learning_rate: 0.0001
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
26.7014	0.0251	5	17.9690	0.4959	0.0229	0.4821	0.4821
22.8828	0.0503	10	17.8948	0.4330	0.0229	0.4394	0.4427
25.3054	0.0754	15	17.7656	0.4335	0.0229	0.4399	0.4431
24.2626	0.1005	20	17.6523	0.4614	0.0229	0.4498	0.4541
26.9164	0.1256	25	17.4744	0.4291	0.0229	0.4298	0.4340
26.7442	0.1508	30	17.3540	0.4528	0.0229	0.4507	0.4560
22.8846	0.1759	35	17.1714	0.4395	0.0229	0.4407	0.4423
23.0382	0.2010	40	17.0225	0.4176	0.0229	0.4200	0.4272
24.5139	0.2261	45	16.9602	0.3881	0.0229	0.3958	0.4018
23.5225	0.2513	50	16.8832	0.4163	0.0229	0.4241	0.4257
24.7283	0.2764	55	16.7457	0.4156	0.0229	0.4235	0.4254
24.5206	0.3015	60	16.4804	0.4285	0.0229	0.4337	0.4356
22.8146	0.3266	65	16.2983	0.4012	0.0229	0.4064	0.4090
21.381	0.3518	70	16.0329	0.3890	0.0229	0.3922	0.3988
23.4543	0.3769	75	15.8419	0.3890	0.0229	0.3922	0.3988
20.3948	0.4020	80	15.6935	0.4034	0.0229	0.4058	0.4152
23.315	0.4271	85	15.5347	0.3758	0.0229	0.3724	0.3848
20.4828	0.4523	90	15.4258	0.3758	0.0229	0.3724	0.3848
22.6177	0.4774	95	15.2736	0.3821	0.0229	0.3830	0.3916
22.7118	0.5025	100	15.0719	0.3408	0.0229	0.3414	0.3506
24.145	0.5276	105	14.8205	0.3314	0.0229	0.3350	0.3426
21.6796	0.5528	110	14.5375	0.3310	0.0229	0.3348	0.3413
21.0313	0.5779	115	14.2323	0.3313	0.0229	0.3349	0.3425
20.1509	0.6030	120	13.9040	0.3306	0.0229	0.3340	0.3412
20.8036	0.6281	125	13.5365	0.3903	0.0493	0.3953	0.3977
18.9977	0.6533	130	13.2251	0.3972	0.0493	0.3996	0.4052
21.1749	0.6784	135	13.0306	0.3848	0.0493	0.3826	0.3906
18.1424	0.7035	140	12.7417	0.3568	0.0493	0.3589	0.3579
17.3758	0.7286	145	12.5348	0.3567	0.0493	0.3590	0.3577
21.9186	0.7538	150	12.2754	0.3735	0.0493	0.3773	0.3764
18.0612	0.7789	155	11.9966	0.3858	0.0493	0.3916	0.3914
16.8307	0.8040	160	11.8330	0.3154	0.0493	0.3172	0.3202
17.5778	0.8291	165	11.6714	0.2914	0.0493	0.2913	0.2967
18.1477	0.8543	170	11.4841	0.2913	0.0493	0.2913	0.2964
15.9704	0.8794	175	11.3850	0.3049	0.0493	0.3017	0.3097
17.1034	0.9045	180	11.2041	0.2991	0.0619	0.2978	0.3027
19.7897	0.9296	185	11.0869	0.3345	0.0662	0.3265	0.3309
16.608	0.9548	190	10.9445	0.3345	0.0662	0.3265	0.3309
17.1781	0.9799	195	10.8493	0.3345	0.0662	0.3264	0.3308
14.5506	1.0050	200	10.7443	0.3557	0.0662	0.3475	0.3524
15.1794	1.0302	205	10.6058	0.3699	0.0662	0.3608	0.3684
14.1433	1.0553	210	10.4693	0.3699	0.0662	0.3608	0.3684
15.3501	1.0804	215	10.2548	0.3697	0.0662	0.3600	0.3678
14.2343	1.1055	220	10.0423	0.3992	0.0662	0.3840	0.3902
13.6561	1.1307	225	9.8362	0.3637	0.0594	0.3538	0.3593
14.1522	1.1558	230	9.6526	0.4294	0.0662	0.4139	0.4260
