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: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
18.0108	0.0203	5	11.5448	3.9300	0.4603	3.3547	3.3632
17.5668	0.0407	10	11.5972	3.9014	0.4501	3.3130	3.3181
19.0556	0.0610	15	11.5125	3.9044	0.4504	3.3225	3.3262
16.6307	0.0813	20	11.4957	3.8681	0.4543	3.3027	3.3068
17.8223	0.1016	25	11.3646	3.8279	0.4525	3.2854	3.2867
16.8407	0.1220	30	11.3086	3.8277	0.4524	3.2854	3.2865
19.0472	0.1423	35	11.2292	3.8043	0.4540	3.2832	3.2943
16.9039	0.1626	40	11.1408	3.8013	0.4331	3.2699	3.2785
17.1096	0.1829	45	11.0139	3.8277	0.4340	3.2868	3.2945
17.0412	0.2033	50	10.9073	3.8309	0.4332	3.2869	3.2928
16.6607	0.2236	55	10.8013	3.8073	0.4150	3.2473	3.2639
16.5624	0.2439	60	10.6754	3.8365	0.4271	3.2722	3.2881
15.8037	0.2642	65	10.5570	3.8600	0.4234	3.3018	3.3157
15.8958	0.2846	70	10.4281	3.9131	0.4247	3.3444	3.3616
16.0049	0.3049	75	10.2931	3.9190	0.4118	3.3249	3.3406
15.1851	0.3252	80	10.1219	3.8818	0.4187	3.3168	3.3305
13.7935	0.3455	85	9.9471	3.8700	0.4145	3.2969	3.3054
15.0027	0.3659	90	9.7600	3.8159	0.4211	3.2540	3.2651
15.3996	0.3862	95	9.6233	3.8580	0.4184	3.2710	3.2782
14.189	0.4065	100	9.4453	3.9250	0.4301	3.3165	3.3252
14.4231	0.4268	105	9.2650	3.8933	0.4263	3.3054	3.3138
14.8272	0.4472	110	9.0932	3.8949	0.4302	3.3236	3.3306
13.1533	0.4675	115	8.8607	3.9270	0.4176	3.3854	3.3892
13.6223	0.4878	120	8.6228	3.8793	0.3861	3.3604	3.3663
12.7113	0.5081	125	8.4188	3.7781	0.3467	3.2851	3.2864
12.5541	0.5285	130	8.2296	3.8065	0.3223	3.2651	3.2694
11.8255	0.5488	135	8.0490	3.9082	0.3657	3.3732	3.3725
12.664	0.5691	140	7.8683	3.9992	0.3731	3.4254	3.4455
10.8239	0.5894	145	7.7140	4.0845	0.4366	3.4650	3.4742
10.25	0.6098	150	7.5566	4.0105	0.3898	3.3940	3.4020
10.5402	0.6301	155	7.4057	3.9363	0.3658	3.3753	3.3842
10.8665	0.6504	160	7.2786	3.8811	0.3543	3.3469	3.3597
9.673	0.6707	165	7.1480	3.7187	0.3318	3.1914	3.1905
9.0383	0.6911	170	7.0333	3.6714	0.3155	3.1956	3.1828
9.1189	0.7114	175	6.9442	3.7038	0.3199	3.2065	3.1968
8.8971	0.7317	180	6.8851	3.6408	0.3490	3.1678	3.1628
8.504	0.7520	185	6.8155	3.6940	0.3810	3.2346	3.2342
9.4379	0.7724	190	6.7635	3.4465	0.3622	3.0650	3.0664
8.3226	0.7927	195	6.7094	3.4300	0.3409	3.0456	3.0430
7.8744	0.8130	200	6.6554	3.4937	0.3494	3.0846	3.0886
8.2568	0.8333	205	6.5959	3.4686	0.3547	3.0518	3.0472
7.856	0.8537	210	6.5439	3.3870	0.3170	2.9934	2.9916
7.8506	0.8740	215	6.4955	3.2677	0.2639	2.9242	2.9198
7.9843	0.8943	220	6.4606	3.2314	0.2205	2.8815	2.8830
8.0429	0.9146	225	6.4291	3.1974	0.1902	2.8733	2.8672
7.7224	0.9350	230	6.3923	3.1009	0.1791	2.7896	2.7864
7.7764	0.9553	235	6.3620	3.1415	0.1798	2.8431	2.8440
7.8474	0.9756	240	6.3385	3.2174	0.2042	2.9180	2.9192
