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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 45
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Bleu	Chrf++	F1	Gen Len
1.5062	1.0	1485	1.3429	25.6029	11.7283	23.5488	23.753	0.0124	13.0823	40.4386	19.9619
1.4881	2.0	2970	1.3335	25.7116	11.8205	23.6827	23.8737	0.0125	13.1671	40.5548	19.9614
1.4774	3.0	4455	1.3220	25.8298	11.9618	23.7697	23.9725	0.0127	13.2465	40.5981	19.9635
1.4594	4.0	5940	1.3118	25.8411	12.0124	23.8195	24.021	0.0126	13.2928	40.6417	19.9631
1.4513	5.0	7425	1.3006	25.9926	12.1855	23.9235	24.1263	0.0129	13.4023	40.7798	19.9621
1.4255	6.0	8910	1.2946	26.26	12.3361	24.1914	24.3947	0.0134	13.5309	40.9229	19.9614
1.4205	7.0	10395	1.2832	26.1618	12.3798	24.1252	24.3218	0.0132	13.5239	40.8967	19.9608
1.4065	8.0	11880	1.2788	26.4143	12.4893	24.3089	24.5138	0.0135	13.6449	41.0338	19.9608
1.4022	9.0	13365	1.2693	26.3817	12.4848	24.283	24.4877	0.0135	13.6422	40.9885	19.9614
1.3947	10.0	14850	1.2635	26.4912	12.6371	24.4041	24.6144	0.0137	13.7143	41.0899	19.9619
1.3807	11.0	16335	1.2556	26.5174	12.6435	24.4404	24.6327	0.0136	13.7252	41.1222	19.9609
1.3696	12.0	17820	1.2488	26.5883	12.7175	24.5004	24.6991	0.0138	13.7714	41.1291	19.9633
1.3591	13.0	19305	1.2430	26.626	12.7825	24.5489	24.7576	0.014	13.8226	41.0921	19.9606
1.3570	14.0	20790	1.2388	26.6863	12.8661	24.5939	24.7962	0.014	13.8611	41.1619	19.9603
1.3530	15.0	22275	1.2333	26.7387	12.9319	24.6663	24.8517	0.0141	13.9063	41.1669	19.9592
1.3343	16.0	23760	1.2272	26.7814	12.9765	24.6644	24.86	0.0143	13.9511	41.1985	19.9614
1.3246	17.0	25245	1.2227	26.8	13.0579	24.6996	24.9041	0.0142	13.9546	41.1806	19.9601
1.3228	18.0	26730	1.2171	26.8662	13.0906	24.7478	24.9428	0.0143	14.0091	41.264	19.9599
1.3139	19.0	28215	1.2156	26.9931	13.1569	24.8493	25.0564	0.0146	14.0802	41.2823	19.9599
1.3125	20.0	29700	1.2106	27.0149	13.2193	24.9087	25.1058	0.0145	14.1095	41.3381	19.9586
1.3190	21.0	31185	1.2067	27.0084	13.228	24.8653	25.0618	0.0146	14.0888	41.3169	19.9603
1.3155	22.0	32670	1.2026	27.0349	13.2667	24.9248	25.1266	0.0146	14.1182	41.3522	19.9591
1.3001	23.0	34155	1.1996	27.0917	13.3104	24.9344	25.1363	0.0147	14.144	41.3756	19.9591
1.3043	24.0	35640	1.1957	27.1726	13.3511	25.0214	25.2124	0.0148	14.1805	41.4131	19.9592
1.2931	25.0	37125	1.1951	27.1423	13.3543	24.9929	25.1952	0.0149	14.1989	41.4103	19.9581
1.2898	26.0	38610	1.1901	27.0899	13.3727	24.9498	25.1611	0.0148	14.1824	41.3881	19.9594
1.2863	27.0	40095	1.1878	27.0954	13.4075	24.9747	25.1778	0.0148	14.183	41.3753	19.9581
1.2884	28.0	41580	1.1862	27.1579	13.4647	25.0357	25.228	0.0149	14.2228	41.401	19.9579
1.2828	29.0	43065	1.1824	27.1882	13.4747	25.0503	25.2599	0.015	14.2522	41.4186	19.9579
1.2751	30.0	44550	1.1812	27.1544	13.4621	25.0335	25.2313	0.0149	14.246	41.449	19.9586
1.2754	31.0	46035	1.1797	27.244	13.5217	25.0988	25.3078	0.0151	14.2867	41.4518	19.9579
1.2727	32.0	47520	1.1781	27.2461	13.5148	25.0875	25.2872	0.015	14.286	41.4687	19.9603
1.2707	33.0	49005	1.1768	27.249	13.5417	25.1162	25.3072	0.0151	14.2962	41.5131	19.9598
1.2678	34.0	50490	1.1749	27.2825	13.5632	25.138	25.3386	0.0152	14.317	41.4794	19.9587
1.2679	35.0	51975	1.1732	27.3079	13.5821	25.1551	25.3552	0.0151	14.3222	41.5345	19.9591
1.2604	36.0	53460	1.1714	27.275	13.5615	25.1178	25.316	0.0151	14.3055	41.5094	19.9591
1.2615	37.0	54945	1.1711	27.3122	13.5727	25.1475	25.3549	0.0152	14.3259	41.5286	19.9591
1.2599	38.0	56430	1.1708	27.3298	13.5941	25.1612	25.3621	0.0152	14.3451	41.54	19.9592
1.2570	39.0	57915	1.1691	27.322	13.5882	25.1582	25.3653	0.0152	14.3341	41.5336	19.9598
1.2601	40.0	59400	1.1688	27.332	13.6108	25.1875	25.3938	0.0152	14.3418	41.5489	19.9591
1.2646	41.0	60885	1.1684	27.3288	13.6272	25.1822	25.3878	0.0152	14.3557	41.5653	19.9599
1.2608	42.0	62370	1.1677	27.3472	13.5869	25.1854	25.3888	0.0152	14.3419	41.5599	19.9589
1.2592	43.0	63855	1.1671	27.3276	13.5815	25.179	25.3812	0.0151	14.3379	41.5377	19.9586
1.2568	44.0	65340	1.1670	27.2974	13.5784	25.147	25.3529	0.0151	14.3338	41.5502	19.9586
1.2546	45.0	66825	1.1670	27.318	13.5923	25.1685	25.3722	0.0151	14.341	41.5514	19.9586

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2