0.3448
1.1731	3.14	980	1.9787	0.304	0.2726	0.3531
1.1819	3.17	990	1.9570	0.2995	0.2715	0.3437
1.2072	3.2	1000	1.9613	0.3004	0.2705	0.3472
1.1214	3.24	1010	1.9670	0.3	0.2723	0.3432
1.226	3.27	1020	1.9676	0.2945	0.2639	0.3422
1.1956	3.3	1030	1.9721	0.2949	0.2657	0.3406
1.2286	3.33	1040	1.9572	0.3046	0.2759	0.3489
1.1786	3.36	1050	1.9549	0.3009	0.2728	0.3448
1.1512	3.4	1060	1.9609	0.2989	0.2699	0.3441
1.1897	3.43	1070	1.9626	0.2983	0.2697	0.3427
1.187	3.46	1080	1.9612	0.3016	0.2731	0.3457
1.1394	3.49	1090	1.9519	0.3015	0.2746	0.3431
1.1088	3.52	1100	1.9674	0.301	0.2709	0.3477
1.1787	3.56	1110	1.9549	0.3009	0.2728	0.3449
1.1961	3.59	1120	1.9545	0.3016	0.2722	0.3476
1.1194	3.62	1130	1.9693	0.3028	0.2735	0.3484
1.1991	3.65	1140	1.9538	0.3002	0.2706	0.3461
1.2109	3.68	1150	1.9428	0.3018	0.2729	0.3465
1.1389	3.72	1160	1.9578	0.3008	0.2723	0.3452
1.1922	3.75	1170	1.9576	0.2992	0.2701	0.3446
1.1002	3.78	1180	1.9571	0.299	0.2696	0.3445
1.1407	3.81	1190	1.9530	0.2979	0.2692	0.3422
1.1882	3.84	1200	1.9491	0.3009	0.2725	0.345
1.1755	3.88	1210	1.9562	0.3024	0.2735	0.3468
1.062	3.91	1220	1.9577	0.302	0.2722	0.3478
1.1965	3.94	1230	1.9575	0.3013	0.2716	0.3472
1.1255	3.97	1240	1.9550	0.3014	0.272	0.3466

Framework versions

Transformers 4.35.2
Pytorch 2.1.1+cu121
Datasets 2.14.5
Tokenizers 0.15.1

silmi224/finetune-led-thousanddata

finetune-led-thousanddata

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

silmi224/
finetune-led-thousanddata