7.3132	0.9959	245	6.3093	3.2292	0.1906	2.9351	2.9334
7.3998	1.0163	250	6.2774	3.1930	0.1732	2.9568	2.9584
7.2203	1.0366	255	6.2460	3.2335	0.1658	2.9483	2.9454
7.3049	1.0569	260	6.2189	3.2481	0.1694	2.9453	2.9452
7.0353	1.0772	265	6.1975	3.2876	0.1564	3.0087	3.0046
6.9	1.0976	270	6.1539	3.2913	0.1536	2.9996	3.0008
6.8139	1.1179	275	6.1047	3.4811	0.1732	3.1349	3.1390
6.902	1.1382	280	6.0440	3.6308	0.1706	3.3246	3.3234
6.9654	1.1585	285	5.9931	3.6252	0.1805	3.3207	3.3277
6.8434	1.1789	290	5.9510	3.5878	0.1408	3.3061	3.3153
6.4474	1.1992	295	5.8972	3.4887	0.1034	3.2221	3.2279
6.6874	1.2195	300	5.8437	3.4791	0.1030	3.2127	3.2192
6.497	1.2398	305	5.7777	3.4709	0.0906	3.2150	3.2178
6.4195	1.2602	310	5.6580	3.8082	0.1531	3.4406	3.4558
6.473	1.2805	315	5.5412	3.9102	0.1725	3.5010	3.5080
6.2156	1.3008	320	5.4296	4.0643	0.1898	3.5671	3.5763
6.3401	1.3211	325	5.3092	4.3524	0.2994	3.8692	3.8805
6.0932	1.3415	330	5.1912	4.4523	0.3417	3.9303	3.9354
6.2575	1.3618	335	5.0914	4.6630	0.4477	4.0695	4.0826
5.941	1.3821	340	4.9969	4.9389	0.4925	4.3100	4.3131
5.9698	1.4024	345	4.9161	5.1712	0.4837	4.4398	4.4476
5.7625	1.4228	350	4.8474	5.3514	0.5243	4.5382	4.5440
5.6139	1.4431	355	4.7795	5.4523	0.5360	4.6344	4.6380
5.5114	1.4634	360	4.7074	5.6188	0.5335	4.6739	4.6785
5.4565	1.4837	365	4.6473	5.8800	0.5351	4.9122	4.9171
5.5245	1.5041	370	4.5967	6.0567	0.5893	5.0300	5.0355
5.6112	1.5244	375	4.5488	6.1792	0.5714	5.1372	5.1390
5.5809	1.5447	380	4.5089	6.1893	0.5835	5.1176	5.1213
5.5151	1.5650	385	4.4584	6.2205	0.6062	5.0569	5.0656
5.1035	1.5854	390	4.4120	6.2272	0.5906	5.0419	5.0478
5.3306	1.6057	395	4.3672	6.4538	0.6838	5.1904	5.1891
5.38	1.6260	400	4.3240	6.4831	0.7128	5.1797	5.1843
5.265	1.6463	405	4.2839	6.4992	0.7125	5.2043	5.2041
5.3303	1.6667	410	4.2356	6.4401	0.6704	5.1652	5.1684
4.9325	1.6870	415	4.1924	6.4591	0.6386	5.1278	5.1308
4.9228	1.7073	420	4.1499	6.4728	0.6064	5.1166	5.1201
4.8428	1.7276	425	4.1129	6.4206	0.6310	5.0960	5.0919
5.0332	1.7480	430	4.0814	6.4917	0.6573	5.1753	5.1774
4.887	1.7683	435	4.0484	6.4957	0.7106	5.2395	5.2416
5.0924	1.7886	440	4.0170	6.6189	0.7384	5.3166	5.3217
5.0251	1.8089	445	3.9924	6.6051	0.7061	5.2867	5.2955
5.0704	1.8293	450	3.9685	6.5558	0.7182	5.2370	5.2404
4.6966	1.8496	455	3.9450	6.6479	0.7181	5.3175	5.3195
4.7904	1.8699	460	3.9275	6.5675	0.7439	5.2862	5.2857
4.6447	1.8902	465	3.9122	6.6267	0.7624	5.3759	5.3792
4.6136	1.9106	470	3.8969	6.8663	0.8278	5.5731	5.5694
4.8622	1.9309	475	3.8812	6.8957	0.9100	5.5617	5.5594
4.6006	1.9512	480	3.8643	6.9135	0.9242	5.5461	5.5430
4.6175	1.9715	485	3.8551	6.9522	0.9010	5.5928	5.5870
4.5605	1.9919	490	3.8478	7.0768	0.9381	5.6953	5.6931
4.5957	2.0122	495	3.8391	7.2254	0.9712	5.8105	5.8225