12.2793	1.1809	235	9.4753	0.4294	0.0662	0.4139	0.4260
12.999	1.2060	240	9.3080	0.4818	0.0662	0.4527	0.4640
12.6114	1.2312	245	9.1514	0.5574	0.0843	0.5411	0.5470
12.6979	1.2563	250	9.0131	0.5651	0.0843	0.5186	0.5269
11.6085	1.2814	255	8.8960	0.5875	0.0761	0.5317	0.5412
11.9352	1.3065	260	8.7639	0.6295	0.1163	0.5637	0.5745
12.1973	1.3317	265	8.6219	0.6309	0.1163	0.5660	0.5754
11.8386	1.3568	270	8.5034	0.6541	0.1163	0.5877	0.5997
11.1427	1.3819	275	8.3913	0.7308	0.1302	0.6666	0.6784
11.6074	1.4070	280	8.2927	0.7541	0.1298	0.6892	0.7015
11.879	1.4322	285	8.1927	0.7048	0.0662	0.6537	0.6663
12.5362	1.4573	290	8.0887	0.7321	0.0662	0.6832	0.6939
11.1034	1.4824	295	7.9519	0.7805	0.0742	0.7167	0.7279
10.742	1.5075	300	7.7980	0.9069	0.1048	0.8357	0.8377
10.3591	1.5327	305	7.6581	1.1354	0.2100	0.9999	1.0172
11.0729	1.5578	310	7.5439	1.2665	0.2698	1.1212	1.1410
11.0979	1.5829	315	7.4250	1.2760	0.2269	1.1590	1.1749
10.7504	1.6080	320	7.3066	1.0842	0.1685	1.0249	1.0238
11.2598	1.6332	325	7.2042	1.1594	0.1687	1.0796	1.0846
10.4366	1.6583	330	7.1160	1.1594	0.1687	1.0796	1.0846
10.0824	1.6834	335	7.0343	1.1730	0.1684	1.0898	1.0982
9.9589	1.7085	340	6.9468	1.1306	0.1686	1.0485	1.0545
10.3309	1.7337	345	6.8646	1.1736	0.1824	1.0809	1.0876
9.6166	1.7588	350	6.7984	1.1165	0.1690	1.0478	1.0482
9.3742	1.7839	355	6.7329	1.2620	0.2014	1.1354	1.1476
9.853	1.8090	360	6.6669	1.2627	0.2014	1.1455	1.1582
10.1404	1.8342	365	6.6068	1.3100	0.2156	1.2083	1.2155
9.3509	1.8593	370	6.5692	1.2194	0.2020	1.1225	1.1152
8.8801	1.8844	375	6.5346	1.1335	0.1431	1.0131	1.0107
9.3656	1.9095	380	6.5026	1.1119	0.1291	1.0170	1.0141
9.0491	1.9347	385	6.4711	1.2375	0.1293	1.1102	1.1153
9.6425	1.9598	390	6.4447	1.2243	0.1409	1.1258	1.1284
8.8074	1.9849	395	6.4136	1.3684	0.2034	1.2323	1.2465
8.6168	2.0101	400	6.3833	1.4884	0.1787	1.3723	1.3762
8.9557	2.0352	405	6.3572	1.4520	0.1638	1.2990	1.2947
9.101	2.0603	410	6.3413	1.6343	0.1641	1.4365	1.4336
8.438	2.0854	415	6.3290	1.6232	0.1505	1.4573	1.4618
8.6262	2.1106	420	6.3048	1.6377	0.1245	1.5029	1.5195
8.9535	2.1357	425	6.2767	1.6318	0.1749	1.5081	1.5353
8.3392	2.1608	430	6.2523	1.6524	0.1743	1.5073	1.5367
8.6226	2.1859	435	6.2420	1.6597	0.1743	1.5340	1.5374
8.5399	2.2111	440	6.2366	1.6552	0.1743	1.5178	1.5262
8.4814	2.2362	445	6.2269	1.6997	0.1599	1.5507	1.5492
8.402	2.2613	450	6.2206	1.7743	0.1729	1.6229	1.6207
8.1715	2.2864	455	6.2157	1.7005	0.1731	1.5580	1.5519
8.3982	2.3116	460	6.2149	1.7912	0.1731	1.6301	1.6303
8.2935	2.3367	465	6.2051	1.8151	0.1813	1.6772	1.6708
8.1023	2.3618	470	6.1992	1.7187	0.1813	1.6023	1.5988
