finetune-led-thousanddata

silmi224/finetune-led-thousanddata

finetune-led-thousanddata is a summarization model from silmi224. Use it when you need a shorter version of a longer text. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Summarization
Library: transformers
Model type: led
Access: Public
Created: Jun 23, 2024
Updated: Jun 23, 2024
SHA: 6df9ce9d

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Model card

From the Hugging Face model README

finetune-led-thousanddata

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.9539
Rouge1 Precision: 0.2722
Rouge1 Recall: 0.3458
Rouge1 Fmeasure: 0.3011

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4
mixed_precision_training: Native AMP

Training results

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

Task: Summarization
Library: transformers
Type: led
Created: Jun 23, 2024
Updated: Jun 23, 2024