4.5938	2.0325	500	3.8291	7.3132	0.9832	5.9090	5.9106
4.5655	2.0528	505	3.8190	7.3358	1.0038	5.9027	5.9068
4.5382	2.0732	510	3.8099	7.3025	1.0034	5.8752	5.8710
4.4525	2.0935	515	3.8025	7.3049	0.9536	5.8523	5.8455
4.4157	2.1138	520	3.7961	7.4072	0.9535	5.9041	5.9008
4.4082	2.1341	525	3.7912	7.4709	0.9522	5.9616	5.9656
4.3671	2.1545	530	3.7876	7.4469	0.9872	5.9326	5.9355
4.7378	2.1748	535	3.7833	7.4974	0.9992	5.9752	5.9837
4.4706	2.1951	540	3.7762	7.4701	0.9856	5.9161	5.9190
4.4955	2.2154	545	3.7710	7.4531	0.9937	5.9156	5.9220
4.4801	2.2358	550	3.7655	7.4544	0.9802	5.9010	5.9051
4.4433	2.2561	555	3.7609	7.4530	0.9821	5.8880	5.8943
4.4976	2.2764	560	3.7573	7.4870	0.9917	5.9409	5.9500
4.4341	2.2967	565	3.7531	7.5217	0.9978	5.9659	5.9805
4.3203	2.3171	570	3.7508	7.5998	1.0437	6.0008	6.0109
4.3169	2.3374	575	3.7483	7.5085	1.0815	6.0146	6.0212
4.662	2.3577	580	3.7471	7.4676	1.0804	5.9955	6.0032
4.6169	2.3780	585	3.7447	7.4787	1.0630	5.9807	5.9814
4.3445	2.3984	590	3.7405	7.5285	1.0614	6.0290	6.0325
4.516	2.4187	595	3.7364	7.4974	1.0552	5.9861	5.9908
4.4071	2.4390	600	3.7330	7.4665	1.0531	5.9722	5.9793
4.4474	2.4593	605	3.7305	7.4660	1.0384	5.9710	5.9789
4.283	2.4797	610	3.7288	7.4973	1.0456	5.9665	5.9795
4.3223	2.5	615	3.7275	7.5151	1.0181	5.9662	5.9806
4.3932	2.5203	620	3.7259	7.5069	0.9933	5.9719	5.9893
4.2437	2.5407	625	3.7243	7.5955	1.0070	6.0214	6.0317
4.2483	2.5610	630	3.7230	7.5689	0.9922	6.0040	6.0167
4.2537	2.5813	635	3.7215	7.4879	0.9556	5.9368	5.9518
4.2733	2.6016	640	3.7198	7.4788	0.9705	5.9586	5.9725
4.5302	2.6220	645	3.7178	7.4790	0.9852	5.9632	5.9778
4.1324	2.6423	650	3.7164	7.4771	0.9865	5.9625	5.9761
4.5736	2.6626	655	3.7155	7.4212	0.9593	5.9265	5.9359
4.5945	2.6829	660	3.7133	7.4485	0.9614	5.9890	6.0049
4.3038	2.7033	665	3.7110	7.5173	0.9909	6.0460	6.0546
4.3281	2.7236	670	3.7092	7.5410	0.9993	6.0549	6.0679
4.1467	2.7439	675	3.7082	7.4982	0.9879	6.0017	6.0106
4.2528	2.7642	680	3.7074	7.4629	0.9702	5.9727	5.9830
4.6152	2.7846	685	3.7074	7.4454	0.9684	5.9447	5.9505
4.3496	2.8049	690	3.7063	7.4183	0.9751	5.9158	5.9190
4.312	2.8252	695	3.7047	7.4795	1.0091	5.9265	5.9343
4.1854	2.8455	700	3.7026	7.4642	0.9984	5.9307	5.9385
4.1499	2.8659	705	3.7006	7.4736	0.9944	5.9241	5.9325
4.4412	2.8862	710	3.6987	7.5272	1.0167	5.9658	5.9697
4.4081	2.9065	715	3.6972	7.4958	1.0027	5.9399	5.9491
4.2851	2.9268	720	3.6958	7.4725	0.9964	5.9096	5.9193
4.4668	2.9472	725	3.6940	7.5229	1.0252	5.9573	5.9714
4.3325	2.9675	730	3.6927	7.4774	1.0188	5.9138	5.9322
4.1743	2.9878	735	3.6916	7.4919	1.0118	5.9390	5.9554
4.0727	3.0081	740	3.6907	7.4959	1.0324	5.9169	5.9329
4.2407	3.0285	745	3.6897	7.4904	1.0223	5.9043	5.9153
4.2583	3.0488	750	3.6887	7.4509	1.0124	5.8934	5.9047