8.4083	2.3869	475	6.1925	1.7018	0.1816	1.5594	1.5567
8.3179	2.4121	480	6.1839	1.7161	0.1941	1.5709	1.5685
7.8477	2.4372	485	6.1699	1.6932	0.1816	1.5782	1.5846
7.9573	2.4623	490	6.1573	1.7737	0.1941	1.6399	1.6485
8.3412	2.4874	495	6.1501	1.6200	0.1553	1.5142	1.5160
8.2275	2.5126	500	6.1420	1.5973	0.1553	1.4930	1.4994
7.7802	2.5377	505	6.1371	1.5843	0.1553	1.4519	1.4570
8.2208	2.5628	510	6.1301	1.5734	0.1552	1.4672	1.4746
7.988	2.5879	515	6.1250	1.6141	0.1552	1.5196	1.5243
8.0406	2.6131	520	6.1216	1.5612	0.1317	1.4781	1.4902
7.6537	2.6382	525	6.1177	1.5042	0.1174	1.4341	1.4512
7.7706	2.6633	530	6.1124	1.5480	0.1110	1.4766	1.4870
7.7587	2.6884	535	6.1041	1.6054	0.0975	1.5301	1.5384
7.5912	2.7136	540	6.0947	1.6413	0.0975	1.5722	1.5747
7.6195	2.7387	545	6.0872	1.6897	0.0975	1.6322	1.6231
7.9719	2.7638	550	6.0840	1.6390	0.0980	1.5458	1.5456
7.5861	2.7889	555	6.0818	1.7055	0.0984	1.6106	1.6002
7.3751	2.8141	560	6.0693	1.7887	0.0984	1.7099	1.6969
7.4287	2.8392	565	6.0521	1.9438	0.0984	1.8707	1.8458
7.8715	2.8643	570	6.0418	1.9864	0.0742	1.9169	1.9069
7.5668	2.8894	575	6.0371	2.0494	0.0742	1.9751	1.9491
7.5644	2.9146	580	6.0284	2.0795	0.0742	2.0033	1.9955
7.5837	2.9397	585	6.0187	2.0435	0.0593	1.9903	1.9873
7.8794	2.9648	590	6.0076	2.1343	0.0593	2.0976	2.0879
7.4229	2.9899	595	5.9940	2.1421	0.0593	2.1090	2.0987
7.3116	3.0151	600	5.9697	2.2915	0.0593	2.2485	2.2434
7.237	3.0402	605	5.9432	2.2761	0.0593	2.2335	2.2297
7.5251	3.0653	610	5.9066	2.3241	0.0593	2.2857	2.2795
7.5311	3.0905	615	5.8749	2.3968	0.0593	2.3618	2.3526
7.3948	3.1156	620	5.8503	2.4292	0.0722	2.3934	2.3859
7.4102	3.1407	625	5.8441	2.5045	0.0722	2.4562	2.4443
7.3152	3.1658	630	5.8373	2.5838	0.0722	2.5309	2.5271
7.2793	3.1910	635	5.8287	2.5969	0.0722	2.5425	2.5405
7.2854	3.2161	640	5.8204	2.6641	0.0722	2.6240	2.6098
7.2151	3.2412	645	5.8081	2.7296	0.0722	2.6823	2.6686
7.1616	3.2663	650	5.7995	2.8340	0.0721	2.7816	2.7651
7.2671	3.2915	655	5.7911	2.9706	0.0721	2.9038	2.8953
7.3364	3.3166	660	5.7806	3.0656	0.0721	2.9978	2.9924
7.345	3.3417	665	5.7695	3.1378	0.0721	3.0744	3.0664
7.3118	3.3668	670	5.7532	3.1238	0.0722	3.0706	3.0618
7.4469	3.3920	675	5.7453	3.1653	0.0722	3.1293	3.1149
7.2567	3.4171	680	5.7376	3.1821	0.0722	3.1314	3.1141
7.1828	3.4422	685	5.7268	3.2855	0.0848	3.2291	3.1953
7.3317	3.4673	690	5.7070	3.3113	0.0848	3.2480	3.2158
7.1762	3.4925	695	5.6925	3.3213	0.0848	3.2595	3.2338
7.0286	3.5176	700	5.6794	3.3345	0.0848	3.2713	3.2443
7.1958	3.5427	705	5.6638	3.3834	0.0848	3.3169	3.2835
7.2112	3.5678	710	5.6573	3.4198	0.0744	3.3372	3.3030
7.0299	3.5930	715	5.6404	3.5031	0.0744	3.4199	3.3898