4.3452	3.0691	755	3.6878	7.4750	1.0069	5.9222	5.9333
4.2949	3.0894	760	3.6868	7.5390	0.9952	5.9733	5.9910
4.2085	3.1098	765	3.6854	7.5405	0.9942	5.9777	5.9908
4.3482	3.1301	770	3.6844	7.5476	1.0170	5.9739	5.9883
4.1785	3.1504	775	3.6833	7.5631	1.0362	5.9782	5.9957
4.2099	3.1707	780	3.6822	7.5461	1.0514	5.9543	5.9681
4.3208	3.1911	785	3.6818	7.5684	1.0562	5.9565	5.9666
4.2931	3.2114	790	3.6811	7.5235	1.0319	5.9354	5.9527
4.2106	3.2317	795	3.6806	7.5078	1.0330	5.9378	5.9489
4.195	3.2520	800	3.6806	7.4847	1.0356	5.9069	5.9228
4.3165	3.2724	805	3.6804	7.4700	1.0188	5.8761	5.8961
4.2902	3.2927	810	3.6797	7.5378	1.0462	5.9404	5.9509
4.1609	3.3130	815	3.6787	7.5259	1.0430	5.9233	5.9359
4.2289	3.3333	820	3.6779	7.5253	1.0387	5.9297	5.9385
4.2448	3.3537	825	3.6774	7.5219	1.0328	5.9296	5.9412
4.0381	3.3740	830	3.6771	7.5243	1.0427	5.9362	5.9482
4.1953	3.3943	835	3.6771	7.5248	1.0506	5.9437	5.9530
4.361	3.4146	840	3.6763	7.5336	1.0596	5.9460	5.9549
4.0716	3.4350	845	3.6754	7.5635	1.0920	5.9728	5.9824
4.3947	3.4553	850	3.6745	7.5557	1.0892	5.9628	5.9698
4.2481	3.4756	855	3.6738	7.5649	1.0911	5.9696	5.9813
4.214	3.4959	860	3.6733	7.5300	1.0755	5.9690	5.9875
4.2825	3.5163	865	3.6730	7.5289	1.0720	5.9665	5.9761
4.4458	3.5366	870	3.6728	7.5113	1.0605	5.9523	5.9651
4.3372	3.5569	875	3.6726	7.5258	1.0739	5.9611	5.9700
4.0246	3.5772	880	3.6723	7.5324	1.0743	5.9610	5.9669
4.1618	3.5976	885	3.6721	7.5468	1.0875	5.9577	5.9690
4.2495	3.6179	890	3.6720	7.5494	1.1002	5.9659	5.9755
4.349	3.6382	895	3.6718	7.5468	1.1054	5.9699	5.9818
4.2023	3.6585	900	3.6714	7.4897	1.0704	5.9180	5.9280
4.3001	3.6789	905	3.6711	7.4895	1.0677	5.9279	5.9382
4.2759	3.6992	910	3.6710	7.5036	1.0668	5.9132	5.9201
4.165	3.7195	915	3.6708	7.5085	1.0801	5.9235	5.9336
4.2758	3.7398	920	3.6705	7.5276	1.1017	5.9339	5.9448
4.3073	3.7602	925	3.6704	7.5367	1.0959	5.9344	5.9455
4.1083	3.7805	930	3.6701	7.5082	1.0752	5.9056	5.9212
4.2257	3.8008	935	3.6698	7.5016	1.0802	5.8981	5.9119
4.3292	3.8211	940	3.6695	7.5069	1.0861	5.9075	5.9141
4.2595	3.8415	945	3.6693	7.5115	1.0882	5.8957	5.8997
4.3752	3.8618	950	3.6691	7.4807	1.0836	5.8884	5.8952
4.1327	3.8821	955	3.6690	7.4807	1.0836	5.8884	5.8952
4.1546	3.9024	960	3.6689	7.4948	1.0882	5.8884	5.8952
4.0501	3.9228	965	3.6689	7.4948	1.0882	5.8884	5.8952
4.2212	3.9431	970	3.6688	7.4948	1.0882	5.8884	5.8952
4.3237	3.9634	975	3.6687	7.4948	1.0882	5.8884	5.8952
4.1791	3.9837	980	3.6687	7.4948	1.0882	5.8884	5.8952

Framework versions

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

benitoals/base-lora-local-combined

base-lora-local-combined

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

benitoals/
base-lora-local-combined