7.4005	3.6181	720	5.6231	3.5545	0.0744	3.4659	3.4403
7.2407	3.6432	725	5.6160	3.6875	0.0744	3.6277	3.6033
7.1189	3.6683	730	5.6075	3.7917	0.0744	3.7315	3.7144
7.0044	3.6935	735	5.5928	3.9431	0.0744	3.8972	3.8828
7.0864	3.7186	740	5.5823	3.9375	0.0593	3.8940	3.8878
7.3772	3.7437	745	5.5713	3.9630	0.0593	3.9203	3.9155
7.0098	3.7688	750	5.5583	4.1243	0.0744	4.0620	4.0602
6.8234	3.7940	755	5.5445	4.1046	0.0593	4.0478	4.0421
7.1442	3.8191	760	5.5222	4.0768	0.0593	4.0170	4.0034
6.9834	3.8442	765	5.5042	4.0013	0.0	3.9340	3.9286
7.0864	3.8693	770	5.4785	4.0012	0.0	3.9335	3.9288
6.863	3.8945	775	5.4519	4.0494	0.0	3.9829	3.9836
6.8511	3.9196	780	5.4235	4.1077	0.0	4.0382	4.0420
6.8788	3.9447	785	5.3975	4.2017	0.0	4.1333	4.1334
6.6429	3.9698	790	5.3782	4.2539	0.0	4.2007	4.1935
6.8546	3.9950	795	5.3626	4.3106	0.0	4.2576	4.2563
6.8145	4.0201	800	5.3410	4.3560	0.0	4.3242	4.3170
6.7826	4.0452	805	5.3263	4.3417	0.0	4.3236	4.3164
6.9502	4.0704	810	5.3144	4.3921	0.0	4.3602	4.3603
6.6682	4.0955	815	5.3021	4.3912	0.0	4.3575	4.3582
6.7195	4.1206	820	5.2895	4.3898	0.0	4.3564	4.3573
6.8389	4.1457	825	5.2787	4.3896	0.0	4.3561	4.3573
6.9199	4.1709	830	5.2683	4.4240	0.0	4.3939	4.3847
6.8859	4.1960	835	5.2588	4.4237	0.0	4.3934	4.3838
6.7521	4.2211	840	5.2438	4.5064	0.0	4.4407	4.4426
6.7168	4.2462	845	5.2284	4.5129	0.0	4.4405	4.4425
7.2573	4.2714	850	5.2204	4.4857	0.0	4.4131	4.4064
6.5104	4.2965	855	5.2126	4.5147	0.0	4.4269	4.4225
6.6178	4.3216	860	5.2034	4.5454	0.0	4.4468	4.4405
6.5719	4.3467	865	5.1942	4.5156	0.0	4.4389	4.4297
6.7698	4.3719	870	5.1824	4.5155	0.0	4.4383	4.4294
6.5936	4.3970	875	5.1708	4.5155	0.0	4.4383	4.4294
6.6705	4.4221	880	5.1564	4.5968	0.0	4.4961	4.5006
6.8366	4.4472	885	5.1465	4.5968	0.0	4.4961	4.5006
6.8101	4.4724	890	5.1397	4.5968	0.0	4.4960	4.5006
6.6961	4.4975	895	5.1324	4.5962	0.0	4.4958	4.5003
6.8763	4.5226	900	5.1243	4.6352	0.0	4.5400	4.5474
6.7891	4.5477	905	5.1166	4.6352	0.0	4.5400	4.5474
6.6563	4.5729	910	5.1063	4.6352	0.0	4.5400	4.5474
6.7201	4.5980	915	5.0973	4.6352	0.0	4.5395	4.5464
6.6108	4.6231	920	5.0880	4.6472	0.0	4.5387	4.5463
6.5714	4.6482	925	5.0786	4.6379	0.0	4.5388	4.5456
6.5445	4.6734	930	5.0695	4.6765	0.0	4.5586	4.5678
6.6799	4.6985	935	5.0614	4.6765	0.0	4.5586	4.5678
6.568	4.7236	940	5.0531	4.6765	0.0	4.5579	4.5675
6.2814	4.7487	945	5.0416	4.6892	0.0	4.5728	4.5799
6.8206	4.7739	950	5.0313	4.6892	0.0	4.5728	4.5799
6.5936	4.7990	955	5.0224	4.6957	0.0	4.5869	4.5870
6.662	4.8241	960	5.0125	4.6957	0.0	4.5869	4.5870
6.6761	4.8492	965	5.0031	4.6957	0.0	4.5869	4.5870
6.8252	4.8744	970	4.9960	4.6957	0.0	4.5868	4.5869
6.4136	4.8995	975	4.9888	4.6957	0.0	4.5868	4.5869
6.6854	4.9246	980	4.9813	4.6957	0.0	4.5868	4.5869
6.3622	4.9497	985	4.9766	4.6957	0.0	4.5868	4.5869
6.6554	4.9749	990	4.9696	4.6960	0.0	4.5868	4.5870
6.4508	5.0	995	4.9637	4.7116	0.0	4.6024	4.6028
6.701	5.0251	1000	4.9589	4.7116	0.0	4.6024	4.6028
6.2751	5.0503	1005	4.9519	4.7116	0.0	4.6024	4.6029
6.5376	5.0754	1010	4.9458	4.7116	0.0	4.6024	4.6029
6.4412	5.1005	1015	4.9417	4.7115	0.0	4.6020	4.6028
6.5644	5.1256	1020	4.9374	4.6979	0.0	4.6030	4.6028
6.1549	5.1508	1025	4.9295	4.6909	0.0	4.5909	4.5974
6.4149	5.1759	1030	4.9197	4.6675	0.0	4.5708	4.5716
6.5379	5.2010	1035	4.9123	4.6675	0.0	4.5585	4.5552
6.3613	5.2261	1040	4.9056	4.6791	0.0	4.5692	4.5656
6.5305	5.2513	1045	4.9001	4.6791	0.0	4.5692	4.5656
6.5593	5.2764	1050	4.8975	4.6661	0.0143	4.5695	4.5663
6.529	5.3015	1055	4.8906	4.6550	0.0143	4.5524	4.5508
6.4264	5.3266	1060	4.8837	4.6550	0.0143	4.5524	4.5508
6.679	5.3518	1065	4.8797	4.6444	0.0143	4.5417	4.5448
6.4163	5.3769	1070	4.8762	4.6444	0.0143	4.5417	4.5448
6.5349	5.4020	1075	4.8723	4.6444	0.0143	4.5417	4.5448
6.469	5.4271	1080	4.8693	4.6444	0.0143	4.5417	4.5448
6.3743	5.4523	1085	4.8668	4.6444	0.0143	4.5417	4.5448
6.3293	5.4774	1090	4.8648	4.6442	0.0143	4.5415	4.5447
6.3905	5.5025	1095	4.8615	4.6442	0.0143	4.5415	4.5447
6.6543	5.5276	1100	4.8589	4.6442	0.0143	4.5415	4.5447
6.2526	5.5528	1105	4.8557	4.6442	0.0143	4.5415	4.5447
6.5861	5.5779	1110	4.8521	4.6586	0.0143	4.5602	4.5559
6.6042	5.6030	1115	4.8498	4.6586	0.0143	4.5602	4.5559
6.5273	5.6281	1120	4.8485	4.6586	0.0143	4.5602	4.5559
6.3963	5.6533	1125	4.8477	4.6586	0.0143	4.5602	4.5559
6.3541	5.6784	1130	4.8468	4.6586	0.0143	4.5602	4.5559
6.2128	5.7035	1135	4.8460	4.6586	0.0143	4.5602	4.5559
6.6066	5.7286	1140	4.8454	4.6586	0.0143	4.5602	4.5559
6.366	5.7538	1145	4.8448	4.6590	0.0143	4.5612	4.5562
6.4843	5.7789	1150	4.8440	4.6590	0.0143	4.5612	4.5562
6.8107	5.8040	1155	4.8434	4.6590	0.0143	4.5612	4.5562
6.2873	5.8291	1160	4.8429	4.6590	0.0143	4.5612	4.5562
6.5391	5.8543	1165	4.8426	4.6911	0.0143	4.5972	4.5916
6.7077	5.8794	1170	4.8425	4.6911	0.0143	4.5972	4.5916
6.5323	5.9045	1175	4.8424	4.6911	0.0143	4.5972	4.5916
6.1429	5.9296	1180	4.8422	4.6911	0.0143	4.5972	4.5916
6.457	5.9548	1185	4.8419	4.6911	0.0143	4.5972	4.5916
6.1296	5.9799	1190	4.8417	4.6911	0.0143	4.5972	4.5916

Framework versions

PEFT 0.14.0
Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.3.2
Tokenizers 0.21.0

benitoals/mt5-lora-hf

mt5-lora-hf

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

benitoals/
mt5-lora-hf